**Part 2**

## **Integrated Chemical Weed Management**

130 Weed Control

Weston, L.A. 1996. Utilization of allelopathy for weed management in agroecosystems:

Willis, R.J. 1985. The historical bases of the concept of allelopathy. *Journal of the History of* 

Wingard, C. 1996. Cover crops in integrated vegetable production systems. SARE Project report # PG95-033. Southern Region SARE. Griffin, GA. www.sare.org/projects. Wolf, R.B., G.F. Spencer, and W.F. Kwolek. 1984. Inhibition of velvetleaf (*Abutilon* 

Wu, L., X. Guo, M.A. Harivandi. 1998. Allelopathic effects of phenolic acids detected in

Yamamoto, Y. 2008. Movement of allelopathic compound coumarin from plant residue of

Yu, J.Q., S.F. Ye, M.F. Zhan, and W.H. Hu. 2003. Effects of root exudates and aqueous root

*theophrasti*) germination and growth by benzyl isothiocyanate, a natural toxicant.

buffalograss (*Buchloe dactyloides*) clippings on growth of annual bluegrass (*Poa annua*) and buffalograss seedlings. *Environmental and Experimental Botany* 39: 159-

Sweet vernalgrass (*Anthoxanthum odoratum* L.) to soil. *Japanese Society of Grassland* 

extracts of cucumber (*Cucumis sativus*) and allelochemicals, on photosynthesis and antioxidant enzymes in cucumber. *Biochemical Systematics and Ecology* 31: 129-139.

Allelopathy in cropping systems. *Agronomy Journal* 88: 860-866.

*Biology* 18: 71-102.

*Science* 55: 36-40.

167.

*Weed Science* 32: 612-615.

**7** 

*Ireland* 

**The Relationship Between Patch Spraying** 

Carroll and Holden (2005) defined a method for quantifying weed distributions using distance transform analysis as a first-step in relating the distribution of weeds in a field to the type and cost of equipment used to spray the field (Carroll and Holden, 2009). The method was developed because in much of Europe, fields are sprayed at a fixed application rate determined by the average weed density of weed patches in the field, despite the fact that some areas of the field are below the economic threshold (ET) for intervention and do not require spraying (Mortensen et al., 1995). Targeted application of herbicides to weed patches, known as patch spraying, has the potential to significantly reduce herbicide use,

Patch spraying of herbicide is only viable if: (i) there is a distinct pattern of within-field variability; (ii) the variability identified can be reliably mapped; (iii) the variability has a known biological or environmental effect once managed; (iv) there is a suitable theoretical means of dealing with the weed that accounts for chemical efficacy and weed reproduction; (v) the mechanical equipment exists that can target the within-field variability in an accurate and precise manner; and (vi) the operation can be undertaken at an acceptable cost and

The work of Carroll and Holden (2005, 2009) provides a method of quantifying weed distributions. It is documented that weeds are clustered and can be mapped (Godwin and Miller, 2003), and there is reasonable evidence to suggest that patch spraying can be a theoretically effective management approach that has an agronomic and environmental advantage (Lutman et al., 1998 and Wilkerson et al., 2004). Ford et al. (2011) showed that less herbicide could be used through variable rate application, when comparing a conventional broadcast herbicide sprayer to a variable spray weed sensing sprayer It is also known that within certain spatial constraints patch spraying can be accurate (Paice et al., 1997), but the question remains as to whether an acceptable cost and return on investment can be achieved. Carroll and Holden (2009) developed generalized relationships between field weed patterns, patch sprayer specifications and the spray quality achieved with the view to use these relationships to specify the most appropriate equipment for a certain field weed

which has both economic and environmental advantages (Lutman et al., 1998).

**1. Introduction** 

return on investment.

pattern based on required spray quality and cost.

**Cost and Target Weed Distribution** 

John Carroll1 and Nicholas Holden2

*1Teagasc Crops Research Centre, 2University College Dublin,* 

## **The Relationship Between Patch Spraying Cost and Target Weed Distribution**

John Carroll1 and Nicholas Holden2 *1Teagasc Crops Research Centre,* 

*2University College Dublin, Ireland* 

#### **1. Introduction**

Carroll and Holden (2005) defined a method for quantifying weed distributions using distance transform analysis as a first-step in relating the distribution of weeds in a field to the type and cost of equipment used to spray the field (Carroll and Holden, 2009). The method was developed because in much of Europe, fields are sprayed at a fixed application rate determined by the average weed density of weed patches in the field, despite the fact that some areas of the field are below the economic threshold (ET) for intervention and do not require spraying (Mortensen et al., 1995). Targeted application of herbicides to weed patches, known as patch spraying, has the potential to significantly reduce herbicide use, which has both economic and environmental advantages (Lutman et al., 1998).

Patch spraying of herbicide is only viable if: (i) there is a distinct pattern of within-field variability; (ii) the variability identified can be reliably mapped; (iii) the variability has a known biological or environmental effect once managed; (iv) there is a suitable theoretical means of dealing with the weed that accounts for chemical efficacy and weed reproduction; (v) the mechanical equipment exists that can target the within-field variability in an accurate and precise manner; and (vi) the operation can be undertaken at an acceptable cost and return on investment.

The work of Carroll and Holden (2005, 2009) provides a method of quantifying weed distributions. It is documented that weeds are clustered and can be mapped (Godwin and Miller, 2003), and there is reasonable evidence to suggest that patch spraying can be a theoretically effective management approach that has an agronomic and environmental advantage (Lutman et al., 1998 and Wilkerson et al., 2004). Ford et al. (2011) showed that less herbicide could be used through variable rate application, when comparing a conventional broadcast herbicide sprayer to a variable spray weed sensing sprayer It is also known that within certain spatial constraints patch spraying can be accurate (Paice et al., 1997), but the question remains as to whether an acceptable cost and return on investment can be achieved. Carroll and Holden (2009) developed generalized relationships between field weed patterns, patch sprayer specifications and the spray quality achieved with the view to use these relationships to specify the most appropriate equipment for a certain field weed pattern based on required spray quality and cost.

The Relationship Between Patch Spraying Cost and Target Weed Distribution 135

dynamics on long-term profitability of weed management decisions. Not treating a near threshold weed population may affect whether or not a threshold will be exceeded in subsequent years due to increases in weed seedbank. Weaver (1996) discussed the importance of seed production by threshold density weed populations, emphasizing the importance of seed viability, dormancy and longevity. Long-term weed management programs must consider weed seed production, as well as yield losses to permit accurate cost estimation and to increase the likelihood that the threshold criterion is adopted by producers. Zanin et al. (1993) determined ET for winter wheat weed control using different

Within the same species the different values of ET result from the different costs and efficacy

Black and Dyson (1993) developed a model for calculating the economic benefit of early spraying of herbicides in wheat and barley crops, using data derived from routine herbicide evaluation field experiments. The absolute yield benefit (kg/ha) from controlling a forecast proportion of estimated weed units present when the crop is sprayed at or before the early tillering stage has 3 determinants: the weed-free yield potential of the crop (kg/ha); the number of weeds /m2 at spraying; and relative growth stages of weeds and crop at spraying. The weight of evidence from the data used indicates that there is an approximately linear relationship between weed density after spraying and grain yield.

Barroso et al. (2004) simulated the effects of weed spatial pattern and resolution of mapping and spraying on economics of site-specific weed management (SSWM). They concluded that the economic benefits of using SSWM are related to the proportion of the field that is weedinfested, the number of weed patches and the spatial resolution of sampling and spraying technologies. Different combinations of these factors were simulated using parameter values obtained for *Avena sterilis ludoviciana* growing in Spanish winter barley crops. The profitability of SSWM systems increased as the proportion of the field infested decreased and when patch distribution was more concentrated. Positive net returns for SSWM were obtained when the weed-infested area was smaller than 30% with the highest return

Paice et al. (1998) evaluated patch spraying using a stochastic simulation model incorporating Lloyd's Patchiness Index to quantify the patchiness of the weed distribution and the negative binomial distribution to measure distribution shape. They concluded that the long-term economic benefits of patch spraying are likely to be related to the initial spatial distribution, the demographic characteristics of the weed species and the weed control and crop husbandry practices to which they are subjected and that for a system conforming to their very exact specifications, patch spraying of *Alopecurus myosuroides* Huds

herbicides or mixtures effective against individual weed species:

• *Alopecurus myosuroides* Hudson = 25 – 35 plants per m2.

occurring at a 12 m X 12 m mapping and spraying resolution.

• *Lolium multiflorum* Lam = 25 – 35 plants per m2.

• *Bromus sterilis* L. = just under 40 per m2.

• *Galium aparine* L. = 2/m2. • *Vicia sativa* L. = 2 – 10 /m2.

**3. Economic simulations** 

of the herbicides.

• *Avena Sterilis* L. subsp. Ludoviciana (Durieu) Nyman = 7 – 12 plants per m2.

The focus of the research presented within this chapter is to define the actual costs associated with using sprayer specifications, derived by analyzing weed distributions, and to compare this cost with that of uniform spraying. Any potential environmental costs or benefits associated with applying excess or precise herbicide amount in whole field or site specific applications are not considered. The analysis was undertaken to quantify the economic benefit of precision agriculture herbicide application technology.

#### **2. Economic thresholds**

Some research has been reported on the economic analysis of the benefits of patch spraying. The first requirement of any economic weed control analysis is the allocation of an ET, which is defined as the weed density at which the control cost equals the crop loss value if no control action is taken (Bauer and Mortensen, 1992) or the weed population at which the cost of control is equal to the crop value increase from control of the weeds present (Coble and Mortensen, 1992).

Coble and Mortensen (1992) wrote that the economic return associated with a crop production practice and the sustainability of that practice is of greatest immediate concern to the producer. As both biological and economic effects and costs are considered, an economic threshold offers a method by which profitable and sustainable weed management decisions can be made. The ET can be estimated by:

$$T\_e = \frac{\mathbf{C}\_h + \mathbf{C}\_a}{\mathbf{YPLH}} \tag{1}$$

Where

Te = economic threshold Ch = herbicide cost Ca = application cost Y = weed free crop yield P = value per unit of crop L = proportional loss per unit weed density H = proportional reduction in weed density by the herbicide treatment.

Equation 1 reveals that any increase in herbicide or application cost will increase ET with other factors being constant. Increase in crop yield, value, degree of weed control or crop loss per unit weed density will lower ET. Three of the factors involved in ET calculations, herbicide cost, application cost and crop value can be estimated fairly accurately by individual growers. However, other factors including potential crop yield, proportional loss per unit weed density and herbicide efficacy are more difficult to estimate because of the variability associated with weather, weed species composition, weed size and cropping system effects on these variables. The focus of this research is on the application cost (Ca). This consists of depreciating sprayer value, cost of operation (including fuel and maintenance) and labor costs. All costs will relate to the size, segmentation and the control system of the sprayer.

Bauer and Mortensen (1992) discussed the Economic Optimum Threshold (EOT) concept. Economic thresholds generally refer to in-season decisions during a single crop year, and do not include a cost factor associated with possible increases in the soil seedbank due to lack of weed control. However, the term EOT is used to include the impact of seedbank dynamics on long-term profitability of weed management decisions. Not treating a near threshold weed population may affect whether or not a threshold will be exceeded in subsequent years due to increases in weed seedbank. Weaver (1996) discussed the importance of seed production by threshold density weed populations, emphasizing the importance of seed viability, dormancy and longevity. Long-term weed management programs must consider weed seed production, as well as yield losses to permit accurate cost estimation and to increase the likelihood that the threshold criterion is adopted by producers. Zanin et al. (1993) determined ET for winter wheat weed control using different herbicides or mixtures effective against individual weed species:


134 Weed Control

The focus of the research presented within this chapter is to define the actual costs associated with using sprayer specifications, derived by analyzing weed distributions, and to compare this cost with that of uniform spraying. Any potential environmental costs or benefits associated with applying excess or precise herbicide amount in whole field or site specific applications are not considered. The analysis was undertaken to quantify the

Some research has been reported on the economic analysis of the benefits of patch spraying. The first requirement of any economic weed control analysis is the allocation of an ET, which is defined as the weed density at which the control cost equals the crop loss value if no control action is taken (Bauer and Mortensen, 1992) or the weed population at which the cost of control is equal to the crop value increase from control of the weeds present (Coble

Coble and Mortensen (1992) wrote that the economic return associated with a crop production practice and the sustainability of that practice is of greatest immediate concern to the producer. As both biological and economic effects and costs are considered, an economic threshold offers a method by which profitable and sustainable weed management

> *h a <sup>e</sup> C C <sup>T</sup> YPLH*

Equation 1 reveals that any increase in herbicide or application cost will increase ET with other factors being constant. Increase in crop yield, value, degree of weed control or crop loss per unit weed density will lower ET. Three of the factors involved in ET calculations, herbicide cost, application cost and crop value can be estimated fairly accurately by individual growers. However, other factors including potential crop yield, proportional loss per unit weed density and herbicide efficacy are more difficult to estimate because of the variability associated with weather, weed species composition, weed size and cropping system effects on these variables. The focus of this research is on the application cost (Ca). This consists of depreciating sprayer value, cost of operation (including fuel and maintenance) and labor costs. All costs will relate

Bauer and Mortensen (1992) discussed the Economic Optimum Threshold (EOT) concept. Economic thresholds generally refer to in-season decisions during a single crop year, and do not include a cost factor associated with possible increases in the soil seedbank due to lack of weed control. However, the term EOT is used to include the impact of seedbank

<sup>+</sup> <sup>=</sup> (1)

economic benefit of precision agriculture herbicide application technology.

**2. Economic thresholds** 

and Mortensen, 1992).

Te = economic threshold Ch = herbicide cost Ca = application cost Y = weed free crop yield P = value per unit of crop

Where

decisions can be made. The ET can be estimated by:

L = proportional loss per unit weed density

H = proportional reduction in weed density by the herbicide treatment.

to the size, segmentation and the control system of the sprayer.

• *Vicia sativa* L. = 2 – 10 /m2.

Within the same species the different values of ET result from the different costs and efficacy of the herbicides.

Black and Dyson (1993) developed a model for calculating the economic benefit of early spraying of herbicides in wheat and barley crops, using data derived from routine herbicide evaluation field experiments. The absolute yield benefit (kg/ha) from controlling a forecast proportion of estimated weed units present when the crop is sprayed at or before the early tillering stage has 3 determinants: the weed-free yield potential of the crop (kg/ha); the number of weeds /m2 at spraying; and relative growth stages of weeds and crop at spraying. The weight of evidence from the data used indicates that there is an approximately linear relationship between weed density after spraying and grain yield.

## **3. Economic simulations**

Barroso et al. (2004) simulated the effects of weed spatial pattern and resolution of mapping and spraying on economics of site-specific weed management (SSWM). They concluded that the economic benefits of using SSWM are related to the proportion of the field that is weedinfested, the number of weed patches and the spatial resolution of sampling and spraying technologies. Different combinations of these factors were simulated using parameter values obtained for *Avena sterilis ludoviciana* growing in Spanish winter barley crops. The profitability of SSWM systems increased as the proportion of the field infested decreased and when patch distribution was more concentrated. Positive net returns for SSWM were obtained when the weed-infested area was smaller than 30% with the highest return occurring at a 12 m X 12 m mapping and spraying resolution.

Paice et al. (1998) evaluated patch spraying using a stochastic simulation model incorporating Lloyd's Patchiness Index to quantify the patchiness of the weed distribution and the negative binomial distribution to measure distribution shape. They concluded that the long-term economic benefits of patch spraying are likely to be related to the initial spatial distribution, the demographic characteristics of the weed species and the weed control and crop husbandry practices to which they are subjected and that for a system conforming to their very exact specifications, patch spraying of *Alopecurus myosuroides* Huds

The Relationship Between Patch Spraying Cost and Target Weed Distribution 137

Table 1. Qualitative wed map class descriptions derived from quantified distance transform

Class 1 Class 2 Class 3

Class 4 Class 5 Class 6

Class 7 Class 8 Class 9

Fig. 1. Example weed maps in each of the 9 classes derived by quantified distance transform

Co (i) Co (ii) Co (iii)

analysis (Carroll and Holden, 2009)

Ci (1)

Ci (2)

Ci (3)

analysis.

**Class Description Ci Co**  1 Widely distributed large patches < 0.05 < 0.025 2 Large patches closer together than in class 1 < 0.05 0.025 – 0.1 3 Large spatially aggregated patches < 0.05 > 0.1 4 Medium, widely distributed patches 0.05 – 0.14 < 0.025 5 Medium patches closer together 0.05 - 0.14 0.025 – 0.1 6 Spatially aggregated medium sized patches 0.05 - 0.14 > 0.1 7 Small, widely distributed patches > 0.14 < 0.025 8 Small patches closer together > 0.14 0.025 – 0.1 9 Small spatially aggregated patches. > 0.14 > 0.1

would not be profitable in the long term if the control area was greater than 6m X 6m. The method was not developed for field application and focuses on the agronomic rather than the mechanization aspects of weed control.

The work presented in this chapter, and the preceding papers (Carroll and Holden, 2005, 2009), provides a practical, readily applied method of selecting the most appropriate spray technology for accurate patch spraying based on the weed distribution to be targeted, and to evaluate whether an economic benefit will arise from using the technology in preference to uniform application of herbicide over the whole field.

#### **4. Materials and methods**

#### **4.1 Weed maps and pattern quantification**

The weed maps used to develop the economic analysis were the same as those used by Carroll and Holden (2005) (all scanned with a resolution of 1 m per pixel and subject to a 2 pixel radius filter after thresholding to remove noise): (i) 23 maps of blackgrass (*Alopecurus myosuroides* Huds.) in cereal fields published in a report for the Home Grown Cereals Authority in the United Kingdom (Lutman et al., 1998) with a critical density of >3 plant/m2 and a minimum patch size of 25 m2; (ii) 14 maps of 'scutch grass' (*Elymus repens* L. Gould) delineated by field scouting after harvest from a farm in Ireland located in Co. Kilkenny (52.6 degrees north, 7.1 degrees west) with a critical density of *c*. 10 plants/m2; (iii) 9 maps delineating blackgrass (*Alopecurus myosuroides* Huds.) in cereals and sugar beet published by Gerhards and Christensen (2003), with a critical density of >5 plants/m2; and (iv) 4 maps published by Barroso et al. (2001) delineating sterile wild oat (*Avena Sterilis*) infestations mapped by 4 different methods: counting panicle contacts, scoring panicle density from the ground, scoring panicle density from a combine and counting seed rain on the ground. 5 plants/m2 was defined as the critical density.

The pattern of weeds as shown in each map was quantified by inward (subscript i) and outward (subscript o) distance transform analysis (Carroll and Holden, 2005) and summarised by an exponential association function fitted to the cumulative area probability distribution derived from the transformed image histogram:

$$y = a \left( b - e^{-c\_n \chi} \right) \tag{2}$$

where *a* and *b* values account for the error of the fitted curve from the data (1 – *ab* indicates the deviation of the fitted curve from the data), and the *cn* parameter represents the steepness of the curve (where n can be inward or outward). Values of cn were collected for each field and were used to group fields with similar weed distribution patterns (Table 1, Figure 1).

#### **4.2 Required resolution**

Carroll and Holden (2009) defined the minimum control requirements for each on the nine weed map classes (Figure 1) in terms of boom segmentation (BS) and control distance (CD) for acceptable patch spraying (based on Spray Quality Index, SQI) for both untreated weed maps (Table 2), and after a dilation and erosion image processing algorithm had been applied to consolidate many small patches into larger patches of lower average weed density (Table 3).

would not be profitable in the long term if the control area was greater than 6m X 6m. The method was not developed for field application and focuses on the agronomic rather than

The work presented in this chapter, and the preceding papers (Carroll and Holden, 2005, 2009), provides a practical, readily applied method of selecting the most appropriate spray technology for accurate patch spraying based on the weed distribution to be targeted, and to evaluate whether an economic benefit will arise from using the technology in preference to

The weed maps used to develop the economic analysis were the same as those used by Carroll and Holden (2005) (all scanned with a resolution of 1 m per pixel and subject to a 2 pixel radius filter after thresholding to remove noise): (i) 23 maps of blackgrass (*Alopecurus myosuroides* Huds.) in cereal fields published in a report for the Home Grown Cereals Authority in the United Kingdom (Lutman et al., 1998) with a critical density of >3 plant/m2 and a minimum patch size of 25 m2; (ii) 14 maps of 'scutch grass' (*Elymus repens* L. Gould) delineated by field scouting after harvest from a farm in Ireland located in Co. Kilkenny (52.6 degrees north, 7.1 degrees west) with a critical density of *c*. 10 plants/m2; (iii) 9 maps delineating blackgrass (*Alopecurus myosuroides* Huds.) in cereals and sugar beet published by Gerhards and Christensen (2003), with a critical density of >5 plants/m2; and (iv) 4 maps published by Barroso et al. (2001) delineating sterile wild oat (*Avena Sterilis*) infestations mapped by 4 different methods: counting panicle contacts, scoring panicle density from the ground, scoring panicle density from a combine and counting seed rain on the ground. 5

The pattern of weeds as shown in each map was quantified by inward (subscript i) and outward (subscript o) distance transform analysis (Carroll and Holden, 2005) and summarised by an exponential association function fitted to the cumulative area probability

where *a* and *b* values account for the error of the fitted curve from the data (1 – *ab* indicates the deviation of the fitted curve from the data), and the *cn* parameter represents the steepness of the curve (where n can be inward or outward). Values of cn were collected for each field and

Carroll and Holden (2009) defined the minimum control requirements for each on the nine weed map classes (Figure 1) in terms of boom segmentation (BS) and control distance (CD) for acceptable patch spraying (based on Spray Quality Index, SQI) for both untreated weed maps (Table 2), and after a dilation and erosion image processing algorithm had been applied to consolidate many small patches into larger patches of lower average weed density (Table 3).

were used to group fields with similar weed distribution patterns (Table 1, Figure 1).

( ) *nc x y ab e*<sup>−</sup> = − (2)

the mechanization aspects of weed control.

**4.1 Weed maps and pattern quantification** 

plants/m2 was defined as the critical density.

**4.2 Required resolution** 

distribution derived from the transformed image histogram:

**4. Materials and methods** 

uniform application of herbicide over the whole field.


Table 1. Qualitative wed map class descriptions derived from quantified distance transform analysis (Carroll and Holden, 2009)

Fig. 1. Example weed maps in each of the 9 classes derived by quantified distance transform analysis.

The Relationship Between Patch Spraying Cost and Target Weed Distribution 139

• Tractor: The size of tractor needed to power each sprayer was determined using ASAE Standards 2002a, EP496.2 (Table 4). List prices for these tractors were found in O' Mahony (2010). Using ASAE Standards 2002b, EP497.4 as a guide, depreciation, repair and maintenance and interest costs were calculated. The costs were then averaged over 10 years to give a cost of tractor use per hectare. Using average values of tractor work schedules (Forristal, 2005), 20% of the tractors yearly work was allocated to spraying. The Hardi Window 3.00 (Drouin, 1989) computer program was used to calculate fuel consumption and hence diesel costs based on tractor and sprayer size, distance to field

• Positioning/Control System (only needed for patch spraying): The costs of a Global Positioning System with the required accuracy (e.g. 1-5 m Carroll and Holden 2009), Geographic Information System to process data and create the various maps required (e.g. ArcView GIS and AgLeader SMS) and control system for patch sprayer operation (e.g. AgLeader Insight) were determined as per manufacturers list price and again

• Labor: The Hardi Window 3.00 (Drouin, 1989) computer program was used to calculate the work rates for each type sprayer in ha/hr over the model 100 ha farm (Table 6). This was then converted to a labor cost by multiplying by the Irish national agricultural

• Herbicide Costs: A herbicide cost of €66/ha for contact herbicide application in winter wheat crops was reported by O' Mahony (2010). There are typically three herbicide applications per year in Irish conditions. The first is a glyphosate spray on stubbled ground post harvest for control of grass weeds including scutch grass (*Elymus repens* L. Gould) and rye grasses (Lolium perenne spp.). The second spray (sulfonylureas) is applied post emergence for control of the common grass and broadleaved weeds including chickweed (Stellaria media spp.), speedwell (Veronica arvensis L.), charlock (Sinapsis arvensis L.) and knotgrass (Paspalum distichum L.). The third spray (amidosulfuron and Fenoxaprop-P (ethyl)) is applied for control of cleavers (Galium aparine) and wild oats (Avena fatua L.). For herbicide costs it was assumed that if a non-weed area is not sprayed, this will have no effect on future weed populations.

Tc = Ct + Cs + Cr + Cl + Ch (3)

Cr = cost of resolution (function of mapping/GPS/control system combination).

wage of €7.50/hr and allowing for three herbicide applications per year.

ASAE Standard 497.4 as a template.

and distance traveled within the field (Table 5).

allocated per unit area using ASAE Standard 497.4.

Total costs were calculated using equation 3.

Costs were calculated under the following headings

Where Tc = total cost Ct = cost of tractor Cs = cost of sprayer

Cl = cost of labor Ch = cost of herbicide

**5. Results and discussion** 

**5.1 Uniform application** 

specified in Tables 2 and 3. Average costs per hectare over 10 years were found using


Table 2. SQIs at different boom segment length (BS, m) and control distance (CD, m) combinations


Table 3. Results of weed map erosion and dilation.

No change in the minimum technology requirements was predicted to be needed for Classes 1 to 6 (i.e. large to medium sized patches from widely distributed to spatially aggregated). This was due to the fact that the erosion and dilation process had very little effect in these situations. Only maps in classes 7 to 9 really benefited from this processing because the small weed patches amalgamate and produce maps classified as class 5 or 6. Pre-processing provides a means to specify readily available and relatively inexpensive spraying technology and still make savings in herbicide use compared to uniform spraying (Carroll and Holden, 2005, 2009).

#### **4.3 Calculation of costs**

For each of the nine classes, costs were calculated for an assumed model tillage farm of 100ha under winter wheat for three situations: uniform application with cheapest possible combination of sprayer and tractor; patch spraying for 75% SQI with no pre-processing algorithm; and patch spraying for 75% SQI after pre-processing with erosion/dilation algorithm. Total costs were calculated using five sub-sections:

• Sprayer: Manufacturers list prices and data from O' Mahony (2010) were used to determine the price of sprayers that could satisfy the boom length and control distance

**(BS = 30, CD = 20) (BS = 3, CD = 2) BS CD** 

**Min Requirements for 75%** 

**Min requirements for 75% SQI (after)** 

**SQI** 

**Min requirements for 75% SQI (before)** 

**BS (m) CD (m) BS (m) CD (m)** 

**Control** 

**% error induced by processing** 

4 13 18 5 12 5 12 5 5 33 41 8 12 6 12 6 6 61 72 11 10 6 10 6 7 9 15 6 4 2 12 6 8 22 34 12 3 3 10 6 9 35 65 30 3 3 10 6

No change in the minimum technology requirements was predicted to be needed for Classes 1 to 6 (i.e. large to medium sized patches from widely distributed to spatially aggregated). This was due to the fact that the erosion and dilation process had very little effect in these situations. Only maps in classes 7 to 9 really benefited from this processing because the small weed patches amalgamate and produce maps classified as class 5 or 6. Pre-processing provides a means to specify readily available and relatively inexpensive spraying technology and still make savings in herbicide use compared to uniform spraying (Carroll

For each of the nine classes, costs were calculated for an assumed model tillage farm of 100ha under winter wheat for three situations: uniform application with cheapest possible combination of sprayer and tractor; patch spraying for 75% SQI with no pre-processing algorithm; and patch spraying for 75% SQI after pre-processing with erosion/dilation

• Sprayer: Manufacturers list prices and data from O' Mahony (2010) were used to determine the price of sprayers that could satisfy the boom length and control distance

1 78 97 30 20 2 72 95 30 13 3 80 96 30 20 4 45 91 12 5 5 50 90 12 6 6 60 88 10 6 7 15 80 4 2 8 27 79 3 3 9 25 80 3 3 Table 2. SQIs at different boom segment length (BS, m) and control distance (CD, m)

**Class SQI @ Min Control SQI @ Max Available** 

**Average weed area after processing (%)**

Table 3. Results of weed map erosion and dilation.

algorithm. Total costs were calculated using five sub-sections:

combinations

**Class Average** 

**initial weed area (%)** 

and Holden, 2005, 2009).

**4.3 Calculation of costs** 

specified in Tables 2 and 3. Average costs per hectare over 10 years were found using ASAE Standard 497.4 as a template.


Total costs were calculated using equation 3.

$$\mathbf{T}\_c = \mathbf{C}\_l + \mathbf{C}\_s + \mathbf{C}\_r + \mathbf{C}\_l + \mathbf{C}\_h \tag{3}$$

Where Tc = total cost Ct = cost of tractor Cs = cost of sprayer Cr = cost of resolution (function of mapping/GPS/control system combination). Cl = cost of labor Ch = cost of herbicide

#### **5. Results and discussion**

#### **5.1 Uniform application**

Costs were calculated under the following headings

The Relationship Between Patch Spraying Cost and Target Weed Distribution 141

3. Positioning/control system: for uniform application no mapping, positioning or control

4. Labor: labor costs were calculated based on a sprayer work rate as shown in Table 6 and the agricultural minimum wage of €7.50 per hour to give a value of €4.09/ha for the 15

5. Herbicide: From O' Mahony (2010) a herbicide cost of €66/ha was allocated for the

Costs were calculated under the same headings as for uniform for systems required for 75% SQI and best available technology in Ireland at the current time before and after processing

1. Sprayer: For each group the cost of sprayers with a resolution required for 75% SQI and best available technology (BAT) from table 2 were calculated. For 75% SQI it was found that the sprayer used in the uniform application had the necessary resolution for groups 1 to 3 to give a cost of €20.73/ha. For groups 4 to 6 a 15 m sprayer with control over 3 x 5 m segments and a control distance of less than 6 m was required. A sprayer with this resolution retailed at €15,000 to give a cost/ha of €25.91. For groups 7 to 9 a 15 m sprayer with control over 5 x 3m segments and a control distance less than 3 m was required. A retail price of €17,000 led to an allocation of €29.36/ha over 10 years. After pre-processing the required resolution of the sprayer remained the same for groups 1 to 6 so the same costs were incurred. For groups 7 to 9 the required resolution was decreased so the same sprayer as used for groups 1 to 6 could be used to give a cost of

From table 2 the best available technology has control over 3m boom sections at a control distance of approximately 2m on a 15m boom. A retail price of €19,000 led to an allocation of €32.82/ha over 10 years to each group. Other emerging technologies may in the future lead to a much higher accuracy but as yet are not suited for herbicide application at high

2. Tractor: For both 75% and best available technology with and without pre-processing, the 15m boom was used for each group as described above to give a cost of €13.82/ha

with the erosion/dilation algorithm. At 75% SQI efficacy of spray is at or near 100%.

**Number of runs Labor** 

**(€/ha)** 

**Labor cost (€/hr)** 

10 4.1 7.50 3 5.63 12 4.5 7.50 3 5.01 15 5.5 7.50 3 4.09 18 6.4 7.50 3 3.52 21 7.4 7.50 3 3.04 24 8.1 7.50 3 2.81 27 9.6 7.50 3 2.34 30 10.2 7.50 3 2.21

systems are used so none of these costs are incurred in this situation.

m sprayer over the 3 herbicide sprays.

three contact herbicide applications.

Table 6. Labor costs/ha calculations

€25.91/ha, a decrease of €3.45/ha.

resolutions in cereal crops.

**5.2 Patch spraying** 

**Work Rate (ha/hr)** 

**Boom Length** 

**(m)** 



Table 4. PTO and Tractor power requirements for different sprayer boom lengths

The costs were calculated as per {Table 7} and 20% of yearly tractor work was allocated to spraying. Diesel costs at €0.40/l (Table 5) were obtained using Hardi Window 3.00 program, which calculates sprayer use based on tractor and sprayer size, distance from field and distance traveled within the field and found to be €2.80/ha over three applications for this situation.


Table 5. Diesel Cost based on different sprayer and tractor combinations.



Table 6. Labor costs/ha calculations

#### **5.2 Patch spraying**

140 Weed Control

1. Sprayer: It was assumed that a simple 15m sprayer with manual valve operation could be used for uniform application of herbicide over the entire 100 ha model farm. This is a fairly typical sprayer used for these operations on Irish farms (Rice, 2005). A typical sprayer of this type has a list price of €12,000 and, allocated over 10 years, gives an average cost per hectare of €20.73. For the largest model available (30m) the extra costs of purchasing the equipment (€49,000) and the larger tractor (€90,000) were not found to be justified in this situation. However for a larger operation economies of scale may

2. Tractor: Using the data from ASAE Standard EP496.2 it was determined that a tractor of 45 kW is required to operate this 15 m sprayer. Allowing for adverse field conditions and the use of a slightly larger tractor also used for many other farm operations, it was decided that a 65kW tractor at cost of €40,000 (O' Mahony, 2010) would be used in this

> **Required Tractor Power (kW)**

> > **Work Rate (ha/hr)**

**Actual Tractor Power (kW)** 

> **Diesel Cost (€/ha)**

lead to larger sprayers being much more economically viable.

10 24.6 29.6 50 12 29.5 35.6 55 15 36.9 44.5 65 18 44.2 53.4 75 21 51.7 62.2 85 24 59.1 71.1 90 27 66.4 80.1 100 30 73.8 88.9 110

Table 4. PTO and Tractor power requirements for different sprayer boom lengths

The costs were calculated as per {Table 7} and 20% of yearly tractor work was allocated to spraying. Diesel costs at €0.40/l (Table 5) were obtained using Hardi Window 3.00 program, which calculates sprayer use based on tractor and sprayer size, distance from field and distance traveled within the field and found to be €2.80/ha over three applications for

> **Tractor size (kW)**

10 800 50 4.1 2.97 12 800 55 4.5 2.91 15 1000 65 5.5 2.80 18 1200 75 6.4 2.76 21 1500 85 7.4 2.76 24 1500 90 8.1 2.67 27 2500 100 9.6 2.46 30 2500 110 10.2 2.55

Table 5. Diesel Cost based on different sprayer and tractor combinations.

situation to give an allocation of €13.82/ha.

**Tank Size** 

**(l)** 

**PTO Power (kW)** 

**Sprayer Width** 

this situation.

**Boom Length** 

**(m)** 

**(m)** 

Costs were calculated under the same headings as for uniform for systems required for 75% SQI and best available technology in Ireland at the current time before and after processing with the erosion/dilation algorithm. At 75% SQI efficacy of spray is at or near 100%.

1. Sprayer: For each group the cost of sprayers with a resolution required for 75% SQI and best available technology (BAT) from table 2 were calculated. For 75% SQI it was found that the sprayer used in the uniform application had the necessary resolution for groups 1 to 3 to give a cost of €20.73/ha. For groups 4 to 6 a 15 m sprayer with control over 3 x 5 m segments and a control distance of less than 6 m was required. A sprayer with this resolution retailed at €15,000 to give a cost/ha of €25.91. For groups 7 to 9 a 15 m sprayer with control over 5 x 3m segments and a control distance less than 3 m was required. A retail price of €17,000 led to an allocation of €29.36/ha over 10 years. After pre-processing the required resolution of the sprayer remained the same for groups 1 to 6 so the same costs were incurred. For groups 7 to 9 the required resolution was decreased so the same sprayer as used for groups 1 to 6 could be used to give a cost of €25.91/ha, a decrease of €3.45/ha.

From table 2 the best available technology has control over 3m boom sections at a control distance of approximately 2m on a 15m boom. A retail price of €19,000 led to an allocation of €32.82/ha over 10 years to each group. Other emerging technologies may in the future lead to a much higher accuracy but as yet are not suited for herbicide application at high resolutions in cereal crops.

2. Tractor: For both 75% and best available technology with and without pre-processing, the 15m boom was used for each group as described above to give a cost of €13.82/ha

The Relationship Between Patch Spraying Cost and Target Weed Distribution 143

4. Labor: For both 75% SQI and best available technology the 15 m boom gave a work rate of 5.5 ha/hr to give a labor cost of €4.09/ha over the three spray applications from

5. Herbicide: Calculated based on average percent weed in each class before and after preprocessing from Carroll and Holden (2009) as shown in table 8. As can be seen for the 75% and best available technology categories the cost of herbicide will depend on the

Once all these costs had been calculated they were collected and combined to give an overall cost for spraying with the three different methods for each group. Examples of 3 groups are

**Sprayer** 20.73 20.73 32.82 20.73 25.91 32.82 20.73 29.36 32.82

**Tractor** 16.62 16.62 16.62 16.62 16.62 16.62 16.62 16.62 16.62 **Labor** 4.09 4.09 4.09 4.09 4.09 4.09 4.09 4.09 4.09 **Herbicide** 66 16.50 13.60 66 27.23 23.96 66 28.88 27.7 **Total** 107.44 82.46 96.56 107.44 98.73 106.92 107.44 103.47 110.68

Table 9. Total cost of uniform versus patch spraying for some sample groups.

 **75% SQI B.A.T. 75% SQI B.A.T.** 1 107.44 82.46 96.56 - - 2 107.44 89.06 102.36 - - 3 107.44 96.49 108.36 - - 4 107.44 81.87 92.31 85.99 95.91 5 107.44 98.73 106.92 104.97 112.73 6 107.44 121.47 128.05 130.54 136.18 7 107.44 82.02 90.09 83.52 94.84 8 107.44 92.74 100.53 99.12 110.11 9 107.44 103.47 110.68 124.77 134.44

**At control requirements for (€/ha)** 

0 24.52 29.43 0 24.52 29.43 0 24.52 29.43

**75% B.A.T. 75% B.A.T. 75% B.A.T.** 

**Before processing After processing** 

**Uniform (€/ha)**  **At control requirements for (€/ha)** 

shown in Table 9 and the final figures for all groups are shown in Table 10.

**Cost Group 1 Group 5 Group 9** 

**Uniform (€/ha)** 

Table 6.

percent weed present in the field.

**At control requirements for (€/ha)** 

**Uniform (€/ha)** 

**Group Total costs (€/ha) @** 

**Spraying** 

Table 10. Total costs at different resolution levels

 **Uniform** 

**GPS/ Mapping/ Control System** 

using the method as shown in Table 7. The first column shows the amount of depreciation (at 15% cumulative per annum) in each of the 10 years that the tractor is used. The second column shows repair and maintenance costs, which will naturally increase, as the tractor gets older. The 3rd column shows interest on capital expenditure at 5% per annum. The costs were then averaged over 10 years per hectare. Diesel costs came to €2.80/ha.


Table 7. Allocation of costs over 10 years in €/ha for a €50,000 tractor


Table 8. Herbicide cost at different accuracy levels

3. GPS/Mapping/Control System: mapping costs of €15/ha for resolution required for 75% SQI and €18/ha for best available technology were allocated (Barroso et al, 2004). The AgLeader Insight system, which provides positioning, control, and analysis components at a retail price of €5,000 was used as the base for the control system. This gave a cost of €9.52/ha. For best available technology a retail price of €6,000 was assumed to give a cost of €11.43/ha over 10 years.

**Year Depreciation R&M Interest Cost/ha**  1 7500 166 2125 97 2 6375 833 1806 90 3 5418 1166 1535 81 4 4605 1666 1305 75 5 3915 3333 1109 83 6 3327 5000 942 92 7 2828 5833 801 94 8 2404 7500 681 105 9 2040 9166 579 117 10 1737 10000 492 122

 **75% SQI B.A.T. 75% SQI B.A.T.**  1 20 20 66 16.50 13.60 16.50 13.60 2 28 28 66 23.10 19.40 23.10 19.40 3 37 37 66 30.53 25.40 30.53 25.40 4 13 18 66 10.73 9.35 14.85 12.95 5 33 41 66 27.73 23.96 33.83 29.77 6 61 72 66 50.33 45.09 59.40 53.22 7 9 15 66 7.43 7.13 12.38 11.88 8 22 34 66 18.15 17.57 28.05 27.15 9 35 65 66 28.88 27.72 53.63 51.48

3. GPS/Mapping/Control System: mapping costs of €15/ha for resolution required for 75% SQI and €18/ha for best available technology were allocated (Barroso et al, 2004). The AgLeader Insight system, which provides positioning, control, and analysis components at a retail price of €5,000 was used as the base for the control system. This gave a cost of €9.52/ha. For best available technology a retail price of €6,000 was

Table 7. Allocation of costs over 10 years in €/ha for a €50,000 tractor

**Group % weed Herbicide Cost (€)** 

Table 8. Herbicide cost at different accuracy levels

assumed to give a cost of €11.43/ha over 10 years.

**After processing** 

came to €2.80/ha.

 **Before** 

**processing** 

using the method as shown in Table 7. The first column shows the amount of depreciation (at 15% cumulative per annum) in each of the 10 years that the tractor is used. The second column shows repair and maintenance costs, which will naturally increase, as the tractor gets older. The 3rd column shows interest on capital expenditure at 5% per annum. The costs were then averaged over 10 years per hectare. Diesel costs

**Uniform Before processing After processing** 


Once all these costs had been calculated they were collected and combined to give an overall cost for spraying with the three different methods for each group. Examples of 3 groups are shown in Table 9 and the final figures for all groups are shown in Table 10.


Table 9. Total cost of uniform versus patch spraying for some sample groups.


Table 10. Total costs at different resolution levels

The Relationship Between Patch Spraying Cost and Target Weed Distribution 145

Barroso, J., Fernandez-Quintilla, C., Maxwell, B. and Rew, L., 2004. Simulating the effects of

Barroso, J., Ruiz, D., Fernandez-Quintanilla, C., Ribeiro, A. and Diaz, B., 2000. Comparison

Bauer, T.A. and Mortensen, D.A., 1992. A comparison of economic and economic optimum thresholds for two annual weeds in soybean. *Weed technology,* 6, 228 – 235. Black, I.D. and Dyson, C.B., 1993. An economic threshold model for spraying herbicides in

Carroll, J.P. and Holden, N.M., 2005. A method to quantify weed distribution for relating to

Carroll, J.P. and Holden, H. H., 2009. Modeling the relationship between patch sprayer performance and weed distribution. *Transactions of the ASABE*, 52:1051-1056. Coble, H.D. and Mortensen, D.A., 1992. The threshold concept and its application to weed

Drouin, B., 1989. Developer of Hardi Window Version 3.00. Application Technology Group for Hardi International A/S. Copyright Baler Software Corporation. Ford, A., Dotray, P., Keeling, J., Wilkerson, J., Wilcut, J. and Gilbert, L., 2011. Site-specific weed management in cotton using WebHADSS. *Weed Technology*, 25, 107 – 112. Forristal, D., 2005. Department of Engineering. Teagasc, OakPark, Ireland. Personal

Gerhards, R. and Christensen, S., 2003. Real-time weed detection, decision making and

Godwin, R.J. and Miller, P.C.H., 2003. A review of the technologies for mapping within-field

Lutman, P.J.W., Rew, L.J., Cussans, G.W., Miller, P.C.H., Paice, M.E.R., & Stafford, J.E. 1998.

Mortensen, D.A., Johnson, G.A., Wyse, W.Y. and Martin, A.R. 1995. Managing Spatially

Paice, M.E.R., Miller, P.C.H. and Lark, A.G., 1997. The response characteristics of a patch spraying system based on injection metering. *Aspects of Applied Biology,* 48, 41 – 48. Paice, M.E.R., Day, W., Rew, L.J. and Howard, A., 1998. A stochastic simulation model for evaluating the concept of patch spraying. *Weed Research*, 38, 373-388. Rice, B., 2005. Head of Engineering Department, Teagasc, OakPark, Ireland. Personal

Weaver, S.E., 1996. Simulation of crop-weed competition models and their applications.

Wilkerson, G., Price, A., Bennett, A., Krueger, D., Roberson, G. and Robinson, B., 2004.

Evaluating the potential for site-specific herbicide application in soybean. *Weed* 

*International Conference,* 397 – 415, ASA, CSSA, CSSA, Madison, WI.

patch spraying in maize, sugar beet, winter wheat and winter barley. *Weed Research*,

Development of a 'Patch Spraying' system to control weeds in winter wheat. *Home* 

Variable Weed Populations. *Site-Specific Management for Agricultural Systems, 2nd*

patch spraying systems. *Transactions of the ASAE* 48(1), 27 – 35.

specific management. *Weed Research*, 44, 460 – 468.

Nationale Superieure Agronomique, 578 – 581.

cereals. *Weed Research,* 33, 279 – 290.

science. *Weed Technology,* 6, 191 – 195.

variability. *Biosystems Engineering,* 84, 393 – 407.

*Grown Cereals Authority Project Report No.* 158, London, U.K.

O' Mahony, J., 2010. Crop Costs and Returns. Teagasc, Oak Park Carlow.

Communication.

Communication.

*Phytoprotection,* 77, 3 – 11.

*Technology*, 18, 1101 – 1110.

43, 385 – 392.

weed spatial pattern and resolution of mapping and spraying on economics of site-

of various sampling methodologies for site-specific sterile wild oat management. *Proceedings of the 3rd European Conference on Precision Agriculture,* Montpellier Ecole

By analyzing the above data, the cost benefit from patch spraying can be described using the following function.

Cost benefit = f (group, percent weed, required resolution)

The required resolution will be determined by the group to which the field is allocated and the resolution cost can be described using the function.

Resolution cost = f(sprayer, tractor, mapping, positioning, control system)

It is clear from the above data that patch spraying at 75% SQI would give a reduction in costs in most cases. This reduction is very much related to the percent weed present with a Pearson's Correlation Coefficient of 0.975 (*p* < 0.0001). As percentage weed increases the benefits derived from patch spraying will decrease linearly. In the groups with well spread out weed patches (1,4 and 7) cost benefits of up to €25/ha could be achieved. Even though the costs were greater in group 7 due to the increased resolution of the spraying system a large benefit could still be achieved due to the major reduction in herbicides. Cost benefits are least in the spatially aggregated patch groups due mostly to the fact that in these groups the percent weed is almost always greater than in other groups. The dilation/erosion preprocessing, while reducing the equipment costs in groups 7 to 9 actually led to an increased patch spraying cost in all cases. This was due to the production of more weed pixels by the process and hence an increase in percent weed, which led to greater herbicide costs.

While these results focus only on the sprayers and tractors required for specific use on a model 100 ha winter wheat farm, the data from Tables 4,5 and 6 could be used to allocate costs based on different sized systems.

#### **6. Conclusion**

Using the above method it is clear that patch spraying using basic, readily available equipment should be economically advantageous in certain situations. For many of the weed map classes containing medium to large weed patches (1 to 6) there should be economic benefits (up to €25/ha) from patch spraying. Some benefits are also expected in fields with smaller, more aggregated weed patches but at higher weed populations the extra cost of more sophisticated equipment may outweigh the savings from reduced herbicide usage. For patch spraying to become a more attractive option to farmers a cheap, standardized mapping method must be maintained and control systems that can adapt normal sprayers for site specific application must become more readily available and cost effective. If these conditions are met and the methods described by Carroll and Holden (2005, 2009) are used to allocate the correct sprayer to the correct field and weed distribution, patch spraying may be of great economic benefit to a large number of farmers as well as decreasing pesticide introduced into the agro-environment.

#### **7. References**


By analyzing the above data, the cost benefit from patch spraying can be described using the

The required resolution will be determined by the group to which the field is allocated and

It is clear from the above data that patch spraying at 75% SQI would give a reduction in costs in most cases. This reduction is very much related to the percent weed present with a Pearson's Correlation Coefficient of 0.975 (*p* < 0.0001). As percentage weed increases the benefits derived from patch spraying will decrease linearly. In the groups with well spread out weed patches (1,4 and 7) cost benefits of up to €25/ha could be achieved. Even though the costs were greater in group 7 due to the increased resolution of the spraying system a large benefit could still be achieved due to the major reduction in herbicides. Cost benefits are least in the spatially aggregated patch groups due mostly to the fact that in these groups the percent weed is almost always greater than in other groups. The dilation/erosion preprocessing, while reducing the equipment costs in groups 7 to 9 actually led to an increased patch spraying cost in all cases. This was due to the production of more weed pixels by the

process and hence an increase in percent weed, which led to greater herbicide costs.

as well as decreasing pesticide introduced into the agro-environment.

While these results focus only on the sprayers and tractors required for specific use on a model 100 ha winter wheat farm, the data from Tables 4,5 and 6 could be used to allocate

Using the above method it is clear that patch spraying using basic, readily available equipment should be economically advantageous in certain situations. For many of the weed map classes containing medium to large weed patches (1 to 6) there should be economic benefits (up to €25/ha) from patch spraying. Some benefits are also expected in fields with smaller, more aggregated weed patches but at higher weed populations the extra cost of more sophisticated equipment may outweigh the savings from reduced herbicide usage. For patch spraying to become a more attractive option to farmers a cheap, standardized mapping method must be maintained and control systems that can adapt normal sprayers for site specific application must become more readily available and cost effective. If these conditions are met and the methods described by Carroll and Holden (2005, 2009) are used to allocate the correct sprayer to the correct field and weed distribution, patch spraying may be of great economic benefit to a large number of farmers

*ASAE Standards,* 49th Edition, 2002a EP496.2 Agricultural Machinery Management. St.

*ASAE Standards,* 49th Edition, 2002b EP497.4 Agricultural Machinery Management. St.

Cost benefit = f (group, percent weed, required resolution)

Resolution cost = f(sprayer, tractor, mapping, positioning, control system)

the resolution cost can be described using the function.

costs based on different sized systems.

Joseph, Michigan: ASAE.

Joseph, Michigan: ASAE.

**6. Conclusion** 

**7. References** 

following function.


**Weeds in Forestry and** 

*Serbia* 

**Possibilities of Their Control** 

*2University of Novi Sad, Faculty of Agriculture, Novi Sad,* 

Verica Vasic1, Branko Konstantinovic2 and Sasa Orlovic1

*1University of Novi Sad, Institute of Lowland Forestry and Environment, Novi Sad,* 

Thanks to wide inter-row spaces and open canopy in the early phases of establishment, forest nurseries and plantations represent ideal places of floristically rich and diverse weed flora. Weeds have an exceptional capacity of adaptation to environmental conditions

Although the geographic weed distribution and composition depends mainly on climate factors, the vegetation within each climate region is differentiated under the effect of edaphic factors. The soil physical and chemical properties, as well as climate conditions,

However, all weeds do not have equal significance. When considering weed control attibutes, perennial weeds present are far greater challange due to difficulties employing mechanical means, because perennials are often stimulated to grow and disperse even more intensively. Perennial weed species, such as *Sorghum halepense, Convolvulus arvensis*, and *Cynodon dactylon,* have well-developed underground organs and are great problems not

The problem of forestry weeds came to the fore in recent years as more and more attention has been paid to establishing and restoring forests. In afforested areas, luxuriate development of weed vegetation, can imperil the survival and development of young seedlings. Harmful effects of weeds are reflected not only in the subtraction of basic living conditions such as humidity, light and nutrients already undergo a poor growth and

When defining a weed, it should be emphasized that there is no simple and precise definition. Kojic et al., (1996) are of the opinion that weeds are plants growing among cultivated plants and interfere with man's activities; Zekic (1983) stated that weeds are plants growing in places they are not wanted. The concept of a weed is relative in nature, i.e., there is no sharp boundary between weeds and cultivated plants. While in some regions, one plant species is considered a weed, the same species is cultivated in others.

because most produce vast quantities of seeds which enable great expansion.

only in agriculture, but also in nursery production of forest planting materials.

have the primary significance for both cultivated plants and weeds.

**1. Introduction** 

receiving of seedlings.

**2. The concept of weeds in forestry** 

Zanin, G., Berti, A. and Taniolo, L., 1993. Estimation of economic thresholds for weed control in winter wheat. *Weed Research,* 33, 459 – 467. **8** 

## **Weeds in Forestry and Possibilities of Their Control**

Verica Vasic1, Branko Konstantinovic2 and Sasa Orlovic1 *1University of Novi Sad, Institute of Lowland Forestry and Environment, Novi Sad, 2University of Novi Sad, Faculty of Agriculture, Novi Sad, Serbia* 

#### **1. Introduction**

146 Weed Control

Zanin, G., Berti, A. and Taniolo, L., 1993. Estimation of economic thresholds for weed

Thanks to wide inter-row spaces and open canopy in the early phases of establishment, forest nurseries and plantations represent ideal places of floristically rich and diverse weed flora. Weeds have an exceptional capacity of adaptation to environmental conditions because most produce vast quantities of seeds which enable great expansion.

Although the geographic weed distribution and composition depends mainly on climate factors, the vegetation within each climate region is differentiated under the effect of edaphic factors. The soil physical and chemical properties, as well as climate conditions, have the primary significance for both cultivated plants and weeds.

However, all weeds do not have equal significance. When considering weed control attibutes, perennial weeds present are far greater challange due to difficulties employing mechanical means, because perennials are often stimulated to grow and disperse even more intensively. Perennial weed species, such as *Sorghum halepense, Convolvulus arvensis*, and *Cynodon dactylon,* have well-developed underground organs and are great problems not only in agriculture, but also in nursery production of forest planting materials.

The problem of forestry weeds came to the fore in recent years as more and more attention has been paid to establishing and restoring forests. In afforested areas, luxuriate development of weed vegetation, can imperil the survival and development of young seedlings. Harmful effects of weeds are reflected not only in the subtraction of basic living conditions such as humidity, light and nutrients already undergo a poor growth and receiving of seedlings.

#### **2. The concept of weeds in forestry**

When defining a weed, it should be emphasized that there is no simple and precise definition. Kojic et al., (1996) are of the opinion that weeds are plants growing among cultivated plants and interfere with man's activities; Zekic (1983) stated that weeds are plants growing in places they are not wanted. The concept of a weed is relative in nature, i.e., there is no sharp boundary between weeds and cultivated plants. While in some regions, one plant species is considered a weed, the same species is cultivated in others.

Weeds in Forestry and Possibilities of Their Control 149

and phenophases of plant development which is expressed differently in individual phases. In most cases rooting intensity is very low or completely absent during summer months. The time prior to or at the end of a vegetation period is the most favorable for vegetative regeneration and maximum regeneration ability occurs during spring and autumn (Vrbvnicanin & Kojic, 2000). Regeneration ability of vegetative ground organs depends greatly on environmental factors. Favorable climatic conditions, and in particular, the optimal condition of soil moisture and temperature, affect the regeneration process of

Large quantities of seed produced by weeds would not be able to establish and develop in the immediate vicinity of a mother plant. Therefore, the fact that weeds are able to find different ways of quick and efficient seed and fruit distribution is fully justified from a biological point of view (Konstantinovic et al., 2005). In order for weed control to be successful, the means of their distribution must be known. Weeds can be spread by the plant

Weeds present a large challenge both in agriculture and in forestry. They form a large mass of aboveground and inground organs engaged in a competitive relationship with cultivated plants for light, water and nutritive components in the soil (Kojic et al., 1972). Damages caused to cultivated plants by weeds can be great. According to some opinions, damages caused by weeds are greater than those caused by diseases and pests together (Kojic et al., 1996). Weedy plants grow relatively faster in forest settings and displace young forest plants living space, overshadow and stifle them, and water and nutritive matters are taken at the

Far less favorable impacts of weeds are found in nursery production. Due to the presence of weeds, nursery plants can experience retardation of growth, chlorosis, reduced resistance to plant diseases and pests, and death of individual parts of branches or crowns; if the weed is abundant, it often leads to drying and deterioration of the entire plant (Zekic, 1983). If weed control in nurseries is lacking, nursery plants of poor quality and fewer total plants are

Competitive division of weeds in forestry is often made according to the degree of harmfulness of weeds to the trees. According to Vajda (1983) weeds in forestry are classified as either useful or harmful; Konstantinovic (1999) categorizes weeds into useful, harmful, or

vegetative organs.



obtained.

**5. The spread of weeds** 

**6. Damage from weeds** 

expense of cultivated plants.

**7. Distribution of weeds in forestry** 

indifferent. According to this classification:

itself, i.e. by self-distribution, and by other factors:

Vajda (1973) considered as forest weeds those plant species interfering with germination and growth of young forest plants and Konstantinovic (1999) those which were unfavourable under certain circumstances in the forest and interfere with forest management. According to Kovacevic (1979) the weeds in forestry are all herbaceous plants, shrubs, and trees which, in forest nurseries, stands, and clear felled areas weaken or prevent the growth and development of cultivated trees.

Evidence of the benefits of weed control for enhanced tree growth is widespread however weeds in forestry are not always harmful. Herbaceous weeds in forest plantations represent food for livestock (DiTomaso, 1997; Papachristou et al., 2009) and dry weights of some weeds are used as bedding for livestock. The fruits of some weeds are edible and weeds while they are somewhere used for human consumption. Some weeds have medicinal properties and are used as medicinal plants (Stepp & Moerman, 2001; Stepp, 2004; Dhole et al., 2009). Weeds prevent soil erosion and can be a shelter for wild animals and birds. However it should be noted that the benefits of weeds significantly less than the damage caused.

#### **3. The properties of weed species**

The knowledge gained from the study weed biology under given agro ecological conditions represents the basis in choosing the appropriate measures for their control. Compared to cultivated plants, weedy plants show considerable plasticity in relation to numerous ecological factors. One of the most important weed traits is the expressed adaptation ability. Another important weed trait is the pronounced resistance to unfavourable environmental conditions (drought, moisture, wind etc.). Many weeds are resistant to plant diseases and pests. Also, one of the weed traits is the periodicity of germination. Very often weed seeds do not germinate at once, but rather in different time periods, and it is hard to control weeds simultaneously. In addition, many weeds produce an enormous quantity of seed, which makes it easier for them to spread and expand in space.

#### **4. Propagation of weeds**

According to reproduction method, weed plants may be divided into those propagating only sexually, i.e. from seed, and those which are also vegetatively propagated. Sexual weed reproduction results in the formation of seed - reproductive organs by which the weeds are dispersed. All annual weedy plants belong to the group of weeds reproducing only by seed. Under favorable conditions, weeds produce an enormous quantity of seed, even several million of seeds per individual plant. After ripening a great part of the weed seed will end up on the soil surface and subsequently incorporated into the soil by tillage or other means. According to Wilson et al. (1985), under conditions of great weed infestations, some 300 million to 3.5 billion seeds can be found per one hectare of soil. Presence of weed seed in the soil depends on many factors and varies from field to field and region to region (Lutman, 2002).

Vegetative reproduction of perennial weeds represents a very efficient tool for their quick regeneration and distribution. Vegetative reproduction, i.e. regeneration ability of ground vegetative organs, depends first of all on their physiological state. In other words, there is a correlation between intensity of rooting of the rhizome and other ground vegetative organs and phenophases of plant development which is expressed differently in individual phases. In most cases rooting intensity is very low or completely absent during summer months. The time prior to or at the end of a vegetation period is the most favorable for vegetative regeneration and maximum regeneration ability occurs during spring and autumn (Vrbvnicanin & Kojic, 2000). Regeneration ability of vegetative ground organs depends greatly on environmental factors. Favorable climatic conditions, and in particular, the optimal condition of soil moisture and temperature, affect the regeneration process of vegetative organs.

### **5. The spread of weeds**

Large quantities of seed produced by weeds would not be able to establish and develop in the immediate vicinity of a mother plant. Therefore, the fact that weeds are able to find different ways of quick and efficient seed and fruit distribution is fully justified from a biological point of view (Konstantinovic et al., 2005). In order for weed control to be successful, the means of their distribution must be known. Weeds can be spread by the plant itself, i.e. by self-distribution, and by other factors:


148 Weed Control

Vajda (1973) considered as forest weeds those plant species interfering with germination and growth of young forest plants and Konstantinovic (1999) those which were unfavourable under certain circumstances in the forest and interfere with forest management. According to Kovacevic (1979) the weeds in forestry are all herbaceous plants, shrubs, and trees which, in forest nurseries, stands, and clear felled areas weaken or prevent

Evidence of the benefits of weed control for enhanced tree growth is widespread however weeds in forestry are not always harmful. Herbaceous weeds in forest plantations represent food for livestock (DiTomaso, 1997; Papachristou et al., 2009) and dry weights of some weeds are used as bedding for livestock. The fruits of some weeds are edible and weeds while they are somewhere used for human consumption. Some weeds have medicinal properties and are used as medicinal plants (Stepp & Moerman, 2001; Stepp, 2004; Dhole et al., 2009). Weeds prevent soil erosion and can be a shelter for wild animals and birds. However it should be noted that the benefits of weeds significantly less than the damage

The knowledge gained from the study weed biology under given agro ecological conditions represents the basis in choosing the appropriate measures for their control. Compared to cultivated plants, weedy plants show considerable plasticity in relation to numerous ecological factors. One of the most important weed traits is the expressed adaptation ability. Another important weed trait is the pronounced resistance to unfavourable environmental conditions (drought, moisture, wind etc.). Many weeds are resistant to plant diseases and pests. Also, one of the weed traits is the periodicity of germination. Very often weed seeds do not germinate at once, but rather in different time periods, and it is hard to control weeds simultaneously. In addition, many weeds produce an enormous quantity of seed, which

According to reproduction method, weed plants may be divided into those propagating only sexually, i.e. from seed, and those which are also vegetatively propagated. Sexual weed reproduction results in the formation of seed - reproductive organs by which the weeds are dispersed. All annual weedy plants belong to the group of weeds reproducing only by seed. Under favorable conditions, weeds produce an enormous quantity of seed, even several million of seeds per individual plant. After ripening a great part of the weed seed will end up on the soil surface and subsequently incorporated into the soil by tillage or other means. According to Wilson et al. (1985), under conditions of great weed infestations, some 300 million to 3.5 billion seeds can be found per one hectare of soil. Presence of weed seed in the soil depends on many factors and varies from field to field and region to region (Lutman,

Vegetative reproduction of perennial weeds represents a very efficient tool for their quick regeneration and distribution. Vegetative reproduction, i.e. regeneration ability of ground vegetative organs, depends first of all on their physiological state. In other words, there is a correlation between intensity of rooting of the rhizome and other ground vegetative organs

the growth and development of cultivated trees.

**3. The properties of weed species** 

**4. Propagation of weeds** 

makes it easier for them to spread and expand in space.

caused.

2002).


#### **6. Damage from weeds**

Weeds present a large challenge both in agriculture and in forestry. They form a large mass of aboveground and inground organs engaged in a competitive relationship with cultivated plants for light, water and nutritive components in the soil (Kojic et al., 1972). Damages caused to cultivated plants by weeds can be great. According to some opinions, damages caused by weeds are greater than those caused by diseases and pests together (Kojic et al., 1996). Weedy plants grow relatively faster in forest settings and displace young forest plants living space, overshadow and stifle them, and water and nutritive matters are taken at the expense of cultivated plants.

Far less favorable impacts of weeds are found in nursery production. Due to the presence of weeds, nursery plants can experience retardation of growth, chlorosis, reduced resistance to plant diseases and pests, and death of individual parts of branches or crowns; if the weed is abundant, it often leads to drying and deterioration of the entire plant (Zekic, 1983). If weed control in nurseries is lacking, nursery plants of poor quality and fewer total plants are obtained.

#### **7. Distribution of weeds in forestry**

Competitive division of weeds in forestry is often made according to the degree of harmfulness of weeds to the trees. According to Vajda (1983) weeds in forestry are classified as either useful or harmful; Konstantinovic (1999) categorizes weeds into useful, harmful, or indifferent. According to this classification:

Weeds in Forestry and Possibilities of Their Control 151

There are numerous measures and procedures for weed control in forestry today, but, in order to fight weeds successfully, they should consist of different care and control measures.

The main goal of preventive measures is to prevent weed distribution. All measures used to protect any surface from weeds, i.e. to prevent weed seed growth in the field are considered preventive measures (Kovacevic & Momirovic, 2004). Preventive measures in forestry weed




Mechanical measures for combating weeds include basic treatment such as ploughing, disking, tilling and etc. Also regular measures in forest nurseries and plantations are hoeing and farrowing, undertaken during the greatest part of the vegetation period and especially

One of the ways of suppressing the already growing weeds and preventing their seed dispersal is mowing. Multiple repetitions exhaust the stored substances in the root and the plant is killed. In addition to mowing, one of the methods of weed suppression in forestry is also the pruning of shoots and stump shoots. However, this weed suppression method is relatively expensive due to intense labor and if repeated pruning is required depending on the weed species present (Vasic et al., 2009). Concerns about increasing pesticide use have been major factors for research in physical weed control methods in Europe (Melander et al.,

Physical weed control measures applied in forestry involve the use of flame and superheated steam. Destruction of weeds by flame can be applied in forest plantations with wider spaces between the rows, provided that the crops are previously protected by metal shields. Burning weeds is carried out on non-productive areas such as forest railways, roads, and canals. Destruction of weeds using steam is applied in forest nurseries in preparation of substrates used for sowing or planting. This is also a form of sterilization which destroys weed seeds in addition to plant diseases and noxious insects. Orloff & Cudney (1993)

Described below are the six classifications of weed control measures.

**9. Weed control in forestry** 

**9.1 Preventive measures** 

control include:

sown surfaces

forest machinery and objects clean.

emphasised during the entire spring and in early summer.

developed (Janjic et al., 2008).

**9.2 Mechanical measures** 

**9.3 Physical measures** 

2005).


The role of light has been of particular importance for emergence of weeds. In relation to light regime, weeds may be classified into **sciophytes** – plants developing in the shadow in weakly thinned forest stands or in dense forest stands and represent no threat to tree development; **semisciophytes** – semi-shadow plants that develop in thinned stands and can do a lot of harm; and **heliophytes** – plants of open habitats such as clearings, strips, burnt areas, etc., and represent a big threat to renovation and development of trees. There are a number of other weed classifications due to their adaptation to abiotic factors such as water regime, temperature, physico-chemical soil characteristics, etc. during their evolutionary development. However, very important weed classifications in forestry, which would have practical significance from the aspect of weed control, are the following weeds of forest nurseries and weeds of forest plantations and forest stands.

#### **8. The most important weeds in forestry**

#### **8.1 Weeds in forest nurseries**

Weed flora in forest nurseries differ from those found in forest plantations and forest stands. Given the extent of care measures applied, weeds in forest nurseries are very similar to those found in cultivated crops (Konstantinović**,** 1999). They are mostly annual and perennial herbaceous weedy species. The most common grass weed species present in the forest nurseries include: *Sorghum halepense, Cynodon dactylon, Alopecurus myosuroides, Digitaria sanquinalis, Echinochloa crus-galli, Poa annua,* and *Setaria spp.* Dominant broadleaf species include: *Amaranthus retroflexus, Ambrosia artemisiifolia, Chenopodium album, Cirsium arvense, Convolvulus arvensis, Erigeron canadensis, Datura stramonium, Galium aparine, Solanum nigrum, Sinapis arvensis,* and *Poligonum spp..*

Control of weediness in forest nurseries is very important and quality planting material is the basic prerequisite for success in forest stand establishment. Since weeds are one of the most limiting factors for the success of nursery production, their control should be approached very seriously (Vasic & Konstantinovic, 2008).

#### **8.2 Weeds in forest plantations and forest stands**

Weeds in forest plantations and forest stands differ from those in forest nurseries, because, in addition to different care measures applied in plantations and stands, the conditions in habitats also differ. Apart from ferns, herbaceous annual and perennial weeds, woody weeds such as shrubs, bushes, and shoots from the stumps of different tree types may also be present in forest plantations and stands. Woody weeds are very hardy and have a great power of regeneration; it is practically impossible to destroy them completely by mechanical means. The most common weed species present in forest plantations and stands are: *Ambrosia artemisiifolia, Amorpha fruticosa, Asclepias syriaca, Erigeron canadensis, Solidago gigantea, Sorghum halepense, Sambucus nigra, Stenactis annua, Pteridium aquilinum, Rubus caesius and etc.* 

#### **9. Weed control in forestry**

150 Weed Control


The role of light has been of particular importance for emergence of weeds. In relation to light regime, weeds may be classified into **sciophytes** – plants developing in the shadow in weakly thinned forest stands or in dense forest stands and represent no threat to tree development; **semisciophytes** – semi-shadow plants that develop in thinned stands and can do a lot of harm; and **heliophytes** – plants of open habitats such as clearings, strips, burnt areas, etc., and represent a big threat to renovation and development of trees. There are a number of other weed classifications due to their adaptation to abiotic factors such as water regime, temperature, physico-chemical soil characteristics, etc. during their evolutionary development. However, very important weed classifications in forestry, which would have practical significance from the aspect of weed control, are the following weeds of forest

Weed flora in forest nurseries differ from those found in forest plantations and forest stands. Given the extent of care measures applied, weeds in forest nurseries are very similar to those found in cultivated crops (Konstantinović**,** 1999). They are mostly annual and perennial herbaceous weedy species. The most common grass weed species present in the forest nurseries include: *Sorghum halepense, Cynodon dactylon, Alopecurus myosuroides, Digitaria sanquinalis, Echinochloa crus-galli, Poa annua,* and *Setaria spp.* Dominant broadleaf species include: *Amaranthus retroflexus, Ambrosia artemisiifolia, Chenopodium album, Cirsium arvense, Convolvulus arvensis, Erigeron canadensis, Datura stramonium, Galium aparine, Solanum* 

Control of weediness in forest nurseries is very important and quality planting material is the basic prerequisite for success in forest stand establishment. Since weeds are one of the most limiting factors for the success of nursery production, their control should be

Weeds in forest plantations and forest stands differ from those in forest nurseries, because, in addition to different care measures applied in plantations and stands, the conditions in habitats also differ. Apart from ferns, herbaceous annual and perennial weeds, woody weeds such as shrubs, bushes, and shoots from the stumps of different tree types may also be present in forest plantations and stands. Woody weeds are very hardy and have a great power of regeneration; it is practically impossible to destroy them completely by mechanical means. The most common weed species present in forest plantations and stands are: *Ambrosia artemisiifolia, Amorpha fruticosa, Asclepias syriaca, Erigeron canadensis, Solidago gigantea, Sorghum halepense, Sambucus nigra, Stenactis annua, Pteridium aquilinum, Rubus* 



hinder development of cultivated plants

nurseries and weeds of forest plantations and forest stands.

**8. The most important weeds in forestry** 

*nigrum, Sinapis arvensis,* and *Poligonum spp..*

approached very seriously (Vasic & Konstantinovic, 2008).

**8.2 Weeds in forest plantations and forest stands** 

**8.1 Weeds in forest nurseries** 

*caesius and etc.* 

There are numerous measures and procedures for weed control in forestry today, but, in order to fight weeds successfully, they should consist of different care and control measures. Described below are the six classifications of weed control measures.

#### **9.1 Preventive measures**

The main goal of preventive measures is to prevent weed distribution. All measures used to protect any surface from weeds, i.e. to prevent weed seed growth in the field are considered preventive measures (Kovacevic & Momirovic, 2004). Preventive measures in forestry weed control include:


#### **9.2 Mechanical measures**

Mechanical measures for combating weeds include basic treatment such as ploughing, disking, tilling and etc. Also regular measures in forest nurseries and plantations are hoeing and farrowing, undertaken during the greatest part of the vegetation period and especially emphasised during the entire spring and in early summer.

One of the ways of suppressing the already growing weeds and preventing their seed dispersal is mowing. Multiple repetitions exhaust the stored substances in the root and the plant is killed. In addition to mowing, one of the methods of weed suppression in forestry is also the pruning of shoots and stump shoots. However, this weed suppression method is relatively expensive due to intense labor and if repeated pruning is required depending on the weed species present (Vasic et al., 2009). Concerns about increasing pesticide use have been major factors for research in physical weed control methods in Europe (Melander et al., 2005).

#### **9.3 Physical measures**

Physical weed control measures applied in forestry involve the use of flame and superheated steam. Destruction of weeds by flame can be applied in forest plantations with wider spaces between the rows, provided that the crops are previously protected by metal shields. Burning weeds is carried out on non-productive areas such as forest railways, roads, and canals. Destruction of weeds using steam is applied in forest nurseries in preparation of substrates used for sowing or planting. This is also a form of sterilization which destroys weed seeds in addition to plant diseases and noxious insects. Orloff & Cudney (1993)

Weeds in Forestry and Possibilities of Their Control 153

Unlike agriculture, the use of herbicides in forestry began much later and generally the application of herbicides in forestry was based on experiences from intensive agricultural production. The results of research in agriculture are applied in forestry with major or minor delays. Due to the lack of labour, high labour costs, and large areas, producers are more often interestedin the use of herbicides. Use of herbicides in forestry decreases weediness, particularly at the initial stages of development of forest nursery plants, when the effect of weeds on plants is the greatest; at the same time, much better economic efficiency in the production process is achieved. Also, possible mechanical damages to the nursery plants can be avoided, and it happens very often that any kind of mechanical treatment is prevented in early stages of plant development due to high soil humidity. Use of herbicides to control competing vegetation in young forests can increase wood volume yields by 50–

According to the type of action herbicides may be divided into the herbicides with contact action and herbicides of translocation. Contact herbicides destroy above ground parts of plant, only the parts of the plant they touch. Translocation or systemic herbicides absorbed

According to the mode of action herbicides may be divided into the total and selective herbicides. Total herbicides kill all plants and selective herbicides kill weeds, and are not

According to the time of application herbicides may be divided into the herbicides applied before sowing or planting, herbicides applied after sowing, and before emergence of weeds and cultivated plants and herbicides applied after emergence of weeds and cultivated plants.

For contact herbicides, action is manifested at the site of penetration. Contact herbicides penetrate quickly through cuticle and epidermal cells of plants and the toxic effects on weeds are quickly observed. In systemic herbicides, absorption may occur through the root, stem, and leaf. If an herbicide is absorbed through the roots it can move via the xylem to the above ground parts; more often, it moves to leaves where disturbed respiration and photosynthesis occur. Herbicides absorbed by the leaves and stem cause harmful effects in absorbed plant parts and by spreading via the phloem to reach the root and ground plant organs (rhizomes). Whether the plant will absorb a higher quantity of the herbicide through above ground parts or the root system depends on herbicide application, type of herbicide,

Herbicides exhibit different mechanisms of action. Some herbicides inhibit synthesis of amino acids in plants, and others help the formation of free radicals in plants. Lipid synthesis is the site of herbicide action used to control monocot weeds, and a great number of different herbicides inhibit the process of photosynthesis (Duke, 1990). While some herbicides act only on one process, others act on multiple processes in plants. If only one process in the cell is disturbed, the whole range of processes is affected. Due to that it is

difficult to determine the primary herbicide action and consequences.

150% (Guynn et al., 2004).

**10. Division of herbicides** 

harmful to cultivated plants.

by leaves are transferred through the whole plant.

**11. Mechanisms of action herbicides** 

and several other factors (Janjic, 2005).

believe that the use of flame for the reduction of weeds is the best at the end of growing seasons, because in this way destroy most weed seeds that are dispersed on the soil surface.

#### **9.4 Mulches**

The covering of soil with a variety of materials such as straw, stubble, polyethyelene films, and others, to prevent the emergence of weeds is utilzed on smaller areas, mostly in forest nurseries. Polyethylene films of varying colors and thickness are most often used. This type of weed control is efficient for annual weeds but has no effect on control of many perennial weeds, and can be expensive compared to other methods used to fight weeds.

Many types of mulches have been tried including: sheets of plastic, newspaper, plywood, various thicknesses of bark, sawdust, sand, straw, sprayed-on petroleum resin, and even large plastic buckets. Most have proven to be ineffective, costly or both. Early trials tended to use small, short-lived materials that aided conifer seedling survival but not growth. Compared to other weed control techniques available in previous years, mulches were rather expensive. Current trends are to apply longer-lived, somewhat larger mulches of mostly sheet materials made of reinforced paper, polyester, or polypropylene (McDonald & Helgerson, 1990).

#### **9.5 Biological weed control**

Biological measures of weed control are based on the application of natural weed enemies such as insects, fungi, viruses, and bacteria in order to prevent their dissemination, and thus spreading. There are numerous examples of successful biological weed control. Application of pathogenic fungus, *Chondrostereum purpureum,* is used to control beech, yellow birch, red maple, sugar maple, trembling aspen, paper birch, and pin cherry (Wall, 1990). Exotic leaf pathogens, *Phaeoramularia* sp. and *Entyloma ageratinae,* were used for control of *Ageratina adenophora* and *Ageratina riparia* (Morris, 1991) in South Africa. Gordon & Kluge (1991) mentioned that control of *Hypericum perforatum* can be done by using insects *Chrysolina quadrigemina* and *Zeuxidiplosis giardi*. For control of *Acacia longifolia,* the widely spread invasive plant species in Portugal, the bee wasp *Trichilogaster acaciaelongifoliae* was used (Marchante et al., 2011). In those parts of the world where *Eucalyptus sp.* presents a problem the pathogen, *Cryphonectria eucalypti,* may be used for its suppression (Gryzenhout et al., 2003).

Application of biological measures in weed suppression has its limitations, though it has several advantages. Cultivated plants can be protected from some weeds, but not from all of them. It is impossible to destroy weeds completely because the biological agent depends upon the weed for survival; moreover, it is difficult to program biological protection for numerous cultivated plants from weeds with certainty since there are many similarities between weed species and cultivated plants (Konstantinovic, 1999).

#### **9.6. Herbicides**

Herbicides are used in forestry to manage tree-species composition, reduce competition from shrubs and herbaceous vegetation, manipulate wildlife habitat, and control invasive exotics (Shepard et al., 2004).

believe that the use of flame for the reduction of weeds is the best at the end of growing seasons, because in this way destroy most weed seeds that are dispersed on the soil surface.

The covering of soil with a variety of materials such as straw, stubble, polyethyelene films, and others, to prevent the emergence of weeds is utilzed on smaller areas, mostly in forest nurseries. Polyethylene films of varying colors and thickness are most often used. This type of weed control is efficient for annual weeds but has no effect on control of many perennial

Many types of mulches have been tried including: sheets of plastic, newspaper, plywood, various thicknesses of bark, sawdust, sand, straw, sprayed-on petroleum resin, and even large plastic buckets. Most have proven to be ineffective, costly or both. Early trials tended to use small, short-lived materials that aided conifer seedling survival but not growth. Compared to other weed control techniques available in previous years, mulches were rather expensive. Current trends are to apply longer-lived, somewhat larger mulches of mostly sheet materials made of reinforced paper, polyester, or polypropylene (McDonald &

Biological measures of weed control are based on the application of natural weed enemies such as insects, fungi, viruses, and bacteria in order to prevent their dissemination, and thus spreading. There are numerous examples of successful biological weed control. Application of pathogenic fungus, *Chondrostereum purpureum,* is used to control beech, yellow birch, red maple, sugar maple, trembling aspen, paper birch, and pin cherry (Wall, 1990). Exotic leaf pathogens, *Phaeoramularia* sp. and *Entyloma ageratinae,* were used for control of *Ageratina adenophora* and *Ageratina riparia* (Morris, 1991) in South Africa. Gordon & Kluge (1991) mentioned that control of *Hypericum perforatum* can be done by using insects *Chrysolina quadrigemina* and *Zeuxidiplosis giardi*. For control of *Acacia longifolia,* the widely spread invasive plant species in Portugal, the bee wasp *Trichilogaster acaciaelongifoliae* was used (Marchante et al., 2011). In those parts of the world where *Eucalyptus sp.* presents a problem the pathogen, *Cryphonectria eucalypti,* may be used for its suppression (Gryzenhout et al.,

Application of biological measures in weed suppression has its limitations, though it has several advantages. Cultivated plants can be protected from some weeds, but not from all of them. It is impossible to destroy weeds completely because the biological agent depends upon the weed for survival; moreover, it is difficult to program biological protection for numerous cultivated plants from weeds with certainty since there are many similarities

Herbicides are used in forestry to manage tree-species composition, reduce competition from shrubs and herbaceous vegetation, manipulate wildlife habitat, and control invasive

between weed species and cultivated plants (Konstantinovic, 1999).

weeds, and can be expensive compared to other methods used to fight weeds.

**9.4 Mulches** 

Helgerson, 1990).

2003).

**9.6. Herbicides** 

exotics (Shepard et al., 2004).

**9.5 Biological weed control** 

Unlike agriculture, the use of herbicides in forestry began much later and generally the application of herbicides in forestry was based on experiences from intensive agricultural production. The results of research in agriculture are applied in forestry with major or minor delays. Due to the lack of labour, high labour costs, and large areas, producers are more often interestedin the use of herbicides. Use of herbicides in forestry decreases weediness, particularly at the initial stages of development of forest nursery plants, when the effect of weeds on plants is the greatest; at the same time, much better economic efficiency in the production process is achieved. Also, possible mechanical damages to the nursery plants can be avoided, and it happens very often that any kind of mechanical treatment is prevented in early stages of plant development due to high soil humidity. Use of herbicides to control competing vegetation in young forests can increase wood volume yields by 50– 150% (Guynn et al., 2004).

#### **10. Division of herbicides**

According to the type of action herbicides may be divided into the herbicides with contact action and herbicides of translocation. Contact herbicides destroy above ground parts of plant, only the parts of the plant they touch. Translocation or systemic herbicides absorbed by leaves are transferred through the whole plant.

According to the mode of action herbicides may be divided into the total and selective herbicides. Total herbicides kill all plants and selective herbicides kill weeds, and are not harmful to cultivated plants.

According to the time of application herbicides may be divided into the herbicides applied before sowing or planting, herbicides applied after sowing, and before emergence of weeds and cultivated plants and herbicides applied after emergence of weeds and cultivated plants.

#### **11. Mechanisms of action herbicides**

For contact herbicides, action is manifested at the site of penetration. Contact herbicides penetrate quickly through cuticle and epidermal cells of plants and the toxic effects on weeds are quickly observed. In systemic herbicides, absorption may occur through the root, stem, and leaf. If an herbicide is absorbed through the roots it can move via the xylem to the above ground parts; more often, it moves to leaves where disturbed respiration and photosynthesis occur. Herbicides absorbed by the leaves and stem cause harmful effects in absorbed plant parts and by spreading via the phloem to reach the root and ground plant organs (rhizomes). Whether the plant will absorb a higher quantity of the herbicide through above ground parts or the root system depends on herbicide application, type of herbicide, and several other factors (Janjic, 2005).

Herbicides exhibit different mechanisms of action. Some herbicides inhibit synthesis of amino acids in plants, and others help the formation of free radicals in plants. Lipid synthesis is the site of herbicide action used to control monocot weeds, and a great number of different herbicides inhibit the process of photosynthesis (Duke, 1990). While some herbicides act only on one process, others act on multiple processes in plants. If only one process in the cell is disturbed, the whole range of processes is affected. Due to that it is difficult to determine the primary herbicide action and consequences.

Weeds in Forestry and Possibilities of Their Control 155

Toxicity is the capacity of a substance to harm or disturb the health of an organism (Sovljanski, 2003). Toxic effects may be immediate (acute) or accumulative (chronic), depending upon the exposure duration, the dose, and the herbicide. The toxicity of a substance varies with the animal species, age, sex, and nutritional status and with the route of exposure—through the stomach (orally), the lungs (by inhalation), or the skin (dermally).

A common way to document toxicity is by oral LD50 values. LD50 is the amount of chemical required to provide a "lethal dose" to 50% of the test population. LD50 is measured in mg of chemical administered per kg of body weight (Fishel et al., 2006). Toxicity tests are conducted on experimental animals, such as white rats, mice, and rabbits. Due to different ways of herbicide action when estimating its toxicity the whole range of data should be

The impact of herbicides on the environment (water, soil, biodiversity, etc.) may have diverse effects depending on the whole range of factors and, initially, on the evironment in which it is found after application. In general, herbicides most commonly used for vegetation management in forestry (glyphosate, triclopyr, imazapyr, sulfometuron and etc.) degrade quickly once they enter the environment and thus are neither persistent nor bioaccumulative (Tatum, 2004). Forest herbicides persist short term in the environment, and have few toxic effects when operationally applied following herbicide labels (Guynn et al., 2004). Single applications of forestry herbicides at stand initiation have minor and temporary impacts on plant communities and wildlife habitat conditions (Miller & Miller, 2004). Studies carried out on the effect of herbicides hexazinone fosamine ammonium and glyphosate in forestry have revealed that these herbicides have minimal effects on soil microorganisms and exhibit little or no potential for bioaccumulation (Ghassemi et al., 1982). If herbicides are properly used, current research indicates that the negative effects on wildlife usually are short-term and that herbicides can be used to meet wildlife habitat

In addition to irrigation, fertilization, and pruning of branches and tender shoots, hoeing and dusting are also very significant care measures in forest nurseries for production of planting material. Hoeing and dusting are regular measures applied during most of the vegetation period, and are particularly pronounced during the spring and at the beginning of summer. The purpose and objective of hoeing and dusting are, in addition to destruction of weeds, the maintenance of such soil structure that provides the optimum water-air

The number of hoeing and farrowing applications required depends on the soil preparation, climate conditions, and on weed emergence. Markovic et al., (1995) claimed that first hoeing

regime of soil layers in which the root system develops (Roncevic et al., 2002).

The skin and eyes are also subject to irritation caused by chemicals.

**15. Impact of herbicides on the environment** 

**16. Possibilities of weed control in forest nurseries** 

known and therefore it is difficult to express herbicide toxicity (Janjic, 2005).

**14. The toxicity of herbicides** 

objectives (Wagner et al., 2004).

**16.1 Mechanical weed control** 

#### **12. Selectivity of herbicides**

Selectivity is a property of herbicides to destroy weeds effectively without harming cultivated plants. Selectivity is not an absolute property of any herbicide. There is a whole range of factors such as morphological, biological and physiological plant traits, chemical composition, and herbicide structure, quantity, mode and time of herbicide application, and translocation of herbicides on which selectivity of some herbicides depend (Kojic & Janjic, 1994; Owen, 1990). Cudney (1996) mentioned that herbicide selectivity is a dynamic process with complex interactions between plant, herbicide and environment.

The main mechanism of herbicide selectivity is the differential metabolism between weeds and crop species, by which susceptible weeds are less able to metabolize selective herbicides (Cole, 1994). The importance of understanding the main stages of differential metabolismbased selectivity derives from the elucidation that plants, to an extent, use cell energy to process and detoxify herbicides (Carvalho et al., 2009).

There is also morphological selectivity based on plant structure. Leaf structure such as vertical and narrow leaf, waxy coat, and a protected vegetation cone contribute to the fact that some grasses are resistant to 2,4-D herbicide. Greater resistance of coniferous species to herbicides compared to broadleaves is also based on leaf structure (Zekic, 1983).

#### **13. Degradation of herbicides**

A great part of the total quantity of herbicides applied in agriculture and in forestry is found in the soil. After introduction into soils, several processes affect the vertical and horizontal distribution of the herbicides including transport by water flow, sorption to soil components and various degradation processes. Degradation can involve biotic and abiotic processes, where microbially facilitated biodegradation is especially interesting, as it is a major process in the complete mineralisation of compounds to harmless inorganic products (Alexander, 1981; Kojic & Janjic, 1994).

Decomposition of herbicides in the soil is a complex process taking place in several stages such as photodegradation, chemical degradation, and microbiological degradation.

Photodegradation is means that some herbicide molecules such as trifluralin, dinitroaniline are degraded by the influence of ultraviolet rays, and these herbicides should be incorporated into soil after application (Konstantinovic, 1999). Chemical degradation of herbicides in the soil is done through processes of oxidation, hydrolisis, hidratation and reduction during which the herbicides are completely or partly degraded. Microbial degradation plays an important role in herbicide breakdown, and of their toxic material found in soil. Herbicides not only influence the activity of microorganisms, but the fate of herbicide in the soil depends on the activity of microorganisms (Janjc, 1996). Abilty of microorganisms to carry out herbicide biodegradation depends on the type af applied herbicide (Govedarica & Mrkovacki, 1993; Dordevic et al., 1994; Milosevic & Govedarica 2000), herbicide chemical properties (Poppell et al., 2002; Martins et al., 2001), applied herbicide concentrations (Gigliotti & Allievi, 2001), and great number of biotic and abiotic factors.

#### **14. The toxicity of herbicides**

154 Weed Control

Selectivity is a property of herbicides to destroy weeds effectively without harming cultivated plants. Selectivity is not an absolute property of any herbicide. There is a whole range of factors such as morphological, biological and physiological plant traits, chemical composition, and herbicide structure, quantity, mode and time of herbicide application, and translocation of herbicides on which selectivity of some herbicides depend (Kojic & Janjic, 1994; Owen, 1990). Cudney (1996) mentioned that herbicide selectivity is a dynamic process with complex interactions between plant, herbicide and

The main mechanism of herbicide selectivity is the differential metabolism between weeds and crop species, by which susceptible weeds are less able to metabolize selective herbicides (Cole, 1994). The importance of understanding the main stages of differential metabolismbased selectivity derives from the elucidation that plants, to an extent, use cell energy to

There is also morphological selectivity based on plant structure. Leaf structure such as vertical and narrow leaf, waxy coat, and a protected vegetation cone contribute to the fact that some grasses are resistant to 2,4-D herbicide. Greater resistance of coniferous species to

A great part of the total quantity of herbicides applied in agriculture and in forestry is found in the soil. After introduction into soils, several processes affect the vertical and horizontal distribution of the herbicides including transport by water flow, sorption to soil components and various degradation processes. Degradation can involve biotic and abiotic processes, where microbially facilitated biodegradation is especially interesting, as it is a major process in the complete mineralisation of compounds to harmless inorganic products (Alexander,

Decomposition of herbicides in the soil is a complex process taking place in several stages

Photodegradation is means that some herbicide molecules such as trifluralin, dinitroaniline are degraded by the influence of ultraviolet rays, and these herbicides should be incorporated into soil after application (Konstantinovic, 1999). Chemical degradation of herbicides in the soil is done through processes of oxidation, hydrolisis, hidratation and reduction during which the herbicides are completely or partly degraded. Microbial degradation plays an important role in herbicide breakdown, and of their toxic material found in soil. Herbicides not only influence the activity of microorganisms, but the fate of herbicide in the soil depends on the activity of microorganisms (Janjc, 1996). Abilty of microorganisms to carry out herbicide biodegradation depends on the type af applied herbicide (Govedarica & Mrkovacki, 1993; Dordevic et al., 1994; Milosevic & Govedarica 2000), herbicide chemical properties (Poppell et al., 2002; Martins et al., 2001), applied herbicide concentrations (Gigliotti & Allievi, 2001), and great number of biotic and abiotic

such as photodegradation, chemical degradation, and microbiological degradation.

herbicides compared to broadleaves is also based on leaf structure (Zekic, 1983).

**12. Selectivity of herbicides** 

process and detoxify herbicides (Carvalho et al., 2009).

**13. Degradation of herbicides** 

1981; Kojic & Janjic, 1994).

factors.

environment.

Toxicity is the capacity of a substance to harm or disturb the health of an organism (Sovljanski, 2003). Toxic effects may be immediate (acute) or accumulative (chronic), depending upon the exposure duration, the dose, and the herbicide. The toxicity of a substance varies with the animal species, age, sex, and nutritional status and with the route of exposure—through the stomach (orally), the lungs (by inhalation), or the skin (dermally). The skin and eyes are also subject to irritation caused by chemicals.

A common way to document toxicity is by oral LD50 values. LD50 is the amount of chemical required to provide a "lethal dose" to 50% of the test population. LD50 is measured in mg of chemical administered per kg of body weight (Fishel et al., 2006). Toxicity tests are conducted on experimental animals, such as white rats, mice, and rabbits. Due to different ways of herbicide action when estimating its toxicity the whole range of data should be known and therefore it is difficult to express herbicide toxicity (Janjic, 2005).

#### **15. Impact of herbicides on the environment**

The impact of herbicides on the environment (water, soil, biodiversity, etc.) may have diverse effects depending on the whole range of factors and, initially, on the evironment in which it is found after application. In general, herbicides most commonly used for vegetation management in forestry (glyphosate, triclopyr, imazapyr, sulfometuron and etc.) degrade quickly once they enter the environment and thus are neither persistent nor bioaccumulative (Tatum, 2004). Forest herbicides persist short term in the environment, and have few toxic effects when operationally applied following herbicide labels (Guynn et al., 2004). Single applications of forestry herbicides at stand initiation have minor and temporary impacts on plant communities and wildlife habitat conditions (Miller & Miller, 2004). Studies carried out on the effect of herbicides hexazinone fosamine ammonium and glyphosate in forestry have revealed that these herbicides have minimal effects on soil microorganisms and exhibit little or no potential for bioaccumulation (Ghassemi et al., 1982). If herbicides are properly used, current research indicates that the negative effects on wildlife usually are short-term and that herbicides can be used to meet wildlife habitat objectives (Wagner et al., 2004).

#### **16. Possibilities of weed control in forest nurseries**

#### **16.1 Mechanical weed control**

In addition to irrigation, fertilization, and pruning of branches and tender shoots, hoeing and dusting are also very significant care measures in forest nurseries for production of planting material. Hoeing and dusting are regular measures applied during most of the vegetation period, and are particularly pronounced during the spring and at the beginning of summer. The purpose and objective of hoeing and dusting are, in addition to destruction of weeds, the maintenance of such soil structure that provides the optimum water-air regime of soil layers in which the root system develops (Roncevic et al., 2002).

The number of hoeing and farrowing applications required depends on the soil preparation, climate conditions, and on weed emergence. Markovic et al., (1995) claimed that first hoeing

Weeds in Forestry and Possibilities of Their Control 157

Very effective

Not effective

Very effective

Weakness effect

More attention is being paid to the application of herbicides as one of the control measures against weeds in forest nurseries. Due to the lack of labour, high labour costs, and large areas, the application of herbicides might be considered as the only possible way of weed

When choosing herbicides, it is important to take care of several factors such as: weed composition, range of herbicide action, phenophase of development of cultivated plants,

Application of herbicides in forest nurseries is that the biological and economic point of view is fully justified. Doses of herbicide application are low and there is no danger to wildlife, crops and watercourses (Zekic, 1979). For weed control in nurseries is necessary to provide a lot of labour and for mitigate this problem and increase the productivity application of herbicide is necessary. Chemical weed control in forest nurseries represents a complex job. In order to obtain the expected effect should take into account number of factors such as composition of the weed, the spectrum of action of herbicides, soil type, rainfall, temperature and etc. Herbicides that can be used in nursery production of some

**Dasomet -** is applied 2 - 5 weeks prior to sowing or planting in quantity of 30 – 60 g/m2 incorporated into the soil up to the depth of 8 – 10 cm. It is used for soil treatment in

**Effectiveness Potential Environmental Impacts** 

soil)

soil)

environment

environment

They can be potential polluters depending on the applied

No adverse effect on the

No adverse effect on the

They can be potential polluters depending on the applied

herbicide and the environment in which the herbicide occurs (water,

herbicide and the environment in which the herbicide occurs (water,

**Weed types Treatment** 

**Woody weeds** 

**Bracken** 

**alternatives** 

herbicides

cutting

herbicides

cutting

**16.2 Application of herbicides** 

forest tree species are as follows:

nurseries before sowing or planting.

and time and manner of herbicide application.

**16.2.1 Herbicides that can be used in forest nurseries** 

control (Table 1).

**Cost Euro/ha**

60 - 120

100 - 200

60 – 120

100 - 200

Table 1. Comments on control methods adopted and impacts in forestry

is a very significant measure that must be paid attention to particularly around cuttings and roots in order to avoid damages of buds and young shoots.

However mechanical measures have no long lasting effect in weed control due to relatively fast regeneration of weed flora (Table 1). Combined chemical and mechanical measures applied in forest nurseries are very effective in weed control. Us of herbicides decrease weediness in the early stages of development of cultivated plants when negative influences of weeds are the most dangerous. Mechanical injuries of nursery plants can be avoided in that way, and very often they are prevented due to high soil moisture. Mechanical measures are aimed at maintaining soil water-air regime and control of weeds that may subsequently have emerged.


is a very significant measure that must be paid attention to particularly around cuttings and

However mechanical measures have no long lasting effect in weed control due to relatively fast regeneration of weed flora (Table 1). Combined chemical and mechanical measures applied in forest nurseries are very effective in weed control. Us of herbicides decrease weediness in the early stages of development of cultivated plants when negative influences of weeds are the most dangerous. Mechanical injuries of nursery plants can be avoided in that way, and very often they are prevented due to high soil moisture. Mechanical measures are aimed at maintaining soil water-air regime and control of weeds that may subsequently

Very effective

Not effective

Effectiveness varies with weed

Only effective on annual weeds

Very effective

Only effective on annual weeds

Only effective on annual weeds

Effectiveness varies with weed

and site Very effective

and site

**Effectiveness Potential Environmental Impacts** 

soil)

They can be potential polluters depending on the applied

No adverse effect on the

No adverse effect on the

No adverse effect on the

No adverse effect on the

No adverse effect on the

No adverse effect on the

They can be potential polluters depending on the applied

herbicide and the environment in which the herbicide occurs (water,

environment

environment

environment

environment

environment

environment

soil)

herbicide and the environment in which the herbicide occurs (water,

roots in order to avoid damages of buds and young shoots.

**Cost Euro/ha**

50 - 100

100 - 200

150 - 190

600 - 820

30 - 100

100 - 200

150 - 190

600 - 820

have emerged.

**Perennial broadleaf and grass weeds**

**Annual broadleaf and grass weeds** 

**Weed types Treatment** 

**alternatives** 

herbicides

cutting

cultivation

mulches

herbicides

cutting

cultivation

mulches


Table 1. Comments on control methods adopted and impacts in forestry

#### **16.2 Application of herbicides**

More attention is being paid to the application of herbicides as one of the control measures against weeds in forest nurseries. Due to the lack of labour, high labour costs, and large areas, the application of herbicides might be considered as the only possible way of weed control (Table 1).

When choosing herbicides, it is important to take care of several factors such as: weed composition, range of herbicide action, phenophase of development of cultivated plants, and time and manner of herbicide application.

#### **16.2.1 Herbicides that can be used in forest nurseries**

Application of herbicides in forest nurseries is that the biological and economic point of view is fully justified. Doses of herbicide application are low and there is no danger to wildlife, crops and watercourses (Zekic, 1979). For weed control in nurseries is necessary to provide a lot of labour and for mitigate this problem and increase the productivity application of herbicide is necessary. Chemical weed control in forest nurseries represents a complex job. In order to obtain the expected effect should take into account number of factors such as composition of the weed, the spectrum of action of herbicides, soil type, rainfall, temperature and etc. Herbicides that can be used in nursery production of some forest tree species are as follows:

**Dasomet -** is applied 2 - 5 weeks prior to sowing or planting in quantity of 30 – 60 g/m2 incorporated into the soil up to the depth of 8 – 10 cm. It is used for soil treatment in nurseries before sowing or planting.

Weeds in Forestry and Possibilities of Their Control 159

Photo 2. Phytotoxic effect of n poplar seedling (*Populus euramericana, Populus deltoides*)

Photo 3. Efficiency of herbicides in nursery production of poplar plants (*Populus* 

**Pendimethalin** – is used for control of many annual grass weeds in production of poplar (*Populus euramericana, Populus deltoides*) nursery plants. It is applied after sowing or planting

*euramericana, Populus deltoides*)

in quantity of 4 – 6 l/ha depending on soil.

**Trifluralin** – registered rate is 1,5 – 2,5 l/ha depending on soil with mandatory incorporation at a depth of 5 – 8 cm. It is used for soil treatment in nurseries before sowing or planting.

**Azafenidin** - is applied in quantity of 100 – 125 g/ha after sowing or planting, and before emergence of cultivated plants. It is used in production of poplar (*Populus euramericana, Populus deltoides*.) for control of a great number of broadleaf weeds (Photo 1).

**Acetochlor** – is applied in quantity of 2 l/ha after sowing or planting, and before emergence of cultivated plants. It is used in production of poplar (*Populus euramericana, Populus deltoides*), oak (*Quercus robur*), and black locust (*Robinia pseudoacacia*) nursery plants for control of a great number of grass and broad leaved weeds.

**Dimethenamid** - is applied in quantity of 1,2 – 1,4 l/ha after sowing or planting, and before emergence of cultivated plants. It is used in production of poplar (*Populus euramericana, Populus deltoides*), oak (*Quercus robur*), and black locust (*Robinia pseudoacacia*) nursery plants for control of great number of grass and broadleave weeds.

**Linuron** – is used for soil treatment after sowing or planting, and before emergence of cultivated plants. It is applied in quantity of 2 l/ha in production of poplar (*Populus euramericana, Populus deltoides*), willow (*Salix* sp.) and oak (*Quercus robur*).

Photo 1. Efficiency ofcombination of herbicids Acetochlor (Relay plus) and Azafenidinin (Evolus 80-WG) in nursery production of poplar (*Populus euramericana, Populus deltoides*)

**Metribuzin** – registered rate is 0,500 – 0,750 kg/ha after planting, and before emergence of poplars (*Populus euramericana, Populus deltoides*) and weeds. It is used for control with a greater number of annual broadleaved weeds. If applied on sandy soil with a lighter mechanical composition, it can have phytotoxic effects on poplar seedling (Photo 2).

**Promethrin** - is used for soil treatment after sowing or planting, and before emergence of cultivated plants and weeds. It is applied in quantity of 2 l/ha in production of poplar (*Populus euramericana, Populus deltoides*) and willow (*Salix* sp.) nursery plants (Photo 3).

**Trifluralin** – registered rate is 1,5 – 2,5 l/ha depending on soil with mandatory incorporation at a depth of 5 – 8 cm. It is used for soil treatment in nurseries before sowing

**Azafenidin** - is applied in quantity of 100 – 125 g/ha after sowing or planting, and before emergence of cultivated plants. It is used in production of poplar (*Populus euramericana,* 

**Acetochlor** – is applied in quantity of 2 l/ha after sowing or planting, and before emergence of cultivated plants. It is used in production of poplar (*Populus euramericana, Populus deltoides*), oak (*Quercus robur*), and black locust (*Robinia pseudoacacia*) nursery plants for

**Dimethenamid** - is applied in quantity of 1,2 – 1,4 l/ha after sowing or planting, and before emergence of cultivated plants. It is used in production of poplar (*Populus euramericana, Populus deltoides*), oak (*Quercus robur*), and black locust (*Robinia pseudoacacia*) nursery plants

**Linuron** – is used for soil treatment after sowing or planting, and before emergence of cultivated plants. It is applied in quantity of 2 l/ha in production of poplar (*Populus* 

Photo 1. Efficiency ofcombination of herbicids Acetochlor (Relay plus) and Azafenidinin (Evolus 80-WG) in nursery production of poplar (*Populus euramericana, Populus deltoides*)

mechanical composition, it can have phytotoxic effects on poplar seedling (Photo 2).

**Metribuzin** – registered rate is 0,500 – 0,750 kg/ha after planting, and before emergence of poplars (*Populus euramericana, Populus deltoides*) and weeds. It is used for control with a greater number of annual broadleaved weeds. If applied on sandy soil with a lighter

**Promethrin** - is used for soil treatment after sowing or planting, and before emergence of cultivated plants and weeds. It is applied in quantity of 2 l/ha in production of poplar (*Populus euramericana, Populus deltoides*) and willow (*Salix* sp.) nursery plants (Photo 3).

*Populus deltoides*.) for control of a great number of broadleaf weeds (Photo 1).

control of a great number of grass and broad leaved weeds.

for control of great number of grass and broadleave weeds.

*euramericana, Populus deltoides*), willow (*Salix* sp.) and oak (*Quercus robur*).

or planting.

Photo 2. Phytotoxic effect of n poplar seedling (*Populus euramericana, Populus deltoides*)

Photo 3. Efficiency of herbicides in nursery production of poplar plants (*Populus euramericana, Populus deltoides*)

**Pendimethalin** – is used for control of many annual grass weeds in production of poplar (*Populus euramericana, Populus deltoides*) nursery plants. It is applied after sowing or planting in quantity of 4 – 6 l/ha depending on soil.

Weeds in Forestry and Possibilities of Their Control 161

larch is completely destroyed. It has a large range of action, long-lasting, so the soil remains

**Fluazifop-p-butyl** – is used for foliar control of annual and perennial grass weeds in production of willow, poplar, oak, maple tree, birch, and beech. It is applied in quantity of

**Haloxyfop-p-methyl** – is used for foliar control of annual and perennial grass weeds in production of poplar, acacia, oak and maple tree nursery plants. It is applied in quantity of 1

**Imazethapyr** – is used in production of acacia nursery plants. It is applied at a quantity of 1 l/ha after black locust emergence and controls a great number of grass and broadleaved weeds.

**Diquat** – is used for total control of emerged weeds on areas planed for sowing or planting, and on areas where sowing or planting have already been done, and before to the

**Quizalofop-p-ethyl** – is used for foliar control of annual and perennial grass weeds in production of willow, poplar, oak, maple tree, birch, and beech nursery plants. It is applied

Control of weed vegetation in forest stands is performed by mowing, cutting, pulling, etc. However, this way of weed control today is slow, inefficient and expensive and it must be repeated. Weed control between the rows is most often by mowing or by weed cutters, and

Due to the presence of both herbaceous and woody weeds, application of herbicides in forest stands is of great significance. Woody weeds are very resistant and have great power of regeneration, therefore difficult to kill completely by mechanical means. It is very important to perform weed control in stands in a timely manner (at the stage of intensive growth) and in the right manner in order to use mininal herbicides inputs with effective results. Costs of weed control in stands by application of herbicides are much lower since reapplications are generally not required and it can be performed with less labour. Weed control in stands can be performed over the entire surface (broadcast application), within rows, or just around the nursery plants. The aim of weed control in forest stands is not complete destruction of weed flora, but prevention of competitive relationships with

However, their application is sometimes impossible in some systems because, very often, agricultural crops such as maize, soya, wheat, etc. are sown between the rows in order to use that space (Photo 5 and 6). In that case, selective herbicides for use in maize and efficient for control of weeds present in stand should be used. The most often used herbicides are

appearance of cultivated or nursery plants. It is applied in a quantity of 4 – 6 l/ha.

within the row, or around the plants weed control is by application of herbicides.

clear throughout vegetation period.

**17.1 Mechanical weed control** 

**17.2 Application of herbicides** 

nursery plants and termination of growth.

those based on fluroxypyr or mesotrione.

1,3 l/ha when weeds are at intensive growth phase.

– 1,5 l/ha when weeds are at intensive growth phase.

in a quantity of 1 – 1,5 l/ha at the intensive growth phase.

**17. Possibilities of weed control in forest plantations** 

Photo 4. Control plot

**S-metolachlor** – registered rate is 1,2 – 1,5 l/ha after sowing or planting, and before emergence of weeds and cultivated plants. It used for control of annual grass and broadleave weeds in production of poplar (*Populus euramericana, Populus deltoides*), black locust (*Robinia pseudoacacia*), willow (*Salix* sp.) and oak (*Quercus robur*) nursery plants.

**Oxyifluorfen** – registered rate is 1 l/ha after sowing or planting, and before emergence of cultivated plants. It is used in production of poplar (*Populus euramericana, Populus deltoides*) nursery plants for control of large number of grass and broadleave weeds.

**Cycloxydim** – is used for foliar control of annual and perennial grass weeds in production of poplar (*Populus euramericana, Populus deltoides*), black locust (*Robinia pseudoacacia*), oak (*Quercus robur*), maple tree (*Acer* sp.) and bee tree (*Evodia hupehensis*) nursery plants. It is applied in quantity of 3 l/ha at the stage of intensive weeds growth.

**Glyphostate** – is used for total control of emerged weeds on areas planned for sowing or planting, and on areas where sowing and planting have already been performed, and prior to appearance of cultivated plants or nursery plants. It is applied in quantity of 2 – 12 l/ha with water consumption of 200-400 l/ha.

**Glufosinate - ammonium** – is used as non-selective, contact herbicide for control of weeds on areas planed for sowing or planting, and on areas where sowing or planting have already been done, and before to the appearance of cultivated or nursery plants. It is applied in quantity of 4 -7,5 l/ha with water consumption of 400-600 l/ha.

**Hexazinone** – registered rate is 1,5 – 2 kg/ha in pine germination chambers 1,5 to 2 months after sowing and 2 – 3 kg/ha in nurseries. Pines are highly resistant to herbicide hexazinone, except *Pinus strob*es and *Pinus contorta*. Brown, necrotic spots appear on Norway spruce and

**S-metolachlor** – registered rate is 1,2 – 1,5 l/ha after sowing or planting, and before emergence of weeds and cultivated plants. It used for control of annual grass and broadleave weeds in production of poplar (*Populus euramericana, Populus deltoides*), black locust (*Robinia pseudoacacia*), willow (*Salix* sp.) and oak (*Quercus robur*) nursery plants.

**Oxyifluorfen** – registered rate is 1 l/ha after sowing or planting, and before emergence of cultivated plants. It is used in production of poplar (*Populus euramericana, Populus deltoides*)

**Cycloxydim** – is used for foliar control of annual and perennial grass weeds in production of poplar (*Populus euramericana, Populus deltoides*), black locust (*Robinia pseudoacacia*), oak (*Quercus robur*), maple tree (*Acer* sp.) and bee tree (*Evodia hupehensis*) nursery plants. It is

**Glyphostate** – is used for total control of emerged weeds on areas planned for sowing or planting, and on areas where sowing and planting have already been performed, and prior to appearance of cultivated plants or nursery plants. It is applied in quantity of 2 – 12 l/ha

**Glufosinate - ammonium** – is used as non-selective, contact herbicide for control of weeds on areas planed for sowing or planting, and on areas where sowing or planting have already been done, and before to the appearance of cultivated or nursery plants. It is applied in

**Hexazinone** – registered rate is 1,5 – 2 kg/ha in pine germination chambers 1,5 to 2 months after sowing and 2 – 3 kg/ha in nurseries. Pines are highly resistant to herbicide hexazinone, except *Pinus strob*es and *Pinus contorta*. Brown, necrotic spots appear on Norway spruce and

nursery plants for control of large number of grass and broadleave weeds.

applied in quantity of 3 l/ha at the stage of intensive weeds growth.

quantity of 4 -7,5 l/ha with water consumption of 400-600 l/ha.

with water consumption of 200-400 l/ha.

Photo 4. Control plot

larch is completely destroyed. It has a large range of action, long-lasting, so the soil remains clear throughout vegetation period.

**Fluazifop-p-butyl** – is used for foliar control of annual and perennial grass weeds in production of willow, poplar, oak, maple tree, birch, and beech. It is applied in quantity of 1,3 l/ha when weeds are at intensive growth phase.

**Haloxyfop-p-methyl** – is used for foliar control of annual and perennial grass weeds in production of poplar, acacia, oak and maple tree nursery plants. It is applied in quantity of 1 – 1,5 l/ha when weeds are at intensive growth phase.

**Imazethapyr** – is used in production of acacia nursery plants. It is applied at a quantity of 1 l/ha after black locust emergence and controls a great number of grass and broadleaved weeds.

**Diquat** – is used for total control of emerged weeds on areas planed for sowing or planting, and on areas where sowing or planting have already been done, and before to the appearance of cultivated or nursery plants. It is applied in a quantity of 4 – 6 l/ha.

**Quizalofop-p-ethyl** – is used for foliar control of annual and perennial grass weeds in production of willow, poplar, oak, maple tree, birch, and beech nursery plants. It is applied in a quantity of 1 – 1,5 l/ha at the intensive growth phase.

#### **17. Possibilities of weed control in forest plantations**

#### **17.1 Mechanical weed control**

Control of weed vegetation in forest stands is performed by mowing, cutting, pulling, etc. However, this way of weed control today is slow, inefficient and expensive and it must be repeated. Weed control between the rows is most often by mowing or by weed cutters, and within the row, or around the plants weed control is by application of herbicides.

#### **17.2 Application of herbicides**

Due to the presence of both herbaceous and woody weeds, application of herbicides in forest stands is of great significance. Woody weeds are very resistant and have great power of regeneration, therefore difficult to kill completely by mechanical means. It is very important to perform weed control in stands in a timely manner (at the stage of intensive growth) and in the right manner in order to use mininal herbicides inputs with effective results. Costs of weed control in stands by application of herbicides are much lower since reapplications are generally not required and it can be performed with less labour. Weed control in stands can be performed over the entire surface (broadcast application), within rows, or just around the nursery plants. The aim of weed control in forest stands is not complete destruction of weed flora, but prevention of competitive relationships with nursery plants and termination of growth.

However, their application is sometimes impossible in some systems because, very often, agricultural crops such as maize, soya, wheat, etc. are sown between the rows in order to use that space (Photo 5 and 6). In that case, selective herbicides for use in maize and efficient for control of weeds present in stand should be used. The most often used herbicides are those based on fluroxypyr or mesotrione.

Weeds in Forestry and Possibilities of Their Control 163

**Glyphosate** – is used for complete weed control during the stage of intensive growth and concentration of 2-3%. Its efficacy on weeds is high due to good translocation into root and rhizomes. Side shoots, if present, should be removed prior to treatment with glyphosate because the preparations based on glyphoste should not reach the leaves of nursery plants (Photo 7 and 8). Glyphostate is used for treating stumps in order to prevent emergence of shoots from stumps. Concentration of 10-15% is applied immediately after cutting, but the treatment may be applied until shoots appear from May through October (Photo 9 and 10).

Photo 7. Efficiency of herbicide glyphosate in poplar plantation

Photo 8. Inter-row application of herbicide glyphosate in the plantation

600 l/ha.

**Glufosinate - ammonium** – is used as non-selective, contact herbicide for control of weeds in forest plantations. It is applied in quantity of 4 -7,5 l/ha with water consumption of 400-

Photo 5. Inter-row sown corn in the poplar plantation

Photo 6. Inter-row sown soybeans in the poplar plantation

#### **17.2.1 Herbicides that can be used for weed control in forest plantations**

Weed control in forest plantations should be carried out until the moment when the seedlings provided normal development and growth acording when seedlings grow beyond the zone of herbaceous weeds and shoots from the stumps. This moment the performance of various plantations in the different age because it depends on many factors such as species and age of seedlings, soil type, ground preparation prior to afforestation and etc. Weed control in forest plantations is not intended to completely destroy weed seedlings than the release from weed competition acording to stop weed growth and development. Application of herbicides in forest plantations can be done on the whole surface in rows or around trees. We should take into account that the applied herbicides do not reach the leaves of seedlings. For this purpose they may use the following herbicides:

Photo 5. Inter-row sown corn in the poplar plantation

Photo 6. Inter-row sown soybeans in the poplar plantation

**17.2.1 Herbicides that can be used for weed control in forest plantations** 

leaves of seedlings. For this purpose they may use the following herbicides:

Weed control in forest plantations should be carried out until the moment when the seedlings provided normal development and growth acording when seedlings grow beyond the zone of herbaceous weeds and shoots from the stumps. This moment the performance of various plantations in the different age because it depends on many factors such as species and age of seedlings, soil type, ground preparation prior to afforestation and etc. Weed control in forest plantations is not intended to completely destroy weed seedlings than the release from weed competition acording to stop weed growth and development. Application of herbicides in forest plantations can be done on the whole surface in rows or around trees. We should take into account that the applied herbicides do not reach the **Glyphosate** – is used for complete weed control during the stage of intensive growth and concentration of 2-3%. Its efficacy on weeds is high due to good translocation into root and rhizomes. Side shoots, if present, should be removed prior to treatment with glyphosate because the preparations based on glyphoste should not reach the leaves of nursery plants (Photo 7 and 8). Glyphostate is used for treating stumps in order to prevent emergence of shoots from stumps. Concentration of 10-15% is applied immediately after cutting, but the treatment may be applied until shoots appear from May through October (Photo 9 and 10).

Photo 7. Efficiency of herbicide glyphosate in poplar plantation

Photo 8. Inter-row application of herbicide glyphosate in the plantation

**Glufosinate - ammonium** – is used as non-selective, contact herbicide for control of weeds in forest plantations. It is applied in quantity of 4 -7,5 l/ha with water consumption of 400- 600 l/ha.

Weeds in Forestry and Possibilities of Their Control 165

decline and slow development, the weed vegetation in young pedunculate oak forest should be suppressed. Prior to acorn planting it is important to perform the site preparation in order to provide the most favourable conditions for oak development. The acorn is planted in the autumn or spring in the soil prepared for the reception of the seed. If renovating surfaces are prepared prior to sowing then the problems with weeds encountered later when maintaining the surfaces are much smaller. But despite the completion of site preparation in the first and the second year after renovation, the occurrence of some weeds that emerged subsequently or were not affected by the preparation treatment before

Removal of debris in the form of wood chips obtained by stump grinding is done completely or partially by collecting heaps, and spreading them on skid trails and then burning. Suppression of shrubs and shoots from the stumps may be accoplished manually by cutting with scythes, scissors, axes and etc. However, such weed suppression is inefficient and expensive, and is being replaced by faster and more efficient ways of

The most often encountered problem in pedunculate oak forest renovation is *Rubus caesius* L. (European dewberry) forming impenetrable thickets. Besides blackberry, *Crataegus monogyna, C.oxyacantha*, and *Rosa arvensis* may also pose a problem although significantly smaller. Also, if the shoots from stumps are not suppressed they can reach the height of up to 1,5 m, and blackberry and other weeds form thick cover, then the chemical control is difficult to perform due to impenetrability and requires a much higher expenditure of funds.

Photo 11. Application herbicides glyphosate after sowing and before emergence of

planting, may be expected.

suppression.

**18.1 Mechanical weed control** 

**18.2 Application of herbicides** 

cultivated plants (*Quercus robur*)

**Triclopyr** – is used for oak stump shoots control in the ration of 1:5 or 1:10 for other broadleaved species. The best way is to treat stumps immediately after cutting, but it can be done until the shoots emerge. It can be performed during entire year, except during freezing.

Photo 9. Treated stumps by glyphosate

Photo 10. Non treated stumps

#### **18. Possibilities of weed control in the natural and artificial regeneration of pedunculate oak forest**

In addition to harmful insects and diseases, weed vegetation represents a great problem in renovated pedunculate oak (*Quercus robur* L.) forests. Presence of weeds and great number of shrubby species per area unit is a basic limiting factor for continual spontaneous rejuvenation and offspring survival (Bobinac et al., 1991). Natural renovation is often poor or completely missing due to the presence of a great number of weeds. Due to the impossibility of preserving seedlings, and formation of quality offspring as well as the

**Triclopyr** – is used for oak stump shoots control in the ration of 1:5 or 1:10 for other broadleaved species. The best way is to treat stumps immediately after cutting, but it can be done until the shoots emerge. It can be performed during entire year, except during freezing.

**18. Possibilities of weed control in the natural and artificial regeneration of** 

In addition to harmful insects and diseases, weed vegetation represents a great problem in renovated pedunculate oak (*Quercus robur* L.) forests. Presence of weeds and great number of shrubby species per area unit is a basic limiting factor for continual spontaneous rejuvenation and offspring survival (Bobinac et al., 1991). Natural renovation is often poor or completely missing due to the presence of a great number of weeds. Due to the impossibility of preserving seedlings, and formation of quality offspring as well as the

Photo 9. Treated stumps by glyphosate

Photo 10. Non treated stumps

**pedunculate oak forest** 

decline and slow development, the weed vegetation in young pedunculate oak forest should be suppressed. Prior to acorn planting it is important to perform the site preparation in order to provide the most favourable conditions for oak development. The acorn is planted in the autumn or spring in the soil prepared for the reception of the seed. If renovating surfaces are prepared prior to sowing then the problems with weeds encountered later when maintaining the surfaces are much smaller. But despite the completion of site preparation in the first and the second year after renovation, the occurrence of some weeds that emerged subsequently or were not affected by the preparation treatment before planting, may be expected.

#### **18.1 Mechanical weed control**

Removal of debris in the form of wood chips obtained by stump grinding is done completely or partially by collecting heaps, and spreading them on skid trails and then burning. Suppression of shrubs and shoots from the stumps may be accoplished manually by cutting with scythes, scissors, axes and etc. However, such weed suppression is inefficient and expensive, and is being replaced by faster and more efficient ways of suppression.

#### **18.2 Application of herbicides**

The most often encountered problem in pedunculate oak forest renovation is *Rubus caesius* L. (European dewberry) forming impenetrable thickets. Besides blackberry, *Crataegus monogyna, C.oxyacantha*, and *Rosa arvensis* may also pose a problem although significantly smaller. Also, if the shoots from stumps are not suppressed they can reach the height of up to 1,5 m, and blackberry and other weeds form thick cover, then the chemical control is difficult to perform due to impenetrability and requires a much higher expenditure of funds.

Photo 11. Application herbicides glyphosate after sowing and before emergence of cultivated plants (*Quercus robur*)

Weeds in Forestry and Possibilities of Their Control 167

**Glyphostate** – is used for total control of weeds emerged on areas planed for sowing or planting, and on areas where sowing or planting have already been done, and before appearance of cultivated or nursery plants (Photo 11 and 12). It is applied in quantity of 2 –

**Glufosinate - ammonium** – is used as a nonselective, contact herbicide for weed control on areas planed for sowing or planting, and on areas where sowing or planting have already been done, and prior to appearance of cultivated or nursery plants. It is applied in quantity

Photo 13. Efficiency of herbicides in artificial regeneration of pedunculate oak forest

Photo 14. Efficiency of herbicide clopyralid (Lontrel-100) in regeneration of pedunculate oak

forest

12 l/ha, with water consumption of 200-400 l/ha.

of 4 – 7,5 l/ha, with water consumption of 400-600 l/ha.

Photo 12. Emergence of oak plants after application of total herbicide

#### **18.3 Herbicides that can be used for weed control in regeneration of pedunculate oak (***Quercus robur* **L.) forest**

To work in weed control in regeneration of pedunculate oak (*Quercus robur* L.) forest were successful among series of measures that are applied herbicide application is necessary. Herbicides that can be used for weed control in regeneration of penduculate oak (*Quercus robur* L.) forest as:

**Cycloxydim** – is used for foliar control of annual and perennial grass weeds. It is applied in quantity of 3 l/ha or applied twice at half-dosage (1,5 + 1,5 l/ha) when weeds are at the intensive growth stage.

**Clopyralid** – is used for control of a great number of broad-leaved weed species such as *Cirsium arvense, Ambrosia artemisifolia, Solanum nigrum, Erigeron canadensis* and etc. in renovated pedunculate oak forests. It causes transient symptoms of phytotoxicity in annual pedunculate oak plants (creating ''a spoon like appearance''), while it is selective toward two-, and three-year oak plants, and causes no symptoms of phytotoxicity. It is applied in quantity of 1 l/ha with water consumption of 300 l/ha.

**Nicosulfuron** – is selective for pedunculate oak plants. It is used for control of a great number of annual broad leaf weeds, and for some perennial weeds. It is applied in the quantity of 1 – 1,2 l/ha.

**Fluazifop-p-butyl** – is used for foliar control of annual and perennial grass weeds in renovated pedunculate oak trees. It is applied in the quantity of 1,3 l/ha at the stage of intensive weed growth.

Photo 12. Emergence of oak plants after application of total herbicide

quantity of 1 l/ha with water consumption of 300 l/ha.

**(***Quercus robur* **L.) forest** 

*robur* L.) forest as:

intensive growth stage.

quantity of 1 – 1,2 l/ha.

intensive weed growth.

**18.3 Herbicides that can be used for weed control in regeneration of pedunculate oak** 

To work in weed control in regeneration of pedunculate oak (*Quercus robur* L.) forest were successful among series of measures that are applied herbicide application is necessary. Herbicides that can be used for weed control in regeneration of penduculate oak (*Quercus* 

**Cycloxydim** – is used for foliar control of annual and perennial grass weeds. It is applied in quantity of 3 l/ha or applied twice at half-dosage (1,5 + 1,5 l/ha) when weeds are at the

**Clopyralid** – is used for control of a great number of broad-leaved weed species such as *Cirsium arvense, Ambrosia artemisifolia, Solanum nigrum, Erigeron canadensis* and etc. in renovated pedunculate oak forests. It causes transient symptoms of phytotoxicity in annual pedunculate oak plants (creating ''a spoon like appearance''), while it is selective toward two-, and three-year oak plants, and causes no symptoms of phytotoxicity. It is applied in

**Nicosulfuron** – is selective for pedunculate oak plants. It is used for control of a great number of annual broad leaf weeds, and for some perennial weeds. It is applied in the

**Fluazifop-p-butyl** – is used for foliar control of annual and perennial grass weeds in renovated pedunculate oak trees. It is applied in the quantity of 1,3 l/ha at the stage of **Glyphostate** – is used for total control of weeds emerged on areas planed for sowing or planting, and on areas where sowing or planting have already been done, and before appearance of cultivated or nursery plants (Photo 11 and 12). It is applied in quantity of 2 – 12 l/ha, with water consumption of 200-400 l/ha.

**Glufosinate - ammonium** – is used as a nonselective, contact herbicide for weed control on areas planed for sowing or planting, and on areas where sowing or planting have already been done, and prior to appearance of cultivated or nursery plants. It is applied in quantity of 4 – 7,5 l/ha, with water consumption of 400-600 l/ha.

Photo 13. Efficiency of herbicides in artificial regeneration of pedunculate oak forest

Photo 14. Efficiency of herbicide clopyralid (Lontrel-100) in regeneration of pedunculate oak forest

Weeds in Forestry and Possibilities of Their Control 169

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#### **19. Acknowledgment**

This paper was realized as a part of the project "Studying climate change and its influence on the environment: impacts, adaptation and mitigation" (43007) financed by the Ministry of Education and Science of the Republic of Serbia within the framework of integrated and interdisciplinary research over the period 2011-2014.

#### **20. References**


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**9** 

*Brazil* 

**Potential Use of Tembotrione** 

Sergio De Oliveira Procópio3, Jamil Constantin1

*3Brazilian Agricultural Research Corporation, Londrina, PR,* 

and Guilherme Braga Pereira Braz1

*State University of Maringá, Paraná, 2University of Rio Verde, Goiás,* 

**(HPPD-Inhibitor Herbicides) in Grain Sorghum** 

Sorghum [*Sorghum bicolor* (L.) Moench] is a very important cultivated species in India, United States and some countries in Africa due to its high nutritional value both for food (grains) and feed (forage and grains) (Dahlberg *et al.,* 2004). In Brazil, sorghum has increasingly attained a level of recognition mainly as an option for the second crop cycle known as "safrinha". It has also been considered a viable alternative to replace crops such as cotton [*Gossypium hirsutum* (L.) Moench], corn (*Zea mays* (L.) Moench] and millet [*Pennisentum glauco* (L.) Moench] in crop rotations, serving not only for straw residue in conservation agriculture systems but also for the production of grains and forage as well

Grown in tropical and subtropical climate regions, grain sorghum presents upright growing habit, mid-range height and uniform development even under limited water availability (Kismann, 2007). Despite its rusticity, grain sorghum has a slow initial growth, becoming vulnerable to the interference caused by weed competition. In this context, weeds may become a limiting factor for the development of the crop. It is estimated that the coexistence of weeds along with grain sorghum during the four first weeks after crop emergence may

In spite of being a remarkable crop on grain production worldwide, there are a limited number of studies on the selectivity of herbicides for this species, making weed control options more limited, mainly in large areas (Abit *et al.,* 2009). One of the major obstacles that has limited sorghum expansion is the difficulty to manage weeds due to the crop sensitivity to grass herbicides currently available (Archangelo *et al.*, 2002). Since the aggravating factor is the difficulty to control grass weeds, new research on this issue must

cause reductions ranging from 40 to 97% in grain yield (Tamado *et al.*, 2002).

**1. Introduction** 

(Gontijo Neto *et al.,* 2002).

be considered.

Rubem Silvério De Oliveira Junior 1, Lilian Gomes De Moraes Dan1,

Hugo De Almeida Dan1, Alberto Leão De Lemos Barroso2,

*1Center for Advanced Studies in Weed Research, Agronomy Department,* 


## **Potential Use of Tembotrione (HPPD-Inhibitor Herbicides) in Grain Sorghum**

Hugo De Almeida Dan1, Alberto Leão De Lemos Barroso2, Rubem Silvério De Oliveira Junior 1, Lilian Gomes De Moraes Dan1, Sergio De Oliveira Procópio3, Jamil Constantin1 and Guilherme Braga Pereira Braz1 *1Center for Advanced Studies in Weed Research, Agronomy Department, State University of Maringá, Paraná, 2University of Rio Verde, Goiás, 3Brazilian Agricultural Research Corporation, Londrina, PR, Brazil* 

#### **1. Introduction**

170 Weed Control

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Wall, R. E. (1990). The fungus Chondrostereumpurpureum as a silvicide to control stump sprouting in hardwoods. North. Journal of Applied Ecology, 7, 17-19 Wilson, R. G.; Kerr, E. D. & Nelson, L. A. (1985). Potential for using weed seed content in the

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soil to predict future weed problems. Weed Science, 33, 171-175

conference on weed control in forestry, Sarajevo, 63 - 69

industrije za preradu drveta Bosne i Herzegovine, Sarajevo

vegetation and management – Serbia. European Science Foundation, Brussels, pp.

herbicides for enhancing forest productivity and conserving land for biodiversity in

Herbicides and Plant Metabolism, Edited by Dodge, A.D., University of Bath,

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Foundation, Brussels, pp. 51 – 60

Weed Research, Vol.39, No.3, 171-180

izdanje, Poljoprivredni fakultet Novi Sad

Ethnopharmacology, Vol.92, No.2-3, 163-166

Acta herbologica, Vol.17, No.2, 145-154

Vol.32, No.4, 1042-1048

Jugoslavica XXV, pp. 1-8

117 – 122

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reproductive and planting stock. Poplar, 169/170, 3-20

4585

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overview. Wildlife Society Bulletin, Vol.32, No.4, 1020-1027

Sorghum [*Sorghum bicolor* (L.) Moench] is a very important cultivated species in India, United States and some countries in Africa due to its high nutritional value both for food (grains) and feed (forage and grains) (Dahlberg *et al.,* 2004). In Brazil, sorghum has increasingly attained a level of recognition mainly as an option for the second crop cycle known as "safrinha". It has also been considered a viable alternative to replace crops such as cotton [*Gossypium hirsutum* (L.) Moench], corn (*Zea mays* (L.) Moench] and millet [*Pennisentum glauco* (L.) Moench] in crop rotations, serving not only for straw residue in conservation agriculture systems but also for the production of grains and forage as well (Gontijo Neto *et al.,* 2002).

Grown in tropical and subtropical climate regions, grain sorghum presents upright growing habit, mid-range height and uniform development even under limited water availability (Kismann, 2007). Despite its rusticity, grain sorghum has a slow initial growth, becoming vulnerable to the interference caused by weed competition. In this context, weeds may become a limiting factor for the development of the crop. It is estimated that the coexistence of weeds along with grain sorghum during the four first weeks after crop emergence may cause reductions ranging from 40 to 97% in grain yield (Tamado *et al.*, 2002).

In spite of being a remarkable crop on grain production worldwide, there are a limited number of studies on the selectivity of herbicides for this species, making weed control options more limited, mainly in large areas (Abit *et al.,* 2009). One of the major obstacles that has limited sorghum expansion is the difficulty to manage weeds due to the crop sensitivity to grass herbicides currently available (Archangelo *et al.*, 2002). Since the aggravating factor is the difficulty to control grass weeds, new research on this issue must be considered.

Potential Use of Tembotrione (HPPD-Inhibitor Herbicides) in Grain Sorghum 173

germination, growth and development, mainly by reducing the incidence of radiation (Rizzardi *et al.,* 2001). In larger areas, weed control is usually accomplished by herbicide applications. Despite the method used for weed control, it is also important to observe that the period within the crop cycle when weed interference is prevented may also be a determinant for crop success. The stage of the crop cycle when weed control is established strongly influences competition levels, bringing about impacts on crop growth,

At the start of crop development, sorghum and weeds can coexist for a given period without the latter affecting either quantitatively or qualitatively crop production. This phase is called 'period prior to interference' (PPI). By determining interference periods in sorghum crops in tropical regions, Rodrigues *et al.* (2010) concluded that sorghum and the weed community could coexist for 42 days (PPI) with no yield reduction (Figure 1). On the other hand, this interference could occur earlier in the crop cycle depending on the density and species of

The period of time after sorghum emergence during which it must be free from weed

By definition, the period in which weeds effectively interfere with the crop and the period during which competition must not exist is called 'critical period of competition' (PCPI) (Pitelli & Durigan, 1984). During this period, there has been observed a drastic crop yield reduction (54%) when the control was achieved late in time. Silva *et al.* (1986) observed that the absence of weed control on the first four weeks after sorghum emergence can lead to a reduction in grain production of 35% and that, without any control during the entire crop

Fig. 1. Sorghum grain yield as a function of periods of weed control and weed coexistence in

competition is called 'total period of interference prevention' (TPIP).

development and grain yield (Silva *et al.,* 2009).

cycle, the reduction can be as high as 70%.

tropical regions (Rodrigues *et al.,* 2010).

weeds.

The identification of post-emergence herbicides capable of controlling grasses, with suitable crop selectivity, is crucially important to keep sorghum cultivated areas expanding. Most of the registered herbicides used on sorghum farming were initially developed to be used on other large scale crops, particularly on corn and sweet corn (Stahlman & Wicks, 2000).

In this regard, the objective of this study is to gather information concerning the actual status of reported effects of weed interference on grain sorghum and also discuss options of chemical weed control through post-emergence herbicides including tembotrione.

#### **2. Importance of weed control in sorghum**

Sorghum, as well as other agricultural plant species, is subjected to a series of biotic and abiotic factors, which directly or indirectly influence its growth and development (Magalhães *et al.,* 2000). Among these factors, weed-imposed interference on crops is one of the most remarkable. Low sorghum yields have been correlated both to the absence and inefficient weed control (Erasmo & Pitelli, 1997).

The negative effects of weeds on sorghum agrosystems occur mainly due to the competition for crops' vital resources, such as water, light and nutrients. Furthermore, weeds can host pests and diseases, raising the cost of production, not to mention the depreciation of the product's quality (Grichar *et al.,* 2005; Andres *et al.,* 2009).

Initial development of sorghum is slow when compared to other cultivated species, which ensures that weeds, mainly those with a more aggressive growth habit, are more advantaged in the competition for resources, making sorghum more susceptible to interference exerted by weed community (Rizzardi *et al.,* 2004). Even showing a slow initial growth, sorghum utilizes a C4 photosynthetic pathway and is able to grow under low soil moisture conditions (Rodrigues *et al.,* 2010). Noteworthy for fodder sorghum, an annual crop used for feeding during dry periods, dense sowing increases this crop's competitive efficiency in relation to weeds, due to a faster land cover and, therefore, to the limited available spaces for weed emergence and growth.

For the majority of cultivated species, most troublesome weeds are those with a similar morphophysiology and life cycle*,* such as *Echinochloa crus-galli* and *Brachiaria plantaginea,* in areas cultivated with corn, sorghum and pear millet (Andres *et al.,* 2009; Rodrigues *et al.,* 2010; Dan *et al.,* 2011a).

However, in the United States and Mexico, the biggest problems to weed competition are related to the presence of broadleaves. These species have caused steep yield reductions, encouraging research focused on such weeds (Grichar *et al.,* 2005; Rosales-Robles *et al.,* 2005). Weed density increases of one single plant of *Amaranthus palmeri* per square meter have caused a 1.8% reduction on grain yield (Moore *et al.,* 2004). In subtropical areas, some grasses are still considered even more aggressive. According to Norris (1980), the presence of 175 *Echinochloa crus-galli* plants per square meter was enough to cause a 52% reduction on grain sorghum yield.

Weed management on sorghum crops in small properties has been carried out during the first 40 to 50 days after emergence, and two to three manual weedings are required. From this point on, the sorghum canopy will contribute to reduced favorable conditions for weed

The identification of post-emergence herbicides capable of controlling grasses, with suitable crop selectivity, is crucially important to keep sorghum cultivated areas expanding. Most of the registered herbicides used on sorghum farming were initially developed to be used on other large scale crops, particularly on corn and sweet corn (Stahlman & Wicks, 2000).

In this regard, the objective of this study is to gather information concerning the actual status of reported effects of weed interference on grain sorghum and also discuss options of

Sorghum, as well as other agricultural plant species, is subjected to a series of biotic and abiotic factors, which directly or indirectly influence its growth and development (Magalhães *et al.,* 2000). Among these factors, weed-imposed interference on crops is one of the most remarkable. Low sorghum yields have been correlated both to the absence and

The negative effects of weeds on sorghum agrosystems occur mainly due to the competition for crops' vital resources, such as water, light and nutrients. Furthermore, weeds can host pests and diseases, raising the cost of production, not to mention the depreciation of the

Initial development of sorghum is slow when compared to other cultivated species, which ensures that weeds, mainly those with a more aggressive growth habit, are more advantaged in the competition for resources, making sorghum more susceptible to interference exerted by weed community (Rizzardi *et al.,* 2004). Even showing a slow initial growth, sorghum utilizes a C4 photosynthetic pathway and is able to grow under low soil moisture conditions (Rodrigues *et al.,* 2010). Noteworthy for fodder sorghum, an annual crop used for feeding during dry periods, dense sowing increases this crop's competitive efficiency in relation to weeds, due to a faster land cover and, therefore, to the limited

For the majority of cultivated species, most troublesome weeds are those with a similar morphophysiology and life cycle*,* such as *Echinochloa crus-galli* and *Brachiaria plantaginea,* in areas cultivated with corn, sorghum and pear millet (Andres *et al.,* 2009; Rodrigues *et al.,*

However, in the United States and Mexico, the biggest problems to weed competition are related to the presence of broadleaves. These species have caused steep yield reductions, encouraging research focused on such weeds (Grichar *et al.,* 2005; Rosales-Robles *et al.,* 2005). Weed density increases of one single plant of *Amaranthus palmeri* per square meter have caused a 1.8% reduction on grain yield (Moore *et al.,* 2004). In subtropical areas, some grasses are still considered even more aggressive. According to Norris (1980), the presence of 175 *Echinochloa crus-galli* plants per square meter was enough to cause a 52% reduction on

Weed management on sorghum crops in small properties has been carried out during the first 40 to 50 days after emergence, and two to three manual weedings are required. From this point on, the sorghum canopy will contribute to reduced favorable conditions for weed

chemical weed control through post-emergence herbicides including tembotrione.

**2. Importance of weed control in sorghum** 

inefficient weed control (Erasmo & Pitelli, 1997).

available spaces for weed emergence and growth.

2010; Dan *et al.,* 2011a).

grain sorghum yield.

product's quality (Grichar *et al.,* 2005; Andres *et al.,* 2009).

germination, growth and development, mainly by reducing the incidence of radiation (Rizzardi *et al.,* 2001). In larger areas, weed control is usually accomplished by herbicide applications. Despite the method used for weed control, it is also important to observe that the period within the crop cycle when weed interference is prevented may also be a determinant for crop success. The stage of the crop cycle when weed control is established strongly influences competition levels, bringing about impacts on crop growth, development and grain yield (Silva *et al.,* 2009).

At the start of crop development, sorghum and weeds can coexist for a given period without the latter affecting either quantitatively or qualitatively crop production. This phase is called 'period prior to interference' (PPI). By determining interference periods in sorghum crops in tropical regions, Rodrigues *et al.* (2010) concluded that sorghum and the weed community could coexist for 42 days (PPI) with no yield reduction (Figure 1). On the other hand, this interference could occur earlier in the crop cycle depending on the density and species of weeds.

The period of time after sorghum emergence during which it must be free from weed competition is called 'total period of interference prevention' (TPIP).

By definition, the period in which weeds effectively interfere with the crop and the period during which competition must not exist is called 'critical period of competition' (PCPI) (Pitelli & Durigan, 1984). During this period, there has been observed a drastic crop yield reduction (54%) when the control was achieved late in time. Silva *et al.* (1986) observed that the absence of weed control on the first four weeks after sorghum emergence can lead to a reduction in grain production of 35% and that, without any control during the entire crop cycle, the reduction can be as high as 70%.

Fig. 1. Sorghum grain yield as a function of periods of weed control and weed coexistence in tropical regions (Rodrigues *et al.,* 2010).

Potential Use of Tembotrione (HPPD-Inhibitor Herbicides) in Grain Sorghum 175

Lack of adoption of weed control measures may affect sorghum quality and/or productivity, and, as a result, decrease a farmer`s profitability. However, management of the weed community at specific periods of time ensures lower damages because sorghum

Local variations on the critical period of weed interference are due to differences in crop genotype, sowing and emergence timing, water and nutrients availability, and density and

Traditionally, sorghum is more susceptible to herbicides than corn, mainly for graminicides applied postemergence. This response limits the utilization of chemical control as the main

To date, most studies have focused on the selectivity of herbicides applied pre-emergence such as s-metolachlor, dimethenamid and atrazine. However, the use of s-metolachlor has always been limited to the utilization of protective agents known as "safeners". Seed treatment using protectors such as fluxofenim, oxabetrinil, benoxacor, cyometrinil and naphthalic anhydride improves selectivity of s-metolachlor for sorghum (Horky & Martin,

It is estimated that approximately 95% of sorghum area is treated with post-emergence herbicides, particularly with atrazine. In Brazil, little attention has been given to preemergent herbicides in sorghum, due to the fact that most areas are cultivated in no or minimum-tillage areas. Therefore, sorghum sowing is often associated with the presence of a variable amount of straw (ranging from 2 to 8 ton dry matter per hectare) from the previous cropping cycle, usually following soybeans. With the increasing area of no-till farming and the growing problems of herbicide-resistant weeds, there has been a growing demand for herbicides with different mechanisms of action, mainly those applied postemergence. Table 1 summarizes main current post-emergence options studied and utilized

can exert the crop's control as well as express its full productive potential.

composition of the weed community.

2005).

in sorghum

**3. Selectivity of herbicides to grain sorghum** 

tool for weed management in sorghum areas.

in weed management for grain sorghum.

Common Name Level of selectivity Author atrazine Good Martin (2004) bentazon Good Ferrell *et al* (2008)

Inter: Intermediate (Some restrictions); Good: (No restrictions)

bromoxynil Good Rosales-Robles *et al.* (2005)

prosulfuron Good Rosales-Robles *et al.* (2005)

Table 1. Compilation of results related to herbicide selectivity in post-emergence application

2,4-D (amine) Inter. Dan *et al.* (2010b) carfentrazone Good Ferrell *et al.* (2008) dicamba Good Smith & Scott (2006) halosulfuron Inter. Ferrell *et al.* (2008) mesotrione Inter. Abit *et al.* (2009)

However, total period of interference prevention (TPIP) was 26 days (Rodrigues *et al.,* 2010). On this basis, it can be concluded that PPI was longer than TPIP, and, in this case, there was no PCPI. Under this scenario, accomplishing weed control just once during the crop cycle would be enough to preserve yield crop potential, as long as it is carried out between the end of PPI and the end of TPIP. Nevertheless, it must be understood that those periods may vary, mainly in relation to the intensity of competitive potential of weeds and to the density range as well as the predominant environmental conditions which may be more or less favorable to weeds. *Abutilon theophrasti* is noted to be more competitive than *Ipomoea purpurea* and *I. hederacea* in relation to sorghum, but the period of competition varies according to soil moisture level, exposure to solar radiation and nitrogen fertilization (Feltner *et al.,* 1973). Further studies should be carried out to determine critical periods of weed interference under different environmental and soil conditions.

Another approach to study weed interference on crops is based on crop development stage. For sorghum, the plant's phenological stage is usually a better indicator than the number of days after crop emergence due to both biotic and abiotic factors affecting crop growth (Larcher, 2000).

Losses can reach 80% of grain production under no weed control method (Andres *et al.,* 2009). Weed control on fodder sorghum crop should be accomplished along the period of the crop cycle between third and seventh leaf emission. Proper weed control during this period ensures no significant damage to the crop's grain yield. Figure 2 represents sorghum grain yield in relation to the phase of crop cycle in which weed control was accomplished (Andres *et al.,* 2009).

Number of leaves (grain sorghum)

Fig. 2. Sorghum grain yield in relation to periods of initial control and coexistence of weeds in sorghum crop cv. BRS 305 in temperate climate lowlands. (x) periods of initial control; (●) periods of initial coexistence (Andres *et al.,* 2009).

Lack of adoption of weed control measures may affect sorghum quality and/or productivity, and, as a result, decrease a farmer`s profitability. However, management of the weed community at specific periods of time ensures lower damages because sorghum can exert the crop's control as well as express its full productive potential.

Local variations on the critical period of weed interference are due to differences in crop genotype, sowing and emergence timing, water and nutrients availability, and density and composition of the weed community.

## **3. Selectivity of herbicides to grain sorghum**

174 Weed Control

However, total period of interference prevention (TPIP) was 26 days (Rodrigues *et al.,* 2010). On this basis, it can be concluded that PPI was longer than TPIP, and, in this case, there was no PCPI. Under this scenario, accomplishing weed control just once during the crop cycle would be enough to preserve yield crop potential, as long as it is carried out between the end of PPI and the end of TPIP. Nevertheless, it must be understood that those periods may vary, mainly in relation to the intensity of competitive potential of weeds and to the density range as well as the predominant environmental conditions which may be more or less favorable to weeds. *Abutilon theophrasti* is noted to be more competitive than *Ipomoea purpurea* and *I. hederacea* in relation to sorghum, but the period of competition varies according to soil moisture level, exposure to solar radiation and nitrogen fertilization (Feltner *et al.,* 1973). Further studies should be carried out to determine critical periods of

Another approach to study weed interference on crops is based on crop development stage. For sorghum, the plant's phenological stage is usually a better indicator than the number of days after crop emergence due to both biotic and abiotic factors affecting crop growth

Losses can reach 80% of grain production under no weed control method (Andres *et al.,* 2009). Weed control on fodder sorghum crop should be accomplished along the period of the crop cycle between third and seventh leaf emission. Proper weed control during this period ensures no significant damage to the crop's grain yield. Figure 2 represents sorghum grain yield in relation to the phase of crop cycle in which weed control was accomplished

Number of leaves (grain sorghum)

Fig. 2. Sorghum grain yield in relation to periods of initial control and coexistence of weeds in sorghum crop cv. BRS 305 in temperate climate lowlands. (x) periods of initial control; (●)

3579

5% of reduction

10% of reduction

weed interference under different environmental and soil conditions.

(Larcher, 2000).

(Andres *et al.,* 2009).

Grain Yield (kg ha-1)

1000

periods of initial coexistence (Andres *et al.,* 2009).

2000

3000

4000

5000

6000

7000

8000

9000

Traditionally, sorghum is more susceptible to herbicides than corn, mainly for graminicides applied postemergence. This response limits the utilization of chemical control as the main tool for weed management in sorghum areas.

To date, most studies have focused on the selectivity of herbicides applied pre-emergence such as s-metolachlor, dimethenamid and atrazine. However, the use of s-metolachlor has always been limited to the utilization of protective agents known as "safeners". Seed treatment using protectors such as fluxofenim, oxabetrinil, benoxacor, cyometrinil and naphthalic anhydride improves selectivity of s-metolachlor for sorghum (Horky & Martin, 2005).

It is estimated that approximately 95% of sorghum area is treated with post-emergence herbicides, particularly with atrazine. In Brazil, little attention has been given to preemergent herbicides in sorghum, due to the fact that most areas are cultivated in no or minimum-tillage areas. Therefore, sorghum sowing is often associated with the presence of a variable amount of straw (ranging from 2 to 8 ton dry matter per hectare) from the previous cropping cycle, usually following soybeans. With the increasing area of no-till farming and the growing problems of herbicide-resistant weeds, there has been a growing demand for herbicides with different mechanisms of action, mainly those applied postemergence. Table 1 summarizes main current post-emergence options studied and utilized in weed management for grain sorghum.


Inter: Intermediate (Some restrictions); Good: (No restrictions)

Table 1. Compilation of results related to herbicide selectivity in post-emergence application in sorghum

Potential Use of Tembotrione (HPPD-Inhibitor Herbicides) in Grain Sorghum 177

reduction. Since sorghum hybrids were able to recover from injury as the growing season progressed, injury symptoms were not good predictors of yield loss. This study demonstrated that post-emergence applications of mesotrione to sorghum grain hybrids caused a differential crop injury response ranging from susceptible to tolerant. To develop mesotrione as a good alternative for post-emergence weed grass management in sorghum, it may be crucially important for regionalized studies to understand the diversity of genotype

Tembotrione was discovered in 1997 and launched as a commercial herbicide in 2007/2008 in Austria, Hungary, USA and Brazil. When tembotrione is applied to the foliage, a very high percentage of the applied compound is rapidly absorbed. In cases where the herbicide comes in contact with the soil, only small amounts enter the plants via the roots. Accordingly, this herbicide acts after post-emergence application predominantly via the foliage. Tembotrione is mobile both in the plant symplast (phloem) and in the apoplast (xylem). The mobility in the phloem is of particular importance, since it ensures that after a post-emergence spray application the herbicide will be distributed in the stream of assimilates from the mature leaves (metabolic sources) to the developing, highly susceptible leaves (metabolic sinks) at the shoot apex. In accordance with the translocation data obtained with 14C-labeled tembotrione, it can be demonstrated that after controlled foliar placement of the herbicide on susceptible weed species new shoot growth is inhibited due to

As a member of the triketone family of active ingredients, tembotrione shows properties of a weak acid (pKa = 3.18), resulting in high water solubility and low lipophilicity, e.g. a low octanol/water partition coefficient. These properties are pH-dependent in the environmentally relevant pH range between pH 5 to 9 (log Pow = –1.09 at pH 7 and –1.37 at pH 9). Consequently, it can be assumed that the behavior of tembotrione in soil and aqueous systems is also influenced by pH. This expectation was confirmed by the differences in the water solubility of tembotrione. Solubility is low at pH 4 (0.22 g L-1) and significantly higher at pH 7 and 9 (28-29 g L-1). The high solubility in water at neutral to weakly alkaline pH correlates favorably with the low logPow. Therefore, under environmentally relevant pH conditions, tembotrione is mainly present in its ionic form indicating a very low potential for accumulation in biological systems and a tendency to form salts in the environment. In addition, with the values determined for vapor pressure and the Henry's law constant it is estimated that no significant volatilization from soil or water surfaces will occur (Tarara *et al.,* 2009). Typical bleaching caused by tembotrione applications in sorghum occurs in leaves

Tembotrione is currently registered for post-emergence use in corn in the United States and Brazil and has showed quite satisfactory results on weed control, particularly for grasses. Commercial formulations of this herbicide include the safener isoxadifen-ethyl, granting higher selectivity to corn and popcorn crops (Waddington & Young, 2006). Field evaluations of crop tolerance provided by mesotrione, topramesone and tembotrione applications in corn, lead to the conclusion that tembotrione caused the least crop injury when compared to

tolerance across different producing regions throughout the world.

**3.1 Selectivity of tembotrione to grain sorghum** 

phloem systemicity (Van Almsick *et al.,* 2009).

that develop after spraying (Figure 3).

topramesone and mesotrione (Bollman *et al.,* 2008).

One of the most commonly used herbicides to control weeds post-emergence in sorghum is atrazine. Atrazine has been the basis of chemical weed control in corn for the last 50 years and its mechanism of action inhibits the electron flow in photosystem II; other than its know selectivity to corn, it has been considered selective to other grass crops such as pear millet and sorghum (Dan *et al.,* 2011a). In contrast, one of the main limitations of this herbicide is its low effectiveness on grasses. Previous reports confirm the limited effectiveness of atrazine postemergence applications to control grass weeds like *Cenchrus echinatus* and *Digitaria horizontalis* in corn and sorghum (Dan *et al.,* 2011a,b).

Herbicides like 2,4-D, carfentrazone and dicamba have also been considered excellent alternatives for the control of broad-leaved weeds. However, they present limitations regarding grass control. Furthermore, additional caution concerning the use of synthetic auxins like 2,4-D and dicamba, should be taken since the combination of late applications and high doses of these chemicals can cause foliar and root dymorphism, which in some cases, leads to yield reduction (Dan *et al.,* 2010b).

Among the graminicides and broadleaf herbicides with potential post-emergence use in sorghum, carotenoid biosynthesis inhibitor herbicides, particularly those that inhibit the enzyme 4-hydroxyphenylpyruvate dioxygenase (HPPD) are noteworthy (Miller & Regehr, 2002). The inhibition of HPPD blocks the pathway of prenylquinone biosynthesis in plants. Early effects, prior to the appearance of visible phytotoxicity symptoms, are decreased levels of tocopherols and plastoquinone in the plant tissue and a reduced photosynthetic yield. Indirect inhibition of phytoene desaturase as an effect of blocked plastoquinone biosynthesis leads to a decrease in carotenoid levels particularly in young, still expanding leaves. This causes typical foliar bleaching symptoms because the photosynthetic apparatus is no longer stabilized by these pigments. Under high light intensity, excess energy is not quenched and chlorophyll molecules are destroyed (Wichert *et al.,* 1999). Since carotenoids play an important role in dissipating the oxidative energy of singlet O2, bleaching occurs due to the loss of the protection provided these pigments, leading to a chlorophyll oxidative degradation and, in some extreme cases, to cell membrane oxidation (Mitchell *et al.,* 2001; Armel *et al.,* 2003; Grossmann & Ehrhardt, 2007). Current carotenoid biosynthesis inhibitors registered for use in Brazil include clomazone, isoxaflutole, mesotrione and tembotrione, but clomazone and isoxaflutole have been limited to pre-emergence applications.

Some crops, such as corn, show good tolerance to these herbicides. It has been suggested that selectivity of HPPD inhibitors occur due to a rapid metabolism of herbicide molecules, mainly caused by the action of cytochrome P450 hemoprotein. The cytochrome P450 enzyme, responsible for this metabolism, is likely encoded by the active allele, *Nsf1* (Pataky *et al.,* 2008). Sweet corn hybrids, homozygous for the inactive allele (*Nsf1*), are highly sensitive to mesotrione (Pataky *et al.,* 2008).

Recent studies have demonstrated the possibility of using mesotrione in sorghum as postemergence applications. Mesotrione is a HPPD inhibitor and belongs to triketone chemical family. It is derived from a natural phytotoxin (callistemone) obtained from the *Callistemon citrinus* plants. A large variability of crop response in the 85 sorghum hybrids treated with 0, 52, 105, 210, and 315 g ha-1 mesotrione was found when plants were sprayed at the 3 to 4 leaf stage (Abit *et al.,* 2009). From the total number of hybrids tested, 23 were classified as susceptible, 45 as intermediate, and 17 as tolerant. From the 17 hybrids classified as tolerant, four were grown in the field. In field, the level of injury symptoms did not correlate to yield reduction. Since sorghum hybrids were able to recover from injury as the growing season progressed, injury symptoms were not good predictors of yield loss. This study demonstrated that post-emergence applications of mesotrione to sorghum grain hybrids caused a differential crop injury response ranging from susceptible to tolerant. To develop mesotrione as a good alternative for post-emergence weed grass management in sorghum, it may be crucially important for regionalized studies to understand the diversity of genotype tolerance across different producing regions throughout the world.

#### **3.1 Selectivity of tembotrione to grain sorghum**

176 Weed Control

One of the most commonly used herbicides to control weeds post-emergence in sorghum is atrazine. Atrazine has been the basis of chemical weed control in corn for the last 50 years and its mechanism of action inhibits the electron flow in photosystem II; other than its know selectivity to corn, it has been considered selective to other grass crops such as pear millet and sorghum (Dan *et al.,* 2011a). In contrast, one of the main limitations of this herbicide is its low effectiveness on grasses. Previous reports confirm the limited effectiveness of atrazine postemergence applications to control grass weeds like *Cenchrus echinatus* and

Herbicides like 2,4-D, carfentrazone and dicamba have also been considered excellent alternatives for the control of broad-leaved weeds. However, they present limitations regarding grass control. Furthermore, additional caution concerning the use of synthetic auxins like 2,4-D and dicamba, should be taken since the combination of late applications and high doses of these chemicals can cause foliar and root dymorphism, which in some

Among the graminicides and broadleaf herbicides with potential post-emergence use in sorghum, carotenoid biosynthesis inhibitor herbicides, particularly those that inhibit the enzyme 4-hydroxyphenylpyruvate dioxygenase (HPPD) are noteworthy (Miller & Regehr, 2002). The inhibition of HPPD blocks the pathway of prenylquinone biosynthesis in plants. Early effects, prior to the appearance of visible phytotoxicity symptoms, are decreased levels of tocopherols and plastoquinone in the plant tissue and a reduced photosynthetic yield. Indirect inhibition of phytoene desaturase as an effect of blocked plastoquinone biosynthesis leads to a decrease in carotenoid levels particularly in young, still expanding leaves. This causes typical foliar bleaching symptoms because the photosynthetic apparatus is no longer stabilized by these pigments. Under high light intensity, excess energy is not quenched and chlorophyll molecules are destroyed (Wichert *et al.,* 1999). Since carotenoids play an important role in dissipating the oxidative energy of singlet O2, bleaching occurs due to the loss of the protection provided these pigments, leading to a chlorophyll oxidative degradation and, in some extreme cases, to cell membrane oxidation (Mitchell *et al.,* 2001; Armel *et al.,* 2003; Grossmann & Ehrhardt, 2007). Current carotenoid biosynthesis inhibitors registered for use in Brazil include clomazone, isoxaflutole, mesotrione and tembotrione, but

clomazone and isoxaflutole have been limited to pre-emergence applications.

Some crops, such as corn, show good tolerance to these herbicides. It has been suggested that selectivity of HPPD inhibitors occur due to a rapid metabolism of herbicide molecules, mainly caused by the action of cytochrome P450 hemoprotein. The cytochrome P450 enzyme, responsible for this metabolism, is likely encoded by the active allele, *Nsf1* (Pataky *et al.,* 2008). Sweet corn hybrids, homozygous for the inactive allele (*Nsf1*), are highly

Recent studies have demonstrated the possibility of using mesotrione in sorghum as postemergence applications. Mesotrione is a HPPD inhibitor and belongs to triketone chemical family. It is derived from a natural phytotoxin (callistemone) obtained from the *Callistemon citrinus* plants. A large variability of crop response in the 85 sorghum hybrids treated with 0, 52, 105, 210, and 315 g ha-1 mesotrione was found when plants were sprayed at the 3 to 4 leaf stage (Abit *et al.,* 2009). From the total number of hybrids tested, 23 were classified as susceptible, 45 as intermediate, and 17 as tolerant. From the 17 hybrids classified as tolerant, four were grown in the field. In field, the level of injury symptoms did not correlate to yield

*Digitaria horizontalis* in corn and sorghum (Dan *et al.,* 2011a,b).

cases, leads to yield reduction (Dan *et al.,* 2010b).

sensitive to mesotrione (Pataky *et al.,* 2008).

Tembotrione was discovered in 1997 and launched as a commercial herbicide in 2007/2008 in Austria, Hungary, USA and Brazil. When tembotrione is applied to the foliage, a very high percentage of the applied compound is rapidly absorbed. In cases where the herbicide comes in contact with the soil, only small amounts enter the plants via the roots. Accordingly, this herbicide acts after post-emergence application predominantly via the foliage. Tembotrione is mobile both in the plant symplast (phloem) and in the apoplast (xylem). The mobility in the phloem is of particular importance, since it ensures that after a post-emergence spray application the herbicide will be distributed in the stream of assimilates from the mature leaves (metabolic sources) to the developing, highly susceptible leaves (metabolic sinks) at the shoot apex. In accordance with the translocation data obtained with 14C-labeled tembotrione, it can be demonstrated that after controlled foliar placement of the herbicide on susceptible weed species new shoot growth is inhibited due to phloem systemicity (Van Almsick *et al.,* 2009).

As a member of the triketone family of active ingredients, tembotrione shows properties of a weak acid (pKa = 3.18), resulting in high water solubility and low lipophilicity, e.g. a low octanol/water partition coefficient. These properties are pH-dependent in the environmentally relevant pH range between pH 5 to 9 (log Pow = –1.09 at pH 7 and –1.37 at pH 9). Consequently, it can be assumed that the behavior of tembotrione in soil and aqueous systems is also influenced by pH. This expectation was confirmed by the differences in the water solubility of tembotrione. Solubility is low at pH 4 (0.22 g L-1) and significantly higher at pH 7 and 9 (28-29 g L-1). The high solubility in water at neutral to weakly alkaline pH correlates favorably with the low logPow. Therefore, under environmentally relevant pH conditions, tembotrione is mainly present in its ionic form indicating a very low potential for accumulation in biological systems and a tendency to form salts in the environment. In addition, with the values determined for vapor pressure and the Henry's law constant it is estimated that no significant volatilization from soil or water surfaces will occur (Tarara *et al.,* 2009). Typical bleaching caused by tembotrione applications in sorghum occurs in leaves that develop after spraying (Figure 3).

Tembotrione is currently registered for post-emergence use in corn in the United States and Brazil and has showed quite satisfactory results on weed control, particularly for grasses. Commercial formulations of this herbicide include the safener isoxadifen-ethyl, granting higher selectivity to corn and popcorn crops (Waddington & Young, 2006). Field evaluations of crop tolerance provided by mesotrione, topramesone and tembotrione applications in corn, lead to the conclusion that tembotrione caused the least crop injury when compared to topramesone and mesotrione (Bollman *et al.,* 2008).

(Figure 4).

Potential Use of Tembotrione (HPPD-Inhibitor Herbicides) in Grain Sorghum 179

and 38% at 7 DAA), respectively for the highest dose of 168 g ha-1. Results are shown below

DKB 599 17.0 6.5 4.4 13.2 abA 11.2 abA AG 1020 23.0 19.4 2.3 12.9 abA 9.1 bB BRS 308 13.5 4.3 1.5 10.3 bA 9.3 bA AG 1040 14.7 8.6 3.2 15.3 aA 13.4 aA AGN-8040 11.3 10.3 4.3 12.6 bA 10.2 aA

7 DAA 14 DAA 21 DAA 0.0 75.5

Dose (g ha-1)

Cultivar Visual crop injury (%) SDW (g plot -1)

CV% 13.12 DMS 3.23

differ from each other by Tukey *p≥0.05* test (Dan *et al.,* 2009a).

3 leaves y= -5.99+0.39x R2

5 leaves y= -5.89+0.31x R2

8 leaves y= -5.56+0.26x R2

after application of two doses of tembotrione.

Crop injury (%)

0

in three crop growth stages. Source: Dan *et al.* (2010a).

10

20

30

40

50

60

70

Means followed by the same letter (low case letter in the column and capital letter in the row) do not

Table 2. Visual rating of crop injury and shoot dry weight (SDW) of five sorghum cultivars

=0.97

=0.96

=0.96

Dose of tembotrione (g ha-1) 0 42 88 126 168

Fig. 4. Sorghum visual injury seven days after application for different doses of tembotrione

Despite the rapid injuries recovery at 21 DAA, the authors have reported that trends evidenced at 7 DAA were maintained, indicating that applications accomplished in the earlier stages of sorghum crop development have provided the highest levels of crop injury,

Fig. 3. Simptoms of tembotrione (200 g ha-1) injuries in late post-emergence application in sorghum.

When assessing selectivity of tembotrione applied to 4-leaf stage in five sorghum cultivars, different levels of crop tolerance were found (Dan *et al.,* 2009a). Results from evaluation performed seven days after application (DAA) of tembotrione demonstrated typical injuries of carotenoid pigment biosynthesis inhibitor herbicides (Figure 3). Throughout the postapplication evaluation period, all cultivars showed intoxication (0 to 23% crop injury) when compared to those plants with no herbicide treatment (Table 2). Although there have been visible injuries in all cultivars at 7 DAA, progressive recovery of sorghum plants lead to less than 5% of visual injuries and no bleaching at 21 DAA (Table 2).

Cultivar AG-1020 was the most susceptible genotype among cultivars, and its shoot dry biomass was severely (~30%) affected when plants were harvested 28 DAA. Cultivars have not differed concerning the extent to herbicide sensitivity after 75.5 ha-1 tembotrione application to sorghum crop in tropical regions.

Based on the effect of crop dose-response in relation to stages when the herbicide application was performed, results so far indicate that earlier applications are more harmful to grain sorghum development (Dan *et al.,* 2010a). In this study, they evaluated the effect of tembotrione (0, 42, 88, 126, and 168 g ha-1) applied to three phenological stages of sorghum (S1: 3-leaf stage, 15 days after emergence; S2: 5-leaf stage, 23 days after emergence; S3: 8-leaf stage, 31 days after emergence). Cultivar AG-1040 presented the greatest injury levels (59, 46

Fig. 3. Simptoms of tembotrione (200 g ha-1) injuries in late post-emergence application in

than 5% of visual injuries and no bleaching at 21 DAA (Table 2).

application to sorghum crop in tropical regions.

When assessing selectivity of tembotrione applied to 4-leaf stage in five sorghum cultivars, different levels of crop tolerance were found (Dan *et al.,* 2009a). Results from evaluation performed seven days after application (DAA) of tembotrione demonstrated typical injuries of carotenoid pigment biosynthesis inhibitor herbicides (Figure 3). Throughout the postapplication evaluation period, all cultivars showed intoxication (0 to 23% crop injury) when compared to those plants with no herbicide treatment (Table 2). Although there have been visible injuries in all cultivars at 7 DAA, progressive recovery of sorghum plants lead to less

Cultivar AG-1020 was the most susceptible genotype among cultivars, and its shoot dry biomass was severely (~30%) affected when plants were harvested 28 DAA. Cultivars have not differed concerning the extent to herbicide sensitivity after 75.5 ha-1 tembotrione

Based on the effect of crop dose-response in relation to stages when the herbicide application was performed, results so far indicate that earlier applications are more harmful to grain sorghum development (Dan *et al.,* 2010a). In this study, they evaluated the effect of tembotrione (0, 42, 88, 126, and 168 g ha-1) applied to three phenological stages of sorghum (S1: 3-leaf stage, 15 days after emergence; S2: 5-leaf stage, 23 days after emergence; S3: 8-leaf stage, 31 days after emergence). Cultivar AG-1040 presented the greatest injury levels (59, 46

sorghum.


and 38% at 7 DAA), respectively for the highest dose of 168 g ha-1. Results are shown below (Figure 4).

Means followed by the same letter (low case letter in the column and capital letter in the row) do not differ from each other by Tukey *p≥0.05* test (Dan *et al.,* 2009a).

Table 2. Visual rating of crop injury and shoot dry weight (SDW) of five sorghum cultivars after application of two doses of tembotrione.

Fig. 4. Sorghum visual injury seven days after application for different doses of tembotrione in three crop growth stages. Source: Dan *et al.* (2010a).

Despite the rapid injuries recovery at 21 DAA, the authors have reported that trends evidenced at 7 DAA were maintained, indicating that applications accomplished in the earlier stages of sorghum crop development have provided the highest levels of crop injury,

Potential Use of Tembotrione (HPPD-Inhibitor Herbicides) in Grain Sorghum 181

Currently, doses ranging from 75.6 to 100.8 g ha-1 of tembotrione are recommended for weed control in corn in Brazil. Taking into account the lowest recommended dose (75.6 g ha-1), for instance, the greatest reduction observed for sorghum grain yield was about 11% when applications were carried out at the 3-leaf stage. Applications performed in other crop stages reached 7.3% and 6.1% in 5- and 8- leaf stages, respectively. These results indicate a potential use of this herbicide on grain sorghum, however, further studies evaluating other

Despite the different levels of crop injury, it is important to highlight that interference caused by weeds could pose a much more important risk due to losses up to 97% on grain

The tolerance of corn to tembotrione in combination with the safener isoxadifenethyl has been attributed to a much faster metabolic degradation of the herbicide than in susceptible dicotyledonous and grass weed species. Herbicide metabolism studies in corn, with and without a safener, reveal that isoxadifen-ethyl enhances tembotrione metabolism resulting in non-phytotoxic products. Corresponding to the specificity of safener action in corn, no significant enhancement of herbicide metabolism is found in *Brachiaria plantaginea* as one

Besides selectivity, another decisive factor leading to the adoption of a certain herbicide is related to the spectrum of weed control. The list of weeds controlled by tembotrione in Brazil comprises important grasses like *Brachiaria decumbens*, *Cenchrus echinatus*, *Digitaria horizontalis, D. ciliaris* and *Brachiaria plantaginea* and broad leaf species like *Alternanthera tenella*, *Commelina benghalensis*, *Ipomoea nil*, *I. purpurea*, *I. acuminate*, *Sida rhombifolia*, *Nicandra physaloides*, *Euphorbia heterophylla*, *Raphanus raphanistrum*, *Bidens pilosa*, *B. subalternans*, *Richardia brasiliensis* and *Leonurus sibiricus*, but the registration is limited to corn. However, the control on broad leaf species such as *A. tenella, B. pilosa* and *Ageratum conyzoides* is usually extended by using the combined use of atrazine and tembotrione (Barroso *et al.,* 2009). The efficiency of tembotrione alone is clearly limited when it is applied to weeds in a

Among main grass species that are present in areas cultivated with sorghum in Midwestern Brazil, post-emergence applications of tembotrione may have a differential level of efficacy. *D. horizontalis* is more sensitive than *Cenchrus echinatus*; control of both species becomes more evident (>80%) in doses ≥88 g ha-1 for *D. horizontalis* in applications carried out before tillering. However, similar levels of control of *C. echinatus* are obtained only by using doses

Other studies have also evaluated the spectrum of weeds controlled by tembotrione. Applied at 92 g ha-1, control of broadleaves and grass species was reported (Hinz *et al.,* 2005; Lamore *et al.,* 2006), including redroot pigweed (*Amaranthus retroflexus* L.), common lambsquarters (*Chenopodium album* L.), common ragweed (*Ambrosia artemisifolia* L.), velvetleaf (*Abutilon theophrasti* Medic.), giant foxtail (*Setaria faberi* Herrm), barnyardgrass [*Echinochloa crusgalli* (L.) Beauv], and woolly cupgrass [*Eriochloa villosa* (Thunb.) Kunth].

Further work on this issue must investigate the possibility of using mixtures with other herbicides such as atrazine, among others. In addition, it is equally important to highlight

cultivars are required to supplement information on the selectivity of this herbicide.

sorghum yield (Tamado *et al.,* 2001), justifying the need for weed control.

example of a representative target weed species (Tarara *et al.,* 2009).

**3.2 Weed control by tembotrione in grain sorghum** 

more advanced growth stage.

of 126 g ha-1 (Figure 6).

implying that herbicide tolerance increases as plants get older. Similar effects related to tembotrione applications in pearl millet have also been described (Dan *et al.,* 2010c). In pear millet, higher tolerance occurred when tembotrione (75 g ha-1) was applied at the beginning of tillering, as compared to prior-tillering.

Increasing doses of tembotrione can trigger significant reductions on the amount of shoot dry weight and final plant height. More evident reductions of sorghum growth were observed when the herbicide application was carried out at earlier growth stages (3 leaves stage) (Dan *et al.,* 2010a). Injury reduction was twice as much more pronounced when compared to applications at 5- and 8-leaf stage. Nevertheless, effects on dry weight are directly related to crop stage at herbicide spraying. Abit *et al.* (2009) observed that all 85 sorghum hybrids evaluated showed significant reductions in the amount of dry weight after exposure to mesotrione, an herbicide which exhibits a very close chemical structure and similar mechanism of action to that of tembotrione.

Results lead to the conclusion that younger plants are less able to recover from injuries caused by tembotrione and that this fact directly reflects on dry weight accumulation, which may represent a negative factor for sorghum crops destined to forage production. For this reason, proper care should be taken concerning the dose and time of application of this herbicide.

In relation to grain yield, intoxication caused by tembotrione can cause significant reductions due to dose increment. Studies carried out with doses ranging from 0 to 168 g ha-1, demonstrated grain yield reductions of 25, 16 and 15% for applications performed at 3, 5 and 8 expanded leaves stages, respectively (Figure 5) (Dan *et al.,* 2010a).

Fig. 5. Sorghum grain yield reduction as a function of increasing doses of tembotrione applied in three crop growth stages (Dan *et al.,* 2010a).

implying that herbicide tolerance increases as plants get older. Similar effects related to tembotrione applications in pearl millet have also been described (Dan *et al.,* 2010c). In pear millet, higher tolerance occurred when tembotrione (75 g ha-1) was applied at the beginning

Increasing doses of tembotrione can trigger significant reductions on the amount of shoot dry weight and final plant height. More evident reductions of sorghum growth were observed when the herbicide application was carried out at earlier growth stages (3 leaves stage) (Dan *et al.,* 2010a). Injury reduction was twice as much more pronounced when compared to applications at 5- and 8-leaf stage. Nevertheless, effects on dry weight are directly related to crop stage at herbicide spraying. Abit *et al.* (2009) observed that all 85 sorghum hybrids evaluated showed significant reductions in the amount of dry weight after exposure to mesotrione, an herbicide which exhibits a very close chemical structure and

Results lead to the conclusion that younger plants are less able to recover from injuries caused by tembotrione and that this fact directly reflects on dry weight accumulation, which may represent a negative factor for sorghum crops destined to forage production. For this reason, proper care should be taken concerning the dose and time of application of this herbicide.

In relation to grain yield, intoxication caused by tembotrione can cause significant reductions due to dose increment. Studies carried out with doses ranging from 0 to 168 g ha-1, demonstrated grain yield reductions of 25, 16 and 15% for applications performed at 3, 5

=0,98

=0.98

=0,95

=0.96

=0.95

=0,96

Recommended dose to corn

75 100

Dose of tembotrione (g ha-1) 0 42 88 126 168

Fig. 5. Sorghum grain yield reduction as a function of increasing doses of tembotrione

applied in three crop growth stages (Dan *et al.,* 2010a).

and 8 expanded leaves stages, respectively (Figure 5) (Dan *et al.,* 2010a).

3 folhas y= 0,14+0,15x R<sup>2</sup>

5 leaves y= - 0.24+0.10x R2

3 leaves y= 0.14+0.15x R2

5 folhas y= - 0,24+0,10x R<sup>2</sup>

8 leaves y= - 1.44+0.10x R2

8 folhas y= - 1,44+0,10x R<sup>2</sup>

of tillering, as compared to prior-tillering.

similar mechanism of action to that of tembotrione.

Grain yield reduction (%)

0

5

10

15

20

25

30

Currently, doses ranging from 75.6 to 100.8 g ha-1 of tembotrione are recommended for weed control in corn in Brazil. Taking into account the lowest recommended dose (75.6 g ha-1), for instance, the greatest reduction observed for sorghum grain yield was about 11% when applications were carried out at the 3-leaf stage. Applications performed in other crop stages reached 7.3% and 6.1% in 5- and 8- leaf stages, respectively. These results indicate a potential use of this herbicide on grain sorghum, however, further studies evaluating other cultivars are required to supplement information on the selectivity of this herbicide.

Despite the different levels of crop injury, it is important to highlight that interference caused by weeds could pose a much more important risk due to losses up to 97% on grain sorghum yield (Tamado *et al.,* 2001), justifying the need for weed control.

The tolerance of corn to tembotrione in combination with the safener isoxadifenethyl has been attributed to a much faster metabolic degradation of the herbicide than in susceptible dicotyledonous and grass weed species. Herbicide metabolism studies in corn, with and without a safener, reveal that isoxadifen-ethyl enhances tembotrione metabolism resulting in non-phytotoxic products. Corresponding to the specificity of safener action in corn, no significant enhancement of herbicide metabolism is found in *Brachiaria plantaginea* as one example of a representative target weed species (Tarara *et al.,* 2009).

#### **3.2 Weed control by tembotrione in grain sorghum**

Besides selectivity, another decisive factor leading to the adoption of a certain herbicide is related to the spectrum of weed control. The list of weeds controlled by tembotrione in Brazil comprises important grasses like *Brachiaria decumbens*, *Cenchrus echinatus*, *Digitaria horizontalis, D. ciliaris* and *Brachiaria plantaginea* and broad leaf species like *Alternanthera tenella*, *Commelina benghalensis*, *Ipomoea nil*, *I. purpurea*, *I. acuminate*, *Sida rhombifolia*, *Nicandra physaloides*, *Euphorbia heterophylla*, *Raphanus raphanistrum*, *Bidens pilosa*, *B. subalternans*, *Richardia brasiliensis* and *Leonurus sibiricus*, but the registration is limited to corn. However, the control on broad leaf species such as *A. tenella, B. pilosa* and *Ageratum conyzoides* is usually extended by using the combined use of atrazine and tembotrione (Barroso *et al.,* 2009). The efficiency of tembotrione alone is clearly limited when it is applied to weeds in a more advanced growth stage.

Among main grass species that are present in areas cultivated with sorghum in Midwestern Brazil, post-emergence applications of tembotrione may have a differential level of efficacy. *D. horizontalis* is more sensitive than *Cenchrus echinatus*; control of both species becomes more evident (>80%) in doses ≥88 g ha-1 for *D. horizontalis* in applications carried out before tillering. However, similar levels of control of *C. echinatus* are obtained only by using doses of 126 g ha-1 (Figure 6).

Other studies have also evaluated the spectrum of weeds controlled by tembotrione. Applied at 92 g ha-1, control of broadleaves and grass species was reported (Hinz *et al.,* 2005; Lamore *et al.,* 2006), including redroot pigweed (*Amaranthus retroflexus* L.), common lambsquarters (*Chenopodium album* L.), common ragweed (*Ambrosia artemisifolia* L.), velvetleaf (*Abutilon theophrasti* Medic.), giant foxtail (*Setaria faberi* Herrm), barnyardgrass [*Echinochloa crusgalli* (L.) Beauv], and woolly cupgrass [*Eriochloa villosa* (Thunb.) Kunth].

Further work on this issue must investigate the possibility of using mixtures with other herbicides such as atrazine, among others. In addition, it is equally important to highlight

Potential Use of Tembotrione (HPPD-Inhibitor Herbicides) in Grain Sorghum 183

Archangelo, E.R. *et al.* Tolerância do sorgo forrageiro ao herbicida Primestra SC. Revista

Armel, G.R.; Wilson, H.P.; Richardson, R.J. Mesotrione combinations in no-till corn (*Zea* 

Barroso, A.L.L. *et al.* Controle de plantas daninhas na cultura do milho In: Worshop Comigo 2009, Rio Verde, GO. Anais... Resultados CTC Comigo 2009, 2009. p.90-96. Bollman, J.D.; Boerboom, C.M.; Becker, R.L. Efficacy and tolerance to HPPD-inhibiting herbicides in sweet corn. Weed Technology, v.22, n.4, p.666-674, 2008. Dahlberg, J.A.; Burke, J.J.; Rosenow, D.T. Development of a sorghum core collection: refinement and evaluation of a subset from sudan. Economic Botany, v.58, n.4, p.556-567, 2004. Dan, H.A. *et al.* Efeito do herbicida atrazine no controle do capim-carrapicho (*Cenchus* 

Dan, H.A. *et al.* Seletividade de Herbicidas para a cultura do sorgo granífero. In: Worshop Comigo 2009, Rio Rerde, GO. Anais. Resultados CTC Comigo 2009, 2009b. p.80-85. Dan, H.A. *et al.* Tolerância do sorgo granífero ao herbicida tembotrione. Planta Daninha,

Dan, H.A *et al.* Tolerância do sorgo granífero ao 2,4-D aplicado em pós-emergência. Planta

Dan, H.A *et al.* Seletividade do herbicida tembotrione à cultura do milheto. Planta Daninha,

Dan, H.A. *et al.* Influência do estádio de desenvolvimento de *Cenchrus echinatus* na supressão

Dan, H.A. *et al.* Supressão imposta pelo atrazine a *Digitaria horizontalis* em função do estádio

Erasmo, E.A.L.; Pitelli, R.A. Efeitos da adubação fosfatada nas relações de interferência entre

Feltner, K. C.; Vanderlip, R. L.; Hurst, H. R. Velvetleaf and morningglory competition in grain sorghum. Kansas Academia Science, v.76, n.4, p.282-288, 1973. Ferrell, J.A.; Macdonald, G.E.; Brecke, B.J. Weed management in sorghum – 2008. Series of

Gontijo Neto, M.M.G. et al. Híbridos de sorgo (*Sorghum bicolor* (L.) Moench) cultivados sob

Grichar, W.J. *et al.* Weed control and grain sorghum (*Sorghum bicolor*) response to

Grossmann, K.; Ehrhardt, T. On the mechanism of action and selectivity of the corn

Hinz, J.; Wollam, J.; Allen, J. Weed control with AE 0172747 in corn. Proc. North Central

Horky, K.T.; Martin, A.R. Evaluation of preemergence weed control programs in grain

Lamore, D.; Simkins, G.; Watteyne, K.; Allen, J. Weed control programs with tembotrione in corn. Proc. North Central Weed Science Society, v.61, n.2, p.119, 2006.

sorgo granífero e tiririca. I. Crescimento inicial. Planta Daninha, v.15, n.2, p.114-121,

níveis crescentes de adubação. Rendimento, proteína bruta e digestibilidade *in* 

postemergence applications of atrazine, pendimethalin, and trifluralin. Weed

herbicide topramezone: a new inhibitor of 4- hydroxyphenylpyruvate dioxygenase.

sorghum. In: Weed Control in Specialty Crops. Lincoln, NE: 2005 NCWSS Research

imposta por atrazine. Planta Daninha, v.29, n.1, p.179-184, 2011a.

de desenvolvimento. Revista Caatinga, v.24, n.1, p. 27-33, 2011b.

*Vitro*. Revista Brasileira de Zootecnia, v.31, n.4, p.1640-1647, 2002

Rerde, GO. Anais... Resultados CTC Comigo 2009, 2009a. p.77-80.

*echinatus*) e capim-colchão (*Digitaria horizontalis*). In: Worshop Comigo 2009, Rio

Brasileira de Milho e Sorgo*,* v.1, n.2, p.59-66, 2002.

*mays*). Weed Technology, v.17, n.3, p.111-116, 2003.

v.28, n.3, p.615-620, 2010a.

v.28, n.4, p.793-799, 2010c.

1997.

Daninha, v.28, n.4, p.785-792, 2010b.

the Agronomy Department. 2008. 5p

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Larcher, W. Ecofisiologia vegetal. São Carlos: RIMA, 2000. 531 p.

other cropping techniques targeted to reduce the infestation in order to reduce pressure by making the control easier to ensure a more successful tillage management.

Fig. 6. Weed control for sorghum crop at 21 days after applying increasing doses of tembotrione (Dan et al., 2009b).

#### **4. Concluding remarks**

Weeds present a great competitive potential with grain sorghum. However, effects are converged by a number of factors such as weed species and densities, moment of crop cycle when control is imposed and farming practices such as tillage system. Results have demonstrated that more intense interference occurs, in most cases, starting at the 4-leaf stage, weed free period. Although sorghum cropping is widespread in a great variety of regions throughout the world, current selective herbicides have not been sufficiently evaluated and the options available so far are not enough. Studies have provided results that confirm HPPD-inhibitor herbicides potential, mainly for mesotrione and tembotrione, assisting mainly in post-mergence grass weed control. Nevertheless, regionalized studies on different genotypes of sorghum must be conducted to supplement information regarding the selectivity of this herbicide for grain sorghum and to support recommendations.

#### **5. References**

Andres, A. *et al.* Períodos de interferência de plantas daninhas na cultura do sorgo forrageiro em terras baixas. Planta Daninha, v.27, n.2, p.229-234, 2009.

Abit, J.M. *et al.* Differential response of grain sorghum hybrids to foliar-applied mesotrione. Weed Technology, v. 23, n.1, p.28-33, 2009.

other cropping techniques targeted to reduce the infestation in order to reduce pressure by

Dose of tembotrione (g ha-1) 0 42 88 126 168

Weeds present a great competitive potential with grain sorghum. However, effects are converged by a number of factors such as weed species and densities, moment of crop cycle when control is imposed and farming practices such as tillage system. Results have demonstrated that more intense interference occurs, in most cases, starting at the 4-leaf stage, weed free period. Although sorghum cropping is widespread in a great variety of regions throughout the world, current selective herbicides have not been sufficiently evaluated and the options available so far are not enough. Studies have provided results that confirm HPPD-inhibitor herbicides potential, mainly for mesotrione and tembotrione, assisting mainly in post-mergence grass weed control. Nevertheless, regionalized studies on different genotypes of sorghum must be conducted to supplement information regarding

Fig. 6. Weed control for sorghum crop at 21 days after applying increasing doses of

the selectivity of this herbicide for grain sorghum and to support recommendations.

Weed Technology, v. 23, n.1, p.28-33, 2009.

Andres, A. *et al.* Períodos de interferência de plantas daninhas na cultura do sorgo forrageiro em terras baixas. Planta Daninha, v.27, n.2, p.229-234, 2009. Abit, J.M. *et al.* Differential response of grain sorghum hybrids to foliar-applied mesotrione.

*C. echinatus* y=-4.06+0.88x-0.001x<sup>2</sup>

*D. horizontalis* y= 0.58+1.25x-0.003x<sup>2</sup>

 R<sup>2</sup> =0.98

> R<sup>2</sup> =0.99

making the control easier to ensure a more successful tillage management.

Control (%)

0

tembotrione (Dan et al., 2009b).

**4. Concluding remarks** 

**5. References** 

20

40

60

80

100


**Part 3** 

**Integrated Weed Management** 

**and Soil Fertility/Quality** 


## **Part 3**

**Integrated Weed Management and Soil Fertility/Quality** 

184 Weed Control

Kissmann, K.G. Plantas infestantes e nocivas. TOMO I. 3º ed. São Paulo: Basf Brasileira S. A.,

Magalhães, P.C. *et al.* Fitotoxicidade causada por herbicidas na fase inicial de desenvolvimento da cultura do sorgo. Planta Daninha, v.18, n.3, p.483-490, 2000. Miller, J.N.; Regehr, D.L. Grain sorghum tolerance to postemergence mesotrione

Mitchell, G.D.W. *et al.* Mesotrione: a new selective herbicide for use in maize. Pest

Moore, J.W.; Murray, D.S.; Westerman, R.B. Palmer amaranth (*Amaranthus palmeri*) effects

Norris, R.F. Barnyardgrass [*Echinochloa crus-galli* (L.) Beauv] competition and seed

Pataky, J.K. *et al.* Genetic basis for varied levels of injury to sweet corn hybrids from three

Pitelli, R.A.; Durigan, J.C. Terminologia para períodos de controle e convivência das plantas

Rizzardi, M.A. *et al.* Competição por recursos do solo entre ervas daninhas e culturas.

Rizzardi, M.A.; KARAM, D.; CRUZ, M.B. Manejo e controle de plantas daninhas em milho e

Plantas Daninhas. Bento Gonçalves: Embrapa Uva e Vinho, 2004. p.571-594. Rodrigues, A.C.P. *et al.* Períodos de interferência de plantas daninhas na cultura do sorgo.

Rosales-Robles, E. *et al.* Broadleaf weed management in grain sorghum with reduced rates of postemergence herbicides. Weed Technology, v.19, n.2, p.385-390, 2005. Silva, A.F. *et al.* Período anterior à interferência na cultura da soja-RR em condições de baixa,

Silva, J.B.; Passini, T.; Viana, A.C. Controle de plantas daninhas na cultura do sorgo. Informe

Smith, K.; Scott, B. Grain Sorghum Production Handbook: Weed control in grain sorghum. Little Rock, AR: University of Arkansas Cooperative Extension Service. 2006. 74 p. Stahlman, P.W.; Wicks, G.A. Weeds and their control in grain sorghum. In: Smith, C.W.;

Tamado, T.; Schu¨tz, W.; Milberg, P. Germination ecology of the weed *Parthenium hysterophorus*  in eastern Ethiopia. Annals of Applied of Biology, v.140, n.3, p.263-270, 2002. Tarara, G.; Fliege, R.; Kley, C.; Peters, B. Environmental fate of tembotrione. Bayer

Van Almsick, A.; Benet-Buchholz, J.; Olenik, B.; Willms, L. Tembotrione, a new exceptionally

Waddington, M.A.; Young, B.G. Interactions of herbicides and adjuvants with AE 0172747 on postemergence grass control. Weed Science, v.61, n.4, p.108-115, 2006. Wichert R.; Townson J.K.; Bartlett D.W.; Foxon G.A. Technical review of mesotrione, a new maize herbicide. The BCPC Conference – Weeds, v.1: p.105–110, 1999.

Frederiksen, R.A. (eds.). Sorghum: Origin, History, Technology, and Production.

safe cross-spectrum herbicide for corn production. Bayer CropScience Journal, v.62,

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New York, NY: John Wiley and Sons. 2000. pp. 535-690.

on the harvest and yield of grain sorghum (*Sorghum bicolor*). Weed Technology,

cytochrome P450-metabolized herbicides. Journal of the American Society

daninhas em culturas anuais e bianuais. In: Congresso Brasileiro de Herbicidas e Plantas Daninhas, 15., 1984, Belo Horizonte. Resumos. Belo Horizonte: SBHED, p. 37,

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2007. CD-ROM.

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1984.

**10** 

Moses Imo

*Kenya* 

**Managing Competition** 

*Department of Forestry and Wood Science,* 

**for Nutrients in Agro-Ecosystems** 

Knowledge of competitive plant interactions is important in designing more productive cropping systems in both agriculture and forestry. These interactions are often variable in nature, and may be competitive, synergistic or complementary depending on several factors such as species mixture, environmental conditions and management practices, which are also influenced by prevailing socioeconomic factors. Most of these interactions often involve primarily competition for the major plant growth resources namely: light, moisture, nutrients and space. Unfortunately, segregating the specific mechanisms involved at any time in the competition process has often been a major problem for many agro-ecologists because of the complex interactive nature of the requirements by plants for these growth resources. Although significant attempts and gains have been made with respect to understanding mechanisms for competition for a single aboveground resource (i.e. light), little progress has been made with respect to competition for a broad range of belowground resources (i.e. nutrients and moisture). This is mainly because of the multiple belowground interactions involving complex processes and mechanisms of availability, uptake and utilization by plants. In the case of nutrients, plants compete for a broad range of essential plant mineral elements that differ in molecular size, valence, oxidation state and mobility within the soil. Unofortunately, and leess understood, belowground competition often reduces plant performance more than aboveground competition (Wilson 1988), and it is the principal form of competition occurring in ecosystems with extremely low plant densities

This chapter reviews the mechanisms and ecological importance of nutrient competition, emphasizing methodologies for measuring nutrient compeition in cropping systems and their advantages and limitations. This is particularly important in understanding the roles of plant competition for nutrients in the productivity of agro-ecosystems, and provide guidelines for their management. The approach is to combine knowledge in soil fertility and plant nutrition with physiological ecology in order to merge various diagnostic tools for decision making at farm level. The goal is to illustrate a simple graphical diagnostic model for identifying overall nutrient interaction effects and how to optimize various factors affecting nutrient competition in different agro-ecosystems. To be useful, such tools must help determine the benefits and consequences of crop and weed management strategies in any give system, and facilitate determination of the relative importance of various

such as arid lands and low fertility sites (Fowler, 1986).

interaction types and the associated specific mechanisms.

**1. Introduction** 

*Chepkoilel University College, Moi University, Eldoret,* 

## **Managing Competition for Nutrients in Agro-Ecosystems**

#### Moses Imo

*Department of Forestry and Wood Science, Chepkoilel University College, Moi University, Eldoret, Kenya* 

#### **1. Introduction**

Knowledge of competitive plant interactions is important in designing more productive cropping systems in both agriculture and forestry. These interactions are often variable in nature, and may be competitive, synergistic or complementary depending on several factors such as species mixture, environmental conditions and management practices, which are also influenced by prevailing socioeconomic factors. Most of these interactions often involve primarily competition for the major plant growth resources namely: light, moisture, nutrients and space. Unfortunately, segregating the specific mechanisms involved at any time in the competition process has often been a major problem for many agro-ecologists because of the complex interactive nature of the requirements by plants for these growth resources. Although significant attempts and gains have been made with respect to understanding mechanisms for competition for a single aboveground resource (i.e. light), little progress has been made with respect to competition for a broad range of belowground resources (i.e. nutrients and moisture). This is mainly because of the multiple belowground interactions involving complex processes and mechanisms of availability, uptake and utilization by plants. In the case of nutrients, plants compete for a broad range of essential plant mineral elements that differ in molecular size, valence, oxidation state and mobility within the soil. Unofortunately, and leess understood, belowground competition often reduces plant performance more than aboveground competition (Wilson 1988), and it is the principal form of competition occurring in ecosystems with extremely low plant densities such as arid lands and low fertility sites (Fowler, 1986).

This chapter reviews the mechanisms and ecological importance of nutrient competition, emphasizing methodologies for measuring nutrient compeition in cropping systems and their advantages and limitations. This is particularly important in understanding the roles of plant competition for nutrients in the productivity of agro-ecosystems, and provide guidelines for their management. The approach is to combine knowledge in soil fertility and plant nutrition with physiological ecology in order to merge various diagnostic tools for decision making at farm level. The goal is to illustrate a simple graphical diagnostic model for identifying overall nutrient interaction effects and how to optimize various factors affecting nutrient competition in different agro-ecosystems. To be useful, such tools must help determine the benefits and consequences of crop and weed management strategies in any give system, and facilitate determination of the relative importance of various interaction types and the associated specific mechanisms.

Managing Competition for Nutrients in Agro-Ecosystems 189

Characteristically, like competition for all the other growth resources, nutrient competition is reciprocal, i.e., it occurs only when nutrient resources are in limited supply. The competing plants can either be of the same species (*intraspecific* competition) or of different species (*interspecific* competition). These responses are usually described by yield-density relationships that follow the 'law of constant final yield' (Begon *et al*. 1996). That is, at low density, total resource availability to each individual plant is high resulting in a few large individuals, and total growth will respond to small changes in density. At high densities, however, resource availability to each individual is low resulting in many small individuals, and total production is less responsive to changes in density and attains a final constant value reflecting complete utilization of available growth resources. When one species has a negative effect on the second species, yet both can utilize more efficiently available resources when in mixture than in monoculture, it is referred to as the *interferenc*e, but it is *facilitation* if one species has a positive effect on the other (Vandermeer, 1984). The term *interaction* is also often used to simply mean mutual or reciprocal effects in situations where species performance in mixtures is not equal to the sum of their performances when they are grown separately. Although plant interactions may also be due to other effects such as allelopathy through production of toxins, parasitism by natural enemies and mutualisms,

the focus in this chapter, however, is on interactions involving soil nutrients.

Elberse 1990; Nambiar and Sands 1993). These factors are discussed below.

The characteristics of plant root systems (e.g. root length, density, surface area and diameter), rates of nutrient diffusion in the soil and uptake by plants, morphological and

**3.2 Nutrient acquisition strategies by plants** 

Soil nutrients reach the root surfaces for uptake through three general processes (Marschner, 1995): *root interception* (the capture of nutrients as the root grows through the soil, physically displacing soil particles and clay surfaces), *mass flow* (the movement of dissolved mineral nutrients in water driven by plant transpiration and is a function of the rate of water movement to the root and the concentration of dissolved nutrients), and *diffusion* of nutrients toward the root surfaces when nutrient uptake exceeds the supply by mass flow thereby creating localized nutrient concentration gradients. Overall, root interception is the least important of the three processes, while diffusion occurs only when mass flow and root interception are inadequate to meet plant requirements. However, these mechanisms almost always work together. Various mechanisms have been proposed to explain partitioning of soil nutrients among neighboring competing plants, and corresponding theories are often linked to a specific theoretical framework developed for a specific type of ecological system under study. Overall, the competitive ability of a plant is determined by its capacity to capture and exploit resources rapidly and the ability to utilize shared nutrient resources in shortest supply by two or more species (Tilman 1988). Thus, understanding mechanisms of nutrient partitioning requires knowledge of factors related to the plant's ability to acquire a greater proportion of nutrients, utilize nutrients more efficiently, and allocate assimilates in ways that maximize the capacity of an individual for survival and growth (Berendse and

**3. Mechanisms of plant competition for nutrients** 

**3.1 Nutrient uptake** 

#### **2. Definition and importance of nutrient competition**

Competition for soil nutrients is one of the major factors that structure plant communities (Grace and Tilman 1990). Understanding the mechanisms that control plant competition for soil nutrients is therefore an essential step in predicting the outcome of interspecific competition in many plant communities, and in designing effective cultural practices in agro-ecosystems. Generally, plant competition is broadly defined as the process by which two or more individual plants or populations of plants interact, such that at least one exerts a negative effect on its neighbor in terms of reduced survivorship, growth or reproduction. Competition for nutrients therefore can be said to occur when a plant depletes a soil nutrient and negatively impacts availability of the nutrient element to which another plant shows a positive response. This definition, which is derived from that of Goldberg (1990), essentially means that the reduced level of the nutrient (an intermediary resource), has a negative effect on the performance of the competing plants measured per individual or per unit size. The advantage of this physiologically based definition identifies how nutrient uptake by one individual plant can affect the quantity of the nutrient taken up by another, and often help determine the consequences for plant performance as shown in Figure 1.

Fig. 1. Plant competition for nutrients showing the effects and responses of competing plants to changing nutrient availability. Both the effect and the response should have the appropriate sign for competition to occur.

Characteristically, like competition for all the other growth resources, nutrient competition is reciprocal, i.e., it occurs only when nutrient resources are in limited supply. The competing plants can either be of the same species (*intraspecific* competition) or of different species (*interspecific* competition). These responses are usually described by yield-density relationships that follow the 'law of constant final yield' (Begon *et al*. 1996). That is, at low density, total resource availability to each individual plant is high resulting in a few large individuals, and total growth will respond to small changes in density. At high densities, however, resource availability to each individual is low resulting in many small individuals, and total production is less responsive to changes in density and attains a final constant value reflecting complete utilization of available growth resources. When one species has a negative effect on the second species, yet both can utilize more efficiently available resources when in mixture than in monoculture, it is referred to as the *interferenc*e, but it is *facilitation* if one species has a positive effect on the other (Vandermeer, 1984). The term *interaction* is also often used to simply mean mutual or reciprocal effects in situations where species performance in mixtures is not equal to the sum of their performances when they are grown separately. Although plant interactions may also be due to other effects such as allelopathy through production of toxins, parasitism by natural enemies and mutualisms, the focus in this chapter, however, is on interactions involving soil nutrients.

#### **3. Mechanisms of plant competition for nutrients**

#### **3.1 Nutrient uptake**

188 Weed Control

Competition for soil nutrients is one of the major factors that structure plant communities (Grace and Tilman 1990). Understanding the mechanisms that control plant competition for soil nutrients is therefore an essential step in predicting the outcome of interspecific competition in many plant communities, and in designing effective cultural practices in agro-ecosystems. Generally, plant competition is broadly defined as the process by which two or more individual plants or populations of plants interact, such that at least one exerts a negative effect on its neighbor in terms of reduced survivorship, growth or reproduction. Competition for nutrients therefore can be said to occur when a plant depletes a soil nutrient and negatively impacts availability of the nutrient element to which another plant shows a positive response. This definition, which is derived from that of Goldberg (1990), essentially means that the reduced level of the nutrient (an intermediary resource), has a negative effect on the performance of the competing plants measured per individual or per unit size. The advantage of this physiologically based definition identifies how nutrient uptake by one individual plant can affect the quantity of the nutrient taken up by another, and often help

Fig. 1. Plant competition for nutrients showing the effects and responses of competing plants

**AVAILABLE SOIL NUTRIENTS** 

Effect

Response Response

Effect Effect

**CROP** 

to changing nutrient availability. Both the effect and the response should have the

**ENVIRONMENTAL** 


**CONDITIONS**  - Water

appropriate sign for competition to occur.

**WEEDS** 

**2. Definition and importance of nutrient competition** 

determine the consequences for plant performance as shown in Figure 1.

Soil nutrients reach the root surfaces for uptake through three general processes (Marschner, 1995): *root interception* (the capture of nutrients as the root grows through the soil, physically displacing soil particles and clay surfaces), *mass flow* (the movement of dissolved mineral nutrients in water driven by plant transpiration and is a function of the rate of water movement to the root and the concentration of dissolved nutrients), and *diffusion* of nutrients toward the root surfaces when nutrient uptake exceeds the supply by mass flow thereby creating localized nutrient concentration gradients. Overall, root interception is the least important of the three processes, while diffusion occurs only when mass flow and root interception are inadequate to meet plant requirements. However, these mechanisms almost always work together. Various mechanisms have been proposed to explain partitioning of soil nutrients among neighboring competing plants, and corresponding theories are often linked to a specific theoretical framework developed for a specific type of ecological system under study. Overall, the competitive ability of a plant is determined by its capacity to capture and exploit resources rapidly and the ability to utilize shared nutrient resources in shortest supply by two or more species (Tilman 1988). Thus, understanding mechanisms of nutrient partitioning requires knowledge of factors related to the plant's ability to acquire a greater proportion of nutrients, utilize nutrients more efficiently, and allocate assimilates in ways that maximize the capacity of an individual for survival and growth (Berendse and Elberse 1990; Nambiar and Sands 1993). These factors are discussed below.

#### **3.2 Nutrient acquisition strategies by plants**

The characteristics of plant root systems (e.g. root length, density, surface area and diameter), rates of nutrient diffusion in the soil and uptake by plants, morphological and

Managing Competition for Nutrients in Agro-Ecosystems 191

Relative allocation of assimilates to root and shoot growth modifies root-shoot ratios of plants and influences the ability of plants to acquire below- and above-ground resources. Thus, one critical point in understanding plant strategies in overcoming nutrient competition is the allocation of growth to below-ground during nutrient limitations (Chapin 1980). Root-shoot ratios increase with reduced nutrient availability (Axelsson and Axelsson 1986; Boot and Mensink 1990) perhaps as a means of enhancing nutrient acquisition from the soil. However, such data must be analyzed with caution since the positive effects of nutrient status (e.g. higher nutrient concentration in plant tissue) on plant growth can overshadow the effect of preferred growth allocation to roots. For example, high soil nutrient availability may enhance both root and shoot growth resulting in a larger root

Since plant growth involves acquisition of multiple resources, interactions among these resources in the environment (particularly in the soil) can complicate interpretation of competition effects. As discussed earlier, soil moisture can greatly affect nutrient availability and uptake, while light can alter demand for the nutrients thus influencing the outcome of competition for the nutrients in short supply. Also, removal of one species may increase availability of all resources together because of reduced uptake, or the removal may indirectly modify resource availability through microclimate modification, as has been demonstrated in other weed control experiments (Smesthurst and Nambiar 1989; Woods *et al*. 1990). In these studies, weed removal by herbicide application increased available moisture and nutrients, and altered soil temperature that favored faster N mineralization. Weed removal (especially in young plantations) also increases light availability that favors faster growth and further creates a larger demand for soil moisture and nutrients. This evidence demonstrates the need for a systematic and integrated approach to resource competition studies to allow a better segregation of the competition processes involved. Figure 2 is a conceptual model for resource competition by plants that provides a comprehensive starting point for any such approach. Since interspecific interactions may result in either reduced growth (competition) or enhanced productivity (synergism), understanding processes of resource partitioning is necessary to increasing productivity in these ecosystems, and requires knowledge of soil and plant processes related to nutrient

**3.4 Carbon allocation** 

system, but root-shoot ratio may decline.

**3.5 Nutrient interactions with other resources** 

availability, uptake and utilization by plants.

**4.1 Ecological relationships** 

**4. Framework for understanding plant competition for nutrients** 

The model is based on Berkowitz's conceptual model of plant competition for resources (Berkowitz 1988), simplified here to illustrate the key processes involved as shown in Figure 2. This diagram distinguishes three major variables: state, rate and intermediate variables. State variables (represented by boxes in Figure 2) are measurable quantities (biomass, nutrient content, soil water, soil nutrients and solar radiation). Each state variable is characterized by a rate or efficiency variable (represented as valves in Figure 2) that determines the rate of flow in material or energy between state variables due to specific

physiological plasticity, and spatial and temporal soil partitioning are the major factors determining the nutrient competitive ability of most plant nutrient competition (Gillespie 1989; Neary *et al*. 1990; Smethurst and Comerford 1993). Generally, these factors have been used to predict nutrient uptake of competing plants as a function of the nutrient concentration in solution at the soil-root interface, which is determined by the balance between plant demand for nutrients and the ability of the soil to supply that demand. As roots absorb nutrients, concentrations around the root surface declines, thus creating 'nutrient depletion zones' around the root surface. Nutrient competition then occurs when the depletion zones for adjacent roots overlap, thus interfering with nutrient availability for each plant and resulting in reduced uptake.

Root morphology plays a major role in determining nutrient depletion. Regardless of nutrient mobility, competition for all nutrients increases with root length or density (Barber 1984; Gillespie 1989). In addition, thicker roots have steeper depletion gradients and wider depletion zones than thinner roots. Hence, thinner and longer roots are less likely to compete than thicker and shorter roots because finer roots will be able to absorb nutrients at much lower nutrient concentration in solution (Sands and Mulligan 1990). Spatial segregation of roots of different species may reduce interspecific competition. For example, the ability of woody plants to develop deep rooting systems (Eastham and Rose 1990; Stone and Kalisz 1991) may be an important strategy to avoid competition with shallow rooted herbaceous plants. However, roots of most woody plants are also concentrated in the surface soil (Nambiar 1990), thus making direct competition with herbaceous plants inevitable.

#### **3.3 Nutrient use by plants**

Plant growth usually increases with the amount of nutrient present in biomass. However, there are considerable species differences in the amount of nutrients required to produce biomass (Wang *et al*. 1991), indicating the differential ability in species to utilize nutrients for growth (i.e. nutrient-use-efficiency [NUE]). Generally, trees produce more biomass per unit of nutrients (i.e. higher NUE) than herbaceous weedy species, probably because trees increasingly produce woody tissue which is low in nutrients as it is not active photosynthetic tissue (Chapin 1990; van den Driessche 1991; Nambiar and Fife, 1991). This mechanism partially explains why the capacity of trees to dominate a site accelerates with increasing age of the trees.

Nutrient losses from a plant (litter fall, leaching, root decay and herbivory) also determine the plant's total nutrient requirements, i.e. the amount of nutrient that must be absorbed by an individual or population just to maintain or replace its biomass (Berendse and Elberse 1990). A species with low nutrient loss rate and high uptake rate and/or higher NUE will have a relatively low demand for external nutrients. According to Tilman (1988), such a species is predicted to be able to meet its nutrient requirements at a lower soil nutrient supply rate, and its total biomass will increase only if it absorbs more nutrients than its demand, but declines if it absorbs less. Hence, partitioning of a limiting nutrient between competing species is expected to be proportional to their demand relative to uptake; therefore, a species with a low demand relative to uptake will have a higher competitive ability.

#### **3.4 Carbon allocation**

190 Weed Control

physiological plasticity, and spatial and temporal soil partitioning are the major factors determining the nutrient competitive ability of most plant nutrient competition (Gillespie 1989; Neary *et al*. 1990; Smethurst and Comerford 1993). Generally, these factors have been used to predict nutrient uptake of competing plants as a function of the nutrient concentration in solution at the soil-root interface, which is determined by the balance between plant demand for nutrients and the ability of the soil to supply that demand. As roots absorb nutrients, concentrations around the root surface declines, thus creating 'nutrient depletion zones' around the root surface. Nutrient competition then occurs when the depletion zones for adjacent roots overlap, thus interfering with nutrient availability for

Root morphology plays a major role in determining nutrient depletion. Regardless of nutrient mobility, competition for all nutrients increases with root length or density (Barber 1984; Gillespie 1989). In addition, thicker roots have steeper depletion gradients and wider depletion zones than thinner roots. Hence, thinner and longer roots are less likely to compete than thicker and shorter roots because finer roots will be able to absorb nutrients at much lower nutrient concentration in solution (Sands and Mulligan 1990). Spatial segregation of roots of different species may reduce interspecific competition. For example, the ability of woody plants to develop deep rooting systems (Eastham and Rose 1990; Stone and Kalisz 1991) may be an important strategy to avoid competition with shallow rooted herbaceous plants. However, roots of most woody plants are also concentrated in the surface soil (Nambiar 1990), thus making direct competition with herbaceous plants

Plant growth usually increases with the amount of nutrient present in biomass. However, there are considerable species differences in the amount of nutrients required to produce biomass (Wang *et al*. 1991), indicating the differential ability in species to utilize nutrients for growth (i.e. nutrient-use-efficiency [NUE]). Generally, trees produce more biomass per unit of nutrients (i.e. higher NUE) than herbaceous weedy species, probably because trees increasingly produce woody tissue which is low in nutrients as it is not active photosynthetic tissue (Chapin 1990; van den Driessche 1991; Nambiar and Fife, 1991). This mechanism partially explains why the capacity of trees to dominate a site accelerates with

Nutrient losses from a plant (litter fall, leaching, root decay and herbivory) also determine the plant's total nutrient requirements, i.e. the amount of nutrient that must be absorbed by an individual or population just to maintain or replace its biomass (Berendse and Elberse 1990). A species with low nutrient loss rate and high uptake rate and/or higher NUE will have a relatively low demand for external nutrients. According to Tilman (1988), such a species is predicted to be able to meet its nutrient requirements at a lower soil nutrient supply rate, and its total biomass will increase only if it absorbs more nutrients than its demand, but declines if it absorbs less. Hence, partitioning of a limiting nutrient between competing species is expected to be proportional to their demand relative to uptake; therefore, a species with a low demand relative to uptake will have a higher competitive

each plant and resulting in reduced uptake.

inevitable.

ability.

**3.3 Nutrient use by plants** 

increasing age of the trees.

Relative allocation of assimilates to root and shoot growth modifies root-shoot ratios of plants and influences the ability of plants to acquire below- and above-ground resources. Thus, one critical point in understanding plant strategies in overcoming nutrient competition is the allocation of growth to below-ground during nutrient limitations (Chapin 1980). Root-shoot ratios increase with reduced nutrient availability (Axelsson and Axelsson 1986; Boot and Mensink 1990) perhaps as a means of enhancing nutrient acquisition from the soil. However, such data must be analyzed with caution since the positive effects of nutrient status (e.g. higher nutrient concentration in plant tissue) on plant growth can overshadow the effect of preferred growth allocation to roots. For example, high soil nutrient availability may enhance both root and shoot growth resulting in a larger root system, but root-shoot ratio may decline.

#### **3.5 Nutrient interactions with other resources**

Since plant growth involves acquisition of multiple resources, interactions among these resources in the environment (particularly in the soil) can complicate interpretation of competition effects. As discussed earlier, soil moisture can greatly affect nutrient availability and uptake, while light can alter demand for the nutrients thus influencing the outcome of competition for the nutrients in short supply. Also, removal of one species may increase availability of all resources together because of reduced uptake, or the removal may indirectly modify resource availability through microclimate modification, as has been demonstrated in other weed control experiments (Smesthurst and Nambiar 1989; Woods *et al*. 1990). In these studies, weed removal by herbicide application increased available moisture and nutrients, and altered soil temperature that favored faster N mineralization. Weed removal (especially in young plantations) also increases light availability that favors faster growth and further creates a larger demand for soil moisture and nutrients. This evidence demonstrates the need for a systematic and integrated approach to resource competition studies to allow a better segregation of the competition processes involved. Figure 2 is a conceptual model for resource competition by plants that provides a comprehensive starting point for any such approach. Since interspecific interactions may result in either reduced growth (competition) or enhanced productivity (synergism), understanding processes of resource partitioning is necessary to increasing productivity in these ecosystems, and requires knowledge of soil and plant processes related to nutrient availability, uptake and utilization by plants.

#### **4. Framework for understanding plant competition for nutrients**

#### **4.1 Ecological relationships**

The model is based on Berkowitz's conceptual model of plant competition for resources (Berkowitz 1988), simplified here to illustrate the key processes involved as shown in Figure 2. This diagram distinguishes three major variables: state, rate and intermediate variables. State variables (represented by boxes in Figure 2) are measurable quantities (biomass, nutrient content, soil water, soil nutrients and solar radiation). Each state variable is characterized by a rate or efficiency variable (represented as valves in Figure 2) that determines the rate of flow in material or energy between state variables due to specific

Managing Competition for Nutrients in Agro-Ecosystems 193

Fig. 2. Conceptual processes of resource competition between a crop and neighboring non-crop. Boxes represent state variables; valves represent rate variables; circles represent intermediate variables; and hexagons represent physiological processes. Solid arrows represent flow of material, while broken arrows are flow of information or signals, and indicate occurrence of feed-back processes. Available resources are partitioned between competing plant species: shaded for the crop, and unshaded for the non-crop. LCE, WCE and NCE are light, water and nutrient capture efficiency, respectively. LUE, WUE and NUE are light-, water- and nutrient-use- efficiency, respectively. Biomass is com posed of carbon (W) and nutrients (U), and nutrients are returned to the soil as mulch as determined by the loss rate (LR). Nutrient application efficiency (NA) determines the

 rate of

conversion

 of added nutrients

 to available nutrients.

 Adapted from Imo (1999), and based on Kropf *et. al* 1984).

processes (represented as hexagons in Figure 2). The intermediate variables (represented as circles in Figure 2) define plant characteristics that directly determine the capture of available resources. The other variables not included in this model are deriving variables that characterize the effect of environmental conditions on the whole system.

The amount of resource acquired by a plant is determined by resource availability and the rate of resource uptake or resource capture efficiency (*LCE*, *WCE* and *NCE* for light, water and nutrient capture efficiency, respectively, in Figure 2). Plant biomass is considered a product of its cumulative total resource uptake and resource use efficiency (i.e. rate of biomass production per unit of resource acquired or *LUE*, *WUE* and *NUE* for light, water and nutrient use efficiencies, respectively, in Figure 2). Biomass is composed of carbon (*W*) and nutrient content (*U*), and the ratio *U*/*W* gives a measure of nutrient concentration (*C*). Carbon is allocated to different plant components (leaves, stem, fruits and roots) that determine the plant's physiological characteristics that further influence resource capture (Berendse and Elberse 1990). Greater proportional allocation to roots increases root density, hence improve efficiency or rate of capturing soil resources, while preferred allocation to shoots enhances growth rate because of higher leaf area index (*LAI*), thus photosynthesis. The loss rate (*LR*) determines the rate of nutrient return to the soil as mulch or root decay. Application efficiency (*NA*) determines the rate by which added nutrients are made available for uptake.

Resource acquisition and use involve three major processes: nutrient uptake, moisture absorption, and photosynthesis. Moisture availability influences growth through photosynthesis because of its effect on plant water status and hence leaf conductance (Burdett 1990), and nutrient absorption since it is required for diffusion and absorption of nutrient ions (Gillespie 1989; Smesthurst and Comerford 1993). In ecosystems in which at least one resource is limiting, biomass production can be regarded as a function of the amount of limiting resource(s) and their utilization efficiency. Interspecific plant interactions occur when one species affects availability of one or more resources to the other species, or simply alters environmental conditions favorable for the flow of material between state variables. Competition will reduce availability and flow of resources, while synergism will increase resource availability to the neighboring species. The mechanisms associated with this model are discussed in the following section.

#### **4.2 Partitioning of soil nutrients**

The conceptual framework in Figure 2 shows that there is mutual interdependence between nutrient uptake, moisture absorption and photosynthesis (Kropff *et al*. 1984). For example, increased nutrient availability and uptake accelerates photosynthesis that, in turn, promotes uptake and use of nutrients, and *vice versa*. Competition for soil nutrients may also reduce nutrient content and leaf area, thus resulting in reduced photosynthesis and growth that, in part, has a negative effect on nutrient uptake. Also, competition for moisture reduces plant water status and leaf conductance (thus reduced photosynthesis and growth), and may impair nutrient uptake since moisture is required for nutrient absorption by the roots.

The question as to which of these resources is the primary (direct) and secondary (indirect) growth limiting factor in competitive situations often poses considerable difficulty in interpreting observed effects of competition (Nambiar and Sands 1993). For example, most

192 Weed Control

processes (represented as hexagons in Figure 2). The intermediate variables (represented as circles in Figure 2) define plant characteristics that directly determine the capture of available resources. The other variables not included in this model are deriving variables

The amount of resource acquired by a plant is determined by resource availability and the rate of resource uptake or resource capture efficiency (*LCE*, *WCE* and *NCE* for light, water and nutrient capture efficiency, respectively, in Figure 2). Plant biomass is considered a product of its cumulative total resource uptake and resource use efficiency (i.e. rate of biomass production per unit of resource acquired or *LUE*, *WUE* and *NUE* for light, water and nutrient use efficiencies, respectively, in Figure 2). Biomass is composed of carbon (

Carbon is allocated to different plant components (leaves, stem, fruits and roots) that determine the plant's physiological characteristics that further influence resource capture (Berendse and Elberse 1990). Greater proportional allocation to roots increases root density, hence improve efficiency or rate of capturing soil resources, while preferred allocation to shoots enhances growth rate because of higher leaf area index (*LAI*), thus photosynthesis. The loss rate (*LR*) determines the rate of nutrient return to the soil as mulch or root decay. Application efficiency (*NA*) determines the rate by which added nutrients are made

Resource acquisition and use involve three major processes: nutrient uptake, moisture absorption, and photosynthesis. Moisture availability influences growth through photosynthesis because of its effect on plant water status and hence leaf conductance (Burdett 1990), and nutrient absorption since it is required for diffusion and absorption of nutrient ions (Gillespie 1989; Smesthurst and Comerford 1993). In ecosystems in which at least one resource is limiting, biomass production can be regarded as a function of the amount of limiting resource(s) and their utilization efficiency. Interspecific plant interactions occur when one species affects availability of one or more resources to the other species, or simply alters environmental conditions favorable for the flow of material between state variables. Competition will reduce availability and flow of resources, while synergism will increase resource availability to the neighboring species. The mechanisms associated with

The conceptual framework in Figure 2 shows that there is mutual interdependence between nutrient uptake, moisture absorption and photosynthesis (Kropff *et al*. 1984). For example, increased nutrient availability and uptake accelerates photosynthesis that, in turn, promotes uptake and use of nutrients, and *vice versa*. Competition for soil nutrients may also reduce nutrient content and leaf area, thus resulting in reduced photosynthesis and growth that, in part, has a negative effect on nutrient uptake. Also, competition for moisture reduces plant water status and leaf conductance (thus reduced photosynthesis and growth), and may impair nutrient uptake since moisture is required for nutrient absorption by the roots.

The question as to which of these resources is the primary (direct) and secondary (indirect) growth limiting factor in competitive situations often poses considerable difficulty in interpreting observed effects of competition (Nambiar and Sands 1993). For example, most

/*W* gives a measure of nutrient concentration (

*W*)

*C*).

that characterize the effect of environmental conditions on the whole system.

*U*

*U*), and the ratio

this model are discussed in the following section.

**4.2 Partitioning of soil nutrients** 

and nutrient content (

available for uptake.

Managing Competition for Nutrients in Agro-Ecosystems 195

reveal the mechanism on how nutrient content and dry mass are related, since changes in concentration may be caused by changes in either biomass or nutrient uptake or both, and there is no way of distinguishing between these mechanisms. Changes in concentration as a result of changes in content implies that the plant itself altered nutrient uptake and synthesis, while changes in concentration due to changes in biomass can be regarded as a growth

Belowground competition is measured by quantifying the extent that root interactions reduce nutrient uptake and plant growth by preventing root interactions using root exclusion tubes, trenching, or neighbor removal to separate the roots of target individuals from those of neighboring plants. Root competition is determined by comparing the growth or survival of target plants inside the partitions with those having root systems that can interact freely with neighboring vegetation. Neighbors within partitions are killed by a fastdegrading herbicide, severed at the soil surface to remove shoots, or removed completely by excavating, sieving, and replacing the soil. Unfortunately, such methods often alter the soil environment and may even affect the availability of resources for which the plants are competing. To address this issue, many studies have proposed various competition indices to characterize the degree to which the growing space of a target crop is shared by weedy vegetation in agro-ecosystems and forest plantations by developing functional relationships between target crop or tree responses to some measure of non-crop (weed) proximity. Some

The traditional approach has been to use competition indices to predict yield losses due to weeds in forestry and in agricultural systems (Morris and McDonald 1991; Wagner, 1994; 1993). Similarly, a competition index (i.e. tree-crop-interaction [TCI] equation) has been proposed to quantify the balance between competitive and beneficial effects of trees on crops in agroforestry systems (Ong 1996). Although these competition indices often demonstrate occurrence of likely competition, they fail to explain the specific processes and mechanisms involved, thus complicating interpretation of competition or beneficial effects between plants and making extrapolation of results to other situations difficult. Available mechanistic models usually focus on competition for a single resource. Physiological models of competition for light or moisture in agricultural ecosystems (e.g. Kropff 1993; Ong *et al*. 1996) and competition for nutrients in natural plant communities (e.g. Berendse *et al*. 1989) are examples of models based on single resources. However, plant growth involves acquisition, partitioning and interactions among multiple resources, making it difficult to determine if competition occurred for one or more resources. Understanding how growth resources are partitioned between neighboring species is, therefore, important in providing

Various experimental methods have been used to elucidate competitive interactions in cropping systems, most of which have often considered only two-species mixtures as

response without any specific effects on metabolism of the nutrient.

**5. Measuring plant competition for nutrients** 

**5.1 Root exclusion experiments** 

of these indices are discussed below.

**5.2.1 Additive and substitutive indices** 

a scientific base for designing more productive cropping systems.

**5.2 Competition indices** 

studies reporting on moisture competition hardly have measurements on tree nutrition even when treatments may have substantial impacts on soil nutrient availability and uptake (Coates *et al.* 1991). Hence, the relative significance of competition for moisture versus nutrients is usually ignored, especially in environments in which both moisture and nutrients are limiting. Unlike moisture, however, nutrients accumulate in plant biomass. Since moisture affects both photosynthesis and nutrient absorption, the approach in this chapter is to evaluate the effects of competition on carbon accumulation and nutrient content as shown in Figure 2. The fundamental question, therefore, is how competition affects carbon assimilation relative to nutrient uptake.

In Figure 2, carbon (*W*) and nutrient content (*U*) are assumed to be an integrated measure of availability, uptake and use of light, moisture and nutrients by plants, and are regulated by other environmental factors (i.e. deriving variables). When competition is primarily for soil nutrients, a reduction in *U* will be large relative to a corresponding reduction in *W*, and *U*/*W* (i.e. nutrient concentration) will also decline. When competition is primarily for light, then reduction in *U* will be small relative to reduction in *W*, and *U*/*W* will increase. When nutrients are non-limiting and competition is primarily for moisture, reduction in *U* will also be small relative to reduction in *W*, and *U*/*W* will increase.

In environments where both water and nutrients are limiting, moisture competition is expected to have overriding effects because water molecules are more mobile than nutrient ions in the soil, hence would have larger and greater overlapping depletion zones than nutrients (Gillespie 1989; Smesthurst and Comerford 1993). Thus, reduction in *U* will be small relative to reduction in *W*, and *U*/*W* will increase. Also, increased growth associated with (1) elevated nutrient levels (both concentration and uptake) reflects positive fertility effects, and (2) increased uptake but decreased nutrient concentration exemplifies improved moisture, light and or microclimate favorable for crop growth without significant effects on nutrient availability. These principles have been illustrated using interactions involving N responses in order to confirm these interpretations (Imo and Timmer, 1999a; 1999b). The treatments in these studies were selected to represent competition-free status and the other three interaction types (antagonistic, synergistic and compensatory). Although this technique may be applicable to all other nutrients when the same resources are removed, total resource availability to the other species should increase resulting in maximum growth and nutrient uptake potential (Imo 1999).

#### **4.3 Nutrient uptake and plant growth relationships**

Since nutrient concentration and content of a plant gives an integrated estimate of both total uptake and use by a plant (Imo 1999), studying the relationships between the two fundamental processes involved (i.e. nutrient accumulation and biomass production) can provide insight into the mechanisms involved. Chemical analysis of plants is frequently used to diagnose the nutritional status of plants since the plant itself is the object of interest, and its nutrient composition reflects many of the factors affecting its nutrition. Traditionally, plant nutrient composition is expressed either in relative term s (i.e. concentration [*C*], the amount of nutrient present per unit amount of biomass) or on total mass basis (i.e. absolute content [*U*], the total amount of nutrient present in a specific amount of plant tissue [*W*]). Total content is obtained by multiplying concentration by dry mass of the sample, thus *U* = *C*(*W*). Imo and Timmer (1997) and Timmer (1991) have previously argued that using concentration alone does not reveal the mechanism on how nutrient content and dry mass are related, since changes in concentration may be caused by changes in either biomass or nutrient uptake or both, and there is no way of distinguishing between these mechanisms. Changes in concentration as a result of changes in content implies that the plant itself altered nutrient uptake and synthesis, while changes in concentration due to changes in biomass can be regarded as a growth response without any specific effects on metabolism of the nutrient.

#### **5. Measuring plant competition for nutrients**

#### **5.1 Root exclusion experiments**

194 Weed Control

studies reporting on moisture competition hardly have measurements on tree nutrition even when treatments may have substantial impacts on soil nutrient availability and uptake (Coates *et al.* 1991). Hence, the relative significance of competition for moisture versus nutrients is usually ignored, especially in environments in which both moisture and nutrients are limiting. Unlike moisture, however, nutrients accumulate in plant biomass. Since moisture affects both photosynthesis and nutrient absorption, the approach in this chapter is to evaluate the effects of competition on carbon accumulation and nutrient content as shown in Figure 2. The fundamental question, therefore, is how competition

In Figure 2, carbon (*W*) and nutrient content (*U*) are assumed to be an integrated measure of availability, uptake and use of light, moisture and nutrients by plants, and are regulated by other environmental factors (i.e. deriving variables). When competition is primarily for soil nutrients, a reduction in *U* will be large relative to a corresponding reduction in *W*, and *U*/*W* (i.e. nutrient concentration) will also decline. When competition is primarily for light, then reduction in *U* will be small relative to reduction in *W*, and *U*/*W* will increase. When nutrients are non-limiting and competition is primarily for moisture, reduction in *U* will

In environments where both water and nutrients are limiting, moisture competition is expected to have overriding effects because water molecules are more mobile than nutrient ions in the soil, hence would have larger and greater overlapping depletion zones than nutrients (Gillespie 1989; Smesthurst and Comerford 1993). Thus, reduction in *U* will be small relative to reduction in *W*, and *U*/*W* will increase. Also, increased growth associated with (1) elevated nutrient levels (both concentration and uptake) reflects positive fertility effects, and (2) increased uptake but decreased nutrient concentration exemplifies improved moisture, light and or microclimate favorable for crop growth without significant effects on nutrient availability. These principles have been illustrated using interactions involving N responses in order to confirm these interpretations (Imo and Timmer, 1999a; 1999b). The treatments in these studies were selected to represent competition-free status and the other three interaction types (antagonistic, synergistic and compensatory). Although this technique may be applicable to all other nutrients when the same resources are removed, total resource availability to the other species should increase resulting in maximum growth

Since nutrient concentration and content of a plant gives an integrated estimate of both total uptake and use by a plant (Imo 1999), studying the relationships between the two fundamental processes involved (i.e. nutrient accumulation and biomass production) can provide insight into the mechanisms involved. Chemical analysis of plants is frequently used to diagnose the nutritional status of plants since the plant itself is the object of interest, and its nutrient composition reflects many of the factors affecting its nutrition. Traditionally, plant nutrient composition is expressed either in relative term s (i.e. concentration [*C*], the amount of nutrient present per unit amount of biomass) or on total mass basis (i.e. absolute content [*U*], the total amount of nutrient present in a specific amount of plant tissue [*W*]). Total content is obtained by multiplying concentration by dry mass of the sample, thus *U* = *C*(*W*). Imo and Timmer (1997) and Timmer (1991) have previously argued that using concentration alone does not

affects carbon assimilation relative to nutrient uptake.

also be small relative to reduction in *W*, and *U*/*W* will increase.

and nutrient uptake potential (Imo 1999).

**4.3 Nutrient uptake and plant growth relationships** 

Belowground competition is measured by quantifying the extent that root interactions reduce nutrient uptake and plant growth by preventing root interactions using root exclusion tubes, trenching, or neighbor removal to separate the roots of target individuals from those of neighboring plants. Root competition is determined by comparing the growth or survival of target plants inside the partitions with those having root systems that can interact freely with neighboring vegetation. Neighbors within partitions are killed by a fastdegrading herbicide, severed at the soil surface to remove shoots, or removed completely by excavating, sieving, and replacing the soil. Unfortunately, such methods often alter the soil environment and may even affect the availability of resources for which the plants are competing. To address this issue, many studies have proposed various competition indices to characterize the degree to which the growing space of a target crop is shared by weedy vegetation in agro-ecosystems and forest plantations by developing functional relationships between target crop or tree responses to some measure of non-crop (weed) proximity. Some of these indices are discussed below.

#### **5.2 Competition indices**

#### **5.2.1 Additive and substitutive indices**

The traditional approach has been to use competition indices to predict yield losses due to weeds in forestry and in agricultural systems (Morris and McDonald 1991; Wagner, 1994; 1993). Similarly, a competition index (i.e. tree-crop-interaction [TCI] equation) has been proposed to quantify the balance between competitive and beneficial effects of trees on crops in agroforestry systems (Ong 1996). Although these competition indices often demonstrate occurrence of likely competition, they fail to explain the specific processes and mechanisms involved, thus complicating interpretation of competition or beneficial effects between plants and making extrapolation of results to other situations difficult. Available mechanistic models usually focus on competition for a single resource. Physiological models of competition for light or moisture in agricultural ecosystems (e.g. Kropff 1993; Ong *et al*. 1996) and competition for nutrients in natural plant communities (e.g. Berendse *et al*. 1989) are examples of models based on single resources. However, plant growth involves acquisition, partitioning and interactions among multiple resources, making it difficult to determine if competition occurred for one or more resources. Understanding how growth resources are partitioned between neighboring species is, therefore, important in providing a scientific base for designing more productive cropping systems.

Various experimental methods have been used to elucidate competitive interactions in cropping systems, most of which have often considered only two-species mixtures as

Managing Competition for Nutrients in Agro-Ecosystems 197

However, it is difficult to determine from these regression models if competition occurred for one or more resources since competitive interactions involve partitioning of multiple resources (Nambiar and Sands 1993; Trenbath 1976). Berkowitz (1988) proposed a conceptual model of resource competition between a target plant and its neighbor that explicitly includes partitioning of the three main limiting resources (light, moisture and nutrients). In this model, competition may result from direct reduction in resource availability because of competitive uptake, or indirectly by reducing the uptake capacity of the other plant through resource-mediated alterations of the environment. These mechanisms are examined in the following section with respect to nutrient partitioning between competing plants, a major factor affecting species performance in plant

Despite general agreement that plant competition usually involves interactions between plants for different resources, most studies reporting competition fail to explain the processes and mechanisms involved (Nambiar and Sands 1993; Wilson and Tilman 1991), thus making it difficult to identify the resources which led to the interactions. According to Goldberg (1990), examining effects and responses of competing plants to resource availability can provide insight into the processes and mechanisms involved. This approach is adopted in this chapter, since both plant effects on resources and plant responses to resource availability can be quantified. As indicated earlier, the objective is to examine the concept of plant competition for nutrient resources, examine mechanisms partitioning of soil nutrients between neighboring plants, explain relationships between nutrient availability and plant growth, and to develop a theoretical basis for elucidating interspecific plant nutrient interactions. Although both light and moisture influence plant growth, the focus here is on interactions involving nutrients. However, attention is given to the role of moisture and light where these resources may directly influence nutrient availability,

Unfortunately, these indices often focus on relating crop responses to some measure of weed proximity (Goldberg and Werner 1983; Radosevich 1988). Also, treatment impacts are often evaluated on the basis of survival, growth and yield of the target crops without considering non-crop responses, a major weakness for ecologically based assessments. Moreover, applications of these indices hardly explain the mechanisms involved. Little attention has also been given to determine whether competition is primarily for light, moisture and/or nutrients. The advantages and limitations of these indices are discussed in a review by Burton (1993) who advocated replacing static competition indices with a more site-specific phytometric approaches that feature greater accounting of neighboring non-

Distinguishing between these processes is important to test hypotheses related to the effect of changing nutrient supply on plant growth and nutrient composition. One way of solving this problem is by first studying the effects of nutrient supply on each of the individual plant response variables (i.e. biomass, nutrient concentration and content), and then examining their interrelationships using *Vector Nutrient Analysis* model (Timmer 1991; Imo and Timmer 1998). Traditionally applied to a single crop, this diagnostic format is unsuitable for interpreting competition effects in which growth resources are partitioned between interacting plants. Thus, the format has been modified by combining responses of

**5.2.3 Limitations of competition indices** 

communities (Berendse and Elberse 1990).

uptake and use by plants.

crops and systematic local calibration and verification.

summarized by Radosevich (1988). The outcome of interspecific plant competition is influenced by factors of plant proximity such as density, spatial arrangement of plants, and the proportion of each species in mixtures. These methods rely mainly on using various regression models based on the growth-density relationship by assessing intercrop productivity in relation to performance in monoculture. To achieve this, three main methods used have been proposed to elucidate interspecific plant competition in agro-ecosystems, additive, substitutive and neighborhood experimentation. In additive experiments, two or more species are grown together with the density of one species held constant while that of the other species is varied. Hence, the additive approach is relevant to studying weed competition in agricultural systems where weeds often invade an area occupied by a fixed density of the crop and typically follows the 'law of diminishing returns' (Roadosevich 1988). Crop productivity diminishes with increasing weed density until weed density does not reduce crop productivity significantly. The main disadvantage of this approach is that both density and proportion of the species under study keep varying making it difficult to assess the relative effects of intraspecific and interspecific competition on total productivity. Some of these limitations have been addressed using the replacement series (or substitutive) experimental designs, whereby proportions of the two species vary in mixture, but total density remain constant. This approach is important where there are management interventions that are likely to have significantly different outcomes depending on which of these principles is in play under a specific set of conditions.

The yield of each species in mixture is expressed relative to their respective yields in monoculture. The sum of the relative yields is referred to as the relative yield total (*RYT*) or land equivalent ratio (LER), and have been used widely to assess the competitive ability of different species in mixture, and to evaluate the advantages associated with intercropping (Spitters 1983). If *RYT* > 1.0, then there is a true advantage of mixed cropping and indicates that the mixture as a total captures more resources than the respective monocultures, whereas competitive effects are indicated by *RYT* < 1.0. The main problem with this approach is that model coefficients vary with total density (Taylor and Aarssen 1989).

#### **5.2.2 Neighborhood competition indices**

To address these issues, Goldberg and Werner (1983) introduced a 'neighborhood' experimental approach, in which performance of a target individual is assessed as a function of the number, biomass or distance of its neighbors. The target species is either grown alone or is surrounded by individuals of the neighboring species. The relationship between target species and its neighbors is then expressed in terms of production of individual plants. This enables the determination of whether competition for resources is occurring since biomass is assumed to be proportional to total resource use by plants (Goldberg 1990). The basic argument is that comparing the slopes of the relationship between competing species provides a useful approach to studying competitive effects of neighboring species (Malik and Timmer 1996; Imo and Timmer 1997). Thus, lack of relationship indicates no interaction (neutral), and that resource use efficiency is constant with increasing neighbor biomass, while positive relationships would show synergistic interactions giving rise to over-yielding at increasing density. On the other hand, negative relationships indicate competition for resources resulting in lower yields with increasing density.

#### **5.2.3 Limitations of competition indices**

196 Weed Control

summarized by Radosevich (1988). The outcome of interspecific plant competition is influenced by factors of plant proximity such as density, spatial arrangement of plants, and the proportion of each species in mixtures. These methods rely mainly on using various regression models based on the growth-density relationship by assessing intercrop productivity in relation to performance in monoculture. To achieve this, three main methods used have been proposed to elucidate interspecific plant competition in agro-ecosystems, additive, substitutive and neighborhood experimentation. In additive experiments, two or more species are grown together with the density of one species held constant while that of the other species is varied. Hence, the additive approach is relevant to studying weed competition in agricultural systems where weeds often invade an area occupied by a fixed density of the crop and typically follows the 'law of diminishing returns' (Roadosevich 1988). Crop productivity diminishes with increasing weed density until weed density does not reduce crop productivity significantly. The main disadvantage of this approach is that both density and proportion of the species under study keep varying making it difficult to assess the relative effects of intraspecific and interspecific competition on total productivity. Some of these limitations have been addressed using the replacement series (or substitutive) experimental designs, whereby proportions of the two species vary in mixture, but total density remain constant. This approach is important where there are management interventions that are likely to have significantly different outcomes depending on which of

The yield of each species in mixture is expressed relative to their respective yields in monoculture. The sum of the relative yields is referred to as the relative yield total (*RYT*) or land equivalent ratio (LER), and have been used widely to assess the competitive ability of different species in mixture, and to evaluate the advantages associated with intercropping (Spitters 1983). If *RYT* > 1.0, then there is a true advantage of mixed cropping and indicates that the mixture as a total captures more resources than the respective monocultures, whereas competitive effects are indicated by *RYT* < 1.0. The main problem with this approach is that model coefficients vary with total density (Taylor

To address these issues, Goldberg and Werner (1983) introduced a 'neighborhood' experimental approach, in which performance of a target individual is assessed as a function of the number, biomass or distance of its neighbors. The target species is either grown alone or is surrounded by individuals of the neighboring species. The relationship between target species and its neighbors is then expressed in terms of production of individual plants. This enables the determination of whether competition for resources is occurring since biomass is assumed to be proportional to total resource use by plants (Goldberg 1990). The basic argument is that comparing the slopes of the relationship between competing species provides a useful approach to studying competitive effects of neighboring species (Malik and Timmer 1996; Imo and Timmer 1997). Thus, lack of relationship indicates no interaction (neutral), and that resource use efficiency is constant with increasing neighbor biomass, while positive relationships would show synergistic interactions giving rise to over-yielding at increasing density. On the other hand, negative relationships indicate competition for

these principles is in play under a specific set of conditions.

resources resulting in lower yields with increasing density.

and Aarssen 1989).

**5.2.2 Neighborhood competition indices** 

However, it is difficult to determine from these regression models if competition occurred for one or more resources since competitive interactions involve partitioning of multiple resources (Nambiar and Sands 1993; Trenbath 1976). Berkowitz (1988) proposed a conceptual model of resource competition between a target plant and its neighbor that explicitly includes partitioning of the three main limiting resources (light, moisture and nutrients). In this model, competition may result from direct reduction in resource availability because of competitive uptake, or indirectly by reducing the uptake capacity of the other plant through resource-mediated alterations of the environment. These mechanisms are examined in the following section with respect to nutrient partitioning between competing plants, a major factor affecting species performance in plant communities (Berendse and Elberse 1990).

Despite general agreement that plant competition usually involves interactions between plants for different resources, most studies reporting competition fail to explain the processes and mechanisms involved (Nambiar and Sands 1993; Wilson and Tilman 1991), thus making it difficult to identify the resources which led to the interactions. According to Goldberg (1990), examining effects and responses of competing plants to resource availability can provide insight into the processes and mechanisms involved. This approach is adopted in this chapter, since both plant effects on resources and plant responses to resource availability can be quantified. As indicated earlier, the objective is to examine the concept of plant competition for nutrient resources, examine mechanisms partitioning of soil nutrients between neighboring plants, explain relationships between nutrient availability and plant growth, and to develop a theoretical basis for elucidating interspecific plant nutrient interactions. Although both light and moisture influence plant growth, the focus here is on interactions involving nutrients. However, attention is given to the role of moisture and light where these resources may directly influence nutrient availability, uptake and use by plants.

Unfortunately, these indices often focus on relating crop responses to some measure of weed proximity (Goldberg and Werner 1983; Radosevich 1988). Also, treatment impacts are often evaluated on the basis of survival, growth and yield of the target crops without considering non-crop responses, a major weakness for ecologically based assessments. Moreover, applications of these indices hardly explain the mechanisms involved. Little attention has also been given to determine whether competition is primarily for light, moisture and/or nutrients. The advantages and limitations of these indices are discussed in a review by Burton (1993) who advocated replacing static competition indices with a more site-specific phytometric approaches that feature greater accounting of neighboring noncrops and systematic local calibration and verification.

Distinguishing between these processes is important to test hypotheses related to the effect of changing nutrient supply on plant growth and nutrient composition. One way of solving this problem is by first studying the effects of nutrient supply on each of the individual plant response variables (i.e. biomass, nutrient concentration and content), and then examining their interrelationships using *Vector Nutrient Analysis* model (Timmer 1991; Imo and Timmer 1998). Traditionally applied to a single crop, this diagnostic format is unsuitable for interpreting competition effects in which growth resources are partitioned between interacting plants. Thus, the format has been modified by combining responses of

Managing Competition for Nutrients in Agro-Ecosystems 199

Vector competition analysis is modeled on regression analysis to evaluate competition effects (Goldberg and Werner 1983; Malik and Timmer 1995), and vector diagnosis is often used to assess plant nutrient status by identifying nutritional effects of nutrient dilution, deficiency, sufficiency and excess uptake (Haase and Rose 1995; Imo and Timmer 1997). The effect of non-crop weedy vegetation (*V*) on the target crop (*T*) is evaluated using the linear relationship {(*TW* = *YW* + *X*(*VW*) or *TU* = *YU* +*X*(*VU*)}, where *Y* represents crop response in the absence of competition; *X* is the slope of the regression; and *W* and *U* signify biomass and nutrient uptake, respectively. According to Goldberg and Werner (1983) and Goldberg (1990), the slope of the regression has a physical meaning: it provides a measure of competition intensity. The slope indicates no interactions when it is not significantly different from zero (0); indicates beneficial or facilitative effects of the vegetation if it is

positive (+); and indicates competitive effects of the vegetation if it is negative (-).

vector showing the combined crop and non-crop response (i.e. vector response).

against that of the other species (e.g. weedy vegetation).

Malik and Timmer (1995; 1996) have shown the effectiveness of this approach in describing relationships between growth of interacting black spruce (*Picea mariana)* seedlings and neighboring vegetation over a two-year period, which clearly demonstrated a significant negative relationship between seedling biomass and neighboring vegetation with seedling performance suffering at the expense of weed growth (Malik and Timmer 1996). Thus, reduction in weed biomass due to herbicide application resulted in reduced weed competition and a corresponding increase in biomass or nutrient content of the target crop. Since such a response often has both direction and magnitude, it can be characterized by a

Also, the slopes of such regressions serve as indicators of competition intensity (Goldberg 1990; Goldberg and Werner 1983), and may vary depending on site quality (Weldon *et al*. 1988). Management practices can also influence competition intensity as was demonstrated with herbicide application and nutrient loading (Malik and Timmer 1995; 1996). On the basis of these results, various possible impacts of weed management practices on the competitive interactions of crops and weeds, for example, herbicide application, may reduce weed competition in favor of the crop because of weed elimination. Fertilization may favor both the crop and weeds because of stimulated growth or may favor weeds more than the crop due to rapid weed growth resulting in shading of the crops; shading under the shelterwood system may reduce growth of both species. These varying treatment responses can be classified into four quadrants by two-dimensional graphical representation of the performance (biomass, nutrient content, yield or density) of one species (i.e. crop) plotted

The principle is illustrated in Figure 3, which displays some possible combinations of the crop and non-crop (i.e. weedy vegetation): crop response plotted on the y-axis, and noncrop responses on the x-axis. First, performance of the crop in the absence of non-crop competition (when there is zero biomass of the non-crop) is plotted on the y-axis, while performance of the non-crop in the absence of the crop ( zero biomass of the crop) is plotted on the x-axis (Figure 3). The process of interspecific interaction can then be visualized as a change in these values along each axis when in mixtures. Competition will reduce performance of either species, while facilitation will increase species performance. Hence, performance of each species in mixture is represented by a point on the graph as summarized in Figure 3 depicting vectors of changing biomass production and nutrient

**6.1 Model theory** 

competing plants to allow interpretation of interspecific interactions in a model called vector competition analysis (Imo and Timmer 1998). The focus, however, remains the same: identifying growth and nutritional interactions within the framework of vector diagnosis by characterizing the relationships between biomass production, nutrient accumulation and nutrient concentration of both species in mixture relative to their status in monoculture. These relationships are then synthesized into a diagnostic model (vector competition analysis) for elucidating interspecific plant growth and nutrient interactions.

In the following section, a graphical framework that helps discern interspecific competition effects involving nutrients and helps understanding probable mechanisms for nutrient competition in cropping systems is outlined. Functioning of the model is demonstrated using case studies from previous studies involving nutrient relationships in plants growing alone or in mixture, thus elucidating crop and weed nutrient interactions by examining the extent to which nutrients influenced the observed growth responses will be used to illustrate functioning of the model in screening impacts of different vegetation management regimes including use of herbicides, fertilization and nutrient loading as weed control measures in young forest plantations (Timmer 1997; Mead and Mansur, 1993), managing tree-crop interactions in alley cropping (Imo and Timmer 2000), and crop and tree interactions in *taungya\** systems of plantation establishment (Imo 2010).

#### **6. The vector competition analysis model**

The objective of this section is to provide the theoretical background of the model and to demonstrate its function in elucidating interspecific growth and nutrient interactions in plants. Although scientists have often studied competition to understand succession patterns, as well as growth, diversity and dynamics of plant communities, such studies have focused mainly on minimizing the effects of competing weeds by developing predictive tools for yield-loss assessment, and also to minimize the use of herbicides (Altieri and Liebman 1988). Also, much research has been conducted to maximize the output of intercropping systems (Willey 1979; Vandermeer 1989; 1998). In agricultural ecosystems, concepts such as 'vegetation management' or 'weed management' are important considerations in yield improvement, and often include a broad spectrum of concerns such as biodiversity, the effects of different management practices on competitive interactions, and sustainable crop production. The general view is that any vegetation or weed management should aim to suppress the non-crop only to the extent that it significantly interferes with the target crop. More recently, similar concepts have been extended to agroforestry where biophysical benefits and consequences of including trees in farm land management (e.g. competition for resources and improvement of soil fertility) are major factors in designing mixed cropping systems (Ong and Huxley 1996). The objective here is to minimize competitive effects between plants, while taking advantage of the beneficial effects of trees on the crop. According to Nambiar and Sands (1993), the difficulty with these concepts is defining levels of 'significant' interference, as well as establishing criteria for segregating competition effects.

<sup>\*</sup> An agroforestry system involving planting tree seedlings in combination with food crops by first growing crops with tree seedlings for 3 - 4 years, after which trees are left to grow alone. This planting sequence eliminates weed competition, while tree and crop competition is minimized.

#### **6.1 Model theory**

198 Weed Control

competing plants to allow interpretation of interspecific interactions in a model called vector competition analysis (Imo and Timmer 1998). The focus, however, remains the same: identifying growth and nutritional interactions within the framework of vector diagnosis by characterizing the relationships between biomass production, nutrient accumulation and nutrient concentration of both species in mixture relative to their status in monoculture. These relationships are then synthesized into a diagnostic model (vector competition

In the following section, a graphical framework that helps discern interspecific competition effects involving nutrients and helps understanding probable mechanisms for nutrient competition in cropping systems is outlined. Functioning of the model is demonstrated using case studies from previous studies involving nutrient relationships in plants growing alone or in mixture, thus elucidating crop and weed nutrient interactions by examining the extent to which nutrients influenced the observed growth responses will be used to illustrate functioning of the model in screening impacts of different vegetation management regimes including use of herbicides, fertilization and nutrient loading as weed control measures in young forest plantations (Timmer 1997; Mead and Mansur, 1993), managing tree-crop interactions in alley cropping (Imo and Timmer 2000), and crop and tree

The objective of this section is to provide the theoretical background of the model and to demonstrate its function in elucidating interspecific growth and nutrient interactions in plants. Although scientists have often studied competition to understand succession patterns, as well as growth, diversity and dynamics of plant communities, such studies have focused mainly on minimizing the effects of competing weeds by developing predictive tools for yield-loss assessment, and also to minimize the use of herbicides (Altieri and Liebman 1988). Also, much research has been conducted to maximize the output of intercropping systems (Willey 1979; Vandermeer 1989; 1998). In agricultural ecosystems, concepts such as 'vegetation management' or 'weed management' are important considerations in yield improvement, and often include a broad spectrum of concerns such as biodiversity, the effects of different management practices on competitive interactions, and sustainable crop production. The general view is that any vegetation or weed management should aim to suppress the non-crop only to the extent that it significantly interferes with the target crop. More recently, similar concepts have been extended to agroforestry where biophysical benefits and consequences of including trees in farm land management (e.g. competition for resources and improvement of soil fertility) are major factors in designing mixed cropping systems (Ong and Huxley 1996). The objective here is to minimize competitive effects between plants, while taking advantage of the beneficial effects of trees on the crop. According to Nambiar and Sands (1993), the difficulty with these concepts is defining levels of 'significant' interference, as well as establishing criteria for

An agroforestry system involving planting tree seedlings in combination with food crops by first growing crops with tree seedlings for 3 - 4 years, after which trees are left to grow alone. This planting

sequence eliminates weed competition, while tree and crop competition is minimized.

analysis) for elucidating interspecific plant growth and nutrient interactions.

interactions in *taungya\** systems of plantation establishment (Imo 2010).

**6. The vector competition analysis model** 

segregating competition effects.

 \*

Vector competition analysis is modeled on regression analysis to evaluate competition effects (Goldberg and Werner 1983; Malik and Timmer 1995), and vector diagnosis is often used to assess plant nutrient status by identifying nutritional effects of nutrient dilution, deficiency, sufficiency and excess uptake (Haase and Rose 1995; Imo and Timmer 1997). The effect of non-crop weedy vegetation (*V*) on the target crop (*T*) is evaluated using the linear relationship {(*TW* = *YW* + *X*(*VW*) or *TU* = *YU* +*X*(*VU*)}, where *Y* represents crop response in the absence of competition; *X* is the slope of the regression; and *W* and *U* signify biomass and nutrient uptake, respectively. According to Goldberg and Werner (1983) and Goldberg (1990), the slope of the regression has a physical meaning: it provides a measure of competition intensity. The slope indicates no interactions when it is not significantly different from zero (0); indicates beneficial or facilitative effects of the vegetation if it is positive (+); and indicates competitive effects of the vegetation if it is negative (-).

Malik and Timmer (1995; 1996) have shown the effectiveness of this approach in describing relationships between growth of interacting black spruce (*Picea mariana)* seedlings and neighboring vegetation over a two-year period, which clearly demonstrated a significant negative relationship between seedling biomass and neighboring vegetation with seedling performance suffering at the expense of weed growth (Malik and Timmer 1996). Thus, reduction in weed biomass due to herbicide application resulted in reduced weed competition and a corresponding increase in biomass or nutrient content of the target crop. Since such a response often has both direction and magnitude, it can be characterized by a vector showing the combined crop and non-crop response (i.e. vector response).

Also, the slopes of such regressions serve as indicators of competition intensity (Goldberg 1990; Goldberg and Werner 1983), and may vary depending on site quality (Weldon *et al*. 1988). Management practices can also influence competition intensity as was demonstrated with herbicide application and nutrient loading (Malik and Timmer 1995; 1996). On the basis of these results, various possible impacts of weed management practices on the competitive interactions of crops and weeds, for example, herbicide application, may reduce weed competition in favor of the crop because of weed elimination. Fertilization may favor both the crop and weeds because of stimulated growth or may favor weeds more than the crop due to rapid weed growth resulting in shading of the crops; shading under the shelterwood system may reduce growth of both species. These varying treatment responses can be classified into four quadrants by two-dimensional graphical representation of the performance (biomass, nutrient content, yield or density) of one species (i.e. crop) plotted against that of the other species (e.g. weedy vegetation).

The principle is illustrated in Figure 3, which displays some possible combinations of the crop and non-crop (i.e. weedy vegetation): crop response plotted on the y-axis, and noncrop responses on the x-axis. First, performance of the crop in the absence of non-crop competition (when there is zero biomass of the non-crop) is plotted on the y-axis, while performance of the non-crop in the absence of the crop ( zero biomass of the crop) is plotted on the x-axis (Figure 3). The process of interspecific interaction can then be visualized as a change in these values along each axis when in mixtures. Competition will reduce performance of either species, while facilitation will increase species performance. Hence, performance of each species in mixture is represented by a point on the graph as summarized in Figure 3 depicting vectors of changing biomass production and nutrient

Managing Competition for Nutrients in Agro-Ecosystems 201

in C exemplify treatments that favor both species (synergistic [+,+]); and shifts in D illustrate

Slopes of the vectors define symmetry of the interactions. If crops and non-crops influence each other such that both species change by the same magnitude and in the same direction (as shown by vectors A, B, C and D in Figure 3), the slope will be one indicating symmetric interaction. Deviations from a slope of unity represent asymmetric interactions, and can be interpreted on the basis of vector orientation and magnitude within each of the four quadrants projected horizontally and vertically from the reference point (Figure 3). Vector deviations closer to the horizontal dashed line imply the non-crop is more sensitive to the treatments than the target crop, while those closer to the vertical dashed line indicate the crop is more sensitive to treatments. Similarly, a slope of zero indicates treatments that affect the non-crop without influencing the crop, while a slope of infinity exemplifies treatments

Since nutrient concentration (*C*) is a function of uptake (*U*) and biomass (*W*), thus *C* = *U*/*W*, interpretation of growth and nutritional interactions for each species can be determined based on the ratio between nutrient content vector (*U*) and biomass vector (*W*), thus (*U*/*W*). This approach is adapted for several reasons: first, *W* and *C* can easily be determined using standard laboratory procedures, and U is calculated for each sample by taking the product of concentration and dry mass, thus *U* = *C\*W*. Secondly, nutrient content (U) gives a direct measure of the amount of nutrients actually absorbed by the plant (Berkowitz 1988). Finally, the relationship *C* = *U*/*W* enables one to determine whether changes in *C* are associated

Diagnostic interpretations of these interactions are summarized in Box II, Figure 3. Nutrient dilution (or decline in concentration) occurs when *W* > *U* (i.e. vector ratio *U*/*W* < 1). Such dilution effect is antagonistic dilution if it is associated with reduced growth and nutrient uptake, or is growth dilution if it is associated with increased growth and nutrient uptake. Nutrient sufficiency occurs when *W* = *U* (i.e. vector ratio *U*/*W* = 1, Box II in Figure 3) indicating that rate of nutrient uptake matched growth increase or steady state nutrition (Ingestad and Lund, 1986; Imo and Timmer 1997). Nutrient accumulation occurs when *W* < *U* (i.e. vector ratio *U*/*W* > 1, Box II in Figure 3) indicating that rate of nutrient uptake was higher than growth rate. Such accumulation is a deficiency response if it is associated with increase in both *W* and *U*, but excess uptake if associated with decline in biomass and or

These interactions can also be determined graphically by plotting both biomass and nutrient uptake responses on the same vector competition diagram (illustrated later) as follows. For the crop component (*y*), drawing a horizontal line (parallel to the *x*-axis) across the point indicating *W* enables determination of the change in relative nutrient concentration depending on the relative position of the point indicating *U*. Nutrient dilution in the crop occurs when *U* is below the horizontal line (i.e. *U* < *W*); nutrient sufficiency occurs when *U* and *W* lie on the same horizontal line (i.e. *U* = *W*); while nutrient accumulation occurs when U is above the horizontal line (i.e. *U* > *W*). Similarly, changes in relative nutrient concentration for the non-crop (*x*) can be determined by drawing a vertical line (parallel to the *y*-axis) across the point indicating *W*. Nutrient dilution in the non-crop occurs when *U* is

treatments that favor the crop but not non-crop (compensatory [+,-]).

that affect the crop without affecting non-crop.

primarily with changes in *U*, W or both.

nutrient uptake (Timmer 1991).

**6.4 Diagnosis of plant growth and nutritional interactions** 

uptake of interacting plants relative to competition-free status as described in the following section.

#### **6.2 Constructing vector competition diagrams**

In this model, biomass or nutrient content of the target crop is plotted on the y-axis against those of the neighboring non-crop vegetation on the x-axis (Figure 3). Although absolute data can be used in plotting vector competition diagrams, use of relative (normalized) values allows multiple comparisons among sites, treatments and nutrient elements by eliminating inherent differences in plant size and nutrient content (Timmer 1991). Normalization is achieved by dividing response parameters (biomass or nutrient content) for each treatment by corresponding values of the control or reference treatment, and expressed as percentage by multiplying by 100% to obtain relative biomass (W) and relative nutrient content (*U*). The choice of the reference treatment depends on the specific objective of the analysis being conducted. For studies in which interspecific interactions is the focus, species performance without competition is used as the reference for comparison with its performance in mixture. Thus, effects of non-crops on the target crop are evaluated when performance of the sole crop is used as the reference (Figure 3). Similarly, effects of the crop on non-crops are assessed when performance of the non-crop in the absence of the target crop is the reference.

After normalizing the data, plotting starts with the reference treatments (i.e. sole crop [*y*] = 100, and sole non-crop [x] = 100) as shown in Figure 3. This reference point represents the combined crop and non-crop response without competition. When plotting, it is important to have the same scale on both axes to form a square so as to avoid visual exaggerations. The second step is to plot crop and non-crop responses in mixture (*W* or *U*) on the same diagram, each point representing the combined crop and non-crop mixture response to different treatments within one of the quadrants A, B, C or D (as shown in Figure3). The third step is to draw vectors from the reference point to the plotted data points to show the combined response in biomass or nutrient content of the crop as well as non-crop in mixture or due to specific treatments.

The final step is to draw a vertical and horizontal dashed line across the reference point to divide the vector competition diagram into four distinct quadrants (A, B, C and D) that help define interaction types (Box I in Fig. 3). Vector shifts below the horizontal dashed line indicate competitive effects of the non-crop since responses are negative, while shifts above indicate beneficial or facilitative effects of the non-crop because responses are positive. Similarly, vector shifts to the left of the vertical dashed line indicate competitive effects of the crop on the non-crop, while shifts to the right signify facilitative effects of the crop. Interpretations of these vector relationships are discussed below.

#### **6.3 Vector interpretations**

Diagnostic interpretation of the impacts of alternative management strategies on crops as well as non-crops is based on vector direction and magnitude observed as no change (0), increase (+), or decrease (-) relative to the reference status (Box I, Figure 3). Vector shifts in quadrant A indicate treatments that inhibit both species (antagonistic [-,-]); shifts in B show treatments that favor non-crop vegetation but not the target crop (compensatory [-,+]); shifts

uptake of interacting plants relative to competition-free status as described in the following

In this model, biomass or nutrient content of the target crop is plotted on the y-axis against those of the neighboring non-crop vegetation on the x-axis (Figure 3). Although absolute data can be used in plotting vector competition diagrams, use of relative (normalized) values allows multiple comparisons among sites, treatments and nutrient elements by eliminating inherent differences in plant size and nutrient content (Timmer 1991). Normalization is achieved by dividing response parameters (biomass or nutrient content) for each treatment by corresponding values of the control or reference treatment, and expressed as percentage by multiplying by 100% to obtain relative biomass (W) and relative nutrient content (*U*). The choice of the reference treatment depends on the specific objective of the analysis being conducted. For studies in which interspecific interactions is the focus, species performance without competition is used as the reference for comparison with its performance in mixture. Thus, effects of non-crops on the target crop are evaluated when performance of the sole crop is used as the reference (Figure 3). Similarly, effects of the crop on non-crops are assessed when performance of the non-crop in the absence of the target

After normalizing the data, plotting starts with the reference treatments (i.e. sole crop [*y*] = 100, and sole non-crop [x] = 100) as shown in Figure 3. This reference point represents the combined crop and non-crop response without competition. When plotting, it is important to have the same scale on both axes to form a square so as to avoid visual exaggerations. The second step is to plot crop and non-crop responses in mixture (*W* or *U*) on the same diagram, each point representing the combined crop and non-crop mixture response to different treatments within one of the quadrants A, B, C or D (as shown in Figure3). The third step is to draw vectors from the reference point to the plotted data points to show the combined response in biomass or nutrient content of the crop as well as non-crop in mixture

The final step is to draw a vertical and horizontal dashed line across the reference point to divide the vector competition diagram into four distinct quadrants (A, B, C and D) that help define interaction types (Box I in Fig. 3). Vector shifts below the horizontal dashed line indicate competitive effects of the non-crop since responses are negative, while shifts above indicate beneficial or facilitative effects of the non-crop because responses are positive. Similarly, vector shifts to the left of the vertical dashed line indicate competitive effects of the crop on the non-crop, while shifts to the right signify facilitative effects of the crop.

Diagnostic interpretation of the impacts of alternative management strategies on crops as well as non-crops is based on vector direction and magnitude observed as no change (0), increase (+), or decrease (-) relative to the reference status (Box I, Figure 3). Vector shifts in quadrant A indicate treatments that inhibit both species (antagonistic [-,-]); shifts in B show treatments that favor non-crop vegetation but not the target crop (compensatory [-,+]); shifts

Interpretations of these vector relationships are discussed below.

section.

crop is the reference.

or due to specific treatments.

**6.3 Vector interpretations** 

**6.2 Constructing vector competition diagrams** 

in C exemplify treatments that favor both species (synergistic [+,+]); and shifts in D illustrate treatments that favor the crop but not non-crop (compensatory [+,-]).

Slopes of the vectors define symmetry of the interactions. If crops and non-crops influence each other such that both species change by the same magnitude and in the same direction (as shown by vectors A, B, C and D in Figure 3), the slope will be one indicating symmetric interaction. Deviations from a slope of unity represent asymmetric interactions, and can be interpreted on the basis of vector orientation and magnitude within each of the four quadrants projected horizontally and vertically from the reference point (Figure 3). Vector deviations closer to the horizontal dashed line imply the non-crop is more sensitive to the treatments than the target crop, while those closer to the vertical dashed line indicate the crop is more sensitive to treatments. Similarly, a slope of zero indicates treatments that affect the non-crop without influencing the crop, while a slope of infinity exemplifies treatments that affect the crop without affecting non-crop.

#### **6.4 Diagnosis of plant growth and nutritional interactions**

Since nutrient concentration (*C*) is a function of uptake (*U*) and biomass (*W*), thus *C* = *U*/*W*, interpretation of growth and nutritional interactions for each species can be determined based on the ratio between nutrient content vector (*U*) and biomass vector (*W*), thus (*U*/*W*). This approach is adapted for several reasons: first, *W* and *C* can easily be determined using standard laboratory procedures, and U is calculated for each sample by taking the product of concentration and dry mass, thus *U* = *C\*W*. Secondly, nutrient content (U) gives a direct measure of the amount of nutrients actually absorbed by the plant (Berkowitz 1988). Finally, the relationship *C* = *U*/*W* enables one to determine whether changes in *C* are associated primarily with changes in *U*, W or both.

Diagnostic interpretations of these interactions are summarized in Box II, Figure 3. Nutrient dilution (or decline in concentration) occurs when *W* > *U* (i.e. vector ratio *U*/*W* < 1). Such dilution effect is antagonistic dilution if it is associated with reduced growth and nutrient uptake, or is growth dilution if it is associated with increased growth and nutrient uptake. Nutrient sufficiency occurs when *W* = *U* (i.e. vector ratio *U*/*W* = 1, Box II in Figure 3) indicating that rate of nutrient uptake matched growth increase or steady state nutrition (Ingestad and Lund, 1986; Imo and Timmer 1997). Nutrient accumulation occurs when *W* < *U* (i.e. vector ratio *U*/*W* > 1, Box II in Figure 3) indicating that rate of nutrient uptake was higher than growth rate. Such accumulation is a deficiency response if it is associated with increase in both *W* and *U*, but excess uptake if associated with decline in biomass and or nutrient uptake (Timmer 1991).

These interactions can also be determined graphically by plotting both biomass and nutrient uptake responses on the same vector competition diagram (illustrated later) as follows. For the crop component (*y*), drawing a horizontal line (parallel to the *x*-axis) across the point indicating *W* enables determination of the change in relative nutrient concentration depending on the relative position of the point indicating *U*. Nutrient dilution in the crop occurs when *U* is below the horizontal line (i.e. *U* < *W*); nutrient sufficiency occurs when *U* and *W* lie on the same horizontal line (i.e. *U* = *W*); while nutrient accumulation occurs when U is above the horizontal line (i.e. *U* > *W*). Similarly, changes in relative nutrient concentration for the non-crop (*x*) can be determined by drawing a vertical line (parallel to the *y*-axis) across the point indicating *W*. Nutrient dilution in the non-crop occurs when *U* is

*U*/*W* = 1).

**7. Practical applications** 

Managing Competition for Nutrients in Agro-Ecosystems 203

For a limiting nutrient, this interaction type is associated with a deficiency response (i.e. vector ratio *U*/*W* > 1, Box II in Figure 3) since uptake is accelerated faster than growth in a manner similar to fertilization response. Synergistic nutrient interactions may also result from improved moisture availability, for example, mulching, reducing surface runoff and evaporation. This interaction may increase growth faster than nutrient uptake, thus resulting in growth dilution of nutrients (i.e. vector ratio *U*/*W* < 1, Box II Figure 3) or sufficiency if both growth and nutrient uptake are increased at the same rate (i.e. vector ratio

Competition for nutrients reduces growth and nutrient uptake of the species in mixture, and often occurs when nutrient availability is not sufficient to support the demand by either species, resulting in antagonistic competition (Shift A in Figure 3). This type of interaction results in antagonistic dilution of nutrients (i.e. *U*/*W* < 1, Box II in Figure 3) indicating that competition reduced nutrient absorption more than photosynthesis. Interspecific nutrient interactions may also result in compensatory competition in favor of one species while the other is suppressed (Figure 3). For example, increasing nutrient availability through fertilization may favor growth of one species and cause preemption of other resources. Since nutrients are not limiting, this type of interaction results in excess nutrient uptake (*U*/*W* > 1, Box II in Figure 3) indicating that photosynthesis was reduced more than nutrient absorption presumably because of light and or moisture preemption. The function of this

It is important to note that this model has been developed primarily for screening alternative strategies of integrated vegetation management in forest plantations, cropping and agroforestry systems by evaluating crop and weed interactions in a bivariate graphical model depicting vectors of changing biomass production and nutrient uptake relative to competition-free status. Conceptually, this approach has the potential to contribute to efficient nutrient management in intensively managed cropping systems by providing a systematic framework for rationalizing management prescriptions as has been demonstrated for agroforestry systems in Western Kenya (Imo and Timmer (2001) and young forest plantations (Imo and Timmer 2000) where management of competing non-crop species is an important objective and the other where complementary use of growth resources by species in mixture is an important consideration in management decisions.

Since nutrient content is often used to give an integrated measure of total nutrient uptake and use by plants, determination of nutrient content of neighboring plants can provide insight into the processes of partitioning of soil nutrient resources between them (Berkowitz 1988). Although Imo and Timmer (1997) have previously diagnosed these nutritional effects using vector competition analysis without linkage to availability of other resources required for growth, it is well-known that plant growth depends on acquisition, retention and use of multiple resources (carbon, water, nutrients and light) as illustrated in Figure 2 (Trenbath 1976). Carbon and nutrients are converted into biomass, while light and water are necessary for growth and other physiological processes (Salisbury and Ross 1992), often involving complex interactions among various resources (Neary *et al*. 1990; Sands and Mulligan 1990; Woods *et al*. 1992). Plant growth characteristics may also influence resource interactions, for example, due to trade-offs in carbon allocation between resource acquiring organs or greater

model is demonstrated with response data from the following study.

to the left (i.e. *U* < *W*), while accumulation occurs when *U* is to the right (i.e. *W* < *U*) of the vertical line. Sufficiency occurs when *U* and *W* lie on the same vertical line (i.e. *W* = *U*).



Fig. 3. Graphical vector competition analysis model showing total nutrient use by neighboring tree crop and non-crop weedy species. Competition-free crop or non-crop weedy vegetation status is normalized to 100% as a reference (R) for comparison with corresponding plants growing in mixture, respectively. The vertical and horizontal dashed lines divide the model into four quadrants (A, B, C and D) that characterize the type of interaction (Box I), while the associated growth and nutritional interactions are identified in terms of vector ratio in Box II. Adapted from Imo (1999); see also Imo and Timmer (2000; 2002).

to the left (i.e. *U* < *W*), while accumulation occurs when *U* is to the right (i.e. *W* < *U*) of the vertical line. Sufficiency occurs when *U* and *W* lie on the same vertical line (i.e. *W* = *U*).

Fig. 3. Graphical vector competition analysis model showing total nutrient use by neighboring tree crop and non-crop weedy species. Competition-free crop or non-crop weedy vegetation status is normalized to 100% as a reference (R) for comparison with corresponding plants growing in mixture, respectively. The vertical and horizontal dashed lines divide the model into four quadrants (A, B, C and D) that characterize the type of interaction (Box I), while the associated growth and nutritional interactions are identified in terms of vector ratio in Box II. Adapted from Imo (1999); see also Imo and Timmer

(2000; 2002).

For a limiting nutrient, this interaction type is associated with a deficiency response (i.e. vector ratio *U*/*W* > 1, Box II in Figure 3) since uptake is accelerated faster than growth in a manner similar to fertilization response. Synergistic nutrient interactions may also result from improved moisture availability, for example, mulching, reducing surface runoff and evaporation. This interaction may increase growth faster than nutrient uptake, thus resulting in growth dilution of nutrients (i.e. vector ratio *U*/*W* < 1, Box II Figure 3) or sufficiency if both growth and nutrient uptake are increased at the same rate (i.e. vector ratio *U*/*W* = 1).

Competition for nutrients reduces growth and nutrient uptake of the species in mixture, and often occurs when nutrient availability is not sufficient to support the demand by either species, resulting in antagonistic competition (Shift A in Figure 3). This type of interaction results in antagonistic dilution of nutrients (i.e. *U*/*W* < 1, Box II in Figure 3) indicating that competition reduced nutrient absorption more than photosynthesis. Interspecific nutrient interactions may also result in compensatory competition in favor of one species while the other is suppressed (Figure 3). For example, increasing nutrient availability through fertilization may favor growth of one species and cause preemption of other resources. Since nutrients are not limiting, this type of interaction results in excess nutrient uptake (*U*/*W* > 1, Box II in Figure 3) indicating that photosynthesis was reduced more than nutrient absorption presumably because of light and or moisture preemption. The function of this model is demonstrated with response data from the following study.

It is important to note that this model has been developed primarily for screening alternative strategies of integrated vegetation management in forest plantations, cropping and agroforestry systems by evaluating crop and weed interactions in a bivariate graphical model depicting vectors of changing biomass production and nutrient uptake relative to competition-free status. Conceptually, this approach has the potential to contribute to efficient nutrient management in intensively managed cropping systems by providing a systematic framework for rationalizing management prescriptions as has been demonstrated for agroforestry systems in Western Kenya (Imo and Timmer (2001) and young forest plantations (Imo and Timmer 2000) where management of competing non-crop species is an important objective and the other where complementary use of growth resources by species in mixture is an important consideration in management decisions.

#### **7. Practical applications**

Since nutrient content is often used to give an integrated measure of total nutrient uptake and use by plants, determination of nutrient content of neighboring plants can provide insight into the processes of partitioning of soil nutrient resources between them (Berkowitz 1988). Although Imo and Timmer (1997) have previously diagnosed these nutritional effects using vector competition analysis without linkage to availability of other resources required for growth, it is well-known that plant growth depends on acquisition, retention and use of multiple resources (carbon, water, nutrients and light) as illustrated in Figure 2 (Trenbath 1976). Carbon and nutrients are converted into biomass, while light and water are necessary for growth and other physiological processes (Salisbury and Ross 1992), often involving complex interactions among various resources (Neary *et al*. 1990; Sands and Mulligan 1990; Woods *et al*. 1992). Plant growth characteristics may also influence resource interactions, for example, due to trade-offs in carbon allocation between resource acquiring organs or greater

Managing Competition for Nutrients in Agro-Ecosystems 205

dilution effects (Imo and Timmer 1997), suggesting that competitive interactions reduced nutrient uptake more than photosynthesis. It was therefore concluded that competition for N between the seedlings and the weedy vegetation was more important than for light and moisture. In contrast, nutrient loading, however, improved competitive ability of the seedlings, presumably because of a build-up of pre-plant N during the nursery phase.

Synergistic interactions occur when performance of one or both species mixture is more than their performance when grown alone (Shift C in Figure 3), and indicates beneficial effects of one species on the other. This interaction type was demonstrated in an alley agroforestry system involving *Leucaena* trees inter-cropped with maize (*Zea mays* L.) in Western Kenya (Imo and Timmer 2000). In this study, maize crop productivity was significantly higher than the sole crop, which was attributed to higher N availability mineralized from added mulch. Apparently, the beneficial effects of the mulch for the crop were higher than the negative effects of competition by the *Leucaena* hedgerows for the other resources (i.e. available light and moisture). The frequent and rapid pruning regime applied ensured return of mulch to the soil hence maintaining high N availability to the crop while ensuring minimal effects of light competition by the hedgerow trees. It was also noted that, although both biomass and nutrient content of the crop increased, the increase in the latter was larger than a corresponding increase in the former, which resulted in a typical of deficiency response

In view of the factors influencing nutrient partitioning between competing plants discussed in the previous sections, crop productivity can be maintained or improved by tilting the balance in resource capture in favor of the target crops. Previous developments on integrated weed management in forestry and agriculture ), and tree and crop inter-cropping in agroforestry (Ong and Huxley 1996) have repeatedly emphasized the need to incorporate a combination of management approaches for different objectives. Given that such integrated methods for crop management are linked to biological, environmental and economic considerations (Nambiar and Sands 1993), the problem often encountered by many decision-makers is related to determining the level of significant interference of beneficial value of non-crop management interventions. The vector competition analysis approach presented in this thesis is a simple decision-support tool to reconcile these variable objectives, especially in relation to nutrient management. In this model, both crop and noncrop interaction types and the associated nutritional mechanism s are evaluated in a

Figure 3 above has shown how this simple approach can be used to determine the partitioning of soil nutrients between neighboring plant species, and how efficiently the available nutrient resources are utilized on a site-specific and management specific-regime basis. Notice that the model emphasizes total nutrient use by the neighboring plant species according to the competitive production principle or facilitation discussed earlier. The lower-left portion (*antagonistic* interactions) in Figure 3 indicates competitive interactions between the species and demonstrates combinations in which total nutrient uptake and use by the two species in mixture is less than their total nutrient uptake and use when grown

(Imo and Timmer 1997) due to improved fertility from mulching.

**7.3 Synergistic interactions** 

**7.4 Maximizing crop nutrient use** 

systematic manner.

growth rate and overall plant size. Trade-offs between uptake organs predict a negative relation between competitive abilities for different resources ,while accelerated growth and resource use predict a positive relation between competitive abilities for different resources (Tilman 1988). Unfortunately, most studies on plant competition often focus on effects of single resources without considering the processes involved (Nambiar and Sands 1993). This makes it difficult to determine whether interactions involved more than one resource. Figure 3 illustrates the impacts of management practices on resource partitioning between target crops and neighboring non-crop weedy vegetation as discussed below.

#### **7.1 Compensatory competition**

Compensatory competition occurs when growth of one species increases, while that of the other decreases (Shift B and C, Figure 3), and indicates re-allocation of available resources from one species for use by the other. This type of interaction occurred between natural vegetation and seedlings after fertilizer addition on the weed prone sites (Imo and Timmer 1999). Although fertilizer addition increased N availability to both species, growth and N uptake of neighboring non-crop vegetation were increased while those of seedlings declined. Nitrogen uptake and growth of the faster growing weeds were higher with carbon assimilates allocated primarily to aboveground, typical of plant responses to increased nutrient availability (Chapin 1987). With this allocation pattern, neighboring vegetation intercepted more light because of larger leaf area. Once these processes were established, a positive feedback mechanism between growth and resource capture (Grime 1979) presumably preempted available light and moisture from seedlings that in effect became suppressed. These responses confirm the hypothesis that competitive advantage of a species for one resource leads to an advantage for acquisition and use of the other resources as well (; Kropff et al. 1984).

Comparison of carbon (*W*) and nitrogen (*U*) accumulation in seedlings shows that both *W* and *U* were reduced, but proportional reduction in *W* was larger than the corresponding reduction in *U*, hence *U*/*W* increased signifying excess uptake of N (Box II in Figure 3). Thus, N was not limiting, and the suppressed seedling growth was probably because of preemption of available light and or moisture by the weeds. The proportionally lower *W* than *U* in weeds after fertilization indicates that U/W also increased signifying a deficiency response (Box II in Figure 3).

#### **7.2 Antagonistic competition**

Antagonistic competition occurs when performance of both species in mixture is reduced (Shift A, Figure 3), probably because consumption by each species reduces resource availability to each other. This interaction type has been demonstrated in conventionally fertilized seedlings planted on high competition sites without any weed control treatment (Imo and Timmer 1998). In this study, growth and N uptake of both the seedlings and neighboring vegetation were reduced exemplifying mutual antagonism due to low N availability that was not sufficient to support growth demand of both the seedlings and competing vegetation, thus reducing uptake of both species. Reduction in weed biomass was, however, small compared to decline in seedling biomass (Imo and Timmer 1999) exemplifying asymmetric competition with the trees being more sensitive to competitive effects than the weeds. Further analysis of the results from this study indicated antagonistic dilution effects (Imo and Timmer 1997), suggesting that competitive interactions reduced nutrient uptake more than photosynthesis. It was therefore concluded that competition for N between the seedlings and the weedy vegetation was more important than for light and moisture. In contrast, nutrient loading, however, improved competitive ability of the seedlings, presumably because of a build-up of pre-plant N during the nursery phase.

#### **7.3 Synergistic interactions**

204 Weed Control

growth rate and overall plant size. Trade-offs between uptake organs predict a negative relation between competitive abilities for different resources ,while accelerated growth and resource use predict a positive relation between competitive abilities for different resources (Tilman 1988). Unfortunately, most studies on plant competition often focus on effects of single resources without considering the processes involved (Nambiar and Sands 1993). This makes it difficult to determine whether interactions involved more than one resource. Figure 3 illustrates the impacts of management practices on resource partitioning between

Compensatory competition occurs when growth of one species increases, while that of the other decreases (Shift B and C, Figure 3), and indicates re-allocation of available resources from one species for use by the other. This type of interaction occurred between natural vegetation and seedlings after fertilizer addition on the weed prone sites (Imo and Timmer 1999). Although fertilizer addition increased N availability to both species, growth and N uptake of neighboring non-crop vegetation were increased while those of seedlings declined. Nitrogen uptake and growth of the faster growing weeds were higher with carbon assimilates allocated primarily to aboveground, typical of plant responses to increased nutrient availability (Chapin 1987). With this allocation pattern, neighboring vegetation intercepted more light because of larger leaf area. Once these processes were established, a positive feedback mechanism between growth and resource capture (Grime 1979) presumably preempted available light and moisture from seedlings that in effect became suppressed. These responses confirm the hypothesis that competitive advantage of a species for one resource leads to an advantage for acquisition and use of the other resources as well

Comparison of carbon (*W*) and nitrogen (*U*) accumulation in seedlings shows that both *W* and *U* were reduced, but proportional reduction in *W* was larger than the corresponding reduction in *U*, hence *U*/*W* increased signifying excess uptake of N (Box II in Figure 3). Thus, N was not limiting, and the suppressed seedling growth was probably because of preemption of available light and or moisture by the weeds. The proportionally lower *W* than *U* in weeds after fertilization indicates that U/W also increased signifying a deficiency

Antagonistic competition occurs when performance of both species in mixture is reduced (Shift A, Figure 3), probably because consumption by each species reduces resource availability to each other. This interaction type has been demonstrated in conventionally fertilized seedlings planted on high competition sites without any weed control treatment (Imo and Timmer 1998). In this study, growth and N uptake of both the seedlings and neighboring vegetation were reduced exemplifying mutual antagonism due to low N availability that was not sufficient to support growth demand of both the seedlings and competing vegetation, thus reducing uptake of both species. Reduction in weed biomass was, however, small compared to decline in seedling biomass (Imo and Timmer 1999) exemplifying asymmetric competition with the trees being more sensitive to competitive effects than the weeds. Further analysis of the results from this study indicated antagonistic

target crops and neighboring non-crop weedy vegetation as discussed below.

**7.1 Compensatory competition** 

(; Kropff et al. 1984).

response (Box II in Figure 3).

**7.2 Antagonistic competition** 

Synergistic interactions occur when performance of one or both species mixture is more than their performance when grown alone (Shift C in Figure 3), and indicates beneficial effects of one species on the other. This interaction type was demonstrated in an alley agroforestry system involving *Leucaena* trees inter-cropped with maize (*Zea mays* L.) in Western Kenya (Imo and Timmer 2000). In this study, maize crop productivity was significantly higher than the sole crop, which was attributed to higher N availability mineralized from added mulch. Apparently, the beneficial effects of the mulch for the crop were higher than the negative effects of competition by the *Leucaena* hedgerows for the other resources (i.e. available light and moisture). The frequent and rapid pruning regime applied ensured return of mulch to the soil hence maintaining high N availability to the crop while ensuring minimal effects of light competition by the hedgerow trees. It was also noted that, although both biomass and nutrient content of the crop increased, the increase in the latter was larger than a corresponding increase in the former, which resulted in a typical of deficiency response (Imo and Timmer 1997) due to improved fertility from mulching.

#### **7.4 Maximizing crop nutrient use**

In view of the factors influencing nutrient partitioning between competing plants discussed in the previous sections, crop productivity can be maintained or improved by tilting the balance in resource capture in favor of the target crops. Previous developments on integrated weed management in forestry and agriculture ), and tree and crop inter-cropping in agroforestry (Ong and Huxley 1996) have repeatedly emphasized the need to incorporate a combination of management approaches for different objectives. Given that such integrated methods for crop management are linked to biological, environmental and economic considerations (Nambiar and Sands 1993), the problem often encountered by many decision-makers is related to determining the level of significant interference of beneficial value of non-crop management interventions. The vector competition analysis approach presented in this thesis is a simple decision-support tool to reconcile these variable objectives, especially in relation to nutrient management. In this model, both crop and noncrop interaction types and the associated nutritional mechanism s are evaluated in a systematic manner.

Figure 3 above has shown how this simple approach can be used to determine the partitioning of soil nutrients between neighboring plant species, and how efficiently the available nutrient resources are utilized on a site-specific and management specific-regime basis. Notice that the model emphasizes total nutrient use by the neighboring plant species according to the competitive production principle or facilitation discussed earlier. The lower-left portion (*antagonistic* interactions) in Figure 3 indicates competitive interactions between the species and demonstrates combinations in which total nutrient uptake and use by the two species in mixture is less than their total nutrient uptake and use when grown

Managing Competition for Nutrients in Agro-Ecosystems 207

(*W*) and nitrogen (*U*) content of the seedlings were maximized, seedling nutrient status can

Several conclusions can be drawn regarding the effectiveness of the vector competition model to elucidate complex interspecific plant growth and nutrient interactions in cropping systems in a simplified graphical format. First, integrating processes of resource acquisition and use within a conceptual graphical framework provides an approach for obtaining insight into the mechanisms involved in nutrient partitioning between competing plants. Thus, appropriate management interventions can be designed to alter allocation of soil nutrients to favor targeted plant crops or to maximize total nutrient use by the competing species. This conceptual approach clearly illustrates the complex nature of resource acquisition, uptake and use by plants, yet the overall effects on growth and nutrient uptake can be elucidated in a very simplified manner using vector competition analysis. Also, these interactions vary over time, and are regulated by feedback processes within the plant itself and affected by many environmental conditions. The conceptual graphical framework may also provide a simplified framework for simulating individual processes of resource availability, uptake and use by the competing species, thus enabling the understanding of how these processes change under specific environmental conditions and management

Specifically, the model enabled identification of the nature of interspecific growth and nutritional interactions in plants competing for the same resources in terms of antagonistic, compensatory or synergistic interactions, as well as discern phenomena of symmetrical interactions by isolating the most responsive species and sites under different management regimes. By comparing biomass production, nutrient accumulation, and nutrient concentration of the competing species, vector competition analysis facilitated characterization of interspecific interactions involving nutrient competition, synergistic nutrient interactions, or non-nutrient competition responses. These interpretations were based on nutritional effects, namely: nutrient dilution, sufficiency or accumulation as summarized in Box II of Figure 3. Thus, the model is an improvement over traditional competition indices based only on morphological parameters. Since biomass and nutritional responses were normalized to a standard reference treatment (100%), it was possible to compare treatments, sites and nutrient elements simultaneously. This standardization permitted ranking of weed problem sites, the model enabled identification of the nature of interspecific growth and nutritional interactions in plants competing for the same growth resources in terms of antagonistic, compensatory or

The model further helped identify phenomena of symmetrical interactions by isolating the most effective vegetation management practices over a wide range of ecological conditions. Thus, the model can provide farm managers with a decision-support mechanism for identifying and ranking weed problem sites, and permits recommendations regarding silvicultural treatments for specific sites. Appropriate management practices that favor resource allocation to target crops or maximize total resource use can be designed to improve productivity of the whole cropping system. The vector competition analysis approach can then be used as a decision-support tool to evaluate and rank such practices in

synergistic interactions as shown in Box I of Figure 3.

a systematic manner.

be considered sufficient for growth requirements under Compensatory competition

**8. Conclusions** 

regimes.

separately. This portion, therefore, represents inefficient exploitation of the site by the two species if planted as monocultures. The upper-right portion (*synergistic* interactions), on the other hand, shows complementary use of nutrients (or facilitation) between the neighboring species, and indicates that total nutrient uptake and use in by the plants in mixture is greater than their total uptake when grown separately, thus represents higher efficiency in resource capture and utilization. In practical terms, management practices that aim to suppress weed competition (as demonstrated in by Imo and Timmer 2001) operate within the lower-left portion, while inter-cropping practices such those in agroforestry (as demonstrated in Imo and Timmer 2000) aim at achieving species mixture within the upper-right portion of Figure 3. The main advantage of this approach is that it provides an instant evaluation of the advantage of intercropping or of specific management practices, and the possible processes involved.

In these studies, herbicide application on young black spruce plantations on high competition forest sites (Imo and Timmer 2001) eliminated weed competition, thus maximizing the amount of available resources to the target tree seedlings whose growth was increased. In theory, supplementing the resource pool of the crop by, for example, fertilization and irrigation should reduce competitive effects of the weeds. Results from this study, however, illustrated one major difficulty in this approach as was demonstrated by Imo and Timmer (2001) after fertilizing weed prone sites. Applied fertilizer was preferentially taken up by the weeds resulting in rapid weed growth and light preemption, consistent with the well established notion that weed resource use often increases more rapidly with added nutrients than that of the target crops.

In agroforestry, tree and crop inter-crops can be managed for spatial or temporal complementary use of nutrients to reduce competitive interactions while enhancing total nutrient use in the whole system (unshaded portion of Figure 3). Here, optimizing tree density and spacing is a key factor in complementary use of nutrients in these systems, and may be explained in terms of either the competitive production principle or facilitation (Vandermeer 1989). Several mechanisms may be associated with complementary use of nutrients in these systems such as nitrogen fixation since *Leucaena* is a N-fixing species (Kang *et al.* 1985), or the ability to access different nutrient pools or use of nutrients by the other species that would otherwise be lost to deep percolation.

These processes were confirmed from results with herbicide application on the high competition sites (Imo and Timmer 1999). Herbicides eliminated weed competition resulting in increased total resource availability to the seedlings. Light availability increased because of removal of aboveground weed biomass, while moisture and nitrogen availability increased presumably due to elimination of uptake by the weeds. Further, the dead weed material was returned to the soil as residue that mineralized to increase available N as was confirmed in the field. Vegetation removal presumably increased soil temperature as well and was favorable for rapid mineralization.

In the absence of weed competition, all available resources were utilized in seedling growth, thus the trees were able to achieve maximum carbon assimilation and nutrient uptake potential. Biomass production under this competition-free status was, therefore, maximized as supported by the significantly higher growth and nitrogen content of seedlings at the end of the growing season after herbicide application (Imo and Timmer 1999). Since both carbon (*W*) and nitrogen (*U*) content of the seedlings were maximized, seedling nutrient status can be considered sufficient for growth requirements under Compensatory competition

#### **8. Conclusions**

206 Weed Control

separately. This portion, therefore, represents inefficient exploitation of the site by the two species if planted as monocultures. The upper-right portion (*synergistic* interactions), on the other hand, shows complementary use of nutrients (or facilitation) between the neighboring species, and indicates that total nutrient uptake and use in by the plants in mixture is greater than their total uptake when grown separately, thus represents higher efficiency in resource capture and utilization. In practical terms, management practices that aim to suppress weed competition (as demonstrated in by Imo and Timmer 2001) operate within the lower-left portion, while inter-cropping practices such those in agroforestry (as demonstrated in Imo and Timmer 2000) aim at achieving species mixture within the upper-right portion of Figure 3. The main advantage of this approach is that it provides an instant evaluation of the advantage of intercropping or of specific management practices, and the possible processes

In these studies, herbicide application on young black spruce plantations on high competition forest sites (Imo and Timmer 2001) eliminated weed competition, thus maximizing the amount of available resources to the target tree seedlings whose growth was increased. In theory, supplementing the resource pool of the crop by, for example, fertilization and irrigation should reduce competitive effects of the weeds. Results from this study, however, illustrated one major difficulty in this approach as was demonstrated by Imo and Timmer (2001) after fertilizing weed prone sites. Applied fertilizer was preferentially taken up by the weeds resulting in rapid weed growth and light preemption, consistent with the well established notion that weed resource use often increases more

In agroforestry, tree and crop inter-crops can be managed for spatial or temporal complementary use of nutrients to reduce competitive interactions while enhancing total nutrient use in the whole system (unshaded portion of Figure 3). Here, optimizing tree density and spacing is a key factor in complementary use of nutrients in these systems, and may be explained in terms of either the competitive production principle or facilitation (Vandermeer 1989). Several mechanisms may be associated with complementary use of nutrients in these systems such as nitrogen fixation since *Leucaena* is a N-fixing species (Kang *et al.* 1985), or the ability to access different nutrient pools or use of nutrients by the

These processes were confirmed from results with herbicide application on the high competition sites (Imo and Timmer 1999). Herbicides eliminated weed competition resulting in increased total resource availability to the seedlings. Light availability increased because of removal of aboveground weed biomass, while moisture and nitrogen availability increased presumably due to elimination of uptake by the weeds. Further, the dead weed material was returned to the soil as residue that mineralized to increase available N as was confirmed in the field. Vegetation removal presumably increased soil temperature as well

In the absence of weed competition, all available resources were utilized in seedling growth, thus the trees were able to achieve maximum carbon assimilation and nutrient uptake potential. Biomass production under this competition-free status was, therefore, maximized as supported by the significantly higher growth and nitrogen content of seedlings at the end of the growing season after herbicide application (Imo and Timmer 1999). Since both carbon

rapidly with added nutrients than that of the target crops.

other species that would otherwise be lost to deep percolation.

and was favorable for rapid mineralization.

involved.

Several conclusions can be drawn regarding the effectiveness of the vector competition model to elucidate complex interspecific plant growth and nutrient interactions in cropping systems in a simplified graphical format. First, integrating processes of resource acquisition and use within a conceptual graphical framework provides an approach for obtaining insight into the mechanisms involved in nutrient partitioning between competing plants. Thus, appropriate management interventions can be designed to alter allocation of soil nutrients to favor targeted plant crops or to maximize total nutrient use by the competing species. This conceptual approach clearly illustrates the complex nature of resource acquisition, uptake and use by plants, yet the overall effects on growth and nutrient uptake can be elucidated in a very simplified manner using vector competition analysis. Also, these interactions vary over time, and are regulated by feedback processes within the plant itself and affected by many environmental conditions. The conceptual graphical framework may also provide a simplified framework for simulating individual processes of resource availability, uptake and use by the competing species, thus enabling the understanding of how these processes change under specific environmental conditions and management regimes.

Specifically, the model enabled identification of the nature of interspecific growth and nutritional interactions in plants competing for the same resources in terms of antagonistic, compensatory or synergistic interactions, as well as discern phenomena of symmetrical interactions by isolating the most responsive species and sites under different management regimes. By comparing biomass production, nutrient accumulation, and nutrient concentration of the competing species, vector competition analysis facilitated characterization of interspecific interactions involving nutrient competition, synergistic nutrient interactions, or non-nutrient competition responses. These interpretations were based on nutritional effects, namely: nutrient dilution, sufficiency or accumulation as summarized in Box II of Figure 3. Thus, the model is an improvement over traditional competition indices based only on morphological parameters. Since biomass and nutritional responses were normalized to a standard reference treatment (100%), it was possible to compare treatments, sites and nutrient elements simultaneously. This standardization permitted ranking of weed problem sites, the model enabled identification of the nature of interspecific growth and nutritional interactions in plants competing for the same growth resources in terms of antagonistic, compensatory or synergistic interactions as shown in Box I of Figure 3.

The model further helped identify phenomena of symmetrical interactions by isolating the most effective vegetation management practices over a wide range of ecological conditions. Thus, the model can provide farm managers with a decision-support mechanism for identifying and ranking weed problem sites, and permits recommendations regarding silvicultural treatments for specific sites. Appropriate management practices that favor resource allocation to target crops or maximize total resource use can be designed to improve productivity of the whole cropping system. The vector competition analysis approach can then be used as a decision-support tool to evaluate and rank such practices in a systematic manner.

Managing Competition for Nutrients in Agro-Ecosystems 209

permitting understanding of how these processes change under specific environmental conditions and management regimes. Such understanding can help to predict the potential role of various environmental, plant attributes and management practices in determining

Funding support from the Moi University Annual Research Grant (ARG) is greatly acknowledged. I am also grateful to many colleagues who have helped review and proof

Altieri, M . A. and Liebm an, M . (eds). 1988. *Weed Management in Agroecosystem s: Ecological* 

Axelsson, E. and Axelsson, B. 1986. Changes in carbon allocation patterns in spruce and pine trees following irrigation and fertilization. *Tree Physiology,* Vol. 2, pp 189-204. Barber, S. A. 1984. *Soil Nutrient Bioavailability: A M echanistic Approach*. John Wiley and Sons,

Begon, M ., Harper, J. L. and Townsend, C. R. 1996. *Ecology,* 3rd ed. Blackwell Sciences Ltd,

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**10. Acknowledgment** 

read this manuscript.

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**11. References** 

#### **9. Future research directions**

As discussed earlier, weed or non-crop vegetation management in forestry and agriculture involves a wide range of complex issues: social, economic, ecological and their interactions. Thus, one of the most important concerns is associated with the use of different analytical criteria for the assessment of alternative crop and non-crop management alternatives. For example, assessments based entirely on yield (a biological criterion) can lead to completely different conclusions regarding desirability of alternative cropping systems than from an assessment based purely on cash value (an economic criterion). Thus, it is possible to recommend an ecologically viable cropping system that lacks economic feasibility, or vice versa. How can the different management criteria be integrated into a single vector competition model? This should allow prescription of appropriate management practices that are acceptable using both ecological and economic criteria.

Secondly, while the principles articulated in this chapter can be of general application over a wide range of interspecific plant growth and nutrient interactions, interpretations thus far are limited to only a two-species mixture, one growing season, and harvesting the total aboveground plant parts for analysis. Thus the question is whether the vector competition analysis approach can be extended to evaluate mutual or competitive effects of more than two species in mixture. An answer to this question is important especially in field studies under natural conditions where individuals of more than two species often affect each other. The other area is whether the vector competition model is able to evaluate interspecific competition responses over several seasons, thus enabling understand long-term dynamics in competition responses. The approach taken by Imo and Timmer (1997) with traditional vector nutrient diagnosis using *Prosopis chilensis* seedlings can provide a basis for modeling time-dependent competition responses in vector competition analysis.

The other issue is whether plant parts (rather than the total aboveground plant) can give an indication of competitive responses. This is an important question especially in forestry and agroforestry where monitoring competition effects will normally not involve harvesting the whole crop. Traditional methods usually applied to assess tree growth such as foliage biomass and chemistry, stem diameter or leaf area index can be useful indices if incorporated into vector competition analysis to assess competition responses. Some of these methods have been applied successfully with the traditional vector nutrient diagnosis (e.g. Imo and Timmer 2000).

Finally, the process model (Figure 2) integrating processes of acquisition, uptake and use of different resources raises a number of fundamental questions in terms of mechanisms involved in partitioning of nutrients between competing plants that need further investigation. For example: the critical *'competition-free'* period during which all nutrient resources should be available for the target crops and the timing of management interventions should be investigated further to help design management interventions to maximize productivity of the target crop. Relating growth and nutritional interactions to environmental or plant variables that can be manipulated to provide further insight of limiting resources. For example, the relative effects of moisture availability on photosynthesis and nutrient uptake need to be quantified. There is a need to integrate vector competition analysis into detailed ecophysiological simulation models for the individual processes of resource availability, uptake and utilization by the competing species thus permitting understanding of how these processes change under specific environmental conditions and management regimes. Such understanding can help to predict the potential role of various environmental, plant attributes and management practices in determining the outcome of competitive interactions.

#### **10. Acknowledgment**

Funding support from the Moi University Annual Research Grant (ARG) is greatly acknowledged. I am also grateful to many colleagues who have helped review and proof read this manuscript.

#### **11. References**

208 Weed Control

As discussed earlier, weed or non-crop vegetation management in forestry and agriculture involves a wide range of complex issues: social, economic, ecological and their interactions. Thus, one of the most important concerns is associated with the use of different analytical criteria for the assessment of alternative crop and non-crop management alternatives. For example, assessments based entirely on yield (a biological criterion) can lead to completely different conclusions regarding desirability of alternative cropping systems than from an assessment based purely on cash value (an economic criterion). Thus, it is possible to recommend an ecologically viable cropping system that lacks economic feasibility, or vice versa. How can the different management criteria be integrated into a single vector competition model? This should allow prescription of appropriate management practices

Secondly, while the principles articulated in this chapter can be of general application over a wide range of interspecific plant growth and nutrient interactions, interpretations thus far are limited to only a two-species mixture, one growing season, and harvesting the total aboveground plant parts for analysis. Thus the question is whether the vector competition analysis approach can be extended to evaluate mutual or competitive effects of more than two species in mixture. An answer to this question is important especially in field studies under natural conditions where individuals of more than two species often affect each other. The other area is whether the vector competition model is able to evaluate interspecific competition responses over several seasons, thus enabling understand long-term dynamics in competition responses. The approach taken by Imo and Timmer (1997) with traditional vector nutrient diagnosis using *Prosopis chilensis* seedlings can provide a basis for modeling

The other issue is whether plant parts (rather than the total aboveground plant) can give an indication of competitive responses. This is an important question especially in forestry and agroforestry where monitoring competition effects will normally not involve harvesting the whole crop. Traditional methods usually applied to assess tree growth such as foliage biomass and chemistry, stem diameter or leaf area index can be useful indices if incorporated into vector competition analysis to assess competition responses. Some of these methods have been applied successfully with the traditional vector nutrient diagnosis (e.g.

Finally, the process model (Figure 2) integrating processes of acquisition, uptake and use of different resources raises a number of fundamental questions in terms of mechanisms involved in partitioning of nutrients between competing plants that need further investigation. For example: the critical *'competition-free'* period during which all nutrient resources should be available for the target crops and the timing of management interventions should be investigated further to help design management interventions to maximize productivity of the target crop. Relating growth and nutritional interactions to environmental or plant variables that can be manipulated to provide further insight of limiting resources. For example, the relative effects of moisture availability on photosynthesis and nutrient uptake need to be quantified. There is a need to integrate vector competition analysis into detailed ecophysiological simulation models for the individual processes of resource availability, uptake and utilization by the competing species thus

that are acceptable using both ecological and economic criteria.

time-dependent competition responses in vector competition analysis.

**9. Future research directions** 

Imo and Timmer 2000).


Managing Competition for Nutrients in Agro-Ecosystems 211

Malik, V. and Timmer, V. R. (1998). Biomass partitioning and nitrogen retranslocation in

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**11** 

**Interrelationships Among Weed Management in** 

**Coffee Plantation and Soil Physical Quality** 

Cezar Francisco Araujo-Junior1, Moacir de Souza Dias Junior2, Elifas Nunes de Alcântara3, Paulo Tácito Gontijo Guimarães3

*3Minas Gerais State Corporation for Agriculture and Livestock Research,* 

*4Soil Water and Environment Section, Department of Agricultural Engineering,* 

Coffee bean is one of the most important commodities produced in Brazil. Brazil is responsible for the supply of about 30% of world coffee bean market. Coffee related enterprises are a major economic driver in the regions where it is cultivated in Brazil and elsewhere as it generates jobs, provide income and stimulate development. However, for greater coffee agribusiness competitiveness , it is necessary to meet social-environmental

Among several social-environmental expectations met by coffee farmers internationally, biodiversity conservation, sustainable management and subsequent improvement or maintenance of soil structure in order to avoid or minimize additional soil compaction resulting from inadequate management are vital (Brazil Specialty Coffee Association [BSCA], 2005). These requirements help the coffee farmers develop eco-friendly production practices/guidelines: environmentally appropriate, economically viable, socially beneficial and culturally acceptable in their production system. These production guidelines, help in balancing environmental and socio-economic factors in coffee bean

Amongst all agronomic practices involved in coffee production, the weed management strategy/system is one of the most intensive in coffee bean production and critical to ecofriendly management ranging from two to five operations per year. The adopted weed management system in coffee plantations can have major effects on the soil environment,

requirements expected by international consumers (Araujo-Junior et al., 2008).

**1. Introduction** 

production.

and Ayodele Ebenezer Ajayi4 *1Agronomic Institute of Paraná – IAPAR,* 

*2Department of Soil Science,* 

*EPAMIG, CTSM* 

*1,2,3Brazil 4Nigeria* 

*Rodovia Celso Garcia Cid, Londrina, State of Paraná,* 

*Federal University of Technology Akure Ondo State,* 

*Federal University of Lavras, Lavras, State of Minas Gerais,* 

Estimation of competition effects. *Netherlands Journal of Agricultural.Sciences*. Vol. 31, pp 1 - 11.


## **Interrelationships Among Weed Management in Coffee Plantation and Soil Physical Quality**

Cezar Francisco Araujo-Junior1, Moacir de Souza Dias Junior2, Elifas Nunes de Alcântara3, Paulo Tácito Gontijo Guimarães3 and Ayodele Ebenezer Ajayi4 *1Agronomic Institute of Paraná – IAPAR, Rodovia Celso Garcia Cid, Londrina, State of Paraná, 2Department of Soil Science, Federal University of Lavras, Lavras, State of Minas Gerais, 3Minas Gerais State Corporation for Agriculture and Livestock Research, EPAMIG, CTSM 4Soil Water and Environment Section, Department of Agricultural Engineering, Federal University of Technology Akure Ondo State, 1,2,3Brazil 4Nigeria* 

#### **1. Introduction**

212 Weed Control

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Wagner, R. G. and Radosevich, S. R. (1991). Neighborhood predictors of interspecific

Wang, D., Bormann, F. H., Lugo, A. E. and Bowden, R. D. (1991). Comparison of nutrient

Willey, R. W. (1979). Intercropping - its importance and its research needs. Part I. Competition and yield advantages. *Field Crop Abstracts*, Vol. 32, pp 73-85. Wilson, J.B. (1988). Shoot competition and root competition. *Journal of Applied Ecology,* Vol*.* 

Wilson, S. D. and Tilman, D. (1991). Components of plant competition along an experimental gradient of nitrogen availability. *Ecology*, Vol. 73, pp 1050-1065. Woods, P. V., Nambiar, E. K. S. and Smethurst, P. J. (1992). Effect of annual weeds on water

competition in young Douglas-fir plantations. *Canadian Journal of Forest Research*,

use efficiency and biomass production in five tropical tree taxa. Forest Ecology and Management, Vol. 46, pp 1-21. Weldon, C. W., Slauson, W. L. and Ward, R. T. (1988). Competition and abiotic stress shrubs in northwest Colorado. *Ecology* Vol.

and nitrogen availability to *Pinus radiata* trees in a young plantation. Forest Ecology

America. *Canadian Journal of Forest Research*, Vol. 23, pp 2317-2327.

competition experiments. *Journal of Ecology*, Vol. 77, pp 975-988.

31, pp 1 - 11.

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*Management*, Vol. 46, pp 59 - 102.

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and Management, Vol. 48, pp 145-163.

Estimation of competition effects. *Netherlands Journal of Agricultural.Sciences*. Vol.

Monographs in Population Biology 20, Princeton University Press, Princeton, New

Driessche, R. (ed.). *Mineral Nutrition of Conifer Seedlings*. CRC Press, Boca Raton. pp. 113-134. Trenbath, B. R. 1976. Plant interactions in mixed crop communities. *In* Stelly, M . (ed.). *Multiple Cropping*. American Society of Agronomy, Special

den Driessche, R. (ed.). *Mineral Nutrition of Conifer Seedlings*. CRS Press, Boca Raton,

Coffee bean is one of the most important commodities produced in Brazil. Brazil is responsible for the supply of about 30% of world coffee bean market. Coffee related enterprises are a major economic driver in the regions where it is cultivated in Brazil and elsewhere as it generates jobs, provide income and stimulate development. However, for greater coffee agribusiness competitiveness , it is necessary to meet social-environmental requirements expected by international consumers (Araujo-Junior et al., 2008).

Among several social-environmental expectations met by coffee farmers internationally, biodiversity conservation, sustainable management and subsequent improvement or maintenance of soil structure in order to avoid or minimize additional soil compaction resulting from inadequate management are vital (Brazil Specialty Coffee Association [BSCA], 2005). These requirements help the coffee farmers develop eco-friendly production practices/guidelines: environmentally appropriate, economically viable, socially beneficial and culturally acceptable in their production system. These production guidelines, help in balancing environmental and socio-economic factors in coffee bean production.

Amongst all agronomic practices involved in coffee production, the weed management strategy/system is one of the most intensive in coffee bean production and critical to ecofriendly management ranging from two to five operations per year. The adopted weed management system in coffee plantations can have major effects on the soil environment,

structure.

coffee powder.

sampling (Table 1).

**2. Site description and characterization** 

1470 mm (Alcântara & Ferreira, 2000a,b).

Interrelationships Among Weed Management in Coffee Plantation and Soil Physical Quality 215

when subjected to external pressure (Dias Junior & Pierce, 1996). Thus, soil water acts as a lubricant and as a binder between the soils particles, affecting the structural stability and strength of geological materials and soil (Topp & Ferré, 2002).Therefore, knowledge of the interrelationship of weed management and its influence on soil structure is essential to establish sustainable management of the soil in coffee plantations. Mentioned previously, soil structure greatly influences the distribution of the pore size, water and gas movement into the soil, soil strength and soil water retention. Few studies have been investigated the effect of weed management system on soil physical quality. In this book chapter, changes in soil physical attributes (soil bulk density, microporosity, macroporosity, total porosity, soil water retention curve, precompression stress and load bearing capacity) are studied in relation to weed management system in coffee plantation. Load bearing capacity models were developed to assess the influence of the different weed management systems on soil

The study site was the Experimental Farm of the Minas Gerais State Department for Agriculture and Livestock Research [EPAMIG] (20°55'00'' S, 47°07'10'' W, ≈ 885 m) in the São Sebastião do Paraíso County, State of Minas Gerais, Brazil. The farm has been used for weed control management system experiments since 1977. The average annual temperature of the area is 20.8 °C, (27.6 °C maximum, 14.1 °C, minimum) and the average annual rainfall is

The soil in the experimental area is derived from basalt and was classified as a Dystroferric Red Latosol according to the Brazilian Soil Classification System (Brazilian Agricultural Research Council [Embrapa], 2006); Oxisol according to USDA soil taxonomy (Soil Survey Staff, 1998) and Ferralsol (Food and Agriculture Organization [FAO], 2006). Analysis of soil collected close to experimental area under natural forest showed that Dystroferric Red Latosol contains 570 g kg-1 clay, 230 g kg-1 silt and 200 g kg-1 sand, in the top 0 to 30 cm depth and also have a homogeneous structure throughout the profile. The soil has low soil bulk density, high total porosity and macroporosity and exhibit a granular structure like a

**2.1 Weed control management systems and conduction of the coffee plantation** 

Seven weed management systems which had been in use for about 30 years in the coffee plantation were considered in this study (Photo 1; Table 1). The management systems were established in a randomized complete block design with three replicates, each plot 36m in length. The experimental design further included a split-plot with each weed management system in use in three interrows as the main-plot factor, and the soil sampling depths (0–3, 10–13 and 25–28 cm) as a split-plot. In the areas under the coffee canopy, the weeds are managed as needed utilizing manual hoeing or with the application of herbicides. The successful weed management system utilized in the coffee plantation experimental area for the 30 years period prior to treatment establishment influenced the number of operations needed as well as the density and diversity of weeds found in the area at the time of the

affecting physical, chemical and biological conditions, resulting in changes soil compressive behavior and load bearing capacity affecting yield potential in coffee plantations (Araujo-Junior et al., 2008; 2011).

Appropriate weed management systems utilized between coffee rows would help in minimizing soil degradation by erosion (Carvalho et al., 2007), reducing compaction and improving soil workability and machines trafficability (Araujo-Junior et al., 2008, 2011). Weed plants utilized as cover crops residues can be left on the soil surface similar to a cereal stubble mulch to protect against evaporations and erosion (Hillel, 1980; Faria et al., 1998). In a newly developed orchard, Yang et al. (2007) observed that the application of herbicides and tillage favored soil erosion. Yang et al. (2007) pointed out that chemical and mechanical methods are the dominant weed control practices in many production systems due to its effectiveness, but noted on the other hand, that weed presence during the rainy season prevented soil erosion. Studies conducted in tropical conditions showed that mechanical and chemical methods for weed control on coffee plantations had a great influence on the soil compaction state (Kurachi & Silveira, 1984; Alcântara & Ferreira, 2000b; Araujo-Junior et al., 2008, 2011), soil surface crust formation, erosion and coffee yield (Silveira et al., 1985; Alcântara & Ferreira, 2000a).

Soil compaction processes are one of the most important causes of soil degradation and changes on soil structure, affecting soil physical quality. Compaction is a reduction of the volume of a given mass of soil and ceases when the soil structure has become strong enough to withstand the applied stress without further failure, in compacted soils volume of pores is reduced (Dexter, 2004). Soil structure is defined as the arrangement of the solid particles and of the pore space located between them (Marshall, 1962). Also, soil structure may be defined as the combination or arrangement of primary soil particles into secondary units or peds. The secondary units are characterized on the basis of size, shape and grade (Soil Science Society American – SSSA, 2008). Structural changes to the soil could alter their physical quality, thereby altering the soil workability and trafficability, infiltrate rate, drainage, water redistribution and water retention, as a function of pore-size distribution. Due to effects of soil residue coverage on soil, the weed management system has direct influence on soil structure management and physical quality and must therefore be considered from both agronomic and environmental viewpoints.

Structural changes resulting from the traditional bare ground weed management system stand out among the main adverse effects of this practice (Kurachi & Silveira, 1984; Silveira & Kurachi, 1985; Faria et al., 1998; Alcântara & Ferreira, 2000a; Araujo-Junior et al., 2008, 2011). Structural changes due to improper soil management make coffee plants more susceptible to dry conditions by the reduction of infiltration rate and gas flow into the soil profile. Inadequate soil aeration and nutritional deficiency, decreases root growth and enhancing soil erosion, resulting in a compromise of the soil and environmental quality in agro-forestry production (Horn, 1988; Dias Junior et al., 2005; Vogeler et al., 2006).

The water content in the soil profile determines the reaction to tillage, and among the physical properties, soil moisture is the most important for soil-machine interactions, since it controls the consistency of the soil (Hillel, 1980) and governs the amount of soil deformation when subjected to external pressure (Dias Junior & Pierce, 1996). Thus, soil water acts as a lubricant and as a binder between the soils particles, affecting the structural stability and strength of geological materials and soil (Topp & Ferré, 2002).Therefore, knowledge of the interrelationship of weed management and its influence on soil structure is essential to establish sustainable management of the soil in coffee plantations. Mentioned previously, soil structure greatly influences the distribution of the pore size, water and gas movement into the soil, soil strength and soil water retention. Few studies have been investigated the effect of weed management system on soil physical quality. In this book chapter, changes in soil physical attributes (soil bulk density, microporosity, macroporosity, total porosity, soil water retention curve, precompression stress and load bearing capacity) are studied in relation to weed management system in coffee plantation. Load bearing capacity models were developed to assess the influence of the different weed management systems on soil structure.

#### **2. Site description and characterization**

214 Weed Control

affecting physical, chemical and biological conditions, resulting in changes soil compressive behavior and load bearing capacity affecting yield potential in coffee plantations (Araujo-

Appropriate weed management systems utilized between coffee rows would help in minimizing soil degradation by erosion (Carvalho et al., 2007), reducing compaction and improving soil workability and machines trafficability (Araujo-Junior et al., 2008, 2011). Weed plants utilized as cover crops residues can be left on the soil surface similar to a cereal stubble mulch to protect against evaporations and erosion (Hillel, 1980; Faria et al., 1998). In a newly developed orchard, Yang et al. (2007) observed that the application of herbicides and tillage favored soil erosion. Yang et al. (2007) pointed out that chemical and mechanical methods are the dominant weed control practices in many production systems due to its effectiveness, but noted on the other hand, that weed presence during the rainy season prevented soil erosion. Studies conducted in tropical conditions showed that mechanical and chemical methods for weed control on coffee plantations had a great influence on the soil compaction state (Kurachi & Silveira, 1984; Alcântara & Ferreira, 2000b; Araujo-Junior et al., 2008, 2011), soil surface crust formation, erosion and coffee yield (Silveira et al., 1985;

Soil compaction processes are one of the most important causes of soil degradation and changes on soil structure, affecting soil physical quality. Compaction is a reduction of the volume of a given mass of soil and ceases when the soil structure has become strong enough to withstand the applied stress without further failure, in compacted soils volume of pores is reduced (Dexter, 2004). Soil structure is defined as the arrangement of the solid particles and of the pore space located between them (Marshall, 1962). Also, soil structure may be defined as the combination or arrangement of primary soil particles into secondary units or peds. The secondary units are characterized on the basis of size, shape and grade (Soil Science Society American – SSSA, 2008). Structural changes to the soil could alter their physical quality, thereby altering the soil workability and trafficability, infiltrate rate, drainage, water redistribution and water retention, as a function of pore-size distribution. Due to effects of soil residue coverage on soil, the weed management system has direct influence on soil structure management and physical quality and must therefore be considered from both

Structural changes resulting from the traditional bare ground weed management system stand out among the main adverse effects of this practice (Kurachi & Silveira, 1984; Silveira & Kurachi, 1985; Faria et al., 1998; Alcântara & Ferreira, 2000a; Araujo-Junior et al., 2008, 2011). Structural changes due to improper soil management make coffee plants more susceptible to dry conditions by the reduction of infiltration rate and gas flow into the soil profile. Inadequate soil aeration and nutritional deficiency, decreases root growth and enhancing soil erosion, resulting in a compromise of the soil and environmental quality in agro-forestry production (Horn, 1988; Dias Junior et al., 2005; Vogeler et al.,

The water content in the soil profile determines the reaction to tillage, and among the physical properties, soil moisture is the most important for soil-machine interactions, since it controls the consistency of the soil (Hillel, 1980) and governs the amount of soil deformation

Junior et al., 2008; 2011).

Alcântara & Ferreira, 2000a).

agronomic and environmental viewpoints.

2006).

The study site was the Experimental Farm of the Minas Gerais State Department for Agriculture and Livestock Research [EPAMIG] (20°55'00'' S, 47°07'10'' W, ≈ 885 m) in the São Sebastião do Paraíso County, State of Minas Gerais, Brazil. The farm has been used for weed control management system experiments since 1977. The average annual temperature of the area is 20.8 °C, (27.6 °C maximum, 14.1 °C, minimum) and the average annual rainfall is 1470 mm (Alcântara & Ferreira, 2000a,b).

The soil in the experimental area is derived from basalt and was classified as a Dystroferric Red Latosol according to the Brazilian Soil Classification System (Brazilian Agricultural Research Council [Embrapa], 2006); Oxisol according to USDA soil taxonomy (Soil Survey Staff, 1998) and Ferralsol (Food and Agriculture Organization [FAO], 2006). Analysis of soil collected close to experimental area under natural forest showed that Dystroferric Red Latosol contains 570 g kg-1 clay, 230 g kg-1 silt and 200 g kg-1 sand, in the top 0 to 30 cm depth and also have a homogeneous structure throughout the profile. The soil has low soil bulk density, high total porosity and macroporosity and exhibit a granular structure like a coffee powder.

#### **2.1 Weed control management systems and conduction of the coffee plantation**

Seven weed management systems which had been in use for about 30 years in the coffee plantation were considered in this study (Photo 1; Table 1). The management systems were established in a randomized complete block design with three replicates, each plot 36m in length. The experimental design further included a split-plot with each weed management system in use in three interrows as the main-plot factor, and the soil sampling depths (0–3, 10–13 and 25–28 cm) as a split-plot. In the areas under the coffee canopy, the weeds are managed as needed utilizing manual hoeing or with the application of herbicides. The successful weed management system utilized in the coffee plantation experimental area for the 30 years period prior to treatment establishment influenced the number of operations needed as well as the density and diversity of weeds found in the area at the time of the sampling (Table 1).

Interrelationships Among Weed Management in Coffee Plantation and Soil Physical Quality 217

1. No-weed control between coffee rows (NWC): the weeds plants were left to grow freely between the coffee rows, thus, high density and diversity of the weed plants were

2. Hand hoeing (HAHO): performed with the aid of a hoe, when the weed reached 45 cm height. These operations were carried out eight times between January 2006 to

3. Post-emergence herbicide (POSH): glyphosate, N-(fosfonometil) glicina, was applied with the aid of a knapsack sprayer, at a rate 2.0 L ha-1 of commercial product and 0.72 Kg active ingredient ha-1, soluble concentrate formulation 0,36 Kg L-1, and applied with spray volume of 400 L ha-1, eight applications were performed between January

4. Mechanical mowing (MMOW): the weed plants were mowed with a mechanical mower Kamaq® model 132 KD, with cutting width of 1.32 m and 340 Kg of static

5. Rotary-tilling (ROTI): the axis has five flanges, as two sides with three knives and threes

6. Coffee tandem disk harrow (CTDH): the equipment is composed by two sections in tandem, each section is equipped with seven flat disks with cut width of 1.3 m and

7. Pre-emergence herbicide (HPRE): oxyfluorfen (2-cloro-a,a,a-trifluoro-p-tolyl-3-ethoxy-4 nitrophenyl ether), was applied with the aid of a knapsack sprayer, at a rate 2.0 L ha-1 of commercial product and 0.48 Kg active ingredient ha-1 in the soluble concentrate formulation 0.24 Kg L-1, and applied with spray volume of 400 L ha-1 (Rodrigues & Almeida, 2005) six applications were performed from January 2006 to December 2007

A

edges with six knives. It's worked at 10 cm depth incorporating the weeds.

(Table 1). For this application, soil surface was free of the vegetation.

found in the plots at the time of sampling (Table 1).

December 2007 (Table 1).

mass

2006 and December 2007 (Table 1).

static mass 300 kg. It's worked at 7 cm depth.


Table 1. Weed management system, numbers of operations performed between January 2006 and December 2007, species, common name and genus observed in an experimental area at the time of soil sampling.

*Marmodica charantia* L., melão-de-são-

*Digitaria insularis* (L.) Mea ex Ekman, capim-

*Panicum maximum* Jacq., capim-colonião,

*Nicandra physaloides* Gaertn., joá-de-capote,

*Ipomoea acuminata*, corda-de-viola,

*Ephorbia heterophylla* L., leiteira,

*Amaranthus viridis,* caruru-de-mancha,

*Digitaria horizontalis* Willd., capim-colchão,

*Cenchrus echinatus* L., timbête, Poaceae.

*Cyperus rotundus L,* tiririca, Cyperaceae; *Cynodon dactylon* (L.) Pers., grama-seda,

Bidens pilosa L., picão-preto, Compositae.

*Amaranthus viridis* (caruru-de-mancha,

*Commelina benghalensis* L. (trapoeraba,

*Cyperus rotundus L,* tiririca, Cyperaceae; *Cynodon dactylon* (L.) Pers., grama-seda,

*Amaranthus viridis,* caruru-de-mancha,

*Brachiaria decumbens* Stapf., braquiária,

*Cyperus rotundus L,* tiririca, Cyperaceae; *Cynodon dactylon* (L.) Pers., grama-seda,

*Brachiaria plantaginea* (Link) Hitchc.,

caetano, Cucurbitaceae; *Ephorbia heterophylla* L., leiteira,

Euphorbiaceae;

Poaceae;

Solanaceae;

Convolvulaceae;

Amaranthaceae

Euphorbiaceae;

Poaceae;

Poaceae;

Poaceae;

Poaceae.

Poaceae;

sampling

Pre-emergence herbicide 6 Without weed plants at the moment of the

Table 1. Weed management system, numbers of operations performed between January 2006 and December 2007, species, common name and genus observed in an experimental

Amaranthaceae);

Commelinaceae).

Amaranthaceae;

marmelada, Poaceae.

amargoso, Poaceae;

Weed management Operations Species weed/common name/families

No-Weed Control (NWC) 0

Hand Hoeing (HAHO) 8

Rotary Tilling (ROTI) 8

(POSH) <sup>8</sup>

(MMOW) <sup>9</sup>

Disk Harrowing (CTDH) 8

area at the time of soil sampling.

Post-Emergence Herbicide

Mechanical Mowing


Interrelationships Among Weed Management in Coffee Plantation and Soil Physical Quality 219

The equipment used to apply tillage treatments was mounted on a two-wheel-drive coffee tractor Valmet® model 68. This tractor has engine capacity of 61.9 CV (45 kW), total weight of tractor with equipment was 38.25 kN, front tyres 6-16 (15.24 cm of width x 40.64 cm rim diameter) in inflation pressure 172 kPa and rear tyres 12.4-R28 in inflation pressure 124 kPa. To determine the maximum stress applied by each tyre, the static weight distribution was considered to be 35% for the front tyres and 65% for the rear tyres. The critical volumetric water content for the traffic of the tractor, were considered as those stress that don't exceed the internal strength of the soil expresses in the precompression stress (Araujo-Junior et al., 2011).

In each weed management system, 15 undisturbed soil samples (early December, 2007) were collected randomly in the traffic line of the machines and equipments, 80 cm from stems of the coffee trees in the 0–3, 10–13 and 25–28 cm layers, totaling 315 soil samples (15 samples x 3 depths x 7 management system). Additional fifteen samples at each depth were collected in a Dystroferric Red Latosol under natural forest (NAFT) adjacent to coffee cultivation, 45 undisturbed soil samples (15 samples x 3 depths) were collected which served as a reference of soil physical quality. The undisturbed soil samples were collected using a cylindrical Uhland sampler (Uhland, 1949) and aluminum rings, 2.54 cm high by 6.35 cm diameter (Photo 2). The Uhland sampler is pressed into the soil sample in the 0–3 cm depth. To collect the sample at 10–13 cm and 25–28 cm depths, the sampling pit were carefully dug to depths

Photo 2. Uhland undisturbed soil sampler components. 1 – driving assembly; 2 – aluminum cylinder room ; 3- graphite lubricant; 4 – plastic film to cover soil sample; 5 – measuring

1 2 4

5

3

6

tape; 6 – mattock for digging soil sampling pit.

**2.2 Soil sampling** 

10 cm and 25 cm.

C

Photo 1. Overview of experimental area at the time of the sampling in December 2007. (A) weedy control between coffee rows; (B) pre-emergence herbicide. Note sheet erosion (B) and decreased infiltration due to surface crusting (C) between coffee rows.

The equipment used to apply tillage treatments was mounted on a two-wheel-drive coffee tractor Valmet® model 68. This tractor has engine capacity of 61.9 CV (45 kW), total weight of tractor with equipment was 38.25 kN, front tyres 6-16 (15.24 cm of width x 40.64 cm rim diameter) in inflation pressure 172 kPa and rear tyres 12.4-R28 in inflation pressure 124 kPa. To determine the maximum stress applied by each tyre, the static weight distribution was considered to be 35% for the front tyres and 65% for the rear tyres. The critical volumetric water content for the traffic of the tractor, were considered as those stress that don't exceed the internal strength of the soil expresses in the precompression stress (Araujo-Junior et al., 2011).

#### **2.2 Soil sampling**

218 Weed Control

B

C Photo 1. Overview of experimental area at the time of the sampling in December 2007. (A) weedy control between coffee rows; (B) pre-emergence herbicide. Note sheet erosion (B) and

decreased infiltration due to surface crusting (C) between coffee rows.

In each weed management system, 15 undisturbed soil samples (early December, 2007) were collected randomly in the traffic line of the machines and equipments, 80 cm from stems of the coffee trees in the 0–3, 10–13 and 25–28 cm layers, totaling 315 soil samples (15 samples x 3 depths x 7 management system). Additional fifteen samples at each depth were collected in a Dystroferric Red Latosol under natural forest (NAFT) adjacent to coffee cultivation, 45 undisturbed soil samples (15 samples x 3 depths) were collected which served as a reference of soil physical quality. The undisturbed soil samples were collected using a cylindrical Uhland sampler (Uhland, 1949) and aluminum rings, 2.54 cm high by 6.35 cm diameter (Photo 2). The Uhland sampler is pressed into the soil sample in the 0–3 cm depth. To collect the sample at 10–13 cm and 25–28 cm depths, the sampling pit were carefully dug to depths 10 cm and 25 cm.

Photo 2. Uhland undisturbed soil sampler components. 1 – driving assembly; 2 – aluminum cylinder room ; 3- graphite lubricant; 4 – plastic film to cover soil sample; 5 – measuring tape; 6 – mattock for digging soil sampling pit.

Interrelationships Among Weed Management in Coffee Plantation and Soil Physical Quality 221

The bulk densities values from soil samples following post-emergence herbicide and mechanical mowing weed management systems at all the depths, and those from the rotary tilling managements (10–13 and 25–28 cm depths), coffee tandem disk harrowing and preemergence herbicide (0–3 and 10–13 cm depths) were considered higher than critical values for clay soils (1.2 Mg m-3) in agreement with other studies including Derpsch et al. (1991); Dexter (2004); Severiano et al. (2011) and critical values for coffee root growth in Dystropherric Red Latosol (Araujo-Junior et al., 2011). The disk harrowing and preemergence herbicide weed management systems promote the crusting in the soil surface

After 30 years of conventional coffee cultivation, the total organic carbon contents were markedly affected by weed control between the coffee rows in the traffic line (Figure 1B). Total organic carbon contents were greater for native forest compared to the coffee plantation at all depths, except at 0–3 cm following mechanical mowing, which had the same total organic carbon (Fig 1A.) this is understandable considering that weed control with mechanical mower cut the weed in all the interrows and concentrate weed near the edge of the equipment increasing the total soil organic carbon in this region, where soil

The next highest contents of total organic carbon were found in the soils samples from handhoed (CAPM), post-emergence herbicide (HPOS), rotary tilling (ENRT) followed by noweed control (SCAP), disk harrow (GRAD), and lowest was found in the soil from preemergence herbicide (Figure 1B). This low organic carbon condition was obviously due to the lack weed on the soil surface in the pre-emergence herbicide management system in agreements with other reports from tropical soil environments (Faria et al., 1998; Alcântara

Published results reveal that weedy soil covers between coffee rows had great influences on the dynamics of total organic carbon content. Plant residues may influence the light soil fraction and thus the organic carbon content as reported by Ding et al. (2006) when these authors assessed the effect of cover crop management on chemical and structural composition of soil organic matter. The constant use of the pre-emergence herbicide for weed control in Dystroferric Red Latosol clay decreases significantly the total organic carbon content in the soil surface, because of the prevalence of soil without weed between the coffee rows. The effect of weed control with pre-emergence herbicide on total soil organic carbon was observed also in the 10–13 cm layer due the absence of weed roots

The different weed management system applied to coffee interrows influenced the soil bulk density and organic carbon content of the Latosol, in the 25-28 cm layer (Fig. 1A and 1B), when compared with the soil under natural forest (NAFT); however, when the soil samples were collected in center of the interrows, differences were not observed (Araujo-Junior et al., 2011). These authors observed that different weed management systems used in the interrows did not influenced soil bulk density and total organic carbon content of the Latosol, in the 25–28 cm layer, compared to the soil under natural forest. In our study, it is important highlight that the soil samples were collected in the traffic line of machines, and the total soil organic carbon content did not differ among the weed management systems in coffee plantation at the 25–28 cm depth (Figure 1B). However, Latosol samples from natural forest had greater total organic carbon content when compared to the soil under the

(Photos 1B and 1C) and increase the values of the bulk density (Fig. 1A).

samples were collected.

(Figure 1B).

& Ferreira, 2000b; Araujo-Junior et al., 2011).

#### **2.3 Laboratory analysis**

In the laboratory, a knife was used to trim the soil from the ends to the exact size of the rings. This was used to determine the volume of soil and its weight. The scrapped soil materials were later used for physical (particle size distribution, soil particle density) and chemical (total soil organic carbon content) characterization of the soil. The soil particle-size distribution was determined by the pipette method (Day, 1965), by chemical dispersion with a 50 mL 0,1 N sodium hydroxide solution, in contact with the samples for 24 hours. Physical dispersion was accomplished by slowly rotating in a Wiegner mixer that shakes 30 times per minute, adding 20 g coarse sand (Grohmann & Raij, 1977). Soil particle density was determined by the pycnometer method (Blake & Hartge, 1986b). The total soil organic carbon content were determined by wet combustion with carbon oxidation adding 10 mL of digest solution (Na2Cr2O7 2H2O 4 N + H2SO4 10 N) (Raij et al., 1987).

Three soil samples for each plot and at the sampled depths were saturated by capillary with distilled water, and equilibrated to a matric potential (Ψm) of - 2 and - 6 kPa, on a suction table (Romano et al., 2002) and - 10, - 33, - 100, - 500 and - 1500 kPa in a ceramic plate inside a pressure chamber (Soilmoisture Equipment Crop., P.O. Box 30025, Santa Barbara, CA 93105) (Dane & Hopmans, 2002). The soil-water retention data were fitted through the van Genuchten (1980) model with Mualen (1976) constraint. The – 6 kPa matric potential was used to separate the pores with effective diameter greater than 50 μm, drained from the cores (macropores). Water retained at this matric potential is considered as a measure of microporosity.

Precompression stresses were determined from the undisturbed soil samples submitted to uniaxial compression tests. The soil samples were kept within the sleeves of the coring cylinder, which were placed in the compression cell, and afterwards subjected pneumatically (Durham Geo Slope Indicator, USA, model S-450 Terraload®) to pressures 25, 50, 100, 200, 400, 800 and 1600 kPa to reach equilibrium (Bowles, 1986). During each test, a normal vertical stress was applied until 90% of the maximum deformation was reached and then the pressure is increased to the next level (Taylor, 1948). After uniaxial compression tests, the undisturbed soil samples were dried in the oven at 105–110 °C for 48 hours to determine the dry soil weight per unit volume, to calculate the soil bulk density (Blake and Hartge, 1986a). The volumetric total porosity (VTP) was estimated using the relationship between bulk density and particle density (Flint & Flint, 2002). Volumetric water content for each sample was also obtained

#### **3. Soil physical properties**

#### **3.1 Bulk density and total soil organic carbon**

The soils samples from the coffee-cultivated plots subjected to different weed management systems in the traffic line, had a higher bulk density and lower total soil organic carbon at the three layers studied, when compared to the soil samples from natural forest soil (Fig. 1A and 1B). These results indicated that land use with coffee plantation using different mechanical and chemical methods for weed control, increased the packing of the solids particles in soil thereby affecting the soil structural sustainability.

In the laboratory, a knife was used to trim the soil from the ends to the exact size of the rings. This was used to determine the volume of soil and its weight. The scrapped soil materials were later used for physical (particle size distribution, soil particle density) and chemical (total soil organic carbon content) characterization of the soil. The soil particle-size distribution was determined by the pipette method (Day, 1965), by chemical dispersion with a 50 mL 0,1 N sodium hydroxide solution, in contact with the samples for 24 hours. Physical dispersion was accomplished by slowly rotating in a Wiegner mixer that shakes 30 times per minute, adding 20 g coarse sand (Grohmann & Raij, 1977). Soil particle density was determined by the pycnometer method (Blake & Hartge, 1986b). The total soil organic carbon content were determined by wet combustion with carbon oxidation adding 10 mL of

Three soil samples for each plot and at the sampled depths were saturated by capillary with distilled water, and equilibrated to a matric potential (Ψm) of - 2 and - 6 kPa, on a suction table (Romano et al., 2002) and - 10, - 33, - 100, - 500 and - 1500 kPa in a ceramic plate inside a pressure chamber (Soilmoisture Equipment Crop., P.O. Box 30025, Santa Barbara, CA 93105) (Dane & Hopmans, 2002). The soil-water retention data were fitted through the van Genuchten (1980) model with Mualen (1976) constraint. The – 6 kPa matric potential was used to separate the pores with effective diameter greater than 50 μm, drained from the cores (macropores). Water retained at this matric potential is considered as a measure of

Precompression stresses were determined from the undisturbed soil samples submitted to uniaxial compression tests. The soil samples were kept within the sleeves of the coring cylinder, which were placed in the compression cell, and afterwards subjected pneumatically (Durham Geo Slope Indicator, USA, model S-450 Terraload®) to pressures 25, 50, 100, 200, 400, 800 and 1600 kPa to reach equilibrium (Bowles, 1986). During each test, a normal vertical stress was applied until 90% of the maximum deformation was reached and then the pressure is increased to the next level (Taylor, 1948). After uniaxial compression tests, the undisturbed soil samples were dried in the oven at 105–110 °C for 48 hours to determine the dry soil weight per unit volume, to calculate the soil bulk density (Blake and Hartge, 1986a). The volumetric total porosity (VTP) was estimated using the relationship between bulk density and particle density (Flint & Flint, 2002). Volumetric water content for

The soils samples from the coffee-cultivated plots subjected to different weed management systems in the traffic line, had a higher bulk density and lower total soil organic carbon at the three layers studied, when compared to the soil samples from natural forest soil (Fig. 1A and 1B). These results indicated that land use with coffee plantation using different mechanical and chemical methods for weed control, increased the packing of the solids

digest solution (Na2Cr2O7 2H2O 4 N + H2SO4 10 N) (Raij et al., 1987).

**2.3 Laboratory analysis** 

microporosity.

each sample was also obtained

**3. Soil physical properties** 

**3.1 Bulk density and total soil organic carbon** 

particles in soil thereby affecting the soil structural sustainability.

The bulk densities values from soil samples following post-emergence herbicide and mechanical mowing weed management systems at all the depths, and those from the rotary tilling managements (10–13 and 25–28 cm depths), coffee tandem disk harrowing and preemergence herbicide (0–3 and 10–13 cm depths) were considered higher than critical values for clay soils (1.2 Mg m-3) in agreement with other studies including Derpsch et al. (1991); Dexter (2004); Severiano et al. (2011) and critical values for coffee root growth in Dystropherric Red Latosol (Araujo-Junior et al., 2011). The disk harrowing and preemergence herbicide weed management systems promote the crusting in the soil surface (Photos 1B and 1C) and increase the values of the bulk density (Fig. 1A).

After 30 years of conventional coffee cultivation, the total organic carbon contents were markedly affected by weed control between the coffee rows in the traffic line (Figure 1B). Total organic carbon contents were greater for native forest compared to the coffee plantation at all depths, except at 0–3 cm following mechanical mowing, which had the same total organic carbon (Fig 1A.) this is understandable considering that weed control with mechanical mower cut the weed in all the interrows and concentrate weed near the edge of the equipment increasing the total soil organic carbon in this region, where soil samples were collected.

The next highest contents of total organic carbon were found in the soils samples from handhoed (CAPM), post-emergence herbicide (HPOS), rotary tilling (ENRT) followed by noweed control (SCAP), disk harrow (GRAD), and lowest was found in the soil from preemergence herbicide (Figure 1B). This low organic carbon condition was obviously due to the lack weed on the soil surface in the pre-emergence herbicide management system in agreements with other reports from tropical soil environments (Faria et al., 1998; Alcântara & Ferreira, 2000b; Araujo-Junior et al., 2011).

Published results reveal that weedy soil covers between coffee rows had great influences on the dynamics of total organic carbon content. Plant residues may influence the light soil fraction and thus the organic carbon content as reported by Ding et al. (2006) when these authors assessed the effect of cover crop management on chemical and structural composition of soil organic matter. The constant use of the pre-emergence herbicide for weed control in Dystroferric Red Latosol clay decreases significantly the total organic carbon content in the soil surface, because of the prevalence of soil without weed between the coffee rows. The effect of weed control with pre-emergence herbicide on total soil organic carbon was observed also in the 10–13 cm layer due the absence of weed roots (Figure 1B).

The different weed management system applied to coffee interrows influenced the soil bulk density and organic carbon content of the Latosol, in the 25-28 cm layer (Fig. 1A and 1B), when compared with the soil under natural forest (NAFT); however, when the soil samples were collected in center of the interrows, differences were not observed (Araujo-Junior et al., 2011). These authors observed that different weed management systems used in the interrows did not influenced soil bulk density and total organic carbon content of the Latosol, in the 25–28 cm layer, compared to the soil under natural forest. In our study, it is important highlight that the soil samples were collected in the traffic line of machines, and the total soil organic carbon content did not differ among the weed management systems in coffee plantation at the 25–28 cm depth (Figure 1B). However, Latosol samples from natural forest had greater total organic carbon content when compared to the soil under the

Interrelationships Among Weed Management in Coffee Plantation and Soil Physical Quality 223

limitation to gas exchange under certain conditions and that air-filled porosities < 10% (v/v)

The lowest macroporosities (0.08 cm3 cm-3) in the 0–3 cm depth (pores with effective diameter greater than 50 μm, drained from cores) were observed for the samples under mechanical mowing and coffee tandem disk harrowing weed management system (Figure 2). The soil compaction process reduces the large pores in size first (Hillel, 1980; Dexter,

are characteristic of deficient aeration.

**TOTAL POROSITY, cm3 cm-3**

**TOTAL POROSITY, cm3 cm-3**

**0.0**

**0.1**

**0.2**

**0.3**

**0.4**

**0.5**

**0.6**

**0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8**

2004; Pires et al., 2008; Ajayi et al., 2009; Severiano et al., 2011).

**A**

**F**

**F**

**C**

**O**

**H**

**W**

**0,33 a 0,36 a 0,36 a 0,36 a 0,36 a 0,35 a 0,37 a 0,34 a**

**0,20** <sup>α</sup> **0,16** <sup>α</sup> **0,15** <sup>α</sup> **0,16** <sup>α</sup> **0,16** <sup>α</sup> **0,16** <sup>α</sup> **0,17** <sup>α</sup> **0,19** <sup>α</sup>

**I**

**H**

**H**

**C**

**B**

**O**

**H**

**<sup>A</sup> <sup>A</sup> <sup>A</sup> A A <sup>A</sup>**

**W**

**MICROPOROSITY: diameter smaller than 50** μ**m MACROPOROSITY: diameter greater than 50** μ**m**

**Depth: 10-13 cm**

**0,29 c 0,40 b 0,38 b 0,41 b 0,45 a 0,39 b 0,45 a 0,39 b**

**0,44** <sup>α</sup> **0,13** <sup>β</sup> **0,15** <sup>β</sup> **0,13** <sup>β</sup> **0,08** <sup>γ</sup> **0,19** <sup>β</sup> **0,08** <sup>γ</sup> **0,12** <sup>β</sup>

**MICROPOROSITY: diameter smaller than 50** μ**m MACROPOROSITY: diameter greater than 50** μ**m**

**Depth: 0-3 cm**

**I**

**B <sup>B</sup> <sup>B</sup> B B <sup>B</sup>**

**H**

**H**

**A A**

different weed management system in coffee plantation. It has been proposed that the conservation of soil organic matter is an essential to protection soil against compaction (Etana et al., 1997; Dexter, 2004; Zhang et al., 2005; Araujo-Junior et al., 2011).

Fig. 1. Soil bulk density (A) and total soil organic carbon (B) of a Dystroferric Red Latosol in 0–3, 10–13 and 25–28 cm layers, affected by different weed management between coffee rows. NATF: natural forest; NWC: no-weed control between coffee rows; POSH: postemergence herbicide; MMOW: mechanical mower; ROTI: rotary-tilling; CTDH: coffee tandem disk harrow; PREH: pre-emergence herbicide. Mean followed by equal letters compare the layers in the same weed management, and uppercase letters among the managements in the same depth of sampling, were not different, at 5% probability by the Scott-Knott test. Letters A to D compare 0-3 cm, X and Y compare managements at the 10-13 cm and Greek letters 25–28 cm depths. The red horizontal dotted line represents the critical soil bulk density for coffee root growth and soil structure sustainability estimate by Araujo-Junior et al. (2011) based on soil compression curves.

#### **3.2 Total porosity and pore size distribution**

Figure 2 shows the total porosity and pore size distribution of the Dystroferric Red Latosol (Oxisol) under native forest compared with the samples from the coffee plantation under different weed management system. We observed that samples taken from natural forest in the 0–3 cm depth have a higher total porosity (0.73 cm3 cm-3), macroporosity (0.44 cm3 cm-3) and lower microporosity (0.29 cm3 cm-3) when compared to the soil in different weed management system in the coffee plantation. For other depths (10–13 cm and 25–28 cm), the Latosol total porosity and pore size distribution were not different under natural forest and coffee plantation in the different weed management systems. Studies have been shown that under native forest the most Latosols found in Brazil with the gibbsite minerals content and high hematite contents on the clay fraction have percentage of macropores higher than 20% (Kemper & Derpsch, 1981; Ferreira et al., 1999; Oliveira et al., 2003a,b; Ajayi et al., 2009; Severiano et al., 2011).

Macropores are the pores in the soil in which water percolates due to gravity and their number is also measure of soil compaction (Kemper & Derpsch, 1981). In addition, macropores facilitates gas movement, thus it relates to the ability of the soil both to store and to transport gas (Stepniewski et al., 1994). These authors concluded that macroporosity of 25% (v/v) provides good aeration while in the 10–25% (v/v) range, there may be a

different weed management system in coffee plantation. It has been proposed that the conservation of soil organic matter is an essential to protection soil against compaction

(Etana et al., 1997; Dexter, 2004; Zhang et al., 2005; Araujo-Junior et al., 2011).

**C**

**<sup>Y</sup> Y Y**

β

**B**

**D**

β

**TOTAL SOIL ORGANIC CARBON, g kg-1**

(A) (B) Fig. 1. Soil bulk density (A) and total soil organic carbon (B) of a Dystroferric Red Latosol in 0–3, 10–13 and 25–28 cm layers, affected by different weed management between coffee rows. NATF: natural forest; NWC: no-weed control between coffee rows; POSH: postemergence herbicide; MMOW: mechanical mower; ROTI: rotary-tilling; CTDH: coffee tandem disk harrow; PREH: pre-emergence herbicide. Mean followed by equal letters compare the layers in the same weed management, and uppercase letters among the managements in the same depth of sampling, were not different, at 5% probability by the Scott-Knott test. Letters A to D compare 0-3 cm, X and Y compare managements at the 10-13 cm and Greek letters 25–28 cm depths. The red horizontal dotted line represents the critical soil bulk density for coffee root growth and soil structure sustainability estimate by Araujo-

Figure 2 shows the total porosity and pore size distribution of the Dystroferric Red Latosol (Oxisol) under native forest compared with the samples from the coffee plantation under different weed management system. We observed that samples taken from natural forest in the 0–3 cm depth have a higher total porosity (0.73 cm3 cm-3), macroporosity (0.44 cm3 cm-3) and lower microporosity (0.29 cm3 cm-3) when compared to the soil in different weed management system in the coffee plantation. For other depths (10–13 cm and 25–28 cm), the Latosol total porosity and pore size distribution were not different under natural forest and coffee plantation in the different weed management systems. Studies have been shown that under native forest the most Latosols found in Brazil with the gibbsite minerals content and high hematite contents on the clay fraction have percentage of macropores higher than 20% (Kemper & Derpsch, 1981; Ferreira et al., 1999; Oliveira et al., 2003a,b; Ajayi et al., 2009;

Macropores are the pores in the soil in which water percolates due to gravity and their number is also measure of soil compaction (Kemper & Derpsch, 1981). In addition, macropores facilitates gas movement, thus it relates to the ability of the soil both to store and to transport gas (Stepniewski et al., 1994). These authors concluded that macroporosity of 25% (v/v) provides good aeration while in the 10–25% (v/v) range, there may be a

**0**

**NATIVE FOREST AND WEED MANAGEMENT SYSTEM NATF NWC HAHO POSH MMOW ROTI CTDH PREH**

**0-3 cm 10-13 cm 25-28 cm** 

**A**

**Y**

**<sup>Z</sup> <sup>Z</sup> <sup>Z</sup> <sup>Z</sup>**

**<sup>C</sup> <sup>B</sup> <sup>B</sup>**

<sup>β</sup> <sup>β</sup>

**Z**

**C**

<sup>β</sup> <sup>β</sup> <sup>β</sup> <sup>β</sup> <sup>β</sup>

**D**

**W**

**B**

**4**

**8**

**12**

**16**

**A**

**X**

α

**20**

**NATIVE FOREST AND WEED MANAGEMENT SYSTEM**

Junior et al. (2011) based on soil compression curves.

**3.2 Total porosity and pore size distribution** 

**NATF NWC HAHO POSH MMOW ROTI CTDH PREH**

**1.4 0-3 cm 10-13 cm 25-28 cm** 

**C**

**Y Y <sup>Y</sup> <sup>Y</sup>**

**B B**

<sup>β</sup> <sup>β</sup>

**C**

<sup>γ</sup> <sup>γ</sup> <sup>γ</sup>

**SOIL BULK DENSITY, Mg m-3**

**0.8 0.9 1.0 1.1 1.2 1.3**

**A X** α

Severiano et al., 2011).

limitation to gas exchange under certain conditions and that air-filled porosities < 10% (v/v) are characteristic of deficient aeration.

The lowest macroporosities (0.08 cm3 cm-3) in the 0–3 cm depth (pores with effective diameter greater than 50 μm, drained from cores) were observed for the samples under mechanical mowing and coffee tandem disk harrowing weed management system (Figure 2). The soil compaction process reduces the large pores in size first (Hillel, 1980; Dexter, 2004; Pires et al., 2008; Ajayi et al., 2009; Severiano et al., 2011).

**F**

**C**

**O**

**W**

**I**

**H**

**H**

**H**

Interrelationships Among Weed Management in Coffee Plantation and Soil Physical Quality 225

quality and is a measure of soil microstructure that can be used as an index of soil physical quality. According to Pires et al. (2008) soil compaction decreases large pores followed by a rising amount of small pores, that committing soil physical quality decreases the S-index values (Dexter, 2004). They showed that large values for S-index indicating good soil

Based on soil water retention curve behaviors for a Eutric Nitossol (430 g kg-1 clay) under coffee plantation Pires et al. (2008) assessed the effect of wetting and drying cycles. They found that the wetting and drying treatments did not affect the S-index for this soil. However, they showed that for the other soils S-index were affecting for the wetting and

**INTERROWS:** θ **= 0,24 + (0,66 - 0,24)/[1+(1,1346 x** <sup>Ψ</sup>**m)**

**TRAFFIC LINE:** θ **= 0,28 + (0,57 - 0,28)/[1+(1,8066 x** <sup>Ψ</sup>**m)**

**MATRIC POTENTIAL, - kPa**

Fig. 3. Soil water retention curves for a Dystroferric Red Latosol in 0–3 cm in two sampling

The soil compression curve is a conceptual and interpretative tool by which the compressive behaviour of the soil can be understood. The soil compression curve or stress-deformation curve can be described as a measure of soil deformation under given external loads (Holtz & Kovacz, 1981) (Figure 4) and defines the relationship between the logarithm of applied normal stress on the top of the sample and some parameter related to the packing state of soil; for example soil void ratio or soil bulk density (Casagrande, 1936; Larson et al., 1980; Holtz & Kovacs, 1981; Horn, 1988; Dias Junior & Pierce, 1995). This curve is divided into two

position interrows (no-wheel tracked soil) and traffic line (wheel-tracked soil).

**3.4 Soil compressive behavior and load bearing capacity models** 

**2 4 6 10 33 100 500 1500**

**1,7069]**

**1,3355]**

**0,4141 R2**

**0,2512 R2**

 **= 0,99\*\***

 **= 0,94\*\***

physical quality and presence of structural pores.

**OXISOL 0-3 cm**

drying cycles.

**VOLUMETRIC WATER CONTENT, cm3 cm-3**

**0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70**

Fig. 2. Pore size distribution for a Dystroferric Red Latosol in 0–3, 10–13 and 25–28 cm layers, under natural forest and coffee plantation affected by different weed management between coffee rows. NATF: native forest; NWC: no-weed control between coffee rows; POSH: post-emergence herbicide; MMOW: mechanical mower; ROTI: rotary-tilling; CTDH: coffee tandem disk harrow; PREH: pre-emergence herbicide. Mean followed by equal letters compare the layers in the same weed management, and uppercase letters among the managements in the same depth of sampling, were not different, at 5% probability by the Scott-Knott test.

#### **3.3 Soil-water retention curve**

The soil-water retention curve defines the relationship between the soil matric potential and soil volumetric water content (Figure 3). This relationship may also assess the effect of weed management practices on soil structure. The differences between water retention behaviour for the soil samples collected at the interrows (center of the coffee rows, non-tracked soil) and the traffic line (wheel-tracked soil) at the 0 to 3 cm depth suggests that these curves are influenced by soil structure. The saturated water content (0.57 cm3 cm-3) for retention curve for traffic line decreased as a consequence of destruction of large pores or structural pores. On the other hand, the non-tracked interrow soil water retention curve revealed higher saturated water content (0.66 cm3 cm-3). As stated earlier, the large pores can be transformed into smaller pores and thus increase the soil-water holding capacity in low matric potential (- 1500 kPa). In this study, residual water content or water content at permanent wilting point (- 1500 kPa) increased in 0.04 cm3 cm-3 in the traffic line as compared to interrows (Figure 3).

Recently, Dexter (2004) proposed to calculate the soil water retention curve parameters at inflection point (slope at inflection point, S-index) to assess soil physical quality. This author showed that the slope at inflection point governs directly many of the principal soil physical

**MICROPOROSITY: diameter smaller than 50** μ**m MACROPOROSITY: diameter greater than 50** μ**m**

**Depth: 25-28 cm**

**<sup>A</sup> <sup>A</sup> <sup>A</sup> <sup>A</sup> <sup>A</sup> <sup>A</sup>**

**NATIVE FOREST AND WEED MANAGEMENT SYSTEM**

**MMOW**

**0,33 a 0,36 a 0,34 a 0,36 a 0,36 a 0,36 a 0,36 a 0,34 a**

**0,16** <sup>α</sup> **0,17** <sup>α</sup> **0,17** <sup>α</sup> **0,16** <sup>α</sup> **0,18** <sup>α</sup> **0,14** <sup>α</sup> **0,18** <sup>α</sup> **0,20** <sup>α</sup>

**ROTI**

**CTDH**

**PREH**

**POSH**

**NATF**

**TOTAL POROSITY, cm3 cm-3**

Scott-Knott test.

(Figure 3).

**3.3 Soil-water retention curve** 

**0.0**

**0.1**

**0.2**

**0.3**

**0.4**

**0.5**

**0.6**

**NWC**

**<sup>A</sup> <sup>A</sup>**

**HAHO**

Fig. 2. Pore size distribution for a Dystroferric Red Latosol in 0–3, 10–13 and 25–28 cm layers, under natural forest and coffee plantation affected by different weed management between coffee rows. NATF: native forest; NWC: no-weed control between coffee rows; POSH: post-emergence herbicide; MMOW: mechanical mower; ROTI: rotary-tilling; CTDH: coffee tandem disk harrow; PREH: pre-emergence herbicide. Mean followed by equal letters

compare the layers in the same weed management, and uppercase letters among the managements in the same depth of sampling, were not different, at 5% probability by the

The soil-water retention curve defines the relationship between the soil matric potential and soil volumetric water content (Figure 3). This relationship may also assess the effect of weed management practices on soil structure. The differences between water retention behaviour for the soil samples collected at the interrows (center of the coffee rows, non-tracked soil) and the traffic line (wheel-tracked soil) at the 0 to 3 cm depth suggests that these curves are influenced by soil structure. The saturated water content (0.57 cm3 cm-3) for retention curve for traffic line decreased as a consequence of destruction of large pores or structural pores. On the other hand, the non-tracked interrow soil water retention curve revealed higher saturated water content (0.66 cm3 cm-3). As stated earlier, the large pores can be transformed into smaller pores and thus increase the soil-water holding capacity in low matric potential (- 1500 kPa). In this study, residual water content or water content at permanent wilting point (- 1500 kPa) increased in 0.04 cm3 cm-3 in the traffic line as compared to interrows

Recently, Dexter (2004) proposed to calculate the soil water retention curve parameters at inflection point (slope at inflection point, S-index) to assess soil physical quality. This author showed that the slope at inflection point governs directly many of the principal soil physical quality and is a measure of soil microstructure that can be used as an index of soil physical quality. According to Pires et al. (2008) soil compaction decreases large pores followed by a rising amount of small pores, that committing soil physical quality decreases the S-index values (Dexter, 2004). They showed that large values for S-index indicating good soil physical quality and presence of structural pores.

Based on soil water retention curve behaviors for a Eutric Nitossol (430 g kg-1 clay) under coffee plantation Pires et al. (2008) assessed the effect of wetting and drying cycles. They found that the wetting and drying treatments did not affect the S-index for this soil. However, they showed that for the other soils S-index were affecting for the wetting and drying cycles.

Fig. 3. Soil water retention curves for a Dystroferric Red Latosol in 0–3 cm in two sampling position interrows (no-wheel tracked soil) and traffic line (wheel-tracked soil).

#### **3.4 Soil compressive behavior and load bearing capacity models**

The soil compression curve is a conceptual and interpretative tool by which the compressive behaviour of the soil can be understood. The soil compression curve or stress-deformation curve can be described as a measure of soil deformation under given external loads (Holtz & Kovacz, 1981) (Figure 4) and defines the relationship between the logarithm of applied normal stress on the top of the sample and some parameter related to the packing state of soil; for example soil void ratio or soil bulk density (Casagrande, 1936; Larson et al., 1980; Holtz & Kovacs, 1981; Horn, 1988; Dias Junior & Pierce, 1995). This curve is divided into two

Interrelationships Among Weed Management in Coffee Plantation and Soil Physical Quality 227

Soil load bearing capacity has been defined as the capability of a soil structure to withstand stresses induced by field traffic without changes in the three-dimensional arrangement of its constituent soil particles (Alakukku et al., 2003). Soil load bearing capacity models (LBC) represents mathematically the relationship between soil volumetric water content (θ) and soil precompression stress (σp) and may be described by the Equation 1 (Dias Junior, 1994). In this model, the precompression stress decreases exponentially with the increases in the

( ) 10 *a b*

Where, precompression stress (σp), estimated linear "a" and angular "b" coefficients and θ the initial volumetric soil water content. All the models obtained for the Dystroferric Red Latosol were significant at 1% probability level, for t-Student test and the coefficient of

The estimated linear "a" and angular "b" coefficients of the load bearing capacity models values varied from 2.57 for the soil under native forest at 0–3 cm depth to 2.89 for the soil samples collected from rotary tiller at 25–28 cm depth, and from -1.60 for the soil samples under pre-emergence herbicide at 25–28 cm depth, to - 0.71, for the soil samples collected from native forest at 0–3 cm depth (Table 2). Others studies done in Brazilian Latosols and Ultisols (Silva & Cabeda, 2006; Oliveira et al., 2003a; Kondo & Dias Junior, 1999) are in agreement with this results, which found lowest linear coefficients for soils under native forest when compared to the soil under different tillage management. The soil samples collected from native forest presented lower soil bulk density, microporosity and higher total organic carbon content, total porosity and macroporosity (Figures 1 and 2) due to the lack of anthropogenic activity and stress history. These findings suggest that the fitted parameter, "a" is interrelated to the packing of the solid particles expressed by soil bulk

θ

<sup>+</sup> = (1)

*p*

σ

density and air-filled porosity (macropores) which affect the pore water pressure.

**3.4.1 Influence of weed management system on soil load bearing capacity**

In all the models, the dependence of soil precompression stress on the water content in the soil was displayed. It was observed that the strength of the Latosol soil samples reduces although not linearly, with increases in the water content of the soil. The observation was consistent with results from several studies on the strength of soil samples (Kondo & Dias Junior, 1999; Peng et al., 2004; Dias Junior et al., 2005; Araujo-Junior et al., 2008, 2011).

Reported results from soil samples from three Ultisols under subtropical climate, Peng et al. (2004) also suggested that precompression stress decreases in exponential way with the initial water content. These authors suggest that the parameter "a" indicates the intrinsic strength of dry soil and the parameter "b" influences of soil properties such as soil texture

To assess the influence of the adoption of different weed management on soil load bearing capacity, undisturbed soil samples collected from native forest and coffee plantation submitted to different weed management system were subjected to uniaxial compression test to obtain the soil compression curves. This load bearing capacity model was used to verify possible effects of different weed management systems on soil structure. This model is based on stress history or either, of the stress and other changes that have occurred during

volumetric soil water content.

determination (R2) ranged from 0.75 to 0.96 (Table 2).

and organic matter on the soil strength.

regions so-called: a region of plastic and unrecoverable deformation called the virgin compression curve, and a region of small, elastic and recoverable deformation called the secondary compression curve (Larson et al., 1980; Holtz & Kovacs, 1981; Dias Junior & Pierce, 1995; Gregory et al., 2006). The point that separates these two regions in a compression curve is the precompression stress or preconsolidation pressure (σp) depending on if air or water is being eliminated from the soil, and can be variously defined.

In this study, we assumed, the precompression stress as indicator of internal strength of soils, which resulted from pedogenetic processes, anthropogenic effects, or hydraulic sitespecific conditions (Horn et al., 2004) the maximum vertical overburden stress that particular sample has sustained in the past (Holtz & Kovacs, 1981) or as a predictor of the critical strength at which root elongation ceases (Römkens & Miller, 1971). This parameter is influenced by the initial soil volumetric water content (θ), initial soil bulk density (Bd), total organic carbon (TOC), soil structure and stress history, as it relates to the different weed management in coffee plantation.

The stress in a logarithmic scale versus strain data were then used to construct the soil compression curves (Larson et al., 1980), from which the precompression stress (σp) were determined (Figure 4) following the procedure of Dias Junior & Pierce (1995). In this procedure, precompression stress was estimated as the intersection of two lines: the regression line obtained for the first two (for soil samples with initial volumetric water content higher than matric potential – 100 kPa) or four points (for soil samples with matric potential lower or equal – 100 kPa) of the applied stress sequence in the secondary compression portion of the compression curve and the extension of the virgin compression line determined from the points associated with applied stress of 800 and 1600 kPa (Figure 4).

Fig. 4. Soil compression curve illustrating the position of the precompression stress Source: "From Dias Junior, 1994"

regions so-called: a region of plastic and unrecoverable deformation called the virgin compression curve, and a region of small, elastic and recoverable deformation called the secondary compression curve (Larson et al., 1980; Holtz & Kovacs, 1981; Dias Junior & Pierce, 1995; Gregory et al., 2006). The point that separates these two regions in a compression curve is the precompression stress or preconsolidation pressure (σp) depending on if air or water is being eliminated from the soil, and can be variously defined. In this study, we assumed, the precompression stress as indicator of internal strength of soils, which resulted from pedogenetic processes, anthropogenic effects, or hydraulic sitespecific conditions (Horn et al., 2004) the maximum vertical overburden stress that particular sample has sustained in the past (Holtz & Kovacs, 1981) or as a predictor of the critical strength at which root elongation ceases (Römkens & Miller, 1971). This parameter is influenced by the initial soil volumetric water content (θ), initial soil bulk density (Bd), total organic carbon (TOC), soil structure and stress history, as it relates to the different weed

The stress in a logarithmic scale versus strain data were then used to construct the soil compression curves (Larson et al., 1980), from which the precompression stress (σp) were determined (Figure 4) following the procedure of Dias Junior & Pierce (1995). In this procedure, precompression stress was estimated as the intersection of two lines: the regression line obtained for the first two (for soil samples with initial volumetric water content higher than matric potential – 100 kPa) or four points (for soil samples with matric potential lower or equal – 100 kPa) of the applied stress sequence in the secondary compression portion of the compression curve and the extension of the virgin compression line determined from the

> **PRECOMPRESSION STRESS**

> > **SHIFT WITH**

**MOISTURE**

**VIRGIN**

**(plastic** **COMPRESSION**

**deformations)**

 **LINE**

**10 100 1000**

**Extension of the virgin compression line Line that passes through the first two points**

**APPLIED STRESS, kPA**

**Laboratory compression curve**

Fig. 4. Soil compression curve illustrating the position of the precompression stress

points associated with applied stress of 800 and 1600 kPa (Figure 4).

**SECONDARY COMPRESSION CURVE (elastic deformations)**

management in coffee plantation.

**1.0**

**1.1**

**1.2**

**1.3**

**1.4**

**1.5**

Source: "From Dias Junior, 1994"

**BULK DENSITY, Mg m-3**

Soil load bearing capacity has been defined as the capability of a soil structure to withstand stresses induced by field traffic without changes in the three-dimensional arrangement of its constituent soil particles (Alakukku et al., 2003). Soil load bearing capacity models (LBC) represents mathematically the relationship between soil volumetric water content (θ) and soil precompression stress (σp) and may be described by the Equation 1 (Dias Junior, 1994). In this model, the precompression stress decreases exponentially with the increases in the volumetric soil water content.

$$
\sigma\_p = 10^{(a+b\theta)} \tag{1}
$$

Where, precompression stress (σp), estimated linear "a" and angular "b" coefficients and θ the initial volumetric soil water content. All the models obtained for the Dystroferric Red Latosol were significant at 1% probability level, for t-Student test and the coefficient of determination (R2) ranged from 0.75 to 0.96 (Table 2).

The estimated linear "a" and angular "b" coefficients of the load bearing capacity models values varied from 2.57 for the soil under native forest at 0–3 cm depth to 2.89 for the soil samples collected from rotary tiller at 25–28 cm depth, and from -1.60 for the soil samples under pre-emergence herbicide at 25–28 cm depth, to - 0.71, for the soil samples collected from native forest at 0–3 cm depth (Table 2). Others studies done in Brazilian Latosols and Ultisols (Silva & Cabeda, 2006; Oliveira et al., 2003a; Kondo & Dias Junior, 1999) are in agreement with this results, which found lowest linear coefficients for soils under native forest when compared to the soil under different tillage management. The soil samples collected from native forest presented lower soil bulk density, microporosity and higher total organic carbon content, total porosity and macroporosity (Figures 1 and 2) due to the lack of anthropogenic activity and stress history. These findings suggest that the fitted parameter, "a" is interrelated to the packing of the solid particles expressed by soil bulk density and air-filled porosity (macropores) which affect the pore water pressure.

In all the models, the dependence of soil precompression stress on the water content in the soil was displayed. It was observed that the strength of the Latosol soil samples reduces although not linearly, with increases in the water content of the soil. The observation was consistent with results from several studies on the strength of soil samples (Kondo & Dias Junior, 1999; Peng et al., 2004; Dias Junior et al., 2005; Araujo-Junior et al., 2008, 2011).

Reported results from soil samples from three Ultisols under subtropical climate, Peng et al. (2004) also suggested that precompression stress decreases in exponential way with the initial water content. These authors suggest that the parameter "a" indicates the intrinsic strength of dry soil and the parameter "b" influences of soil properties such as soil texture and organic matter on the soil strength.

#### **3.4.1 Influence of weed management system on soil load bearing capacity**

To assess the influence of the adoption of different weed management on soil load bearing capacity, undisturbed soil samples collected from native forest and coffee plantation submitted to different weed management system were subjected to uniaxial compression test to obtain the soil compression curves. This load bearing capacity model was used to verify possible effects of different weed management systems on soil structure. This model is based on stress history or either, of the stress and other changes that have occurred during

Interrelationships Among Weed Management in Coffee Plantation and Soil Physical Quality 229

The load bearing capacity models of the sample collected from different land uses (native forest and coffee plantation), but at different depths, and those of the various weed management systems were compared in multiple scatter plots (Fig. 5 – 7) and using the test of homogeneity for comparison of regression lines (Snedecor & Cochran, 1989). In the multiple scatter plots, the entire soil moisture and the corresponding preconsolidation value data in the different sites are pulled together on a single graph. For the homogeneity test, two models are picked and compared together by examining the intercept (a), slope (b) and the homogeneity parameter data (F). To obtain a and b values in each model for comparison, the model equation in the exponential form (Eq. 1) was transformed into a linear model by computing the logarithm of both sides of the equation giving equation of the form (Eq. 2)

( ) log log 10 log *a b*

*p p a b* θ

We observed that soils under natural forest and no-weed control exhibited the lowest load bearing capacities at the 0-3 cm depth when compared with those under the varied weed management system used in coffee plantation (Figure 5 to 7). This observation can be associated with initial soil bulk density and soil organic carbon content (Figure 1A and 1B) and be associated with the absence of stress history and anthropogenic activities on the soil under native forest. On the other hand, the weed control using mechanical mower exhibited the highest load bearing capacity at that depth (Figure 5). The final results are presented in Fig. 5 for the models of the sample collected from different weed management systems at depth 0–3 cm depth. Homogeneity tests of the regression equations (Snedecor & Cochran, 1989) indicated that the soil under hand hoeing and pre-emergence herbicide weed management; post-emergence herbicide and coffee tandem disk harrow weed management had the similar load bearing capacities at the 0-3 cm depth (Table 3). Therefore, the dataset of the homogeneous models were combined and a new equation was fitted to each data set, considering all the values of preconsolidation pressure and volumetric soil water content for these treatments (Figure 5). Generally, it was observed that the load bearing capacity for the Dystroferric Red Latosol under the different weed management systems at the soil surface(0-3 cm depth) decreases in a following order: mechanical mower > post-emergence herbicide = coffee tandem disk harrow > rotary tiller > hand hoeing = pre-emergence herbicide > natural forest > no-weed control (Figure 5). The highest soil load bearing capacity was observed for the Latosol under mechanical mower in 0–3 cm depth (Fig. 5). Others studies, have been shown that high traffic intensity necessary to satisfactory weed control in coffee plantation throughout the year (5 to 6 times) increases the risk of soil compaction (Silveira & Kurachi, 1984; Alcântara & Ferreira, 2000b; Silva et al., 2006) mainly in the rainy season (October to March) when the soils has high soil water content and consequently lower load bearing capacity (Silva et al., 2006) increases the soil susceptibility to compaction. On the other hand, when soil is drier present higher resistance to compression and high load bearing capacity that decreases soil susceptibility to compaction

Our results suggested the mechanical mower had a greater potential for causing soil compaction due to high traffic intensity to satisfactory weed control through the year (5 operations) and this operation must be accomplished when the soil has water content lower

 σ

θ<sup>+</sup> = = =+ (2)

(Dias Junior et al., 2005; Araujo-Junior et al., 2011).

(Dias Junior et al., 2005; Araujo-Junior et al., 2008; 2011).

than 0.30 cm3 cm-3 to minimize or avoid additional soil compaction.

σ


their history, and these changes are preserved in the soil structure (Casagrande, 1932 cited by Holtz & Kovacs, 1932).

Table 2. Linear (a) and angular (b) coefficients of the load bearing capacity models [σp = 10(a + b<sup>θ</sup>)], with respective coefficients of determination (R2), and number of undisturbed soil samples (n) collected at 0–3, 10–13 and 25–28 cm depths in the traffic line in a Dystroferric Red Latosol (Oxisol) under native forest and coffee plantation submitted to different weed management systems.

their history, and these changes are preserved in the soil structure (Casagrande, 1932 cited

Depth: 0–3 cm Native forest 2,57 - 0,71 0,80\*\* 15 No-weed control between coffee rows 2,65 - 1,26 0,96\*\* 15 Hand hoe 2,82 - 1,56 0,84\*\* 15 Post-emergence herbicide 2,72 - 0,92 0,92\*\* 15 Mechanical mower 2,86 - 1,19 0,83\*\* 15 Rotary-tilling 2,74 - 1,14 0,79\*\* 14 Coffee tandem disk harrow 2,73 - 0,84 0,77\*\* 15 Pre-emergence herbicide 2,78 - 1,35 0,86\*\* 15 Depth: 10-13 cm Native forest 2,61 - 0,90 0,77\*\* 15 No-weed control between coffee rows 2,77 - 1,26 0,84\*\* 15 Hand hoe 2,77 - 1,05 0,86\*\* 15 Post-emergence herbicide 2,77 - 1,43 0,87\*\* 15 Mechanical mower 2,79 - 1,37 0,82\*\* 15 Rotary-tilling 2,82 - 1,24 0,81\*\* 15 Coffee tandem disk harrow 2,71 - 0,92 0,77\*\* 15 Pre-emergence herbicide 2,83 - 1,49 0,78\*\* 14 Depth: 25-28 cm Native forest 2,66 - 1,11 0,90\*\* 14 No-weed control between coffee rows 2,66 - 0,93 0,82\*\* 15 Hand hoe 2,76 - 1,40 0,94\*\* 15 Post-emergence herbicide 2,86 - 1,51 0,86\*\* 15 Mechanical mower 2,80 - 1,26 0,75\*\* 15 Rotary-tilling 2,89 - 1,45 0,83\*\* 15 Coffee tandem disk harrow 2,76 - 1,27 0,84\*\* 14 Pre-emergence herbicide 2,81 - 1,60 0,83\*\* 14

Table 2. Linear (a) and angular (b) coefficients of the load bearing capacity models

soil samples (n) collected at 0–3, 10–13 and 25–28 cm depths in the traffic line in a Dystroferric Red Latosol (Oxisol) under native forest and coffee plantation submitted to

different weed management systems.

[σp = 10(a + b<sup>θ</sup>)], with respective coefficients of determination (R2), and number of undisturbed

a b R2 n

by Holtz & Kovacs, 1932).

t

Native forest and weed management

The load bearing capacity models of the sample collected from different land uses (native forest and coffee plantation), but at different depths, and those of the various weed management systems were compared in multiple scatter plots (Fig. 5 – 7) and using the test of homogeneity for comparison of regression lines (Snedecor & Cochran, 1989). In the multiple scatter plots, the entire soil moisture and the corresponding preconsolidation value data in the different sites are pulled together on a single graph. For the homogeneity test, two models are picked and compared together by examining the intercept (a), slope (b) and the homogeneity parameter data (F). To obtain a and b values in each model for comparison, the model equation in the exponential form (Eq. 1) was transformed into a linear model by computing the logarithm of both sides of the equation giving equation of the form (Eq. 2) (Dias Junior et al., 2005; Araujo-Junior et al., 2011).

$$\log \sigma\_p = \log 10^{\left(a + b\theta\right)} = \log \sigma\_p = a + b\theta \tag{2}$$

We observed that soils under natural forest and no-weed control exhibited the lowest load bearing capacities at the 0-3 cm depth when compared with those under the varied weed management system used in coffee plantation (Figure 5 to 7). This observation can be associated with initial soil bulk density and soil organic carbon content (Figure 1A and 1B) and be associated with the absence of stress history and anthropogenic activities on the soil under native forest. On the other hand, the weed control using mechanical mower exhibited the highest load bearing capacity at that depth (Figure 5). The final results are presented in Fig. 5 for the models of the sample collected from different weed management systems at depth 0–3 cm depth. Homogeneity tests of the regression equations (Snedecor & Cochran, 1989) indicated that the soil under hand hoeing and pre-emergence herbicide weed management; post-emergence herbicide and coffee tandem disk harrow weed management had the similar load bearing capacities at the 0-3 cm depth (Table 3). Therefore, the dataset of the homogeneous models were combined and a new equation was fitted to each data set, considering all the values of preconsolidation pressure and volumetric soil water content for these treatments (Figure 5). Generally, it was observed that the load bearing capacity for the Dystroferric Red Latosol under the different weed management systems at the soil surface(0-3 cm depth) decreases in a following order: mechanical mower > post-emergence herbicide = coffee tandem disk harrow > rotary tiller > hand hoeing = pre-emergence herbicide > natural forest > no-weed control (Figure 5). The highest soil load bearing capacity was observed for the Latosol under mechanical mower in 0–3 cm depth (Fig. 5). Others studies, have been shown that high traffic intensity necessary to satisfactory weed control in coffee plantation throughout the year (5 to 6 times) increases the risk of soil compaction (Silveira & Kurachi, 1984; Alcântara & Ferreira, 2000b; Silva et al., 2006) mainly in the rainy season (October to March) when the soils has high soil water content and consequently lower load bearing capacity (Silva et al., 2006) increases the soil susceptibility to compaction. On the other hand, when soil is drier present higher resistance to compression and high load bearing capacity that decreases soil susceptibility to compaction (Dias Junior et al., 2005; Araujo-Junior et al., 2008; 2011).

Our results suggested the mechanical mower had a greater potential for causing soil compaction due to high traffic intensity to satisfactory weed control through the year (5 operations) and this operation must be accomplished when the soil has water content lower than 0.30 cm3 cm-3 to minimize or avoid additional soil compaction.

al., 2005).

Interrelationships Among Weed Management in Coffee Plantation and Soil Physical Quality 231

(Etana et al., 1997) while increasing compressibility due to higher soil resilience (Zhang et

The hand hoeing, pre-emergence herbicide and rotary tilling weed management systems load bearing capacities models were intermediate in the behaviour for the studied depth relative to mechanical mowing (highest) and no weed control between coffee rows (lowest). At this depth, our results for the load bearing capacity models were similar to the obtained by Kurachi & Silverira (1984) starting from medium profiles of mechanical resistance of the profile of the soil under different weed management systems. These authors also observed that the mechanical mower was the implement that impact more on the soil strength,

> **NATIVE FOREST** <sup>σ</sup>**p = 10(2,57 - 0,71**<sup>θ</sup>**)**

> **WITHOUT HOE** <sup>σ</sup>**p = 10(2,65 - 1,26**<sup>θ</sup>**)**

> <sup>σ</sup>**p = 10(2,80 - 1,45**<sup>θ</sup>**)**

<sup>σ</sup>**p = 10(2,72 - 0,86**<sup>θ</sup>**)**

**ROTARY TILLER** <sup>σ</sup>**p = 10(2,74 - 1,14**<sup>θ</sup>**)**

**MECHANICAL MOWER** <sup>σ</sup>**p = 10(2,86 - 1,19**<sup>θ</sup>**)**

**<sup>R</sup>2 = 0,80\*\* n = 15** 

**<sup>R</sup>2 = 0,96\*\* n = 15**

**<sup>R</sup>2 = 0,86\*\* n = 30**

**<sup>R</sup>2 = 0,83\*\* n = 30**

**<sup>R</sup>2 = 0,83\*\* n = 15**

**<sup>R</sup>2 = 0,79\*\* n = 14**

**HAND HOE and PRE-EMERG. HERBIC.**

**POST-EMERG. and DISK HARROW**

followed by the herbicide sprayer and the rotary tilling.

**Dystroferric Red-Latosol 0-3 cm depth**

Fig. 5. Load bearing capacity models of a Dystroferric Red Latosol in 0–3 cm layer, cultivated with coffee plants affected by different weed management in interrows of the

**PRECOMPRESSION STRESS, kPa**

coffee plantation.

> **WATER CONTENT, cm3 cm-3 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50**


H: homogeneous; \*\* significant at 1 % probability level; \* significant at 5 % probability level; ns: not significant

Table 3. Comparison of the load bearing capacity models for homogeneity of a Dystroferric Red-Latosol at 0-3 cm depth under native forest and in a coffee plantation submitted to different weed management systems

According to Yang et al. (2007) the weed control in an orchard citrus by mowing three times during the growing season could improve soil and mitigate negative effects of weeds on crops. In the study by Zhang et al. (2006), it was observed that the first three passes of the tractor caused the largest increments in the mechanical resistance of the soil in the first 12cm depth. In conservation tillage systems, no - till management promotes higher soil organic carbon content and contribute to aggregate stability under loading, due to improved structural stability (Silva & Cabeda, 2006). Similarly, others authors have shown that increases in the soil organic carbon content reduces the adverse effects of soil compaction

Depth: 0-3 cm

HAND HOE vs PRE-EMERGENCE HERBICIE H ns ns

ROTARY-TILLING H \* ns

MECHANICAL MOWER H \*\* ns

NATIVE FOREST H \*\* ns

WEED CONTROL H \* \* POST-EMERGENCE HERBICIDE vs DISK HARROW H ns ns

ROTARY-TILLING H ns \*\*

MECHANICAL MOWER H \* ns

HAND HOE and PRE-EMERGENCE HERBICIDE H \*\* \*\*

NATIVE FOREST H \* \*\*

NO-WEED CONTROL H ns \*\* MECHANICAL MOWER vs ROTARY-TILLING H \*\* \*\* MECHANICAL MOWER vs NO-WEED CONTROL H ns \*\* NATIVE FOREST vs NO-WEED CONTROL H \*\* ns NATIVE FOREST vs ROTARY-TILLING H \* ns NATIVE FOREST vs MECHANICAL MOWER H \*\* \*\* H: homogeneous; \*\* significant at 1 % probability level; \* significant at 5 % probability level; ns: not

Table 3. Comparison of the load bearing capacity models for homogeneity of a Dystroferric Red-Latosol at 0-3 cm depth under native forest and in a coffee plantation submitted to

According to Yang et al. (2007) the weed control in an orchard citrus by mowing three times during the growing season could improve soil and mitigate negative effects of weeds on crops. In the study by Zhang et al. (2006), it was observed that the first three passes of the tractor caused the largest increments in the mechanical resistance of the soil in the first 12cm depth. In conservation tillage systems, no - till management promotes higher soil organic carbon content and contribute to aggregate stability under loading, due to improved structural stability (Silva & Cabeda, 2006). Similarly, others authors have shown that increases in the soil organic carbon content reduces the adverse effects of soil compaction

F

F

Intercept of regression, a

Angular coefficient, b

MANAGEMENT WEED SYSTEM

HAND HOE and PRE-EMERGENCE HERBICIDE vs

HAND HOE and PRE-EMERGENCE HERBICIDE vs

HAND HOE and PRE-EMERGENCE HERBICIDE vs NO-

POST-EMERGENCE HERBICIDE and DISK HARROW vs

POST-EMERGENCE HERBICIDE and DISK HARROW vs

POST-EMERGENCE HERBICIDE and DISK HARROW vs

POST-EMERGENCE HERBICIDE and DISK HARROW vs

POST-EMERGENCE HERBICIDE and DISK HARROW vs

significant

different weed management systems

HAND HOE and PRE-EMERGENCE HERB. vs

(Etana et al., 1997) while increasing compressibility due to higher soil resilience (Zhang et al., 2005).

The hand hoeing, pre-emergence herbicide and rotary tilling weed management systems load bearing capacities models were intermediate in the behaviour for the studied depth relative to mechanical mowing (highest) and no weed control between coffee rows (lowest). At this depth, our results for the load bearing capacity models were similar to the obtained by Kurachi & Silverira (1984) starting from medium profiles of mechanical resistance of the profile of the soil under different weed management systems. These authors also observed that the mechanical mower was the implement that impact more on the soil strength, followed by the herbicide sprayer and the rotary tilling.

Fig. 5. Load bearing capacity models of a Dystroferric Red Latosol in 0–3 cm layer, cultivated with coffee plants affected by different weed management in interrows of the coffee plantation.

(Figure 6).

**PRECOMPRESSION STRESS, kPa**

coffee plantation.

Interrelationships Among Weed Management in Coffee Plantation and Soil Physical Quality 233

values of precompression stress consequently, smaller load bearing capacity at all soil water content. The weed management systems of hand hoeing, rotary tilling and coffee tandem disk harrow had higher soil load bearing capacity at all soil water content (Figure 6). The disturbed soil on soil surface for these weed management favor the stress distribution to 16- 21 cm depth (Araujo-Junior et al., 2011), increases the soil load bearing capacity of the samples at the 10-13 cm depth, being the area mainly affected by the distributed stresses

**<sup>R</sup>2 = 0,77\*\* n = 15**

**<sup>R</sup>2 = 0,82\*\* n = 59**

**<sup>R</sup>2 = 0,79\*\* n = 45**

**WITHOUT HOE, POST-EMERG., MECHANICAL MOWER and PRE-EMERG. HERBICIDE**

**HAND HOE, ROTARY TILLER and DISK HARROW** 

**Dystroferric Red-Latosol 10-13 cm depth**

**NATIVE FOREST** <sup>σ</sup>**p = 10(2,61 - 0,90**<sup>θ</sup>**)**

<sup>σ</sup>**p = 10(2,78 - 1,36**<sup>θ</sup>**)**

<sup>σ</sup>**p = 10(2,77 - 1,08**<sup>θ</sup>**)**

**WATER CONTENT, cm3 cm-3 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50**

At the 25-28 cm depth, the weed management systems sets consisting of mechanical mowing , post-emergence herbicide and rotary tilling; hand hoeing, pre-emergence herbicide and coffee tandem disk harrow; resulted in homogenous load bearing capacity models (Table 5). Therefore, for each homogeneous set, the data set consisting all the values of preconsolidation pressure and volumetric soil water content were combined and a new equation was fitted (Figure 7). We observed that the load bearing capacity of the soils were similar and decreased in the following order: post-emergence herbicide = mechanical

Fig. 6. Load bearing capacity models of a Dystroferric Red Latosol in 10–13 cm layer, cultivated with coffee plants affected by different weed management in interrows of the

The homogeneity tests of the regression equations for the samples collected in the 10-13 cm depths showed that there were two homogeneous dataset. The mechanical mowing, preemergence herbicide, no-weed control and post-emergence herbicide; and rotary-tilling exhibited similarity, while hand hoeing, and coffee tandem disk harrowing were similar (Table 4). Therefore, for each homogeneous dataset, a new equation was fitted, combining all the values of preconsolidation pressure and volumetric soil water content (Figure 6).


H: homogeneous; \*\* significant at 1 % probability level; \* significant at 5 % probability level; ns: not significant

Table 4. Comparison of the load bearing capacity models for homogeneity of a Dystroferric Red-Latosol at 10-13 cm depth under native forest and in a coffee plantation submitted to different weed management systems.

In general, at 10-13 cm depth the load bearing capacity models for studied area under varying weed management systems were similar and decreased in the following order: hand hoeing = rotary tilling = coffee tandem disk harrow > no-weed control = postemergence herbicide = mechanical mower = pre-emergence herbicide > natural forest (Figure 6). These responses are associated with lowest soil bulk density value and the greatest soil organic carbon content of the soil under natural forest (Figure 1A and 1B). The lack of anthropogenic activities in the soil under natural forest provides the greater soil organic carbon content and smaller values of soil bulk density, which contribute to smaller

The homogeneity tests of the regression equations for the samples collected in the 10-13 cm depths showed that there were two homogeneous dataset. The mechanical mowing, preemergence herbicide, no-weed control and post-emergence herbicide; and rotary-tilling exhibited similarity, while hand hoeing, and coffee tandem disk harrowing were similar (Table 4). Therefore, for each homogeneous dataset, a new equation was fitted, combining all the values of preconsolidation pressure and volumetric soil water content (Figure 6).

HERBICIDE H ns ns

HERBICIDE vs NO-WEED CONTROL H ns ns

ROTARY-TILLING vs HAND HOE H ns ns

HARROW H ns ns

HARROW vs NATIVE FOREST H \*\* \*\*

H: homogeneous; \*\* significant at 1 % probability level; \* significant at 5 % probability level; ns: not

Table 4. Comparison of the load bearing capacity models for homogeneity of a Dystroferric Red-Latosol at 10-13 cm depth under native forest and in a coffee plantation submitted to

In general, at 10-13 cm depth the load bearing capacity models for studied area under varying weed management systems were similar and decreased in the following order: hand hoeing = rotary tilling = coffee tandem disk harrow > no-weed control = postemergence herbicide = mechanical mower = pre-emergence herbicide > natural forest (Figure 6). These responses are associated with lowest soil bulk density value and the greatest soil organic carbon content of the soil under natural forest (Figure 1A and 1B). The lack of anthropogenic activities in the soil under natural forest provides the greater soil organic carbon content and smaller values of soil bulk density, which contribute to smaller

F

Intercept of regression, a

Angular coefficient, b

H ns ns

H \*\* ns

H \* \*\*

MANAGEMENT WEED SYSTEM F

MECHANICAL MOWER vs PRE-EMERGENCE

MECHANICAL MOWER and PRE-EMERGENCE

MECHANICAL MOWER and PRE-EMERGENCE HERBICIDE and NO-WEED CONTROL vs POST-

ROTARY-TILLING and HAND HOE vs DISK

ROTARY-TILLING and HAND HOE and DISK

MECHANICAL MOWER and PRE-EMERGENCE HERBICIDE and NO-WEED CONTROL vs POST-EMERGENCE HERBICIDE vs NATIVE FOREST

MECHANICAL MOWER and PRE-EMERGENCE HERBICIDE and NO-WEED CONTROL vs POST-EMERGENCE HERBICIDE vs ROTARY-TILLING and

HAND HOE and DISK HARROW

different weed management systems.

significant

Depth: 10-13 cm

EMERGENCE HERBICIDE

values of precompression stress consequently, smaller load bearing capacity at all soil water content. The weed management systems of hand hoeing, rotary tilling and coffee tandem disk harrow had higher soil load bearing capacity at all soil water content (Figure 6). The disturbed soil on soil surface for these weed management favor the stress distribution to 16- 21 cm depth (Araujo-Junior et al., 2011), increases the soil load bearing capacity of the samples at the 10-13 cm depth, being the area mainly affected by the distributed stresses (Figure 6).

Fig. 6. Load bearing capacity models of a Dystroferric Red Latosol in 10–13 cm layer, cultivated with coffee plants affected by different weed management in interrows of the coffee plantation.

At the 25-28 cm depth, the weed management systems sets consisting of mechanical mowing , post-emergence herbicide and rotary tilling; hand hoeing, pre-emergence herbicide and coffee tandem disk harrow; resulted in homogenous load bearing capacity models (Table 5). Therefore, for each homogeneous set, the data set consisting all the values of preconsolidation pressure and volumetric soil water content were combined and a new equation was fitted (Figure 7). We observed that the load bearing capacity of the soils were similar and decreased in the following order: post-emergence herbicide = mechanical

Interrelationships Among Weed Management in Coffee Plantation and Soil Physical Quality 235

rotary tilling increased the soil's mechanical resistance in the moisture levels of 15 cm3 cm-3 and 20 cm3 cm-3, when compared to hand hoeing. Looking at data presented in Figure 7, it is possible to conclude that, even with the absence of mechanical soil disturbance weed management systems, the soil can still be compacted when wet, when stresses travel up to a

**<sup>R</sup>2 = 0,90\*\* n = 14**

**<sup>R</sup>2 = 0,82\*\* n = 15**

**<sup>R</sup>2 = 0,87\*\* n = 43**

**<sup>R</sup>2 = 0,80\*\* n = 44**

**HAND HOE, PRE-EMERG. and DISK HARROW**

**POST-EMERG., MECHANICAL MOWER and ROTARY TILLER** 

**NATIVE FOREST** <sup>σ</sup>**p = 10(2,66 - 1,11**θ**)**

**WITHOUT HOE** <sup>σ</sup>**p = 10(2,66 - 0,93**θ**)**

<sup>σ</sup>**p = 10(2,77 - 1,40**θ**)**

<sup>σ</sup>**p = 10(2,85 - 1,41**θ**)**

**Dystroferric Red-Latosol 25-28 cm depth**

**WATER CONTENT, cm3 cm-3**

Fig. 7. Load bearing capacity models of a Dystroferric Red Latosol in 25–28 cm layer, cultivated with coffee plants affected by different weed management in interrows of the

**3.4.2 Critical volumetric soil water content for traffic of tractor based on soil load** 

According to Hillel (1980) soil moisture is the most important soil physical properties to determine soil-machine interactions. This soil physical property also, governs soil deformation when submitted to external loads (Dias Junior, 1994; Dias Junior & Pierce, 1996). To determine the critical volumetric soil water content (θcritical) for traffic of

**0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50**

depth of 25-28 cm (Figure 7).

**PRECOMPRESSION STRESS, kPa**

coffee plantation.

**bearing capacity**


mower = rotary tilling > hand hoeing = pre-emergence herbicide = coffee tandem disk harrow > no-weed control > natural forest (Figure 7).

H: homogeneous; \*\* significant at 1 % probability level; \* significant at 5 % probability level; ns: not significant

Table 5. Comparison of the load bearing capacity models for homogeneity of a Dystroferric Red-Latosol at 25-28 cm depth under native forest and in a coffee plantation submitted to different weed management systems.

The weed management systems consisting of post-emergence herbicide, mechanical mowing and rotary tilling resulted in most comparisons, higher soil load bearing capacity for the Latosol, indicating that the effect of the traffic of machines in mechanical weed control induced the compaction of the soil in sub-soil region. Kurachi & Silveira (1984) suggest that the weed managements systems that involve the disturbance of the soil had the tendency to increase compaction at the surface; when there is no disturbance, increase compaction is more accentuated starting from the depth of operation of the equipment. However, our result show that the herbicide applicator and mechanical mower as well as

mower = rotary tilling > hand hoeing = pre-emergence herbicide = coffee tandem disk

Depth: 25–28 cm

HERBICIDE H ns ns

HERBICIDE vs ROTARY-TILLING H ns ns HAND HOE vs PRE-EMERGENCE HERBICIDE H ns ns

DISK HARROW H ns ns

DISK HARROW vs NO-WEED CONTROL NH \*\* \*\*

DISK HARROW vs NATIVE FOREST NH \*\* ns

NATIVE FOREST vs NO-WEED CONTROL H ns \*\*

H: homogeneous; \*\* significant at 1 % probability level; \* significant at 5 % probability level; ns: not

Table 5. Comparison of the load bearing capacity models for homogeneity of a Dystroferric Red-Latosol at 25-28 cm depth under native forest and in a coffee plantation submitted to

The weed management systems consisting of post-emergence herbicide, mechanical mowing and rotary tilling resulted in most comparisons, higher soil load bearing capacity for the Latosol, indicating that the effect of the traffic of machines in mechanical weed control induced the compaction of the soil in sub-soil region. Kurachi & Silveira (1984) suggest that the weed managements systems that involve the disturbance of the soil had the tendency to increase compaction at the surface; when there is no disturbance, increase compaction is more accentuated starting from the depth of operation of the equipment. However, our result show that the herbicide applicator and mechanical mower as well as

F

F

Intercept of regression, a

Angular coefficient, b

NH \*\* \*\*

H \*\* \*

H \*\* \*

harrow > no-weed control > natural forest (Figure 7).

MECHANICAL MOWER vs POST-EMERGENCE

MECHANICAL MOWER and POST-EMERGENCE

HAND HOE and PRE-EMERGENCE HERBICIDE vs

HAND HOE and PRE-EMERGENCE HERBICIDE and

HAND HOE and PRE-EMERGENCE HERBICIDE and

HAND HOE and PRE-EMERGENCE HERBICIDE and DISK HARROW vs MECHANICAL MOWER and POST-EMERGENCE HERBICIDE and ROTARY-

MECHANICAL MOWER and POST-EMERGENCE HERBICIDE and ROTARY-TILLING vs NO-WEED

MECHANICAL MOWER and POST-EMERGENCE HERBICIDE and ROTARY-TILLING vs NATIVE

different weed management systems.

MANAGEMENT WEED SYSTEM

TILLING

CONTROL

FOREST

significant

rotary tilling increased the soil's mechanical resistance in the moisture levels of 15 cm3 cm-3 and 20 cm3 cm-3, when compared to hand hoeing. Looking at data presented in Figure 7, it is possible to conclude that, even with the absence of mechanical soil disturbance weed management systems, the soil can still be compacted when wet, when stresses travel up to a depth of 25-28 cm (Figure 7).

Fig. 7. Load bearing capacity models of a Dystroferric Red Latosol in 25–28 cm layer, cultivated with coffee plants affected by different weed management in interrows of the coffee plantation.

#### **3.4.2 Critical volumetric soil water content for traffic of tractor based on soil load bearing capacity**

According to Hillel (1980) soil moisture is the most important soil physical properties to determine soil-machine interactions. This soil physical property also, governs soil deformation when submitted to external loads (Dias Junior, 1994; Dias Junior & Pierce, 1996). To determine the critical volumetric soil water content (θcritical) for traffic of

Interrelationships Among Weed Management in Coffee Plantation and Soil Physical Quality 237

be used to define the optimum moisture content for machine traffic without degrading the

Our results reveal that the weed management system and traffic by machines had a great influence on soil physical quality attributes, mainly on the surface soil (0–3 cm depth) on the inherent strength. The greatest changes in the Latosol structure were observed under mechanical mowing, disk harrowing and pre-emergence herbicide weed management. These observations are related to the applied stress by the machines and direct raindrop impacts to bare soil systems that favored crust formation, thereby increasing the soil strength on the soil surface. In addition, weed control practices that result in the total removal of the soil cover was more prone to compaction due to applied soil stress by

The soil load bearing capacity and the water content at the time of the traffic machines are the most important soil physical properties; thus these attributes must be considered to minimize additional soil compaction and soil structure damage on coffee plantations under different weed management systems. Recommendations for the sustainable weed management system in coffee plantation must consider the inherent internal strength of the

The authors are grateful to Brazilian Consortium for Research and Coffee Development (CBP&D – Café) provided financial support for this study and CAPES agency a

Ajayi, A. E.; Dias Junior, M. de S.; Curi, N.; Araujo-Junior, C. F.; Souza, T. T. T. & Inda

Alakukku, L.; Weisskopf, P.; Chamen, W. C. T.; Tijink, F. G. J.; van der Linden, J. P.; Pires, S.;

Alcântara, E. N. & Ferreira, M. M. (2000b). Efeitos de métodos de controle de plantas

No.4, (October to December 2000), pp. 711–721, ISSN 1806-9657

*Research*, Vol. 73, No. 1/2, (October 2003), pp. 145–160, ISSN 0167-1987 Alcântara, E. N. & Ferreira, M. M. (2000a). Efeito de diferentes métodos de controle de

Junior, A. V. (2009). Strength attributes and compaction susceptibility of Brazilian Latosols. *Soil & Tillage Research*, Vol. 105, No. 1, (September 2009), pp. 122–127,

Sommer, C. & Spoor, G. (2003). Prevention strategies for field traffic-induced subsoil compaction: a review Part 1. Machines/soil interactions. *Soil & Tillage* 

plantas daninhas sobre a produção de cafeeiros instalados em Latossolo Roxo distrófico. (In Portuguese, with English abstract). *Ciência & Agrotecnologia*, Vol.24,

daninhas na cultura do cafeeiro (*Coffea arabica* L.) sobre a qualidade física do solo. (In Portuguese, with English abstract). *Revista Brasileira de Ciência do Solo*, Vol.24,

governmental in scholarship to Dr. Cezar Francisco Araujo Junior.

No.1, (January 2000), pp. 54–61, ISSN 1413-7054.

soil structure.

**4. Conclusions** 

machines and equipments.

**5. Acknowledgments** 

ISSN 0167-1987

**6. References** 

soil expressed by precompression stress.

machines and tools, we considered only those stress that can cause additional soil compaction or change the initial state of the soil structure, and are considered that stress do not exceed internal strength expressed by precompression stress (Araujo-Junior et al., 2011). The maximum vertical stress exerted by the tractor and equipments (σmax) and the stress distribution in various wheeled and soil conditions were obtained using the Tyres/Tracks and Soil Compaction-TASC program (Diserens, 2005).

The maximum stress exerted by a tractor Valmet® model 68 was 220 kPa for front tyres 6-16 inflation pressure 172 kPa. The lowest critical water content was 0.27 cm3 cm-3 for the Dystroferric Red Latosol in the without hoe no inter-rows control at the 0–3 cm depth and the higher 0.48 cm3 cm-3 for the soil managed with pre-emergence herbicide in the 0–3 cm layer.

Fig. 8. Soil load bearing capacity models of a Dystroferric Red Latosol in 0–3, 10–13 and 25– 28 cm layers, cultivated with coffee plants affected by different weed management in interrows in coffee plantation. ROÇA: mechanical mower. The dotted vertical line represents critical water content (θcritical) for tractor traffic above the soil under mechanical mower management. The dotted horizontal line represents the maximum vertical stress exerted by a tractor (σmax).

Our results show that load bearing capacity models might be useful to assess the effect of the weed management on soil strength or inherent ability of the soil samples to withstand applied pressure without degrading their structure. Also, this soil mechanic approach could be used to define the optimum moisture content for machine traffic without degrading the soil structure.

### **4. Conclusions**

236 Weed Control

machines and tools, we considered only those stress that can cause additional soil compaction or change the initial state of the soil structure, and are considered that stress do not exceed internal strength expressed by precompression stress (Araujo-Junior et al., 2011). The maximum vertical stress exerted by the tractor and equipments (σmax) and the stress distribution in various wheeled and soil conditions were obtained using the Tyres/Tracks

The maximum stress exerted by a tractor Valmet® model 68 was 220 kPa for front tyres 6-16 inflation pressure 172 kPa. The lowest critical water content was 0.27 cm3 cm-3 for the Dystroferric Red Latosol in the without hoe no inter-rows control at the 0–3 cm depth and the higher 0.48 cm3 cm-3 for the soil managed with pre-emergence herbicide in the 0–3 cm

 **R2 = 0,72\*\* n = 45**

**0-3 e 10-13 e 25-28 cm** <sup>σ</sup>**p = 10(2,85 - 1,46**<sup>θ</sup>**)**

**Dystroferric Red Latosol Interrows - mechanical mower**

σ**máx = 220 kPa** 

**VOLUMETRIC WATER CONTENT, cm3** 

Fig. 8. Soil load bearing capacity models of a Dystroferric Red Latosol in 0–3, 10–13 and 25– 28 cm layers, cultivated with coffee plants affected by different weed management in interrows in coffee plantation. ROÇA: mechanical mower. The dotted vertical line

represents critical water content (θcritical) for tractor traffic above the soil under mechanical mower management. The dotted horizontal line represents the maximum vertical stress

Our results show that load bearing capacity models might be useful to assess the effect of the weed management on soil strength or inherent ability of the soil samples to withstand applied pressure without degrading their structure. Also, this soil mechanic approach could

**0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50**

**cm -3**

**critical = 0,35 cm3 cm-3**

and Soil Compaction-TASC program (Diserens, 2005).

layer.

**PRECOMPRESSION STRESS, kPa**

exerted by a tractor (σmax).

Our results reveal that the weed management system and traffic by machines had a great influence on soil physical quality attributes, mainly on the surface soil (0–3 cm depth) on the inherent strength. The greatest changes in the Latosol structure were observed under mechanical mowing, disk harrowing and pre-emergence herbicide weed management. These observations are related to the applied stress by the machines and direct raindrop impacts to bare soil systems that favored crust formation, thereby increasing the soil strength on the soil surface. In addition, weed control practices that result in the total removal of the soil cover was more prone to compaction due to applied soil stress by machines and equipments.

The soil load bearing capacity and the water content at the time of the traffic machines are the most important soil physical properties; thus these attributes must be considered to minimize additional soil compaction and soil structure damage on coffee plantations under different weed management systems. Recommendations for the sustainable weed management system in coffee plantation must consider the inherent internal strength of the soil expressed by precompression stress.

### **5. Acknowledgments**

The authors are grateful to Brazilian Consortium for Research and Coffee Development (CBP&D – Café) provided financial support for this study and CAPES agency a governmental in scholarship to Dr. Cezar Francisco Araujo Junior.

#### **6. References**


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**1. Introduction** 

**12** 

*Estonia* 

**Weed Responses to Soil** 

Endla Reintam and Jaan Kuht *Estonian University of Life Sciences,* 

**Compaction and Crop Management** 

Soil compaction first affects physical properties, as compaction occurs when soil particles are pressed together, reducing pore space between them and increasing the soil bulk density (Lipiec & Hatano, 2003; Raper, 2005; Reintam, 2006; Reintam et al., 2009). Soil compaction also influences chemical and biological processes, such as decreasing organic carbon (C) and N mineralization, the concentration of CO2 in the soil (Conlin & Driessche, 2000), nitrification and denitrification, and activity of earthworms and other soil organisms (Ferrero et al., 2002). At high soil moisture, the difference in soil resistance between noncompacted and compacted soil is low and may be smaller than the value that limits root growth (>2 MPa). But as the soil dries, soil compaction is more observable (Hamza & Anderson, 2005). Further soil compaction effects are decreased root size, retarded root penetration, smaller rooting depth (Unger, and Kaspar, 1994), decreased plant nutrient availability and uptake (Kuchenbuch & Ingram, 2003; Reintam, 2006), and greater plant stress (Reintam et al., 2003), which are among the major reasons for reduced plant

When estimating the decreased plant productivity in agro-ecosystems due to compaction, the greatest attention is usually paid to cultivated plant yields. On arable land, different weed species communities exist not only due to the different type of soil, but also because of cultivated plant diversity in agro-ecosystem, in response to different cultures, management intensity, and agro-ecosystems isolation from natural vegetation (van Elsen, 2000). Changing tillage practices consequently changes plant species composition, vertical distribution, and density of weed seed banks in agricultural soils (Buhler, 2002; Carter & Ivany, 2005). Pollard and Cussans (1981) reported that most weeds showed no consistent response to tillage and Derksen et al. (1993) suggested that composition changes in weed communities were influenced more by environmental factors (location and year) than by tillage systems. However, many weed species are more tolerant to poor soil conditions than cultivated plants. Because weeds are more efficient in nutrient uptake, the nutrient content

of a crop decreases when competition with weeds increases (Koch & Köcher, 1968).

The composition of weed community is widely reported in intensive management systems. In experiments in Norway, there were no changes in the weed community during five years, even at the highest herbicide intensities (Fykse & Wærnhus, 1999). However, changing

productivity and yield (Arvidsson, 1999; Reintam et al., 2009).


## **Weed Responses to Soil Compaction and Crop Management**

Endla Reintam and Jaan Kuht

*Estonian University of Life Sciences, Estonia* 

#### **1. Introduction**

242 Weed Control

Uhland, R. E. (1949). Physical properties of soils as modified by crops and management. *Soil* 

van Genuchten, M. Th. (1980). A closed-form equation for predicting the hydraulic

Vogeler, I.; Horn, R.; Wetzel, H. & Krümmbelbein, J. (2006). Tillage effects on soil strength

Yang, Y.; Wang, H.; Tang, J. & Chen, X. (2007). Effects of weed management practices on

Zhang, B.; Horn, R. & Hallet, P. D. (2005). Mechanical resilience of degraded soil amended

Zhang, X. Y.; Cruse, R. M.; Sui, Y. Y.; Jhao, Z. (2006). Soil compaction induced by small

*Research*, Vol. 93, No. 1, (March 2007), pp. 179–185, ISSN 0167-1987

conductivity of unsaturated soils. *Soil Science Society of America Journal*, Vol.44, No.

and solute transport. *Soil & Tillage Research*, Vol. 88, No. 1/2, (July 2006), pp. 193–

orchard soil biological and fertility properties in southeatern China. *Soil & Tillage* 

with organic matter. *Soil Science Society of America Journal*, Vol.69, No. 3, (May 2005),

tractor traffic in northeast China. *Soil Science Society of America Journal*, Vol.70, No.1,

*Science Society Proceedings*, (August 1949), pp. 361–366

5, (September 1980), pp. 892–898, ISSN 0361-5995

204, ISSN 0167-1987

pp. 450–457, ISSN 0361-5995

(Feb. 2006), pp. 613–619, ISSN 0361-5995

Soil compaction first affects physical properties, as compaction occurs when soil particles are pressed together, reducing pore space between them and increasing the soil bulk density (Lipiec & Hatano, 2003; Raper, 2005; Reintam, 2006; Reintam et al., 2009). Soil compaction also influences chemical and biological processes, such as decreasing organic carbon (C) and N mineralization, the concentration of CO2 in the soil (Conlin & Driessche, 2000), nitrification and denitrification, and activity of earthworms and other soil organisms (Ferrero et al., 2002). At high soil moisture, the difference in soil resistance between noncompacted and compacted soil is low and may be smaller than the value that limits root growth (>2 MPa). But as the soil dries, soil compaction is more observable (Hamza & Anderson, 2005). Further soil compaction effects are decreased root size, retarded root penetration, smaller rooting depth (Unger, and Kaspar, 1994), decreased plant nutrient availability and uptake (Kuchenbuch & Ingram, 2003; Reintam, 2006), and greater plant stress (Reintam et al., 2003), which are among the major reasons for reduced plant productivity and yield (Arvidsson, 1999; Reintam et al., 2009).

When estimating the decreased plant productivity in agro-ecosystems due to compaction, the greatest attention is usually paid to cultivated plant yields. On arable land, different weed species communities exist not only due to the different type of soil, but also because of cultivated plant diversity in agro-ecosystem, in response to different cultures, management intensity, and agro-ecosystems isolation from natural vegetation (van Elsen, 2000). Changing tillage practices consequently changes plant species composition, vertical distribution, and density of weed seed banks in agricultural soils (Buhler, 2002; Carter & Ivany, 2005). Pollard and Cussans (1981) reported that most weeds showed no consistent response to tillage and Derksen et al. (1993) suggested that composition changes in weed communities were influenced more by environmental factors (location and year) than by tillage systems. However, many weed species are more tolerant to poor soil conditions than cultivated plants. Because weeds are more efficient in nutrient uptake, the nutrient content of a crop decreases when competition with weeds increases (Koch & Köcher, 1968).

The composition of weed community is widely reported in intensive management systems. In experiments in Norway, there were no changes in the weed community during five years, even at the highest herbicide intensities (Fykse & Wærnhus, 1999). However, changing

Weed Responses to Soil Compaction and Crop Management 245

during vegetation period 2–3 generations, but the young plants have a higher mineral content than more mature plants (Bockholt & Schnittke, 1996). Chickweed emerges continually from spring to autumn and starts flowering within one or two months after emergence. Chickweed seed germinate in response to soil disturbance rather than seasonal cues (Miura & Kusanagi, 2001). Both species, corn mayweed and chickweed, tolerate compacted soil (Reintam et al., 2006). Walter et al. (2002) found that chickweed was positively cross-correlated with clay and

The objective of our experiment was to investigate continuous soil compaction effects on plant community composition and nutrient content in some of the most widespread weed

Data presented in current chapter were collected from the research field at the Estonian University of Life Sciences (58º23´N, 26º44´E) on a sandy loam soil, *Stagnic Luvisol*, at Tartu

Soil compaction was accomplished using a 4.9 Mg tractor MTZ-82 before sowing time in spring 2001, 2002, 2003 and 2004. Passes of one, three and six passes with a wheeled vehicle loaded with 2.22 Mg on the first axle and 2.62 Mg on the rear axle (total load was 4.84 Mg) uniformly covered the entire experimental plot area. The inflation pressures in the wheels of the tractor were 150 kPa. An area without applied compaction served as the control, thus four compaction treatments were established on the experimental field. The compaction treatments were split to four replications and the size of each experimental plot (16 plots) was 12 x 9 m (108 m2). Direct seeding of barley utilizing a drill (crosswise to compaction treatments) in rate of 450 germinating seeds per m2 was accomplished in the middle of May. No fertilizers and herbicides were applied to decrease interactions during the compaction investigation on weed species and barley. Every autumn (in September) the soil was

Soil was classified a sandy loam *Stagnic Luvisol* according to the WRB 1998 classification. From the genetic and diagnostic horizons the humus (32 cm), ferralic accumulation (8 cm), stagnic (10 cm) and argillic (29 cm) horizons were defined in the soil. The soil characteristics of the humus horizon (in beginning of experiment in 2001) are presented as follows: C 1.4%, N 0.11%, K 164 mg kg–1, P 183 mg kg–1, Ca 674 mg kg–1, Mg 101 mg kg–1, pHKCl 6.2, sand (2.0–0.02 mm) 67.9%, silt (0.02–0.002 mm) 22.9% and clay (<0.002 mm) 9.2%. The investigated soil formed on bisequal-textured reddish-brown till and is sensitive to soil compaction. This type of soil covers 5.9% of the total area, and 15.1% of the arable land in

The sampling of soil and plants were accomplished in the earing phase of barley in growth stage 75–79 by numeric code description according by BBCH Growth Scale of plants. All

Estonia, mostly in southern and south-eastern part (Reintam& Köster, 2006).

negatively cross-correlated with pH and potassium (K) content.

species found in barley (*Hordeum vulgare* L.) production.

**2. Material and methods** 

County in 2001–2004.

**2.1 Experiment design** 

ploughed to the 0.21– 0.22 m depth.

**2.2 Soil description** 

**2.3 Field sampling** 

tillage or management intensity and soil physical parameters, following compaction, caused changes in weed flora. Without regular ploughing, selection for annual weeds decreases and selection for perennial weeds increases. On the other hand, in the experiments of Carter and Ivany (2005), direct seeding did not reduce the soil weed seed bank, but mouldboard ploughing for 14 years did reduce the weeds seed bank. Soil compaction caused by traffic (Jurik & Zhang ShuYu, 1999), or soil compaction in a first year's no-tillage system (Lampurlanés & Cantero-Martínez, 2003) changes dominant weed species in the community due to higher soil bulk density and penetration resistance. Many investigations have compared conventional tillage to reduced- or no-tillage systems and reported increasing numbers of perennial weed species, such couch grass (*Elytrigia repens* L.), Canadian thistle (*Cirsium arvense* L.), perennial sow thistle (*Sonchus arvensis* L.), and decrease of cultivated plant production (Blackshaw et al., 2001; Reintam et al., 2008) under no-tillage systems. Stevenson *et al*. (1998) reported that the reduction in midseason dry weight of 36% and seed yield of 59% of barley whole plant weight due to the chisel plough relative to the mouldboard plough treatment. Yield loss in this experiment was associated with interference from broadleaf plantain (*Plantago major* L.) and dandelion (*Taraxacum officinale* Weber in Wiggers). In central Iowa, a single wheel-tracking pass at crop sowing increased the cumulative number of seedlings of giant (*Setaria faberi* L.) and yellow foxtails (*S. glauca* L. [*S. pumila*]) by 187%, common water hemp (*Amaranthus rudis* L.) by 102% and common lambsquarter (*Chenopodium album* L.) by 30%. Researchers have suggested that compaction from wheel traffic apparently did not create a physical impediment to emergence; rather, it altered micro-environmental conditions in ways that stimulated weed germination and emergence (Jurik & Zhang ShuYu, 1999). Tillage effect on soil properties influences both number and diversity of weed populations (Hooker et al., 1997).

Most weeds have higher dry matter nutrient content than crops. Certain weed species have a lower optimal N requirement than crops, giving those weeds a competitive advantage in some situations (Di Tomaso, 1995). When growing with cereal crops, weeds can benefit from fertilizers (Bischoff & Mahn, 2000) irrespective of fertilizer placement (Salonen, 1992). On the other hand, many emerging weeds gain little advantage from fertilization when competing with established crops because of light competition. Nitrogen application rate weakly influences the weed flora (Andersson & Milberg, 1998); soil tillage influenced weeds more than the source of nutrients (McCloskey et al., 1996). Corn spurry (*Spergula arvensis* L.) is reported to be dominant on sandy soils and also clay soils where soil fertility and the competition with other plants are low (Mahn & Muslemanie, 1989). In addition, dry matter of corn spurry grown alone increased with increasing N up to 60 kg ha-1. Competition from rye (*Secale cereale* L.) severely reduced dry matter production of corn spurry and the weed itself was only weakly competitive under increasing N rates. Furthermore, common lambsquarter is reported to dominant in biomass where N was applied, while corn spurry and shepherds-purse (*Capsella bursa-pastoris* L.) dominated on experimental plots without N (Mahn & Muslemanie, 1989). Common lambsquarter and wild mustard (*Sinapis arvensis* L.) are the most widespread weed species on mouldboard ploughed, nutrient rich, neutral soil (Zanin et al., 1997). However, common lambsquarter and wild mustard are not the major species present in cases of low fertility and dense soil (Shrestha et al., 2002).

Plant age plays an essential role on nutrient uptake by weeds. Some weed species, such as corn mayweed (*Matricaria inodora* L.) and common chickweed (*Stellaria media* (L.) Vill.), grow during vegetation period 2–3 generations, but the young plants have a higher mineral content than more mature plants (Bockholt & Schnittke, 1996). Chickweed emerges continually from spring to autumn and starts flowering within one or two months after emergence. Chickweed seed germinate in response to soil disturbance rather than seasonal cues (Miura & Kusanagi, 2001). Both species, corn mayweed and chickweed, tolerate compacted soil (Reintam et al., 2006). Walter et al. (2002) found that chickweed was positively cross-correlated with clay and negatively cross-correlated with pH and potassium (K) content.

The objective of our experiment was to investigate continuous soil compaction effects on plant community composition and nutrient content in some of the most widespread weed species found in barley (*Hordeum vulgare* L.) production.

#### **2. Material and methods**

244 Weed Control

tillage or management intensity and soil physical parameters, following compaction, caused changes in weed flora. Without regular ploughing, selection for annual weeds decreases and selection for perennial weeds increases. On the other hand, in the experiments of Carter and Ivany (2005), direct seeding did not reduce the soil weed seed bank, but mouldboard ploughing for 14 years did reduce the weeds seed bank. Soil compaction caused by traffic (Jurik & Zhang ShuYu, 1999), or soil compaction in a first year's no-tillage system (Lampurlanés & Cantero-Martínez, 2003) changes dominant weed species in the community due to higher soil bulk density and penetration resistance. Many investigations have compared conventional tillage to reduced- or no-tillage systems and reported increasing numbers of perennial weed species, such couch grass (*Elytrigia repens* L.), Canadian thistle (*Cirsium arvense* L.), perennial sow thistle (*Sonchus arvensis* L.), and decrease of cultivated plant production (Blackshaw et al., 2001; Reintam et al., 2008) under no-tillage systems. Stevenson *et al*. (1998) reported that the reduction in midseason dry weight of 36% and seed yield of 59% of barley whole plant weight due to the chisel plough relative to the mouldboard plough treatment. Yield loss in this experiment was associated with interference from broadleaf plantain (*Plantago major* L.) and dandelion (*Taraxacum officinale* Weber in Wiggers). In central Iowa, a single wheel-tracking pass at crop sowing increased the cumulative number of seedlings of giant (*Setaria faberi* L.) and yellow foxtails (*S. glauca* L. [*S. pumila*]) by 187%, common water hemp (*Amaranthus rudis* L.) by 102% and common lambsquarter (*Chenopodium album* L.) by 30%. Researchers have suggested that compaction from wheel traffic apparently did not create a physical impediment to emergence; rather, it altered micro-environmental conditions in ways that stimulated weed germination and emergence (Jurik & Zhang ShuYu, 1999). Tillage effect on soil properties influences both

Most weeds have higher dry matter nutrient content than crops. Certain weed species have a lower optimal N requirement than crops, giving those weeds a competitive advantage in some situations (Di Tomaso, 1995). When growing with cereal crops, weeds can benefit from fertilizers (Bischoff & Mahn, 2000) irrespective of fertilizer placement (Salonen, 1992). On the other hand, many emerging weeds gain little advantage from fertilization when competing with established crops because of light competition. Nitrogen application rate weakly influences the weed flora (Andersson & Milberg, 1998); soil tillage influenced weeds more than the source of nutrients (McCloskey et al., 1996). Corn spurry (*Spergula arvensis* L.) is reported to be dominant on sandy soils and also clay soils where soil fertility and the competition with other plants are low (Mahn & Muslemanie, 1989). In addition, dry matter of corn spurry grown alone increased with increasing N up to 60 kg ha-1. Competition from rye (*Secale cereale* L.) severely reduced dry matter production of corn spurry and the weed itself was only weakly competitive under increasing N rates. Furthermore, common lambsquarter is reported to dominant in biomass where N was applied, while corn spurry and shepherds-purse (*Capsella bursa-pastoris* L.) dominated on experimental plots without N (Mahn & Muslemanie, 1989). Common lambsquarter and wild mustard (*Sinapis arvensis* L.) are the most widespread weed species on mouldboard ploughed, nutrient rich, neutral soil (Zanin et al., 1997). However, common lambsquarter and wild mustard are not the major

number and diversity of weed populations (Hooker et al., 1997).

species present in cases of low fertility and dense soil (Shrestha et al., 2002).

Plant age plays an essential role on nutrient uptake by weeds. Some weed species, such as corn mayweed (*Matricaria inodora* L.) and common chickweed (*Stellaria media* (L.) Vill.), grow Data presented in current chapter were collected from the research field at the Estonian University of Life Sciences (58º23´N, 26º44´E) on a sandy loam soil, *Stagnic Luvisol*, at Tartu County in 2001–2004.

#### **2.1 Experiment design**

Soil compaction was accomplished using a 4.9 Mg tractor MTZ-82 before sowing time in spring 2001, 2002, 2003 and 2004. Passes of one, three and six passes with a wheeled vehicle loaded with 2.22 Mg on the first axle and 2.62 Mg on the rear axle (total load was 4.84 Mg) uniformly covered the entire experimental plot area. The inflation pressures in the wheels of the tractor were 150 kPa. An area without applied compaction served as the control, thus four compaction treatments were established on the experimental field. The compaction treatments were split to four replications and the size of each experimental plot (16 plots) was 12 x 9 m (108 m2). Direct seeding of barley utilizing a drill (crosswise to compaction treatments) in rate of 450 germinating seeds per m2 was accomplished in the middle of May. No fertilizers and herbicides were applied to decrease interactions during the compaction investigation on weed species and barley. Every autumn (in September) the soil was ploughed to the 0.21– 0.22 m depth.

#### **2.2 Soil description**

Soil was classified a sandy loam *Stagnic Luvisol* according to the WRB 1998 classification. From the genetic and diagnostic horizons the humus (32 cm), ferralic accumulation (8 cm), stagnic (10 cm) and argillic (29 cm) horizons were defined in the soil. The soil characteristics of the humus horizon (in beginning of experiment in 2001) are presented as follows: C 1.4%, N 0.11%, K 164 mg kg–1, P 183 mg kg–1, Ca 674 mg kg–1, Mg 101 mg kg–1, pHKCl 6.2, sand (2.0–0.02 mm) 67.9%, silt (0.02–0.002 mm) 22.9% and clay (<0.002 mm) 9.2%. The investigated soil formed on bisequal-textured reddish-brown till and is sensitive to soil compaction. This type of soil covers 5.9% of the total area, and 15.1% of the arable land in Estonia, mostly in southern and south-eastern part (Reintam& Köster, 2006).

#### **2.3 Field sampling**

The sampling of soil and plants were accomplished in the earing phase of barley in growth stage 75–79 by numeric code description according by BBCH Growth Scale of plants. All

Weed Responses to Soil Compaction and Crop Management 247

The one-way, two-way and three-way analysis of variance (ANOVA) was used to determine the impact of trial factors based on the collected data. Soil bulk density (soil compaction), and fertilization rate were considered fixed effects while year was considered random. The significance of experiment factors was calculated using the Fischer test and the level of significance *P*<0.05 was used. To compare the differences between values the standard Student's *t*-test was used and least significant differences (LSD) at significance *P*<0.05. Correlation analysis was also used to process the data. The program Statistica 7.0 was used

The values of soil bulk density and penetration resistance (Fig. 1 and 2) changed among the experiment years due to the different weather conditions during sampling. The values of both, penetration resistance and bulk density depend on the soil moisture content (Fig. 3). However, the effect of traffic on the soil properties was significant in every experimental year between the non-compacted and the six times compacted soil. The differences in soil penetration resistance between one and three times compacted soil were significant after four years of soil compaction (Fig. 2). In average of the four years data there were no significant differences between those treatments. However, the six passes increased the soil

Soil compaction did not caused significant (p<0.05) differences in soil nutrient uptake in first year (Table 1). After four years with continuous compaction and without fertilizers use, significant positive effects of soil compaction on organic carbon, total nitrogen, available phosphorus and potassium content were detected in soil. Compaction by one pass, three and six passes increased organic carbon content 13%, 32% and 39%, respectively, compared to non-compacted soil. Three and six passes increased total nitrogen content 16% and 9%, available phosphorus content 17% and 7%, and potassium 16% and 66%, respectively, compared to non-compacted soil. One pass compaction did not cause significant changes in nitrogen and phosphorus content. Without fertilizer use, the carbon phosphorus and potassium content decreased with four years in the control by one time and three pass compacted treatments. The highest decrease by was in non-compacted soil, where the decrease was 28% in case of carbon, 13% in case phosphorus and 41% in case of potassium. In six times compacted soil the content of carbon was 4%, content of nitrogen 23% and

Compaction effect on soil properties depended on the machinery weight and number of passes, but also from soil moisture and weather conditions during compaction treatment application and vegetation period. Moist soil is more sensitive to soil compaction than dry. Low impact of soil compaction on soil bulk density and penetration resistance in the first year was caused by dry soil (110 g kg-1) at the application of compaction and the subsequent rainy growing season following application. In following years, at the time of compaction application the soil was moist (200 g kg-1) and compaction had a higher effect. Soils with higher clay and moisture content are more sensitive to soil compaction. Dry summers after a

penetration resistance by 2.0–3.0 MPa compared with to non-compacted soil.

**2.6 Statistics** 

for data analysis.

**3. Results and discussion** 

**3.1 Compaction effect on soil properties** 

content of phosphorus 15% higher than in first year.

rainy spring make soil more susceptible to compaction.

barley fruits reached final size in the middle of July in all experimental plots. Data regarding the content of the plant community were obtained from taking vegetation samples from a 0.25 m2 plot (*n*=4). Partitioned plant part components (barley and observed weed species) were determined, counted, measured and weighed (wet weight). Parts of plants were taken to dry them in oven at 60°C temperature to calculate dry matter content and dry weight. Homogenised plant part samples from each treatment were taken for measuring nutrient content. Root samples were taken by 1131 cm3 (h=15 cm, Ø=9.8 cm) steel cylinders in 15 cm layers down to 60 cm in 4 replications in years 2002–2004. Before root washing on 0.5 mm sieve, the soil from cylinders was weighted and soil bulk density calculated. No root measures were made in 2001. The soil bulk density was also measured with 50 cm3 (h=5 cm, Ø=3.5 cm) cylinders in 0.1 m layers down to 0.4 m in four replications. At the each layer depth, samples were taken for measuring soil moisture, pHKCl and nutrient (Corg, Ntotal, plant available P, K, Ca, Mg) content. Penetration resistance was measured with a cone penetrometer (cone angle 60º, stick diameter 12 mm) in every 0.05 m layer down to 0.6 m in six replications from every experimental plot. Soil moisture and penetration resistance was measured also every spring after compaction.

#### **2.4 Laboratory analyses**

Soil and plant analyses were carried out at the laboratories of the Department of Soil Science and Agrochemistry, Estonian University of Life Sciences. The plant samples (aboveground and root parts separately) were dried at 60°C temperature and milled after removing the plants from a field. The Kjeldahl method was used to determine the content of total N of plants. The content of phosphorus (P) was determined colorimetrically on the basis of yellow phosphorus-molybdatic. Potassium content was determined by flame photometer in dipping solution diluted with distilled water. Air–dried soil samples were sieved through a 2 mm sieve and used to determine: soil reaction (pH) in 1M KCl 1:2.5, organic carbon (Corg) after Tjurin, calcium, magnesium, sodium in NH4OAc at pH 7 and phosphorus and potassium after Melich-3 method. To determine water content in the soil, the soil samples taken from the field were weighted and dried at 105 °C to the constant weight and weighted again. After that the water content was calculated. Samples for the particle size determination were treated with sodium pyrophosphate to break down aggregates. Sands were sieved and fractions finer than 0.05 mm were determined by pipette analysis.

#### **2.5 Weather conditions**

In 2001 and 2003 the barley growing period (from May to August) was relatively rainy and cold. The precipitation totals were 373 mm and 450 mm, respectively. Average air temperature was 15.8°C in 2001 and 15°C in 2003 during the barley growing period. More precipitation occurred in May and August and less in June and July. Average air temperature was highest in July (20.1°C) and lowest in May (11.6°C). In 2002 the growing period was relatively warm and dry. During the vegetation period only 163 mm of rain fell and the average air temperature was 17.4°C. More precipitation occurred in June and in end of July, less in May and August. Average air temperature was highest in July (20.1°C) and lowest in May (13.9°C). The rainiest year was 2004, when 475 mm total precipitation fell during the growing period; average air temperature was 16.2°C. More precipitation occurred in June and less in May. Average air temperature was highest in August (17.1°C) and lowest in May (12.1°C).

#### **2.6 Statistics**

246 Weed Control

barley fruits reached final size in the middle of July in all experimental plots. Data regarding the content of the plant community were obtained from taking vegetation samples from a 0.25 m2 plot (*n*=4). Partitioned plant part components (barley and observed weed species) were determined, counted, measured and weighed (wet weight). Parts of plants were taken to dry them in oven at 60°C temperature to calculate dry matter content and dry weight. Homogenised plant part samples from each treatment were taken for measuring nutrient content. Root samples were taken by 1131 cm3 (h=15 cm, Ø=9.8 cm) steel cylinders in 15 cm layers down to 60 cm in 4 replications in years 2002–2004. Before root washing on 0.5 mm sieve, the soil from cylinders was weighted and soil bulk density calculated. No root measures were made in 2001. The soil bulk density was also measured with 50 cm3 (h=5 cm, Ø=3.5 cm) cylinders in 0.1 m layers down to 0.4 m in four replications. At the each layer depth, samples were taken for measuring soil moisture, pHKCl and nutrient (Corg, Ntotal, plant available P, K, Ca, Mg) content. Penetration resistance was measured with a cone penetrometer (cone angle 60º, stick diameter 12 mm) in every 0.05 m layer down to 0.6 m in six replications from every experimental plot. Soil moisture and penetration resistance was

Soil and plant analyses were carried out at the laboratories of the Department of Soil Science and Agrochemistry, Estonian University of Life Sciences. The plant samples (aboveground and root parts separately) were dried at 60°C temperature and milled after removing the plants from a field. The Kjeldahl method was used to determine the content of total N of plants. The content of phosphorus (P) was determined colorimetrically on the basis of yellow phosphorus-molybdatic. Potassium content was determined by flame photometer in dipping solution diluted with distilled water. Air–dried soil samples were sieved through a 2 mm sieve and used to determine: soil reaction (pH) in 1M KCl 1:2.5, organic carbon (Corg) after Tjurin, calcium, magnesium, sodium in NH4OAc at pH 7 and phosphorus and potassium after Melich-3 method. To determine water content in the soil, the soil samples taken from the field were weighted and dried at 105 °C to the constant weight and weighted again. After that the water content was calculated. Samples for the particle size determination were treated with sodium pyrophosphate to break down aggregates. Sands

were sieved and fractions finer than 0.05 mm were determined by pipette analysis.

In 2001 and 2003 the barley growing period (from May to August) was relatively rainy and cold. The precipitation totals were 373 mm and 450 mm, respectively. Average air temperature was 15.8°C in 2001 and 15°C in 2003 during the barley growing period. More precipitation occurred in May and August and less in June and July. Average air temperature was highest in July (20.1°C) and lowest in May (11.6°C). In 2002 the growing period was relatively warm and dry. During the vegetation period only 163 mm of rain fell and the average air temperature was 17.4°C. More precipitation occurred in June and in end of July, less in May and August. Average air temperature was highest in July (20.1°C) and lowest in May (13.9°C). The rainiest year was 2004, when 475 mm total precipitation fell during the growing period; average air temperature was 16.2°C. More precipitation occurred in June and less in May. Average air temperature was highest in August (17.1°C)

measured also every spring after compaction.

**2.4 Laboratory analyses** 

**2.5 Weather conditions** 

and lowest in May (12.1°C).

The one-way, two-way and three-way analysis of variance (ANOVA) was used to determine the impact of trial factors based on the collected data. Soil bulk density (soil compaction), and fertilization rate were considered fixed effects while year was considered random. The significance of experiment factors was calculated using the Fischer test and the level of significance *P*<0.05 was used. To compare the differences between values the standard Student's *t*-test was used and least significant differences (LSD) at significance *P*<0.05. Correlation analysis was also used to process the data. The program Statistica 7.0 was used for data analysis.

#### **3. Results and discussion**

#### **3.1 Compaction effect on soil properties**

The values of soil bulk density and penetration resistance (Fig. 1 and 2) changed among the experiment years due to the different weather conditions during sampling. The values of both, penetration resistance and bulk density depend on the soil moisture content (Fig. 3). However, the effect of traffic on the soil properties was significant in every experimental year between the non-compacted and the six times compacted soil. The differences in soil penetration resistance between one and three times compacted soil were significant after four years of soil compaction (Fig. 2). In average of the four years data there were no significant differences between those treatments. However, the six passes increased the soil penetration resistance by 2.0–3.0 MPa compared with to non-compacted soil.

Soil compaction did not caused significant (p<0.05) differences in soil nutrient uptake in first year (Table 1). After four years with continuous compaction and without fertilizers use, significant positive effects of soil compaction on organic carbon, total nitrogen, available phosphorus and potassium content were detected in soil. Compaction by one pass, three and six passes increased organic carbon content 13%, 32% and 39%, respectively, compared to non-compacted soil. Three and six passes increased total nitrogen content 16% and 9%, available phosphorus content 17% and 7%, and potassium 16% and 66%, respectively, compared to non-compacted soil. One pass compaction did not cause significant changes in nitrogen and phosphorus content. Without fertilizer use, the carbon phosphorus and potassium content decreased with four years in the control by one time and three pass compacted treatments. The highest decrease by was in non-compacted soil, where the decrease was 28% in case of carbon, 13% in case phosphorus and 41% in case of potassium. In six times compacted soil the content of carbon was 4%, content of nitrogen 23% and content of phosphorus 15% higher than in first year.

Compaction effect on soil properties depended on the machinery weight and number of passes, but also from soil moisture and weather conditions during compaction treatment application and vegetation period. Moist soil is more sensitive to soil compaction than dry. Low impact of soil compaction on soil bulk density and penetration resistance in the first year was caused by dry soil (110 g kg-1) at the application of compaction and the subsequent rainy growing season following application. In following years, at the time of compaction application the soil was moist (200 g kg-1) and compaction had a higher effect. Soils with higher clay and moisture content are more sensitive to soil compaction. Dry summers after a rainy spring make soil more susceptible to compaction.

Year/

2001

2004

Compaction variant

a Least significant difference at p<0.05 b No significant differences between variants

decrease at higher moisture contents.

Weed Responses to Soil Compaction and Crop Management 249

Control 13.95 1.29 217.7 181.3 1 time compacted 14.20 1.21 238.5 156.0 3 times compacted 13.32 1.27 252.2 149.0 6 times compacted 13.37 1.20 176.4 217.2 LSD0.05a (comp.) nsb ns ns ns

Control 10.02 1.36 189.5 107.7 1 time compacted 11.33 1.36 188.3 130.5 3 times compacted 13.25 1.58 220.8 125.2 6 times compacted 13.88 1.48 203.7 178.9 LSD0.05 (comp.) 1.04 0.14 35.9 3.1 LSD0.05 (year) 0.81 0.15 16.2 27.0

Table 1. Changes in soil organic carbon (Corg), total nitrogen (Ntot), available phosphorus (P) and potassium (K) content due to soil compaction and experiment year of upper soil layer

Compaction by a 4.9 Mg tractor with tire inflation pressure 150 kPa increased soil bulk density and penetration resistance in the first and second year. However, no hardpan was formed in subsoil, likely due to deep-freezing (up to 0.5 m) in those years and because of the moderate tractor weight. However, after the third year of continuous direct compaction, a hardpan formed below the plough layer even after one tractor pass. The soils of the experiment area have a medium fine texture. They are moderately susceptible to soil compaction when moist but not particularly vulnerable when dry. The recommended maximum tire inflation pressure for medium fine textured soils is 120 to 160 kPa (van den Akker, 2002). Significant compaction has commonly been observed to a depth of about 30 cm at an axle load of 4 Mg. The natural processes of freezing/thawing, wetting/drying and bioactivity alleviate topsoil compaction. In Sweden, one pass by a 5.4 Mg tractor brought resulted in little compaction, but repeated passes led to over-compaction. In the same time one pass by the wheel-loader (9.9 Mg) increased the degree of compaction almost as much as three passes by the tractor (Etana & Håkansson, 1996). When the plough layer is severely compacted, however, the recovery of heavy clay soils may take five years in spite of annual ploughing and frost heaving. In our experiment, the highest values of soil bulk density occurred in 2002 in all compaction treatments. In other experiment years, the highest values of soil bulk density were caused from low soil moisture content (110 g kg-1) and the lowest soil bulk density values occurred in 2001 and 2004 when high soil moisture content (210 g kg-1) was present at compaction application. In experiments of Pickering and Veneman (1984), soil dry density increased to the soil moisture content 0.11–0.12 kg kg-1 and started to

(0–0.3 m) in earing phase of spring barley (*Hordeum vulgare* L.)

Corg Ntot P K

(g kg–1) (g kg–1) (mg kg–1) (mg kg–1)

Fig. 1. Effect of soil compaction on soil bulk density in earing phase of spring barley (*Hordeum vulgare* L.) in years 2001—2004; 0x – non-compacted control, 1, 3, 6 – number of passes; LSD0.05 – least significant differences at significance at p<0.05; ns – differences are not significant

Fig. 2. Effect of soil compaction on soil penetration resistance in earing phase of spring barley (*Hordeum vulgare* L.) in years 2001—2004; 0 – non-compacted control, 1, 3, 6 – number of passes; LSD0.05 – least significant differences at significance at p<0.05; ns – differences are not significant

Fig. 3. Effect of soil compaction on soil moisture content in earing phase of spring barley (*Hordeum vulgare* L.) in years 2001—2004; 0 – non-compacted control, 1, 3, 6 – numbers of passes; LSD0.05 – least significant differences at significance at p<0.05; ns – differences are not significant


Weed Responses to Soil Compaction and Crop Management 249

248 Weed Control

0

**Soil bulk density (Mg m-3)**

0.1

0.2

0.3

0.4

6x **2003**

0x 1x 3x LSD05

1.4 1.5 1.6 1.7 1.8

ns

ns

0x 1x 3x

6x **<sup>2004</sup>**

0 0.1 0.2 0.3 0.4 0.5

LSD05

0x 1x

50 100 150 200 250

6x **2003**

**2004**

LSD05

0 0.1 0.2 0.3 0.4 0.5 0.6

02468

6x **2004**

0

0.1

0.2

0.3

0.4

LSD05

1.4 1.5 1.6 1.7 1.8

ns

LSD05 ns ns

> 0x 1x 3x 6x

ns

0x 1x 3x ns ns ns

LSD05

50 100 150 200 250

02468

0x 1x 3x 6x

LSD05

Fig. 1. Effect of soil compaction on soil bulk density in earing phase of spring barley (*Hordeum vulgare* L.) in years 2001—2004; 0x – non-compacted control, 1, 3, 6 – number of passes; LSD0.05 – least significant differences at significance at p<0.05; ns – differences are not

0 2 4 6 8 10 12

LSD05

0 0.1 0.2 0.3 0.4 0.5 0.6

**Penetration resistance (MPa)**

ns

0x 1x 3x

Fig. 2. Effect of soil compaction on soil penetration resistance in earing phase of spring barley (*Hordeum vulgare* L.) in years 2001—2004; 0 – non-compacted control, 1, 3, 6 – number of passes; LSD0.05 – least significant differences at significance at p<0.05; ns – differences are

6x **<sup>2003</sup>**

0 0.1 0.2 0.3 0.4 0.5

**Soil moisture content (g kg-1)**

3x **<sup>2002</sup>** 6x ns

ns ns

0x 1x

Fig. 3. Effect of soil compaction on soil moisture content in earing phase of spring barley (*Hordeum vulgare* L.) in years 2001—2004; 0 – non-compacted control, 1, 3, 6 – numbers of passes; LSD0.05 – least significant differences at significance at p<0.05; ns – differences are not

LSD05

50 100 150 200 250

1.4 1.5 1.6 1.7 1.8

**2001**

ns ns ns

0x 1x 3x

6x **<sup>2002</sup>**

0 0.1 0.2 0.3 0.4 0.5

3x **<sup>2001</sup>** 6x

ns

0x 1x 3x 6x

LSD05

ns ns

6x **2002**

0 0.1 0.2 0.3 0.4 0.5 0.6 0x 1x 3x

0

0.1

0.2

0.3

0.4

ns

ns

LSD05

1.4 1.5 1.6 1.7 1.8

0

0.1

0.2

**Soil depth (m)**

0.3

0x 1x 3x

**2001**

50 100 150 200 250

LSD05 <sup>0</sup>

0 2 4 6 8 10 12

0.4

significant

0.1 0.2 0.3 0.4 0.5 0.6

not significant

0 0.1 0.2 0.3 0.4 0.5

significant

**Soil depth (m)**

**Soil depth (m)** a Least significant difference at p<0.05

b No significant differences between variants

Table 1. Changes in soil organic carbon (Corg), total nitrogen (Ntot), available phosphorus (P) and potassium (K) content due to soil compaction and experiment year of upper soil layer (0–0.3 m) in earing phase of spring barley (*Hordeum vulgare* L.)

Compaction by a 4.9 Mg tractor with tire inflation pressure 150 kPa increased soil bulk density and penetration resistance in the first and second year. However, no hardpan was formed in subsoil, likely due to deep-freezing (up to 0.5 m) in those years and because of the moderate tractor weight. However, after the third year of continuous direct compaction, a hardpan formed below the plough layer even after one tractor pass. The soils of the experiment area have a medium fine texture. They are moderately susceptible to soil compaction when moist but not particularly vulnerable when dry. The recommended maximum tire inflation pressure for medium fine textured soils is 120 to 160 kPa (van den Akker, 2002). Significant compaction has commonly been observed to a depth of about 30 cm at an axle load of 4 Mg. The natural processes of freezing/thawing, wetting/drying and bioactivity alleviate topsoil compaction. In Sweden, one pass by a 5.4 Mg tractor brought resulted in little compaction, but repeated passes led to over-compaction. In the same time one pass by the wheel-loader (9.9 Mg) increased the degree of compaction almost as much as three passes by the tractor (Etana & Håkansson, 1996). When the plough layer is severely compacted, however, the recovery of heavy clay soils may take five years in spite of annual ploughing and frost heaving. In our experiment, the highest values of soil bulk density occurred in 2002 in all compaction treatments. In other experiment years, the highest values of soil bulk density were caused from low soil moisture content (110 g kg-1) and the lowest soil bulk density values occurred in 2001 and 2004 when high soil moisture content (210 g kg-1) was present at compaction application. In experiments of Pickering and Veneman (1984), soil dry density increased to the soil moisture content 0.11–0.12 kg kg-1 and started to decrease at higher moisture contents.

Weed Responses to Soil Compaction and Crop Management 251

perennial weed density and weight were not statistically significant. In 2002, one, three and six pass treatments had positive effect on annual weed shoot dry weight. This was mainly caused by changes in weight and density of common lambsquarters, which comprised more

Weed species 2001 2004 Common name Scientific name Number of passesa

Field pennycress *Thlaspi arvense* L. + + + + + + + + Corn bindweed *Polygonum convolvulus* L. + + + + + + Hairy tare (vetch) *Vicia villosa* Roth. + + + + + + + + Common chickweed *Stellaria media* (L.) Vill. + + + + + + + + Wall speedwell *Veronica arvensis* L. + + + + + + + + Field pansy *Viola arvensis* Murr. + + + + + + + + Red dead nettle *Lamium purpureum* L. + + + + + + + +

Common fumitory *Fumaria officinalis* L. + + + + + + Corn bugloss *Lycopsis arvensis* L. + + + + Cleavers *Galium aparine* L. + + + + + +

Shepherds-purse *Capsella bursa-pastoris* (L.) Med. + + + + + + + Corn spurry *Spergula arvensis* L. (coll.) + + + +

*Myosotis arvensis* (L.) Hill + +

Wild mustard *Sinapis arvensis* L. + + + + + + +

Corn mayweed *Matricaria inodora* L. + + +

Sun spurge *Euphorbia helioscopia* L. +

Canadian thistle *Cirsium arvense* (L.) Scop. + + + Coltsfoot *Tussilago farfara* L. + + Perennial sowthistle *Sonchus arvensis* L. + + + Corn mint *Mentha arvensis* L. +

Mugwort *Artemisia vulgaris* L. + + + + + + + +

Table 2. Presence of weed species depending on soil compaction and year in earing phase of spring barley (*Hordeum vulgare* L.) in the first (2001) and last (2004) year of the experiment

Knotgrass *Polygonum aviculare* L. +

Common hemp nettle *Galeopsis tetrahit* L. +

Storks-bill *Erodium cicutarium* (L.)

L´Her.

Peachwort *Polygonum persicaria* L. +

Field horsetail *Equisetum arvense* L. +

a 0 – control plot without special compaction; 1, 3, 6 number of special passes

*Chenopodium album* L. +b + + + + + + +

+ +

*Plantago major* L. + + + + +

0 1 3 6 0 1 3 6

than 50% of the weed community (Table 4).

Annual weeds Common lambsquarters

Common scorpion

Perennial weeds

(broadleaf) plantain

b Presence (+) or absence of weeds

grass

Great

Changes in soil nutrient availability due to compaction were reflected in both reduced plant growth (see Tables 3 and 4) and changed soil physical parameters (Fig. 1 and 2). Soil compaction influences both physical properties and chemical and biological processes in the soil (Ferrero et al., 2002). Higher amounts of free P and K in six times compacted soils were directly correlated with reduced nutrient removal. As the nutrient acquisition by plants was reduced, there were higher amounts of free nutrients in the soil. Phosphorus and K ions are more sensitive to soil compaction than N ions. In a rainy year the nutrients, especially P, were leached to deeper soil layers. Phosphorus is more mobile than K. The less mobile K tended to concentrate near the soil surface. A compacted soil layer, because of its high strength and low porosity, confines the crop roots to the top layer and reduces the volume of soil that can be explored by the plants for nutrients and water (Lipiec et al., 2003). There is also an interaction between compaction and soil water content. Carbon mineralization increases with increasing water content in loose soil but decreases in compact soil (Ball et al., 2000) and may increase the total amount of nutrients in soil. There is an increased the amount of total N in the compacted soil, as total N content in soil is connected with organic C content. Also Lipiec and Stepniewski (1995) found reduced N mineralization in compacted soil and Motavalli et al. (2003) reported N recovery efficiency from 290 to 140 g kg–1 by compaction of the soil. However, De Neve and Hofman (2000) concluded that rates of N and C mineralization may or may not be affected by compacted conditions.

#### **3.2 Compaction effect on plant growth**

Twenty-eight weed species were identified from the experimental area during four years of the experiment. In the 2001, 24 weed species were described, mostly annual weeds. The amount of perennial weed species increased from 3 species in the first year to 7 species in fourth year due to repeatedly growing barley in monoculture without herbicides and fertilizers. Only 13 weed species emerged on six-times passed soil in 2004. The most widespread weed species were common lambsquarter, field pennycress, common fumitory and common chickweed. After four years without fertilizer use, the dominating annual weed species were field pennycress and common chickweed (Table 2).

Though the changes in soil bulk density (Fig. 1) and penetration resistance (Fig. 2) due to compaction were exiguous in 2001, there was still a decrease of plant shoot dry weight and number of plants (Table 3, 4,). Significant barley dry weight decrease was observed following the six passes treatment and also on barley plants density following the one and six pass treatments, respectively (Table 3, 2001). Compaction had higher effect on annual weeds than perennial weeds. In 2001 weeds formed 6.5% from the total plant shoot dry weight and 23.4% from plants density in non-compacted and 2.8% and 17.2% in six times passed soil, respectively. Compaction decreased common lambsquarters dry weight, but did not affect density (Table 4). Common lambsquarters weed mass was 15.3% to 32.5% of the total weed mass and comprised 25.3% to 44.4% of the weed density, depending on compaction treatment. The most sensitive weed to the soil compaction was common fumitory; its mass decreased 53%–86% and density 20%–76%, depending on compaction level. Compaction affected also other investigated weed species, but the differences were not biologically significant.

In 2002, compaction increased weed shoot mass from 12% in the control up to 52% in the six times pass treatment and decreased significantly barley yield (Table 3). Changes in

Changes in soil nutrient availability due to compaction were reflected in both reduced plant growth (see Tables 3 and 4) and changed soil physical parameters (Fig. 1 and 2). Soil compaction influences both physical properties and chemical and biological processes in the soil (Ferrero et al., 2002). Higher amounts of free P and K in six times compacted soils were directly correlated with reduced nutrient removal. As the nutrient acquisition by plants was reduced, there were higher amounts of free nutrients in the soil. Phosphorus and K ions are more sensitive to soil compaction than N ions. In a rainy year the nutrients, especially P, were leached to deeper soil layers. Phosphorus is more mobile than K. The less mobile K tended to concentrate near the soil surface. A compacted soil layer, because of its high strength and low porosity, confines the crop roots to the top layer and reduces the volume of soil that can be explored by the plants for nutrients and water (Lipiec et al., 2003). There is also an interaction between compaction and soil water content. Carbon mineralization increases with increasing water content in loose soil but decreases in compact soil (Ball et al., 2000) and may increase the total amount of nutrients in soil. There is an increased the amount of total N in the compacted soil, as total N content in soil is connected with organic C content. Also Lipiec and Stepniewski (1995) found reduced N mineralization in compacted soil and Motavalli et al. (2003) reported N recovery efficiency from 290 to 140 g kg–1 by compaction of the soil. However, De Neve and Hofman (2000) concluded that rates

of N and C mineralization may or may not be affected by compacted conditions.

weed species were field pennycress and common chickweed (Table 2).

Twenty-eight weed species were identified from the experimental area during four years of the experiment. In the 2001, 24 weed species were described, mostly annual weeds. The amount of perennial weed species increased from 3 species in the first year to 7 species in fourth year due to repeatedly growing barley in monoculture without herbicides and fertilizers. Only 13 weed species emerged on six-times passed soil in 2004. The most widespread weed species were common lambsquarter, field pennycress, common fumitory and common chickweed. After four years without fertilizer use, the dominating annual

Though the changes in soil bulk density (Fig. 1) and penetration resistance (Fig. 2) due to compaction were exiguous in 2001, there was still a decrease of plant shoot dry weight and number of plants (Table 3, 4,). Significant barley dry weight decrease was observed following the six passes treatment and also on barley plants density following the one and six pass treatments, respectively (Table 3, 2001). Compaction had higher effect on annual weeds than perennial weeds. In 2001 weeds formed 6.5% from the total plant shoot dry weight and 23.4% from plants density in non-compacted and 2.8% and 17.2% in six times passed soil, respectively. Compaction decreased common lambsquarters dry weight, but did not affect density (Table 4). Common lambsquarters weed mass was 15.3% to 32.5% of the total weed mass and comprised 25.3% to 44.4% of the weed density, depending on compaction treatment. The most sensitive weed to the soil compaction was common fumitory; its mass decreased 53%–86% and density 20%–76%, depending on compaction level. Compaction affected also other investigated weed species, but the differences were

In 2002, compaction increased weed shoot mass from 12% in the control up to 52% in the six times pass treatment and decreased significantly barley yield (Table 3). Changes in

**3.2 Compaction effect on plant growth** 

not biologically significant.

perennial weed density and weight were not statistically significant. In 2002, one, three and six pass treatments had positive effect on annual weed shoot dry weight. This was mainly caused by changes in weight and density of common lambsquarters, which comprised more than 50% of the weed community (Table 4).


a 0 – control plot without special compaction; 1, 3, 6 number of special passes

b Presence (+) or absence of weeds

Table 2. Presence of weed species depending on soil compaction and year in earing phase of spring barley (*Hordeum vulgare* L.) in the first (2001) and last (2004) year of the experiment

2001

2002

2003

2004

LSD0.05 (year)

b Number of passes

experiment

a Least significant difference at p<0.05

c No significant differences between treatments

Weed Responses to Soil Compaction and Crop Management 253

Treatment Dry weight (g m–2) LSD0.05a Density (plants m–2) LSD0.05

*Chenopodium album* L. 8.5 9.8 7.2 5.2 2.1 68 88 71 68 nsc *Fumaria officinalis* L. 5.8 0.8 2.7 1.4 4.7 25 12 20 6 14 *Lamium purpureum* L. 2.6 5.5 1.0 0.6 ns 23 10 9 14 9 *Thlaspi arvense* L. 2.0 1.4 0.3 0.8 ns 16 18 8 14 ns *Stellaria media* (L.) Vill. 4.8 4.3 1.5 1.7 ns 43 63 35 24 ns *Plantago major* L. 0 0.1 0 0 ns 0 3 0 0 ns Weeds total 58 42 22 16 19 285 250 200 153 ns

*Chenopodium album* L. 21.8 30.6 32.7 36.0 8.4 91 95 68 36 25 *Fumaria officinalis* L. 1.0 1.0 1.1 0.1 ns 14 8 6 1 7 *Lamium purpureum* L. 0.5 0.6 0.7 0 ns 9 6 4 0 6 *Thlaspi arvense* L. 0.7 0.2 1.9 1.5 ns 11 4 7 8 ns *Stellaria media* (L.) Vill. 1.0 0.3 5.8 1.7 ns 26 3 19 10 16 *Plantago major* L. 1.6 0 0.2 1.8 ns 4 0 1 2 ns Weeds total 42 55 61 65 21.3 219 180 187 87 73

*Chenopodium album* L. 1.8 3.3 3.7 1.2 0.7 91 97 109 56 20 *Fumaria officinalis* L. 0.3 0 0.2 0 0.1 16 0 13 0 5 *Lamium purpureum* L. 2.4 3.3 1.6 0.1 0.3 66 66 31 3 7 *Thlaspi arvense* L. 0.6 1.7 0.6 1.1 ns 22 38 9 19 ns *Stellaria media* (L.) Vill. 9.2 3.2 10.7 3.8 5.8 109 50 63 41 45 *Plantago major* L. 0.1 2.4 0.2 1.2 0.1 3 3 6 13 7 Weeds total 22 18 30 10 5 477 384 363 200 95

*Chenopodium album* L. 0.4 0.3 0.6 0.4 0.2 25 21 17 33 3 *Fumaria officinalis* L. 0 0.8 0.3 0 0.3 0 22 9 0 9 *Lamium purpureum* L. 0.1 0.2 0.1 0.2 0.08 4 20 14 24 5 *Thlaspi arvense* L. 3.0 2.4 5.1 3.1 1.7 66 34 40 48 11 *Stellaria media* (L.) Vill. 1.0 1.4 0.8 3.7 1.4 50 25 30 150 86 *Plantago major* L. 0.02 0.3 0.6 25.2 0.4 1 2 3 6 1 Weeds total 28 16 27 33 4 342 302 284 486 97

*Chenopodium album* L. 9.8 11.4 20.4 18.5 25 31 19 28 *Fumaria officinalis* L. 4.3 ns 2.4 1.3 14 11 ns 2 *Lamium purpureum* L. 1.8 ns ns 0.3 7 8 11 11 *Thlaspi arvense* L. ns ns 2.3 1.7 10 15 12 20 *Stellaria media* (L.) Vill. ns 3.2 6.9 ns ns 38 ns 81 *Plantago major* L. ns 0.1 0.1 0.6 ns ns 5 2 Weeds total 24.7 17.2 32.1 22.3 128 81 69 102

Table 4. Soil compaction effects on the most abundant six weed species dry mass and density in weed community in earing phase of spring barley (*Hordeum vulgare* L.) during

0b 1 3 6 (comp.) 0 1 3 6 (comp.)


a Least significant difference at p<0.05

b Number of passes

c No significant differences between treatments

Table 3. Soil compaction effect on spring barley (*Hordeum vulgare* L.) and weed dry mass and density in community in earing phase of spring barley during experiment

Common fumitory did not emerge following the six pass compaction treatment. However, compaction had no strongly positive or negative effect on pennycress and chickweed.

After three years of soil compaction without fertilizer use, the total shoot dry weight and barley yield was only ¼ from first year shoot dry mass on non-compacted soil (Table 3). Changes in barley shoot dry weight were similar to earlier years, one and three pass treatments decreased barley density; however, six passes increased it. Weeds formed 9.7% to 14.9% from total shoot mass, depending on treatment and year. There was significant increase of perennial plant dry weight and density, mostly great plantain, which composition increased from 0.5% on non-compacted soil to 12.2% on six pass compacted soil in the weed community (Table 4). Again, the dominating weed species was common chickweed, with a shoot mass of 43.2% in the control and 17.6%, 36.1% and 38.8% in one, three and six pass compacted treatments, respectively. Field pennycress increased in dry weight with compaction. Again, common fumitory was not found following the six pass treated soil in the earing phase of barley.


Treatment Dry weight (g m–2) LSD0.05a Density (plants m–2) LSD0.05

Spring barley 797 632 758 551 190 883 778 859 738 104 Annual weeds 54 31 21 15 18 245 236 187 146 86 Perennial weeds 1.4 1.2 1.2 1.0 nsc 24 14 13 7 ns Total biomass 855 674 780 567 215 1151 1028 1059 890 169

Spring barley 302 262 169 61 78 450 228 266 137 132 Annual weeds 33 39 58 60 22.6 213 148 182 80 73 Perennial weeds 8.3 16 2.2 5.5 ns 6 32 5 7 19 Total biomass 344 317 230 126 68 669 408 453 224 119

Spring barley 186 167 169 91 21 550 466 463 475 58 Annual weeds 21 17 27 9 7.2 462 350 344 181 107 Perennial weeds 0.3 1.2 2.6 0.8 1.7 15 34 19 19 15 Total biomass 208 185 199 101 19 1028 850 825 791 100

Spring barley 151 164 132 92 18 573 496 429 462 97 Annual weeds 9 11 12 19 2.4 264 264 210 464 101 Perennial weeds 19 4.5 15 15 ns 88 38 74 22 22 Total biomass 180 179 159 125 20 920 781 669 1043 179

Table 3. Soil compaction effect on spring barley (*Hordeum vulgare* L.) and weed dry mass and

Common fumitory did not emerge following the six pass compaction treatment. However, compaction had no strongly positive or negative effect on pennycress and chickweed.

After three years of soil compaction without fertilizer use, the total shoot dry weight and barley yield was only ¼ from first year shoot dry mass on non-compacted soil (Table 3). Changes in barley shoot dry weight were similar to earlier years, one and three pass treatments decreased barley density; however, six passes increased it. Weeds formed 9.7% to 14.9% from total shoot mass, depending on treatment and year. There was significant increase of perennial plant dry weight and density, mostly great plantain, which composition increased from 0.5% on non-compacted soil to 12.2% on six pass compacted soil in the weed community (Table 4). Again, the dominating weed species was common chickweed, with a shoot mass of 43.2% in the control and 17.6%, 36.1% and 38.8% in one, three and six pass compacted treatments, respectively. Field pennycress increased in dry weight with compaction. Again, common fumitory was not found following the six pass

Spring barley 161 124 143 55 134 99 112 100 Annual weeds 44.2 20.2 34.1 24.0 120 72 59 112 Perennial weeds 8.6 16.5 3.7 6.5 24 19 18 7 Total biomass 196 134 144 53 175 165 134 184

density in community in earing phase of spring barley during experiment

2001

2002

2003

2004

LSD0.05 (year)

b Number of passes

a Least significant difference at p<0.05

c No significant differences between treatments

treated soil in the earing phase of barley.

0b 1 3 6 (comp.) 0 1 3 6 (comp.)


a Least significant difference at p<0.05

b Number of passes

c No significant differences between treatments

Table 4. Soil compaction effects on the most abundant six weed species dry mass and density in weed community in earing phase of spring barley (*Hordeum vulgare* L.) during experiment

Weed Responses to Soil Compaction and Crop Management 255

grow well in a wide range of climates and soils, especially those with high organic matter content (Mitch, 1988). It possesses a prolific rooting system, which allows it to resist adverse environmental conditions, such as soil compaction. Common lambsquarters emergence rate has been reported to increase with temperature and decreases with increasing soil

> 0x 1x 3x 6x 0x 1x 3x 6x 2003 2004

> > 0 50 100 150 200 250 300

**Roots dry weight (g m-2)**

**2004**

0x 1x 3x 6x

0 100 200 300 400

0 0.1 0.2 0.3 0.4 0.5 0.6

LSD05

LSD05

0x 1x 3x 6x 0x 1x 3x 6x 2001 2002

*Hordeum vulgare Chenopodium album Thlaspi arvense Stellaria media*

**2003**

Fig. 5. Effect of soil compaction on plant roots (barley and weeds) dry weight in earing phase of spring barley (*Hordeum vulgare* L.) in years 2002, 2003 and 2004; 0 – non-compacted control, 1, 3, 6 – number of passes; LSD0.05 – least significant differences at significance at

0x 1x 3x 6x

Fig. 4. Effect of soil compaction on the height of the most common seven weeds observed in earing phase of spring barley (*Hordeum vulgare* L.) in years 2001—2004; 0 – non-compacted

*Lamium purpureum Fumaria officinalis Plantago major*

control, 1, 3, 6 – number of passes; bars indicates the standard deviation

0 0.1 0.2 0.3 0.4 0.5 0.6

LSD05

penetration resistance and depth (Vleeshouwers, 1997).

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

0 0.1 0.2 0.3 0.4 0.5 0.6

**Soil depth (m)**

**2002**

0x 1x 3x 6x

p<0.05; ns – differences are not significant

0 50 100 150 200 250 300

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

**Height of plants (m)**

In 2004, due to persistent rain and lower penetration resistance (Fig. 2), the impact of soil compaction on barley dry weight and density was lower than in previous two years (Table 3). The lowest barley weed dry weight and density were recorded following one time passed soil, where their share from total shoot dry weight was 8.6% and from total plant density 37.9%. Following six times passed soil weeds formed 27% total shoot dry weight and 51.2% total plant density. Perennial weed dry weight was 29% to 67% of total, depending on treatment. Also in 2004, annual weed mass increased with increasing soil bulk density; however, this was likely due to higher plant density on compacted soil and not likely due to higher plant shoot mass like in 2002 (Table 4). Compaction had significant effect on all investigated weed species dry weight and density. In six pass treated soil the most widespread weed species was great plantain by comprising 74.1% of the weed community. No common fumitory plants were detected on non-compacted and six times compacted soil in earing phase of barley after four years. Year had less impact on field pennycress shoot mass and common chickweed shoot mass and density (Table 4). Again, the most affected weed species were common lambsquarters and common fumitory.

The visible result of soil compaction on plant growth is plants height reduction. Compaction had more effect on barley stalks length than weed density (Fig. 4). One compaction pass reduced barley height by 0.02 to 0.04 m, three passes 0.03 to 0.06 m and six passes 0.08 to 0.2 m depending on experiment year. In the same level of vegetation mixture with barley we observed wild mustard, corn bindweed and common lambsquarters plants. Most of the observed weed species were shorter than barley, except in 2002, when weeds over-topped barley. Compaction also reduced average weed height, but increased the differences of plant height for only common lambsquarter. Although compaction affected common fumitory mass and density, there was no impact on common fumitory height.

As compaction changes soil properties, the direct effect will be on plant root growth. Our results showed that moderate compaction (one and three compaction passes) might increase root mass in the upper part of soil compare to non-compacted soil (Fig. 5). In the draughty year 2002, the highest root mass in the top 45 cm depth was detected in three times compacted soil, two times higher than in non-compacted soil. One time and six times compaction decreased root mass by 37% and 13%, respectively, especially in deeper soil layers. In 2003, one compaction pass increased root mass in the upper 15 cm soil layer by 50 g m-2, but decreased in deeper layers to 54%. Following three and six compaction passes total root mass decreased 66% and 80% respectively. In 2004, compaction decreased root mass relative to the increase of soil bulk density. Still the highest root mass was detected in three and six pass compacted soil in 15–30 cm depth likely due to high composition of perennial weed roots. In the very rainy year 2004, total root mass was four times higher than in the droughty year 2002, and two times higher than in year 2003.

Changes of dominant weed species in plant community during the four year experiment were likely caused by changes in soil conditions: decrease of available nutrients in soil, higher soil penetration resistance and soil bulk density. Low availability of major nutrients such as N, P and K play an essential role in maintaining species richness in weed communities. Like cultivated plants, weeds have different advantages depending on environmental parameters. In this experiment, decrease of common lambsquarters dry weight was due to nutrient availability and high soil resistance to root growth, conditions where it thrived compared to other species. Common lambsquarters has been shown to

In 2004, due to persistent rain and lower penetration resistance (Fig. 2), the impact of soil compaction on barley dry weight and density was lower than in previous two years (Table 3). The lowest barley weed dry weight and density were recorded following one time passed soil, where their share from total shoot dry weight was 8.6% and from total plant density 37.9%. Following six times passed soil weeds formed 27% total shoot dry weight and 51.2% total plant density. Perennial weed dry weight was 29% to 67% of total, depending on treatment. Also in 2004, annual weed mass increased with increasing soil bulk density; however, this was likely due to higher plant density on compacted soil and not likely due to higher plant shoot mass like in 2002 (Table 4). Compaction had significant effect on all investigated weed species dry weight and density. In six pass treated soil the most widespread weed species was great plantain by comprising 74.1% of the weed community. No common fumitory plants were detected on non-compacted and six times compacted soil in earing phase of barley after four years. Year had less impact on field pennycress shoot mass and common chickweed shoot mass and density (Table 4). Again,

the most affected weed species were common lambsquarters and common fumitory.

mass and density, there was no impact on common fumitory height.

in the droughty year 2002, and two times higher than in year 2003.

The visible result of soil compaction on plant growth is plants height reduction. Compaction had more effect on barley stalks length than weed density (Fig. 4). One compaction pass reduced barley height by 0.02 to 0.04 m, three passes 0.03 to 0.06 m and six passes 0.08 to 0.2 m depending on experiment year. In the same level of vegetation mixture with barley we observed wild mustard, corn bindweed and common lambsquarters plants. Most of the observed weed species were shorter than barley, except in 2002, when weeds over-topped barley. Compaction also reduced average weed height, but increased the differences of plant height for only common lambsquarter. Although compaction affected common fumitory

As compaction changes soil properties, the direct effect will be on plant root growth. Our results showed that moderate compaction (one and three compaction passes) might increase root mass in the upper part of soil compare to non-compacted soil (Fig. 5). In the draughty year 2002, the highest root mass in the top 45 cm depth was detected in three times compacted soil, two times higher than in non-compacted soil. One time and six times compaction decreased root mass by 37% and 13%, respectively, especially in deeper soil layers. In 2003, one compaction pass increased root mass in the upper 15 cm soil layer by 50 g m-2, but decreased in deeper layers to 54%. Following three and six compaction passes total root mass decreased 66% and 80% respectively. In 2004, compaction decreased root mass relative to the increase of soil bulk density. Still the highest root mass was detected in three and six pass compacted soil in 15–30 cm depth likely due to high composition of perennial weed roots. In the very rainy year 2004, total root mass was four times higher than

Changes of dominant weed species in plant community during the four year experiment were likely caused by changes in soil conditions: decrease of available nutrients in soil, higher soil penetration resistance and soil bulk density. Low availability of major nutrients such as N, P and K play an essential role in maintaining species richness in weed communities. Like cultivated plants, weeds have different advantages depending on environmental parameters. In this experiment, decrease of common lambsquarters dry weight was due to nutrient availability and high soil resistance to root growth, conditions where it thrived compared to other species. Common lambsquarters has been shown to grow well in a wide range of climates and soils, especially those with high organic matter content (Mitch, 1988). It possesses a prolific rooting system, which allows it to resist adverse environmental conditions, such as soil compaction. Common lambsquarters emergence rate has been reported to increase with temperature and decreases with increasing soil penetration resistance and depth (Vleeshouwers, 1997).

Fig. 4. Effect of soil compaction on the height of the most common seven weeds observed in earing phase of spring barley (*Hordeum vulgare* L.) in years 2001—2004; 0 – non-compacted control, 1, 3, 6 – number of passes; bars indicates the standard deviation

Fig. 5. Effect of soil compaction on plant roots (barley and weeds) dry weight in earing phase of spring barley (*Hordeum vulgare* L.) in years 2002, 2003 and 2004; 0 – non-compacted control, 1, 3, 6 – number of passes; LSD0.05 – least significant differences at significance at p<0.05; ns – differences are not significant

occur.

**3.3 Compaction effect on plant nutrition** 

Weed Responses to Soil Compaction and Crop Management 257

(no additional tillage or cultivation operations, except ploughing, were made to control weeds). Without herbicides use also weed density increased in all compaction treatments (Table 3). While specific soil conditions have been associated with weed infestations, it should also be recognized that these same soil conditions may reduce the vigour of the crop, making the crop less competitive with weeds. Therefore, the weeds associated with a specific soil condition may be a secondary effect related to crop vigour rather than a weed response to soil conditions (Buhler, 2003). However, the soil physical properties and the position of weed seeds within the soil matrix play an important role in seedling emergence and seed survival. Grundy et al. (2003) found that the weed species with smaller seeds, such corn mayweed and wall speedwell showed a sharp decline in emergence when burial depth exceeded 1 cm, but some species (common chickweed and common lambsquarter) have the physical reserves to emerge from a wider range of burial depths and soil densities than normally observed in the field, suggesting an ability to exploit opportunities when they

Most of the observed weed species had higher nitrogen content in their shoots than barley, especially common chickweed and common lambsquarters (Fig. 6). Nitrogen content in common chickweed and common lambsquarters dry matter reached 27 g kg–1. Only in 2001, barley had higher nitrogen content than common lambsquarters, common fumitory and field pennycress and lower nitrogen content than common chickweed. In droughty 2002, the nitrogen content in weeds was more than 2 times higher than in barley. Also in 2003 and 2004, barley contained the lowest nitrogen content. Plant root (barley and weeds) nitrogen content was similar to barley in 2002 and 2003, while barley had the most roots mass. In 2004, the nitrogen content was higher in barley roots than in observed weed species. The lowest nitrogen contents during experiment were measured in 2004. Compaction did not cause any significant changes in plant nitrogen content after first year of soil compaction (Fig. 6). There was some decrease due to one and six pass compaction in case of barley, common chickweed and common lambsquarters. In 2002 and 2003, soil compaction decreased nitrogen content in most investigated weed species while three the pass compaction and six pass compaction treatments increased nitrogen content again, except in case of common chickweed (Fig. 6, 2002 and 2003). Nitrogen content in barley dry matter increased with increasing of soil bulk density. In 2004, compaction had only negative effect on plant nitrogen content regardless of compaction intensity (Fig. 6). Nitrogen decrease was detected regardless of species. Changes in root nitrogen content due to the compaction were similar to aboveground plant parts in 2002 and 2003. In 2004 increasing amount of perennial

weed roots in compacted soil also increased the root nitrogen content.

Phosphorus content in plant dry matter was highest in common chickweed (3 to 4.5 g kg–1) in all years (Fig. 7). Lowest phosphorus content was detected in barley in all years (1.0 to 1.7 g kg–1). Changes in phosphorus content in plants due to compaction were similar to nitrogen changes in 2001. In 2002, soil compaction in most cases decreased phosphorus content. Increase of phosphorus content due to the six pass compaction treatment was observed only for common fumitory in 2002. Compaction had the highest negative effect on common lambsquarters and barley phosphorus content. In 2004, one pass compaction increased while three and six times compaction decreased phosphorus content in barley, common

Common fumitory favours well-drained soil. The plant is suited to sandy and medium loamy acid, neutral and alkaline soils (Mitch, 1997). In this respect, common fumitory has only limited ability to compete with other weeds and suffers strongly from intraspecific competition. We observed some common fumitory in tillering phase of barley, but they did not survive in competition with other weeds on compacted soil until the earing phase of barley. Field pennycress also thrives on fertile soils but the plant can also tolerate dense soils. If the intensity of tilling is reduced, the field pennycress composition density in weed community increases (Stevenson et al., 1998).

Great plantain and corn mayweed are commonly observed on edges of field and waysides, while corn spurry is observed on soils with low fertility (Trivedi & Tripathi, 1982). Great plantain is characteristic of relatively fertile, disturbed habitats and where its root system is restricted by compaction (Whitfield et al., 1996). Few great plantain plants were observed in the weed community at the beginning of this experiment but were likely out competed by other weed species. Following, compaction, soil strength inhibited establishment of most of other existing weed species and great plantain started to dominate in weed flora (Photo 1). Species which increase in abundance under changed soil properties or low nutrient conditions (Liebman, 1989) may do so due to their intolerance of earlier conditions or high nutrient levels, or they may be suppressed by other species which respond better.

Common chickweed is a cosmopolitan species, common in cereal and broad-leaved cultivated crops (Lutman et al., 2000). Walter et al. (2002) found that chickweed was positively cross-correlated with clay and negatively cross-correlated with pH and potassium content. In our experiment common chickweed tolerated moderately compacted soil more than severely compacted soil in most years of our investigation.

Photo 1. Differences in great plantain (*Plantago major* L.) abundance on compacted and noncompacted soil after two years of continuous compaction (A) and great plantain on field edge likely indicating compaction problems (B)

Heterogeneous occurrence of some weed species, such common hemp nettle, common scorpion grass, storks-bill, spun spurge and peachwort, was caused by their heterogeneous seed distribution in the experimental area soil (Table 2). Increase of perennial weed species after four years was the result of herbicides free management and reduced tillage intensity

Common fumitory favours well-drained soil. The plant is suited to sandy and medium loamy acid, neutral and alkaline soils (Mitch, 1997). In this respect, common fumitory has only limited ability to compete with other weeds and suffers strongly from intraspecific competition. We observed some common fumitory in tillering phase of barley, but they did not survive in competition with other weeds on compacted soil until the earing phase of barley. Field pennycress also thrives on fertile soils but the plant can also tolerate dense soils. If the intensity of tilling is reduced, the field pennycress composition density in weed

Great plantain and corn mayweed are commonly observed on edges of field and waysides, while corn spurry is observed on soils with low fertility (Trivedi & Tripathi, 1982). Great plantain is characteristic of relatively fertile, disturbed habitats and where its root system is restricted by compaction (Whitfield et al., 1996). Few great plantain plants were observed in the weed community at the beginning of this experiment but were likely out competed by other weed species. Following, compaction, soil strength inhibited establishment of most of other existing weed species and great plantain started to dominate in weed flora (Photo 1). Species which increase in abundance under changed soil properties or low nutrient conditions (Liebman, 1989) may do so due to their intolerance of earlier conditions or high

Common chickweed is a cosmopolitan species, common in cereal and broad-leaved cultivated crops (Lutman et al., 2000). Walter et al. (2002) found that chickweed was positively cross-correlated with clay and negatively cross-correlated with pH and potassium content. In our experiment common chickweed tolerated moderately compacted soil more

A B Photo 1. Differences in great plantain (*Plantago major* L.) abundance on compacted and noncompacted soil after two years of continuous compaction (A) and great plantain on field

Heterogeneous occurrence of some weed species, such common hemp nettle, common scorpion grass, storks-bill, spun spurge and peachwort, was caused by their heterogeneous seed distribution in the experimental area soil (Table 2). Increase of perennial weed species after four years was the result of herbicides free management and reduced tillage intensity

nutrient levels, or they may be suppressed by other species which respond better.

than severely compacted soil in most years of our investigation.

edge likely indicating compaction problems (B)

community increases (Stevenson et al., 1998).

(no additional tillage or cultivation operations, except ploughing, were made to control weeds). Without herbicides use also weed density increased in all compaction treatments (Table 3). While specific soil conditions have been associated with weed infestations, it should also be recognized that these same soil conditions may reduce the vigour of the crop, making the crop less competitive with weeds. Therefore, the weeds associated with a specific soil condition may be a secondary effect related to crop vigour rather than a weed response to soil conditions (Buhler, 2003). However, the soil physical properties and the position of weed seeds within the soil matrix play an important role in seedling emergence and seed survival. Grundy et al. (2003) found that the weed species with smaller seeds, such corn mayweed and wall speedwell showed a sharp decline in emergence when burial depth exceeded 1 cm, but some species (common chickweed and common lambsquarter) have the physical reserves to emerge from a wider range of burial depths and soil densities than normally observed in the field, suggesting an ability to exploit opportunities when they occur.

#### **3.3 Compaction effect on plant nutrition**

Most of the observed weed species had higher nitrogen content in their shoots than barley, especially common chickweed and common lambsquarters (Fig. 6). Nitrogen content in common chickweed and common lambsquarters dry matter reached 27 g kg–1. Only in 2001, barley had higher nitrogen content than common lambsquarters, common fumitory and field pennycress and lower nitrogen content than common chickweed. In droughty 2002, the nitrogen content in weeds was more than 2 times higher than in barley. Also in 2003 and 2004, barley contained the lowest nitrogen content. Plant root (barley and weeds) nitrogen content was similar to barley in 2002 and 2003, while barley had the most roots mass. In 2004, the nitrogen content was higher in barley roots than in observed weed species. The lowest nitrogen contents during experiment were measured in 2004. Compaction did not cause any significant changes in plant nitrogen content after first year of soil compaction (Fig. 6). There was some decrease due to one and six pass compaction in case of barley, common chickweed and common lambsquarters. In 2002 and 2003, soil compaction decreased nitrogen content in most investigated weed species while three the pass compaction and six pass compaction treatments increased nitrogen content again, except in case of common chickweed (Fig. 6, 2002 and 2003). Nitrogen content in barley dry matter increased with increasing of soil bulk density. In 2004, compaction had only negative effect on plant nitrogen content regardless of compaction intensity (Fig. 6). Nitrogen decrease was detected regardless of species. Changes in root nitrogen content due to the compaction were similar to aboveground plant parts in 2002 and 2003. In 2004 increasing amount of perennial weed roots in compacted soil also increased the root nitrogen content.

Phosphorus content in plant dry matter was highest in common chickweed (3 to 4.5 g kg–1) in all years (Fig. 7). Lowest phosphorus content was detected in barley in all years (1.0 to 1.7 g kg–1). Changes in phosphorus content in plants due to compaction were similar to nitrogen changes in 2001. In 2002, soil compaction in most cases decreased phosphorus content. Increase of phosphorus content due to the six pass compaction treatment was observed only for common fumitory in 2002. Compaction had the highest negative effect on common lambsquarters and barley phosphorus content. In 2004, one pass compaction increased while three and six times compaction decreased phosphorus content in barley, common

Weed Responses to Soil Compaction and Crop Management 259

compared to barley. The lower nitrogen need of many weed species can give them advantage in competition with cereals (Di Tomaso, 1995) and thus they have a greater ability to compete with barley for nutrients, water and light. Because weeds are more efficient in nutrient uptake, in particular nitrogen, the nitrogen content of a crop decreases with increasing competition with weeds. However, some researchers suggest that competition between weeds and crops is lower on nutrient rich than on nutrient-poor soils (Pyšek et al., 2005) and competition is most intense in plots with lowest resource levels (Wilson & Tilman, 1993). In our experiment, weeds were more able to compete with barley under moderate compaction conditions (3 pass treatment), where in many cases nutrient content in weeds increased, especially in common lambsquarters and common chickweed.

**2001**

0x 1x 3x 6x

0x 1x 3x 6x *Hordeum vulgare Chenopodium album Fumaria officinalis Thlaspi arvense*

monocotyledonous plants (Materachera et al., 1991).

**2003**

0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

**Phosphorus content (g kg-1)**

0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

LSD05

LSD05

number of passes; LSD0.05 – least significant differences at significance at p<0.05

Fig. 7. Effect of soil compaction on plants and roots phosphorus content in earing phase of spring barley (*Hordeum vulgare* L.) in years 2001—2004; 0 – non-compacted control, 1, 3, 6 –

Also Bockholt and Schnittke (1996) observed the high nutrient, especially potassium assimilation of young chickweed plants. Common lambsquarters was especially rich in nitrogen, but also potassium. Common lambsquarters is reported as the highest competitor for the nutrients to cultivated plants because of its high mass and nutrient content (Parylak, 1996) and high ability to compete with cultivated plants. Common lambsquarters taproot makes it more competiveness on dense soil compared to the barley, which have fibrous roots. Thicker roots are better able to penetrate the compacted soil compared to thinner roots (Whitely & Dexter, 1984) and compaction influences less dicotyledonous than

0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 **2002**

0x 1x 3x 6x

**2004**

0x 1x 3x 6x *Stellaria media* Roots

LSD05

LSD05

lambsquarters and common chickweed. Compaction had no significant impact on roots phosphorus content. However, the six pass compaction treatment increased roots phosphorus content in all years.

Fig. 6. Effect of soil compaction on plants and roots nitrogen content in earing phase of spring barley (*Hordeum vulgare* L.) in years 2001—2004; 0 – non-compacted control, 1, 3, 6 – number of passes; LSD0.05 – least significant differences at significance at p<0.05

The weeds highest in potassium were common chickweed and common lambsquarters in all years of experiment (Fig. 8). The potassium content in common chickweed ranged from 55 g kg–1 to 80 g kg–1 and in common lambs quarters from 25 to 62 g kg–1, depending on soil compaction and year. Stabile low potassium content was observed in barley and field pennycress dry matter with 12 to 20 g kg–1 and in roots with 5 to 12 g kg–1, depending on compaction and year. No positive correlation between nutrient content and soil compaction was observed in case of potassium in plants aboveground parts. Compaction inhibited potassium uptake of all investigated species and the differences between compaction treatments were significant in 2001. The six pass compaction treatment caused highest decrease on common fumitory by 50%, on common lambsquarters and common chickweed by 21% in 2003, and on common lambsquarters and common chickweed by 33% and 35%, respectively, in 2004. Significant increase of roots potassium content was observed in 2003 due to the six pass compaction treatment and in 2004 due to one, three and six pass compaction treatments.

Similar to this experiment, our earlier investigations (Reintam & Kuht, 2004) and investigations of other researchers (Salonen, 1992) reported higher nutrient content in weeds

lambsquarters and common chickweed. Compaction had no significant impact on roots phosphorus content. However, the six pass compaction treatment increased roots

LSD05

LSD05

Fig. 6. Effect of soil compaction on plants and roots nitrogen content in earing phase of spring barley (*Hordeum vulgare* L.) in years 2001—2004; 0 – non-compacted control, 1, 3, 6 –

The weeds highest in potassium were common chickweed and common lambsquarters in all years of experiment (Fig. 8). The potassium content in common chickweed ranged from 55 g kg–1 to 80 g kg–1 and in common lambs quarters from 25 to 62 g kg–1, depending on soil compaction and year. Stabile low potassium content was observed in barley and field pennycress dry matter with 12 to 20 g kg–1 and in roots with 5 to 12 g kg–1, depending on compaction and year. No positive correlation between nutrient content and soil compaction was observed in case of potassium in plants aboveground parts. Compaction inhibited potassium uptake of all investigated species and the differences between compaction treatments were significant in 2001. The six pass compaction treatment caused highest decrease on common fumitory by 50%, on common lambsquarters and common chickweed by 21% in 2003, and on common lambsquarters and common chickweed by 33% and 35%, respectively, in 2004. Significant increase of roots potassium content was observed in 2003 due to the six pass compaction treatment and in 2004 due to one, three and six pass

Similar to this experiment, our earlier investigations (Reintam & Kuht, 2004) and investigations of other researchers (Salonen, 1992) reported higher nutrient content in weeds

number of passes; LSD0.05 – least significant differences at significance at p<0.05

**2002**

0x 1x 3x 6x

**2004**

0x 1x 3x 6x

*Stellaria media* Roots

LSD05

LSD05

phosphorus content in all years.

**Nitrogen content (g kg-1)**

compaction treatments.

**2001**

0x 1x 3x 6x

0x 1x 3x 6x *Hordeum vulgare Chenopodium album Fumaria officinalis Thlaspi arvense*

**2003**

compared to barley. The lower nitrogen need of many weed species can give them advantage in competition with cereals (Di Tomaso, 1995) and thus they have a greater ability to compete with barley for nutrients, water and light. Because weeds are more efficient in nutrient uptake, in particular nitrogen, the nitrogen content of a crop decreases with increasing competition with weeds. However, some researchers suggest that competition between weeds and crops is lower on nutrient rich than on nutrient-poor soils (Pyšek et al., 2005) and competition is most intense in plots with lowest resource levels (Wilson & Tilman, 1993). In our experiment, weeds were more able to compete with barley under moderate compaction conditions (3 pass treatment), where in many cases nutrient content in weeds increased, especially in common lambsquarters and common chickweed.

Fig. 7. Effect of soil compaction on plants and roots phosphorus content in earing phase of spring barley (*Hordeum vulgare* L.) in years 2001—2004; 0 – non-compacted control, 1, 3, 6 – number of passes; LSD0.05 – least significant differences at significance at p<0.05

Also Bockholt and Schnittke (1996) observed the high nutrient, especially potassium assimilation of young chickweed plants. Common lambsquarters was especially rich in nitrogen, but also potassium. Common lambsquarters is reported as the highest competitor for the nutrients to cultivated plants because of its high mass and nutrient content (Parylak, 1996) and high ability to compete with cultivated plants. Common lambsquarters taproot makes it more competiveness on dense soil compared to the barley, which have fibrous roots. Thicker roots are better able to penetrate the compacted soil compared to thinner roots (Whitely & Dexter, 1984) and compaction influences less dicotyledonous than monocotyledonous plants (Materachera et al., 1991).

Weed Responses to Soil Compaction and Crop Management 261

decreasing distance from straw residues and air permeability, and with increasing cone

No increase of phosphorus and potassium content in plants (barley and weeds) due to increasing soil bulk density and penetration resistance were detected during this experiment (Fig. 7 and 8). Due to compacted soil, the plants are in stress and in the stressed conditions (increased cellular pH) plants nutrient acquisition through proton pumping via the H+- ATPase and transporters from roots to the stems and leaves is reduced (Bucher et al., 2001; Reintam & Kuht, 2003) and results in increased nutrients uptake by roots. These processes likely explain the increase of nutrients in the roots due to the compaction. Liepiec and Stepniewski (1995) found that root growth greatly affects uptake of nutrients transported by

Soil over-compaction inhibits the nutrition of cultivated plants and decreases their ability to compete with weeds. Changing the field conditions also changes the weed composition with which cultivated plants will compete. In compacted soils without fertilizer use, relatively easily controlled weed species will likely be replaced with harder to control weed species due to selection for competitive species. Weeds are serious competitors in agricultural systems; they accumulated free nutrients from soil, especially in dense soil, at the detriment to less competitive cultivated crops. At the same time the nutrient assimilation by weeds may stop their leaching from soil and store the nutrients in organic matter also for the next growing period. However, in severely compacted soil even weeds are not able to flourish and free nutrients may start to pollute the environment. Both, changes in weed community composition and nutrient assimilation deserves further investigations to understand better plant–soil and plant–plant interactions of other cultivated plants and soils under stress

The study was supported by Estonian Science Foundation grant No 5418, 4991 and 7622.

Andersson, T.N. & Milberg, P. (1998). Weed flora and the relative importance of site, crop,

Arvidsson, J. (1999). Nutrient uptake and growth of barley as affected by soil compaction.

Ball, B.C.; Horgan, G.W. & Parker, J.P. (2000). Short-range spatial variation of nitrous oxide

Bischoff, A. & Mahn, E.-G. (2000). The effect of nitrogen and diaspore availability on the

Blackshaw, R.E.; Larney, F.J.; Lindwall, C.W.; Watson, P.R. & Derksen, D.A. 2001. Tillage

cropping system. *Canadian Journal of Plant Sciences*, Vol. 81, pp. 805–813.

fluxes in relation to compaction and straw residues. *European Journal of Soil Science*,

regeneration of weed communities following extensification. *Agriculture, Ecosystems* 

intensity and crop rotation affect weed community dynamics in a winter wheat

crop rotation, and nitrogen. *Weed Science,* Vol. 46, pp. 30–38.

resistance and wet bulk density (Ball et al., 2000).

diffusion, such as phosphorus.

conditions, such is soil compaction.

*Plant and Soil,* Vol. 208, pp. 9–19.

*and Environment*, Vol. 77, pp. 237–246.

Vol. 51, pp. 607–616.

**5. Acknowledgment** 

**6. References** 

**4. Conclusion** 

Fig. 8. Effect of soil compaction on plants and roots potassium content in earing phase of spring barley (*Hordeum vulgare* L.) in years 2001–2004; 0 – non-compacted control, 1, 3, 6 – number of passes; LSD0.05 – least significant differences at significance at p<0.05; ns – differences are not significant

The increased nitrogen content in barley and weed species dry matter with increasing soil bulk density was likely due to better competitive conditions available to the survived plants on the most compacted soil. Plant density was there 1.3 to 3-times lower than on non-compacted soil and nutrient area per one plant was higher. In unsuitable conditions, barley tillering is higher and in dry year new sprouts may grow after adequate rainfall. In 2002, barley grew new sprouts in middle of the summer, and young plant tissues are always richer in nutrients than older. In the better competitive conditions plants are also producing more leaves than under lover radiation and less competitive conditions, and leaves usually containing more nitrogen than in stems. Planting density and ontogenetic processes significantly influence dry matter partitioning between leaves and stems (Röhrig & Stützel, 2001; Causin, 2004). With increasing competition (on non-compacted soil) common lambsquarters, field pennycress and common fumitory allocated relatively more biomass to stems than to leaves. In addition, higher soil moisture content over time is observed in compacted soil in dry seasons compared to less compacted soil. In moist soil, there are more plant available nitrates than in dry soil. In a dry year, due to compacted soil, uptake of elements such as nitrogen, calcium and magnesium, which are moving into the plant with water, might increase. Decrease of nitrogen content in plant dry matter in wet years, especially in 2001 and 2004, was probably connected next to poorer root development also with increased denitrification and decreased mineralization of organic matter in highly compacted soil due to decreased soil aeration. N2O flux increases with decreasing distance from straw residues and air permeability, and with increasing cone resistance and wet bulk density (Ball et al., 2000).

No increase of phosphorus and potassium content in plants (barley and weeds) due to increasing soil bulk density and penetration resistance were detected during this experiment (Fig. 7 and 8). Due to compacted soil, the plants are in stress and in the stressed conditions (increased cellular pH) plants nutrient acquisition through proton pumping via the H+- ATPase and transporters from roots to the stems and leaves is reduced (Bucher et al., 2001; Reintam & Kuht, 2003) and results in increased nutrients uptake by roots. These processes likely explain the increase of nutrients in the roots due to the compaction. Liepiec and Stepniewski (1995) found that root growth greatly affects uptake of nutrients transported by diffusion, such as phosphorus.

#### **4. Conclusion**

260 Weed Control

**2001**

0x 1x 3x 6x

**2003**

LSD05 <sup>0</sup>

0x 1x 3x 6x *Hordeum vulgare Chenopodium album Fumaria officinalis Thlaspi arvense*

**Potassium content (g kg-1)**

differences are not significant

LSD05

Fig. 8. Effect of soil compaction on plants and roots potassium content in earing phase of spring barley (*Hordeum vulgare* L.) in years 2001–2004; 0 – non-compacted control, 1, 3, 6 – number of passes; LSD0.05 – least significant differences at significance at p<0.05; ns –

The increased nitrogen content in barley and weed species dry matter with increasing soil bulk density was likely due to better competitive conditions available to the survived plants on the most compacted soil. Plant density was there 1.3 to 3-times lower than on non-compacted soil and nutrient area per one plant was higher. In unsuitable conditions, barley tillering is higher and in dry year new sprouts may grow after adequate rainfall. In 2002, barley grew new sprouts in middle of the summer, and young plant tissues are always richer in nutrients than older. In the better competitive conditions plants are also producing more leaves than under lover radiation and less competitive conditions, and leaves usually containing more nitrogen than in stems. Planting density and ontogenetic processes significantly influence dry matter partitioning between leaves and stems (Röhrig & Stützel, 2001; Causin, 2004). With increasing competition (on non-compacted soil) common lambsquarters, field pennycress and common fumitory allocated relatively more biomass to stems than to leaves. In addition, higher soil moisture content over time is observed in compacted soil in dry seasons compared to less compacted soil. In moist soil, there are more plant available nitrates than in dry soil. In a dry year, due to compacted soil, uptake of elements such as nitrogen, calcium and magnesium, which are moving into the plant with water, might increase. Decrease of nitrogen content in plant dry matter in wet years, especially in 2001 and 2004, was probably connected next to poorer root development also with increased denitrification and decreased mineralization of organic matter in highly compacted soil due to decreased soil aeration. N2O flux increases with

**2002**

LSD05 <sup>0</sup>

LSD05 <sup>0</sup>

*Stellaria media* Roots

0x 1x 3x 6x

0x 1x 3x 6x

**2004**

Soil over-compaction inhibits the nutrition of cultivated plants and decreases their ability to compete with weeds. Changing the field conditions also changes the weed composition with which cultivated plants will compete. In compacted soils without fertilizer use, relatively easily controlled weed species will likely be replaced with harder to control weed species due to selection for competitive species. Weeds are serious competitors in agricultural systems; they accumulated free nutrients from soil, especially in dense soil, at the detriment to less competitive cultivated crops. At the same time the nutrient assimilation by weeds may stop their leaching from soil and store the nutrients in organic matter also for the next growing period. However, in severely compacted soil even weeds are not able to flourish and free nutrients may start to pollute the environment. Both, changes in weed community composition and nutrient assimilation deserves further investigations to understand better plant–soil and plant–plant interactions of other cultivated plants and soils under stress conditions, such is soil compaction.

#### **5. Acknowledgment**

The study was supported by Estonian Science Foundation grant No 5418, 4991 and 7622.

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## *Edited by Andrew J. Price*

Crop loss due to weeds has challenged agricultural managers since man began to develop the first farming systems. In the past century, however, much progress has been made to reduce weed interference in crop settings through effective yet mostly non-sustainable weed control strategies. With the commercial introduction of herbicides during the mid-1900's, advancements in chemical weed control tactics have provided efficient suppression of a broad range of weed species for most agricultural practices. Currently, with the necessity to design effective sustainable weed management systems, research has been pushing new frontiers on investigating integrated weed management options including chemical, mechanical as well as cultural practices. Author contributions to Weed Science present significant topics of research that examine a number of options that can be utilized to develop successful and sustainable weed management systems for many areas of crop production

Weed Control

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