**3. Main fungal diseases of wheat in Luxembourg and associated decision support systems**

Wheat represents one of the most widely cultivated cereals with a production area of 215 million ha worldwide [11]. Unfortunately, wheat diseases remain a major constraint to wheat production [12]. Crop protection often relies on calendar-date applied, preventive fungicide applications, and small grain cereals are typically treated with two or three foliar fungicide applications in Luxembourg and Belgium [13, 14]. The marginal cost/revenue ratio for winter wheat and the potential negative impacts that overuse of pesticides can have on the environment are compelling arguments to minimize inputs, including fungicides. Effective estimation of the risk of disease epidemic development can minimize the number of fungicide spray applied, leading to a more sustainable and environmentally friendly system of wheat production. Using tools to develop integrated pest management can lead to fungicides being applied only at particular stages that are at risk of infection, and only when the pathogen has been correctly identified (accurate identification and/or estimation of severity of disease can be critical to effective management). Diseases of wheat that have become economically important in Luxembourg include Septoria leaf blotch (SLB) caused by *Zymoseptoria tritici Roberge in Desmaz.*, wheat leaf rust (WLR) caused by *Puccinia triticina Eriks.*, wheat stripe rust (WSR) caused by *Puccinia striiformis Westend. f. sp. Tritici Eriks.*, and Fusarium Head Blight (FHB) caused mainly by *Fusarium graminearum*. The control of the diseases caused by these pathogens is a high priority to minimize yield and grain quality losses.

#### **3.1. Septoria leaf blotch**

The majority of the SLB disease prediction systems proposed for the management of *Z. tritici* assume that the main risk of infection of the upper leaves (the most critical for grain fill [15]) comes from the inoculum that developed on the leaves during the winter and spring before the extension of the stem [16]. These prediction systems are based solely on rainfall occurring during stem extension, without considering the development of individual leaves [17–19]. The importance of rain and splash dispersal for development of severe SLB has been demonstrated in several studies (e.g., [16, 20–22]). Shaw and Royle [19] suggested that the amount of Septoria inoculum at GS31 (first node detectable) [23] was only a partial guide to forecast the inoculum available during the expansion of the last two leaves. The progression of the disease on the upper leaves depends on the sensitivity of the cultivar, and the period of infection (infections occurring during and/or just after the emergence of these leaves could lead to severe impacts if the weather conditions are favorable) [24]. The mechanisms by which the pathogen population increases on the upper leaves are determined by the interaction of plant growth, the meteorological conditions allowing the dispersal of the inoculum and thus opportunity for new infections, and the availability of that inoculum in sufficient proximity to the upper leaves [19]. El Jarroudi et al. [20] suggested that the greatest risk to a wheat crop occurs from infections arising between the emergences of leaf 2 (L2) and the flag leaf and roughly two latent periods before these leaves would naturally begin senescence. If the upper leaves are infected early in the cropping season, they are likely to suffer much more severe disease for two reasons: a) there is sufficient time for the pathogen to have more than one multiplication cycle on the leaves, with a longer time during which dissemination and infection may occur, resulting in premature loss of leaf area; b) these leaves are closer to the sources of the inoculum and extreme splashing events will no longer be necessary to disperse sufficient number of spore onto a susceptible tissue that is higher in the crop canopy. Furthermore, the structure of the wheat plants and the position of the source of the inoculum on specific leaves relative to each other are constantly changing and thus the risk of disease progression is dynamically complex and specific to each crop, cultivar and season [22]. In addition, the life of the upper leaves is considerably shortened by secondary infections resulting from the inoculum produced by primary lesions in the same leaf layer [19]. The detection of spores of *Z. tritici* during the season demonstrates the need for a predictive model [25, 26]. Both asexually produced pycnidiospores and sexually produced ascospores of *Z. tritici* are known to cause disease in wheat [22, 27], with ascospores being aerially dispersed over relatively long distances, and the pycnidiospores being primarily splash dispersed. Furthermore, the ascospores have an impact not only as primary inoculum in autumn and winter [27], but also as secondary inoculum at the end of spring and in summer. This airborne inoculum could help to colonize the upper leaves without the need for splash-dispersed pycnidiospores or could exacerbate the damage caused by splash-dispersed *Z. tritici* (**Photo 1**) due to the presence of the additional ascospore inoculum [28].

blight) of winter wheat in Luxembourg. A description of the models is provided along with the constraints associated with their use for in-season disease monitoring. The challenges

Wheat represents one of the most widely cultivated cereals with a production area of 215 million ha worldwide [11]. Unfortunately, wheat diseases remain a major constraint to wheat production [12]. Crop protection often relies on calendar-date applied, preventive fungicide applications, and small grain cereals are typically treated with two or three foliar fungicide applications in Luxembourg and Belgium [13, 14]. The marginal cost/revenue ratio for winter wheat and the potential negative impacts that overuse of pesticides can have on the environment are compelling arguments to minimize inputs, including fungicides. Effective estimation of the risk of disease epidemic development can minimize the number of fungicide spray applied, leading to a more sustainable and environmentally friendly system of wheat production. Using tools to develop integrated pest management can lead to fungicides being applied only at particular stages that are at risk of infection, and only when the pathogen has been correctly identified (accurate identification and/or estimation of severity of disease can be critical to effective management). Diseases of wheat that have become economically important in Luxembourg include Septoria leaf blotch (SLB) caused by *Zymoseptoria tritici Roberge in Desmaz.*, wheat leaf rust (WLR) caused by *Puccinia triticina Eriks.*, wheat stripe rust (WSR) caused by *Puccinia striiformis Westend. f. sp. Tritici Eriks.*, and Fusarium Head Blight (FHB) caused mainly by *Fusarium graminearum*. The control of the diseases caused by these

The majority of the SLB disease prediction systems proposed for the management of *Z. tritici* assume that the main risk of infection of the upper leaves (the most critical for grain fill [15]) comes from the inoculum that developed on the leaves during the winter and spring before the extension of the stem [16]. These prediction systems are based solely on rainfall occurring during stem extension, without considering the development of individual leaves [17–19]. The importance of rain and splash dispersal for development of severe SLB has been demonstrated in several studies (e.g., [16, 20–22]). Shaw and Royle [19] suggested that the amount of Septoria inoculum at GS31 (first node detectable) [23] was only a partial guide to forecast the inoculum available during the expansion of the last two leaves. The progression of the disease on the upper leaves depends on the sensitivity of the cultivar, and the period of infection (infections occurring during and/or just after the emergence of these leaves could lead to severe impacts if the weather conditions are favorable) [24]. The mechanisms by which the pathogen population increases on the upper leaves are determined by the interaction of plant growth, the meteorological conditions allowing the dispersal of the inoculum and thus opportunity for new infections, and the availability of that inoculum in sufficient proximity to the upper leaves [19]. El Jarroudi et al. [20] suggested that the greatest risk to a wheat crop occurs

faced using weather-based models in a changing climate are also discussed.

pathogens is a high priority to minimize yield and grain quality losses.

**decision support systems**

6 Advances in Plant Pathology

**3.1. Septoria leaf blotch**

**3. Main fungal diseases of wheat in Luxembourg and associated** 

Due to the potential for yield loss from SLB, growers tend to spray fungicides several times during the winter wheat season to protect their crops. The development of resistance in

**Photo 1.** Symptoms of Septoria leaf blotch caused by *Zymoseptoria tritici* on leaf L3 of the cultivar Achat. The black dots in the tan lesions are the pycnidia that produce the splash dispersed pycnidiospores (photo taken on May 30, 2007 at Everlange, Luxembourg; photo credit: El Jarroudi M.).

*Z. tritici* to the main fungicides used for its control [20] has been demonstrated in many countries. Moreover, actual disease severity does not always justify a fungicide spray. In years with a low disease risk, a lower fungicide dose could be used [29]. There are several weatherbased Decision Support Systems (DSSs) available to help a grower decide whether a fungicide application is required [30–32]. These models rely mainly on rainfall measurement, or in some case more comprehensively on weather data, without considering the development of the different leaf layers during stem elongation [18, 33–37].

SLB during full emergence. Consequently, a fungicide treatment against the risk of SLB is recommended if a latency period of the disease is completed at 75% emergence and favorable weather conditions forecasted. Overall, the assessment of the infection periods achieved an accuracy of 85%. The results showed that the PROCULTURE model satisfactorily recommended none or a single fungicide treatment at each study site, regardless of geographical

Modeling the Main Fungal Diseases of Winter Wheat: Constraints and Possible Solutions

http://dx.doi.org/10.5772/intechopen.75983

9

The PROCULTURE model is being used in early warning systems in Belgium and Luxembourg to define, in real time, the risk of SLB developing on the upper leaves of winter wheat during stem elongation. However, setting up an operational network for recommending the optimal time for fungicide application requires a representative network of weather stations throughout the region where the DSS will be used. In our studies (e.g., [20, 46]) overestimation or underestimation of the risk of SLB progression could often be traced back to differences in rain events captured by the tipping-bucket rain-gauges at the weather station compared with the rainfall to which a particular field was actually exposed. Rainfall data could be interpolated between weather stations, but precipitation between fields are characterized by high spatial and temporal variability [47, 48], making the interpolation

Radar may provide a solution for improving the interpolation of precipitation data [51, 52]. Over the past few years, radar-derived estimates have been increasingly used in disease fore-

**Figure 1.** Descriptive flowchart of the PROCULTURE model for predicting the risk of Septoria leaf blotch (SLB) infection

casting applications as an alternative to gauge-derived measurements [51, 53].

location or possible variability among the fungal diseases involved [45].

*3.1.2. Spatialization of PROCULTURE alerts using radar rainfall*

unreliable [49, 50].

events. T: Air temperature; RH: Relative humidity.

However, many models neglect the periods of interruption of acceptable temperature or humidity for infection which are important factors in disease development, and can be an indispensable element in developing more accurate models. According to Shaw [38], interruptions in periods at 75% relative humidity for 48 h slightly reduced the efficiency of the infection process, but interruptions at 50% relative humidity resulted in major effects, but still allowed infection to occur. To simulate infection, some models take daily conditions [39, 40], while others, for example the PROCULTURE model are based on hourly weather conditions [14, 20].

#### *3.1.1. The PROCULTURE model*

The PROCULTURE model is an interactive web-based, field-specific, DSS based on the mechanistic modeling of the development of the last five leaf layers of the wheat plant coupled with the progress of SLB on these layers [14, 20, 41, 42]. A descriptive flowchart of the model is presented in **Figure 1**. The main inputs include weather data (hourly air temperature, rainfall and relative humidity) and field-specific data including the location, sowing date and cultivar susceptibility. Field observations are also important since a fine-tuning of the model may be required based on the actual growth stage (around the first node stage, GS32) and the severity of SLB on the particular leaf layer as specified by the model. The model considers infection to have occurred when, during a 2 h rainfall event, precipitation for the first hour is at least 0.1 mm (to allow for the swelling of pycnidia), followed by a second hour with at least 0.5 mm precipitation (**Figure 1**), leading to the release and splash dispersal of the conidia [14, 20]. In addition, after rainfall, relative humidity should be higher than 60% during the following 16 h [20, 43] and the temperature should remain above 4°C for 24 h [20] for germination and infection.

The evaluation of the PROCULTURE model at several sites in Belgium [14, 44] and Luxembourg [20] demonstrated that the model can explain disease progression in the canopy (**Figure 2**) and can be used to advise farmers when to apply fungicides during stem elongation, as the three upper leaves emerge. The need for and timing of a single fungicide spray using the PROCULTURE model is based on the observed disease severity earlier in the cropping season (i.e., severity on the lower leaves L5-L4 at GS 31–37, L1 being the flag leaf), the susceptibility of the cultivar, past and forecasted weather conditions, and the predicted development of leaves based on the output of the PROCULTURE model. Furthermore, historical data (weather and disease incidence and severities) were used as a basis for similarity analysis to further evaluate the risk of severe disease development. Given the threshold level of observed disease severity (namely on the lower leaves) and weather conditions (actual and forecasted), an advice for fungicide treatment was taken and fungicides applied only if required to protect the upper leaves. For example, a 5% of emergence of L3 coinciding with SLB symptoms on L5 and a rainfall event, results in a greater risk that L3 will be affected by SLB during full emergence. Consequently, a fungicide treatment against the risk of SLB is recommended if a latency period of the disease is completed at 75% emergence and favorable weather conditions forecasted. Overall, the assessment of the infection periods achieved an accuracy of 85%. The results showed that the PROCULTURE model satisfactorily recommended none or a single fungicide treatment at each study site, regardless of geographical location or possible variability among the fungal diseases involved [45].

#### *3.1.2. Spatialization of PROCULTURE alerts using radar rainfall*

*Z. tritici* to the main fungicides used for its control [20] has been demonstrated in many countries. Moreover, actual disease severity does not always justify a fungicide spray. In years with a low disease risk, a lower fungicide dose could be used [29]. There are several weatherbased Decision Support Systems (DSSs) available to help a grower decide whether a fungicide application is required [30–32]. These models rely mainly on rainfall measurement, or in some case more comprehensively on weather data, without considering the development of the dif-

However, many models neglect the periods of interruption of acceptable temperature or humidity for infection which are important factors in disease development, and can be an indispensable element in developing more accurate models. According to Shaw [38], interruptions in periods at 75% relative humidity for 48 h slightly reduced the efficiency of the infection process, but interruptions at 50% relative humidity resulted in major effects, but still allowed infection to occur. To simulate infection, some models take daily conditions [39, 40], while others, for example the PROCULTURE model are based on hourly weather conditions [14, 20].

The PROCULTURE model is an interactive web-based, field-specific, DSS based on the mechanistic modeling of the development of the last five leaf layers of the wheat plant coupled with the progress of SLB on these layers [14, 20, 41, 42]. A descriptive flowchart of the model is presented in **Figure 1**. The main inputs include weather data (hourly air temperature, rainfall and relative humidity) and field-specific data including the location, sowing date and cultivar susceptibility. Field observations are also important since a fine-tuning of the model may be required based on the actual growth stage (around the first node stage, GS32) and the severity of SLB on the particular leaf layer as specified by the model. The model considers infection to have occurred when, during a 2 h rainfall event, precipitation for the first hour is at least 0.1 mm (to allow for the swelling of pycnidia), followed by a second hour with at least 0.5 mm precipitation (**Figure 1**), leading to the release and splash dispersal of the conidia [14, 20]. In addition, after rainfall, relative humidity should be higher than 60% during the following 16 h [20, 43] and the temperature should remain above 4°C for 24 h [20] for germination and infection.

The evaluation of the PROCULTURE model at several sites in Belgium [14, 44] and Luxembourg [20] demonstrated that the model can explain disease progression in the canopy (**Figure 2**) and can be used to advise farmers when to apply fungicides during stem elongation, as the three upper leaves emerge. The need for and timing of a single fungicide spray using the PROCULTURE model is based on the observed disease severity earlier in the cropping season (i.e., severity on the lower leaves L5-L4 at GS 31–37, L1 being the flag leaf), the susceptibility of the cultivar, past and forecasted weather conditions, and the predicted development of leaves based on the output of the PROCULTURE model. Furthermore, historical data (weather and disease incidence and severities) were used as a basis for similarity analysis to further evaluate the risk of severe disease development. Given the threshold level of observed disease severity (namely on the lower leaves) and weather conditions (actual and forecasted), an advice for fungicide treatment was taken and fungicides applied only if required to protect the upper leaves. For example, a 5% of emergence of L3 coinciding with SLB symptoms on L5 and a rainfall event, results in a greater risk that L3 will be affected by

ferent leaf layers during stem elongation [18, 33–37].

*3.1.1. The PROCULTURE model*

8 Advances in Plant Pathology

The PROCULTURE model is being used in early warning systems in Belgium and Luxembourg to define, in real time, the risk of SLB developing on the upper leaves of winter wheat during stem elongation. However, setting up an operational network for recommending the optimal time for fungicide application requires a representative network of weather stations throughout the region where the DSS will be used. In our studies (e.g., [20, 46]) overestimation or underestimation of the risk of SLB progression could often be traced back to differences in rain events captured by the tipping-bucket rain-gauges at the weather station compared with the rainfall to which a particular field was actually exposed. Rainfall data could be interpolated between weather stations, but precipitation between fields are characterized by high spatial and temporal variability [47, 48], making the interpolation unreliable [49, 50].

Radar may provide a solution for improving the interpolation of precipitation data [51, 52]. Over the past few years, radar-derived estimates have been increasingly used in disease forecasting applications as an alternative to gauge-derived measurements [51, 53].

**Figure 1.** Descriptive flowchart of the PROCULTURE model for predicting the risk of Septoria leaf blotch (SLB) infection events. T: Air temperature; RH: Relative humidity.

**Field sites Observation** 

HUMAINf 21/05 to

USELDANGEg 13/05 to

BURMERANGEg 17/05 to

a

b

c

e

f

g

**period**

05/07

03/05 to 28/06

20/05 to 15/07

29/06

16/05 to 09/07

14/05 to 12/07

03/07

05/05 to 13/07

12/05 to 04/07

27/05 to 05/07

16/05 to 11/07

against the number of infections observed. Perfect forecast = 1.

<sup>h</sup>Average for each field site over three cropping seasons indicated in bold.

during three cropping seasons in Luxembourg and Belgium [42].

infections observed in the field. Perfect forecast = 0.

estimates. Perfect value = 1.

Site in Belgium.


Site in Luxembourg.

**Year Eventsa Duration** 

**of infection periodb**

REULERg — 2003 — — — — — — — — —

All 148 433 413 0.79 0.84 0.01 0.02 0.77 0.83

Number of infection events deduced from visually observed symptoms in the field sites on the upper three leaves.

Probability of Detection of infection by *Z. tritici* is the number of cases where infections are both simulated and observed

dFalse Alarms Ratio of infection by *Z. tritici* is the number of observed infections not simulated against the number of

Critical Success Index of *Z. tritici* infection takes into account both false alarms and missed events. The PODso, FARso and CSIso show the infection occurrence comparison between infection periods (on the last 3 leaves) determined by visual observations and simulated by the PROCULTURE model using measurements from four rain-gauges or radar-based

**Table 1.** Comparison of the performance when using rain-gauge or radar-based rainfall measurements in the PROCULTURE model for estimating the risk of infection events in winter wheat by *Zymoseptoria tritici* at four sites

Total number of hours with a high probability of infection simulated by PROCULTURE.

**PODso**

Modeling the Main Fungal Diseases of Winter Wheat: Constraints and Possible Solutions

2003 18 60 62 0.93 0.83 0 0 0.93 0.83

2004 16 46 40 0.73 0.87 0 0 0.73 0.87

2005 8 24 27 0.86 1.00 0 0.12 0.85 0.87

2003 15 56 44 0.87 0.80 0 0 0.87 0.80

2004 18 48 48 0.72 0.78 0 0 0.72 0.78

2005 15 33 32 0.71 0.86 0.09 0.07 0.67 0.81

2003 10 30 22 0.70 0.70 0 0 0.70 0.70

2004 15 43 55 0.73 0.93 0 0 0.73 0.93

2005 12 24 28 0.91 0.83 0 0 0.91 0.83

2004 10 45 32 0.70 0.70 0 0 0.70 0.70

2005 11 24 23 0.82 1.00 0 0 0.82 1.00

42 130 129 **0.84**<sup>h</sup> **0.90 0 0.04 0.84 0.86**

48 137 124 **0.77 0.81 0.03 0.02 0.75 0.80**

37 97 105 **0.78 0.82 0 0 0.78 0.82**

21 69 55 **0.76 0.85 0 0 0.76 0.85**

**<sup>c</sup> FARso**

**Gauge Radar Gauge Radar Gauge Radar Gauge Radar**

**<sup>d</sup> CSIso**

http://dx.doi.org/10.5772/intechopen.75983

**e**

11

**Figure 2.** Output of the Septoria risk simulation model from 2006 in winter wheat fields at Reuler Luxembourg. A: Observed daily values of air mean temperature (°C) and rainfall (mm). B: Number of hours per day with a > 80% probability of infection. C: Lines: Leaf area development (0–100%) of leaves L5–L1 (flag leaf is L1). Gray: Accumulation of hours of primary infection expressed on leaves L5–L1 (maximum of 100 h) (Reuler is one of the representative sites of winter wheat cropping regions in Luxembourg selected for field experiments in the framework of the SENTINELLE project. It is located in the northern part of Luxembourg).

Mahtour et al. [42] validated the simulation of infection periods for *Z. tritici* calculated by PROCULTURE using radar-based rainfall measurements. The duration of periods with a high probability of infection by *Z. tritici* was calculated by PROCULTURE and using radar rainfall data for these trials was similar to that based on gauge measurements (**Table 1**). A better spatial representation of precipitation will inevitably improve present DSSs. Consequently, the DSSs could more accurately be the basis for recommending appropriate fungicide applications. If the results of the radar-based rainfall measurements combined with PROCULTURE are confirmed for a larger precipitation dataset and a larger number of stations, the sole use of radar data in the disease-warning system will be considered in the future. The results from this work should encourage research on additional radar-based rainfall applications for diseases of other crops.

#### **3.2. Wheat leaf rust**

WLR is of major historical significance and is of economic importance worldwide. It is the most widespread of the three species of rusts causing significant yield losses over large geographical areas [54–59]. Several studies in major cereal-producing areas have revealed


a Number of infection events deduced from visually observed symptoms in the field sites on the upper three leaves. b Total number of hours with a high probability of infection simulated by PROCULTURE.

c Probability of Detection of infection by *Z. tritici* is the number of cases where infections are both simulated and observed against the number of infections observed. Perfect forecast = 1.

dFalse Alarms Ratio of infection by *Z. tritici* is the number of observed infections not simulated against the number of infections observed in the field. Perfect forecast = 0.

e Critical Success Index of *Z. tritici* infection takes into account both false alarms and missed events. The PODso, FARso and CSIso show the infection occurrence comparison between infection periods (on the last 3 leaves) determined by visual observations and simulated by the PROCULTURE model using measurements from four rain-gauges or radar-based estimates. Perfect value = 1.

f Site in Belgium.

g Site in Luxembourg.

<sup>h</sup>Average for each field site over three cropping seasons indicated in bold.


Mahtour et al. [42] validated the simulation of infection periods for *Z. tritici* calculated by PROCULTURE using radar-based rainfall measurements. The duration of periods with a high probability of infection by *Z. tritici* was calculated by PROCULTURE and using radar rainfall data for these trials was similar to that based on gauge measurements (**Table 1**). A better spatial representation of precipitation will inevitably improve present DSSs. Consequently, the DSSs could more accurately be the basis for recommending appropriate fungicide applications. If the results of the radar-based rainfall measurements combined with PROCULTURE are confirmed for a larger precipitation dataset and a larger number of stations, the sole use of radar data in the disease-warning system will be considered in the future. The results from this work should encourage research on additional radar-based rainfall applications for dis-

**Figure 2.** Output of the Septoria risk simulation model from 2006 in winter wheat fields at Reuler Luxembourg. A: Observed daily values of air mean temperature (°C) and rainfall (mm). B: Number of hours per day with a > 80% probability of infection. C: Lines: Leaf area development (0–100%) of leaves L5–L1 (flag leaf is L1). Gray: Accumulation of hours of primary infection expressed on leaves L5–L1 (maximum of 100 h) (Reuler is one of the representative sites of winter wheat cropping regions in Luxembourg selected for field experiments in the framework of the SENTINELLE

WLR is of major historical significance and is of economic importance worldwide. It is the most widespread of the three species of rusts causing significant yield losses over large geographical areas [54–59]. Several studies in major cereal-producing areas have revealed

eases of other crops.

10 Advances in Plant Pathology

project. It is located in the northern part of Luxembourg).

**3.2. Wheat leaf rust**

**Table 1.** Comparison of the performance when using rain-gauge or radar-based rainfall measurements in the PROCULTURE model for estimating the risk of infection events in winter wheat by *Zymoseptoria tritici* at four sites during three cropping seasons in Luxembourg and Belgium [42].

that epidemics of WLR occur under (i) favorable conditions for overwintering spores as a source of primary inoculum, (ii) rapid and abundant production of wind-dispersed urediniospores, and (iii) a complex interaction between environmental conditions and host resistance [54, 60]. The dispersal of foliar pathogens and WLR in particular around a spore source has been described in many studies, sometimes confirming dispersal over large distances [61] but most often at the spatial scale of an infected plant or group of plants [62, 63], or even a single leaf [64]. Although these studies give valuable insights to allow understanding of epidemic spread of diseases like WLR and to parameterize simulation models, they most often do not take into account the local structure of the host crop and its potential effect on disease distribution [64].

Two different approaches have been used to forecast development of epidemics of WLR. Some forecasting systems consider the effect of weather on the disease by means of empirical rules, flow charts [65], disease indices [66, 67], or regression equations [68, 69]. Other models forecast severity of WLR on the basis of the dynamic of the epidemic, using a fixed relative growth rate of the disease [70–72].

> light rain (0.1–1.0 mm) in the first hour of an infection event supposing that this rainfall allows the first deposition of the inoculum in the field. This light rain event is not a necessity once the primary infection has occurred. The model has led to a DSS that allows optimizing timing of

> **Figure 3.** Descriptive flowchart of the model used for predicting wheat leaf rust (WLR) infection events caused by

**Photo 2.** A leaf showing symptoms of infection by *Puccinia triticina,* causing pathogen of wheat leaf rust (photo taken on

Modeling the Main Fungal Diseases of Winter Wheat: Constraints and Possible Solutions

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13

applications of the fungicide for controlling WLR in fields in Luxembourg.

June 2009 at Burmerange, South Luxembourg; photo credit: El Jarroudi M.).

*Puccinia triticina* [80].

Moisture and temperature are reported to be the most important meteorological parameters influencing the development of epidemics of WLR [73]. Nevertheless, the genetic resistance of wheat cultivars is critically important factor in determining the impact of the disease [74]. Urediniospores are deposited by wind or rain on the adaxial and abaxial surfaces of wheat leaves. Rain on, or turbulence around the leaf surface allows the dispersal of urediniospores. In addition, wet deposition (spore scavenged from the air by rain) is considered an important mechanism of crop contamination by some rusts [75]. Although most rainfall events promote spore dispersal in the field, heavy rain may also induce the leaching of spores deposited on leaves and may totally deplete the lesions in the process [76]. When the urediniospores of WLR are in contact with susceptible wheat leaves, the success of infection requires a minimal duration of surface wetness, which varies as a function of temperature [50, 77]. De Vallavieille-Pope et al. [77] showed that optimum temperatures for uredospore germination ranged from 12 to 15°C and that the germination process ceased above 35°C. As noted, the presence of free water on the leaf surface is essential for urediniospore germination. In an earlier study, Eversmeyer [78] proposed an optimum temperature of 16°C for completion of the infection process by uredinisospores of *P. triticina*, with infection needing a dew period of at least 3–4 h. In the same study, it was shown that the latent period for WLR ranged from 8 to 20 days for air temperatures between 10 and 20°C. The process of infection has an approximately linear relationship with the sum of base 0 degree-days. It has also been demonstrated that germination of urediniospores of *P. triticina* could be delayed or inhibited by increasing light intensity [78, 79]. For this reason, infections occur preferentially at night (**Photo 2**).

Considering these data, an empirical approach for simulating infection by WLR and progress of the disease on the upper three leaf layers has been proposed and validated in Luxembourg [2]. The model used only weather data logged between 8 pm to 5 am based on the assumption that spore germination is inhibited by light. Each infection event was deemed to require a period of at least 12 consecutive hours counted on at least two nights with air temperatures ranging between 8 and 16°C and a relative humidity greater than 60% (**Figure 3**). Moreover, the hourly rainfall totals during these 12-hour periods must be less than 1 mm to avoid the leaching of spores present on leaves. Furthermore, the primary infection in a field requires a Modeling the Main Fungal Diseases of Winter Wheat: Constraints and Possible Solutions http://dx.doi.org/10.5772/intechopen.75983 13

that epidemics of WLR occur under (i) favorable conditions for overwintering spores as a source of primary inoculum, (ii) rapid and abundant production of wind-dispersed urediniospores, and (iii) a complex interaction between environmental conditions and host resistance [54, 60]. The dispersal of foliar pathogens and WLR in particular around a spore source has been described in many studies, sometimes confirming dispersal over large distances [61] but most often at the spatial scale of an infected plant or group of plants [62, 63], or even a single leaf [64]. Although these studies give valuable insights to allow understanding of epidemic spread of diseases like WLR and to parameterize simulation models, they most often do not take into account the local structure of the host crop and its potential effect on disease distri-

Two different approaches have been used to forecast development of epidemics of WLR. Some forecasting systems consider the effect of weather on the disease by means of empirical rules, flow charts [65], disease indices [66, 67], or regression equations [68, 69]. Other models forecast severity of WLR on the basis of the dynamic of the epidemic, using a fixed relative growth

Moisture and temperature are reported to be the most important meteorological parameters influencing the development of epidemics of WLR [73]. Nevertheless, the genetic resistance of wheat cultivars is critically important factor in determining the impact of the disease [74]. Urediniospores are deposited by wind or rain on the adaxial and abaxial surfaces of wheat leaves. Rain on, or turbulence around the leaf surface allows the dispersal of urediniospores. In addition, wet deposition (spore scavenged from the air by rain) is considered an important mechanism of crop contamination by some rusts [75]. Although most rainfall events promote spore dispersal in the field, heavy rain may also induce the leaching of spores deposited on leaves and may totally deplete the lesions in the process [76]. When the urediniospores of WLR are in contact with susceptible wheat leaves, the success of infection requires a minimal duration of surface wetness, which varies as a function of temperature [50, 77]. De Vallavieille-Pope et al. [77] showed that optimum temperatures for uredospore germination ranged from 12 to 15°C and that the germination process ceased above 35°C. As noted, the presence of free water on the leaf surface is essential for urediniospore germination. In an earlier study, Eversmeyer [78] proposed an optimum temperature of 16°C for completion of the infection process by uredinisospores of *P. triticina*, with infection needing a dew period of at least 3–4 h. In the same study, it was shown that the latent period for WLR ranged from 8 to 20 days for air temperatures between 10 and 20°C. The process of infection has an approximately linear relationship with the sum of base 0 degree-days. It has also been demonstrated that germination of urediniospores of *P. triticina* could be delayed or inhibited by increasing light intensity

[78, 79]. For this reason, infections occur preferentially at night (**Photo 2**).

Considering these data, an empirical approach for simulating infection by WLR and progress of the disease on the upper three leaf layers has been proposed and validated in Luxembourg [2]. The model used only weather data logged between 8 pm to 5 am based on the assumption that spore germination is inhibited by light. Each infection event was deemed to require a period of at least 12 consecutive hours counted on at least two nights with air temperatures ranging between 8 and 16°C and a relative humidity greater than 60% (**Figure 3**). Moreover, the hourly rainfall totals during these 12-hour periods must be less than 1 mm to avoid the leaching of spores present on leaves. Furthermore, the primary infection in a field requires a

bution [64].

12 Advances in Plant Pathology

rate of the disease [70–72].

**Photo 2.** A leaf showing symptoms of infection by *Puccinia triticina,* causing pathogen of wheat leaf rust (photo taken on June 2009 at Burmerange, South Luxembourg; photo credit: El Jarroudi M.).

light rain (0.1–1.0 mm) in the first hour of an infection event supposing that this rainfall allows the first deposition of the inoculum in the field. This light rain event is not a necessity once the primary infection has occurred. The model has led to a DSS that allows optimizing timing of applications of the fungicide for controlling WLR in fields in Luxembourg.

**Figure 3.** Descriptive flowchart of the model used for predicting wheat leaf rust (WLR) infection events caused by *Puccinia triticina* [80].

The presence of primary inoculum in the air is not considered as a limited factor in this model. We assumed that spores of *P. triticina* are already present in fields during the period of study. A fine-tuning of the DSS will include an effective assessment (i.e., spore dispersion estimates) for the spores in the same field, since spores from outside the field are only required to initiate the first infection (exogenous inoculum). Indeed, the assessment of the model coupled with detection of spores showed that the infection periods on susceptible cultivars (**Figure 4**) were well predicted [81].

Thus, the detection of airborne inoculum by sensors and its coupling to a reliable model of dispersion could help improve forecasting the occurrence of WLR. In Belgium, a recent study on the spatio-temporal distribution of the airborne inoculum of *P. triticina* indicated that infection on the three youngest leaf layers could originate from endogenous and/or exogenous inoculum. The first symptoms observed on crops can be the result of either infection by urediniospores carried upwind by air masses from distant infected fields or the consequence of sporulating lesions occurring in the fall and remaining active after the winter [82]. Airborne inoculum was generally detected in fields during the growing season between March and May (during the spring green-up). Various densities of airborne inoculum were observed depending of the site and the year, and the severity of WLR on the upper leaf layers during the grain filling was strongly influenced by the density of spores collected during the development of these leaf layers [83].

Molecular diagnostics combined with sampling of airborne inoculum could be exploited to more accurately predict the risk of epidemics in wheat agro-ecosystems. Strategies for controlling WLR in fields include the use of resistant cultivars. But a prolonged period of monitoring WLR involving susceptible cultivars and favorable night conditions conducive to spore production, dispersal of, and infection by *P. triticina* with subsequent development of WLR should demonstrate the capability of the DSS in these situations. Junk et al. [84] studied the potential infection periods of WLR in a changing climate at two selected sites in Luxembourg (Burmerange and Christnach) using a weather threshold-based model for infection and development and progress of WLR that involved hourly night-time data for air temperature, relative humidity and rainfall. Their findings revealed that highest proportions of favorable days for infection with *P. triticina* and development of WLR in the future would occur during spring and summer at both sites, with the proportions more marked at Burmerange.

#### **3.3. Wheat stripe rust**

WSR is an example of a disease of world-wide importance and ability for long distance dispersal. Crop pathogens with worldwide prevalence and potential for long distance migration and thus invasions into new areas may pose a serious threat to food security regionally or globally [85]. WSR of wheat is among the most important crop diseases causing a continuous threat to crop production [86, 87]. Worldwide. the virulence and race diversity of populations of *P. striiformis* is apparent. Races from regionally prevalent lineages cause epidemic outbreaks resulting in widespread economic losses in wheat production [85, 88]. Virulence to most of the characterized resistance genes has been observed in Europe, reflecting the large-scale deployment of these genes in Europe in the past [89–93]. More recently, the footprint of epidemics of WSR appears to be moving into non-traditional, warmer and dryer areas suggesting a wider range of adaption [85]. Based on an ostensibly representative selection of isolates of WSR collected from the United States (and genetically similar isolates from Denmark, Mexico and

**Figure 4.** Severity of wheat leaf rust (WLR) on the three upper leaves in wheat plants. Severity of WLR, infection by, and latent periods of *P. triticina* were determined based on favorable night weather conditions at Perwez, Belgium in 2009 (a), 2011 (B) and 2013 (C). The arrows show the time of the first disease observation in the field. Phenology of the plants including the appearance of the three upper leaves is represented at the bottom of each figure. The airborne inoculum trapped in the field allows determination of when the "inoculum condition" was reached (black bars). The gray bars

Modeling the Main Fungal Diseases of Winter Wheat: Constraints and Possible Solutions

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symbolize the moment when the "rain conditions" of the original model were reached. (source: [81]).

Modeling the Main Fungal Diseases of Winter Wheat: Constraints and Possible Solutions http://dx.doi.org/10.5772/intechopen.75983 15

The presence of primary inoculum in the air is not considered as a limited factor in this model. We assumed that spores of *P. triticina* are already present in fields during the period of study. A fine-tuning of the DSS will include an effective assessment (i.e., spore dispersion estimates) for the spores in the same field, since spores from outside the field are only required to initiate the first infection (exogenous inoculum). Indeed, the assessment of the model coupled with detection of spores showed that the infection periods on susceptible cultivars (**Figure 4**) were

Thus, the detection of airborne inoculum by sensors and its coupling to a reliable model of dispersion could help improve forecasting the occurrence of WLR. In Belgium, a recent study on the spatio-temporal distribution of the airborne inoculum of *P. triticina* indicated that infection on the three youngest leaf layers could originate from endogenous and/or exogenous inoculum. The first symptoms observed on crops can be the result of either infection by urediniospores carried upwind by air masses from distant infected fields or the consequence of sporulating lesions occurring in the fall and remaining active after the winter [82]. Airborne inoculum was generally detected in fields during the growing season between March and May (during the spring green-up). Various densities of airborne inoculum were observed depending of the site and the year, and the severity of WLR on the upper leaf layers during the grain filling was strongly influenced by the density of spores collected during the development of these leaf layers [83].

Molecular diagnostics combined with sampling of airborne inoculum could be exploited to more accurately predict the risk of epidemics in wheat agro-ecosystems. Strategies for controlling WLR in fields include the use of resistant cultivars. But a prolonged period of monitoring WLR involving susceptible cultivars and favorable night conditions conducive to spore production, dispersal of, and infection by *P. triticina* with subsequent development of WLR should demonstrate the capability of the DSS in these situations. Junk et al. [84] studied the potential infection periods of WLR in a changing climate at two selected sites in Luxembourg (Burmerange and Christnach) using a weather threshold-based model for infection and development and progress of WLR that involved hourly night-time data for air temperature, relative humidity and rainfall. Their findings revealed that highest proportions of favorable days for infection with *P. triticina* and development of WLR in the future would occur during

spring and summer at both sites, with the proportions more marked at Burmerange.

WSR is an example of a disease of world-wide importance and ability for long distance dispersal. Crop pathogens with worldwide prevalence and potential for long distance migration and thus invasions into new areas may pose a serious threat to food security regionally or globally [85]. WSR of wheat is among the most important crop diseases causing a continuous threat to crop production [86, 87]. Worldwide. the virulence and race diversity of populations of *P. striiformis* is apparent. Races from regionally prevalent lineages cause epidemic outbreaks resulting in widespread economic losses in wheat production [85, 88]. Virulence to most of the characterized resistance genes has been observed in Europe, reflecting the large-scale deployment of these genes in Europe in the past [89–93]. More recently, the footprint of epidemics of WSR appears to be moving into non-traditional, warmer and dryer areas suggesting a wider range of adaption [85]. Based on an ostensibly representative selection of isolates of WSR collected from the United States (and genetically similar isolates from Denmark, Mexico and

well predicted [81].

14 Advances in Plant Pathology

**3.3. Wheat stripe rust**

**Figure 4.** Severity of wheat leaf rust (WLR) on the three upper leaves in wheat plants. Severity of WLR, infection by, and latent periods of *P. triticina* were determined based on favorable night weather conditions at Perwez, Belgium in 2009 (a), 2011 (B) and 2013 (C). The arrows show the time of the first disease observation in the field. Phenology of the plants including the appearance of the three upper leaves is represented at the bottom of each figure. The airborne inoculum trapped in the field allows determination of when the "inoculum condition" was reached (black bars). The gray bars symbolize the moment when the "rain conditions" of the original model were reached. (source: [81]).

Eritrea) before and after 2000 [94], it was demonstrated that isolates collected after 2000 were more aggressive and had adapted to produce more urediniospores in a shorter time period, and at higher temperatures. The pathogen has been highly mobile and the geography of its genetics has changed and expanded, especially since 2000. Multiple new incursions of the pathogen have been reported in Australia and South Africa [95, 96] and international movement of spores of *P. striiformis* from Europe (in 1979) and North America (in 2002) has been implicated on the clothing of travelers [97]. Indeed, in 2011 a new race of *P. striiformis*, named "Warrior," was detected in various European countries including France, Germany and the UK [93]. Since urediniospores of P. striiformis can spread over large distances [98], the race Warrior is probably already present in Luxembourg. Confirming the existence of Warrior in commercial Luxembourgish wheat fields was not part of this study.

In most seasons, environmental conditions during spring and early summer are conducive to the production of large quantities of spores of *P. striiformis*, which are dispersed from distances of a few centimeters to thousands of kilometers (**Photo 3**), where they might reach a susceptible host plant [76, 98]. The sporulation capacity and infection efficiency of *P. striiformis* are affected mainly by air temperature, leaf-wetness duration and light intensity [77]. Urediniospores of *P. striiformis* require a relative humidity near saturation for at least three hours to germinate [99] and are sensitive to an interruption of the wet period during germination [77]. The presence of free water on the leaf surface is also essential for spore germination [77, 99, 100]. Thus, rain is often considered conductive to disease spread because rain events are generally followed by extended periods of leaf wetness [76, 99].

**Photo 3.** Fungicide treated and non-treated plots of winter wheat and a leaf (inset) showing symptoms of wheat stripe rust caused by *Puccinia striiformis* (photo taken on 2015 in Burmerange, South Luxembourg. Photo credit: Beyer M.)*.*

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**Figure 6.** Example of simulated infection events by *Puccinia striiformis* (cause of wheat stripe rust (WSR)), observed green leaf area (GLA) and severity of WSR on the three upper leaves (L3 to L1, L1 being flag leaf) at Burmerange, Luxembourg

during the 2015 cropping season. The severity of WSR is expressed as percentage leaf area diseased [13].

The model developed is based on a stepwise approach (**Figure 5**) consisting of (1) the determination of the potential range of weather conditions conducive to WSR in Luxembourg using a stochastic approach, and (2) the determination of optimum classes of combined weather variables

**Figure 5.** Descriptive flowchart of the modeling approach for predicting infection events of wheat stripe rust caused by *Puccinia striiformis* [13]. Air temperature (T), relative humidity (RH) and rainfall (R).

Modeling the Main Fungal Diseases of Winter Wheat: Constraints and Possible Solutions http://dx.doi.org/10.5772/intechopen.75983 17

**Photo 3.** Fungicide treated and non-treated plots of winter wheat and a leaf (inset) showing symptoms of wheat stripe rust caused by *Puccinia striiformis* (photo taken on 2015 in Burmerange, South Luxembourg. Photo credit: Beyer M.)*.*

**Figure 6.** Example of simulated infection events by *Puccinia striiformis* (cause of wheat stripe rust (WSR)), observed green leaf area (GLA) and severity of WSR on the three upper leaves (L3 to L1, L1 being flag leaf) at Burmerange, Luxembourg during the 2015 cropping season. The severity of WSR is expressed as percentage leaf area diseased [13].

**Figure 5.** Descriptive flowchart of the modeling approach for predicting infection events of wheat stripe rust caused by

Eritrea) before and after 2000 [94], it was demonstrated that isolates collected after 2000 were more aggressive and had adapted to produce more urediniospores in a shorter time period, and at higher temperatures. The pathogen has been highly mobile and the geography of its genetics has changed and expanded, especially since 2000. Multiple new incursions of the pathogen have been reported in Australia and South Africa [95, 96] and international movement of spores of *P. striiformis* from Europe (in 1979) and North America (in 2002) has been implicated on the clothing of travelers [97]. Indeed, in 2011 a new race of *P. striiformis*, named "Warrior," was detected in various European countries including France, Germany and the UK [93]. Since urediniospores of P. striiformis can spread over large distances [98], the race Warrior is probably already present in Luxembourg. Confirming the existence of Warrior in

In most seasons, environmental conditions during spring and early summer are conducive to the production of large quantities of spores of *P. striiformis*, which are dispersed from distances of a few centimeters to thousands of kilometers (**Photo 3**), where they might reach a susceptible host plant [76, 98]. The sporulation capacity and infection efficiency of *P. striiformis* are affected mainly by air temperature, leaf-wetness duration and light intensity [77]. Urediniospores of *P. striiformis* require a relative humidity near saturation for at least three hours to germinate [99] and are sensitive to an interruption of the wet period during germination [77]. The presence of free water on the leaf surface is also essential for spore germination [77, 99, 100]. Thus, rain is often considered conductive to disease spread because rain events

The model developed is based on a stepwise approach (**Figure 5**) consisting of (1) the determination of the potential range of weather conditions conducive to WSR in Luxembourg using a stochastic approach, and (2) the determination of optimum classes of combined weather variables

commercial Luxembourgish wheat fields was not part of this study.

16 Advances in Plant Pathology

are generally followed by extended periods of leaf wetness [76, 99].

*Puccinia striiformis* [13]. Air temperature (T), relative humidity (RH) and rainfall (R).

(air temperature (T), relative humidity (RH) and rainfall (R)) conducive to the disease and building of a weather threshold based model for predicting WSR infection events [13].

Weather is a critical factor influencing FHB. Frequent rainfall, high humidity and warm temperatures, coinciding with flowering and early kernel filling, favor infection by *Fusarium* spp. and development of the disease [107]. Numerous research and survey reports have shown that the main environmental factors influencing the development of FHB (**Photo 4**) are temperature and humidity/wetness [108, 109] It has been speculated that the difference observed in severity of FHB between 2007 and 2008 (**Figure 7**) (21.0 ± 17.8% versus 13.5 ± 16.2%) may, at least in part, be explained by the warmer temperature observed in 2007 (11.9°C) compared to 2008 (9.4°C) [103, 110]. Climatic factors can also influence the impact of fungicide application

Modeling the Main Fungal Diseases of Winter Wheat: Constraints and Possible Solutions

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Many studies have highlighted the relationship between the severity of FHB in specific fields where certain cereals particularly maize, were the previous crop [103, 112]. Maize residues are a host for several *Fusarium* species and thus provide a source of inoculum for infection of any susceptible crops planted in that land [113, 114]. Suitable cultural practices (e.g., crop rotation) aiming to reduce inoculum borne plant residues could be effective in controlling

A simulation model for predicting the periods of infection by *Fusarium* spp. was developed and evaluated at various sites in Luxembourg during 2007–2009 [115]. Like the models developed for other fungal diseases, the main inputs are T, R and RH. Information on the cultivar and the previous crop are also considered while using the model outputs for recommending fungicide sprays (i.e., the model is only used when sensitive cultivars are planted after maize or sorghum).

**Figure 7.** Incidence of fusarium head blight (% infected wheat spikes), caused by *Fusarium* spp. in various districts of

Luxembourg (n = 17) in 2007 (a) and 2008 (B) as assessed between GS 77 and GS 87 ([45]).

and its effect on Fusarium strain population [111].

FHB in winter wheat fields.

The threshold-based model for development of WSR was evaluated using independent data from experiments in Luxembourg in 2002–2015 [13]. Infection days and latency periods for *P. striiformis* (**Figure 6**) were calculated based on periods when the combined favorable weather variables (4°C < T < 16°C, RH > 92% and R ≤ 0.1 mm) were met. The overall performance of the threshold-based weather model developed in this study is quite similar to that developed for WLR across the same geographical region. Although the findings are area-specific and may differ in other geographic regions, the underlying hypothesis and approach can be extended to different locations and/or explored for other economically important fungal diseases of other crops.

#### **3.4. Fusarium head blight**

Besides the yield loss that it can cause, FHB can negatively affect the entire human food and animal feed chain through the contamination of wheat grains with mycotoxins. Contamination with fumonisins can result in grains unusable for consumption or for further processing into bakery products, breakfast cereals, pasta, snacks, beer or animal feed, etc., [101–106].

**Photo 4.** Fusarium growth on wheat (Photo credit: Giraud F.)*.*

Weather is a critical factor influencing FHB. Frequent rainfall, high humidity and warm temperatures, coinciding with flowering and early kernel filling, favor infection by *Fusarium* spp. and development of the disease [107]. Numerous research and survey reports have shown that the main environmental factors influencing the development of FHB (**Photo 4**) are temperature and humidity/wetness [108, 109] It has been speculated that the difference observed in severity of FHB between 2007 and 2008 (**Figure 7**) (21.0 ± 17.8% versus 13.5 ± 16.2%) may, at least in part, be explained by the warmer temperature observed in 2007 (11.9°C) compared to 2008 (9.4°C) [103, 110]. Climatic factors can also influence the impact of fungicide application and its effect on Fusarium strain population [111].

(air temperature (T), relative humidity (RH) and rainfall (R)) conducive to the disease and building of a weather threshold based model for predicting WSR infection events [13].

The threshold-based model for development of WSR was evaluated using independent data from experiments in Luxembourg in 2002–2015 [13]. Infection days and latency periods for *P. striiformis* (**Figure 6**) were calculated based on periods when the combined favorable weather variables (4°C < T < 16°C, RH > 92% and R ≤ 0.1 mm) were met. The overall performance of the threshold-based weather model developed in this study is quite similar to that developed for WLR across the same geographical region. Although the findings are area-specific and may differ in other geographic regions, the underlying hypothesis and approach can be extended to different locations and/or explored for other economically important fungal

Besides the yield loss that it can cause, FHB can negatively affect the entire human food and animal feed chain through the contamination of wheat grains with mycotoxins. Contamination with fumonisins can result in grains unusable for consumption or for further processing into

bakery products, breakfast cereals, pasta, snacks, beer or animal feed, etc., [101–106].

diseases of other crops.

18 Advances in Plant Pathology

**3.4. Fusarium head blight**

**Photo 4.** Fusarium growth on wheat (Photo credit: Giraud F.)*.*

Many studies have highlighted the relationship between the severity of FHB in specific fields where certain cereals particularly maize, were the previous crop [103, 112]. Maize residues are a host for several *Fusarium* species and thus provide a source of inoculum for infection of any susceptible crops planted in that land [113, 114]. Suitable cultural practices (e.g., crop rotation) aiming to reduce inoculum borne plant residues could be effective in controlling FHB in winter wheat fields.

A simulation model for predicting the periods of infection by *Fusarium* spp. was developed and evaluated at various sites in Luxembourg during 2007–2009 [115]. Like the models developed for other fungal diseases, the main inputs are T, R and RH. Information on the cultivar and the previous crop are also considered while using the model outputs for recommending fungicide sprays (i.e., the model is only used when sensitive cultivars are planted after maize or sorghum).

**Figure 7.** Incidence of fusarium head blight (% infected wheat spikes), caused by *Fusarium* spp. in various districts of Luxembourg (n = 17) in 2007 (a) and 2008 (B) as assessed between GS 77 and GS 87 ([45]).

Wheat diseases present a constant and evolving threat to food security. Decision-support tools based on in-season disease monitoring and disease progress models in relation to weather variables present various advantages for managing the development of epidemics of those diseases, while limiting potentially harmful side effects of excessive fungicide applications while ensuring economic benefit. Embedded in operational warning systems for plant disease monitoring, DSSs could provide a valuable service to the farmer community for pest and disease management through integrated and environmentally friendly

Modeling the Main Fungal Diseases of Winter Wheat: Constraints and Possible Solutions

, Bernard Tychon<sup>1</sup>

1 Department of Environmental Sciences and Management, Université de Liège, Arlon,

2 International Centre for Applied Climate Sciences, University of Southern Queensland,

3 Laboratory of Mathematics and Applications, Department of Mathematics, Université

4 Department Environment and Agro-Biotechnologies, Luxembourg Institute of Science and

[1] Campbell CL, Madden LV. Introduction to Plant Disease Epidemiology. New York: John

[2] El Jarroudi M, Kouadio L, Delfosse P, Tychon B. Brown rust disease control in winter wheat: I. Exploring an approach for disease progression based on night weather condi-

[3] El Jarroudi M, Kouadio L, Beyer M, Junk J, Hoffmann L, Tychon B, Maraite H, Bock CH, Delfosse P. Economics of a decision–support system for managing the main fungal diseases of winter wheat in the grand-duchy of Luxembourg. Field Crops Research.

[4] Hardwick NV. Disease forecasting. In: Jones DG, editor. The Epidemiology of Plant

tions. Environmental Science and Pollution Research. 2014;**21**:4797-4808

Disease. Dordrecht: Kluwer Publishers; 1998. pp. 207-230

, Mustapha El Jarroudi3

,

http://dx.doi.org/10.5772/intechopen.75983

21

methods.

**Author details**

Moussa El Jarroudi<sup>1</sup>

, Clive Bock5

Abdelmalek Essaâdi, Tangier, Morocco

5 USDA-ARS-SEFTNRL, Byron, GA, United States

Technology, Belvaux, Luxembourg

Wiley and Sons; 1990

2015;**172**:32-41

Toowoomba, QLD, Australia

Jürgen Junk<sup>4</sup>

Belgium

**References**

\*, Louis Kouadio2

\*Address all correspondence to: meljarroudi@ulg.ac.be

and Philippe Delfosse<sup>4</sup>

**Figure 8.** Example of simulated infection events by *Fusarium spp.* at Reuler, Luxembourg during the 2007 cropping season.

An example of the number of infection events by *Fusarium* spp. is depicted in **Figure 8**. Because of the changes in the composition of Fusarium population across sites and other site-specific characteristics related to the climate and topography, a mixed performance of the model. Thus, knowledge of the spatial patterns of epidemics of FHB, along with information on the Fusarium species involved are crucial to developing improved control and management measures relevant to each region, as in Luxembourg [116]. Furthermore, management strategies based on fungicide application should also take into account the effect chemical treatments may have on toxin induction by Fusarium species [103, 111]. Management tools in the future might include a weather-based DSS to help predict and eventually manage FHB.

## **4. Concluding remarks**

Meteorological variables are most often used as the input data for disease forecasting models of fungal diseases of winter wheat in Luxembourg and elsewhere. For disease risk assessments at the regional scale, the meteorological data in these forecasting models must originate from local weather stations which are part of a meteorological networks consisting of automatic weather stations (AWSs). However, the choice of location for an AWS within a field or the distance between AWSs locations are both factors that hamper accurate forecasting of fungal diseases at regional scales. Moreover, techniques used to interpolate weather data from a set of neighboring sites suffer from some potential sources of error, e.g., difficulty in capturing small scale variation, failure to account for local topographical features, etc.

With the changes in the patterns of world climate expected during the coming decades [117], the pattern of corresponding distributions of fungal diseases will be affected accordingly. Thus, new challenges are emerging that need to be addressed. Climate change affects pathogen biology not only directly but also indirectly through effects on host development and phenology. Modeling to predict new disease threats is expected to be beneficial since many years of data are needed to prepare appropriate solutions to developing issues. However, although the impacts of climate change on crop disease are being studied, uncertainties inherent in crop disease models remain largely unexplored and unreported [118]. Moreover, acclimation to future climatic conditions by both the pathogen and the host can significantly alter the outcome of the plant–pathogen interaction [119].

Wheat diseases present a constant and evolving threat to food security. Decision-support tools based on in-season disease monitoring and disease progress models in relation to weather variables present various advantages for managing the development of epidemics of those diseases, while limiting potentially harmful side effects of excessive fungicide applications while ensuring economic benefit. Embedded in operational warning systems for plant disease monitoring, DSSs could provide a valuable service to the farmer community for pest and disease management through integrated and environmentally friendly methods.

## **Author details**

An example of the number of infection events by *Fusarium* spp. is depicted in **Figure 8**. Because of the changes in the composition of Fusarium population across sites and other site-specific characteristics related to the climate and topography, a mixed performance of the model. Thus, knowledge of the spatial patterns of epidemics of FHB, along with information on the Fusarium species involved are crucial to developing improved control and management measures relevant to each region, as in Luxembourg [116]. Furthermore, management strategies based on fungicide application should also take into account the effect chemical treatments may have on toxin induction by Fusarium species [103, 111]. Management tools in the future might include a weather-based DSS to help predict and eventually manage FHB.

**Figure 8.** Example of simulated infection events by *Fusarium spp.* at Reuler, Luxembourg during the 2007 cropping

Meteorological variables are most often used as the input data for disease forecasting models of fungal diseases of winter wheat in Luxembourg and elsewhere. For disease risk assessments at the regional scale, the meteorological data in these forecasting models must originate from local weather stations which are part of a meteorological networks consisting of automatic weather stations (AWSs). However, the choice of location for an AWS within a field or the distance between AWSs locations are both factors that hamper accurate forecasting of fungal diseases at regional scales. Moreover, techniques used to interpolate weather data from a set of neighboring sites suffer from some potential sources of error, e.g., difficulty in capturing

With the changes in the patterns of world climate expected during the coming decades [117], the pattern of corresponding distributions of fungal diseases will be affected accordingly. Thus, new challenges are emerging that need to be addressed. Climate change affects pathogen biology not only directly but also indirectly through effects on host development and phenology. Modeling to predict new disease threats is expected to be beneficial since many years of data are needed to prepare appropriate solutions to developing issues. However, although the impacts of climate change on crop disease are being studied, uncertainties inherent in crop disease models remain largely unexplored and unreported [118]. Moreover, acclimation to future climatic conditions by both the pathogen and the host can significantly alter

small scale variation, failure to account for local topographical features, etc.

the outcome of the plant–pathogen interaction [119].

**4. Concluding remarks**

season.

20 Advances in Plant Pathology

Moussa El Jarroudi<sup>1</sup> \*, Louis Kouadio2 , Bernard Tychon<sup>1</sup> , Mustapha El Jarroudi3 , Jürgen Junk<sup>4</sup> , Clive Bock5 and Philippe Delfosse<sup>4</sup>

\*Address all correspondence to: meljarroudi@ulg.ac.be

1 Department of Environmental Sciences and Management, Université de Liège, Arlon, Belgium

2 International Centre for Applied Climate Sciences, University of Southern Queensland, Toowoomba, QLD, Australia

3 Laboratory of Mathematics and Applications, Department of Mathematics, Université Abdelmalek Essaâdi, Tangier, Morocco

4 Department Environment and Agro-Biotechnologies, Luxembourg Institute of Science and Technology, Belvaux, Luxembourg

5 USDA-ARS-SEFTNRL, Byron, GA, United States

## **References**


[5] Madden LV, Ellis MA. How to develop plant disease forecasters. In: Kranz J, Rotem J, editors. Experimental Techniques in Plant Disease Epidemiology. Berlin: Springer; 1988. pp. 191-208

[21] Geagea L, Huber L, Sache I, Flura D, Mac Cartney HA, Fitt BDL. Influence of simulated rain on dispersal of rust spores from infected wheat seedlings. Agricultural and Forest

Modeling the Main Fungal Diseases of Winter Wheat: Constraints and Possible Solutions

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**Chapter 2**

**Provisional chapter**

**The Biology of** *Thecaphora frezii* **Smut and Its Effects**

**The Biology of** *Thecaphora frezii* **Smut and Its Effects** 

*Thecaphora frezii* was first reported in 1962 in wild peanut from Aquidauana, Mato Grosso do Sul, Brazil. In Argentina, it was first detected in 1995 in commercial crops from the central-northern area of Córdoba province. The fungus can survive in the soil as teliospores. When peanut gynophore penetrates the soil, their exudates disrupt telial dormancy; *T. frezii* penetrates and colonizes the tissues and replaces the cells with teliospores. Since its first report, peanut smut prevalence has gradually increased in peanut areas to reach a 100% in 2012. Currently, it is the most important peanut disease in Argentina, not only for its destructive power on crop but also for its quick spread throughout the growing region of Córdoba and the lack of effective tools for its management. It is important for additional research to find effective agronomical practice that reaches high control efficiencies. The collaboration of all those involved in Argentinian peanut production systems is necessary

Peanut is an herbaceous plant from South America. Its origin is located specifically in southeastern Bolivia and northwestern Argentina, where its parental species are found in wild habits [1]. In 1753 the cultivated species of peanut was classified as *Arachis hypogaea* L. in two subspecies, *hypogaea* and *fastigiata* [2]. *Arachis hypogaea* belongs to the family Leguminosae, subfamily Papilionoidea, and gender Arachis [3]. Peanut is an annual plant, and its growth habits are described as bunch, decumbent, or runner. The bunch types can

> © 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

DOI: 10.5772/intechopen.75837

**on Argentine Peanut Production**

**on Argentine Peanut Production**

Luis Ignacio Cazón, Juan Andrés Paredes and

Luis Ignacio Cazón, Juan Andrés Paredes and

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

for the management of peanut smut to be successful.

**Keywords:** peanut smut, teliospores, peg, basidiospores, phytopathology

http://dx.doi.org/10.5772/intechopen.75837

Alejandro Mario Rago

Alejandro Mario Rago

**Abstract**

**1. Introduction**

[119] Váry Z, Mullins E, McElwain JC, Doohan FM. The severity of wheat diseases increases when plants and pathogens are acclimatized to elevated carbon dioxide. Global Change Biology. 2015;**21**:2661-2669

#### **The Biology of** *Thecaphora frezii* **Smut and Its Effects on Argentine Peanut Production The Biology of** *Thecaphora frezii* **Smut and Its Effects on Argentine Peanut Production**

DOI: 10.5772/intechopen.75837

Luis Ignacio Cazón, Juan Andrés Paredes and Alejandro Mario Rago Luis Ignacio Cazón, Juan Andrés Paredes and Alejandro Mario Rago

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.75837

#### **Abstract**

[118] Newbery F, Qi A, Fitt BDL. Modelling impacts of climate change on arable crop diseases: Progress, challenges and applications. Current Opinion in Plant Biology. 2016;

[119] Váry Z, Mullins E, McElwain JC, Doohan FM. The severity of wheat diseases increases when plants and pathogens are acclimatized to elevated carbon dioxide. Global Change

**32**:101-109

30 Advances in Plant Pathology

Biology. 2015;**21**:2661-2669

*Thecaphora frezii* was first reported in 1962 in wild peanut from Aquidauana, Mato Grosso do Sul, Brazil. In Argentina, it was first detected in 1995 in commercial crops from the central-northern area of Córdoba province. The fungus can survive in the soil as teliospores. When peanut gynophore penetrates the soil, their exudates disrupt telial dormancy; *T. frezii* penetrates and colonizes the tissues and replaces the cells with teliospores. Since its first report, peanut smut prevalence has gradually increased in peanut areas to reach a 100% in 2012. Currently, it is the most important peanut disease in Argentina, not only for its destructive power on crop but also for its quick spread throughout the growing region of Córdoba and the lack of effective tools for its management. It is important for additional research to find effective agronomical practice that reaches high control efficiencies. The collaboration of all those involved in Argentinian peanut production systems is necessary for the management of peanut smut to be successful.

**Keywords:** peanut smut, teliospores, peg, basidiospores, phytopathology

#### **1. Introduction**

Peanut is an herbaceous plant from South America. Its origin is located specifically in southeastern Bolivia and northwestern Argentina, where its parental species are found in wild habits [1]. In 1753 the cultivated species of peanut was classified as *Arachis hypogaea* L. in two subspecies, *hypogaea* and *fastigiata* [2]. *Arachis hypogaea* belongs to the family Leguminosae, subfamily Papilionoidea, and gender Arachis [3]. Peanut is an annual plant, and its growth habits are described as bunch, decumbent, or runner. The bunch types can

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

reach 40 cm in height. They have an upright growth habit with flowering on the main stem and lateral branches. Runner types can reach 120 cm in diameter, and they are considered to have a prostrate growth habit and do not flower on the main stem. Decumbent varieties have an intermediate growth habit between a runner and bunch [4]. Peanut has vegetative and reproductive stages. The vegetative stage involves germination and formation of stems and leaves. Reproductive stage goes from flowering (R1) to the obtaining of overripe fruit (R9) [5, 6]. The flowering process covers almost 80% of the peanut life cycle and overlaps with the fruiting period. After the flower fertilization, the cells located under the receptacle divide rapidly, giving rise to the gynophore or "peg." Gynophore grows toward the ground by stimulation of the light, carrying at its end the ovary protected by a layer of lignified cells [7]. Once introduced into the soil, the elongation stops, and the fruit begins to develop. This requires darkness, mechanical stimulation of the medium, humidity, and the presence of calcium [1]. In Argentina, peanut represents one of most important local economies. More than 92% of Argentine peanut production and processing is in the center of the country, mainly in the province of Córdoba. In this region, more than 12,000 jobs are directly or indirectly related to peanut production [8, 9]. In 2016/2017, peanut was cultivated over 328,600 ha, producing a total of 1.17 MT of peanut and an average yield of 3.69 T/ ha. Peanut industry is characterized as an "exporter industry" in Argentina. More than 80% of Argentine production is exported to the European Union (mainly the Netherlands, Germany, England, Spain, Italy, Greece, and France) and other countries such as the USA, Canada, China, and India. All these facts show that peanut industry is not only important to Argentina but also the world peanut market [10]. In Argentina, during the 1980s, peanut production changed to adapt to the demand of the international market for edible peanut. New cultivars were used, passing from bunch-type cultivars to runner types. However, the prevailing climatic conditions were conducive for the development of soilborne fungal diseases [11, 12]. Therefore, peanut production was moved to more southern areas of Cordoba in the early 1990s to avoid the consequences of the production issues in the northern region [13]. During this migration process, emerged peanut smut caused by *Thecaphora frezii*. It was first detected in commercial peanut in the northern producing areas in Córdoba province and then established on the central region where the main grain processing industries are located [14]. Currently, Argentina is the only country that has reported peanut smut in commercial crops. Both Bolivia and Brazil, however, have only reported cases of smut in wild peanuts [15–17]. *T. frezii* was first reported in 1962 in wild peanut samples from Aquidauana, Mato Grosso do Sul, Brazil [15, 18] (**Figure 1**). In that time, fungus was classified based on disease symptoms and morphology of teliospores. 51 years later this classification was confirmed using molecular tools [19, 20]. In Argentina, *T. frezii* was first detected in commercial crops of peanuts from the central-northern area of Córdoba province: Pampayasta (32°15′07″S 63°39′20″W), Villa Ascasubi (32°10′00″S 63°53′00″W), and Ticino (32°41′25″S 63°23′14″W) [14]. By this time, the presence of affected pods was more frequent year by year in different plots across the peanut area, to finally being found in all production fields in the 2011/2012 growing season [21, 22]. 2 years later, the prevalence was 100% in Argentinian production area, including Salta, Jujuy, La Pampa, and San Luis [23]. During the last 10 years, this disease has caused significant decreases in yield production in Argentina, resulting in 51% losses in some locations [13, 21, 22, 24].

**2. Peanut smut symptoms and disease assessment**

The smut symptoms are very characteristics on peanut and easy to identify. Affected pods shows hypertrophy and spongy consistence when the infection is highly severe. The wall of pods tends to thin, and the grains inside could be totally or partially transformed in a reddish-brown smutted mass (**Figure 2**). According to the symptoms, it is possible to assess the disease in mature pods (R8) from a given field. In this stage, the disease expression is very clear [17]. There are two parameters to consider when quantifying the disease intensity in affected fields: in terms of incidence and severity. The first is the proportion of infected pods out of a total sample, and the second is the proportion of damaged pod tissue. Disease severity can be estimated using a diagrammatic scale representing five different severity levels [25]. Ordinal levels of 0, healthy pods; 1, normal pod with a small sorus in single kernel; 2,

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**Figure 2.** Peanut pods affected by smut. A: Hypertrophied pod due to smut. B: Totally smutted pod. C: Partially smutted pod.

**Figure 1.** Seed and wild peanut pod totally damaged by smut (mass of teliospores replacing the grain tissue).

The Biology of *Thecaphora frezii* Smut and Its Effects on Argentine Peanut Production http://dx.doi.org/10.5772/intechopen.75837 33

**Figure 1.** Seed and wild peanut pod totally damaged by smut (mass of teliospores replacing the grain tissue).

### **2. Peanut smut symptoms and disease assessment**

reach 40 cm in height. They have an upright growth habit with flowering on the main stem and lateral branches. Runner types can reach 120 cm in diameter, and they are considered to have a prostrate growth habit and do not flower on the main stem. Decumbent varieties have an intermediate growth habit between a runner and bunch [4]. Peanut has vegetative and reproductive stages. The vegetative stage involves germination and formation of stems and leaves. Reproductive stage goes from flowering (R1) to the obtaining of overripe fruit (R9) [5, 6]. The flowering process covers almost 80% of the peanut life cycle and overlaps with the fruiting period. After the flower fertilization, the cells located under the receptacle divide rapidly, giving rise to the gynophore or "peg." Gynophore grows toward the ground by stimulation of the light, carrying at its end the ovary protected by a layer of lignified cells [7]. Once introduced into the soil, the elongation stops, and the fruit begins to develop. This requires darkness, mechanical stimulation of the medium, humidity, and the presence of calcium [1]. In Argentina, peanut represents one of most important local economies. More than 92% of Argentine peanut production and processing is in the center of the country, mainly in the province of Córdoba. In this region, more than 12,000 jobs are directly or indirectly related to peanut production [8, 9]. In 2016/2017, peanut was cultivated over 328,600 ha, producing a total of 1.17 MT of peanut and an average yield of 3.69 T/ ha. Peanut industry is characterized as an "exporter industry" in Argentina. More than 80% of Argentine production is exported to the European Union (mainly the Netherlands, Germany, England, Spain, Italy, Greece, and France) and other countries such as the USA, Canada, China, and India. All these facts show that peanut industry is not only important to Argentina but also the world peanut market [10]. In Argentina, during the 1980s, peanut production changed to adapt to the demand of the international market for edible peanut. New cultivars were used, passing from bunch-type cultivars to runner types. However, the prevailing climatic conditions were conducive for the development of soilborne fungal diseases [11, 12]. Therefore, peanut production was moved to more southern areas of Cordoba in the early 1990s to avoid the consequences of the production issues in the northern region [13]. During this migration process, emerged peanut smut caused by *Thecaphora frezii*. It was first detected in commercial peanut in the northern producing areas in Córdoba province and then established on the central region where the main grain processing industries are located [14]. Currently, Argentina is the only country that has reported peanut smut in commercial crops. Both Bolivia and Brazil, however, have only reported cases of smut in wild peanuts [15–17]. *T. frezii* was first reported in 1962 in wild peanut samples from Aquidauana, Mato Grosso do Sul, Brazil [15, 18] (**Figure 1**). In that time, fungus was classified based on disease symptoms and morphology of teliospores. 51 years later this classification was confirmed using molecular tools [19, 20]. In Argentina, *T. frezii* was first detected in commercial crops of peanuts from the central-northern area of Córdoba province: Pampayasta (32°15′07″S 63°39′20″W), Villa Ascasubi (32°10′00″S 63°53′00″W), and Ticino (32°41′25″S 63°23′14″W) [14]. By this time, the presence of affected pods was more frequent year by year in different plots across the peanut area, to finally being found in all production fields in the 2011/2012 growing season [21, 22]. 2 years later, the prevalence was 100% in Argentinian production area, including Salta, Jujuy, La Pampa, and San Luis [23]. During the last 10 years, this disease has caused significant decreases in yield production in

32 Advances in Plant Pathology

Argentina, resulting in 51% losses in some locations [13, 21, 22, 24].

The smut symptoms are very characteristics on peanut and easy to identify. Affected pods shows hypertrophy and spongy consistence when the infection is highly severe. The wall of pods tends to thin, and the grains inside could be totally or partially transformed in a reddish-brown smutted mass (**Figure 2**). According to the symptoms, it is possible to assess the disease in mature pods (R8) from a given field. In this stage, the disease expression is very clear [17]. There are two parameters to consider when quantifying the disease intensity in affected fields: in terms of incidence and severity. The first is the proportion of infected pods out of a total sample, and the second is the proportion of damaged pod tissue. Disease severity can be estimated using a diagrammatic scale representing five different severity levels [25]. Ordinal levels of 0, healthy pods; 1, normal pod with a small sorus in single kernel; 2,

**Figure 2.** Peanut pods affected by smut. A: Hypertrophied pod due to smut. B: Totally smutted pod. C: Partially smutted pod.

When peanut pegs penetrate the soil, their exudates promote spore germination and initiate local infections [30, 31]. The process of teliospore germination includes the formation of a probasidium, followed by a basidium which forms basidiospores. When basidiospores germinate, compatible haploid germ tubes fuse and produce a dikaryotic infective mycelium that penetrate the peanut gynophore in the soil, colonize the tissues, and replace the cells with reddish-brown teliospores [32, 33]. When the affected mature pods open, start the dispersion process (**Figure 5**). There are three important dispersion methods: wind, machinery and seeds. During the harvest activities, a cloud of dust is generated. Teliospores are transported by wind to adjacent fields. According to the Ref. [34], teliospores can travel at least 400 m depending on the wind rate. Peanut processing plants are one of the most important sources of teliospores. In the shelling process, a totally smutted pods release millions of spores that are transported by wind. Long-distance dispersion is attributed to infested machinery and infected seeds. Infested machinery can carry teliospores from one infected field to another located in other provinces or bordering countries [17]. Using the seeds, the pathogen can disperse even to other continents through exportation activities [35]. Teliospores can infest externally asymptomatic seeds or in small lesions that are not detected in the process of selection of seeds [31, 36].

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**Figure 4.** Teliospores of *Thecaphora frezii*. A, B: Teliospores observed under light microscope 40X. C: Scanning electron

micrographs of multicellular teliospores.

**Figure 3.** Peanut smut severity scale. A: level 0. B: level 1. C: level 2. D: level 3. E: level 4.

deformed or normal pod with half of the kernels affected; 3, deformed pod and a completely smutted kernel; and 4, deformed pod, two completely smutted kernels (**Figure 3**). Combining both parameters, incidence and severity, it is possible to calculate "intensity" [26]. For disease assessment, [27] surveyed different fields in Córdoba production area in two consecutives growing seasons (2015/2016 and 2016/2017) and determined the amount of samples according the regional average incidence. They concluded that regions with low incidence values need to be assessed using the highest amount of samples than regions with high incidence values. It is important to emphasize that the evaluation of the disease in the field is a tool to know the final sanitary status. To avoid high levels of disease, it is necessary to adopt other management strategies prior to harvest.

## **3. The pathogen life cycle**

*T. frezii* can survive in the soil as teliospores; those are resistant structures that enable the fungus to be infective for many years in the soil. Ref. [28] studied the infection capacity of teliospores in the field, and they observed that it can be infective for more than 4 years. Smut spores are brown, 20–40 μm in size, and have an echinulate surface [29]. They are thick-walled structures that enable the fungus to survive in different environmental conditions (**Figure 4**). When peanut pegs penetrate the soil, their exudates promote spore germination and initiate local infections [30, 31]. The process of teliospore germination includes the formation of a probasidium, followed by a basidium which forms basidiospores. When basidiospores germinate, compatible haploid germ tubes fuse and produce a dikaryotic infective mycelium that penetrate the peanut gynophore in the soil, colonize the tissues, and replace the cells with reddish-brown teliospores [32, 33]. When the affected mature pods open, start the dispersion process (**Figure 5**). There are three important dispersion methods: wind, machinery and seeds. During the harvest activities, a cloud of dust is generated. Teliospores are transported by wind to adjacent fields. According to the Ref. [34], teliospores can travel at least 400 m depending on the wind rate. Peanut processing plants are one of the most important sources of teliospores. In the shelling process, a totally smutted pods release millions of spores that are transported by wind. Long-distance dispersion is attributed to infested machinery and infected seeds. Infested machinery can carry teliospores from one infected field to another located in other provinces or bordering countries [17]. Using the seeds, the pathogen can disperse even to other continents through exportation activities [35]. Teliospores can infest externally asymptomatic seeds or in small lesions that are not detected in the process of selection of seeds [31, 36].

deformed or normal pod with half of the kernels affected; 3, deformed pod and a completely smutted kernel; and 4, deformed pod, two completely smutted kernels (**Figure 3**). Combining both parameters, incidence and severity, it is possible to calculate "intensity" [26]. For disease assessment, [27] surveyed different fields in Córdoba production area in two consecutives growing seasons (2015/2016 and 2016/2017) and determined the amount of samples according the regional average incidence. They concluded that regions with low incidence values need to be assessed using the highest amount of samples than regions with high incidence values. It is important to emphasize that the evaluation of the disease in the field is a tool to know the final sanitary status. To avoid high levels of disease, it is necessary to adopt other manage-

**Figure 3.** Peanut smut severity scale. A: level 0. B: level 1. C: level 2. D: level 3. E: level 4.

*T. frezii* can survive in the soil as teliospores; those are resistant structures that enable the fungus to be infective for many years in the soil. Ref. [28] studied the infection capacity of teliospores in the field, and they observed that it can be infective for more than 4 years. Smut spores are brown, 20–40 μm in size, and have an echinulate surface [29]. They are thick-walled structures that enable the fungus to survive in different environmental conditions (**Figure 4**).

ment strategies prior to harvest.

34 Advances in Plant Pathology

**3. The pathogen life cycle**

**Figure 4.** Teliospores of *Thecaphora frezii*. A, B: Teliospores observed under light microscope 40X. C: Scanning electron micrographs of multicellular teliospores.

**Figure 5.** Peanut smut disease cycle [17].

## **4. Distribution of the disease and yield losses**

Peanut smut is distributed across the entire production area in Argentina [22]. It was first reported in the north of Córdoba [14] and from this region began to expand. The first survey was performed in 2008 [31]. The data show that the prevalence was 10% in 1997 and increased to 24% in the next 10 years. In 2012, the prevalence was 100% in Córdoba, and 2 years later, the prevalence was 100% in Argentina peanut area, including Salta, Jujuy, La Pampa, and San Luis [22, 23]. To determine the yield losses in Córdoba province, [37] assessed peanut smut in 40 fields from peanut area in 2015/2016 growing season. The data show yield losses of 27.419 tons (USD 14.151.800), representing 3.15% of the total production. In some fields, yield losses of 35% with incidence values to 52% could be observed. The most affected region was in the north of Córdoba peanut area, with average incidence of 17% and yield losses of 21.894 tons. They observed that disease intensity decreases southward. This gradient is because the new production areas are there, away from processing plants, and a much smaller history of peanut crop than north (**Figure 6**). There are some studies about the yield loss estimation. Peanut smut incidence above 14% can be considered as a damage threshold, and it is estimated that 1% increase in incidence can correspond to a 1.2% decrease in yield. The loss estimation in field with low inoculum density is erratic, whereas the correlation was high between the losses and the n° teliospores/gr. of soil in field with high inoculum density [38]. In Ref. [37], peanut smut was evaluated in different fields, and a linear relationship (R2 0.92–0.97) between estimated yield losses and disease intensity was observed.

**5. Teliospore detection**

in different zones [37].

Detection and quantification of spores, both in soil and seeds, represent an important tool to epidemiological management of disease. Peanut smut is considered a monocyclic disease since there is no secondary inoculum produced in the same growing season and polyetic since annual inoculum accumulation affects subsequent seasons [17]. Knowing the amount of teliospores in the soil, it is possible to predict the incidence of the disease in the harvest [24]. On the other hand, by determining the amount of inoculum transported by seeds, it is possible to identify the contribution of teliospores to the field, which increases the probability of occurrence of the disease in future peanut plantations. In 2008, the detection of teliospores from soil samples using a microscope was performed [31]. The same technique was also employed to quantify spores on peanut seeds [39]. Using molecular methods, in 2014, a PCR protocol to detect teliospores was described. This begins with washing off a kernel sample with distilled water, separating supernatant water from kernels and extracting fungal DNA from the obtained pellet. PCR amplification is then performed using specific primers designed for *T. frezii*. This method is highly sensitive and can detect the presence of ten teliospores (10−4 pg. DNA) from a sample of 400 kernels. Its specificity is achieved by using primers that do not hybridize with the DNA of other seed-borne pathogens, such as *Sclerotinia minor, S.* 

**Figure 6.** Gradient of disease incidence decreasing southward. Different colors represent peanut smut incidence found

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The Biology of *Thecaphora frezii* Smut and Its Effects on Argentine Peanut Production http://dx.doi.org/10.5772/intechopen.75837 37

**Figure 6.** Gradient of disease incidence decreasing southward. Different colors represent peanut smut incidence found in different zones [37].

## **5. Teliospore detection**

**4. Distribution of the disease and yield losses**

**Figure 5.** Peanut smut disease cycle [17].

36 Advances in Plant Pathology

fields, and a linear relationship (R2

intensity was observed.

Peanut smut is distributed across the entire production area in Argentina [22]. It was first reported in the north of Córdoba [14] and from this region began to expand. The first survey was performed in 2008 [31]. The data show that the prevalence was 10% in 1997 and increased to 24% in the next 10 years. In 2012, the prevalence was 100% in Córdoba, and 2 years later, the prevalence was 100% in Argentina peanut area, including Salta, Jujuy, La Pampa, and San Luis [22, 23]. To determine the yield losses in Córdoba province, [37] assessed peanut smut in 40 fields from peanut area in 2015/2016 growing season. The data show yield losses of 27.419 tons (USD 14.151.800), representing 3.15% of the total production. In some fields, yield losses of 35% with incidence values to 52% could be observed. The most affected region was in the north of Córdoba peanut area, with average incidence of 17% and yield losses of 21.894 tons. They observed that disease intensity decreases southward. This gradient is because the new production areas are there, away from processing plants, and a much smaller history of peanut crop than north (**Figure 6**). There are some studies about the yield loss estimation. Peanut smut incidence above 14% can be considered as a damage threshold, and it is estimated that 1% increase in incidence can correspond to a 1.2% decrease in yield. The loss estimation in field with low inoculum density is erratic, whereas the correlation was high between the losses and the n° teliospores/gr. of soil in field with high inoculum density [38]. In Ref. [37], peanut smut was evaluated in different

0.92–0.97) between estimated yield losses and disease

Detection and quantification of spores, both in soil and seeds, represent an important tool to epidemiological management of disease. Peanut smut is considered a monocyclic disease since there is no secondary inoculum produced in the same growing season and polyetic since annual inoculum accumulation affects subsequent seasons [17]. Knowing the amount of teliospores in the soil, it is possible to predict the incidence of the disease in the harvest [24]. On the other hand, by determining the amount of inoculum transported by seeds, it is possible to identify the contribution of teliospores to the field, which increases the probability of occurrence of the disease in future peanut plantations. In 2008, the detection of teliospores from soil samples using a microscope was performed [31]. The same technique was also employed to quantify spores on peanut seeds [39]. Using molecular methods, in 2014, a PCR protocol to detect teliospores was described. This begins with washing off a kernel sample with distilled water, separating supernatant water from kernels and extracting fungal DNA from the obtained pellet. PCR amplification is then performed using specific primers designed for *T. frezii*. This method is highly sensitive and can detect the presence of ten teliospores (10−4 pg. DNA) from a sample of 400 kernels. Its specificity is achieved by using primers that do not hybridize with the DNA of other seed-borne pathogens, such as *Sclerotinia minor, S.*  *sclerotiorum, Sclerotium rolfsii*, or *Fusarium solani* [40, 41]. These primers can be adapted for teliospore quantification using real-time PCR (RT-PCR), with a detection sensitivity of two teliospores in a sample of 400 seeds [42]. The development and use of detection techniques are important, because Argentina is the only country in South America that has reported this disease, hence the importance of implementing effective peanut smut management strategies that can minimize yield losses and contaminations of exportation products [8, 35, 43].

incidence of peanut smut than those preceded by soybean [35]. Different authors used deep tillage to burying teliospores 20 cm of depth reducing disease incidence, since peanut pods develop at a planting depth between 5 and 7 cm [54, 55]. Other practices were focused on modifying the soil chemical and physical properties. The objective is making the soil sup-

The Biology of *Thecaphora frezii* Smut and Its Effects on Argentine Peanut Production

contribute to a partial reduction of peanut smut intensity [21]. Phosphate-containing products were assessed to reduce the smut damage. This provided a control efficiency of 16% reduction

This area is not highly developed to peanut smut management. There are only some experiments done that used bioformulations based on *Trichoderma harzianum.* Control efficiency reaches 24% in incidence and 25% in severity [58, 59]. Researchers of IPAVE-CIAP-INTA have assessed the bioformulations based on *Bacillus subtilis* in different doses combining with soil amendments. Therefore, cultural practices and biological control still need to be studied in

The first experiences were performed in vitro using seed treatment fungicides. Teliospores were germinated using leaves and fruit extracts with the addition of PDA medium (39 gr/l). Later, a single colony was picked in media with different fungicides and evaluated the mycelial growth (**Figure 7**). All tested fungicides were effective, but the result could not be extrapolated to the field [33]. This is because the infection process occurred when fungicides from seed treatments have lost their protective effects [17]. The highest control efficiencies were achieved using crop protection fungicides in soil-directed applications. This is because the infective processes occur in the soil, where teliospores infect the gynophore [25, 31, 44]. Refs. [37, 60-62] show that these strategies were more effective at controlling peanut smut than leaf applications. They performed night spraying because in this moment, peanut leaves fold up, and the soil surface is easily reached [17, 63, 64]. Using strobilurin/triazole mixtures like

**Figure 7.** Colonies of *T. frezii*. A: Teliospores germinated in PDA medium (39 gr/l) composed by fruit extracts. B: *T. frezii* 

micelial growth of 7 days old plated on PDA medium (39 g / l).

greater depth for sustainable and economic management of the disease.

) and dolomite to modify the pH of soil can

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39

pressive to *T. frezii*. The use of gypsum (CaSO4

in peanut smut intensity [56, 57].

**6.4. Crop protection fungicides**

**6.3. Biological control**

## **6. Peanut smut management**

In the last years, various researchers have been studying different strategies for peanut smut control. Among them are the development of resistant cultivars, cultural practice, chemical and biological control.

#### **6.1. Host resistance**

High levels of infestation in the soils of the northern Córdoba peanut area and the lack of commercial fungicide that provides high disease control make the genetic resistance the main tool for an integrated management approach [17, 44]. Currently, 100% of cultivars widely planted are susceptible, which have favored *T. frezii* to quickly spread throughout the growing region of Córdoba [45, 46]. There is differential response of some cultivars against peanut smut. Pepe ASEM-INTA cultivar had 34% disease incidence and Colorado Irradiado-INTA 71% under favorable conditions [47]. Granoleico, the most widely distributed cultivar on Córdoba peanut area, shows incidence of 50% in high infested soil [27, 37, 47–49]. In 2011, it was reported that wild species *Arachis correntina* and *Arachis valida* show resistance to *T. frezii* [32]. Recently, INTA released a new cultivar, Ascasubi Hispano, with high tolerance to peanut smut (less than 2% of affected pods in high infested soil), but is not high-oleic cultivar. Among the tools used to facilitate the transfer of resistance are the molecular methods. In 2015, molecular markers associated with the tolerance to peanut smut were found [50]. Marker-assisted selection represents an important tool for breeding programs, as it would save time and money in the development of smut-tolerant cultivars [51]. There are cultivars highly tolerant to peanut smut in the final stages of evaluation [52]. This material would also be useful to prevent the spread of the pathogen to new areas of production [17]. Another aspect to consider is the ability of *T. frezii* to adapt to new cultivars. Cazón (*unpublished*) developed molecular markers to study the temporal variation of *T. frezii* isolates from different years and locations. If the rate of genetic variation of the pathogen is high, the resistance of the new cultivars against smut could be broken. Because of this, it is necessary that breeding programs continue to develop peanut smut-resistant cultivars.

#### **6.2. Cultural practice**

These strategies were focused on the reduction of initial inoculum. For peanut smut management, crop rotation schemes of more than 3 years without peanuts showed low *T. frezii* teliospore density in soil [53]. In addition, peanut crops preceded by corn exhibited lower incidence of peanut smut than those preceded by soybean [35]. Different authors used deep tillage to burying teliospores 20 cm of depth reducing disease incidence, since peanut pods develop at a planting depth between 5 and 7 cm [54, 55]. Other practices were focused on modifying the soil chemical and physical properties. The objective is making the soil suppressive to *T. frezii*. The use of gypsum (CaSO4 ) and dolomite to modify the pH of soil can contribute to a partial reduction of peanut smut intensity [21]. Phosphate-containing products were assessed to reduce the smut damage. This provided a control efficiency of 16% reduction in peanut smut intensity [56, 57].

#### **6.3. Biological control**

*sclerotiorum, Sclerotium rolfsii*, or *Fusarium solani* [40, 41]. These primers can be adapted for teliospore quantification using real-time PCR (RT-PCR), with a detection sensitivity of two teliospores in a sample of 400 seeds [42]. The development and use of detection techniques are important, because Argentina is the only country in South America that has reported this disease, hence the importance of implementing effective peanut smut management strategies

In the last years, various researchers have been studying different strategies for peanut smut control. Among them are the development of resistant cultivars, cultural practice, chemical

High levels of infestation in the soils of the northern Córdoba peanut area and the lack of commercial fungicide that provides high disease control make the genetic resistance the main tool for an integrated management approach [17, 44]. Currently, 100% of cultivars widely planted are susceptible, which have favored *T. frezii* to quickly spread throughout the growing region of Córdoba [45, 46]. There is differential response of some cultivars against peanut smut. Pepe ASEM-INTA cultivar had 34% disease incidence and Colorado Irradiado-INTA 71% under favorable conditions [47]. Granoleico, the most widely distributed cultivar on Córdoba peanut area, shows incidence of 50% in high infested soil [27, 37, 47–49]. In 2011, it was reported that wild species *Arachis correntina* and *Arachis valida* show resistance to *T. frezii* [32]. Recently, INTA released a new cultivar, Ascasubi Hispano, with high tolerance to peanut smut (less than 2% of affected pods in high infested soil), but is not high-oleic cultivar. Among the tools used to facilitate the transfer of resistance are the molecular methods. In 2015, molecular markers associated with the tolerance to peanut smut were found [50]. Marker-assisted selection represents an important tool for breeding programs, as it would save time and money in the development of smut-tolerant cultivars [51]. There are cultivars highly tolerant to peanut smut in the final stages of evaluation [52]. This material would also be useful to prevent the spread of the pathogen to new areas of production [17]. Another aspect to consider is the ability of *T. frezii* to adapt to new cultivars. Cazón (*unpublished*) developed molecular markers to study the temporal variation of *T. frezii* isolates from different years and locations. If the rate of genetic variation of the pathogen is high, the resistance of the new cultivars against smut could be broken. Because of this, it is necessary that breeding programs continue

These strategies were focused on the reduction of initial inoculum. For peanut smut management, crop rotation schemes of more than 3 years without peanuts showed low *T. frezii* teliospore density in soil [53]. In addition, peanut crops preceded by corn exhibited lower

that can minimize yield losses and contaminations of exportation products [8, 35, 43].

**6. Peanut smut management**

to develop peanut smut-resistant cultivars.

**6.2. Cultural practice**

and biological control.

**6.1. Host resistance**

38 Advances in Plant Pathology

This area is not highly developed to peanut smut management. There are only some experiments done that used bioformulations based on *Trichoderma harzianum.* Control efficiency reaches 24% in incidence and 25% in severity [58, 59]. Researchers of IPAVE-CIAP-INTA have assessed the bioformulations based on *Bacillus subtilis* in different doses combining with soil amendments. Therefore, cultural practices and biological control still need to be studied in greater depth for sustainable and economic management of the disease.

#### **6.4. Crop protection fungicides**

The first experiences were performed in vitro using seed treatment fungicides. Teliospores were germinated using leaves and fruit extracts with the addition of PDA medium (39 gr/l). Later, a single colony was picked in media with different fungicides and evaluated the mycelial growth (**Figure 7**). All tested fungicides were effective, but the result could not be extrapolated to the field [33]. This is because the infection process occurred when fungicides from seed treatments have lost their protective effects [17]. The highest control efficiencies were achieved using crop protection fungicides in soil-directed applications. This is because the infective processes occur in the soil, where teliospores infect the gynophore [25, 31, 44]. Refs. [37, 60-62] show that these strategies were more effective at controlling peanut smut than leaf applications. They performed night spraying because in this moment, peanut leaves fold up, and the soil surface is easily reached [17, 63, 64]. Using strobilurin/triazole mixtures like

**Figure 7.** Colonies of *T. frezii*. A: Teliospores germinated in PDA medium (39 gr/l) composed by fruit extracts. B: *T. frezii*  micelial growth of 7 days old plated on PDA medium (39 g / l).

understanding about the pathosystem and effectiveness of various techniques described, it is

The Biology of *Thecaphora frezii* Smut and Its Effects on Argentine Peanut Production

http://dx.doi.org/10.5772/intechopen.75837

41

**i.** The use of pathogen-free seed: This is important to not increase the amount of teliospores

**ii.** Plant peanuts in fields that have low inoculum density of *T. frezii* teliospores: In Córdoba province the healthiest area is in the south, to correspond to new production areas.

**iii.** The fields chosen to cultivate peanut are not to be close to peanut processing factories: Peanut processing plants are the most important sources of teliospores. Those teliospores

**iv.** Spray fungicide mixtures including strobilurins and triazoles: azoxystrobin and cyproconazole are the most effective fungicides. Soil-directed spraying at the beginning of crop

It is important that additional research will be focused in determining the most effective agronomical practice with the most suitable application technologies that reach high control efficiencies. It is necessary that the recommended agricultural practices will be implemented in an entire peanut area, like a regional management. The development of molecular tools is important to facilitate the transfer of resistance to new cultivars and speed up the processes of obtaining varieties with good agronomic characteristics. The collaboration of all those involved in Argentinian peanut production systems is necessary for the management of pea-

We wish to thank Fundación Maní Argentino for providing resources for many research

and Alejandro Mario Rago1,2

All authors declare no conflict of interest about this publication.

\*, Juan Andrés Paredes1

1 Instituto de Patología Vegetal, CIAP – INTA, Córdoba, Argentina 2 Facultad de Agronomía y Veterinaria, UNRC, Córdoba, Argentina

\*Address all correspondence to: cazon.ignacio@inta.gob.ar

possible to recommend disease management tactics to minimize yield losses:

can be transported by wind to adjacent fields.

pegging and 7 days after.

nut smut to be successful.

**Acknowledgements**

**Conflict of interest**

**Author details**

Luis Ignacio Cazón1

experiments cited in this work.

in the soil.

**Figure 8.** Experimental granulate fungicide applied on peanut.

picoxystrobin + cyproconazole, control efficiencies reach 47% at a rate of 900 cc/ha or 1000 cc/ha in two applications in R2 (beginning peg) [65] and 10 days [17, 47]. An experimental granular fungicide with a slow release for longer protection during crop pegging was assessed [26]. Control efficiency reached 42% in incidence if the granulate is applied at flowering (**Figure 8**). Another study performed in INTA affirms that using high dose and night spraying for the first late leaf spot (*Cercosporidium personatum*) application ensures a 35% smut incidence reduction [37]. Control efficiencies close to 50% for peanut smut is an acceptable value, so accompanied by efficient molecules, correct dose and good times, and application technologies, chemical control could contribute to integrated disease management [17].

## **7. Conclusions**

Argentina is the only country that has reported the disease in cultivated peanuts. Currently, it is the most economically important peanut disease in the country. This is due to the characteristics of the pathosystem. These represent an important prejudice since the peanut industry has clearly agroexporting characteristics. The alert generated by smut in the producing countries is mainly due to the destructive power of the disease in the crop but also to the lack of efficient strategies to control the disease and the speed with which the pathogen spreads throughout the Argentinean peanut area in a short time. There were many factors that contributed to the spread of the pathogen. Among them are the low diversification of cultivars used in recent years, the lack of crop rotations and the use of nonspecific fungicides. One of the most important facts is related to the increase of production scale in the 1990s. Many small-sized growers that used to harvest their own seeds were removed from the production system since they could not afford the costs of change of scale. Big growers produced and processed peanuts on a larger scale which included both healthy and diseased fields. This encouraged seed contamination and spread of the pathogen spores. Based on the current understanding about the pathosystem and effectiveness of various techniques described, it is possible to recommend disease management tactics to minimize yield losses:


It is important that additional research will be focused in determining the most effective agronomical practice with the most suitable application technologies that reach high control efficiencies. It is necessary that the recommended agricultural practices will be implemented in an entire peanut area, like a regional management. The development of molecular tools is important to facilitate the transfer of resistance to new cultivars and speed up the processes of obtaining varieties with good agronomic characteristics. The collaboration of all those involved in Argentinian peanut production systems is necessary for the management of peanut smut to be successful.

## **Acknowledgements**

picoxystrobin + cyproconazole, control efficiencies reach 47% at a rate of 900 cc/ha or 1000 cc/ha in two applications in R2 (beginning peg) [65] and 10 days [17, 47]. An experimental granular fungicide with a slow release for longer protection during crop pegging was assessed [26]. Control efficiency reached 42% in incidence if the granulate is applied at flowering (**Figure 8**). Another study performed in INTA affirms that using high dose and night spraying for the first late leaf spot (*Cercosporidium personatum*) application ensures a 35% smut incidence reduction [37]. Control efficiencies close to 50% for peanut smut is an acceptable value, so accompanied by efficient molecules, correct dose and good times, and application technologies, chemical

Argentina is the only country that has reported the disease in cultivated peanuts. Currently, it is the most economically important peanut disease in the country. This is due to the characteristics of the pathosystem. These represent an important prejudice since the peanut industry has clearly agroexporting characteristics. The alert generated by smut in the producing countries is mainly due to the destructive power of the disease in the crop but also to the lack of efficient strategies to control the disease and the speed with which the pathogen spreads throughout the Argentinean peanut area in a short time. There were many factors that contributed to the spread of the pathogen. Among them are the low diversification of cultivars used in recent years, the lack of crop rotations and the use of nonspecific fungicides. One of the most important facts is related to the increase of production scale in the 1990s. Many small-sized growers that used to harvest their own seeds were removed from the production system since they could not afford the costs of change of scale. Big growers produced and processed peanuts on a larger scale which included both healthy and diseased fields. This encouraged seed contamination and spread of the pathogen spores. Based on the current

control could contribute to integrated disease management [17].

**Figure 8.** Experimental granulate fungicide applied on peanut.

**7. Conclusions**

40 Advances in Plant Pathology

We wish to thank Fundación Maní Argentino for providing resources for many research experiments cited in this work.

## **Conflict of interest**

All authors declare no conflict of interest about this publication.

## **Author details**

Luis Ignacio Cazón1 \*, Juan Andrés Paredes1 and Alejandro Mario Rago1,2

\*Address all correspondence to: cazon.ignacio@inta.gob.ar

1 Instituto de Patología Vegetal, CIAP – INTA, Córdoba, Argentina

2 Facultad de Agronomía y Veterinaria, UNRC, Córdoba, Argentina

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2016;**98**(2):327-330


[56] Kearney MI, Cerioni GA, Morla FD, Bonvilliani D, Tello RD, Avellaneda M, Díaz Menaches J, Picco F, Segovia P. Avances en el control de carbón (*Thecaphora frezzii*) con la aplicación de fosfitos en el cultivo de maní. 30° Jornada Nacional de Maní. General Cabrera, Córdoba; 2015. pp. 83-84

**Section 2**

**Advances in Bacterial Plant Pathology**


**Advances in Bacterial Plant Pathology**

[56] Kearney MI, Cerioni GA, Morla FD, Bonvilliani D, Tello RD, Avellaneda M, Díaz Menaches J, Picco F, Segovia P. Avances en el control de carbón (*Thecaphora frezzii*) con la aplicación de fosfitos en el cultivo de maní. 30° Jornada Nacional de Maní. General

[57] Morla F, Kearney M, Cerioni G, Pichetti L, Bonvilliani D, Tello D, Avellaneda M, Díaz Menaches J, Picco F, Segovia P. 15° Jornadas Fitosanitarias Argentinas; 2015. 109 p [58] Pastor NA, Ganuza M, Reynoso MM, Folguera J, Rovera M, Torres AM. Bioproducto a base de *Trichoderma* controla el carbón de maní y aumenta de rendimiento del cultivo.

[59] Ganuza MR, Pastor NA, Folguera J, Andrés J, Reynoso MM, Rovera M, Torres AM. Perspectivas de aplicación de bioformulado de Trichoderm*a harzianum* ITEM 3636 para el control del carbón del maní. 31° Jornada Nacional de Maní. 22 de septiembre. General

[60] Paredes JA, Cazón LI, Bisonard EM, Oddino C, Rago AM. Efecto de ingredientes activos fungicidas sobre la intensidad del carbón del maní. 30° Jornada Nacional de Maní.

[61] Paredes JA, Cazón LI, Bisonard EM, Rago AM. Blanco de aplicación de diferentes fungicidas para el manejo del carbón del maní (Thecaphora frezii). 15° Jornadas fitosanitarias

[62] Paredes JA, Cazón LI, Bisonard EM, Edwards Molina JP, Rago AM. Uso de fungicidas para el control de *Thecaphora frezii* en ensayos a campo. 15° Jornadas fitosanitarias

[63] Augusto J, Brenneman TB, Culbreath AK, Sumner P. Night spraying peanut fungicides I. Extended fungicide residual and integrated disease management. Plant Disease. 2010a;

[64] Augusto J, Brenneman TB, Culbreath AK, Sumner P. Night spraying peanut fungicides II. Application timings and spray deposition in the lower canopy. Plant Disease.

[65] Boote KJ. Growth stages of peanut. Peanut Science. 1982;**9**(1):35-39

30° Jornada Nacional de Maní. General Cabrera, Córdoba; 2015. pp. 81-82

Cabrera, Córdoba; 2015. pp. 83-84

46 Advances in Plant Pathology

Cabrera, Córdoba; 2016. pp. 28-30

Argentinas; 2015. 119 p

Argentinas; 2015. 235 p

**04**:676-682

2010b;**94**:683-689

General Cabrera, Córdoba; 2015. p. 68-69

**Chapter 3**

**Provisional chapter**

**Systematic Identification of the** *Xylophilus* **Group in**

**Systematic Identification of the** *Xylophilus* **Group in** 

The pine wood nematode (PWN) *Bursaphelenchus xylophilus* (Steiner & Buhrer, 1934) Nickle, 1970 is the agent responsible for pine wilt disease (PWD). This nematode has been killing native pine trees (*Pinus densiflora*, *P. thunbergii*, *P. luchuensis*) in Japan since the early twentieth century. It is the number one forest pest in Japan and has been spread to China, Korea, Portugal, and Spain. The nematode is native to North America (Canada, USA, Mexico) and is thought to have been carried to Japan at the beginning of the twentieth century on timber exports. Up to now, the genus *Bursaphelenchus* Fuchs, 1937 comprises nearly 120 species (14 groups). Around 14 species very similar to *B. xylophilus* are put together and named the *xylophilus* group. This chapter presents the grouping history, subspecies or genetic types in species of the *xylophilus* group, and an identification key for 14 species of the *xylophilus* group, ITS-RFLP identification, and other molecular iden-

Pine wilt disease (PWD), which is caused by pine wood nematode (PWN), *Bursaphelenchus xylophilus* (Steiner & Buhrer [6]) Nickle [1], has been devastating Japanese pine forests since the beginning of the twentieth century. For many years, the mass mortality of pine trees was supposed by attacks of beetles. Until 1971, *Bursaphelenchus* sp. was demonstrated as the causal agent of PWD by inoculation tests on *Pinus* spp. [2], and subsequently the nematode was described as *Bursaphelenchus lignicolus* [3]. After that, the PWN was first reported in the United States in 1979 [4]. Extensive surveys revealed the widespread distribution of the

> © 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

DOI: 10.5772/intechopen.77096

**the Genus** *Bursaphelenchus*

**the Genus** *Bursaphelenchus*

http://dx.doi.org/10.5772/intechopen.77096

tification methods are also discussed.

**Keywords:** morphology, molecular, ITS-RFLP, DNA barcoding

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

Jianfeng Gu

Jianfeng Gu

**Abstract**

**1. Introduction**

#### **Systematic Identification of the** *Xylophilus* **Group in the Genus** *Bursaphelenchus* **Systematic Identification of the** *Xylophilus* **Group in the Genus** *Bursaphelenchus*

DOI: 10.5772/intechopen.77096

#### Jianfeng Gu Jianfeng Gu

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.77096

#### **Abstract**

The pine wood nematode (PWN) *Bursaphelenchus xylophilus* (Steiner & Buhrer, 1934) Nickle, 1970 is the agent responsible for pine wilt disease (PWD). This nematode has been killing native pine trees (*Pinus densiflora*, *P. thunbergii*, *P. luchuensis*) in Japan since the early twentieth century. It is the number one forest pest in Japan and has been spread to China, Korea, Portugal, and Spain. The nematode is native to North America (Canada, USA, Mexico) and is thought to have been carried to Japan at the beginning of the twentieth century on timber exports. Up to now, the genus *Bursaphelenchus* Fuchs, 1937 comprises nearly 120 species (14 groups). Around 14 species very similar to *B. xylophilus* are put together and named the *xylophilus* group. This chapter presents the grouping history, subspecies or genetic types in species of the *xylophilus* group, and an identification key for 14 species of the *xylophilus* group, ITS-RFLP identification, and other molecular identification methods are also discussed.

**Keywords:** morphology, molecular, ITS-RFLP, DNA barcoding

## **1. Introduction**

Pine wilt disease (PWD), which is caused by pine wood nematode (PWN), *Bursaphelenchus xylophilus* (Steiner & Buhrer [6]) Nickle [1], has been devastating Japanese pine forests since the beginning of the twentieth century. For many years, the mass mortality of pine trees was supposed by attacks of beetles. Until 1971, *Bursaphelenchus* sp. was demonstrated as the causal agent of PWD by inoculation tests on *Pinus* spp. [2], and subsequently the nematode was described as *Bursaphelenchus lignicolus* [3]. After that, the PWN was first reported in the United States in 1979 [4]. Extensive surveys revealed the widespread distribution of the

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

nematode throughout the country [5], but no epidemic was found, and the disease occurred only on a few exotic pine species. The PWN was later proven to be the same species as the one described in Florida in 1934 [6], the name was then changed from *B. lignicolus* to *B. xylophilus* [7], and it has been indigenous to North America [8].

**Species Main characters Typical spicule shape Typical female tail**

Systematic Identification of the *Xylophilus* Group in the Genus *Bursaphelenchus*

http://dx.doi.org/10.5772/intechopen.77096

51

Female tail cylindrical, terminus broadly rounded, without mucro (if there's a mucro, usually less than 2 μm)

Like R form, but all females have a mucro, less than 3 μm on average (1.5–4.2 μm)

clearly expanded, female tail cylindrical, c' = 2.7–3.4, mucro usually present, about

Female tail cylindrical or subcylindrical, mucro usually offset from the tail, mean length more than 4 μm

ventrally bent, mucro about 2~3 μm, at the ventral position

*conicaudatus*, female tail conical, a small mucro present, length not clear

and clearly ventrally bent, terminus without mucro, roughed or irregular *B. paraluxuriosae* Similar to *B. luxuriosae*,

> but female tail only slightly bent, without mucro, spicule without

cucullus

1.5~2.6 μm

Female tail subcylindrical or conical, mucro not offset from the tail, about 4~7 μm

*B. conicaudatus* Female tail conical,

*B. baujardi* Similar to *B.* 

*B. luxuriosae* Female tail conical

*B. fraudulentus* Spicule cucullus not

*B. xylophilus* (R form)

*B. xylophilus* (M form)

*B. mucronatus kolymensis*

*B. mucronatus mucronatus*

Later, the disease has spread into China in 1982, Korea in 1988, Mexico in 1993, Portugal in 1999, and Spain in 2011 [9], and it is now still a potential threat to pine forests worldwide.

In nature, *B. xylophilus* is spread from tree to tree through the activity of adult stages of woodinhabiting longhorn beetles of the genus *Monochamus* (Coleoptera: Cerambycidae) for short distance. This transmits the nematode either to the shoots of living trees during maturation feeding either by sex or by oviposition of females. But human activity is responsible for the long-distance spread. It is widely accepted that national and international trade of pine logs and related packaging wood is the causal of PWN spreading, so national and international regulations (e.g., ISPM 15: FAO, 2003, revised 2009) were accompanied by intensive sampling and laboratory investigations for the presence of PWD in imported wood worldwide in order to significantly reduce the risk of the pest's spread. So, it is important to identify *B. xylophilus* to manage its further spreading and conduct early eradication plan.

Before 2000, there were only other two closely related species: *B. fraudulentus* Rühm [10] and *B. mucronatus* Mamiya and Enda [11] (*B. kolymensis* Korenchenko [12] was later considered as being synonymous with *B. mucronatus*). For a long time, in diagnostic protocol of *B. xylophilus*, it was morphologically compared with only *B. mucronatus* and *B. fraudulentus*, many PCRbased methods also used only these three species samples.

Since 2000, with further study of packaging wood and phoretic insects, more *Bursaphelenchus* species were discovered. Now, there are 110–120 known species in this genus [9] and 14 species in the *xylophilus* group. *B. xylophilus* (Steiner & Buhrer [6]) Nickle [1]; *B. fraudulentus* Rühm [10] (J. B. Goodey, 1960); *B. mucronatus* Mamiya and Enda [11]; *B. conicaudatus* Kanzaki et al. [13]; *B. baujardi* Walia, Negi et al. [14]; *B. luxuriosae* Kanzaki and Futai [15]; *B. doui* Braasch et al. [16]; *B. singaporensis* Zhang et al. [17]; *B. macromucronatus* Gu et al. [18]; *B. populi* Tomalak and Filipiak [19]; *B. paraluxuriosae* Gu et al. [20]; *B. firmae* Kanzaki et al. [21]; *B. koreanus* Gu et al. [22]; and *B. gillanii* Schönfeld et al. [23].

## **2. Grouping history**

Giblin and Kaya [24] first separated five groups within *Bursaphelenchus* mainly according to spicule morphology; the *xylophilus* group contains three species, namely, *B. xylophilus*, *B. mucronatus*, and *B. fraudulentus*, all have large, paired, arcuate spicules with a sharply pointed rostrum, and a disk-like expansion, cucullus, and females of this group have a vulval flap (**Table 1**). Braasch [25] studied the morphological relationship between European *Bursaphelenchus* species in order to provide key characters for their taxonomic identification. She considered the number of incisures in the lateral field as a basic grouping feature, together with other features like spicule shape, number and position of caudal papillae, presence and size of a vulval flap, and the shape of female tail. Among the 28


nematode throughout the country [5], but no epidemic was found, and the disease occurred only on a few exotic pine species. The PWN was later proven to be the same species as the one described in Florida in 1934 [6], the name was then changed from *B. lignicolus* to

Later, the disease has spread into China in 1982, Korea in 1988, Mexico in 1993, Portugal in 1999, and Spain in 2011 [9], and it is now still a potential threat to pine forests worldwide.

In nature, *B. xylophilus* is spread from tree to tree through the activity of adult stages of woodinhabiting longhorn beetles of the genus *Monochamus* (Coleoptera: Cerambycidae) for short distance. This transmits the nematode either to the shoots of living trees during maturation feeding either by sex or by oviposition of females. But human activity is responsible for the long-distance spread. It is widely accepted that national and international trade of pine logs and related packaging wood is the causal of PWN spreading, so national and international regulations (e.g., ISPM 15: FAO, 2003, revised 2009) were accompanied by intensive sampling and laboratory investigations for the presence of PWD in imported wood worldwide in order to significantly reduce the risk of the pest's spread. So, it is important to identify *B. xylophilus*

Before 2000, there were only other two closely related species: *B. fraudulentus* Rühm [10] and *B. mucronatus* Mamiya and Enda [11] (*B. kolymensis* Korenchenko [12] was later considered as being synonymous with *B. mucronatus*). For a long time, in diagnostic protocol of *B. xylophilus*, it was morphologically compared with only *B. mucronatus* and *B. fraudulentus*, many PCR-

Since 2000, with further study of packaging wood and phoretic insects, more *Bursaphelenchus* species were discovered. Now, there are 110–120 known species in this genus [9] and 14 species in the *xylophilus* group. *B. xylophilus* (Steiner & Buhrer [6]) Nickle [1]; *B. fraudulentus* Rühm [10] (J. B. Goodey, 1960); *B. mucronatus* Mamiya and Enda [11]; *B. conicaudatus* Kanzaki et al. [13]; *B. baujardi* Walia, Negi et al. [14]; *B. luxuriosae* Kanzaki and Futai [15]; *B. doui* Braasch et al. [16]; *B. singaporensis* Zhang et al. [17]; *B. macromucronatus* Gu et al. [18]; *B. populi* Tomalak and Filipiak [19]; *B. paraluxuriosae* Gu et al. [20]; *B. firmae* Kanzaki et al. [21]; *B. koreanus* Gu

Giblin and Kaya [24] first separated five groups within *Bursaphelenchus* mainly according to spicule morphology; the *xylophilus* group contains three species, namely, *B. xylophilus*, *B. mucronatus*, and *B. fraudulentus*, all have large, paired, arcuate spicules with a sharply pointed rostrum, and a disk-like expansion, cucullus, and females of this group have a vulval flap (**Table 1**). Braasch [25] studied the morphological relationship between European *Bursaphelenchus* species in order to provide key characters for their taxonomic identification. She considered the number of incisures in the lateral field as a basic grouping feature, together with other features like spicule shape, number and position of caudal papillae, presence and size of a vulval flap, and the shape of female tail. Among the 28

*B. xylophilus* [7], and it has been indigenous to North America [8].

to manage its further spreading and conduct early eradication plan.

based methods also used only these three species samples.

et al. [22]; and *B. gillanii* Schönfeld et al. [23].

**2. Grouping history**

50 Advances in Plant Pathology


Ryss [26] considered that those characters like lateral lines, number and position of caudal papillae, and vulval flap are available for only some of the nominal species; thereby, their utility is limited. So, he studied 75 valid species of the genus *Bursaphelenchus* known that time. Only based on spicule structure, he sorted this genus into six groups: *hunti*, *aberrans*, *eidmanni*, *borealis*, *xylophilus*, and *piniperdae* groups. For the *xylophilus* group, its spicule is characterized by capitulum flattened anteriorly, small condylus, dorsal contour of the lamina distinctly angular in last third, and cucullus usually present (except in *B. crenati*). He listed ten species: *B. xylophilus*, *B. abruptus*, *B. baujardi*, *B. conicaudatus*, *B. crenati*, *B. eroshenkii*, *B. fraudulentus*, *B. kolymensis*, *B. luxuriosae*, and *B. mucronatus*. Later study showed that *B. abruptus*, *B. crenati*, and *B. eroshenkii* were definitely different from the *xylophilus* group [27]. *B. crenati* has a different position of the caudal papillae (the double pair in front of the bursa is missing), the presence of a vulval flap is questionable, and the spicules do not show a cucullus. Additionally, it is transmitted by a bark beetle, a scenario not typical for the *xylophilu*s group. *B. eroshenkii* has five incisures in the lateral field, only five caudal papillae (seven in the *xylophilus* group) and

Systematic Identification of the *Xylophilus* Group in the Genus *Bursaphelenchus*

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53

Braasch [29] stated that the *xylophilus* group of the genus *Bursaphelenchus* can be clearly distinguished from other species of the genus by the presence of four lateral lines, the presence of a vulval flap in females, a characteristic shape of the male spicules, and the arrangement of the seven caudal papillae. An identification key of the nine species of the *xylophilus* group was presented, and *B. kolymensis* was considered to be the European type of *B. mucronatus*. Later, with the development of the molecular methods, especially sequencing technique, more *Bursaphelenchus* sequences are available in the GenBank. Based on morphological characters and phylogenetic analysis [27], the genus is divided into eight groups with four incisures in the lateral field (*xylophilus*, *okinawaensis*, *africanus*, *fungivorus*, *cocophilus*, *kevini*, *tokyoensis* and *sexdentati* groups), four groups with three incisures (*eggersi*, *eremus*, *hofmanni*, and *leoni* groups), and two groups with two incisures (*abietinus* and *sinensis* groups). Most of

no vulval flap [28]. The spicule shape of *B. abruptus* is not typical.

the groups are well separated by both morphological and molecular studies.

argued against this idea [32].

**3. Subspecies or genetic types in species of the** *xylophilus* **group**

*Bursaphelenchus mucronatus* Mamiya and Enda [11] was first found from pine trees in Japan. Braasch [30] reported for the first time *B. mucronatus* in timber imports from Siberia and in forest trees in Germany. These populations (later on named "European genotype" or "European type") had shown morphological and morphometric deviations from Japanese *B. mucronatus* isolate. Separate species status for Japanese and European *B. mucronatus* was postulated on the basis of sequence differences of an amplified fragment of the heat shock 70A gene [31]. However, successful mating experiments of a European *B. mucronatus* with a Japanese isolate

Braasch et al. [33] proposed the two *Bursaphelenchus mucronatus* types to be subspecies. The European type is named *B. mucronatus kolymensis*, and the East Asian type is named *B. mucronatus mucronatus*. The earlier described *Bursaphelenchus kolymensis* corresponds to *B. mucronatus kolymensis* in morphological characters. The two subspecies show morphological

**Table 1.** Main morphological characters of the *xylophilus* group.

conifer-inhabiting European species, she proposed eight groups. The *xylophilus* group (*B. xylophilus*, *B. mucronatus*, and *B. fraudulentus*) can easily be separated from all other species by the presence of four incisures, the typical shape of spicules, the special position of the caudal papillae, and the large vulval flap of females.

Ryss [26] considered that those characters like lateral lines, number and position of caudal papillae, and vulval flap are available for only some of the nominal species; thereby, their utility is limited. So, he studied 75 valid species of the genus *Bursaphelenchus* known that time. Only based on spicule structure, he sorted this genus into six groups: *hunti*, *aberrans*, *eidmanni*, *borealis*, *xylophilus*, and *piniperdae* groups. For the *xylophilus* group, its spicule is characterized by capitulum flattened anteriorly, small condylus, dorsal contour of the lamina distinctly angular in last third, and cucullus usually present (except in *B. crenati*). He listed ten species: *B. xylophilus*, *B. abruptus*, *B. baujardi*, *B. conicaudatus*, *B. crenati*, *B. eroshenkii*, *B. fraudulentus*, *B. kolymensis*, *B. luxuriosae*, and *B. mucronatus*. Later study showed that *B. abruptus*, *B. crenati*, and *B. eroshenkii* were definitely different from the *xylophilus* group [27]. *B. crenati* has a different position of the caudal papillae (the double pair in front of the bursa is missing), the presence of a vulval flap is questionable, and the spicules do not show a cucullus. Additionally, it is transmitted by a bark beetle, a scenario not typical for the *xylophilu*s group. *B. eroshenkii* has five incisures in the lateral field, only five caudal papillae (seven in the *xylophilus* group) and no vulval flap [28]. The spicule shape of *B. abruptus* is not typical.

Braasch [29] stated that the *xylophilus* group of the genus *Bursaphelenchus* can be clearly distinguished from other species of the genus by the presence of four lateral lines, the presence of a vulval flap in females, a characteristic shape of the male spicules, and the arrangement of the seven caudal papillae. An identification key of the nine species of the *xylophilus* group was presented, and *B. kolymensis* was considered to be the European type of *B. mucronatus*.

Later, with the development of the molecular methods, especially sequencing technique, more *Bursaphelenchus* sequences are available in the GenBank. Based on morphological characters and phylogenetic analysis [27], the genus is divided into eight groups with four incisures in the lateral field (*xylophilus*, *okinawaensis*, *africanus*, *fungivorus*, *cocophilus*, *kevini*, *tokyoensis* and *sexdentati* groups), four groups with three incisures (*eggersi*, *eremus*, *hofmanni*, and *leoni* groups), and two groups with two incisures (*abietinus* and *sinensis* groups). Most of the groups are well separated by both morphological and molecular studies.

## **3. Subspecies or genetic types in species of the** *xylophilus* **group**

conifer-inhabiting European species, she proposed eight groups. The *xylophilus* group (*B. xylophilus*, *B. mucronatus*, and *B. fraudulentus*) can easily be separated from all other species by the presence of four incisures, the typical shape of spicules, the special position of the caudal papillae, and the large

**Species Main characters Typical spicule shape Typical female tail**

*B. doui* Spicule length in chord

52 Advances in Plant Pathology

*B. singaporensis* Female tail without

line

*B. populi* Vulval flap ventrally

*B. firmae* Female mucro thick,

*B. koreanus* Spicule length along

terminus

the base

*B. gillanii* Female tail conical,

*B.* 

*macromucronatus*

34~44 μm, the middle part nearly straight, female tail variable, usually show a mucro at the ventral position, about 2~4 μm

mucro, spicule length along the curved median line 41–48 μm, condylus continuous with the dorsal spicule

Female tail conical, straight mucro usually continuous with tail, about 4.5 μm(2.5~6.5 μm)

bent with its distal half sunken in a conspicuous, sharp depression immediately posterior to the vulva

terminus bluntly pointed

the curved median line 35–44 μm, condylus set off from dorsal spicule line, female tail conical and ventrally bent with slightly pointed, irregular, or roughened

mucro 5–7 μm, wide at

**Table 1.** Main morphological characters of the *xylophilus* group.

vulval flap of females.

*Bursaphelenchus mucronatus* Mamiya and Enda [11] was first found from pine trees in Japan. Braasch [30] reported for the first time *B. mucronatus* in timber imports from Siberia and in forest trees in Germany. These populations (later on named "European genotype" or "European type") had shown morphological and morphometric deviations from Japanese *B. mucronatus* isolate. Separate species status for Japanese and European *B. mucronatus* was postulated on the basis of sequence differences of an amplified fragment of the heat shock 70A gene [31]. However, successful mating experiments of a European *B. mucronatus* with a Japanese isolate argued against this idea [32].

Braasch et al. [33] proposed the two *Bursaphelenchus mucronatus* types to be subspecies. The European type is named *B. mucronatus kolymensis*, and the East Asian type is named *B. mucronatus mucronatus*. The earlier described *Bursaphelenchus kolymensis* corresponds to *B. mucronatus kolymensis* in morphological characters. The two subspecies show morphological differences in the shape of female tail, length of mucro, position of excretory pore, and also small differences in spicule shape. They can be distinguished by their ITS-RFLP patterns based on restriction fragments obtained with enzymes *Rsa* I and *Hae* III. Based on sequence analysis of ribosomal ITS1/ITS2, LSU D2/D3, and mitochondrial COI regions, a clear subdivision of the two isolate groups (subspecies) has been confirmed.

But the mucro character of the R form of *B. xylophilus* is not always stable; it depends on different hosts and environmental situations. Braasch [36] reported that when an R form *B. xylophilus* isolate (US15) was re-extracted from trees 3 months after inoculation experiment, 35% of females were round-tailed, 8% had conical tails, and 17% had a distinct mucro (up to 4–5 μm), whereas 40% had a very small mucro of 1 μm length. Zheng et al. [37] reported that an R form *B. xylophilus* was detected from a pine tree in Ningbo, China; all females had a distinct mucro, ranging from 0.5 to 2.9 μm (mean 1.7 μm), but the mucro disappeared after culturing on *B. fuckeliana*. Gu et al. [35] also reported an R form *B. xylophilus* isolate (4049); about half of the females detected from packaging wood had a round tail, and the other half showed a

Systematic Identification of the *Xylophilus* Group in the Genus *Bursaphelenchus*

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55

**Figure 1.** Light photomicrographs of female tails of "R" form of *Bursaphelenchus xylophilus* (isolate 4049) in different situations: A–D, detected from the packaging wood; E–H, after culturing on *B. fuckeliana*; and I–L, after culturing on

*Pestalotiopsis* sp. (scale bars = 10 μm).

Since the report of a mucronate ("M") form of *B. xylophilus* detected from balsam fir (*Abies balsamea*) in Minnesota and Wisconsin, USA [34], uncertainty in morphological distinction of *B. xylophilus* from related species became evident. For a long time, it is morphological and molecular characters are not clear. Gu et al. [35] made a morphological and molecular study based on five isolates of "M" form of *Bursaphelenchus xylophilus*, together with the round-tailed ("R") form of *B. xylophilus* and *B. mucronatus* (both subspecies), and founded that the spicules of these species (types or forms) are similar. The "M" form of *B. xylophilus* is distinguished from the "R" form of *B. xylophilus* by a distinct mucro at the female tail end. It differs from the *B. mucronatus kolymensis* by slightly shorter female tail mucro and position of excretory pore. It is distinguished from *B. mucronatus mucronatus* by female tail shape and shorter female tail mucro. The conventional five restriction endonucleases (*Rsa* I, *Hae* III, *Msp* I, *Hinf* I, and *Alu* I) used for obtaining ITS-RFLP patterns of *Bursaphelenchus* species cannot distinguish the "M" and "R" form of *B. xylophilus*, but the two forms can be differentiated by the use of two additional restriction endonucleases (*Hpy*188 I and *Hha* I). The molecular phylogenetic analysis based on the sequences of D2D3 LSU rDNA, ITS1/2 region, and mtCOI revealed that the "M" form of *B. xylophilus* is genetically closest to the "R" form of *B. xylophilus*, and that their sequence divergence is small.

## **4. Morphological characters of the** *xylophilus* **group**

According to Braasch et al. [27], the *xylophilus* group is characterized by four lateral lines; seven caudal papillae; conspicuous P4, P3, and P4 papillae adjacent to each other (double pair) just anterior to bursa; spicules long, slender, and semicircular with angular lamina in posterior third; capitulum fattened with small condylus and distinct rostrum; cucullus present (for *B. fraudulentus* and *B. paraluxuriosae*, spicule cucullus is not clearly visible); and large vulval flap.

But lateral lines and caudal papillae are not easy to be seen sometimes, so typical male spicule shape and female vulval flap should be the main grouping characters [35]. In all known *Bursaphelenchus* species, only *B. masseyi*, *B. trypophloei*, and *B. abruptus* may be confused with *B. xylophilus* group. All their females have a vulval flap, but their spicules are not typical. *B. trypophloei* and *B. masseyi* differ in having relatively short rostrum, and the angular contour of the dorsal lamina is usually indistinct. *B. abruptus* differs in different ventral curvatures of the spicules.

## **5. Morphological identification of** *B. xylophilus* **with a key**

Usually, R form of *B. xylophilus* is distinguished from other species by cylindrical female tail with bluntly rounded terminus, without mucro, or in some cases, some females will show a mucro, which is less than 2 μm.

But the mucro character of the R form of *B. xylophilus* is not always stable; it depends on different hosts and environmental situations. Braasch [36] reported that when an R form *B. xylophilus* isolate (US15) was re-extracted from trees 3 months after inoculation experiment, 35% of females were round-tailed, 8% had conical tails, and 17% had a distinct mucro (up to 4–5 μm), whereas 40% had a very small mucro of 1 μm length. Zheng et al. [37] reported that an R form *B. xylophilus* was detected from a pine tree in Ningbo, China; all females had a distinct mucro, ranging from 0.5 to 2.9 μm (mean 1.7 μm), but the mucro disappeared after culturing on *B. fuckeliana*. Gu et al. [35] also reported an R form *B. xylophilus* isolate (4049); about half of the females detected from packaging wood had a round tail, and the other half showed a

differences in the shape of female tail, length of mucro, position of excretory pore, and also small differences in spicule shape. They can be distinguished by their ITS-RFLP patterns based on restriction fragments obtained with enzymes *Rsa* I and *Hae* III. Based on sequence analysis of ribosomal ITS1/ITS2, LSU D2/D3, and mitochondrial COI regions, a clear subdivision of the

Since the report of a mucronate ("M") form of *B. xylophilus* detected from balsam fir (*Abies balsamea*) in Minnesota and Wisconsin, USA [34], uncertainty in morphological distinction of *B. xylophilus* from related species became evident. For a long time, it is morphological and molecular characters are not clear. Gu et al. [35] made a morphological and molecular study based on five isolates of "M" form of *Bursaphelenchus xylophilus*, together with the round-tailed ("R") form of *B. xylophilus* and *B. mucronatus* (both subspecies), and founded that the spicules of these species (types or forms) are similar. The "M" form of *B. xylophilus* is distinguished from the "R" form of *B. xylophilus* by a distinct mucro at the female tail end. It differs from the *B. mucronatus kolymensis* by slightly shorter female tail mucro and position of excretory pore. It is distinguished from *B. mucronatus mucronatus* by female tail shape and shorter female tail mucro. The conventional five restriction endonucleases (*Rsa* I, *Hae* III, *Msp* I, *Hinf* I, and *Alu* I) used for obtaining ITS-RFLP patterns of *Bursaphelenchus* species cannot distinguish the "M" and "R" form of *B. xylophilus*, but the two forms can be differentiated by the use of two additional restriction endonucleases (*Hpy*188 I and *Hha* I). The molecular phylogenetic analysis based on the sequences of D2D3 LSU rDNA, ITS1/2 region, and mtCOI revealed that the "M" form of *B. xylophilus* is genetically closest to the "R" form of *B. xylophilus*, and that their sequence divergence is small.

According to Braasch et al. [27], the *xylophilus* group is characterized by four lateral lines; seven caudal papillae; conspicuous P4, P3, and P4 papillae adjacent to each other (double pair) just anterior to bursa; spicules long, slender, and semicircular with angular lamina in posterior third; capitulum fattened with small condylus and distinct rostrum; cucullus present (for *B. fraudulentus* and *B. paraluxuriosae*, spicule cucullus is not clearly visible); and large vulval flap. But lateral lines and caudal papillae are not easy to be seen sometimes, so typical male spicule shape and female vulval flap should be the main grouping characters [35]. In all known *Bursaphelenchus* species, only *B. masseyi*, *B. trypophloei*, and *B. abruptus* may be confused with *B. xylophilus* group. All their females have a vulval flap, but their spicules are not typical. *B. trypophloei* and *B. masseyi* differ in having relatively short rostrum, and the angular contour of the dorsal lamina is usually indistinct. *B. abruptus* differs in different ventral curvatures of the spicules.

Usually, R form of *B. xylophilus* is distinguished from other species by cylindrical female tail with bluntly rounded terminus, without mucro, or in some cases, some females will show a

two isolate groups (subspecies) has been confirmed.

54 Advances in Plant Pathology

**4. Morphological characters of the** *xylophilus* **group**

**5. Morphological identification of** *B. xylophilus* **with a key**

mucro, which is less than 2 μm.

**Figure 1.** Light photomicrographs of female tails of "R" form of *Bursaphelenchus xylophilus* (isolate 4049) in different situations: A–D, detected from the packaging wood; E–H, after culturing on *B. fuckeliana*; and I–L, after culturing on *Pestalotiopsis* sp. (scale bars = 10 μm).

very small mucro about 0.5–1 μm long. But after culturing on *B. fuckeliana* for 1 month, more than half of females showed a mucro of about specimens, a mucro of less than 0.5 μm long, or no mucro. However, after being cultured on *Pestalotiopsis* sp., apart from some round-tailed females, most females had a bluntly pointed tail terminus (**Figure 1**).

Typical R form of *B. xylophilus* can be distinguished from other species of the *xylophilus* group by the female tail shape. *B. populi* sometimes also shows a cylindrical female tail without mucro, but they can be separated by the vulval flap ventrally bent with its distal half sunken in a conspicuous, sharp depression. Identification of the M form of *B. xylophilus* is more difficult. Females in mucronate populations generally show a mucro on the female tail end, on average 2.2–3.0 μm long (1.5–4.2 μm). Its mucro shape does not change even after culturing for many years. The M form of *B. xylophilus* is morphologically most similar to the *B. mucronatus kolymensis*. It is distinguished from it by slightly shorter mucro on female tail (mean 2.2–3.0 μm *vs.* 3.0–5.0 μm) and the position of excretory pore. Up to now, M form of *B. xylophilus* has only been reported in North America, and its report in China and Taiwan is still questionable. Due to a certain variation in characters between populations and different hosts and environmental situations, it is essential to perform molecular test in case of doubt.

The following dichotomous key of species of the *xylophilus* group is based on the female tail shape (conical or cylindrical, with or without mucro, and mucro length), vulval flap shape (straight or bent), and spicule size and shape (with or without cucullus).

**6. Identification of the** *xylophilus* **group species with ITS-RFLP** 

9. (a) Spicule length in chord 34~44 μm, the middle part nearly straight *B. doui* (b) Spicule length in chord <34 μm, the middle part slightly ventrally curved 10

*B. xylophilus* (R form)

http://dx.doi.org/10.5772/intechopen.77096

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11

Systematic Identification of the *Xylophilus* Group in the Genus *Bursaphelenchus*

10. (a) Female tail cylindrical, terminus broadly rounded, without mucro (some females may possess a short process at the tail terminus, usually less than

(b) Female tail cylindrical, subcylindrical, or conical, terminus with mucro,

11. (a) Mucro usually continuous with tail 12 (b) Mucro usually offset from tail 15

(b) Spicule condylus dorsally offset, body stout (a < 40) 13 13. (a) Female mucro terminus pointed 14

(b) Female mucro terminus bluntly pointed *B. firmae* 14. (a) Female tail straight *B. macromucronatus* (b) Female tail slightly bent, dorsally stronger bent than ventrally *B. gillanii*

12. (a) Spicule condylus dorsally not offset, body slim (a > 40) *B. mucronatus mucronatus*

15. (a) Mucro mean length more than 4 μm *B. mucronatus kolymensis* (b) Mucro mean length less than 3 μm *B. xylophilus* (M form)

Application of ITS-RFLP analysis to *Bursaphelenchus* species identification was first described in 1998 [38, 39]. In this technique, a region of ribosomal DNA (rDNA), containing the internal transcribed spacer regions ITS1 and ITS2, is amplified by PCR method with forward primer F194 5'-CGTAACAAGGTAGCTGTAG-3′ (Ferris et al.) and reverse primer 5368 5'-TTTCACTCGCCGTTACTAAGG-3′ (Vrain) [40, 41], and, subsequently, the PCR products were digested with five restriction endonucleases *Alu* I, *Hae* III, *Hinf* I, *Msp* I, and *Rsa* I to get the restriction fragment length polymorphisms. Using the same set of five restriction enzymes, species-specific ITS-RFLP reference patterns were compiled for 11 *Bursaphelenchus* species in 1999 [42] and extended to 26 species in 2005 [43]. The technique has proven to be a valuable tool in identification of nematodes isolated from imported wood in quarantine control or forest surveys [44–47]. Wolfgang et al. (2009) produced ITS-RFLP reference profiles of 44 *Bursaphelenchus* species [48], including two intraspecific types in each of *B. mucronatus* and *B. leoni*. Though in the case of *B. corneolus*, *B. lini* (later identified as *Devibursaphelenchus lini*), *B. singaporensis*, *B. sexdentati*, and *B. doui* [49], additional bands in the patterns of certain isolates or individual nematodes were observed which may be explained by ITS sequence microheterogeneity, i.e., the presence of ITS sequence variants within the number of rDNA tandem repeats, but they did not seriously impair identification of species based on the overall reference patterns. ITS-RFLP analysis has proven valuable not only for differentiation of the pathogenic pine wood nematode, *B. xylophilus*, from related species but also useful in

**method**

2 μm

more than 2 μm



very small mucro about 0.5–1 μm long. But after culturing on *B. fuckeliana* for 1 month, more than half of females showed a mucro of about specimens, a mucro of less than 0.5 μm long, or no mucro. However, after being cultured on *Pestalotiopsis* sp., apart from some round-tailed

Typical R form of *B. xylophilus* can be distinguished from other species of the *xylophilus* group by the female tail shape. *B. populi* sometimes also shows a cylindrical female tail without mucro, but they can be separated by the vulval flap ventrally bent with its distal half sunken in a conspicuous, sharp depression. Identification of the M form of *B. xylophilus* is more difficult. Females in mucronate populations generally show a mucro on the female tail end, on average 2.2–3.0 μm long (1.5–4.2 μm). Its mucro shape does not change even after culturing for many years. The M form of *B. xylophilus* is morphologically most similar to the *B. mucronatus kolymensis*. It is distinguished from it by slightly shorter mucro on female tail (mean 2.2–3.0 μm *vs.* 3.0–5.0 μm) and the position of excretory pore. Up to now, M form of *B. xylophilus* has only been reported in North America, and its report in China and Taiwan is still questionable. Due to a certain variation in characters between populations and different hosts and environmental situations, it is essential to perform molecular test in case of doubt.

The following dichotomous key of species of the *xylophilus* group is based on the female tail shape (conical or cylindrical, with or without mucro, and mucro length), vulval flap shape

females, most females had a bluntly pointed tail terminus (**Figure 1**).

56 Advances in Plant Pathology

(straight or bent), and spicule size and shape (with or without cucullus).

1. (a) Posterior to the vulva *B. populi* (b) Vulval flap bent and to the vulva not clear 2 2. (a) Spicule cucullus not clearly expanded 3 (b) Spicule cucullus expanded 4 3. (a) Female tail cylindrical, c' = 2.7–3.4, mucro present *B. fraudulentus*

4. (a) Average c' > 4, female tail conical 5 (b) Average c' < 4, female tail cylindrical, subcylindrical, or conical 9 5. (a) Female tail without mucro 6 (b) Female tail with mucro 8 6. (a) Spicule length along the curved median line 27–30 μm *B. luxuriosae* (b) Spicule length along the curved median line more than 35 μm 7

7. (a) Spicule length along the curved median line 35–44 μm, condylus set off

(b) Spicule length along the curved median line 41–48 μm, condylus

(b) Stylet without small knob, excretory pore at the position of median bulb,

8. (a) Stylet with small knob, excretory pore ranging from median bulb to

from dorsal spicule line

hemizonid, c' = 3.6–5

c' = 3–4

continuous with the dorsal spicule line

(b) Female tail conical, c' = 4–5, without mucro *B. paraluxuriosae*

*B. koreanus*

*B. singaporeinsis*

*B. conicaudatus*

*B. baujardi*
