**Nutrient Mobility and Availability with Selected Irrigation and Drainage Systems for Vegetable Crops on Sandy Soils**

Shinjiro Sato1 and Kelly T. Morgan2

*1Department of Environmental Engineering for Symbiosis, Soka University, Tokyo, 2Southwest Florida Research and Education Center, University of Florida, Florida, 1Japan 2USA* 

## **1. Introduction**

A wide variety of vegetable crops is produced on varying types of soils including sandy soils where the production can be maximized as long as proper fertilization, irrigation and drainage systems are implemented. However, most sandy soils have low water- and nutrient-holding capacities, hence appropriate irrigation scheduling is critical for proper plant health as well as for minimizing water requirement. Healthy crops are better able to withstand pest and disease pressures, as well as produce a high quality commercial product. Irrigation management should be geared towards maintaining optimum moisture and nutrient concentrations within the plant root zone. If this goal is achieved, crops will take up their maximum amounts of water and nutrients with minimum wastage. Equally important, excessive irrigation will reduce water use efficiency, as well as require more water and contribute to potentially negative environmental impacts.

It is crucial to recognize how nutrients move and transform in soils after the application for improved application efficiencies and reduced environmental losses. However, different irrigation and drainage systems practiced on sandy soils for vegetable production can complicate the dynamics of mobility and availability of nutrients and water. Yet, the number of researches on this matter has not been as many as needed. Therefore, this review attempts to summarize characteristics of sandy soils for vegetable production (Section 2), clarify pros and cons of different irrigation and drainage systems practiced on sandy soils (Section 3), and elucidate the nutrient mobility and availability for vegetable production under different irrigation systems specifically on sandy soils (Section 4), in which the soil environment can greatly differ from other soil types in terms of nutrient dynamics in soil.

#### **2. Characteristics of sandy soils for vegetable production**

#### **2.1 Types and physiochemical properties of sandy soils**

Soils on which crops are grown greatly influence how irrigation water, nutrients, and other agrichemicals should be managed to maximize the production while minimizing resource use and effects on the environment. Soil properties that influence soil water management

Nutrient Mobility and Availability with

use efficiency by crops.

Selected Irrigation and Drainage Systems for Vegetable Crops on Sandy Soils 91

such as Riviera (57 to 135 cm), Winder (30 to 122 cm), Pomona (52 to 65 cm), and Wabasso series (62 to 85 cm) have relatively shallow argillic or spodic layers that can be excavated during the bedding process (Obreza & Collins, 2002; Gilbert et al., 2008). As a result, these subsurface materials are sometimes mixed into the root zone affecting physicochemical

Irrigation can be defined as the artificial application of water to the soil for assisting in growing crops and is considered one of the most important cultivation practices in dry or limited rainfall areas and during periods with no or little rainfall. An approach to conserving water is to maximize the irrigation efficiency and to minimize water loss. Irrigation efficiency is a measure of the effectiveness of an irrigation system in delivering water to a crop and/or the effectiveness of irrigation in increasing crop yields. Good irrigation practices imply good irrigation efficiency and can be achieved by maintaining a good irrigation water application uniformity and improve water uptake efficiency of the irrigation water. Uniformity can be defined as the ratio of the volume of water used or available for use in crop production to the volume pumped or delivered for use. Crop uptake efficiency may be expressed as the ratio of increase in yield over non-irrigated production to the volume of irrigation water used. Irrigation efficiencies thus provide a basis for the comparison of irrigation systems from the standpoint of water beneficially used and from the standpoint of yield per unit of water used (Haman et al., 2005). Irrigation system efficiency depends primarily on design, installation and maintenance, and management. Thus, a properly designed and maintained system can be inefficient if mismanaged just as a well-designed system can be inefficient if managed effectively with poor maintenance. Irrigation management of vegetable crops includes: 1) combination of target irrigation volume, 2) measure of soil moisture to adjust this volume based on crop age and weather conditions, 3) knowledge of how much the root zone can hold, and 4) assessment of how rainfall contributes to replenishing soil moisture. (Hochmuth, 2007). Concerns about the environmental impact of water and fertilizer uses by agriculture have dramatically increased in the past few decades. Crop production is linked to leaf photosynthesis and canopy size, and water stress drastically reduces both components (Kramer & Boyer, 1995). Adequate water supply is, therefore, critical in maximizing crop production, nutrient use efficiency (NUE), and quality of most horticultural crops. Efficient water use may promote an increase in fertilizer retention in the effective root zone, maximizing crop production and minimizing the potential of groundwater degradation (e.g., nitrate-nitrogen (NO3––N) leaching) (Scholberg et al., 2002). A simple goal of the ideal irrigation scheduling would be to increase crop production with the least amount of water, therefore minimizing water loss by deep percolation, runoff or evaporation. However, no irrigation system has the capability of completely avoiding water losses, although several irrigation methods and techniques can be adopted to minimize losses and increase the water

One of the most important irrigation management factors is irrigation uniformity, which is how evenly water is distributed across the field. Non-uniform distribution of irrigation water may create over- and/or under-irrigated areas which can lead to yield reduction due to excessive nutrient leaching or plant water stress. For a sprinkler irrigation system, the

properties of the surface sandy soils (Obreza & Collins, 2002).

**3. Irrigation and drainage systems on sandy soils** 

include soil texture, hydraulic conductivity, water-holding capacity, and natural drainage, which also affect soil nutrient management that differs depending on soil organic matter (OM) content, pH, cation exchange capacity (CEC), and coatings on sand grains.

Soil texture is the relative proportion of sand, silt, and clay in a mineral soil. Texture influences how much water a soil can hold against drainage by gravity and how quickly water drains away if it has an outlet. Sandy soils contain 80% or more sand in the root zone (Shirazi and Boersma, 1982). The high sand contents make irrigation water management extremely difficult because sands are dominated by large pores that have little capacity to hold water through capillarity (Kern, 1992). Therefore, if too much water is applied to the sandy soil, the excess is lost below the root zone and can induce nutrient leaching.

Soil OM includes anything that was once alive, from freshly deposited plant residues to highly decomposed humus. In their native state, sandy soils may contain as much as 5% OM under grass vegetation, and somewhat less under forest cover (Six et al., 1998). Cultivated soils usually contain less OM than native soils, typically less than 3%, due to decreased plant diversity and the use of herbicides or plastic mulches that reduce weed growth. Under welldrained conditions, soil OM is rapidly lost as carbon dioxide by oxidation in warm and humid climates, and is not replaced in large quantities by crop production because relatively low area is covered by plant materials at any given time. In sandy soils, OM is an extremely valuable component because it provides both water and nutrient-holding capacities, and its decomposition provides recycled nutrients to plants (Khaleel et al., 1981).

Soil water-holding capacity is provided by the smaller pores that exist between and within the smallest fraction of soil and OM particles (Khaleel et al., 1981). Therefore, the waterholding capacity is directly related to amounts of silt, clay, and OM present. Since sandy soils contain only minimal amounts of these components, their water-holding capacity is rarely greater than 2.5 cm per 30 cm of soil depth, and are often less than 1.9 cm per 30 cm.

#### **2.2 Characteristics of subsurface layers**

Argillic and spodic layers can be found underneath many surface sandy soils, and have considerably different physicochemical properties from the surface soils. The argillic layer is created by the deposition of clay particles and is usually mottled gray in color and sandy or sandy loam in texture. This horizon can be either acidic or alkaline with high clay content. The spodic layer is composed of OM that is leached down the profile by both physical and chemical means and deposited in the lower part of the soil profile. This distinct brown or black layer is often high in OM, aluminum, and iron, usually with a low pH, and almost always sandy in texture. Both argillic and spodic layers impede vertical water percolation and causes water to accumulate above these horizons because their permeability is low. This water accumulation is referred to as a perched water table, and is beneficial for maintaining a constant water table for subsurface irrigation for vegetable production (Muchovej et al., 2005). In addition, the water-holding capacity and CEC are typically higher in these subsurface layers than in the surface soils (Obreza & Collins, 2002).

The nutrient and irrigation managements can be different and may be complicated when these layers are excavated and mixed in as a result of the bedding process. The subsurface layers can be found relatively deep in some Alfisols and Spodosols in USA such as Holopaw (70 to 162 cm depth), Pineda (95 to 130 cm), Immokalee (90 to 137 cm), and Oldsmar series (95 to 125 cm), hence remain undisturbed following the bedding process. Other sandy soils such as Riviera (57 to 135 cm), Winder (30 to 122 cm), Pomona (52 to 65 cm), and Wabasso series (62 to 85 cm) have relatively shallow argillic or spodic layers that can be excavated during the bedding process (Obreza & Collins, 2002; Gilbert et al., 2008). As a result, these subsurface materials are sometimes mixed into the root zone affecting physicochemical properties of the surface sandy soils (Obreza & Collins, 2002).

## **3. Irrigation and drainage systems on sandy soils**

90 Soil Health and Land Use Management

include soil texture, hydraulic conductivity, water-holding capacity, and natural drainage, which also affect soil nutrient management that differs depending on soil organic matter

Soil texture is the relative proportion of sand, silt, and clay in a mineral soil. Texture influences how much water a soil can hold against drainage by gravity and how quickly water drains away if it has an outlet. Sandy soils contain 80% or more sand in the root zone (Shirazi and Boersma, 1982). The high sand contents make irrigation water management extremely difficult because sands are dominated by large pores that have little capacity to hold water through capillarity (Kern, 1992). Therefore, if too much water is applied to the sandy soil, the excess is lost below the root zone and can induce nutrient

Soil OM includes anything that was once alive, from freshly deposited plant residues to highly decomposed humus. In their native state, sandy soils may contain as much as 5% OM under grass vegetation, and somewhat less under forest cover (Six et al., 1998). Cultivated soils usually contain less OM than native soils, typically less than 3%, due to decreased plant diversity and the use of herbicides or plastic mulches that reduce weed growth. Under welldrained conditions, soil OM is rapidly lost as carbon dioxide by oxidation in warm and humid climates, and is not replaced in large quantities by crop production because relatively low area is covered by plant materials at any given time. In sandy soils, OM is an extremely valuable component because it provides both water and nutrient-holding capacities, and its

Soil water-holding capacity is provided by the smaller pores that exist between and within the smallest fraction of soil and OM particles (Khaleel et al., 1981). Therefore, the waterholding capacity is directly related to amounts of silt, clay, and OM present. Since sandy soils contain only minimal amounts of these components, their water-holding capacity is rarely greater than 2.5 cm per 30 cm of soil depth, and are often less than 1.9 cm per 30 cm.

Argillic and spodic layers can be found underneath many surface sandy soils, and have considerably different physicochemical properties from the surface soils. The argillic layer is created by the deposition of clay particles and is usually mottled gray in color and sandy or sandy loam in texture. This horizon can be either acidic or alkaline with high clay content. The spodic layer is composed of OM that is leached down the profile by both physical and chemical means and deposited in the lower part of the soil profile. This distinct brown or black layer is often high in OM, aluminum, and iron, usually with a low pH, and almost always sandy in texture. Both argillic and spodic layers impede vertical water percolation and causes water to accumulate above these horizons because their permeability is low. This water accumulation is referred to as a perched water table, and is beneficial for maintaining a constant water table for subsurface irrigation for vegetable production (Muchovej et al., 2005). In addition, the water-holding capacity and CEC are typically higher in these

The nutrient and irrigation managements can be different and may be complicated when these layers are excavated and mixed in as a result of the bedding process. The subsurface layers can be found relatively deep in some Alfisols and Spodosols in USA such as Holopaw (70 to 162 cm depth), Pineda (95 to 130 cm), Immokalee (90 to 137 cm), and Oldsmar series (95 to 125 cm), hence remain undisturbed following the bedding process. Other sandy soils

(OM) content, pH, cation exchange capacity (CEC), and coatings on sand grains.

decomposition provides recycled nutrients to plants (Khaleel et al., 1981).

subsurface layers than in the surface soils (Obreza & Collins, 2002).

**2.2 Characteristics of subsurface layers** 

leaching.

Irrigation can be defined as the artificial application of water to the soil for assisting in growing crops and is considered one of the most important cultivation practices in dry or limited rainfall areas and during periods with no or little rainfall. An approach to conserving water is to maximize the irrigation efficiency and to minimize water loss. Irrigation efficiency is a measure of the effectiveness of an irrigation system in delivering water to a crop and/or the effectiveness of irrigation in increasing crop yields. Good irrigation practices imply good irrigation efficiency and can be achieved by maintaining a good irrigation water application uniformity and improve water uptake efficiency of the irrigation water. Uniformity can be defined as the ratio of the volume of water used or available for use in crop production to the volume pumped or delivered for use. Crop uptake efficiency may be expressed as the ratio of increase in yield over non-irrigated production to the volume of irrigation water used. Irrigation efficiencies thus provide a basis for the comparison of irrigation systems from the standpoint of water beneficially used and from the standpoint of yield per unit of water used (Haman et al., 2005). Irrigation system efficiency depends primarily on design, installation and maintenance, and management. Thus, a properly designed and maintained system can be inefficient if mismanaged just as a well-designed system can be inefficient if managed effectively with poor maintenance. Irrigation management of vegetable crops includes: 1) combination of target irrigation volume, 2) measure of soil moisture to adjust this volume based on crop age and weather conditions, 3) knowledge of how much the root zone can hold, and 4) assessment of how rainfall contributes to replenishing soil moisture. (Hochmuth, 2007).

Concerns about the environmental impact of water and fertilizer uses by agriculture have dramatically increased in the past few decades. Crop production is linked to leaf photosynthesis and canopy size, and water stress drastically reduces both components (Kramer & Boyer, 1995). Adequate water supply is, therefore, critical in maximizing crop production, nutrient use efficiency (NUE), and quality of most horticultural crops. Efficient water use may promote an increase in fertilizer retention in the effective root zone, maximizing crop production and minimizing the potential of groundwater degradation (e.g., nitrate-nitrogen (NO3––N) leaching) (Scholberg et al., 2002). A simple goal of the ideal irrigation scheduling would be to increase crop production with the least amount of water, therefore minimizing water loss by deep percolation, runoff or evaporation. However, no irrigation system has the capability of completely avoiding water losses, although several irrigation methods and techniques can be adopted to minimize losses and increase the water use efficiency by crops.

One of the most important irrigation management factors is irrigation uniformity, which is how evenly water is distributed across the field. Non-uniform distribution of irrigation water may create over- and/or under-irrigated areas which can lead to yield reduction due to excessive nutrient leaching or plant water stress. For a sprinkler irrigation system, the

Nutrient Mobility and Availability with

(Smajstrla et al., 2002).

Selected Irrigation and Drainage Systems for Vegetable Crops on Sandy Soils 93

**Microirrigation systems**: Application efficiencies of microirrigation systems are typically high because these systems distribute water near or directly into the crop root zone, and water losses due to wind drift and evaporation are typically small (Boman & Parsons, 2002; Locascio, 2005). This highly efficient water system (90% to 95%; Table 1) is widely used on high value vegetables and tree fruit crops. The advantages of microirrigation over sprinkler include reduced water use, ability to apply fertilizer with the irrigation, precise water distribution, reduced foliar diseases, and the ability to electronically scheduled irrigation on large areas with relatively smaller pumps. If micro-sprinkler systems are operated under windy conditions on hot, dry days, wind drift and evaporation losses can be high. Thus, management to avoid these losses is important to achieving high application efficiencies with these systems. The most common application of microirrigation in Florida, USA is that of under-tree micro-sprinkler systems for citrus. Less efficiency has been found for microsprinkler system compared to drip irrigation system. Application efficiencies of drip and line source systems are primarily dependent on hydraulics of design of these systems and

**Sprinkler system**: Sprinkler systems are designed to use overlapping patterns to provide uniform coverage over an irrigated area. Sprinklers are normally spaced 50-60% of their diameter of coverage to provide uniform application in low wind conditions. Studies have shown that 1.5% to 7.6% of irrigated water can be lost due to wind drift and evaporation during application (Dukes et al., 2010). Application efficiencies of sprinkler systems are relatively low at less than 80% (Table 1). Because networks of pressurized pipelines are used to distribute water in these systems, the uniformity of water application and the irrigation efficiency is more strongly dependent on the hydraulic properties of the pipe network. Thus, application efficiencies of well-designed and well-managed pressurized sprinkler systems are much less variable than those of gravity flow irrigation systems, which depend heavily on soil hydraulic characteristics. Therefore, during water applications, sprinkler irrigation systems lose water due to evaporation and wind drift (Haman et al., 2005). More water is lost during windy conditions than calm conditions. More is also lost during high evaporative demand periods (hot and dry days) than during low demand periods (cool, cloudy, and humid days). Thus, sprinkler irrigation systems usually apply water more efficiently at night (and early mornings and late evenings) than during the day. It is not possible to apply water with perfect uniformity because of friction losses, elevation changes, manufacturing variation in components, and other factors. Traveling guns typically have greater application efficiencies than portable guns because of the greater uniformity that occurs in the direction of travel (Smajstrla et al., 2002). Periodic move lateral systems are designed to apply water uniformly along the laterals. No uniformity and low application efficiencies occur when the laterals are not properly positioned between settings. Nonuniformity also occurs at the ends of the laterals where sprinkler overlap is not adequate

**Surface and Seepage systems**: Water is distributed by flow through the soil profile or over the soil surface. The uniformity and efficiency of the irrigation water applied by the surface

Table 1. Application efficiency for water delivery system (Simonne & Dukes, 2009)

on their maintenance and management (Boman & Parsons, 2002).

Irrigation system Application efficiency Microirrigation 80-95% Sprinkler 60-80% Surface/Seepage 20-70%

uniformity of application can be evaluated by placing containers in a geometric configuration and measuring the amount of water caught in each container. Dukes et al. (2006) utilized this type of testing to show the effect of pressure and wind speed on operating performance of two types of center pivot sprinkler system nozzle packages. Furthermore, Dukes and Perry (2006) showed that uniformity of a variable rate control system was not different from a traditional control system on two typical center pivot/linear move irrigation systems used in the southeast USA. However, the problem with sprinkler systems is that the water application pattern is susceptible to distortion by the wind. While wind speed and direction are not controlled variables, their effect on irrigation uniformity is significant, so that sprinkler system design must be done with anticipated wind conditions. Drip irrigation systems are very efficient in terms of water distribution and reduction of water losses. The uniformity is directly related to the pressure variation within the entire system and the variability of the emissions of each individual emitter. Several factors contribute to reduce the uniformity of water application such as excessive length of laterals, excessive pressure losses due to changes in elevation along the laterals, emitter clogging, and soil characteristics. Limited lateral water mobility in sandy soils under drip irrigation drastically affects root distribution (Zotarelli et al., 2009), and nutrient interception in the sides of the raised bed. This could be a problem for double row crops like peppers and squash when a single drip tape is placed in center of the bed.

Non-uniform distribution of water in the bed may also compromise the acquisition of nutrients by the root system. Since NO3––N is a highly mobile, non-adsorbing ion, low rooting densities may not be sufficient for NO3 ––N acquisition, and a larger fraction of the N applied through fertigation can escape below the root zone. The basis for this lies in previous field observations which demonstrated that the displacement of irrigation water and nutrients is primarily vertical and confined to a 30–38 cm wide zone, due to the extremely high hydraulic conductivity of sandy soils (Zotarelli et al., unpublished data). The use of appropriate irrigation scheduling facilitates more frequent applications of small volumes of water and improves matching of water supply and crop water demand which is critical to reduce potential crop water stress and leaching losses in sandy soils (Zotarelli et al., 2008a, 2008b, 2009). Since applying frequent small volume irrigation with conventional systems tends to be labor-intensive and/or technically difficult to employ, sensor-based irrigation systems may facilitate the successful employment of low volume-high frequency irrigation systems in commercial vegetable systems. In addition, reduction in emitter spacing and also the use of double drip tapes placed closer to the crop rows may improve the uniformity of water and nutrient distribution along the beds, while reducing the amount of water required. However, there is a lack of information about the effectiveness of this system for double row crops.

#### **3.1 Irrigation types and performance characteristics**

Irrigated acreage world-wide spans a range of irrigation delivery systems depending on the type of crop and cultural conditions. Irrigation can be grouped into the following general categories: low volume (also known as microirrigation, trickle irrigation, or drip irrigation), sprinkler, surface (also known as gravity or flood irrigation), and seepage (also known as subsurface irrigation or water table control). These irrigation systems vary by application efficiency with surface and seepage being less efficient than microirrigation (Table 1).

uniformity of application can be evaluated by placing containers in a geometric configuration and measuring the amount of water caught in each container. Dukes et al. (2006) utilized this type of testing to show the effect of pressure and wind speed on operating performance of two types of center pivot sprinkler system nozzle packages. Furthermore, Dukes and Perry (2006) showed that uniformity of a variable rate control system was not different from a traditional control system on two typical center pivot/linear move irrigation systems used in the southeast USA. However, the problem with sprinkler systems is that the water application pattern is susceptible to distortion by the wind. While wind speed and direction are not controlled variables, their effect on irrigation uniformity is significant, so that sprinkler system design must be done with anticipated wind conditions. Drip irrigation systems are very efficient in terms of water distribution and reduction of water losses. The uniformity is directly related to the pressure variation within the entire system and the variability of the emissions of each individual emitter. Several factors contribute to reduce the uniformity of water application such as excessive length of laterals, excessive pressure losses due to changes in elevation along the laterals, emitter clogging, and soil characteristics. Limited lateral water mobility in sandy soils under drip irrigation drastically affects root distribution (Zotarelli et al., 2009), and nutrient interception in the sides of the raised bed. This could be a problem for double row crops like peppers and squash when a single drip tape is

Non-uniform distribution of water in the bed may also compromise the acquisition of nutrients by the root system. Since NO3––N is a highly mobile, non-adsorbing ion, low rooting densities may not be sufficient for NO3––N acquisition, and a larger fraction of the N applied through fertigation can escape below the root zone. The basis for this lies in previous field observations which demonstrated that the displacement of irrigation water and nutrients is primarily vertical and confined to a 30–38 cm wide zone, due to the extremely high hydraulic conductivity of sandy soils (Zotarelli et al., unpublished data). The use of appropriate irrigation scheduling facilitates more frequent applications of small volumes of water and improves matching of water supply and crop water demand which is critical to reduce potential crop water stress and leaching losses in sandy soils (Zotarelli et al., 2008a, 2008b, 2009). Since applying frequent small volume irrigation with conventional systems tends to be labor-intensive and/or technically difficult to employ, sensor-based irrigation systems may facilitate the successful employment of low volume-high frequency irrigation systems in commercial vegetable systems. In addition, reduction in emitter spacing and also the use of double drip tapes placed closer to the crop rows may improve the uniformity of water and nutrient distribution along the beds, while reducing the amount of water required. However, there is a lack of information about the effectiveness of this

Irrigated acreage world-wide spans a range of irrigation delivery systems depending on the type of crop and cultural conditions. Irrigation can be grouped into the following general categories: low volume (also known as microirrigation, trickle irrigation, or drip irrigation), sprinkler, surface (also known as gravity or flood irrigation), and seepage (also known as subsurface irrigation or water table control). These irrigation systems vary by application

efficiency with surface and seepage being less efficient than microirrigation (Table 1).

placed in center of the bed.

system for double row crops.

**3.1 Irrigation types and performance characteristics** 


Table 1. Application efficiency for water delivery system (Simonne & Dukes, 2009)

**Microirrigation systems**: Application efficiencies of microirrigation systems are typically high because these systems distribute water near or directly into the crop root zone, and water losses due to wind drift and evaporation are typically small (Boman & Parsons, 2002; Locascio, 2005). This highly efficient water system (90% to 95%; Table 1) is widely used on high value vegetables and tree fruit crops. The advantages of microirrigation over sprinkler include reduced water use, ability to apply fertilizer with the irrigation, precise water distribution, reduced foliar diseases, and the ability to electronically scheduled irrigation on large areas with relatively smaller pumps. If micro-sprinkler systems are operated under windy conditions on hot, dry days, wind drift and evaporation losses can be high. Thus, management to avoid these losses is important to achieving high application efficiencies with these systems. The most common application of microirrigation in Florida, USA is that of under-tree micro-sprinkler systems for citrus. Less efficiency has been found for microsprinkler system compared to drip irrigation system. Application efficiencies of drip and line source systems are primarily dependent on hydraulics of design of these systems and on their maintenance and management (Boman & Parsons, 2002).

**Sprinkler system**: Sprinkler systems are designed to use overlapping patterns to provide uniform coverage over an irrigated area. Sprinklers are normally spaced 50-60% of their diameter of coverage to provide uniform application in low wind conditions. Studies have shown that 1.5% to 7.6% of irrigated water can be lost due to wind drift and evaporation during application (Dukes et al., 2010). Application efficiencies of sprinkler systems are relatively low at less than 80% (Table 1). Because networks of pressurized pipelines are used to distribute water in these systems, the uniformity of water application and the irrigation efficiency is more strongly dependent on the hydraulic properties of the pipe network. Thus, application efficiencies of well-designed and well-managed pressurized sprinkler systems are much less variable than those of gravity flow irrigation systems, which depend heavily on soil hydraulic characteristics. Therefore, during water applications, sprinkler irrigation systems lose water due to evaporation and wind drift (Haman et al., 2005). More water is lost during windy conditions than calm conditions. More is also lost during high evaporative demand periods (hot and dry days) than during low demand periods (cool, cloudy, and humid days). Thus, sprinkler irrigation systems usually apply water more efficiently at night (and early mornings and late evenings) than during the day. It is not possible to apply water with perfect uniformity because of friction losses, elevation changes, manufacturing variation in components, and other factors. Traveling guns typically have greater application efficiencies than portable guns because of the greater uniformity that occurs in the direction of travel (Smajstrla et al., 2002). Periodic move lateral systems are designed to apply water uniformly along the laterals. No uniformity and low application efficiencies occur when the laterals are not properly positioned between settings. Nonuniformity also occurs at the ends of the laterals where sprinkler overlap is not adequate (Smajstrla et al., 2002).

**Surface and Seepage systems**: Water is distributed by flow through the soil profile or over the soil surface. The uniformity and efficiency of the irrigation water applied by the surface

Nutrient Mobility and Availability with

are 66-cm apart from each other on the bed

maintained regardless of an increasing crop requirement.

efficiency of the gradient-mulch system.

reduces production efficiency (Geraldson, 1981).

Selected Irrigation and Drainage Systems for Vegetable Crops on Sandy Soils 95

Fig. 2. Diagram of typical gradient-mulch system in a field. A ditch runs between every six raised beds of 91-cm width with 1.8-m distance between beds. For example, tomato plants

Use of a full-bed synthetic mulches on soil beds can serve for minimum nutrient loss by leaching, minimum evaporation loss, optimum soil temperature and moisture/air ratio, and weed and ground rots control (Geraldson, 1981). A reciprocal moisture-air gradient is provided by maintaining the constant water table a given distance below the flat topped soil bed. Thus, a two-dimensional range of decreasing moisture/increasing air is established from a level of saturation to the bed surface. A three-dimensional concentration gradient decreasing with distance from the surface applied fertilizers is superimposed on the moisture-air gradient. Thus, the root from a germinating seed or transplanted seedling can develop in that portion of the bed where the most favorable levels of nutrients, moisture, and air coincide. Once the root system becomes established in a favorable portion of the soil bed, then nutrients and moisture must continue to be supplied to the root as removed by the root; soluble nutrients move by gradient diffusion from the surface to the root. The less soluble nutrients mixed in soil bed continue to become available by equilibrium action, also as removed by the root. Thus, a minimal stress root environment is established and

Moisture is similarly supplied from the water table as required. It is important to recognize that a fluctuating water table can alter the stability of both the moisture and nutrient gradients. The depth of the water table can be a function of the design and management of both the drainage and irrigation system (Geraldson, 1981). Many sandy soils in Florida such as Spodosols favor the use of a constant water table which is basic to the functional

The required quantities of fertilizers used under the mulch for intensive production are no problem if used as recommended. However, when finished and the mulch is removed, it would be preferable to have a minimal residue of salts, thus minimal leaching of salts out of the field (minimal pollution) and minimal salts that might accumulate (minimal stress). Residual salts, irrigation water salts, and misplaced fertilizer salts contribute to a salt buildup in the root environment. Accumulation beyond a given concentration progressively

irrigation system depends strongly on the soil topography and hydraulic properties (Boman & Parsons, 2002). Florida's humid climate requires drainage on high water table soils, and field slope is necessary for surface drainage. But surface runoff also occurs because of field slope. Runoff reduces irrigation application efficiencies unless this water is collected in detention ponds and used for irritation at a later time (Smajstrla et al., 2002). Water distribution from seepage irrigation system occurs below the soil surface. Therefore, wind and other climatic factors do not affect the uniformity of water application. Use of a welldesigned and well-maintained irrigation systems can reduce the loss of water and thereby increase application efficiency as well as uniformity (Boman & Parsons, 2002).

#### **3.2 Development and characteristics of "gradient-mulch" system**

In the 1960's, a vegetable production system on sandy soils was developed in south Florida using a "gradient-mulch" concept to supply nutrients to plants under seepage irrigation (Geraldson, 1962; Geraldson et al., 1965). This system dominates contemporary vegetable production on Florida's sandy soils. The gradient-mulch system involves soil fumigation and banded application of soluble fertilizers beneath full bed plastic mulch. The system has been proven to provide a controlled environment within the bedded soil for sufficient nutrient supply, optimum soil moisture content, stable root growth, and managements for weed, disease, and insect.

Basic components of the gradient-mulch system include 70- to 90 cm-wide (depending on vegetables) flat topped soil beds raised to 25- to 30-cm above from ground, covered by full plastic mulch (Fig. 1). Soluble fertilizers such as N and potassium (K) are applied as band on or near (top 0 to 4 cm) the soil bed surface with the more insoluble nutrients such as phosphorus (P) and micronutrients mixed in the bed. Seepage irrigation is provided to maintain a constant water table levels that are typically 40 to 45 cm deep in Florida sandy soils. Intermittent ditches are also provided for irrigation and drainage purposes from a precisely leveled field with a slope of about 2.5 cm in 30 m (Fig. 2).

Fig. 1. Diagram of the gradient-mulch system. A. Three-dimensional nutrient gradient where salts diffuse outward from level of highest concentration, and move upward with moisture. B. Two-dimensional moisture-air gradient where moisture moves upward (modified from Geraldson, 1980)

irrigation system depends strongly on the soil topography and hydraulic properties (Boman & Parsons, 2002). Florida's humid climate requires drainage on high water table soils, and field slope is necessary for surface drainage. But surface runoff also occurs because of field slope. Runoff reduces irrigation application efficiencies unless this water is collected in detention ponds and used for irritation at a later time (Smajstrla et al., 2002). Water distribution from seepage irrigation system occurs below the soil surface. Therefore, wind and other climatic factors do not affect the uniformity of water application. Use of a welldesigned and well-maintained irrigation systems can reduce the loss of water and thereby

In the 1960's, a vegetable production system on sandy soils was developed in south Florida using a "gradient-mulch" concept to supply nutrients to plants under seepage irrigation (Geraldson, 1962; Geraldson et al., 1965). This system dominates contemporary vegetable production on Florida's sandy soils. The gradient-mulch system involves soil fumigation and banded application of soluble fertilizers beneath full bed plastic mulch. The system has been proven to provide a controlled environment within the bedded soil for sufficient nutrient supply, optimum soil moisture content, stable root growth, and managements for

Basic components of the gradient-mulch system include 70- to 90 cm-wide (depending on vegetables) flat topped soil beds raised to 25- to 30-cm above from ground, covered by full plastic mulch (Fig. 1). Soluble fertilizers such as N and potassium (K) are applied as band on or near (top 0 to 4 cm) the soil bed surface with the more insoluble nutrients such as phosphorus (P) and micronutrients mixed in the bed. Seepage irrigation is provided to maintain a constant water table levels that are typically 40 to 45 cm deep in Florida sandy soils. Intermittent ditches are also provided for irrigation and drainage purposes from a

Fig. 1. Diagram of the gradient-mulch system. A. Three-dimensional nutrient gradient where salts diffuse outward from level of highest concentration, and move upward with moisture. B. Two-dimensional moisture-air gradient where moisture moves upward

increase application efficiency as well as uniformity (Boman & Parsons, 2002).

**3.2 Development and characteristics of "gradient-mulch" system** 

precisely leveled field with a slope of about 2.5 cm in 30 m (Fig. 2).

weed, disease, and insect.

(modified from Geraldson, 1980)

Fig. 2. Diagram of typical gradient-mulch system in a field. A ditch runs between every six raised beds of 91-cm width with 1.8-m distance between beds. For example, tomato plants are 66-cm apart from each other on the bed

Use of a full-bed synthetic mulches on soil beds can serve for minimum nutrient loss by leaching, minimum evaporation loss, optimum soil temperature and moisture/air ratio, and weed and ground rots control (Geraldson, 1981). A reciprocal moisture-air gradient is provided by maintaining the constant water table a given distance below the flat topped soil bed. Thus, a two-dimensional range of decreasing moisture/increasing air is established from a level of saturation to the bed surface. A three-dimensional concentration gradient decreasing with distance from the surface applied fertilizers is superimposed on the moisture-air gradient. Thus, the root from a germinating seed or transplanted seedling can develop in that portion of the bed where the most favorable levels of nutrients, moisture, and air coincide. Once the root system becomes established in a favorable portion of the soil bed, then nutrients and moisture must continue to be supplied to the root as removed by the root; soluble nutrients move by gradient diffusion from the surface to the root. The less soluble nutrients mixed in soil bed continue to become available by equilibrium action, also as removed by the root. Thus, a minimal stress root environment is established and maintained regardless of an increasing crop requirement.

Moisture is similarly supplied from the water table as required. It is important to recognize that a fluctuating water table can alter the stability of both the moisture and nutrient gradients. The depth of the water table can be a function of the design and management of both the drainage and irrigation system (Geraldson, 1981). Many sandy soils in Florida such as Spodosols favor the use of a constant water table which is basic to the functional efficiency of the gradient-mulch system.

The required quantities of fertilizers used under the mulch for intensive production are no problem if used as recommended. However, when finished and the mulch is removed, it would be preferable to have a minimal residue of salts, thus minimal leaching of salts out of the field (minimal pollution) and minimal salts that might accumulate (minimal stress). Residual salts, irrigation water salts, and misplaced fertilizer salts contribute to a salt buildup in the root environment. Accumulation beyond a given concentration progressively reduces production efficiency (Geraldson, 1981).

Nutrient Mobility and Availability with

maintained to 10% (Dukes et al., 2006).

(Leinweber et al., 1999; Djodjic et al., 2004).

Selected Irrigation and Drainage Systems for Vegetable Crops on Sandy Soils 97

When a probe installed in the tomato bed was set the threshold for soil VWC of 13%, the amount of excess irrigation water leached out of the root zone (below 60 cm) was 84% less compared to that with the fixed time scheduling irrigation system (6.8 vs. 42.8 mm). Similarly, the amount of NO3––N leached was reduced to 82% between the controlled and fixed time scheduling irrigation systems (7 vs. 37 kg NO3––N ha–1). On bell pepper using the threshold of 10% and 13% VWC, the controlled irrigation system reduced water leaching by 81% and 51%, respectively, as well as NO3––N leaching by 84% and 20%, respectively, compared to the fixed irrigation system. While tomato showed an increase in crop yield, bell pepper exhibited a significant reduction in crop yield especially when the VWC was

Otherwise, the less mobile nutrients such as P are transported mainly by diffusion, hence its mobility in soils is less strongly governed by water mobility than the mobile nutrients. Triple superphosphate (0–45–0) was applied as P fertilizer with four different rates (0, 30, 60, and 90 kg ha–1) and two different water management regimes (drip irrigation and nonirrigation) to tomatoes grown on Granby loamy sands (77 to 82% sand), Canada for two consecutive years (2007-2008) (Liu et al., 2011). For both growing seasons, in the 0-40 cm soil profile, water extractable P (WEP) content was lower in the drip irrigation treatment than in the non-irrigation treatment (Fig. 3a). However, irrigation management did not have significant effects on WEP below 40-cm depth. Similarly, soil WEP content significantly increased with increasing fertilizer P rate applied only in the top 0-40 cm profile, but not below the depth of 40 cm (Fig. 3b). It appeared that the reduced WEP in the top 0-40 cm with the drip irrigation treatment may have caused by increased crop uptake of P with drip irrigation rather than the vertical mobility of P with water. The drip irrigation of P can provide precise amounts of water and nutrients in an efficient manner, optimizing plant uptake and minimizing environmental losses (Hartz & Hochmuth, 1996). However, environmental losses of P through vertical leaching can occur in sandy soils with high hydraulic conductivity, low P adsorption capacity, and shallow water table levels

It appears that fertigation with 100% water-soluble fertilizers applied through drip irrigation (i.e., drip fertigation) can reduce the amount of leachable fertilizers such as NO3––N and K to deeper soil layers, compared to soluble fertilizers applied to soil with water applied by drip irrigation. However, less mobile nutrients such as P tend to be fixed at the point of application. Yet, subsurface drip fertigation can cause higher available P in deeper layers. Tomatoes were grown on sandy loam soil in India with fertilizer rates of 180–66–99.6 kg N– P–K ha–1 using urea, single superphosphate, and muriate of potash as normal fertilizer for drip irrigation, and urea, mono-ammonium phosphate (12–26.84–0), and potassium nitrate (13–0–38.18) as 100% water-soluble fertilizer for fertigation, both applied daily through inline drippers (Hebbar et al., 2004). After 2 years of growing seasons of 116 and 119 days, respectively, lower residual NO3––N was observed at 30-45 cm soil layer in the fertigation treatment (55 kg ha–1) than in the drip irrigation treatment (66 kg ha–1). The reduction in the residual NO3––N was further enhanced at 45-60 cm soil layer. Similarly, the residual exchangeable K (by 1 *N* ammonium acetate) accumulation was higher at deeper layers (30- 45 and 45-60 cm) in drip irrigation treatment, compared with the fertigation treatment (93 vs. 83 kg ha–1 and 95 vs. 72 kg ha–1, respectively). However, the level of available P (by Bray 1) was significantly higher in 15-30 cm (62 kg ha–1), 30-45 cm (36 kg ha–1), and 45-60 cm (22 kg ha–1) depths in the subsurface drip irrigation compared to the drip irrigation treatment.

For tomato production, for example, under the gradient-mulch system with seepage irrigation, all P2O5, micronutrients, and 20 to 25% of N and K2O are broadcast and incorporated into the bed (i.e., "bottom" or "cold" mix). The remaining N and K2O are placed in narrow grooves 5 to 8 cm deep and 30 to 35 cm offset from the plant bed center (i.e., "top" or "hot" mix). Supplemental N and K2O at 13.6 and 9.1 kg, respectively, can be applied by liquid fertilizer injection wheel to replace leached N and K2O (Olson et al., 2009). Therefore, nutrient concentrations can differ considerably with location in the bed and with time throughout the growing season. For example, soil solution NO3––N concentrations at the fertilizer band and crop row of a tomato bed at the beginning of the growing season were 4200 and 263 mg L–1 at the 0–5 cm depth and 900 and 25 mg L–1 at the 10–20 cm depth, respectively. By the end of the growing season, NO3––N concentrations at the band and row had significantly decreased to 250 and 129 mg L–1 at the shallow depth and 115 and 10 mg L– 1 at the deeper depth, respectively (Geraldson, 1999).

The gradient-mulch system was an important factor in improving production efficiency in the Florida tomato industry during the 1970's. The productivity improved 2.5 times with the system compared with that without the system, and the value of the 1979-80 Florida tomato crop increased to \$228 million compared with \$92 million in 1972-73. This system started to de adopted for other crops such as pepper, sweet corn, cauliflower, eggplant, and squash (Geraldson, 1981). Today, as of 2009, the state of Florida has grown to be the second state following California in acreage for fresh tomato production (14,800 and 14,000 ha in California and Florida, respectively), and the leading state in fresh tomato production value in the USA exceeding \$520 million which accounted for 26% of the state's total crop production value (USDA/NASS, 2011).

#### **4. Nutrient mobility and availability for vegetable production on sandy soils**

#### **4.1 Nutrient availability under drip irrigation systems**

The use of plastic mulch and drip irrigation has become more common in high intensity vegetable production on sandy soils than sprinkler and seepage irrigation systems because water application efficiency, defined as the fraction of the water applied and that is available to plant for use, is greater with drip system than with sprinkler and seepage systems (Simonne & Dukes, 2009; Table 1). However, excess irrigation practices can cause reduced water application efficiency and leaching of soluble nutrients out of the root zone. Although irrigation and fertigation practices vary widely among growers, irrigation typically occurs once or twice each day with regularly scheduled time normally and prolonged time during peak growth stages, while fertigation only takes place 1 to 2 times each week.

Soluble nutrients such as NO3 –-N are transported mainly by convection with water mobility, therefore can move through soil profile with water applied using drip irrigation system. It is evident that the reduction in water moving through the root zone corresponds to a reduction in the amount of NO3––N lost below the root zone. When drip irrigation management was improved using more controlled irrigation scheduling than traditional fixed time scheduling, the amounts of water thus NO3 ––N leached out of the root zone were reduced (Dukes et al., 2006). In experiments for tomato and green bell pepper grown on Candler and Tavares sands (both 97% sand), USA, N was applied at 192 and 208 kg ha–1 to tomato and bell pepper, respectively. Electric probes installed in soil beds measured soil water content in the beds and functioned as a bypass controller to skip a scheduled timed irrigation event if the soil volumetric water content (VWC) was above a preset threshold.

For tomato production, for example, under the gradient-mulch system with seepage irrigation, all P2O5, micronutrients, and 20 to 25% of N and K2O are broadcast and incorporated into the bed (i.e., "bottom" or "cold" mix). The remaining N and K2O are placed in narrow grooves 5 to 8 cm deep and 30 to 35 cm offset from the plant bed center (i.e., "top" or "hot" mix). Supplemental N and K2O at 13.6 and 9.1 kg, respectively, can be applied by liquid fertilizer injection wheel to replace leached N and K2O (Olson et al., 2009). Therefore, nutrient concentrations can differ considerably with location in the bed and with time throughout the growing season. For example, soil solution NO3––N concentrations at the fertilizer band and crop row of a tomato bed at the beginning of the growing season were 4200 and 263 mg L–1 at the 0–5 cm depth and 900 and 25 mg L–1 at the 10–20 cm depth, respectively. By the end of the growing season, NO3––N concentrations at the band and row had significantly decreased to 250 and 129 mg L–1 at the shallow depth and 115 and 10 mg L–

The gradient-mulch system was an important factor in improving production efficiency in the Florida tomato industry during the 1970's. The productivity improved 2.5 times with the system compared with that without the system, and the value of the 1979-80 Florida tomato crop increased to \$228 million compared with \$92 million in 1972-73. This system started to de adopted for other crops such as pepper, sweet corn, cauliflower, eggplant, and squash (Geraldson, 1981). Today, as of 2009, the state of Florida has grown to be the second state following California in acreage for fresh tomato production (14,800 and 14,000 ha in California and Florida, respectively), and the leading state in fresh tomato production value in the USA exceeding \$520 million which accounted for 26% of the state's total crop

**4. Nutrient mobility and availability for vegetable production on sandy soils** 

The use of plastic mulch and drip irrigation has become more common in high intensity vegetable production on sandy soils than sprinkler and seepage irrigation systems because water application efficiency, defined as the fraction of the water applied and that is available to plant for use, is greater with drip system than with sprinkler and seepage systems (Simonne & Dukes, 2009; Table 1). However, excess irrigation practices can cause reduced water application efficiency and leaching of soluble nutrients out of the root zone. Although irrigation and fertigation practices vary widely among growers, irrigation typically occurs once or twice each day with regularly scheduled time normally and prolonged time during

therefore can move through soil profile with water applied using drip irrigation system. It is evident that the reduction in water moving through the root zone corresponds to a reduction in the amount of NO3––N lost below the root zone. When drip irrigation management was improved using more controlled irrigation scheduling than traditional fixed time scheduling, the amounts of water thus NO3––N leached out of the root zone were reduced (Dukes et al., 2006). In experiments for tomato and green bell pepper grown on Candler and Tavares sands (both 97% sand), USA, N was applied at 192 and 208 kg ha–1 to tomato and bell pepper, respectively. Electric probes installed in soil beds measured soil water content in the beds and functioned as a bypass controller to skip a scheduled timed irrigation event if the soil volumetric water content (VWC) was above a preset threshold.

–-N are transported mainly by convection with water mobility,

peak growth stages, while fertigation only takes place 1 to 2 times each week.

1 at the deeper depth, respectively (Geraldson, 1999).

**4.1 Nutrient availability under drip irrigation systems** 

production value (USDA/NASS, 2011).

Soluble nutrients such as NO3

When a probe installed in the tomato bed was set the threshold for soil VWC of 13%, the amount of excess irrigation water leached out of the root zone (below 60 cm) was 84% less compared to that with the fixed time scheduling irrigation system (6.8 vs. 42.8 mm). Similarly, the amount of NO3––N leached was reduced to 82% between the controlled and fixed time scheduling irrigation systems (7 vs. 37 kg NO3 ––N ha–1). On bell pepper using the threshold of 10% and 13% VWC, the controlled irrigation system reduced water leaching by 81% and 51%, respectively, as well as NO3 ––N leaching by 84% and 20%, respectively, compared to the fixed irrigation system. While tomato showed an increase in crop yield, bell pepper exhibited a significant reduction in crop yield especially when the VWC was maintained to 10% (Dukes et al., 2006).

Otherwise, the less mobile nutrients such as P are transported mainly by diffusion, hence its mobility in soils is less strongly governed by water mobility than the mobile nutrients. Triple superphosphate (0–45–0) was applied as P fertilizer with four different rates (0, 30, 60, and 90 kg ha–1) and two different water management regimes (drip irrigation and nonirrigation) to tomatoes grown on Granby loamy sands (77 to 82% sand), Canada for two consecutive years (2007-2008) (Liu et al., 2011). For both growing seasons, in the 0-40 cm soil profile, water extractable P (WEP) content was lower in the drip irrigation treatment than in the non-irrigation treatment (Fig. 3a). However, irrigation management did not have significant effects on WEP below 40-cm depth. Similarly, soil WEP content significantly increased with increasing fertilizer P rate applied only in the top 0-40 cm profile, but not below the depth of 40 cm (Fig. 3b). It appeared that the reduced WEP in the top 0-40 cm with the drip irrigation treatment may have caused by increased crop uptake of P with drip irrigation rather than the vertical mobility of P with water. The drip irrigation of P can provide precise amounts of water and nutrients in an efficient manner, optimizing plant uptake and minimizing environmental losses (Hartz & Hochmuth, 1996). However, environmental losses of P through vertical leaching can occur in sandy soils with high hydraulic conductivity, low P adsorption capacity, and shallow water table levels (Leinweber et al., 1999; Djodjic et al., 2004).

It appears that fertigation with 100% water-soluble fertilizers applied through drip irrigation (i.e., drip fertigation) can reduce the amount of leachable fertilizers such as NO3––N and K to deeper soil layers, compared to soluble fertilizers applied to soil with water applied by drip irrigation. However, less mobile nutrients such as P tend to be fixed at the point of application. Yet, subsurface drip fertigation can cause higher available P in deeper layers. Tomatoes were grown on sandy loam soil in India with fertilizer rates of 180–66–99.6 kg N– P–K ha–1 using urea, single superphosphate, and muriate of potash as normal fertilizer for drip irrigation, and urea, mono-ammonium phosphate (12–26.84–0), and potassium nitrate (13–0–38.18) as 100% water-soluble fertilizer for fertigation, both applied daily through inline drippers (Hebbar et al., 2004). After 2 years of growing seasons of 116 and 119 days, respectively, lower residual NO3––N was observed at 30-45 cm soil layer in the fertigation treatment (55 kg ha–1) than in the drip irrigation treatment (66 kg ha–1). The reduction in the residual NO3––N was further enhanced at 45-60 cm soil layer. Similarly, the residual exchangeable K (by 1 *N* ammonium acetate) accumulation was higher at deeper layers (30- 45 and 45-60 cm) in drip irrigation treatment, compared with the fertigation treatment (93 vs. 83 kg ha–1 and 95 vs. 72 kg ha–1, respectively). However, the level of available P (by Bray 1) was significantly higher in 15-30 cm (62 kg ha–1), 30-45 cm (36 kg ha–1), and 45-60 cm (22 kg ha–1) depths in the subsurface drip irrigation compared to the drip irrigation treatment.

Nutrient Mobility and Availability with

WAP), indicating translocation (lateral mobility) of NH4

cm depth under seepage irrigation (Sato et al., 2009a).

Selected Irrigation and Drainage Systems for Vegetable Crops on Sandy Soils 99

because N was applied in the top mix placed directly above B1 and B2, and also in the bottom mix that was incorporated into the soil (C1 and C2) when the raised bed was formed. However, the N rate applied as the bottom mix (17 kg N ha–1, equivalent to 8.5 mg N kg-1 when broadcast in the top 15 cm of soil) was too small to account for the NH4+–N concentrations found at C1 and C2 locations (ranging from 50 and 100 mg N kg-1 until 5

The NH4+–N concentrations below 20-cm depth were consistently low or non-existent throughout the season, indicating that NH4+–N virtually did not move vertically below 20-

The NO3––N concentration at B1 location peaked during 3 WAP, which was 2 to 3 wk later than the NH4+–N peak, then slowly decreased with time (Fig. 5b). This may indicate that it took about 3 wk for NH4+–N at B1 to process nitrification that commenced about 2 wk into the season. The most notable behavior of NO3––N was an elevated concentration peak during 6 to 8 WAP at every location in the bed except for B1 (which remained much higher than the rest of the locations throughout the season), and subsequent decrease to almost zero at the end of the season. This peak corresponded with a raised water table level and accordingly increased soil water content, especially in the middle and bottom layers during 5 to 7 WAP. Since water is supplied to the root zone by capillarity under seepage irrigation, it is critical to maintain the water table at a depth that supplies sufficient upward flux. The water table depth below the bed surface was relatively stable at recommended levels between 45 and 60 cm for seepage-irrigated tomato bed (Stanley and Clark, 2003) except for two elevations that occurred during 2 WAP (43 cm) and 5 WAP (26 cm). On the other hand, the water table fluctuation did not appear to influence NH4+–N in any part of the bed since the NH4+–N steadily decreased after 5 WAP at every location in the bed. This difference could be due mainly to different diffusivity of the two ions, given the same soil properties

Fig. 4. Cross-sectional diagram of tomato bed and sampling locations in the bed. B1: band and top, B2: band and middle, B3: band and bottom, C1: centerline and top, C2: centerline

and middle, and C3: centerline and bottom (modified from Sato et al., 2009a)

+–N most likely from B1 location.

Because P fertilizer was delivered at 20 cm below the surface by means buried laterals, more concentration of P was observed at deeper depths (Hebbar et al., 2004).

Fig. 3. Post-harvest water-extractable P in the 0- to 100-cm soil profile as affected by (a) different water management regimes and (b) fertilizer P application rates under processing tomato at Harrow, Ontario, 2007-2008 (adapted from Liu et al., 2011)

#### **4.2 Nutrient mobility in soil under seepage irrigation**

The spatial and temporal distribution of nutrients and transformation between chemical forms within the soil bed throughout the growing season under seepage-irrigated sandy soils have rarely documented. It is critical, however, to understand dynamics of nutrient mobility in the soil bed for proper fertilization and irrigation managements or best management practices for vegetable production. Tomatoes were grown on Holopaw fine sand (98% sand), USA using the gradient-mulch system with plastic mulch under seepage irrigation (Sato et al., 2009a, 2009b). Total fertilizer rates applied were 224–61–553 kg N–P–K ha–1. While N and K were applied with the bottom (17 kg N ha–1 and 27 kg K ha–1) and top mix (207 kg N ha–1 and 526 kg K ha–1), all P was applied only with the bottom mix. Soil samples were collected using an auger weekly or biweekly for 18 wk after planting (WAP) at two locations (the fertilizer band and the bed centerline) at three different depths with 10 cm increment (Fig. 4). Each sampling location was denoted as B1: band and top, B2: band and middle, B3: band and bottom, C1: centerline and top, C2: centerline and middle, and C3: centerline and bottom. The soil samples were analyzed for ammonium-N (NH4 +–N) and NO3––N by 2 *M* KCl, and available P and K by Mehlich-1 extractions.

The NH4+–N concentration at B1 location was highest throughout the season compared with other locations, and in general steadily decreased with time (Fig. 5a). Initially low NH4+–N at C1 location peaked during 3 to 5 WAP, and elevated NH4 +–N concentrations were found at C2 location as well until 5 WAP. Most NH4+–N resided at B1, B2, C1, and C2 locations

Because P fertilizer was delivered at 20 cm below the surface by means buried laterals, more

Fig. 3. Post-harvest water-extractable P in the 0- to 100-cm soil profile as affected by (a) different water management regimes and (b) fertilizer P application rates under processing

centerline and bottom. The soil samples were analyzed for ammonium-N (NH4

The NH4+–N concentration at B1 location was highest throughout the season compared with other locations, and in general steadily decreased with time (Fig. 5a). Initially low NH4+–N at C1 location peaked during 3 to 5 WAP, and elevated NH4+–N concentrations were found at C2 location as well until 5 WAP. Most NH4+–N resided at B1, B2, C1, and C2 locations

NO3––N by 2 *M* KCl, and available P and K by Mehlich-1 extractions.

+–N) and

The spatial and temporal distribution of nutrients and transformation between chemical forms within the soil bed throughout the growing season under seepage-irrigated sandy soils have rarely documented. It is critical, however, to understand dynamics of nutrient mobility in the soil bed for proper fertilization and irrigation managements or best management practices for vegetable production. Tomatoes were grown on Holopaw fine sand (98% sand), USA using the gradient-mulch system with plastic mulch under seepage irrigation (Sato et al., 2009a, 2009b). Total fertilizer rates applied were 224–61–553 kg N–P–K ha–1. While N and K were applied with the bottom (17 kg N ha–1 and 27 kg K ha–1) and top mix (207 kg N ha–1 and 526 kg K ha–1), all P was applied only with the bottom mix. Soil samples were collected using an auger weekly or biweekly for 18 wk after planting (WAP) at two locations (the fertilizer band and the bed centerline) at three different depths with 10 cm increment (Fig. 4). Each sampling location was denoted as B1: band and top, B2: band and middle, B3: band and bottom, C1: centerline and top, C2: centerline and middle, and C3:

tomato at Harrow, Ontario, 2007-2008 (adapted from Liu et al., 2011)

**4.2 Nutrient mobility in soil under seepage irrigation** 

concentration of P was observed at deeper depths (Hebbar et al., 2004).

(a) (b)

because N was applied in the top mix placed directly above B1 and B2, and also in the bottom mix that was incorporated into the soil (C1 and C2) when the raised bed was formed. However, the N rate applied as the bottom mix (17 kg N ha–1, equivalent to 8.5 mg N kg-1 when broadcast in the top 15 cm of soil) was too small to account for the NH4+–N concentrations found at C1 and C2 locations (ranging from 50 and 100 mg N kg-1 until 5 WAP), indicating translocation (lateral mobility) of NH4 +–N most likely from B1 location. The NH4+–N concentrations below 20-cm depth were consistently low or non-existent throughout the season, indicating that NH4+–N virtually did not move vertically below 20 cm depth under seepage irrigation (Sato et al., 2009a).

The NO3––N concentration at B1 location peaked during 3 WAP, which was 2 to 3 wk later than the NH4 +–N peak, then slowly decreased with time (Fig. 5b). This may indicate that it took about 3 wk for NH4+–N at B1 to process nitrification that commenced about 2 wk into the season. The most notable behavior of NO3––N was an elevated concentration peak during 6 to 8 WAP at every location in the bed except for B1 (which remained much higher than the rest of the locations throughout the season), and subsequent decrease to almost zero at the end of the season. This peak corresponded with a raised water table level and accordingly increased soil water content, especially in the middle and bottom layers during 5 to 7 WAP. Since water is supplied to the root zone by capillarity under seepage irrigation, it is critical to maintain the water table at a depth that supplies sufficient upward flux. The water table depth below the bed surface was relatively stable at recommended levels between 45 and 60 cm for seepage-irrigated tomato bed (Stanley and Clark, 2003) except for two elevations that occurred during 2 WAP (43 cm) and 5 WAP (26 cm). On the other hand, the water table fluctuation did not appear to influence NH4 +–N in any part of the bed since the NH4+–N steadily decreased after 5 WAP at every location in the bed. This difference could be due mainly to different diffusivity of the two ions, given the same soil properties

Fig. 4. Cross-sectional diagram of tomato bed and sampling locations in the bed. B1: band and top, B2: band and middle, B3: band and bottom, C1: centerline and top, C2: centerline and middle, and C3: centerline and bottom (modified from Sato et al., 2009a)

Nutrient Mobility and Availability with

1981).

Selected Irrigation and Drainage Systems for Vegetable Crops on Sandy Soils 101

affecting the ion mobility in soil such as soil hydraulic conductivity, water-holding capacity, texture, porosity, and density. Increased diffusivity of NO3––N with increased water content in the middle and bottom layers during 5 to 7 WAP would have greatly facilitated both lateral and vertical mobility of NO3––N (Sato et al., 2009a). The key to the stability of moisture and nutrient gradients and the root environment in the gradient-mulch system under seepage irrigation is the constant water table that is often difficult to maintain due to periodic rains and complex management of drainage and irrigation (Geraldson, 1980,

Most of P remained at C1 and C2 locations in the bed ranging between 150 and 400 mg kg-1 with gradual decrease at the end of the season, while P in the rest of the bed did not change to a great extent maintaining less than 100 mg kg-1 throughout the season. Since it is a common practice under the seepage irrigation system to apply P fertilizer only in the bottom mix to target the root zone, the bottom mix was broadcast on the surface soil before bedding, and the soil was then pushed by discs at the outer edges of the bedding apparatus toward center to form the raised bed. Therefore, most of P was initially placed in C1 and C2 and remained in the root zone. This implies that P did not move outside the root zone

Most of K in the bed remained in B1 location at an order of magnitude higher concentration (from 4000 up to 12000 mg kg-1) than other bed locations because most of K fertilizer was applied in the top mix. The K concentrations in C1 and C2 locations maintained relatively high concentrations (100 to 180 mg kg-1) until 7 to 8 WAP, then decreased to low values, possibly because of the bottom mix of some of K fertilizer. The K concentrations in the bottom layer were consistently low and did not greatly change throughout the season, except a raised concentration during 7 WAP. Although the substantial amounts of K were present in B1 location at the end of the season, remarkably low K concentrations were found in the rest of the bed after production. This indicates that under gradient-mulch system minimum K leaching occurred during the growing season, except for possible K loss through leaching to some extent during 5 to 7 WAP when water table fluctuation was

The use of controlled-release fertilizers (CRFs) and slow-release fertilizers (SRFs) for vegetable productions on sandy soils has been investigated with varying results, and nutrient availability on sandy soils from CRFs and SRFs has been increasingly clarified (Simonne and Hutchinson, 2005). Nitrogen (NH4+–N + NO3––N) availability from the total of 18 different CRFs (plus 4 different soluble fertilizers as comparison) mixed in Ellzey fine sand (94% sand), USA was evaluated in a plastic pot through which 400 mL of water was leached every 7 d for 12 times. The N rates applied were 6.18 and 4.80 g pot–1 for 2001 and 2002 trials, respectively (the 2001 rate corresponded to 224 kg N ha–1). While the soluble fertilizers leached 82−98% of applied N after 12 leaching events, leached N from CRFs ranged between 13−38% in 2001 and 22−49% in 2002. Some CRFs may not release 100% of the coated N as the thickness of some prills may permanently prevent N release. The fraction of N never released is termed "locked-up N" (Simonne & Hutchinson, 2005). Nevertheless, all CRFs tested did not release N rapidly enough to supply adequate N to vegetable crops. However, since all CRFs tested except for 2 types had urea as the only N source, most of the N recovered was in the NH4+–N form. The NH4+–N release pattern from

regardless of water table fluctuations and soil water content (Sato et al., 2009b).

observed, as similarly seen with NO3––N (Sato et al., 2009b).

**4.3 Nutrient availability from controlled- and slow-release fertilizers** 

Fig. 5. Cross-sectional diagrams of spatial distribution of (a) NH4 +–N and (b) NO3––N in soil bed during 1, 4, 8, and 18 wk after planting (WAP). The concentrations in the soil bed are assumed to be symmetrical about the centerline on both sides of fertilizer band

Fig. 5. Cross-sectional diagrams of spatial distribution of (a) NH4

bed during 1, 4, 8, and 18 wk after planting (WAP). The concentrations in the soil bed are

assumed to be symmetrical about the centerline on both sides of fertilizer band

+–N and (b) NO3––N in soil

affecting the ion mobility in soil such as soil hydraulic conductivity, water-holding capacity, texture, porosity, and density. Increased diffusivity of NO3 ––N with increased water content in the middle and bottom layers during 5 to 7 WAP would have greatly facilitated both lateral and vertical mobility of NO3––N (Sato et al., 2009a). The key to the stability of moisture and nutrient gradients and the root environment in the gradient-mulch system under seepage irrigation is the constant water table that is often difficult to maintain due to periodic rains and complex management of drainage and irrigation (Geraldson, 1980, 1981).

Most of P remained at C1 and C2 locations in the bed ranging between 150 and 400 mg kg-1 with gradual decrease at the end of the season, while P in the rest of the bed did not change to a great extent maintaining less than 100 mg kg-1 throughout the season. Since it is a common practice under the seepage irrigation system to apply P fertilizer only in the bottom mix to target the root zone, the bottom mix was broadcast on the surface soil before bedding, and the soil was then pushed by discs at the outer edges of the bedding apparatus toward center to form the raised bed. Therefore, most of P was initially placed in C1 and C2 and remained in the root zone. This implies that P did not move outside the root zone regardless of water table fluctuations and soil water content (Sato et al., 2009b).

Most of K in the bed remained in B1 location at an order of magnitude higher concentration (from 4000 up to 12000 mg kg-1) than other bed locations because most of K fertilizer was applied in the top mix. The K concentrations in C1 and C2 locations maintained relatively high concentrations (100 to 180 mg kg-1) until 7 to 8 WAP, then decreased to low values, possibly because of the bottom mix of some of K fertilizer. The K concentrations in the bottom layer were consistently low and did not greatly change throughout the season, except a raised concentration during 7 WAP. Although the substantial amounts of K were present in B1 location at the end of the season, remarkably low K concentrations were found in the rest of the bed after production. This indicates that under gradient-mulch system minimum K leaching occurred during the growing season, except for possible K loss through leaching to some extent during 5 to 7 WAP when water table fluctuation was observed, as similarly seen with NO3––N (Sato et al., 2009b).

#### **4.3 Nutrient availability from controlled- and slow-release fertilizers**

The use of controlled-release fertilizers (CRFs) and slow-release fertilizers (SRFs) for vegetable productions on sandy soils has been investigated with varying results, and nutrient availability on sandy soils from CRFs and SRFs has been increasingly clarified (Simonne and Hutchinson, 2005). Nitrogen (NH4+–N + NO3––N) availability from the total of 18 different CRFs (plus 4 different soluble fertilizers as comparison) mixed in Ellzey fine sand (94% sand), USA was evaluated in a plastic pot through which 400 mL of water was leached every 7 d for 12 times. The N rates applied were 6.18 and 4.80 g pot–1 for 2001 and 2002 trials, respectively (the 2001 rate corresponded to 224 kg N ha–1). While the soluble fertilizers leached 82−98% of applied N after 12 leaching events, leached N from CRFs ranged between 13−38% in 2001 and 22−49% in 2002. Some CRFs may not release 100% of the coated N as the thickness of some prills may permanently prevent N release. The fraction of N never released is termed "locked-up N" (Simonne & Hutchinson, 2005). Nevertheless, all CRFs tested did not release N rapidly enough to supply adequate N to vegetable crops. However, since all CRFs tested except for 2 types had urea as the only N source, most of the N recovered was in the NH4 +–N form. The NH4 +–N release pattern from

Nutrient Mobility and Availability with

Selected Irrigation and Drainage Systems for Vegetable Crops on Sandy Soils 103

applied, but not when 168 and 224 kg N ha–1 were applied (Hutchinson et al., 2003). Other studies showed similar results of higher NUE for N with CRFs compared with soluble fertilizers only with lower N application rate applied, but not with higher rate (140 vs. 280 kg N ha–1; Zvomuya et al., 2003; and 146 vs. 225 kg N ha–1; Pack et al., 2006; both for potato production on sandy soils). Nutrient availability caused by the use of CRFs and SRFs over the soluble fertilizer can be improved by the interaction and competition between plant roots, soil microorganisms, chemical reactions, and pathways for loss, and matching

Nutrient availability from organic waste materials such as composts and biosolids when applied in sandy soils is different from, and more difficult to be clarified as organic materials involve more factors and processes in determining nutrient availability than chemical fertilizers (Mylavarapu & Zinati, 2009). Particularly, N is more complicated than other nutrients because the transformation of compost N varies widely among different sources and is affected by soil properties, compost characteristics, and environmental factors (Sims, 1995; Amlinger et al., 2003). The mineralization of one biosolid and two composts with C/N ratio ranging between 5.8 and 38.0 and organic N between 2.9 and 49.0 g kg–1 was evaluated for one-year period in field-conditioned columns packed with Oldsmar sand,

from the biosolid (lower C/N ratio and higher organic N content) were greater than those from the composts (higher C/N ratio and lower organic N content) during the incubation. While the biosolid reached a peak of the mineralized N within the first 90 d of the incubation, the composts had two distinct peaks at about 90 d and about 280 d of the incubation. The first and second peaks of N mineralization from these materials might be the results of its relatively uniform components made of sewage sludge in both materials and grass clippings and wood chips mixed only in the composts, respectively (He et al., 2000). The mobility of the mineralized N in soil column also differed among these materials. While only small portion of the mineralized NH4+–N (9% of the total NH4+–N for the biosolid) leached out of the column of 20-cm depth, 56% of the mineralized NO3––N in the total NO3––N for the biosolid leached out almost constantly throughout the incubation. On the other hand, the composts had leaching of the mineralized NH4+–N (57–65% of the total NH4+–N for the composts) constantly throughout the incubation, on average 75−85% of the mineralized NO3––N of the total NO3––N for the composts leached out during the second half of the incubation. Application and management of the organic materials when utilized

+–N + NO3––N)

––N leaching on

nutrient release with plant demand (Shaviv & Mikkelsen, 1993).

**4.4 Availability of nutrients applied with organic waste materials** 

USA (He et al., 2000). Both rate and the total amount of mineralized N (NH4

as N fertilization need to be carefully considered to minimize the risk of NO3

ratio such as sewage sludge-derived biosolids.

sandy soils, especially when the materials contain high amounts of materials with low C/N

The biosolids produced by different treatment processes contain varying amounts of mineral and organic N and the organic components may be stabilized to varying degrees. Unstabilized biosolids usually contains high available C, thus high C/N ratio, and may cause a net reduction of mineralized N in amended soils due to immobilization during incubation (Epstein et al., 1978; Parker & Sommers, 1983). The N immobilization in the amended soil may occur when the C/N ratio of the biosolids exceeds 15 (Epstein et al., 1978) or 20 (Parker & Sommers, 1983). Twelve different biosolids produced at 8 different sewage treatment facilities in the UK were incubated for 73 d in a loamy sand soil (86% sand) (Smith

some CRFs was similar to those of the soluble fertilizers and closely desirable for vegetable production.

Many researches have demonstrated that CRFs and SRFs can reduce N, particularly NO3––N leaching on sandy soils compared with soluble fertilizers (Alva, 1992; Wang & Alva, 1996; Paramasivam & Alva, 1997; Fan & Li, 2009). Moreover, not only NO3 ––N but also other nutrients such as P, K, calcium (Ca), magnesium (Mg), and copper (Cu) can CRFs reduce leaching compared with uncoated fertilizers. Four different coated CRFs were compared with an uncoated (soluble) fertilizer on a sandy clay loam of Bungor soil (Typic Paleudult), Malaysia for nutrient leaching in soil columns for 30 d (Hanafi et al., 2002). The percentages of the amount of nutrients leached on the nutrients initially applied ranged 23−33%, 2−4%, 10−19%, 2−9%, 4−9%, and 1−3%, whereas those of the uncoated fertilizer were 80%, 28%, 90%, 29%, 20%, and 6% for N, P, K, Ca, Mg, and Cu, respectively. On the other hand, the distribution of the amount of N, P, and K left in the soil profile after 30 d of leaching differed depending on the type of fertilizers (Table 2). Nitrogen and K left in the soil from the uncoated fertilizer were almost evenly distributed among 0–6, 6–12, and 12–18 cm depths, with almost all P was accumulated only in the top 0–6 cm depth. Almost a half of N and K left in the soil from the coated fertilizers was found only in the top 0–6 cm depth, while up to 90% of P left in the soil was in the top depth with up to 15% found in the 6–12 cm depth. Accumulation of nutrients released from CRFs in upper soil layers appears to enhance the reduction of nutrient leaching from CRFs compared with those from soluble fertilizers.


Table 2. The percentage of the amount of nutrients left at different soil depths after leaching for 30 d on the total amount of nutrients left in the soil profile (0–30 cm). Coated fertilizers show ranges of the percentage of 4 different CRFs tested (made from Hanafi et al., 2002)

Nutrient use efficiency, particularly for N using CRFs or SRFs may be improved compared to that using soluble fertilizers (Shaviv & Mikkelsen, 1993). The NUE for N ranged between 10% and 32% for uncoated fertilizer and between 79% and 94% for coated fertilizers when peanut was grown on a sandy soil (Typic Udipsamment), Japan under drip irrigation with N application rates of 30 to 120 kg ha–1 (Wen et al., 2001). Seepage-irrigated Irish potato produced on Elley fine sand (90–95% sand), USA had a significantly higher NUE for N with CRFs treatment compared with soluble fertilizer treatment only when 112 kg N ha–1 was

some CRFs was similar to those of the soluble fertilizers and closely desirable for vegetable

Many researches have demonstrated that CRFs and SRFs can reduce N, particularly NO3––N leaching on sandy soils compared with soluble fertilizers (Alva, 1992; Wang & Alva, 1996;

nutrients such as P, K, calcium (Ca), magnesium (Mg), and copper (Cu) can CRFs reduce leaching compared with uncoated fertilizers. Four different coated CRFs were compared with an uncoated (soluble) fertilizer on a sandy clay loam of Bungor soil (Typic Paleudult), Malaysia for nutrient leaching in soil columns for 30 d (Hanafi et al., 2002). The percentages of the amount of nutrients leached on the nutrients initially applied ranged 23−33%, 2−4%, 10−19%, 2−9%, 4−9%, and 1−3%, whereas those of the uncoated fertilizer were 80%, 28%, 90%, 29%, 20%, and 6% for N, P, K, Ca, Mg, and Cu, respectively. On the other hand, the distribution of the amount of N, P, and K left in the soil profile after 30 d of leaching differed depending on the type of fertilizers (Table 2). Nitrogen and K left in the soil from the uncoated fertilizer were almost evenly distributed among 0–6, 6–12, and 12–18 cm depths, with almost all P was accumulated only in the top 0–6 cm depth. Almost a half of N and K left in the soil from the coated fertilizers was found only in the top 0–6 cm depth, while up to 90% of P left in the soil was in the top depth with up to 15% found in the 6–12 cm depth. Accumulation of nutrients released from CRFs in upper soil layers appears to enhance the reduction of nutrient leaching from CRFs compared with those from soluble

––N but also other

Paramasivam & Alva, 1997; Fan & Li, 2009). Moreover, not only NO3

Soil depth Uncoated fertilizer Coated fertilizers

cm % %

N P K N P K

0–6 35 97 26 47–49 84–90 43–51 6–12 29 2 24 13–15 8–15 19–29 12–18 29 1 21 13–14 1–2 16–19 18–24 4 0 16 13–16 0–1 5–14 24–30 3 0 13 9–12 0 1–8

mg kg-1 mg kg-1 0–30 732 172 497 2341–2694 235–267 3410–4043 Table 2. The percentage of the amount of nutrients left at different soil depths after leaching for 30 d on the total amount of nutrients left in the soil profile (0–30 cm). Coated fertilizers show ranges of the percentage of 4 different CRFs tested (made from Hanafi et al., 2002)

Nutrient use efficiency, particularly for N using CRFs or SRFs may be improved compared to that using soluble fertilizers (Shaviv & Mikkelsen, 1993). The NUE for N ranged between 10% and 32% for uncoated fertilizer and between 79% and 94% for coated fertilizers when peanut was grown on a sandy soil (Typic Udipsamment), Japan under drip irrigation with N application rates of 30 to 120 kg ha–1 (Wen et al., 2001). Seepage-irrigated Irish potato produced on Elley fine sand (90–95% sand), USA had a significantly higher NUE for N with CRFs treatment compared with soluble fertilizer treatment only when 112 kg N ha–1 was

production.

fertilizers.

applied, but not when 168 and 224 kg N ha–1 were applied (Hutchinson et al., 2003). Other studies showed similar results of higher NUE for N with CRFs compared with soluble fertilizers only with lower N application rate applied, but not with higher rate (140 vs. 280 kg N ha–1; Zvomuya et al., 2003; and 146 vs. 225 kg N ha–1; Pack et al., 2006; both for potato production on sandy soils). Nutrient availability caused by the use of CRFs and SRFs over the soluble fertilizer can be improved by the interaction and competition between plant roots, soil microorganisms, chemical reactions, and pathways for loss, and matching nutrient release with plant demand (Shaviv & Mikkelsen, 1993).

#### **4.4 Availability of nutrients applied with organic waste materials**

Nutrient availability from organic waste materials such as composts and biosolids when applied in sandy soils is different from, and more difficult to be clarified as organic materials involve more factors and processes in determining nutrient availability than chemical fertilizers (Mylavarapu & Zinati, 2009). Particularly, N is more complicated than other nutrients because the transformation of compost N varies widely among different sources and is affected by soil properties, compost characteristics, and environmental factors (Sims, 1995; Amlinger et al., 2003). The mineralization of one biosolid and two composts with C/N ratio ranging between 5.8 and 38.0 and organic N between 2.9 and 49.0 g kg–1 was evaluated for one-year period in field-conditioned columns packed with Oldsmar sand, USA (He et al., 2000). Both rate and the total amount of mineralized N (NH4+–N + NO3––N) from the biosolid (lower C/N ratio and higher organic N content) were greater than those from the composts (higher C/N ratio and lower organic N content) during the incubation. While the biosolid reached a peak of the mineralized N within the first 90 d of the incubation, the composts had two distinct peaks at about 90 d and about 280 d of the incubation. The first and second peaks of N mineralization from these materials might be the results of its relatively uniform components made of sewage sludge in both materials and grass clippings and wood chips mixed only in the composts, respectively (He et al., 2000). The mobility of the mineralized N in soil column also differed among these materials. While only small portion of the mineralized NH4 +–N (9% of the total NH4+–N for the biosolid) leached out of the column of 20-cm depth, 56% of the mineralized NO3––N in the total NO3––N for the biosolid leached out almost constantly throughout the incubation. On the other hand, the composts had leaching of the mineralized NH4 +–N (57–65% of the total NH4+–N for the composts) constantly throughout the incubation, on average 75−85% of the mineralized NO3––N of the total NO3––N for the composts leached out during the second half of the incubation. Application and management of the organic materials when utilized as N fertilization need to be carefully considered to minimize the risk of NO3 ––N leaching on sandy soils, especially when the materials contain high amounts of materials with low C/N ratio such as sewage sludge-derived biosolids.

The biosolids produced by different treatment processes contain varying amounts of mineral and organic N and the organic components may be stabilized to varying degrees. Unstabilized biosolids usually contains high available C, thus high C/N ratio, and may cause a net reduction of mineralized N in amended soils due to immobilization during incubation (Epstein et al., 1978; Parker & Sommers, 1983). The N immobilization in the amended soil may occur when the C/N ratio of the biosolids exceeds 15 (Epstein et al., 1978) or 20 (Parker & Sommers, 1983). Twelve different biosolids produced at 8 different sewage treatment facilities in the UK were incubated for 73 d in a loamy sand soil (86% sand) (Smith

Nutrient Mobility and Availability with

**5. Conclusion** 

**6. Acknowledgement** 

Selected Irrigation and Drainage Systems for Vegetable Crops on Sandy Soils 105

the biosolids releasing OM-bound P as surplus to leachable P from the biosolids. However, the surplus P can be adsorbed on surfaces of Fe and Al oxides/hydroxides or immobilized to microbial biomass by increased microorganisms due to freshly added organic C from the biosolids, eventually reducing P leaching. More water-soluble P was incorporated into microbial biomass and organic fractions as evidenced by the increased microbial biomass P and microbial biomass carbon in the biosolids-amended soils (Yang et al., 2008). Nevertheless, mineralization of OM in sandy soils is generally fast particularly in humid climate conditions (Kang et al., 2011), therefore organic P including microbial biomass P can

Sandy-textured soils generally have low water- and nutrient-holding capacities, which, coupled with different irrigation systems used on sandy soils, make nutrient and irrigation managements difficult for suitable vegetable crop production. The nutrient and irrigation managements can be different and may be further complicated when impermeable layers such as argillic and spodic layers are excavated and mixed in as a result of the bedding process. Sandy soils, however, can be utilized for maximized crop production if proper managements for nutrients, irrigation, and drainage systems are implemented. The "gradient-mulch" system under seepage irrigation developed in 1960's in Florida, USA has become the dominant system to provide a controlled environment within the bedded soil for sufficient nutrient supply, optimum soil moisture content, stable root growth, and managements for weed, disease, and insect. More importantly, nowadays, the gradientmulch system has been proven to minimize environmental losses of nutrients, particularly NO3––N and P below the root zone. Maintaining constant water table levels under seepage irrigation is, however, the most crucial factor for the gradient-mulch system for providing the maximized crop yields and minimized environmental losses of nutrients. In the light of the environmental concerns, CRFs and SFRs have been spotlighted for improved NUE, particularly for N for crop production under sandy soils. However, the effect of application of CRFs on vegetable crop production still need to be clearly understood in order for growers to receive full benefits from the use of these materials. Nutrient availability from organic waste materials such as composts and biosolids when applied in sandy soils is complex as the organic materials involve many factors and processes in determining nutrient availability. Particularly, the understanding on mineralization patterns of the organic materials with different properties under different soil and water managements is critical in determining the nutrient availability in soil and environmental fate of nutrients. Sandy soils can provide proper environmental conditions for appropriate vegetable crop production with suitable nutrient and irrigation management systems, therefore more studies are needed to elucidate the effect of different aspects of the production system in order for the producers to continue vegetable production without environmental damages, particularly from NO3––N which can be the most prone nutrient for leaching in sandy soils.

Parts of the studies in this article was funded by Florida Department of Agriculture and Consumer Services (No. 12280) and institutionally made possible by Southwest Florida

be considered to be available or potentially available to crop uptake.

et al., 1998). All biosolids showed a concomitant reduction in NH4 +–N concentration with the formation of NO3––N in the amended soil with increasing time of incubation. Indeed, all except dewatered undigested biosolids exhibited an initial rapid NO3 ––N accumulation followed by a slower release reaching maximum amounts of NO3 ––N production. In contrast, the dewatered undigested biosolids, which contained higher amounts of OM among the biosolids tested, showed a significant immobilization of mineral N in the amended soil during the initial stages of incubation. Based on the N mineralization patterns in incubated soil, 4 categories of the biosolids were proposed (Smith et al., 1998). They are: category 1 – liquid digested and lagooned liquid undigested biosolids that have the greatest NO3––N accumulation potential due to large content of NH4+–N; category 2 – liquid undigested, lagooned liquid digested, and dewatered digested biosolids have low to intermediate NO3––N accumulation potential; category 3 – dewatered undigested biosolids are high in available C and may produce an initial net N immobilization followed by NO3 –– N accumulation after soil microbes has metabolized the added substrate C; and category 4 – air-dried digested biosolids are relatively resistant to mineralization and NO3 ––N formation in soil. The C/N ratio of the biosolids of category 1 to 4 is generally ordered from the lowest to the highest.

When the biosolids and manure are applied to soil based on crop N requirements, P in excess of crop needs is usually supplied due to high P content in the organic wastes. Environmental loss of P by surface runoff or leaching from organic wastes application can be significant in areas with shallow groundwater and coarse-textured soils of low P-holding capacities (Eghball et al., 1996; Lu & O'Connor, 2001). Leachability of eight different biosolids was compared with that of a chemical fertilizer (triple superphosphate, TSP) in a column study for 4 months on Candler and Immokalee sandy soils, USA with the application rates of 56 and 224 kg ha–1, corresponding to typical application rates based on P-based and N-based fertility, respectively (Elliott et al., 2002). Candler and Immokalee soils had moderate and very low P-sorbing capacities, respectively, as indicated by the sum of oxalate-extractable Fe and Al. On Candler sand, the percentage of applied P leached ranged between 1.7% and 21.7% in the TSP treatment, and 0.05% and 0.45% in the biosolids treatment among two application rates. The percentage on Immokalee sand increased ranging between 13.6% and 20.7% from TSP, and 0.05% and 11.1% from the biosolids regardless of the application rates. It appears that the leachability of P from the biosolids is lower than that from the chemical fertilizer and considered as minor or negligible in many soils (Peterson et al., 1994; Sui et al., 1999).

However, the P leachability of the biosolids depends on the P-sorbing capacity of the soil; soils with lower sorbing capacity are more susceptible to P leaching. The extent of the biosolids-P leachability also appears to be explained by the P saturation index (PSI) of the biosolids, calculated as the ratio of oxalate-extractable P to the sum of oxalate-extractable Fe and Al (Jaber et al., 2006). The PSI is a measure of the degree to which biosolids P is potentially bound with Fe and Al. Therefore, PSI values < 1 suggest excess Fe and Al for binding P, and values > 1 suggest available P beyond that associated with Fe and Al precipitates. For the biosolids tested in Elliotte et al. (2002), no appreciable P leaching occurred from soils amended with biosolids of PSI < 1.1, and Immokalee soil amended with biosolids of PSI > 1.3 exhibited substantial P leaching. The microbiological processes in the soil also play an important role in reducing P leaching from the biosolids in sandy soils (Yang et al., 2008). Application of the biosolids can result in the mineralization of OM from the biosolids releasing OM-bound P as surplus to leachable P from the biosolids. However, the surplus P can be adsorbed on surfaces of Fe and Al oxides/hydroxides or immobilized to microbial biomass by increased microorganisms due to freshly added organic C from the biosolids, eventually reducing P leaching. More water-soluble P was incorporated into microbial biomass and organic fractions as evidenced by the increased microbial biomass P and microbial biomass carbon in the biosolids-amended soils (Yang et al., 2008). Nevertheless, mineralization of OM in sandy soils is generally fast particularly in humid climate conditions (Kang et al., 2011), therefore organic P including microbial biomass P can be considered to be available or potentially available to crop uptake.

## **5. Conclusion**

104 Soil Health and Land Use Management

except dewatered undigested biosolids exhibited an initial rapid NO3––N accumulation followed by a slower release reaching maximum amounts of NO3––N production. In contrast, the dewatered undigested biosolids, which contained higher amounts of OM among the biosolids tested, showed a significant immobilization of mineral N in the amended soil during the initial stages of incubation. Based on the N mineralization patterns in incubated soil, 4 categories of the biosolids were proposed (Smith et al., 1998). They are: category 1 – liquid digested and lagooned liquid undigested biosolids that have the greatest NO3––N accumulation potential due to large content of NH4+–N; category 2 – liquid undigested, lagooned liquid digested, and dewatered digested biosolids have low to intermediate NO3––N accumulation potential; category 3 – dewatered undigested biosolids are high in available C and may produce an initial net N immobilization followed by NO3

N accumulation after soil microbes has metabolized the added substrate C; and category 4 – air-dried digested biosolids are relatively resistant to mineralization and NO3––N formation in soil. The C/N ratio of the biosolids of category 1 to 4 is generally ordered from the lowest

When the biosolids and manure are applied to soil based on crop N requirements, P in excess of crop needs is usually supplied due to high P content in the organic wastes. Environmental loss of P by surface runoff or leaching from organic wastes application can be significant in areas with shallow groundwater and coarse-textured soils of low P-holding capacities (Eghball et al., 1996; Lu & O'Connor, 2001). Leachability of eight different biosolids was compared with that of a chemical fertilizer (triple superphosphate, TSP) in a column study for 4 months on Candler and Immokalee sandy soils, USA with the application rates of 56 and 224 kg ha–1, corresponding to typical application rates based on P-based and N-based fertility, respectively (Elliott et al., 2002). Candler and Immokalee soils had moderate and very low P-sorbing capacities, respectively, as indicated by the sum of oxalate-extractable Fe and Al. On Candler sand, the percentage of applied P leached ranged between 1.7% and 21.7% in the TSP treatment, and 0.05% and 0.45% in the biosolids treatment among two application rates. The percentage on Immokalee sand increased ranging between 13.6% and 20.7% from TSP, and 0.05% and 11.1% from the biosolids regardless of the application rates. It appears that the leachability of P from the biosolids is lower than that from the chemical fertilizer and considered as minor or negligible in many

However, the P leachability of the biosolids depends on the P-sorbing capacity of the soil; soils with lower sorbing capacity are more susceptible to P leaching. The extent of the biosolids-P leachability also appears to be explained by the P saturation index (PSI) of the biosolids, calculated as the ratio of oxalate-extractable P to the sum of oxalate-extractable Fe and Al (Jaber et al., 2006). The PSI is a measure of the degree to which biosolids P is potentially bound with Fe and Al. Therefore, PSI values < 1 suggest excess Fe and Al for binding P, and values > 1 suggest available P beyond that associated with Fe and Al precipitates. For the biosolids tested in Elliotte et al. (2002), no appreciable P leaching occurred from soils amended with biosolids of PSI < 1.1, and Immokalee soil amended with biosolids of PSI > 1.3 exhibited substantial P leaching. The microbiological processes in the soil also play an important role in reducing P leaching from the biosolids in sandy soils (Yang et al., 2008). Application of the biosolids can result in the mineralization of OM from

––N in the amended soil with increasing time of incubation. Indeed, all

+–N concentration with

––

et al., 1998). All biosolids showed a concomitant reduction in NH4

the formation of NO3

to the highest.

soils (Peterson et al., 1994; Sui et al., 1999).

Sandy-textured soils generally have low water- and nutrient-holding capacities, which, coupled with different irrigation systems used on sandy soils, make nutrient and irrigation managements difficult for suitable vegetable crop production. The nutrient and irrigation managements can be different and may be further complicated when impermeable layers such as argillic and spodic layers are excavated and mixed in as a result of the bedding process. Sandy soils, however, can be utilized for maximized crop production if proper managements for nutrients, irrigation, and drainage systems are implemented. The "gradient-mulch" system under seepage irrigation developed in 1960's in Florida, USA has become the dominant system to provide a controlled environment within the bedded soil for sufficient nutrient supply, optimum soil moisture content, stable root growth, and managements for weed, disease, and insect. More importantly, nowadays, the gradientmulch system has been proven to minimize environmental losses of nutrients, particularly NO3––N and P below the root zone. Maintaining constant water table levels under seepage irrigation is, however, the most crucial factor for the gradient-mulch system for providing the maximized crop yields and minimized environmental losses of nutrients. In the light of the environmental concerns, CRFs and SFRs have been spotlighted for improved NUE, particularly for N for crop production under sandy soils. However, the effect of application of CRFs on vegetable crop production still need to be clearly understood in order for growers to receive full benefits from the use of these materials. Nutrient availability from organic waste materials such as composts and biosolids when applied in sandy soils is complex as the organic materials involve many factors and processes in determining nutrient availability. Particularly, the understanding on mineralization patterns of the organic materials with different properties under different soil and water managements is critical in determining the nutrient availability in soil and environmental fate of nutrients. Sandy soils can provide proper environmental conditions for appropriate vegetable crop production with suitable nutrient and irrigation management systems, therefore more studies are needed to elucidate the effect of different aspects of the production system in order for the producers to continue vegetable production without environmental damages, particularly from NO3––N which can be the most prone nutrient for leaching in sandy soils.

## **6. Acknowledgement**

Parts of the studies in this article was funded by Florida Department of Agriculture and Consumer Services (No. 12280) and institutionally made possible by Southwest Florida

Nutrient Mobility and Availability with

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Research and Education Center, University of Florida, USA. The authors thank a farmer in southwest Florida for tomato fields for the experiment, and Ms. Jin Wu for a diagram drawing of tomato bed.

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430.


**7** 

*1Ethiopia 2Madagascar* 

**Forest Preservation, Flooding and Soil Fertility:** 

In several developing countries, forest preservation programs have been put in place with an economic justification based on the local ecological services that they provide (Pagiola et al., 2002). It is argued that the presence of forests preserve the hydrological balance; reduce soil erosion due to increased soil stability; reduce flooding and regulate flows (Perrot-Maitre and Davis, 2001; Johnson et al., 2002; Pattanayak and Kramer, 2001a,b). However, other authors dispute the domestic benefits of forests and state that natural scientists often overvalue forests (Chomitz and Kumari, 1998; Aylward and Echeverria, 2001; Calder, 1999). Assuming that an externality costs of deforestation exists, policy makers have started to look at how to correct for this and how a workable system can be put in place to pay for ecological services locally. Increasing attention is going towards the direct payment for environmental services (Ferraro and Simpson, 2002; Durbin, 2002;

We look at this issue in a case study in Madagascar. Multiple studies have shown the high and accelerating deforestation rate in Madagascar (McConnell, 2002). Causes of deforestation are multiple and have been linked to poverty (Zeller et al., 2000), conversion of forest land to pastures (McConnel, 2002), use of wood for charcoal (Casse et al., 2004), wood exports or household fuel consumption (Minten and Moser, 2003), slash-and burn agriculture (Barrett, 1999; Keck et al., 1994; FOFIFA, 2001; Casse et al., 2004; Terretany, 1997), rural insecurity (Minten and Moser, 2003), and land tenure problems (Freudenberger, 1999). While deforestation threatens the unique eco-system of Madagascar, it has also been linked to higher incidences of flooding and greater soil erosion and damages therefore the agricultural resource base domestically (Freudenberger, 1999, Kramer et al., 1997). Overall, it is estimated that the damage of soil erosion in Madagascar is high (Kramer et al., 1997; World Bank, 2005) although the numbers that have been suggested might have been

In this analysis, we study the potential domestic benefits of forests on lowland agriculture. While we do not try to establish explicit linkages between deforestation and sedimentation off-site, we do look at the effects of flooding and sedimentation downstream as perceived by rice farmers. The analysis is based on a small-scale survey in Northern Madagascar where we try to monetize the cost to farmers of flooding and sedimentation on their rice fields

**1. Introduction** 

Pagiola et al., 2002).

exaggerated (see f.ex., Larson, 1994).

**Evidence from Madagascar** 

Bart Minten1 and Claude Randrianarisoa2

*1 International Food Policy Research Institute, Addis Ababa, 2United States Agency for International Development (USAID),* 

Zvomuya, F.; Rosen, C.J.; Russelle, M.P. & Gupta, S.C. (2003). Nitrate leaching and nitrogen recovery following application of polyolefin-coated urea to potato, *Journal of Environmental Quality*, Vol.32, pp.480-489.

Bart Minten1 and Claude Randrianarisoa2 *1 International Food Policy Research Institute, Addis Ababa,* 

*2United States Agency for International Development (USAID), 1Ethiopia 2Madagascar* 

## **1. Introduction**

110 Soil Health and Land Use Management

Zvomuya, F.; Rosen, C.J.; Russelle, M.P. & Gupta, S.C. (2003). Nitrate leaching and nitrogen

*Environmental Quality*, Vol.32, pp.480-489.

recovery following application of polyolefin-coated urea to potato, *Journal of* 

In several developing countries, forest preservation programs have been put in place with an economic justification based on the local ecological services that they provide (Pagiola et al., 2002). It is argued that the presence of forests preserve the hydrological balance; reduce soil erosion due to increased soil stability; reduce flooding and regulate flows (Perrot-Maitre and Davis, 2001; Johnson et al., 2002; Pattanayak and Kramer, 2001a,b). However, other authors dispute the domestic benefits of forests and state that natural scientists often overvalue forests (Chomitz and Kumari, 1998; Aylward and Echeverria, 2001; Calder, 1999). Assuming that an externality costs of deforestation exists, policy makers have started to look at how to correct for this and how a workable system can be put in place to pay for ecological services locally. Increasing attention is going towards the direct payment for environmental services (Ferraro and Simpson, 2002; Durbin, 2002; Pagiola et al., 2002).

We look at this issue in a case study in Madagascar. Multiple studies have shown the high and accelerating deforestation rate in Madagascar (McConnell, 2002). Causes of deforestation are multiple and have been linked to poverty (Zeller et al., 2000), conversion of forest land to pastures (McConnel, 2002), use of wood for charcoal (Casse et al., 2004), wood exports or household fuel consumption (Minten and Moser, 2003), slash-and burn agriculture (Barrett, 1999; Keck et al., 1994; FOFIFA, 2001; Casse et al., 2004; Terretany, 1997), rural insecurity (Minten and Moser, 2003), and land tenure problems (Freudenberger, 1999). While deforestation threatens the unique eco-system of Madagascar, it has also been linked to higher incidences of flooding and greater soil erosion and damages therefore the agricultural resource base domestically (Freudenberger, 1999, Kramer et al., 1997). Overall, it is estimated that the damage of soil erosion in Madagascar is high (Kramer et al., 1997; World Bank, 2005) although the numbers that have been suggested might have been exaggerated (see f.ex., Larson, 1994).

In this analysis, we study the potential domestic benefits of forests on lowland agriculture. While we do not try to establish explicit linkages between deforestation and sedimentation off-site, we do look at the effects of flooding and sedimentation downstream as perceived by rice farmers. The analysis is based on a small-scale survey in Northern Madagascar where we try to monetize the cost to farmers of flooding and sedimentation on their rice fields

where Xj is a vector of socio-economic characteristics for household j and ηj is an error term. Such a model can be further refined to allow for dynamic behavior (Holden and Shiferaw, 2002). If we let W2 represent the subjective present value of future land productivity gains by switching from no interventions to conservation efforts in the uplands, the following equation holds in the case of the maximization of an expected intertemporal utility

0 - Wj) = ∑ t=1<sup>∞</sup>(1+δj) -t EU<sup>j</sup>

t(C1jt – C0jt) is the utility gain in time t when switching from no interventions to conservation efforts in the uplands. Nonseparability in a dynamic context implies that intertemporal markets do not work well and that W would then vary over time with household discount rates that can be very high for poor liquidity constrained households.3 W can then be specified as a random variable which is a continuous function of observational variables that appear in the expenditure function such as farm, technology

Wj = Zjβ +μ<sup>j</sup>

where μj ~ (0, σ2) where Z is a vector of explanatory variables, μi is the error term and σ is the standard

An agricultural household survey was organized in November 2001 in an area northwest of Maroantsetra, in the northeast of Madagascar. The area was selected on the basis of the high diversity in watershed forms and areas and the perceived clear link between upstream activities and lowland impacts. First, a census of all the watersheds was done. In total, 65 watersheds were identified. Due to logistical reasons, only 52 watersheds were sampled. In each watershed, a stratified sample of rice plots was done. Rice plots were stratified based on the distance to the main river. In each watershed, around six fields were sampled, depending on the size of the watershed. In total, data on 268 rice farmers were obtained. The questionnaire that was implemented consisted of four parts. The first part dealt with plot characteristics (including a land valuation question), the second with questions on the rice harvest of last year on that plot, and the third on the overall structure of the agricultural firm. The final part described a willingness to pay scenario where households were asked to

Instead of the widely used and recommended dichotomous choice valuation question (Arrow et al., 1993), a stochastic payment card method (Wang and Whittington, 2005) was implemented for different reasons: (1) Given logistical constraints, a relatively small sample had to be relied upon. The payment card format gives the benefit of having extra information beyond the yes/no question (For papers that discuss the benefits of information

3 Dasgupta (1993) has demonstrated this theoretically and Pender (1996) and Holden, Shifraw and Wik

value their desire to avoid flooding and sedimentation in their rice fields.

t

(C1jt – C0j<sup>t</sup>

t is the expected utility for individual j in time t,

)

function:

and U<sup>j</sup>

deviation.

**3. Methodology and data** 

(1998) provide good empirical evidence on this.

Uj 0 (C<sup>j</sup>

Where δj is individual j time preference, EU<sup>j</sup>

0) - U<sup>j</sup>

and socio-economic characteristics. W can thus be written as

0 (C<sup>j</sup>

downstream.1 If the link between forest cover and flooding would exist in this area and if the link is strong, a positive willingness to pay might then justify investments in conservation measures upstream.

We contribute to the literature in two ways. First, we show that an important percentage of rice farmers benefit from flooding and sedimentation (as shown in higher land values after sedimentation and refusal for contribution towards conservation) and that current economic returns to investment in forest preservation, largely beneficial because of averted rice productivity declines, might thus be overestimated.2 Second, in the rural scarcely monetized settings of developing countries where land transactions are rare, we develop an alternative to the hedonic price analysis of land values using willingness-to-accept scenario's explicitly allowing for uncertainty.

The structure of the paper is as follows. First, we discuss the conceptual framework. Second, the methodology, data sources and the structure of the survey are presented. Third, we look at descriptive statistics describing households as well as sedimentation and flooding incidence. Fourth, the determinants of land values, incorporating the impact of sedimentation, and the results of a willingness to pay question to avoid flooding and sedimentation are discussed. We finish with the conclusions.

#### **2. Conceptual framework**

Assume an expenditure minimization problem where expenditures are minimized subject to the constraint that utility equal or exceed some stated level, U0. The solution to this minimization problem is the restricted expenditure function

$$\mathbf{e} = \mathbf{e}(\mathbf{p}^0, \mathbf{T}^0, \mathbf{U}^0, \mathbf{e}^0)$$

where p0 can be thought of as a vector of prices, T0 is land availability to the household and ε0 represents uncertain factors not reflected in p0, T0 and U0.

In a first offer, the household is asked to sell land for a total payment of P1. In a second offer, the household is asked to pay for conservation for a total payment of P2. The change from T0 to Ti in either of the two scenarios will result in a new expenditure function with a new set of prices and environmental and resource flows, i.e. e = e(p1, T1, U0, ε1) in the first scenario and e = e(p2, T2, U0, ε2) in the second scenario. It seems reasonable if you take away land or income, and given imperfect markets, that the shadow prices and wages are likely to change, i.e. we do not assume the price vector to be independent in the two scenarios.

In such a set-up, the welfare change - the Hicksian compensating surplus - is defined as the difference between the two expenditure functions,

$$\left(\mathbf{e}(\mathbf{p}^{i}, \mathbf{T}^{i}, \mathbf{U}^{0}, \varepsilon^{i}) \cdot \mathbf{e}(\mathbf{p}^{0}, \mathbf{T}^{0}, \mathbf{U}^{0}, \varepsilon^{0})\right)$$

where i is 1 (scenario 1) or 2 (scenario 2). The value of the welfare change is established by using contingent valuation measures and the Willingness to Accept/Pay (W) at the farm household level might be represented by Wj for household j

$$\mathbf{W}\_{\mathbf{j}} = \mathbf{e}(\mathbf{p}^{\mathbf{i}}, \mathbf{T}\_{\mathbf{j}}^{\mathbf{i}}, \mathbf{U}\_{\mathbf{j}}^{0}, \varepsilon\_{\mathbf{j}}^{\mathbf{i}}, \mathbf{X}\_{\mathbf{j}}) - \mathbf{e}(\mathbf{p}^{0}, \mathbf{T}\_{\mathbf{j}}^{0}, \mathbf{U}\_{\mathbf{j}}^{0}, \mathbf{e}\_{\mathbf{j}}^{0}, \mathbf{X}\_{\mathbf{j}}) + \mathbf{n}\_{\mathbf{j}}$$

<sup>1</sup> While there is some rice cultivation on upland, the majority happens in the lowlands.

<sup>2</sup> For example, the World Bank (2005) estimates in its economic calculations that most of the benefits of the national environmental program (EP3) are obtained from avoiding productivity losses on rice fields.

where Xj is a vector of socio-economic characteristics for household j and ηj is an error term. Such a model can be further refined to allow for dynamic behavior (Holden and Shiferaw, 2002). If we let W2 represent the subjective present value of future land productivity gains by switching from no interventions to conservation efforts in the uplands, the following equation holds in the case of the maximization of an expected intertemporal utility function:

$$\mathbf{U}\_{\mathbf{j}}^{\mathrm{o}} \left( \mathbf{C}\_{\mathbf{j}}^{\mathrm{o}} \right) \cdot \mathbf{U}\_{\mathbf{j}}^{\mathrm{o}} \left( \mathbf{C}\_{\mathbf{j}}^{\mathrm{o}} \cdot \mathbf{W}\_{\mathbf{j}} \right) = \sum\_{\mathbf{t} = \mathbf{l}} {}^{\mathrm{e}} (\mathbf{1} + \mathbf{\mathcal{S}}\_{\mathbf{j}}) \cdot {}^{\mathrm{t}} \operatorname{ELU}\_{\mathbf{j}} \left( \mathbf{C}\_{\mathbf{l}\mathbf{j}}^{\mathrm{t}} - \mathbf{C}\_{\mathbf{0}\mathbf{\dot{j}}} \mathbf{t} \right)$$

Where δj is individual j time preference, EU<sup>j</sup> t is the expected utility for individual j in time t, and U<sup>j</sup> t(C1jt – C0jt) is the utility gain in time t when switching from no interventions to conservation efforts in the uplands. Nonseparability in a dynamic context implies that intertemporal markets do not work well and that W would then vary over time with household discount rates that can be very high for poor liquidity constrained households.3 W can then be specified as a random variable which is a continuous function of observational variables that appear in the expenditure function such as farm, technology and socio-economic characteristics. W can thus be written as

$$\mathbf{W}\_{\mathbf{j}} = \mathbf{Z}\_{\mathbf{j}} \mathbf{B} + \mathbf{\mu}\_{\mathbf{j}}$$

$$\text{where } \mathfrak{p}\_{\mathfrak{j}} \sim (0, \sigma^2)$$

where Z is a vector of explanatory variables, μi is the error term and σ is the standard deviation.

#### **3. Methodology and data**

112 Soil Health and Land Use Management

downstream.1 If the link between forest cover and flooding would exist in this area and if the link is strong, a positive willingness to pay might then justify investments in

We contribute to the literature in two ways. First, we show that an important percentage of rice farmers benefit from flooding and sedimentation (as shown in higher land values after sedimentation and refusal for contribution towards conservation) and that current economic returns to investment in forest preservation, largely beneficial because of averted rice productivity declines, might thus be overestimated.2 Second, in the rural scarcely monetized settings of developing countries where land transactions are rare, we develop an alternative to the hedonic price analysis of land values using willingness-to-accept scenario's explicitly

The structure of the paper is as follows. First, we discuss the conceptual framework. Second, the methodology, data sources and the structure of the survey are presented. Third, we look at descriptive statistics describing households as well as sedimentation and flooding incidence. Fourth, the determinants of land values, incorporating the impact of sedimentation, and the results of a willingness to pay question to avoid flooding and

Assume an expenditure minimization problem where expenditures are minimized subject to the constraint that utility equal or exceed some stated level, U0. The solution to this

e = e(p0, T0, U0, ε0) where p0 can be thought of as a vector of prices, T0 is land availability to the household and

In a first offer, the household is asked to sell land for a total payment of P1. In a second offer, the household is asked to pay for conservation for a total payment of P2. The change from T0

where i is 1 (scenario 1) or 2 (scenario 2). The value of the welfare change is established by using contingent valuation measures and the Willingness to Accept/Pay (W) at the farm

, Xj) - e(p0, T<sup>j</sup>

2 For example, the World Bank (2005) estimates in its economic calculations that most of the benefits of the national environmental program (EP3) are obtained from avoiding productivity losses on rice fields.

 in either of the two scenarios will result in a new expenditure function with a new set of prices and environmental and resource flows, i.e. e = e(p1, T1, U0, ε1) in the first scenario and e = e(p2, T2, U0, ε2) in the second scenario. It seems reasonable if you take away land or income, and given imperfect markets, that the shadow prices and wages are likely to change, i.e. we do not assume the price vector to be independent in the two scenarios. In such a set-up, the welfare change - the Hicksian compensating surplus - is defined as the

) - e(p0, T0, U0, ε0)

0, U<sup>j</sup> 0, ε<sup>j</sup>

0, Xj) + η<sup>j</sup>

sedimentation are discussed. We finish with the conclusions.

minimization problem is the restricted expenditure function

ε0 represents uncertain factors not reflected in p0, T0 and U0.

difference between the two expenditure functions,

e(pi , Ti , U0, ε<sup>i</sup>

household level might be represented by Wj for household j

, T<sup>j</sup> i , U<sup>j</sup> 0, ε<sup>j</sup> i

1 While there is some rice cultivation on upland, the majority happens in the lowlands.

Wj = e(pi

conservation measures upstream.

allowing for uncertainty.

**2. Conceptual framework** 

to Ti

An agricultural household survey was organized in November 2001 in an area northwest of Maroantsetra, in the northeast of Madagascar. The area was selected on the basis of the high diversity in watershed forms and areas and the perceived clear link between upstream activities and lowland impacts. First, a census of all the watersheds was done. In total, 65 watersheds were identified. Due to logistical reasons, only 52 watersheds were sampled. In each watershed, a stratified sample of rice plots was done. Rice plots were stratified based on the distance to the main river. In each watershed, around six fields were sampled, depending on the size of the watershed. In total, data on 268 rice farmers were obtained. The questionnaire that was implemented consisted of four parts. The first part dealt with plot characteristics (including a land valuation question), the second with questions on the rice harvest of last year on that plot, and the third on the overall structure of the agricultural firm. The final part described a willingness to pay scenario where households were asked to value their desire to avoid flooding and sedimentation in their rice fields.

Instead of the widely used and recommended dichotomous choice valuation question (Arrow et al., 1993), a stochastic payment card method (Wang and Whittington, 2005) was implemented for different reasons: (1) Given logistical constraints, a relatively small sample had to be relied upon. The payment card format gives the benefit of having extra information beyond the yes/no question (For papers that discuss the benefits of information

<sup>3</sup> Dasgupta (1993) has demonstrated this theoretically and Pender (1996) and Holden, Shifraw and Wik (1998) provide good empirical evidence on this.

While non-responses were not a problem in the plot valuation question, about one third of the respondents did not answer the willingness to pay question to avoid flooding or sedimentation. The characteristics of the respondents that refused to answer are not randomly distributed and might therefore cause inconsistency and inefficiency in the estimation of the coefficients in the regression of the willingness to pay question. A common method to control for non-responses to the willingness to pay question is to estimate a sample selection model (Messonier et al., 2000; Mekkonen, 2001), usually referred to as the

Y\*=β'X +ε

Y=0 if Y\*≤0

and Y=Y\* otherwise

Z=α'V + μ

Z=1 if Z\*>0

and Z=0 if Z\*≤0 where Y is willingness to pay (censored at 0); X is a vector of explanatory exogenous variables that explain Y; Z is 1 when there is a valid response and 0 otherwise; V is vector of explanatory exogenous that influence the probability of giving a valid response; α and β are

The Maroantsetra area in the Northeast of Madagascar is a humid area characterized by two types of agriculture: slash-and-burn cultivation ("tavy") on the hillsides and lowland rice cultivation. The area is isolated from the rest of Madagascar and is highly dependent on agriculture for income. The region is also still highly forested and is one of the largely untouched areas in Madagascar. Table 1 shows the basic descriptive statistics of the households in the survey. The head of households have a low average level of education, i.e. only three years. 10% of the households are female headed and these are mostly poorer households (Razafindravonona et al., 2000). The average size of the household is six members. Almost all the households are natives from the region and all the households

An average household in the sample possesses 62 ares4 of lowland and 73 ares of upland. As in most of Madagascar, the main staple is rice. The average production is just below 1 ton which is estimated to be sufficient for subsistence by almost 70% of the population. However, most households - even some that declare to be self-sufficient in rice - reduce overall consumption during the lean period. The average length of this lean period is estimated to be three months. A household possesses on average 2 zebus. Total annual monetary household income is estimated at 2.7 M Fmg5, i.e. around 415\$US, i.e. low but

parameters to be estimated; ε and μ are disturbances; Y\* and Z\* are latent variables.

**4. Descriptive statistics** 

4 1 are = 0.01 ha

report to depend on agriculture for their livelihood.

5 Malagasy Franc; 1 USD\$ = 6500 Fmg at the time of the survey

Heckman two stage approach (Heckman, 1997). In this case, we estimate:

beyond dichomotous choices, see Blamey et al. (1999) and Ready et al. (2001)). (2) Whittington (1998) and Wang and Whittington (2005) show that a main problem in contingent valuation studies is that the range that is offered is often not large enough to allow for a robust estimation of the valuation function. Moreover, as we had little a priori knowledge about the valuation function, we had to make sure that extreme levels were included in the bids on the payment card. Given the small sample, this could not have been achieved in the dichotomous choice variable format. (3) Uncertainty (for example on the future price evolution of agricultural products) and imperfect information (household chief had to answer immediately during the interview and could not consult with family members and/or village leaders) is allowed for in this format. Wang (1997), Wang and Whittington (2005) and Alberini et al. (2003) show the benefits of the explicit modeling of uncertain responses in contingent valuation data.

Two valuation questions were asked. The valuation questions were set up in such a way to reduce as much as possible the problem with starting point bias and with yea-saying: therefore, it started with an open-ended question (no starting point bais) followed by a payment card (additional information). In the case of the land valuation, a willingness to accept scenario was described where a certain monetary payment was given in exchange for the plot studied. As previous surveys in Madagascar had shown the reluctance of farmers to give a sales price for land - they would often report they would be unwilling to sell the plot whatever happened - it was made clear from the beginning that this was a hypothetical situation where we like to know their approximate financial value of the plot in their farming enterprise. The respondent was presented with a payment card in local currency but with references to values of local rice units, bikes, and value of livestock. On this payment card, the enumerator proceeded to fill in for every amount that was mentioned a code corresponding to 1. Accept to pay for sure; 2. A little bit in doubt but would say yes; 3. Not yes or no, do not know; 4. A little bit in doubt but would say no; 5. Will not pay for sure.

In the case of the question on willingness to pay for reduction in flooding and sedimentation, the valuation scenario was constructed as follows. Respondents were first asked if they thought if flooding and sediments had a negative, neutral or positive influence on rice productivity, in general and on the specific plot that was studied. A scenario was then described in the following way:

*" Suppose that we leave the situation as it is and we leave damage as it is without any intervention to limit deposits on this rice field or to reduce the frequency of flooding on this field. In a second situation, actions will be undertaken in the watershed upstream of your fields. In this case, you will not suffer anymore from problems of flooding and sediments. However, you know that these actions will cost money. We would like to know how much you would be willing to pay for these actions, taking into account your possibilities. If you do not pay as much than what you would be really able to pay, actions will not be sufficient to reduce flooding and sedimentation. On the other hand, if you give a level that is higher than you can afford, functional interventions can not be agreed upon. How much would you be willing to pay? x sobika of rice?"* 

The question was formulated in local units of rice as this measure was easily recognizable by farmers. To finish the valuation section, a question was asked to the farmers on where they would get the rice from for the amount that they were willing to contribute. It was hoped that this would remind them of their budget constraint. Corrections on the payment card were allowed for afterwards.

While non-responses were not a problem in the plot valuation question, about one third of the respondents did not answer the willingness to pay question to avoid flooding or sedimentation. The characteristics of the respondents that refused to answer are not randomly distributed and might therefore cause inconsistency and inefficiency in the estimation of the coefficients in the regression of the willingness to pay question. A common method to control for non-responses to the willingness to pay question is to estimate a sample selection model (Messonier et al., 2000; Mekkonen, 2001), usually referred to as the Heckman two stage approach (Heckman, 1997). In this case, we estimate:

> Y\*=β'X +ε Y=0 if Y\*≤0 and Y=Y\* otherwise Z=α'V + μ Z=1 if Z\*>0 and Z=0 if Z\*≤0

where Y is willingness to pay (censored at 0); X is a vector of explanatory exogenous variables that explain Y; Z is 1 when there is a valid response and 0 otherwise; V is vector of explanatory exogenous that influence the probability of giving a valid response; α and β are parameters to be estimated; ε and μ are disturbances; Y\* and Z\* are latent variables.

## **4. Descriptive statistics**

114 Soil Health and Land Use Management

beyond dichomotous choices, see Blamey et al. (1999) and Ready et al. (2001)). (2) Whittington (1998) and Wang and Whittington (2005) show that a main problem in contingent valuation studies is that the range that is offered is often not large enough to allow for a robust estimation of the valuation function. Moreover, as we had little a priori knowledge about the valuation function, we had to make sure that extreme levels were included in the bids on the payment card. Given the small sample, this could not have been achieved in the dichotomous choice variable format. (3) Uncertainty (for example on the future price evolution of agricultural products) and imperfect information (household chief had to answer immediately during the interview and could not consult with family members and/or village leaders) is allowed for in this format. Wang (1997), Wang and Whittington (2005) and Alberini et al. (2003) show the benefits of the explicit modeling of

Two valuation questions were asked. The valuation questions were set up in such a way to reduce as much as possible the problem with starting point bias and with yea-saying: therefore, it started with an open-ended question (no starting point bais) followed by a payment card (additional information). In the case of the land valuation, a willingness to accept scenario was described where a certain monetary payment was given in exchange for the plot studied. As previous surveys in Madagascar had shown the reluctance of farmers to give a sales price for land - they would often report they would be unwilling to sell the plot whatever happened - it was made clear from the beginning that this was a hypothetical situation where we like to know their approximate financial value of the plot in their farming enterprise. The respondent was presented with a payment card in local currency but with references to values of local rice units, bikes, and value of livestock. On this payment card, the enumerator proceeded to fill in for every amount that was mentioned a code corresponding to 1. Accept to pay for sure; 2. A little bit in doubt but would say yes; 3. Not yes or no, do not know; 4. A little bit in doubt but would say no; 5. Will not pay for

In the case of the question on willingness to pay for reduction in flooding and sedimentation, the valuation scenario was constructed as follows. Respondents were first asked if they thought if flooding and sediments had a negative, neutral or positive influence on rice productivity, in general and on the specific plot that was studied. A scenario was

*" Suppose that we leave the situation as it is and we leave damage as it is without any intervention to limit deposits on this rice field or to reduce the frequency of flooding on this field. In a second situation, actions will be undertaken in the watershed upstream of your fields. In this case, you will not suffer anymore from problems of flooding and sediments. However, you know that these actions will cost money. We would like to know how much you would be willing to pay for these actions, taking into account your possibilities. If you do not pay as much than what you would be really able to pay, actions will not be sufficient to reduce flooding and sedimentation. On the other hand, if you give a level that is higher than you can afford, functional interventions can not be agreed upon. How* 

The question was formulated in local units of rice as this measure was easily recognizable by farmers. To finish the valuation section, a question was asked to the farmers on where they would get the rice from for the amount that they were willing to contribute. It was hoped that this would remind them of their budget constraint. Corrections on the payment

uncertain responses in contingent valuation data.

then described in the following way:

card were allowed for afterwards.

*much would you be willing to pay? x sobika of rice?"* 

sure.

The Maroantsetra area in the Northeast of Madagascar is a humid area characterized by two types of agriculture: slash-and-burn cultivation ("tavy") on the hillsides and lowland rice cultivation. The area is isolated from the rest of Madagascar and is highly dependent on agriculture for income. The region is also still highly forested and is one of the largely untouched areas in Madagascar. Table 1 shows the basic descriptive statistics of the households in the survey. The head of households have a low average level of education, i.e. only three years. 10% of the households are female headed and these are mostly poorer households (Razafindravonona et al., 2000). The average size of the household is six members. Almost all the households are natives from the region and all the households report to depend on agriculture for their livelihood.

An average household in the sample possesses 62 ares4 of lowland and 73 ares of upland. As in most of Madagascar, the main staple is rice. The average production is just below 1 ton which is estimated to be sufficient for subsistence by almost 70% of the population. However, most households - even some that declare to be self-sufficient in rice - reduce overall consumption during the lean period. The average length of this lean period is estimated to be three months. A household possesses on average 2 zebus. Total annual monetary household income is estimated at 2.7 M Fmg5, i.e. around 415\$US, i.e. low but

<sup>4 1</sup> are = 0.01 ha

<sup>5</sup> Malagasy Franc; 1 USD\$ = 6500 Fmg at the time of the survey

**variable Unit N mean median min max** 

area ares 268 2,16 1,2 0,1 25 distance from house minutes 268 15,40 10 1 90 isolated parcel yes=1 268 0,04 0 0 1 parcel along river yes=1 268 0,14 0 0 1 traditional perimetre yes=1 268 0,82 1 0 1 parcel far from river yes=1 268 0,57 1 0 1 parcel in terras yes=1 268 0,17 0 0 1 parcel close to river (<100m) yes=1 268 0,15 0 0 1 parcel between 100 and 200m of river yes=1 268 0,10 0 0 1 interior of bend of river yes=1 268 0,04 0 0 1 exterior of bend of river yes=1 268 0,19 0 0 1 parallel to river yes=1 268 0,56 1 0 1 irrigated by rainfall yes=1 268 0,04 0 0 1 irrigated by dam yes=1 268 0,96 1 0 1 distance river parcel meters 268 103,45 40 0,2 1200 height difference parcel river meters 266 2,51 2 0,2 20 order in irrigation (rank) number 268 9,51 5 1 99 soil depth cm 267 26,34 20 3 120

no deposits yes=1 268 0,44 0 0 1 deposits of clay yes=1 268 0,26 0 0 1 deposits of sand yes=1 268 0,30 0 0 1

length flooding days 218 1,66 1 0 30 maximal depth of water cm 202 116,46 100 0 600 no impact on yields yes=1 268 0,56 1 0 1 little impact on yields yes=1 268 0,05 0 0 1 medium impact on yields yes=1 268 0,04 0 0 1 strong impact on yields yes=1 268 0,03 0 0 1

length flooding days 144 1,31 1 0 17 maximal depth of water cm 122 117,37 100 0 500 no impact on yields yes=1 268 0,41 0 0 1

**Parcel characteristics** 

**Sedimentation and flooding** 

*Cyclone Huddah 2000* 

*Cyclone Gloria 1997* 

consistent with the high poverty levels and the low GNP of Madagascar (Razafindravonona et al., 2001).

Tables 2 presents the descriptive statistics of the rice plots that will be analyzed in more detail later on. The average plot size is small, 2.1 ares, with a range between 1 and 25 ares. Most of the plots are reported to be irrigated through a dam (96%). When asked about production problems in the last agricultural year, 28% of the farmers complained of droughts, 21% of sedimentation problems, and 14% of floods. Average yields during the previous agricultural year were estimated at 3.3 ton per hectare, high compared to the rest of the country but consistent with the excellent country-wide production conditions in 2001.6


Table 1. Descriptive statistics of household variables

Two major cyclones hit the area in the last five years: Huddah in 2000 and Gloria in 1997. The majority of the farmers state that production of plots was not affected by these events. Even when plots were affected, the perceived impact was reported to be small. Only 12% and 3% of the farmers declare that these cyclones had an impact on their rice yields in 2000 and 1997 respectively. Of these farmers, only 3% and 1% state that the impact on rice yield had been very high. Hence, it seems that the direct overall impact of these cyclones has been very small. This might be because the cyclones normally hit outside the regular growing period in Maroantsetra.7

<sup>6</sup> However, few farmers use modern inputs yet.

<sup>7</sup> The reported median harvest month is around November – in contrast to the rest of the country where main harvest are in April/May - while cyclones often hit in the beginning of the year.

consistent with the high poverty levels and the low GNP of Madagascar (Razafindravonona

Tables 2 presents the descriptive statistics of the rice plots that will be analyzed in more detail later on. The average plot size is small, 2.1 ares, with a range between 1 and 25 ares. Most of the plots are reported to be irrigated through a dam (96%). When asked about production problems in the last agricultural year, 28% of the farmers complained of droughts, 21% of sedimentation problems, and 14% of floods. Average yields during the previous agricultural year were estimated at 3.3 ton per hectare, high compared to the rest of the country but consistent with the excellent country-wide production conditions in

**variable Unit N mean median min max**  size of household number of people 268 5,65 5 1 14 education level head of hh years 268 3,13 3 0 12 age years 268 45,55 44 15 81 gender man=1 268 0,90 1 0 1 native of region yes=1 268 0,99 1 0 1 lowland ares 268 61,87 50 0 340 upland ares 268 73,40 50 0 1000 forest savoka ares 268 33,09 0 0 500 primary forest ares 268 30,06 0 0 600 zebus number 268 1,75 0 0 18 total production of rice kg 268 913,46 720 60 4500 total income 1000 Fmg 268 2695,57 1635 0 30100 rice production is enough yes=1 268 0,27 0 0 1 length of lean period number of months 268 2,81 3 0 10 potential access to credit 1000 Fmg 268 706,03 100 0 25000

Two major cyclones hit the area in the last five years: Huddah in 2000 and Gloria in 1997. The majority of the farmers state that production of plots was not affected by these events. Even when plots were affected, the perceived impact was reported to be small. Only 12% and 3% of the farmers declare that these cyclones had an impact on their rice yields in 2000 and 1997 respectively. Of these farmers, only 3% and 1% state that the impact on rice yield had been very high. Hence, it seems that the direct overall impact of these cyclones has been very small. This might be because the cyclones normally hit outside the regular growing

7 The reported median harvest month is around November – in contrast to the rest of the country where

main harvest are in April/May - while cyclones often hit in the beginning of the year.

Table 1. Descriptive statistics of household variables

period in Maroantsetra.7

6 However, few farmers use modern inputs yet.

et al., 2001).

2001.6


**variables Unit Coefficient t-value P>|t|** 

area log(ares) 0,503 **7,590 0,000**  parcel in terras yes=1 -0,343 **-1,840 0,067**  parcel along river yes=1 -0,617 -1,470 0,142 tradional perimeter yes=1 -0,215 -0,530 0,596 interior bend of river yes=1 -0,371 -1,600 0,111 exterior bend of river yes=1 -0,045 -0,300 0,765 distance river parcel log(meters) 0,027 0,700 0,488 height difference parcel river log(meters) 0,040 0,300 0,762 soil depth log(cm) 0,260 **2,140 0,034**  irrgation directly from river yes=1 0,216 **1,700 0,091**  irrigated by dam yes=1 -0,305 -1,000 0,320 clay deposit after cyclones yes=1 0,299 **1,990 0,048**  sandy deposits after cyclones yes=1 0,429 **2,980 0,003** 

education head of household years 0,016 0,740 0,463 age of head of household years 0,001 0,130 0,893 gender head of household man=1 -0,237 -1,110 0,267 annual monetary income log(Fmg) 0,041 1,330 0,185 length of lean period months 0,017 0,690 0,492 potential access to credit log(Fmg) -0,010 -0,870 0,384 owned number of zebus log(number) 0,300 **3,460 0,001**  owned agricultural land log(ares) -0,070 -0,950 0,341 intercept 12,248 **14,700 0,000** 

Most of the physical variables turn out not significant at the conventional statistical levels, indicating that these are not major determinants of sales prices. However, there are a few exceptions. Plots in terraces, at the top of the river, are less valuable. This might be because these plots are more likely to be affected by drought. The impact is shown to reduce the

**plot characteristics** 

**household characteristics** 

Number of observations 256 F( 21, 234) 9,62 Prob > F 0 R-squared 0,3929 Root MSE 0,9256

Table 3. Hedonic price regression

(dep. var. = log (value of land); robust standard errors)


Table 2. Descriptive statistics parcel, flooding, and sedimentation

Runoff and erosion happen often during rare events such as cyclones and heavy, intense rainfall (Kaimowitz, 2000; Brand et al., 2002). While direct impact on productivity might be small, long-term impacts through increased sedimentation might be large. In the next section, we will evaluate the values these rice farmers attach to sedimentation and flooding. We will estimate these through well-established methods in environmental economics: (1) an indirect valuation method using the hedonic pricing methodology and (2) a direct valuation method using the contingent valuation technique.

## **5. Regression results**

#### **5.1. Land valuation**

To evaluate to what extent farmers incorporate physical and environmental amenities in land valuation, a modified hedonic pricing analysis was done. Given that land sales are rare in the region and good land valuations are therefore more difficult to get at, a stochastic payment card method was implemented to arrive at approximate land valuations of the rice plot in the sample. The stated price at which households are willing to sell their plot for sure is used as dependent variable in the regression analysis. The results of this regression are shown in Table 3.

The results illustrate that farmers are well aware of the effect of the physical characteristics on the value of their plots. As expected, area is shown to be a significant determinant of value (see Figure 1). A doubling in area increases the value of the plot by only 0.54, i.e. significantly different from one. This result indicates that larger plots are relatively less valuable than smaller plots, controlling for physical characteristics. On first sight, this implies that there are potential profits to be made by repacking plots in smaller units.8 While returns to scale would result in relatively higher values for larger plots, a potential explanation might be that farmers prefer different smaller plots compared to one big plot as in this way, farmers are able to diversify their risk.9 The likelihood that small plots, that are spatially segregated, are all hit by calamities at the same time - such as flooding, drought, sedimentation problems or plant diseases - is less than for one big plot. This risk averseness, typical for poor small farmers, might be an important explanation of the concave land price relationship.

 8 Similar results have been found in other countries as mentioned by Lin and Evans (2000).

<sup>9</sup> Blarel et al. (1992) study this phenomena in depth in Ghana and Rwanda.


**variable Unit N mean median min max**  little impact on yields yes=1 268 0,01 0 0 1 medium impact on yields yes=1 268 0,01 0 0 1 strong impact on yields yes=1 268 0,01 0 0 1

problems with flooding yes=1 268 0,14 0 0 1 problems with drought yes=1 268 0,28 0 0 1 problems with deposit sand yes=1 268 0,21 0 0 1

Runoff and erosion happen often during rare events such as cyclones and heavy, intense rainfall (Kaimowitz, 2000; Brand et al., 2002). While direct impact on productivity might be small, long-term impacts through increased sedimentation might be large. In the next section, we will evaluate the values these rice farmers attach to sedimentation and flooding. We will estimate these through well-established methods in environmental economics: (1) an indirect valuation method using the hedonic pricing methodology and (2) a direct

To evaluate to what extent farmers incorporate physical and environmental amenities in land valuation, a modified hedonic pricing analysis was done. Given that land sales are rare in the region and good land valuations are therefore more difficult to get at, a stochastic payment card method was implemented to arrive at approximate land valuations of the rice plot in the sample. The stated price at which households are willing to sell their plot for sure is used as dependent variable in the regression analysis. The results of this regression are

The results illustrate that farmers are well aware of the effect of the physical characteristics on the value of their plots. As expected, area is shown to be a significant determinant of value (see Figure 1). A doubling in area increases the value of the plot by only 0.54, i.e. significantly different from one. This result indicates that larger plots are relatively less valuable than smaller plots, controlling for physical characteristics. On first sight, this implies that there are potential profits to be made by repacking plots in smaller units.8 While returns to scale would result in relatively higher values for larger plots, a potential explanation might be that farmers prefer different smaller plots compared to one big plot as in this way, farmers are able to diversify their risk.9 The likelihood that small plots, that are spatially segregated, are all hit by calamities at the same time - such as flooding, drought, sedimentation problems or plant diseases - is less than for one big plot. This risk averseness, typical for poor small farmers, might be an important explanation of the concave land price

8 Similar results have been found in other countries as mentioned by Lin and Evans (2000).

9 Blarel et al. (1992) study this phenomena in depth in Ghana and Rwanda.

Table 2. Descriptive statistics parcel, flooding, and sedimentation

valuation method using the contingent valuation technique.

*This harvest* 

**5. Regression results** 

**5.1. Land valuation** 

shown in Table 3.

relationship.


Table 3. Hedonic price regression

(dep. var. = log (value of land); robust standard errors)

Most of the physical variables turn out not significant at the conventional statistical levels, indicating that these are not major determinants of sales prices. However, there are a few exceptions. Plots in terraces, at the top of the river, are less valuable. This might be because these plots are more likely to be affected by drought. The impact is shown to reduce the

relation was negative (Table 4). However, 38% of the farmers thought that it was actually good for rice yields (while 9% thought its effect was neutral). In a follow-up question, it was asked what the rice farmers expected of the effect of sedimentation and flooding on the rice plot in the sample. Farmers were evenly divided on the question: 37% thought that the effect would be negative, 38% expected a positive effect and 26% reported to

**variable Unit N mean** 

positive yes=1 268 0,38 neutral yes=1 268 0,09 negative yes=1 268 0,53

positive yes=1 268 0,37 neutral yes=1 268 0,26 negative yes=1 268 0,38

Finally, farmers were asked what they were willing to pay to avoid flooding and sedimentation. Figure 2 illustrates, for the respondents that were willing to pay, how the willingness to pay varies for the different levels that were offered to the respondent. We see that the median willingness to pay (at 95% for sure) to avoid flooding is just over 2 sobika, the local unit for a rice basket containing 12 kgs on average per household per year. This amounts to around 4\$. This implies that if a vote would be held in the region, more than 4\$ would not be accepted by a majority of the population. 50% of the farmers would refuse to pay more than 4.5 sobika for sure. On average, this corresponds to 7% of their total rice

The number of farmers that were undecided about accepting or refusing the offer is largest in the middle of the graph, as could be expected (see Wang (1997; p. 223)). For some bids, the indecision domain contains up to 15% of the farmers. This high number indicates the importance of allowing farmers to convey information beyond the simple yes/no format in contingent valuation studies as has been shown by other authors (Blamey et al., 1999; Ready

Regressions were run to look at the determinants of the willingness to pay to avoid flooding and sedimentation on the plot in the sample. These results serve to validate the WTP answers. A two-step approach was used. In a first step, a selection equation was run to explain the characteristics of the households that are willing to contribute to avoid flooding and sedimentation. In this step, variables are included that are potential determinants of the likelihood of the plot to be subject to flooding and sedimentation. In a second step controlling for the characteristics of the plot and the household which explain if it is willing to contribute - economic variables are included in the regression to measure to what extent they are able to contribute, taking into account their socio-economic background. A selectivity coefficient was then included in the second-stage willingness to pay regression.

expect a neutral effect.

production of last year.

et al., 2001; Alberini et al., 2003).

Overall effect sediment/flooding on rice yield…

Table 4. Perceived effect of sedimentation/flooding

Effect on studied parcel of sediments/flooding on rice yield…

value of the plot by around 34%. The perceived cultivable soil depth is a highly important determinant of land prices. A doubling of soil depth increases the value of rice land by 26%. Agronomic evidence suggests that soil depth is crucial for root development which has been shown to be an important constraint on rice production in Madagascar.

Fig. 1. Willingness-to-accept the sales price 'for sure' (by plot size quintile)

In line with de Janvry et al. (1991), we assume imperfect or missing markets where farm households are the decision makers and production and consumption decision are not separable. This implies that land prices would also depend on household characteristics and they were thus included in the regression. Few of these variables come out significant. Only the ownership of cattle leads to significant higher land values. This seems linked to the importance of ownership of cattle to access to manure, an important lasting fertility and land quality enhancing input in these environments (Minten et al., 2007; Barrett et al., 2002).

To measure the effect of sedimentation, we created dummies for clay and sand deposits during recent floods. Compared to soils without deposits during floods, these plots are estimated to be significantly more valuable. The plots affected by soil and sand deposits are estimated to be respectively 30% and 43% more valuable. The latter results might seem surprising at first sight. However, sand deposits come usually together with organic material that might significantly improve the fertility of soils. Farmers also often remove the more damaging sand from the plot. These results indicate overall that sedimentation does not reduce the value of the plot per se, ceteris paribus. We discuss this in more detail below.

#### **5.2 Willingness-to-pay to avoid sedimentation and flooding**

All sedimentation is not perceived to be bad for rice productivity. In fact, erosion and heavy rainfall might induce runoff of the good topsoil of the uplands that ends up in the lowland ricefields (Chomitz and Kumari, 1998). This seems also to be the case in the lowlands of the Maroantsetra region. When asked about the perceived effect of flooding and sedimentation on rice yields overall, 53% of the farmers reported that they thought this

value of the plot by around 34%. The perceived cultivable soil depth is a highly important determinant of land prices. A doubling of soil depth increases the value of rice land by 26%. Agronomic evidence suggests that soil depth is crucial for root development which has been

shown to be an important constraint on rice production in Madagascar.

Fig. 1. Willingness-to-accept the sales price 'for sure' (by plot size quintile)

**5.2 Willingness-to-pay to avoid sedimentation and flooding** 

2002).

In line with de Janvry et al. (1991), we assume imperfect or missing markets where farm households are the decision makers and production and consumption decision are not separable. This implies that land prices would also depend on household characteristics and they were thus included in the regression. Few of these variables come out significant. Only the ownership of cattle leads to significant higher land values. This seems linked to the importance of ownership of cattle to access to manure, an important lasting fertility and land quality enhancing input in these environments (Minten et al., 2007; Barrett et al.,

To measure the effect of sedimentation, we created dummies for clay and sand deposits during recent floods. Compared to soils without deposits during floods, these plots are estimated to be significantly more valuable. The plots affected by soil and sand deposits are estimated to be respectively 30% and 43% more valuable. The latter results might seem surprising at first sight. However, sand deposits come usually together with organic material that might significantly improve the fertility of soils. Farmers also often remove the more damaging sand from the plot. These results indicate overall that sedimentation does not reduce the value of the plot per se, ceteris paribus. We discuss this in more detail below.

All sedimentation is not perceived to be bad for rice productivity. In fact, erosion and heavy rainfall might induce runoff of the good topsoil of the uplands that ends up in the lowland ricefields (Chomitz and Kumari, 1998). This seems also to be the case in the lowlands of the Maroantsetra region. When asked about the perceived effect of flooding and sedimentation on rice yields overall, 53% of the farmers reported that they thought this relation was negative (Table 4). However, 38% of the farmers thought that it was actually good for rice yields (while 9% thought its effect was neutral). In a follow-up question, it was asked what the rice farmers expected of the effect of sedimentation and flooding on the rice plot in the sample. Farmers were evenly divided on the question: 37% thought that the effect would be negative, 38% expected a positive effect and 26% reported to expect a neutral effect.


Table 4. Perceived effect of sedimentation/flooding

Finally, farmers were asked what they were willing to pay to avoid flooding and sedimentation. Figure 2 illustrates, for the respondents that were willing to pay, how the willingness to pay varies for the different levels that were offered to the respondent. We see that the median willingness to pay (at 95% for sure) to avoid flooding is just over 2 sobika, the local unit for a rice basket containing 12 kgs on average per household per year. This amounts to around 4\$. This implies that if a vote would be held in the region, more than 4\$ would not be accepted by a majority of the population. 50% of the farmers would refuse to pay more than 4.5 sobika for sure. On average, this corresponds to 7% of their total rice production of last year.

The number of farmers that were undecided about accepting or refusing the offer is largest in the middle of the graph, as could be expected (see Wang (1997; p. 223)). For some bids, the indecision domain contains up to 15% of the farmers. This high number indicates the importance of allowing farmers to convey information beyond the simple yes/no format in contingent valuation studies as has been shown by other authors (Blamey et al., 1999; Ready et al., 2001; Alberini et al., 2003).

Regressions were run to look at the determinants of the willingness to pay to avoid flooding and sedimentation on the plot in the sample. These results serve to validate the WTP answers. A two-step approach was used. In a first step, a selection equation was run to explain the characteristics of the households that are willing to contribute to avoid flooding and sedimentation. In this step, variables are included that are potential determinants of the likelihood of the plot to be subject to flooding and sedimentation. In a second step controlling for the characteristics of the plot and the household which explain if it is willing to contribute - economic variables are included in the regression to measure to what extent they are able to contribute, taking into account their socio-economic background. A selectivity coefficient was then included in the second-stage willingness to pay regression.

area of the plot log(ares) -0,014 -0,230 0,816 education head of household years -0,016 -0,820 0,410 age of head of household years -0,006 -1,320 0,187 gender head of household man=1 -0,108 -0,610 0,542 owned lowland log(ares) 0,112 **1,950 0,051**  annual monetary income log(Fmg) 0,036 1,090 0,276 length of lean period months -0,058 **-2,460 0,014**  potential access to credit log(Fmg) 0,021 **1,910 0,057**  owned number of zebus log(number) 0,030 0,400 0,691 intercept 0,116 0,220 0,822

**dep. var.: willingness to pay in log(Fmg)** 

expected effect of sedimentation on plot

**selection equation** 

variables Unit Coef. z P>z

1=pos; 2=neutral;

area of the plot log(ares) -0,161 -1,440 0,150 parcel along river yes=1 -1,074 -1,500 0,134 traditional perimetre yes=1 -0,704 -1,060 0,287 parcel in terras yes=1 -0,442 -1,460 0,144 parcel close to river (<100m) yes=1 -0,448 -1,500 0,134 parcel between 100 and 200m of river yes=1 -0,030 -0,070 0,943 interior of bend of river yes=1 -0,216 -0,400 0,689 exterior of bend of river yes=1 0,836 **3,200 0,001**  irrigated by dam yes=1 0,970 **1,960 0,050**  distance river parcel meters 0,005 0,080 0,939 height difference parcel river meters 0,113 0,810 0,420 order in irrigation (rank) number -0,087 -0,910 0,362 slope (distance one sees w/o obstacle) meters 0,056 0,640 0,525 soil depth cm 0,464 **2,740 0,006**  estimated age of plot years -0,003 -0,410 0,680 little impact on yields of cyclone 1 yes=1 -0,087 -0,180 0,859 medium impact on yields of cyclone 1 yes=1 -1,182 **-1,780 0,075**  strong impact on yields of cyclone 1 yes=1 -1,642 **-2,510 0,012**  little impact on yields of cyclone 2 yes=1 -0,333 -0,390 0,695 medium impact on yields of cyclone 2 yes=1 -0,087 -0,060 0,949 strong impact on yields of cyclone 2 yes=1 0,434 0,290 0,770

3=neg. 0,683 **4,990 0,000** 

This set-up would allow us to obtain efficient and unbiased estimates in the second stage regression.

Fig. 2. Willingness to pay to avoid flooding/sedimentation

The results are largely conforming to expectations. The coefficient that measures the expected effect of sedimentation and flooding on the plot and the household perceived effect of sedimentation are significant determinants of the probability that the household is willing to contribute.10 Households with plots on the exterior bend of the river, bigger soil depth and irrigated by dams are significantly more willing to contribute. These plots might be more exposed to risk or are more valuable. It is interesting to note that negative experiences with the last two cyclones make the household less likely to contribute. These households might believe that there is not much that can be done or, alternatively, that this type of adversity might easily be overcome.

The results on the amount that households are willing to contribute – the second stage regression - suggest that wealthier households are willing to pay more. Different measures of wealth were included. A doubling of the area of lowland in possession would increase the willingness to pay significantly by 11%. A lean period that lasts one month longer as measured by the period that they do not have sufficient rice, an indicator of poverty of the household (Barrett and Dorosh, 1998; Minten and Zeller, 2000), reduces the willingness to pay of the household by 6%. Potential access to credit increases the willingness to pay significantly. However, its coefficient is small. Overall income and the number of zebus owned by the household show the expected positive sign but are not significant at the 10% level. Household characteristics, such as level of education, gender and age of the head of household, do not influence the willingness to pay significantly.

<sup>10</sup> However, the match is not perfect. 72% of the households that expected a positive impact were not willing to pay to participate. This compares to 91% of the households that expected a neutral impact and 8% of the households that expected a negative impact.


122 Soil Health and Land Use Management

This set-up would allow us to obtain efficient and unbiased estimates in the second stage

The results are largely conforming to expectations. The coefficient that measures the expected effect of sedimentation and flooding on the plot and the household perceived effect of sedimentation are significant determinants of the probability that the household is willing to contribute.10 Households with plots on the exterior bend of the river, bigger soil depth and irrigated by dams are significantly more willing to contribute. These plots might be more exposed to risk or are more valuable. It is interesting to note that negative experiences with the last two cyclones make the household less likely to contribute. These households might believe that there is not much that can be done or, alternatively, that this

The results on the amount that households are willing to contribute – the second stage regression - suggest that wealthier households are willing to pay more. Different measures of wealth were included. A doubling of the area of lowland in possession would increase the willingness to pay significantly by 11%. A lean period that lasts one month longer as measured by the period that they do not have sufficient rice, an indicator of poverty of the household (Barrett and Dorosh, 1998; Minten and Zeller, 2000), reduces the willingness to pay of the household by 6%. Potential access to credit increases the willingness to pay significantly. However, its coefficient is small. Overall income and the number of zebus owned by the household show the expected positive sign but are not significant at the 10% level. Household characteristics, such as level of education, gender and age of the head of

10 However, the match is not perfect. 72% of the households that expected a positive impact were not willing to pay to participate. This compares to 91% of the households that expected a neutral impact

Fig. 2. Willingness to pay to avoid flooding/sedimentation

household, do not influence the willingness to pay significantly.

and 8% of the households that expected a negative impact.

type of adversity might easily be overcome.

regression.

it seems that farmers that might cause erosion and those that suffer from it are often the

Secondary sources of information further seem to indicate that siltation and erosion might be a relatively minor problem in Malagasy agriculture overall and this despite the high recent deforestation rates in Madagascar.13 The 2001 national household survey asked farmers about the biggest constraints they faced to improved agricultural productivity. The same question was asked in the 2004 national household survey, based on a different sampling frame and with a bigger sample. Respondents had to rank options from 'not important' to 'very important'. The results are presented in Table 6 ordered in decreasing percentage of households that identified the constraint as 'quite' or 'very' important. Answers were strikingly consistent between the two surveys, three years apart and with a different sample. The most and least frequently cited constraints were common to both surveys. Access to agricultural equipment, access to cattle for traction and transport and access to labor are ranked among the top four constraints in both surveys. The clear pattern in these answers is that inputs that complement labor and boost its productivity are most limiting in farmers' opinion (Minten et al., 2007). By contrast, less than 40 percent of households identify the siltation of land as an important constraint and it is more commonly identified as not a constraint on agricultural productivity. Farmers were further asked for each plot in the national household survey of 2001 about the production problems in the year preceding the survey. Siltation was mentioned as a problem on less than 1% of the rice

Flooding and sedimentation downstream are often linked to deforestation upstream. While the debate is on-going and results seem to be variable and site specific (Chomitz and Kumari, 1998; Calder, 1999), policy makers are looking for ways to solve this externality problem to ensure sustainable financing for ecological services of conservation efforts such as reforestation and soil conservation measures. Based on interviews with almost 300 rice farmers - users of land downstream - in the Northeast of Madagascar, this paper tries to shed light on the willingness to pay for ecological services for forests, in this case to avoid

The results of our analysis show that the rice farmers are clearly aware of the effect of sedimentation on production. Sedimentation is not perceived to be unambiguously bad for lowland productivity. Policy interventions that focus on only correcting the perceived negative relationship are therefore misguided. A hedonic pricing analysis on riceland

12 Lowlands are further divided depending on the type of irrigation scheme. The World Bank (2005) estimates the total lowland area at 1.1 million hectares, representing 40% of the cultivated area. The bulk of these lowland areas, 800,000 hectares or 70 percent of the total irrigated lands, are very small in terms of average superficies (a few hectares), and are not equipped with improved irrigation infrastructure. 300,000 hectares is equipped with infrastructure meant to improve water management. Uplands can further be divided in uplands that are cultivated on a permanent basis and land that is

13 It is estimated that Madagascar lost about 12 million ha of forest between 1960 and 2000, effectively

cultivated for three to four years and is then followed by long fallow periods.

reducing forest cover by 50% in just 40 years (World Bank, 2003).

same households.12 This makes a compensation mechanism cumbersome.

plots.

**6. Conclusions** 

flooding and sedimentation.


Table 5. Willingness to pay to avoid flooding on rice plot (Heckman selection model)

The coefficients on the explanatory variables show that the stated amount is consistent with economic logic. Households with access to liquidity and who perceive to suffer from flooding are willing to pay more. To further test for robustness, regressions were run without the selectivity coefficient and with the refusal to pay for sure as dependent variable. The coefficients obtained - but not reported - confirm the results discussed earlier.

We end this section with a final note on the significance of these results at the national level. There are two main differences of the surveyed farmers with the rest of the country. First, the watersheds in this area are small and sedimentation downstream can easy be linked to upstream activities. This is however not the case in the rest of Madagascar and the link between sedimentation downstream and corrective measures upstream are more difficult to make as watersheds are larger.11 Second, the rice harvest in the Maroantsetra region is at the end of the year, i.e. before major cyclones hit the country. This might reduce the willingness to pay for a reduction of floods. In the rest of the country, the main harvest is in the beginning of the year and it might thus more directly be affected by rice losses due to floods and submersion. The run-off of good soils might then only affect the subsequent harvests.

Based on the data of the national household survey of 2001, it is found that 15% of agricultural households cultivate lowland, 21% upland and 64% both. This compares to respectively 25% and 75% of the households that we interviewed in the Maroantsetra area (because of the study subject, only rice farmers were selected). The majority of the households in Madagascar and in our dataset cultivate thus both uplands and lowlands and

<sup>11</sup> Brand et al. (2002) show how the size and the shape of watersheds are important determinants for run-off.

it seems that farmers that might cause erosion and those that suffer from it are often the same households.12 This makes a compensation mechanism cumbersome.

Secondary sources of information further seem to indicate that siltation and erosion might be a relatively minor problem in Malagasy agriculture overall and this despite the high recent deforestation rates in Madagascar.13 The 2001 national household survey asked farmers about the biggest constraints they faced to improved agricultural productivity. The same question was asked in the 2004 national household survey, based on a different sampling frame and with a bigger sample. Respondents had to rank options from 'not important' to 'very important'. The results are presented in Table 6 ordered in decreasing percentage of households that identified the constraint as 'quite' or 'very' important. Answers were strikingly consistent between the two surveys, three years apart and with a different sample. The most and least frequently cited constraints were common to both surveys. Access to agricultural equipment, access to cattle for traction and transport and access to labor are ranked among the top four constraints in both surveys. The clear pattern in these answers is that inputs that complement labor and boost its productivity are most limiting in farmers' opinion (Minten et al., 2007). By contrast, less than 40 percent of households identify the siltation of land as an important constraint and it is more commonly identified as not a constraint on agricultural productivity. Farmers were further asked for each plot in the national household survey of 2001 about the production problems in the year preceding the survey. Siltation was mentioned as a problem on less than 1% of the rice plots.

## **6. Conclusions**

124 Soil Health and Land Use Management

intercept -3,186 1,018 -3,130

Table 5. Willingness to pay to avoid flooding on rice plot (Heckman selection model)

The coefficients on the explanatory variables show that the stated amount is consistent with economic logic. Households with access to liquidity and who perceive to suffer from flooding are willing to pay more. To further test for robustness, regressions were run without the selectivity coefficient and with the refusal to pay for sure as dependent variable. The coefficients obtained - but not reported - confirm the results discussed

We end this section with a final note on the significance of these results at the national level. There are two main differences of the surveyed farmers with the rest of the country. First, the watersheds in this area are small and sedimentation downstream can easy be linked to upstream activities. This is however not the case in the rest of Madagascar and the link between sedimentation downstream and corrective measures upstream are more difficult to make as watersheds are larger.11 Second, the rice harvest in the Maroantsetra region is at the end of the year, i.e. before major cyclones hit the country. This might reduce the willingness to pay for a reduction of floods. In the rest of the country, the main harvest is in the beginning of the year and it might thus more directly be affected by rice losses due to floods and submersion. The run-off of good soils might then only affect the subsequent

Based on the data of the national household survey of 2001, it is found that 15% of agricultural households cultivate lowland, 21% upland and 64% both. This compares to respectively 25% and 75% of the households that we interviewed in the Maroantsetra area (because of the study subject, only rice farmers were selected). The majority of the households in Madagascar and in our dataset cultivate thus both uplands and lowlands and

11 Brand et al. (2002) show how the size and the shape of watersheds are important determinants for

rho -0,744 0,119 sigma 0,778 0,050 lambda -0,578 0,118

LR test of indep. eqns. (rho = 0): chi2(1) = 13.88 Prob > chi2 = 0.0002

Number of obs 265 Censored obs 82 Uncensored obs 183 Wald chi2(9) 21,33 Prob > chi2 0,011 Log likelihood -296,3996

overall perceived impact of

sedimentation

earlier.

harvests.

run-off.

variables Unit Coef. z P>z

3=neg. 0,385 **2,580 0,010** 

1=pos; 2=neutral;

Flooding and sedimentation downstream are often linked to deforestation upstream. While the debate is on-going and results seem to be variable and site specific (Chomitz and Kumari, 1998; Calder, 1999), policy makers are looking for ways to solve this externality problem to ensure sustainable financing for ecological services of conservation efforts such as reforestation and soil conservation measures. Based on interviews with almost 300 rice farmers - users of land downstream - in the Northeast of Madagascar, this paper tries to shed light on the willingness to pay for ecological services for forests, in this case to avoid flooding and sedimentation.

The results of our analysis show that the rice farmers are clearly aware of the effect of sedimentation on production. Sedimentation is not perceived to be unambiguously bad for lowland productivity. Policy interventions that focus on only correcting the perceived negative relationship are therefore misguided. A hedonic pricing analysis on riceland

<sup>12</sup> Lowlands are further divided depending on the type of irrigation scheme. The World Bank (2005) estimates the total lowland area at 1.1 million hectares, representing 40% of the cultivated area. The bulk of these lowland areas, 800,000 hectares or 70 percent of the total irrigated lands, are very small in terms of average superficies (a few hectares), and are not equipped with improved irrigation infrastructure. 300,000 hectares is equipped with infrastructure meant to improve water management. Uplands can further be divided in uplands that are cultivated on a permanent basis and land that is cultivated for three to four years and is then followed by long fallow periods.

<sup>13</sup> It is estimated that Madagascar lost about 12 million ha of forest between 1960 and 2000, effectively reducing forest cover by 50% in just 40 years (World Bank, 2003).

sedimentation. This seems related to the fact that flooding occurs outside the main harvest period and thus therefore not seem to cause any large immediate production damage. Damage depends then on the type of deposits as flooding can actually cause valuable soils and organic material to be transported to the ricefield and to be beneficial for rice productivity. The negative or positive effect of flooding seems to depend on spatial determinants, i.e. location with respect to the main river that irrigates the rice fields

However, a significant part of the farmers also realize the bad effects that sedimentation can have on their rice production. Therefore, they are willing to contribute to avoid flooding and sedimentation on their fields. These farmers are willing to contribute 4\$ per household per year. The magnitude of the amount that they are willing to pay corresponds to spatial as well as economic rationales. Households that are richer, not credit constrained, and that suffer less from seasonality problems are willing to pay significantly more to avoid this flooding and sedimentation damage. Given beneficial effects of sedimentation for some farmers and small willingness to pay by other farmers, our results overall thus suggest that current economic rates of return on forest preservation projects in Madagascar, largely beneficial because of across-the-board domestic agricultural benefits on lowlands, might be

We would like to thank Jurg Brand, Tim Healy, and Andy Keck for help with the set-up of survey, helpful discussions and comments on preliminary results. The field work for this research was financed by ONE (Office National de l'Environnement), PAGE (Projet d'Appuie à la Gestion de l'Environnement) and by the Ilo program. The two last projects were financed by USAID-Madagascar. Any remaining errors are solely the authors'

Alberini, A., Boyle, K., Welsh, M., Analysis of Contingent Valuation Data with Multiple Bids

Arrow, K., Solow, P., Portney, P. Leamer, E.E., Radner, R., Schuman, K., Report of the

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<sup>14</sup> One caveat of the analysis is whether poor farmers' own high discount rates should be the basis for assessing the severity of this type of environmental problem or whether the perceived damage from society's perspective should be based on the much lower social discount rates. While this will change the overall end result, it can be expected, given positive and negative effects of sedimentation, that this is partly canceled out, no matter low or high discount rates. In any case, the objective of the analysis is not to do a full-blown cost-benefit analysis (in which case we would also have to value the loss of production on the uplands) but to show that some of the assumptions in the calculation of current

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overestimated.14

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**8. References** 

pp. 359-381

economic rates of return might be questionable.

**7. Acknowledgement** 

values shows that farmers take sedimentation into consideration in the valuation of their rice plots but that rice plots with sedimentation are valued significantly higher, ceteris paribus.


Table 6. Farm households' reported constraints on improved agricultural productivity

The results of the survey further show that, while 10% of the farmers believe that flooding and sedimentation has no effect, a significant part of the farmers (almost 40% of the rice farmers in the sample) feels that their plots actually benefit from flooding and sedimentation. This seems related to the fact that flooding occurs outside the main harvest period and thus therefore not seem to cause any large immediate production damage. Damage depends then on the type of deposits as flooding can actually cause valuable soils and organic material to be transported to the ricefield and to be beneficial for rice productivity. The negative or positive effect of flooding seems to depend on spatial determinants, i.e. location with respect to the main river that irrigates the rice fields matters.

However, a significant part of the farmers also realize the bad effects that sedimentation can have on their rice production. Therefore, they are willing to contribute to avoid flooding and sedimentation on their fields. These farmers are willing to contribute 4\$ per household per year. The magnitude of the amount that they are willing to pay corresponds to spatial as well as economic rationales. Households that are richer, not credit constrained, and that suffer less from seasonality problems are willing to pay significantly more to avoid this flooding and sedimentation damage. Given beneficial effects of sedimentation for some farmers and small willingness to pay by other farmers, our results overall thus suggest that current economic rates of return on forest preservation projects in Madagascar, largely beneficial because of across-the-board domestic agricultural benefits on lowlands, might be overestimated.14

## **7. Acknowledgement**

126 Soil Health and Land Use Management

values shows that farmers take sedimentation into consideration in the valuation of their rice plots but that rice plots with sedimentation are valued significantly higher, ceteris

**Variables not a bit quite very** 

Access to agricultural equipment 19 18 27 35 Access to land 27 19 29 25 Access to cattle for traction and transport 24 23 29 24 Access to labor 22 28 30 20 Access to credit 36 19 23 22

environmental problems 29 31 22 18 Access to agricultural inputs (e.g. fertilizer) 34 26 19 21 Access to cattle for fertilizer 42 23 19 16 Land tenure insecurity 44 26 22 8 Silting of land 46 29 18 7

Access to agricultural equipment 11 14 32 43 Access to irrigation 13 21 29 37 Access to cattle for traction and transport 16 20 35 29 Access to labor 17 22 37 24 Avoid droughts 20 19 27 34 Access to agricultural inputs (e.g. fertilizer) 24 20 26 30 Phyto-sanitary diseases 19 25 30 26 Avoid flooding 25 20 26 29 Access to cattle for fertilizer 28 22 25 25 Access to credit 31 23 22 24 Silting of land 33 29 23 15 Land tenure insecurity 38 24 23 15 Table 6. Farm households' reported constraints on improved agricultural productivity

The results of the survey further show that, while 10% of the farmers believe that flooding and sedimentation has no effect, a significant part of the farmers (almost 40% of the rice farmers in the sample) feels that their plots actually benefit from flooding and

**Constraints to overall agricultural productivity** 

Degradation of irrigation infrastructure due to

*EPM 2004, 3543 agricultural households* 

*EPM 2001, 2470 agricultural households* 

**Percentage of households that state this constraint is … important** 

paribus.

We would like to thank Jurg Brand, Tim Healy, and Andy Keck for help with the set-up of survey, helpful discussions and comments on preliminary results. The field work for this research was financed by ONE (Office National de l'Environnement), PAGE (Projet d'Appuie à la Gestion de l'Environnement) and by the Ilo program. The two last projects were financed by USAID-Madagascar. Any remaining errors are solely the authors' responsibility.

### **8. References**


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**Part 4** 

**Soil Nitrogen Management**


**Part 4** 

**Soil Nitrogen Management**

130 Soil Health and Land Use Management

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**8** 

*China* 

**Strategies for Managing Soil Nitrogen to** 

Nitrogen fertilizer has played a major role in the global food production over the past 60 years. And about 50 percent of total N comes from fertilizer supply. However, fertilizer N has a low efficiency of use in agriculture (10-50 percent for crops grown in the fields). One of the main causes of low efficiency is the large of N by leaching, runoff, ammonia volatilization or denitrification with resulting in pollution of groundwater and atmosphere. With the limitation on arable land area and the demand for more and more food production, the only way is to increase the efficiency of use of fertilizer N. Thus, it is important to know the forms and pathways of N loss and the factors controlling them so that procedures can be developed to minimize the loss and increase N use efficiency (NUE). A conceptual scheme indicates the nitrogen cycle in crop production systems. Annual N input was about 170 Tg, and about half of added N is removed from the field as harvested crop (85 Tg). The remainder of the N,

defined as surplus N, either is lost to the environment or accumulates in the soil (Fig.1). Food demand of the public is the major promotion for rapid development on intensive agriculture, which is becoming a dynamic industry in China. However, with an excess amount of nitrogen from animal manures and commercial fertilizers, many pollutant incidents have been found and reported on nitrate contamination in intensive agriculture especially in greenhouse vegetable production systems (Zhang, 1996; Ju, 2007; Li, 2002; Song, 2009). Environmental and economic concerns have prompted agriculture researchers and producers to seek for more and more efficient strategies for nutrient managements. The present public concerns on nitrate management are focusing on N, which exceeds crop demand and might migrate from agro-ecosystem to groundwater and surface water (Daniel, 1994). Economic considerations in nitrate managements mainly focus on efforts to improve N utilization and reduce costs of N inputs. Based on its necessity for mobility in the soil and risk to environment systems, popular N management thus aims to balance N inputs with

Song Xiaozong, Jiang Lihua, Lin Haitao, Xu Yu, Gao Xinhao, Zheng Fuli, Tan Deshui, Wang Mei,

*Institute of Agricultural Resources and Environment, Shandong Academy of Agricultural Sciences Jinan, 250100,* 

**1. Introduction** 

 \*

*China*

Shi Jing and Shen Yuwen

**Prevent Nitrate-N Leaching in Intensive** 

**Agriculture System** 

*Institute of Agricultural Resources and Environment, Shandong Academy of Agricultural Sciences Jinan, 250100,* 

Liu Zhaohui et al.\*
