ia(l ELSEVIER Agriculture, Ecosystems and Environment 64 (1997) 233-244

A g r i c u l t u r e E c o s y s t e m s & E n v i r o n m e n t

Application of crop simulation modeling and GIS to agroclimatic assessment in Burkina Faso

Oumarou Badini b,1 Claudio O. St~Sckle a, * Eldon H. Franz c,2 9

Department of Biological Systems Engineering, Washington State Unff, ersity, Pullman, WA 99164-6120, USA b Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164-6420, USA

c Programme in Environmental Science and Regional Planning, Washington State Universi~, Pullman, WA 99164-4430, USA

Accepted 10 January 1997

Abstract

Rainfed crop production is the main source of food and income in Burkina Faso. Environmental conditions are characterized by low and erratic rainfall, compounded by high temperatures and radiation loads. These factors make inadequate water availability the major cause for low crop yields and frequent crop failures. This paper outlines the procedure used to investigate the water-limited growth environment of an improved millet cultivar in Burkina Faso. A daily time-step cropping system simulation model (CropSyst) was used to simulate the soil water budget components and millet production potential, both spatially and temporally, by coupling the model with databases of soil type, long-term weather, and crop management using a geographic information system (GIS). From the cropping model outputs, two agroclimatic indices (Aridity Index and Crop Water Stress Index) that show the water-limited growth environment of the millet crop throughout the country were quantified and mapped with the help of the GIS. This allowed the identification of agroclimatic zones, as determined by the crop water needs. Millet productivity decreased from the south to the north of the country in relation with rainfall isolines and soil types. Locations with less than 500 mm of annual rainfall are marginal for millet, particularly on planosols and arenosols. In regions with rainfall above 700 mm, moisture availability is not a major limiting factor for the 90-day millet production, especially on regosols, cambisols, acrisols and nitosols. Overall, the approach followed in this study appeared promising for quantifying the growth environment of millet as affected by soil type and weather. It could also help to provide guidelines for crop water management and analysis of the suitability of improved crop cultivars. © 1997 Elsevier Science B.V.

Keywords: Millet; Sahel; Climatic index; Crop growth; GIS; Mapping

I. Introduct ion

Like other countries in the West African Sudano- sahelian zone, 90% of the population in Burkina

+ Corresponding author. stockle@wsu.edu

i Fax: (509) 335-8674. 2 Fax: (509) 335-7636.

Fax: (509) 335-2722; e-mail:

Faso lives in rural areas where rainfed crop produc- tion is the major source of food and income. The climatic conditions are unique and characterized by an extreme combination of monomodal, variable and erratic rainfall with high temperatures and radiation loads during the rainy season (Lal, 1991). Also, very high temperatures and very low atmospheric humid- ities produce high evaporative demand in the dry season (Van Keulen and Breman, 1990; Carberry

0167-8809/97/$17.00 © 1997 Elsevier Science B.V. All rights reserved. PII S0 167-8809(97)0004 1-8

234 O. Badini et al. / Agriculture, Ecosystems and Environment 64 (1997) 233-244

and Abretch, 1991). The conditions lead to a very difficult environment for perennial plants. They also negatively impact the length of the growing cycle for annual crops leading to low yields and frequent crop failures.

Production of crops requires the availability of sufficient water. An examination of the soil water balance in the country shows that only a small fraction (10-15%) of the annual precipitation is used by plants with a relatively low efficiency (Van Keulen and Breman, 1990). Because of the low infiltration rate of many soils, runoff is high (30% in some cases). In addition, much of the small proportion of infiltrated water is evaporated from the soil surface because of light plant cover due to the low availabil- ity of nitrogen and phosphorus. This low availability of plant nutrients contributes to the low efficiency of water use. Water availability and nutrients are thus the major constraints limiting crop production in Burkina Faso and in the region as a whole.

To facilitate locally-based application of crop management options, transfer of technologies, and planning alternatives commensurate with the bio- physical attributes of each location, a quantitative understanding of the relationships among crop, cli- mate and soil are needed. To satisfy such an aim, the development of methods that allow the classification of climate and soils into relevant agronomically ho- mogeneous zones is a necessary and important pre- requisite.

There are many agroclimatic classifications seek- ing to describe the moisture conditions of crops which have been applied to the arid and semi-arid regions of Africa and elsewhere in the tropics (e.g. Thornthwaite, 1948, 1955; Reddy, 1984). These clas- sifications covered a vast array of complexity, rang- ing from the use of one parameter to methods incor- porating a number of parameters. More often, these classifications are country or region-specific (Thornthwaite, 1955; Virmani et al., 1980; Reddy, 1984; Sivakumar and Gnoumou, 1985; Ofori- Sarpong, 1987).

In most of these agroclimatic classifications, rain- fall and potential evapotranspiration isolines were often used to delimit the growth environment of crops. Recommendations for the introduction of new crops or varieties are often based on this classifica- tion with little regard for soil types and the level of

production risk. The main limitation of this proce- dure is that it does not take into account the variabil- ity of the soils (depth, water holding capacity, bulk density, etc.) and the dynamics of crop growth in relation to these physical conditions.

The use of new methodologies such as crop mod- eling and GIS may be useful and perhaps the only viable approach to overcome these problems. To classify and display agroclimatic datasets obtained from simulation modeling, geographic information system (GIS) technology provides a valuable set of tools (Pollak and Corbett, 1993) which allow the data to be transformed into information more useful for decision-making.

The objective of this study was to apply crop growth simulation modeling (CropSyst; St6ckle and Nelson, 1993a; St~Sckle et al., 1994) and a GIS (ARC/INFO from ESRI, 1990) to quantify and map agroclimatic indices based on weather patterns and variability in soils describing the growth environ- ment and production potential of millet (Pennisetum americanum) in Burkina Faso.

2. Materials and methods

2.1. The study area

The study area includes the entire country of Burkina Faso (Upper Volta, until 1984) which is part of the West African Sahelian and Sudanian zones. Burkina Faso is a landlocked country stretching across 274 200 km 2. The country lies between 6°W and 2°30'E longitude and between 9 and 15°N lati- tude. The country is mainly fiat, with a mean altitude of about 300 m.

The soils of the country have been studied by diverse research and development institutions. The French soil classification scheme has been applied in most cases. Following the classification of FAO (1977), the soils have been regrouped into ten major types (Sivakumar and Gnoumou, 1985). Luvisols are the most important (39%) followed by Regosols (26%). Cambisols (13%) are frequent in the western zone. The other types by decreasing order of impor- tance include Acrisols, Fluvisols, Lithosols, Arenosols, Vertisols, Planosols and Nitosols (Fig. 1, Table 1). The soils are generally poor in organic

O. Badini et al. / Agriculture, Ecosystems and Emfironment 64 (1997) 233-244 235

Fig. l. Soil map of Burkina Faso( l :5000000)(FAO, 1977 a~erSivakumarand Gnoumou, 1985).

matter (OM) and macro-nutrients such as nitrogen, phosphorus and potassium. Also, most of the soils have a low water holding capacity and a very low infiltration rate resulting in high runoff of the incom- ing water.

Climatic patterns are characterized by the atmo- spheric circulation over Burkina Faso, which is

mainly influenced by two of the three major perma- nent centers of high pressure (or anticyclones) of the region: the Saharan and the Southern Hemisphere anticyclones (Sivakumar and Gnoumou, 1985). Un- der the influence of these anticyclones and the north-south fluctuation of their contact zone called the Intertropical Convergence Zone (ITCZ), climatic

Table 1 General description of the ten main types of soils found in Burkina Faso

Soil type Soil description

Acrisols Fluvisols

Regosols

Lithosots

Arenosols Vertisols Cambisols

Luvisols Planosols Nitosols

Soils usually leached with a low base saturation Soils formed by alluvial deposits and mostly spread in the valleys. Commonly high fertility soil, suitable lor planting various crops Soils have only a light colored A horizon with little or no development. They are generally poor because of low moisture and nutrient holding capacity Soils with hard rock generally on hill slopes. They have a very shallow depth and are poor with very little potential for improved agriculture Soils are gravelly and stony with weak development and low water holding capacity Heavy dark soils, with a good structure. They are generally rich in clay and fertile but have poor drainage Sandy and rapidly permeable soils. Poor in phosphates and bases. Organic matter is sufficient for good physical conditions and they respond well to fertilizer and management Leached soils presenting a horizon of clay alluvium and high base saturation Soils generally developed in leveled or depressed topography with poor drainage Soils with a shiny surface and clay content increasing downward

236 O. Badini et aL / Agriculture, Ecosystems and Environment 64 (1997) 233-244

conditions are characterized by a unimodal rainy season (from May to October) with erratic rainfall amounts starting earlier and diminishing from south to north across the country. During the dry season (November to May) the influence of the Saharan anticyclone is felt through flows northeast to south of dry air fluxes. This pattern is locally known as 'harmattan' and is characterized by hot days and cool nights. High temperatures, radiation and poten- tial evapotranspiration are also common features, especially in the northern part of the country.

2.2. The simulation model: CropSyst

CropSyst is a multi-year, multi-crop, daily time- step simulation model developed to serve as an analytic tool for investigating the effect of cropping systems management on crop productivity in relation to environmental patterns. The model consists of several integrated components and different manage- ment options. Details about components and uses can be found in the model user's manual (Stickle and Nelson, 1993a) and elsewhere (Sttickle et al., 1994).

2.2.1. Soil water balance modeling For the present purpose of agroclimatic character-

ization, precipitation, canopy and residue intercep- tion, runoff, infiltration, redistribution in the soil, and maximum and actual evapotranspiration were con- sidered (St~Sckle et al., 1994).

Cropsyst provides several options for the compu- tation of the daily maximum evapotranspiration (ETma x) defined as the amount of water that would be transpired by the crop and evaporated from the soil if available water were not limiting. These op- tions include a temperature- based simple model (Ndlovu, 1994), the Priestley-Taylor model (Priest- ley and Taylor, 1972) or the Penman-Monteith model (Monteith, 1965). Owing to the limited available weather data, the simple temperature-based ET model was selected for the computation of ETma x. In this simple model, air temperature is used to predict net solar radiation, which is then used to estimate ETma X with the Priestley-Taylor equation.

The daily actual evapotranspiration (ET a) ac- counts for the actual soil evaporation and crop tran- spiration. Actual soil evaporation is simulated by postulating two stages of drying. First, the evapora-

tion proceeds at the potential rate until the water content in the top evaporation soil layer (top 10 cm of soil) reaches the permanent wilting point. For second stage drying, the equation outlined by Camp- bell and Diaz (1988) was used. Actual transpiration and crop water uptake are considered equal in Crop- Syst, i.e. crop water storage is assumed to be negligi- ble. The calculation of crop actual transpiration fol- lows the approach proposed by St6ckle et al. (1994).

2.2.2. Crop development and growth modeling Crop development is determined as a function of

thermal time required to reach specified growth stages, modulated by photoperiod and vernalization effects if appropriate. Crop growth is determined by above ground biomass accumulation, which is de- pendent on intercepted radiation converted to biomass (radiation-limited growth) (Monteith, 1977), transpi- ration (water-limited growth) and nitrogen uptake (nitrogen-dependent growth). Each of these factors is capable of limiting growth of plants. For this study, where the main objective was to quantify water-re- lated agroclimatic indices, nitrogen limitations were not explicitly simulated, but played a role in the selection of other growth parameters so as to limit maximum attainable yields.

The water-limited growth rate is calculated using (Tanner and Sinclair, 1983)

G w = Kr, ct/VPD

where G w is waterqimited biomass production (kg m -2 day ~), T.~c t is the actual transpiration (m day ~ ), K is the above ground biomass transpiration coefficient (kg-kPa m-3), and VPD is daily mean vapor pressure deficit (kPa).

Radiation-limited growth rate (G r) (kg m 2 day - l ) is given by Monteith (1977)

Gr = efS,

where e is a radiation conversion efficiency for the crop (kg MJ-~), S t is the total estimated daily solar radiation above the crop canopy (MJ m -2 day-1), and f is the fraction of incident radiation intercepted by the canopy. The minimum of G w and G r defines the actual biomass growth for a given day. Leaf area index and root growth are related to biomass accu- mulation (Sti3ckle and Nelson, 1993a; StiSckle et al., 1994).

O. Badini et al . / Agriculture, Ecosystems and Em, ironment 64 (1997) 233-244 237

2.3. Input data requirements for CropSyst

The input data sets needed for Cropsyst describe the location, weather, soils, crop and management practices.

2.3.1. Location-weather database In Cropsyst, location-related parameters refer to

information that is specific to the study site such as name, latitude, and daily weather database. Based on existing weather records (Sivakumar and Gnoumou, 1985), 17 locations (given in Fig. 2) were selected to build the location-weather database. Each location- weather polygon was referenced to a given planting date as an attribute of the polygon. To satisfy the need for the long-term daily weather records re- quired by the model, the weather generator CLIMGEN (Campbell, 1990) was used, since com- plete daily weather data were not available for all years and all locations.

CLIMGEN uses historical monthly weather statis- tics to generate daily weather data. The ability of the weather generator to generate long series of daily weather from limited data has been successfully tested with a worldwide database (Ndlovu, 1994).

Synthetic records representing 30 years of daily rain- fall, and maximum and minimum temperature values were generated for the 17 locations. This was done using historical monthly data (30-60 years of record) from literature as well as data obtained from the national meteorological service in Burkina Faso.

2.3.2. Soil database A parameter file was generated for each main soil

type in Burkina Faso (Fig. 1) using soil data assem- bled by Som4 (1989). Each soil parameter file was composed of the number of soil layers, layer thick- ness (m), volumetric water content (m 3 m -3) at permanent wilting point and field capacity, and bulk density (g cm-3).

2.3.3. Crop database To test the water-related constraints to crop pro-

duction in the country, a 90-day improved millet cultivar (IKMV 8201) was chosen as reference. This cultivar was proposed for introduction in farmers fields following a joint breeding program between ICRISAT and the National Agricultural Research Institute of Burkina Faso (INERA). The proposed agroclimatic zones supposedly suitable for the vari-

Burkina Kmrkoye

n ~<~! G!~O-70Ornra m 850-080rdrn m alaov~, 1050 rn~n

Fig. 2. Weather and locations map of Burkina Faso.

238 O. Badini et al. / Agriculture, Ecosystems and Environment 64 (1997) 233-244

ety were those with annual rainfall ranging from 500 to 700 mm for a planting date around the first of July, and rainfall ranging from 700 to 900 mm with a delayed planting date around 15 July. The crop file included parameters such as the thermal time re- quirements for given phenological events, the maxi- mum root depth, the maximum leaf area index (LAI), the biomass-transpiration coefficient and other pa-

Table 2 Crop parameters used to simulate millet in CropSyst

Parameter Value Units

Thermal time requirements Emergence 90 °C-day Begin flowering 900 °C-day Begin grain filling 1000 °C-day Physiological maturity 650 °C-day Base temperature 12 °C Cutoff temperature 35 °C Phenologic sensitivity to water 1 - stress

Morphology Maximum rooting depth 0.70 m Maximum leaf area index (LAI) 2.50 m 2 m- 2 Fraction of max. LAI at 0.50 - physiological maturity Specific leaf area 20 m e kg t Leaf stem partition 4.0 - Leaf duration 800 °C-day Extinction coefficient for 0.45 - solar radiation Full-cover ET crop coefficient 1.20 -

Growth Biomass-transpiration coefficient 5.00 * kg-kPa m - 3 Light to above ground biomass 4.00 g MJ- I conversion At l e t ratio where crop growth 0.90 - ceases Optimum temperature for early 30 °C growth Maximum water uptake 10 mm day- t Critical xylem water potential - 1200 J kg-1 Wilting xylem water potential - 2600 J kg- i

Residue Residue decomposition time 60 days coefficient Area to mass ratio of residue cover 1.0 m 2 kg-

* Typical values for millet are higher, but this was selected to represent potential growth under the harsh conditions of Burkina, limited by nutrients and soil degradation.

rameters (Table 2). Owing to limited information on the cultivar a thorough calibration was not possible and most parameters were used as suggested in the model user's manual (StiSckle and Nelson, 1993a) or were derived from literature (Goldsworthy and Fisher, 1984; Pearson, 1984).

2.3.4. Management database From the many management options allowed by

Cropsyst, only automatic irrigation and dry land (actual rainfed) 30-year runs were selected for the computation of maximum evapotranspiration, poten- tial biomass production, actual evapotranspiration and actual biomass production.

2.4. Geographic information system mapping proce- dures

A vector-based geographical information system (ARC/INFO) was used for the construction of cov- erages of soils and location-weather as well as for the mapping and display of agroclimatic indices.

2.4.1. The soil and location-weather coverages The soil coverage was created from a small-scale

soils map (1:5 000 000) of Burkina Faso (Sivakumar and Gnoumou, 1985). All polygons representing the ten main types of soils identified in the country were digitized and geo-referenced to UTM coordinates (Fig. 1).

To create the location-weather coverage, seven- teen homogeneous areas were drawn based on exist- ing meteorological stations and rainfall isolines with increment of 50 mm from north to south of the country (Fig. 2). (For convenience, the two planting dates were also provided in the location-weather database file because they corresponded to the loca- tions.)

2.4.2. Preparing a combined simulation map The next step was to create a combined simula-

tion map. Each polygon number from each coverage (soil and location-weather) was associated with the corresponding CropSyst (soil and location-weather) parameter files. This was accomplished by creating a simple text file consisting of the polygon numbers and the corresponding parameter file names, which

o. Badini et al./ Agriculture, Ecosystems and Environment 64 (1997) 233-244 239

was used as input to the ARCINFO-CropSyst Coop- erator (ARCCS) program (StiSckle and Nelson, 1993b), which controls model execution and gener- ates the simulation outputs.

2.5. Simulation outputs

The ARCCS program generates CropSyst simula- tions to be run for all the uniquely identified soil and location-weather combination polygons. Thirty-year simulation runs were performed for the two manage- ment conditions (rainfed and irrigated). Daily and yearly biophysical parameters characterizing the ac- tual and potential growth environments of the crop were generated. The soil water budget variables of interest were precipitation, ETma x, and ET a. The millet production variables included the actual and potential above ground biomass (kg ha- l ) .

2.6. Agroclimatic assessment

The outputs of the soil water budget and the crop variables obtained from the simulation were used either singly or in combination to compute two agroclimatic indices that helped determine the water-limited growth environment of the improved millet cultivar. These were named the Aridity Index and the Crop Water Stress Index, and are described below. Using these indices, agroclimatic zones indi- cating the spatial distribution of aridity and millet water stress in relation to soil types and weather patterns were mapped with the help of the GIS.

2.6.1. Aridity index (AI) To determine the adequacy of rainfall in satisfy-

ing the water needs of the crop, an aridity index, defined as the ratio between the accumulated rainfall and the maximum millet evapotranspiration during the growing season, was computed for all locations and mapped for the country. The aridity index was used to express the relative degree of moistness of a climate in any location of the country in relation to millet water use. This index helps to characterize the growth environment of the millet on the basis of climatic variability but does not include the effect of soil variability.

2.6.2. Crop water stress index (CWSI) The crop water stress index is defined as the ratio

between actual millet evapotranspiration (ET a) and

maximum millet evapotranspiration (ETma x) during the crop growth cycle. ET a represents the amount of water transpired from the crop and evaporated from the soil surface under rainfed conditions. ETma x is the amount of water that would be transpired by the crop and evaporated from the soil if available water were not limiting. Under very limited water condi- tions, the deficit can be so severe as to cause crop failure ( E T a is near zero) and thus the ratio becomes very small. The CWSI index provides an insight into the crop response to the environment taking into account both the soils and weather variability. It can be thought of as integrating all factors affecting water availability for crop growth (water holding capacity, soil depth, runoff, etc.). A map of CWSI was constructed for the whole country with eight classes of indices ranging from 0 (extreme water stress) to 0.9 (very low water stress).

3. Results and discussion

3.1. The aridity index (AI)

Values of AI ranged from 0.40 to 1.88, where the lowest ratio corresponded to high aridity (or low degree of moistness), and the highest to low aridity (or high degree of moistness). A linear regression between simulated 30-year averages of biomass pro- duction and AI values for corresponding locations

Y = 2.916x + 0.175

6 II | R square = 0.82 m / , , ~

/ / , -

v i i I

1

0 r I I

0,4 0.6 0.8 1 1.2 1.4 1.6 1.8

Addi~/Index

Fig. 3. Plot of simulated biomass production (t ha -I) (30-year average) versus AI values for corresponding locations.

240 O. Badini et al. / Agriculture, Ecosystems and Environment 64 (1997) 233-244

Burkina ...... ....

Faso .....

m > 1.6 <$ubhumi d)

Fig. 4. Millet AI map for Burkina Faso.

(Fig. 3) gave a good relationship (R 2= 0.82). The outliers, which depart significantly from the regres- sion line in Fig. 3, are consistent with some soil types such as the planosols and arenosols which exhibit very low biomass production potential in all AI classes. Based on the spatial distribution of the AI throughout the country, four homogeneous classes of AI were defined. These classes were used to represent four main climatic zones on the aridity index map (Fig. 4) based on rainfall and ETma x.

Zone 1 on the map corresponded to the zone with A I < 0.6. This zone, covering the locations of Markoye and Dori in the northern part of the coun- try, was considered to be arid with rainfall ranging from 400 to 600 mm. The average value of ETma x over the growing season was about 850 mm, and simulated millet biomass production was less than 2 t ha -1

Zones 2 and 3 (Fig. 4) included areas with 30-year average aridity indices of 0.6 to 1.! and 1.1 to 1.6 respectively. Zone 2, defined here as semi-arid re- gion 1, included locations such as Ouahigouya, Tougan, and Kaya. Rainfall ranged from 650 to 800 mm year-l with an average ETma x during growing season of about 760 mm. Zone 3, including locations

such as Ouagadougou, Fada, Tenkodogo and Dia- paga, was defined as semi-arid region 2, with annual rainfall ranging from 800 to 950 mm and an average ETma x during growing season of about 700 mm. These two zones combined covered the largest part of the country. Simulated biomass production varied greatly, from less than 1.7 to 3.5 t ha-I for zone 2, and from 2 to 5 t ha-~ for zone 3. Despite the higher productivity in these zones compared with the arid region, year-to-year variability in production is large, especially in zone 3.

The last zone, zone 4, with AI values over 1.6, covered the southwest of the country including the locations of Bobo-Dioulasso, Niangoloko, Gaoua. This zone corresponded to the subhumid region of the country with annual rainfall ranging from 950 to 1300 mm and ETma x values around 613 mm during the growing period. The simulated millet biomass production ranged from 4 to more than 6 t ha-1 in this zone.

In general, aridity indices delimited climatic zones that could be useful in pinpointing areas suitable for millet if soil data were not available. Millet produc- tivity decreased from south to north in parallel with rainfall isolines and aridity index zones. The simu-

O. Badini et al. /Agriculture, Ecosystems and Enuironment 64 (1997) 233-244 241

lated millet cultivar is considered by crop breeders in Burkina Faso to be drought-resistant and adapted to the rainfall zones of 500-700 mm and 700-900 mm, with respective planting dates on 1 and 15 July. In this study, the simulation results indicate good adapt- ability of millet in the region with rainfall above 500 mm, in agreement with the view of local breeders, although great year-to-year variability in productivity could occur. Also, in the arid zone, with rainfall below 500 mm, the results suggest that the produc- tion of millet will be limited by water, in agreement with the recommendations of the breeders.

FAO studies defining agroclimatic zones of simi- lar characteristics have been based on the concept of the length of growing season (LGS) estimated from monthly average water balance (Virmani et al., 1980; Sivakumar and Gnoumou, 1985; FAO, 1986). The AI map based on rainfall and maximum evapotran- spiration values is of the same order of detail and provides a first approximation of the suitability of a given climate for agriculture. However, other factors such as soil type and crop growth characteristics also contribute to potential productivity.

3.2. The crop water stress index (CWSI)

CWSI values ranged from zero to 1, where an index of zero represented maximum stress and an index of 1 represented no stress (optimum growth conditions). Since the index included the actual crop evapotranspiration it was found to be better corre- lated to biomass production (Fig. 5) (with R 2 = 0.86) than the AI. In contrast to the AI, that does not take into account soil attributes, the CWSI values were dependent upon the rainfall isolines and the variabil- ity in soil water holding capacity, allowing a more detailed classification.

Fig. 6 shows a relationship between CWSI and the coefficient of variations (CV) of biomass produc- tion simulated throughout the region. Based on this, we have divided the country into four classes which includes different soil types. The average and coeffi- cient of variations of biomass production for four classes and different soil types are given in Table 3. Also, a map of the spatial distribution of the CWSI is shown in Fig. 7. Computed values for Burkina Faso ranged from 0.1 (very high stress) to 0.7 (low stress).

From the map (Fig. 7), the areas corresponding to

7

Y = 10.83x - 1.067

6 R square = 0.86 "

i ,

-

2

0 ~ I J I

0.1 0.2 0.3 0.4 0.5 0.6 0.7

CWSI

Fig. 5. Plot of simulated biomass production (t ha - j ) (30-year average) versus CWSI values for corresponding locations.

the CWSI < 0.4 (very high to high stress) had biomass production ranging from less than 1 to 3 t ha-1 depending on the type of soil and location- weather. Although these areas with high stress were found mostly in the northem arid region, similar patches are present in other locations of the country. Comparison of millet biomass production levels as a function of soils found in these areas (CWSI < 0.4 in Table 3), showed that fluvisols appeared to be the best soils. Average simulated biomass production on fluvisols reached 2.9 t ha -1 with a coefficient of variation (CV) between 30 years of 29%, probably due to its higher water holding capacity. Following

6 5 ¢ l m 1

4 5

35 c ~ , 2

cl~s 3

Ms 4 1 5 .

5 :

0.1 0.2 0.3 0.4 0.5 0.6 0.7

CWSI

Fig. 6. PLot of CV for 30-year average values of simulated biomass production versus CWSI values for corresponding loca- tions.

242 O. Badini et al . / Agriculture, Ecosystems and Environment 64 (1997) 233-244

Table 3 Simulated 30-year averages of simulated millet biomass produc- tion (t ha - l ) per soil type found in each of four CWSI classes throughout Burkina Faso. Values in parentheses correspond to the coefficient of variation (%)

Soils Class 1 a Class 2 b Class 3 c Class 4 d

Acrisols 4.16 (27) 5.25 (15) Arenosols 1.75 (43) 3.38 (25) 5.63 (12) Cambisols 1.77 (41) 4.09 (16) 4.97 (19) 5.95 (10) Fluvisols 2.92 (29) 3.41 (23) 4.52 (25) Lithosols 3.85 (19) 5.07 (19) Luvisols 2.30 (35) 3.60 (22) 4.56 (22) 5.56 (14) Nitosols 4.16 (27) 5.25 (15) Planosols 1.53 (43) Regosols 2.26 (33) 3.92 (17) 4.93 (17) 5.94 (9) Vertisols 2.19 (38) 3.67 (18) 4.27 (18)

a Locations with CWSI < 0.4. b Locations with CWSI = 0.4-0.5. c Locations with CWSI = 0.5-0.6. d Locations with CWSI > 0.6.

the fluvisols are luvisols and regosols (2.3 t ha - l ) (CV = 35% and 33%, respectively) and vertisols (2.2 t ha -~) with a CV = 38%. The planosols were the least productive with an average biomass production of 1.5 t ha- 1 and a very high CV of 43%.

Values of CWSI ranging from 0.4 to 0.5 (medium stress) corresponded to simulated biomass produc- tion ranging from 3.4 to 4.2 t ha ~ with variability also related to the soil types and locations. Acrisols and nitosols, for which similar parameters were used in the model, had the highest biological productivity (4.2 t ha - l , CV= 15%) followed by cambisols (4.1 t ha -1, CV= 16%) and regosols (3.9 t ha -1, CV= 17%).

The areas with values of CWSI ranging from 0.5 to 0.6 (medium to low stress) covered the largest part of the country. Simulated biomass production ranged from 4 to more than 5 t ha-1 (Table 3). Arenosols had the highest productivity (5.6 t ha -1, CV = 12%), followed by acrisols-nitosols (5 t ha -~, CV = 15%) and cambisols (5 t ha - I , CV = 16%).

The areas with CWSI > 0.6 (low stress) had mil- let biomass production ranging from 5 t ha - t to around 6 t ha - l . This zone covered mostly the southern subhumid part of the country (Fig. 7). Cambisols, regosols and luvisols expressed the high- est biomass production with 6 t ha - l (CV= 10%), 5.9 t ha- i (CV = 9%) and 5.6 t ha- l (CV = 14%), respectively.

e.4 +.c, B.5 <rn4,ai,Jm) I 0.6 ~.~, O.~

Fig. 7. Map of CWSI for Burkina Faso.

O. Badini et al. /Agriculture, Ecosystems and Environment 64 (1997) 233-244 243

In contrast to the AI, that does not account for soil attributes, the CWSI is dependent upon the rainfall isolines and the variability in soil water holding capacity. The CWSI map (Fig. 7) suggested that millet could be grown, although with some limitations, in all weather zones but soil types should be an important discriminatory element for the intro- duction of the crop in a given environment. Soil types become increasingly important as the CWSI become more extreme such as in class 1 (Table 3). Drier areas show a wider spread of productivity as a function of soil (e.g. CWSI < 0.40) than the more humid areas (e.g. CWSI > 0.60). Also, Fig. 6 shows that C V decreases with increases in the value of CWSI, as affected by weather. On the other hand, we see that the spread in the values of C V is much larger for class 1 (arid) and decrease as the class reflects more humid conditions (i.e. as CWSI in- crease) as affected by soil type. In the extreme areas corresponding to CWSI < 0.4 for example, soils with higher holding capacity such as fluvisols were more favorable to the millet production. Broad zones as defined from rainfall isolines solely or even from the aridity index map (Fig. 4) could be misleading in the determination of suitable growth environment for the millet.

4. Conclusions

= 0.82) and the Crop Water Stress Index (R 2 = 0.86) were obtained. The spatial distribution of these in- dices helped locate suitable millet growth environ- ments, and indicated where crop failures due to water limitation were likely to occur. The results showed that below 500 mm of annual rainfall, millet production was not promising.

Distribution of biomass productivity was a func- tion of weather and soil types. Productivity de- creased along rainfall isolines from the south to the north of the country. Overall, the central and subhu- mid zones have better productivity and less variabil- ity from season to season. The arid zone, in the northern part of the country, was found to be very marginal for the improved millet cultivar, with lesser productivity and greater year-to-year variability par- ticularly on planosols and arenosols. Soil types mod- ulated responses to weather in all zones.

The approach followed here, which included the use of growth simulators, weather generators, and GIS technology, provides a basis for examining the effects of water stress on crop productivity. Such an approach can provide guidelines for resource man- agement and could be extended to crop production forecasting, water management, and comparison of new crop cultivars for introduction in different agro- climatic zones.Validation of model results with real data would be desirable for future progress.

The objective of this study was to quantify agro- climatic indices to assess water stress effects on millet production. Two indices were derived from crop growth simulation outputs: A I and CWSI. The A I defined as the ratio between the accumulated rainfall and the maximum crop evapotranspiration during the growing season, was used to characterize the growth environment of the millet crop based on climatic variability without including the effect of soil type. On the other hand, the CWSI, defined as the ratio between actual millet evapotranspiration and maximum millet evapotranspiration during the crop growth cycle, integrated both the soils and weather variability throughout the country. These indices appeared as promising quantitative approxi- mations of agricultural productive capacity in Burk- ina Faso. Good correlations between the simulated biomass production and both the Aridity Index (R 2

Acknowledgements

The authors gratefully acknowledge Roger Nelson for assistance in the use of the CropSyst and GIS models. The financial support of the USAID Thomas Jefferson Fellow Program helped make this study possible.

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