A Crop Model and Fuzzy Rule Based Approach for Optimizing Maize Planting Dates in Burkina Faso, West Africa
By Kunstmann, Harald | |
Proquest LLC |
ABSTRACT
In sub-Saharan Africa, with its high rainfall variability and limited irrigation options, the crop planting date is a crucial tactical decision for farmers and therefore a major concern in agricultural decision making. To support decision making in rainfed agriculture, a new approach has been developed to optimize crop planting date. The General Large-AreaModel for Annual Crops (GLAM) has been used for the first time to simulate maize yields in
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1. Introduction
Rainfed agriculture in sub-Saharan Africa (SSA) is characterized by prolonged dry spells, droughts, and low inputs of manures, chemical fertilizers, and insecticides. Farmers still suffer from low productivity. Nevertheless, this agricultural system remains the dominant source of staple food production and the livelihood foundation for SSA countries. Several studies addressing the specific agricultural problems have shown that SSA is a waterscarce region (Challinor et al. 2007; Roudier et al. 2011; Biazin et al. 2012), where farmers have to cope with high rainfall variability. Different soil and water management techniques have been developed and promoted throughout SSA countries to optimize water consumption by plants (Rockstrom et al. 2002; Kabore and Reij 2004). However, with prolonged dry spells at the beginning of the rainy season, the risk of resowing and crop failures during the first stage of plant development is still a major concern in smallholder farming systems in SSA. Consequently, strategic agricultural decisions such as planting dates help reduce the need for crop resowing and crop failure and are, therefore, a key element in agricultural decision support. For farmers in SSA, crop planting date estimation, which is closely linked to the onset of the rainy season, is an important tactical operation as it determines the length of the plant growing period for the ongoing agricultural season. Accordingly, it is also related to the choice of crop and cultivar to plant.
Various definitions of the onset of the rainy season (ORS) in relation to the crop growing season have been developed for water-limited areas. Among them, rainfall-based approaches have been developed and are currently in use in SSA (e.g., Stern et al. 1981; Sivakumar 1988, 1990; Dodd and Jolliffe 2001;
With the increased development of process-based crop models in agricultural impact studies, new crop-specific approaches have been developed to estimate crop planting dates. These approaches have been used either at plot scale or regional scale and can be subdivided into two groups.
The first group consists of methods using only crop models to derive suitable planting dates. In this group, a crop yield optimization method is required (e.g., Stehfest et al. 2007). Depending on the crop model and the optimization method, this approach can be computationally time demanding. To overcome this issue, specific assumptions are usually made. For instance, Folberth et al. (2012) estimated crop planting dates by employing a crop model at a monthly or weekly time step. According to the region, they limited the planting date computation period by using a reported earliest and latest planting date.Although a time windowof 1month for crop planting is valuable in general, it is not favorable for regions in SSA where the growing season lasts only 3 months. In this first group, in addition to the high demand in computing time, crop models require a significant amount of input data. Therefore, this is a limitation for crop simulation, particularly in the data-scarce region of SSA.
The second group consists of a combination of crop models and rainfall distribution characteristics (e.g., Laux et al. 2010). In this approach, the first step is to derive planting dates that fulfill specific agronomical criterions using rainfall information only. Then, the resulting planting dates are used as input into a crop model to derive optimized planting rules by applying a suitable objective function and an optimization algorithm. This approach reduces significantly the required computation time and can be used to improve rainfall-based methods (Laux et al. 2010). This latter approach may open a new avenue in planting date estimates, since it can be used to derive crop and location-specific planting dates. However, determining the appropriate agrometeorological criteria to derive planting dates and the application of optimization methods to support agricultural decision making remain challenges.
This study fits into the second group. The research question is how to use crop planting date as an agricultural management strategy to support agricultural decision making in SSA. This research question is addressed by an approach aimed at optimizing crop and locationspecific planting dates. For this purpose, fuzzy logic- based planting rules in combination with a large-scale crop model have been used. As a staple crop in
The article is composed of three main parts. The first part deals with the study area, the input data, data processing, and the applied crop model [i.e., the General Large-Area Model for Annual Crops (GLAM)]. The second part deals with calibration of
2. Study area
The climate of BF is characterized by two distinct seasons: a rainy season and a dry season. The dry season ranges from November to April and the rainy season ranges from May to October. During the dry season, the country is influenced by the Saharan anticyclone, which causes a flux of dry and cool air, the so-calledHarmattan, over the country. The highest temperatures occur mainly in April-May while the coolest temperatures occurmainly in December-January (Sivakumar and Gnoumou 1987). At large scale, the rainy season is driven by the anomalies of the sea surface temperature (SST) in the tropical
To capture rainfall variability in the study area, observed daily rainfall data provided by the
The mean temperature of the wet season has been estimated to range between 208 and 368C and decreases from north to south across the country (Sivakumar and Gnoumou 1987). The agroecological zones match with the north-south distribution of the rainfall. The interannual and intraseasonal variability of rainfall is one of the major limiting factors of rainfed crop production in
3. Materials and methods
a. Climate data
The large area process-based model for annual crops (GLAM) requires daily weather data, mainly precipitation, mean temperature, and solar radiation (Challinor et al. 2004). Two sources of data have been used within the context of this study. Daily precipitation data from 141 rain gauges (Fig. 1a) have been provided by the DGM for a time series of 31 yr (1980-2010). These precipitation data have been gridded at a resolution of 0.758 3 0.758 (i.e., 51 grid points for the study area) using ordinary kriging (OK). The OK technique is one of the most commonly used methods for interpolation. In this study, the number of rain gauges (141) was assumed to be acceptable for 0.758 3 0.758 gridcell interpolation, using OK. The anisotropy of rainfall variability was well captured. Figure 2 illustrates the gridded mean annual precipitation (1980-2010) (Fig. 2a) as well as the error map (Fig. 2b).
For the study domain,
b. Soil data
Gridded soil types and their hydrological properties (soil water content at saturation and soil water content at field capacity, soil water content at the wilting point) have been derived from the Harmonized World Soil Data (HWSD) dataset in combination with ArcInfo and a soil water content computation algorithm. First, based on the climate data grid coordinates for the target area, the matching soil mapping unit was derived using a geographic information system (ArcInfo). Then, the dominant soil type for each grid location, and its associated soil properties, are summarized using the tabular soil database from HWSD (FAO 1991). Finally, the soil water content parameters were estimated following an algorithm designed for the computation of soil water limits (Ritchie et al. 1999; Suleiman and Ritchie 2001).
c. Crop yield data
Province-level maize yields (kg ha21) over 27yr (1984- 2010) from all 45 provinces in
...
where Ygrid (kg ha21) is the gridded crop yield, Ydistrict(i) is the crop yield in district i, wi is the fraction of land area of district i within the grid cell, and n is the number of grid cells that share the land area of the grid cell.
d. Large-scale crop model
Spatiotemporal variability in crop yields is associated with climate variability. For the first studies attempting to link climate to agriculture outputs, statistical tools were used to derive quantitative or qualitative relationships between crop production and climate variables such as precipitation and temperature. Nowadays, efforts are being made to describe the dynamic relationship between crop production and climate by using process-based crop models (Robert and Bruce 1998; Wallach et al. 2006). Most crop models have been designed to be used at plot scale and therefore specific assumptions have to be made to upscale results to larger scales (Hoogenboom 2000). In recent years, large-scale process-based crop models are increasingly designed and being used in the analysis of regional agricultural production systems (e.g., Moen et al. 1994; Brock and Brink 1996; Challinor et al. 2005; Tao et al. 2009). In this study,
In
In this study,
e.
The capability of GAs to approach (and eventually to find) the global optimum in an optimization problem is based on the choice of reproduction operators, their appropriate representation, and the formulation of the objective function [the so-called fitness function; Sivanandam and Deepa (2008)]. The latter is specific to the problem that one is dealing with in terms of the objective to be reached.
The first step in the implementation of any genetic algorithm is to generate an initial population that consists of random selections of potential solutions in the parameter space. In this study, a binary encoding is used to encode each member of the population as a binary string of length p 3 2n, where p denotes the number of parameters to be calibrated in theGLAMand n denotes the number of bits (2n is the number of possible values for a given parameter) (Carroll 1996a,b).
In
The GDD range for each crop development stage is crucial for the simulation, since the crop phenology and growing period heavily depend on it. To deal with the GDD variability in the target area, the 85-100-day growing period of the maize crop have been transformed into GDDs considering four maize growth stages (vegetative growth, flowering, grain filling, and maturity). The range of GDDs for each development stage has been computed using daily mean temperatures for the target area and crop phenological base temperatures TB. We have chosen TB to be in the range of 88-148C (Birch et al. 1998). The GDDs have been calculated for each grid cell and for each crop development stage. Then, the computation of the GDD mean value (GDDm) and GDD standard deviation (GDDstd) for each development stage is performed over the target area. Finally, assuming a normal distribution, a GDD ranging from GDDm223GDDstd toGDDm123GDDstd is set for each development stage of maize crop. For the other parameters, the selected range has been taken from
In addition to the 32 parameters, planting dates are needed to perform crop simulations with
The different steps in the process of
...
where r denotes the Pearson correlation coefficient between the simulated and observed yields, rRMSE is the relative root-mean-square error, and YGPmin and YGPmax denote the minimum and maximum values of the yield gap parameter, respectively.
f. Fuzzy logic approach for crop planting date estimation
The term fuzzy logic emerged in the development of the theory of fuzzy sets by Zadeh (1965). It refers to the principles and methods of representing knowledge that employs intermediate truth values. Fuzzy logic provides a way to represent subjective attributes of real-world problems in computing (Belohlavek and Klir 2011).
Optimized maize crop planting dates have been derived from rainfall time series data using a fuzzy logic approach in combination with GA. For agronomists, wet conditions are crucial after the planting date. They are necessary to ensure crop emergence and an optimum first-stage development. During the first stage of crop development, the root system of the crop is still not well developed enough to cope with longer dry spells. Therefore, crop failure and resowing might be avoided if wet conditions during the first vegetative growth stage occur. The rainfall-based estimation of planting dates for agricultural decision support uses threshold values for relevant agrometeorological variables such as rainfall amount and the number of wet- and dry-spell lengths, for a given period. However, the uncertainties due to the limited number of observations and measurement errors have to be taken into account when dealing with hydrometeorological variables. To cope with rainfall data uncertainties and the vagueness around the explicit value of these variables, a fuzzy logic-based approach has been used to compute optimized planting dates and for improved crop production (Laux et al. 2010, 2008). This approach uses the concept of fuzzy logic membership functions to deal with the cumulative rainfall amount and the wet- and dry-spell lengths.
Following Laux et al. (2008, 2010), three fuzzy functions g1, g2, and g3 for cumulative rainfall amount within a 5-day spell, the number of rainy days within a 5-day spell, and the longest dry-spell length in the next 30 days after the planting day, respectively, have been defined (Fig. 4). The variables a1 and a2 of the membership g1 vary between 10 and 30mm, b1 and b2 of the membership g2 vary between 1 and 5 days, and c1 and c2 of membership g3 vary between 5 and 10 days. The defuzzification parameter k varies between 0.1 and 1. Using a list of if-then clauses, g1 is set to 0 if the 5-day cumulative rainfall is less than a1mm and 1 if the 5-day cumulative rainfall is greater or equal to a2mm. For a 5-day cumulative rainfall ranging between a1 and a2, the value of g1 is obtained by a linear interpolation between a1 and a2. Similarly, g2 and g3 are computed based on their specific parameters.
The GA, coupled with this fuzzy logic approach and
The optimized fuzzy parameters are crop and location specific. For the optimization process, a fitness function is defined to discriminate among the different sets of parameters in terms of performance. The objective is to optimize planting dates so that they increase crop production and also reduce the coefficient of variation (CV). Therefore, the fitness function is defined as
...
For a specific location, the optimization process yielded a set of optimum fuzzy parameters. From this set, an ensemble of 10 members is retained. The 10 ensemble members consist of parameter sets, which result in high crop yields and a low variability of simulated crop yield (i.e., high fitness) over time. Using a time series of rainfall of a specific grid cell with the ensemble of optimized fuzzy parameter sets, an ensemble of optimized planting dates for maize has been computed by applying the proposed fuzzy logic approach algorithm. The flowchart in Fig. 6 illustrates the individual steps.
g. Evaluation of planting dates
Optimized planting dates (OPDs) are computed using the derived optimum fuzzy parameters in combination with daily rainfall time series. To evaluate the efficiency of the OPDs, two well-known and regionally established approaches have been used to calculate planting dates for comparison. These two approaches are as follow:
(i) Diallo (2001)-the date after 1 May, when at least 20mm of rainfall accumulates over three consecutive days and when no dry spell of more than 10 days occurs within the next 30 days; this approach is currently used at the AGRHYMET Regional Centre in
(ii) Dodd and Jolliffe (2001)-the first day of a spell of 5 days in which at least 25mmof rain falls, on condition that no dry period of more than 7 days occurs in the following 30 days; this approach is currently in operation as an agricultural decision support tool at theBurkina Faso DirectorateGeneral ofMeteorology.
A deviation of planting dates and a relative deviation of maize mean yield are used to compare the different approaches. The deviation of planting date (DPD) is calculated as
...
where PD is the planting date based on either Diallo (2001) or Dodd and Jolliffe (2001).
The relative deviation of the mean maize yields (Dyield) is given as
...
where YIELD is the mean yield either based on Diallo (2001) or Dodd and Jolliffe (2001), We denote the mean yield based on OPD as YIELDOPD.
4. Results
a.
Since
b. Maize-optimized planting dates and yield
A 10-member ensemble of fuzzy logic parameter sets is used to derive OPDs over the period 1980-2010. The ensemble mean values of the fuzzy logic parameters are presented in appendix B. The results shown in Fig. 8 depict (a) the mean OPD (OPD) and (b) the standard deviation of OPD (sOPD) for a sample of 310 (10 members331 yr) optimized planting dates for each grid cell. Between 7 May and 5 July OPDs vary across the country following a north-south gradient. In general, the earliest OPDs occur in May in the southern part of BF, whereas the latest OPDs occur in June-July in the northern part of the country. Following a similar spatial pattern, sOPD varies between 2 and 18 days. The variability of OPDs is greater in the northern than in the southern parts of the country.
The OPDs have been used as input in
c. Comparative analysis of planting date approaches
Planting dates and resulting simulated maize yields are computed for the approaches of Diallo (2001) and Dodd and Jolliffe (2001), and then compared to the OPD approach. On average, the deviation in planting dates between the OPD approach and the approaches of Diallo (2001) and Dodd and Jolliffe (2001) varies between220 and112 days for both Diallo (2001) (Fig. 9a) and Dodd and Jolliffe (2001) (Fig. 9b). The lowest (highest) deviation magnitude is mainly located in the southwestern (northern) part of BF. In general, the OPD approach yielded the earliest planting dates if compared to the planting dates computed by the approaches of Diallo (2001) and Dodd and Jolliffe (2001).
The deviation of maize potential yield ranges between 210% and 160% while positive values prevail (Figs. 9c and 9d). Except for the southern part, the potential yield obtained by OPDs results in an increase of at least 10% in mean yield relative to those obtained by Diallo (2001) and Dodd and Jolliffe (2001). For the southern part of the country, however, this increase in mean yield is less pronounced.
5. Discussion and conclusions
An approach to objectively derive crop planting dates is presented and applied for the first time to maize cultivation in
For SSA, several methods of estimating the onset of the rainy season are in operation, giving recommendations for planting dates. These approaches are usually applied at the local scale. At the BF National Meteorological Services and the AGRHYMET Regional Centre, the approaches of Diallo (2001) and Dodd and Jolliffe (2001), which are regionally adapted versions of Stern et al. (1981, 1982), are currently in operation in support of agricultural decision making in SSA. For the southeast of
In comparison with these approaches in operation, the proposed OPD has the following advantages:
(i) Once a calibrated process-based crop model is available, agrometeorological and crop yield data are required to derive crop and location-specific planting rules and to estimate planting dates. Besides the required knowledge needed to calibrate the crop model, this approach can be seen as fully objective. However, agronomic and agrometeorological knowledge is still required to validate the outcome of this study.
(ii) Instead of relying exclusively on rainfall amount and distribution around planting, the OPD approach not only accounts for plant water requirements and availability throughout the whole growing period, but also for radiation and temperature. This information is inherently included by coupling the planting rules to a process-based crop model.
(iii) The use of fuzzy logic to estimate planting rules instead of binary logic gives further flexibility in estimating reliable planting dates where strict thresholds may fail. This is exemplarily illustrated for the amount of rainfall in a 5-day spell. A strict value of, for example, 25mm, as used in the approach of Dodd and Jolliffe (2001), would exclude a reasonable planting date in which, for instance, 24.9mm of rain are recorded, even if significant rain and favorable conditions for crop growth follow.
(iv) Finally, theOPDapproach is not elaborating a single specific planting date, but rather it is suggesting a set of reasonable planting rules, leading to a time window for planting of approximately 2 weeks. This can help to increase the adoptability of this approach for smallholders, because their decisions about planting also depend on other external factors such as the availability of seeds, labor, machinery, etc.
This approach achieves higher potential yields across BF compared with the methods currently in operation. Detailed in-field validation is required before being implemented at agricultural national and regional centers. Further studies will be conducted in order to evaluate the potential benefits of the OPD approach if combined with improved seasonal climate predictions accounting for the intraseasonal rainfall variability.
Acknowledgments. This work has been funded by the
* Denotes Open Access content.
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MOUSSA WAONGO
Research,
SEYDOU B. TRAORÉ
AGRHYMET Regional Centre,
Institute de l'
Research,
(Manuscript received
Corresponding author address: Moussa Waongo,
E-mail: [email protected]
APPENDIX A
Summary of the Range of Variability of GLAM Calibrated Parameters
See Table A1 for a summary of the range of variability of calibrated parameters in the study area.
APPENDIX B
Mean Values of Optimized Fuzzy Parameters Set
See Table B1 for a presentation of the ensemble mean values of the fuzzy logic parameters.
Copyright: | (c) 2014 American Meteorological Society |
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