A Genetic Algorithm Approach for Location-Specific Calibration of Rainfed Maize Cropping in the Context of Smallholder Farming in West Africa

Smallholder farming in West Africa faces various challenges, such as limited access to seeds, fertilizers, modern mechanization, and agricultural climate services. Crop productivity obtained under these conditions varies significantly from one farmer to another, making it challenging to accurately estimate crop production through crop models. This limitation has implications for the reliability of using crop models as agricultural decision-making support tools. To support decision making in agriculture, an approach combining a genetic algorithm (GA) with the crop model AquaCrop is proposed for a location-specific calibration of maize cropping. In this approach, AquaCrop is used to simulate maize crop yield while the GA is used to derive optimal parameters set at grid cell resolution from various combinations of cultivar parameters and crop management in the process of crop and management options calibration. Statistics on pairwise simulated and observed yields indicate that the coefficient of determination varies from 0.20 to 0.65, with a yield deviation ranging from 8% to 36% across Burkina Faso (BF). An analysis of the optimal parameter sets shows that regardless of the climatic zone, a base temperature of 10˚C and an upper temperature of 32˚C is observed in at least 50% of grid cells. The growing season length and the harvest index vary significantly across BF, with the highest values found in the Soudanian zone and the lowest values in the Sahelian zone. Regarding management strategies, the fertility mean rate is approximately 35%, 39%, and 49% for the Sahelian, Soudano-sahelian, and Soudanian zones, respectively. The mean weed cover is around 36%, with the Sahelian and Soudano-sahelian zones showing the highest variability. The proposed approach can be an alternative to the conventional one-size-fits-all approach commonly used for regional crop modeling. Moreover, it has the potential to explore the performance of cropping strategies to adapt to changing climate conditions.