Crop yield is usually affected by impending weather and climate conditions, as well as human interventions like irrigation. Hence, the prompt detection of regions experiencing water scarcity can aid in implementing effective mitigation strategies. Our study utilized a data-driven approach to compute a Water-Demand-Index (WDI), which incorporates crucial first-order geophysical variables like ambient temperature, vegetation status, and soil moisture, to identify water-stressed fields in Senegal’s agricultural regions during the millet planting, growing, and harvesting periods. We have also explored various scenarios for enhancing the accuracy of millet yield prediction by incorporating other drought indices, soil characteristics, and a bias correction factor. To optimize the hyperparameters of machine learning (ML) models, various techniques were utilized. Meanwhile, the performance of these ML models was evaluated using a nested cross-validation approach. The outcomes of the analysis demonstrate that the Random Forest Regressor model exhibits superior predictive performance. The outcomes of this study also indicate that integrating soil moisture-based indices generated from advanced satellite-based high-resolution soil moisture observations, accounting for individual phases of millet growth, and encompassing millet production regions at the department (administrative unit) level, can significantly enhance the overall predictive capacity of the model. The results imply that a holistic approach, encompassing diverse environmental factors and crop growth stages, could result in more precise and dependable millet yield predictions. Such refined yield predictions could aid in making informed agricultural planning and intervention decisions.
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