Crop yield forecasting in Senegal: application of Machine Learning methods

Two approaches are generally used to predict crop yields. The first is based on Machine Learning methods and the second on mechanistic models. In this study, the robustness of Machine Learning methods in predicting groundnut, millet and cotton yields in Senegal is assessed. These methods are Multiple stepwise regression, Least Absolute Selection and Shrinkage Operator (LASSO) regression and Random Forest regression. These prediction models were tested using a collection of historical agricultural and climatic data for Senegal from 1980 to 2021. Analysis of agricultural trends reveals marked inter-annual variability depending on the crop and period: low variability between 1990–2000 for groundnuts and millet, and between 2011–2021 for cotton. High variability between 2000–2010 for groundnuts and cotton, and between 1980–1990 for millet. Overall, area, production and yields fluctuate widely depending on the periods and crops studied. The crop yield prediction models of groundnut, millet and cotton performed satisfactorily for test dataset except for cotton. They perform well for groundnut and millet, with high R ² for LASSO regression (0.96 and 0.98) and stepwise multiple regression (0.93 and 0.98). Cotton had a low R ² for Random Forest (0.01). The LASSO regression gave the lowest values of RMSE and MAE. Overall, it is the best model for predicting groundnut, millet and cotton yields in Senegal.