Sarr Alioune Badara auteur aut IRD Sultan Benjamin auteur aut IRD text journalArticle eng text/pdf reformatted digital access Agriculture plays an important role in Senegalese economy and annual early warning predictions of crop yields are highly relevant in the context of climate change. In this study, we used three main machine learning methods (support vector machine, random forest, neural network) and one multiple linear regression method, namely Least Absolute Shrinkage and Selection Operator (LASSO), to predict yields of the main food staple crops (peanut, maize, millet and sorghum) in 24 departments of Senegal. Three combination of predictors (climate data, vegetation data or a combination of both) are used to compare the respective contribution of statistical methods and inputs in the predictive skill. Our results showed that the combination of climate and vegetation with the machine learning methods gives the best performance. The best prediction skill is obtained for peanut yield likely due to its high sensitivity to interannual climate variability. Although more research is needed to integrate the results of this study into an operational framework, this paper provides evidence of the promising performance machine learning methods. The development and operationalization of such prediction and their integration into operational early warning systems could increase resilience of Senegal to climate change and contribute to food security. specialized climate change scenario crop yield prediction machine learning Senegal SENEGAL 072 020 43 4 1817-1838 2023 0899-8418 https://www.documentation.ird.fr/hor/fdi:010086735 10.1002/joc.7947 0899-8418 [F B010086735] https://www.documentation.ird.fr/hor/fdi:010086735 https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2025-09/010086735.pdf IRD - Base Horizon / Pleins textes 2023-02-08 2025-09-24 fdi:010086735 fre