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Article Dans Une Revue International Journal of Climatology Année : 2022

Predicting crop yields in Senegal using machine learning methods

Alioune Badara Sarr

Résumé

Abstract 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.

Domaines

Climatologie

Dates et versions

hal-04384038 , version 1 (10-01-2024)

Identifiants

Citer

Alioune Badara Sarr, Benjamin Sultan. Predicting crop yields in Senegal using machine learning methods. International Journal of Climatology, 2022, 43 (4), pp.1817-1838. ⟨10.1002/joc.7947⟩. ⟨hal-04384038⟩
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