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Knowledge4Policy
Knowledge for policy
Supporting policy with scientific evidence

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  • Publication | 2025
Assessing the Impact of Climate Projections on Agricultural Yields in Central Africa: A Machine Learning Approach
Global Food and Nutrition Security

Climate change poses significant challenges to agricultural production, particularly in Central Africa, where the livelihoods of millions depend on key crops such as maize, groundnut, soybean, and rice. The potential effects of climate projections on agricultural yields are significant, as variations in temperature, rainfall, humidity, and soil moisture can lead to substantial changes in crop performance. The research aims to model and predict crop yields based on these meteorological variables by utilizing machine learning models, including Gaussian process and Random forest. The findings demonstrate that regional agricultural production differences may arise from future climatic conditions. The random forest model aligned more closely with observed values, achieving better average accuracies depending on the season. The performance of the machine learning models is closely tied to the specific crops and countries within the study region. Furthermore, the insights gained can greatly benefit political decision-makers and stakeholders in developing targeted adaptation plans and policies.