Effective agricultural and rural development policies require a solid understanding of similarities and differences among farm systems. However, in many developing regions, the complexity and diversity of these systems are not well documented yet. In response, this study is aimed at developing a typology of Malian farming households. For the analysis, we rely on the nationally representative Living Standards Measurement Study household dataset provided by the World Bank filtered to include only households engaged in farming activities ( n = 3215). We identify clusters that have similar characteristics but differ from each other using the machine learning clustering method Partitioning Around Medoids. We identified five distinct farming household clusters in Mali. We call them ‘better‐off farms’, ‘resource‐constrained subsistence farms’, ‘diversified family farms’, ‘rice‐focused farms’ and ‘diverse high‐input farms’. All clusters face the challenge of increasing agricultural production while simultaneously improving climate resilience, but the possible responses to these challenges should be adapted to the characteristics of each cluster. Knowledge about farming systems can contribute to a well‐suited and targeted agricultural policy development. Since the agricultural sector is of prime importance in Mali, both in terms of economic contribution and source of livelihoods, our work is relevant for policymakers, donors, researchers and other stakeholders in Mali.
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Ölkers et al. (2025) studied this question.
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