Spatially explicit information on farmers’ crop choice and how it is impacted by changing environmental or economic conditions is essential to foster food security and predict future crop production. While such knowledge could be particularly beneficial in world regions with large rural populations and high climate risk, data scarcity often impedes crop production mapping and modelling. We present a novel approach that links environmental data, satellite imagery, and regional statistics using economic modelling and generative AI for high-resolution crop choice mapping and modelling without labelled observations. The approach builds on two components: first, we employ a reduced-form model based on economic theory to express the cultivation probability of a crop at a specific location as a function of environmental and potentially economic conditions. Second, we use k-Deep Variational Autoencoders, a class of generative neural networks, to cluster pixels with similar appearance on satellite imagery into groups that can be associated to crop types. By linking both components and jointly estimating all model parameters, the economic model provides prior knowledge to the clustering approach
while benefiting from the information entailed in the remote sensing imagery. Validation for France indicates high overall accuracies of the obtained crop maps. We additionally apply the approach to northern Ghana and simulate how an increase in in-season droughts would impact the spatial distribution of major food crops, information crucial for food security policies. Our method is applicable to numerous world regions.
