Targeting agricultural research to benefit poor farmers: Relating poverty mapping to maize environments in Mexico

Abstract

We explore approaches for targeting agricultural research to benefit poor farmers. Using small area estimation methods and spatial analysis, we generated high-resolution poverty maps and combined them with geo-referenced biophysical data relevant to maize-based agriculture in Mexico. We used multivariate classification and cluster analysis to synthesize biophysical data relevant for crop performance with rural poverty data. Results show that the rural poor are concentrated in particular regions and under particular circumstances. Formal maize germplasm improvement trials were largely outside the core areas of rural poverty and there was little evidence for direct spillover of improved germplasm. Agro-climatic classification used for targeting breeding is useful but often ignores some important factors identified as relevant for the poor. Combining this method with poverty mapping improves stratifying and targeting crop breeding efforts to meet the demands of resource-poor farmers. We believe this integrated approach will help increase benefits from agricultural research to poor rural communities.