Closing the gaps in experimental and observational crop response estimates: a bayesian approach

Abstract

Abstract A stylized fact of African agriculture is that crop responses to inorganic fertilizer application derived from experimental studies are often substantially greater than those from observational studies (e.g. surveys and administrative data). Recent debates on relative costs and benefits of expensive farm input subsidy programs in Africa, have raised the importance of reconciling these estimates. Beyond mean response differences, this paper argues for including parameter uncertainty and heterogeneity arising from variations in soil types, environmental conditions, and management practices. We use a Bayesian approach that combines information from experimental and observational data to model uncertainty and heterogeneity in crop yield responses. Using nationally representative experimental, survey, and administrative datasets from Malawi, we find that: (1) crop responses are low in observational data, (2) there are large spatial heterogeneities, and (3) based on sensitivity analysis, ignoring parameter uncertainty and spatial heterogeneity in crop responses can lead to questionable policy prescriptions.