Satellite-based assessment of yield variation and its determinants in smallholder African systems
This article (PDF) in PNAS Journal, demonstrates the potential to track smallholder maize yield variation in western Kenya, using a combination of 1-m imagery and intensive field sampling on thousands of fields over 2 years. The emergence of satellite sensors that can routinely observe millions of individual smallholder farms raises possibilities for monitoring and understanding agricultural productivity in many regions of the world. Results show that satellite-based measures are able to detect positive yield responses to fertilizer and hybrid seed inputs and that the inferred responses are statistically indistinguishable from estimates based on survey-based yields. These results suggest that high-resolution satellite imagery can be used to make predictions of smallholder agricultural productivity that are roughly as accurate as the survey-based measures traditionally used in research and policy applications. Results also indicate a substantial near-term potential to quickly generate useful datasets on productivity in smallholder systems, even with minimal or no field training data. Such datasets could rapidly accelerate learning about which interventions in smallholder systems have the most positive impact, thus enabling more rapid transformation of rural livelihoods. The authors stress that the results suggest a range of potential capabilities, including inexpensive estimates of yields, which could enable better targeting of agricultural interventions and better evaluation of their impact, the broader characterization of the source and magnitude of yield gaps, and the development of financial products aimed at African smallholders.