Satellite-based assessment of yield variation and its determinants in smallholder African systems

Abstract

<p id="d11599588e207">Improvements in agricultural productivity in developing countries are thought to play a key role in poverty reduction. Unfortunately, such productivity remains poorly measured throughout much of the world, hampering efforts to evaluate and target productivity-enhancing interventions. Using high-resolution satellite imagery in combination with field data we collected from thousands of smallholder plots in Kenya, we show that satellite imagery can be used to estimate and understand yield variation at the field scale across African smallholders. Our results suggest a range of potential capabilities, including the inexpensive measurement of the impact of specific interventions, the broader characterization of the source and magnitude of yield gaps, and the development of financial products aimed at African smallholders. </p><p class="first" id="d11599588e210">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. Here we demonstrate the potential to track smallholder maize yield variation in western Kenya, using a combination of 1-m Terra Bella imagery and intensive field sampling on thousands of fields over 2 y. We find that agreement between satellite-based and traditional field survey-based yield estimates depends significantly on the quality of the field-based measures, with agreement highest ( <span class="inline-formula"> <math id="i1" overflow="scroll"> <msup> <mtext>R</mtext> <mn>2</mn> </msup> </math> </span> up to 0.4) when using precise field measures of plot area and when using larger fields for which rounding errors are smaller. We further 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, and they 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. </p>