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Mapping Smallholder Yield Heterogeneity at Multiple Scales in Eastern Africa

Department of Earth System Science and Center on Food Security and the Environment, Stanford University, Stanford, CA 94305, USA
National Bureau of Economic Research, Cambridge, MA 02138, USA
One Acre Fund, Kigali, Rwanda
Author to whom correspondence should be addressed.
Remote Sens. 2017, 9(9), 931;
Received: 3 August 2017 / Revised: 1 September 2017 / Accepted: 4 September 2017 / Published: 8 September 2017
Accurate measurements of crop production in smallholder farming systems are critical to the understanding of yield constraints and, thus, setting the appropriate agronomic investments and policies for improving food security and reducing poverty. Nevertheless, mapping the yields of smallholder farms is challenging because of factors such as small field sizes and heterogeneous landscapes. Recent advances in fine-resolution satellite sensors offer promise for monitoring and characterizing the production of smallholder farms. In this study, we investigated the utility of different sensors, including the commercial Skysat and RapidEye satellites and the publicly accessible Sentinel-2, for tracking smallholder maize yield variation throughout a ~40,000 km2 western Kenya region. We tested the potential of two types of multiple regression models for predicting yield: (i) a “calibrated model”, which required ground-measured yield and weather data for calibration, and (ii) an “uncalibrated model”, which used a process-based crop model to generate daily vegetation index and end-of-season biomass and/or yield as pseudo training samples. Model performance was evaluated at the field, division, and district scales using a combination of farmer surveys and crop cuts across thousands of smallholder plots in western Kenya. Results show that the “calibrated” approach captured a significant fraction (R2 between 0.3 and 0.6) of yield variations at aggregated administrative units (e.g., districts and divisions), while the “uncalibrated” approach performed only slightly worse. For both approaches, we found that predictions using the MERIS Terrestrial Chlorophyll Index (MTCI), which included the red edge band available in RapidEye and Sentinel-2, were superior to those made using other commonly used vegetation indices. We also found that multiple refinements to the crop simulation procedures led to improvements in the “uncalibrated” approach. We identified the prevalence of small field sizes, intercropping management, and cloudy satellite images as major challenges to improve the model performance. Overall, this study suggested that high-resolution satellite imagery can be used to map yields of smallholder farming systems, and the methodology presented in this study could serve as a good foundation for other smallholder farming systems in the world. View Full-Text
Keywords: smallholder farms; yield; Africa; remote sensing; maize; agriculture smallholder farms; yield; Africa; remote sensing; maize; agriculture
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MDPI and ACS Style

Jin, Z.; Azzari, G.; Burke, M.; Aston, S.; Lobell, D.B. Mapping Smallholder Yield Heterogeneity at Multiple Scales in Eastern Africa. Remote Sens. 2017, 9, 931.

AMA Style

Jin Z, Azzari G, Burke M, Aston S, Lobell DB. Mapping Smallholder Yield Heterogeneity at Multiple Scales in Eastern Africa. Remote Sensing. 2017; 9(9):931.

Chicago/Turabian Style

Jin, Zhenong, George Azzari, Marshall Burke, Stephen Aston, and David B. Lobell 2017. "Mapping Smallholder Yield Heterogeneity at Multiple Scales in Eastern Africa" Remote Sensing 9, no. 9: 931.

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