Eyes in the Sky, Boots on the Ground: Assessing Satellite- and Ground-Based Approaches to Crop Yield Measurement and Analysis

David B. Lobell, George Azzari, Marshall Burke, Sydney Gourlay, Zhenong Jin, Talip Kilic, Siobhan Murray

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Understanding the determinants of agricultural productivity requires accurate measurement of crop output and yield. In smallholder production systems across low- and middle-income countries, crop yields have traditionally been assessed based on farmer-reported production and land areas in household/farm surveys, occasionally by objective crop cuts for a sub-section of a farmer's plot, and rarely using full-plot harvests. In parallel, satellite data continue to improve in terms of spatial, temporal, and spectral resolution needed to discern performance on smallholder plots. This study evaluates ground- and satellite-based approaches to estimating crop yields and yield responsiveness to inputs, using data on maize from Eastern Uganda. Using unique, simultaneous ground data on yields based on farmer reporting, sub-plot crop cutting, and full-plot harvests across hundreds of smallholder plots, we document large discrepancies among the ground-based measures, particularly among yields based on farmer-reporting versus sub-plot or full-plot crop cutting. Compared to yield measures based on either farmer-reporting or sub-plot crop cutting, satellite-based yield measures explain as much or more variation in yields based on (gold-standard) full-plot crop cuts. Further, estimates of the association between maize yield and various production factors (e.g., fertilizer, soil quality) are similar across crop cut- and satellite-based yield measures, with the use of the latter at times leading to more significant results due to larger sample sizes. Overall, the results suggest a substantial role for satellite-based yield estimation in measuring and understanding agricultural productivity in the developing world.

Original languageEnglish (US)
Pages (from-to)202-219
Number of pages18
JournalAmerican Journal of Agricultural Economics
Volume102
Issue number1
DOIs
StatePublished - Jan 1 2020

Bibliographical note

Funding Information:
Field data collection for MAPS II (2016) was financed by the World Bank Innovations in Big Data Analytics Program, the World Bank Trust Fund for Statistical Capacity Building—Innovations in Development Data Window, and the CGIAR Standing Panel on Impact Assessment. Terra Bella (now Skysat) provided free high‐resolution satellite imagery for the MAPS remote sensing tasking area for research purposes. MAPS I and MAPS II were both implemented using the World Bank Survey Solutions Computer‐Assisted Personal Interviewing (CAPI) platform. The research team would like to thank the dedicated management and field staff of the Uganda Bureau of Statistics regarding fieldwork implementation; Mr. Wilbert Drazi Vundru for Survey Solutions programming, fieldwork supervision and survey data quality control; and Ms. Madeline Lisaius for help with image processing. The authors thank the Global Innovation Fund and USAID/BFS for additional funding.

Funding Information:
Field data collection for MAPS II (2016) was financed by the World Bank Innovations in Big Data Analytics Program, the World Bank Trust Fund for Statistical Capacity Building?Innovations in Development Data Window, and the CGIAR Standing Panel on Impact Assessment. Terra Bella (now Skysat) provided free high-resolution satellite imagery for the MAPS remote sensing tasking area for research purposes. MAPS I and MAPS II were both implemented using the World Bank Survey Solutions Computer-Assisted Personal Interviewing (CAPI) platform. The research team would like to thank the dedicated management and field staff of the Uganda Bureau of Statistics regarding fieldwork implementation; Mr. Wilbert Drazi Vundru for Survey Solutions programming, fieldwork supervision and survey data quality control; and Ms. Madeline Lisaius for help with image processing. The authors thank the Global Innovation Fund and USAID/BFS for additional funding.

Publisher Copyright:
© The Author(s) 2019. Published by Oxford University Press on behalf of the Agricultural and Applied Economics Association. All rights reserved.

Keywords

  • Agricultural productivity
  • Uganda
  • crop cutting
  • crop yield estimation
  • maize
  • remote sensing

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