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Addressing Soil Quality Data Gaps with Imputation: Evidence from Ethiopia and Uganda

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  • Dang, Hai-Anh
  • Carleto, Gero
  • Gourlay, Sydney
  • Abanokova, Kseniya

Abstract

Monitoring soil quality provides indispensable inputs for effective policy advice, but very few poorer countries can implement high-quality surveys on soil. We offer an alternative, low-cost imputation-based approach to generating various soil quality indicators. The estimation results validate well against objective measures based on benchmark surveys for Ethiopia and Uganda both for the mean values and the entire distributions of these indicators based on multiple imputation (MI) methods. Machine learning methods also perform well but mostly for the mean values. Furthermore, our imputation models can be combined with other publicly available, large-scale datasets on soil quality generated by model-based analysis with earth observations to provide improved estimates. Our results offer relevant inputs for future data collection efforts.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Dang, Hai-Anh & Carleto, Gero & Gourlay, Sydney & Abanokova, Kseniya, 2023. "Addressing Soil Quality Data Gaps with Imputation: Evidence from Ethiopia and Uganda," 2023 Annual Meeting, July 23-25, Washington D.C. 335648, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea23:335648
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    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • O12 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Microeconomic Analyses of Economic Development
    • Q1 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture
    • Q2 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation

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