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

Hai-Anh Dang (), Calogero Carletto (), Sydney Gourlay () and Kseniya Abanokova ()
Additional contact information
Calogero Carletto: World Bank
Sydney Gourlay: World Bank
Kseniya Abanokova: World Bank

No 17064, IZA Discussion Papers from Institute of Labor Economics (IZA)

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.

Keywords: soil quality; multiple imputation; missing data; survey data; Ethiopia; Uganda (search for similar items in EconPapers)
JEL-codes: C8 O12 Q1 Q2 (search for similar items in EconPapers)
Pages: 85 pages
Date: 2024-06
New Economics Papers: this item is included in nep-dev
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Working Paper: Addressing Soil Quality Data Gaps with Imputation: Evidence from Ethiopia and Uganda (2024)
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