Yielding Insights : Machine Learning-Driven Imputations to Filling Agricultural Data Gaps (English)

This paper addresses the challenge of missing crop yield data in large-scale agricultural surveys, where crop-cutting, the most accurate method for yield measurement, is often limited due to cost constraints. Multiple imputation techniques, supported by machine learning models are used to predict missing yield data. This method is validated using survey data from Mali, which includes both crop-cut and self-reported yield information. The analysis...

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Author

Ismael Yacoubou Djima;

Marco Tiberti;

Talip Kilic;
Document Date
2024/11/04
Document Type
Policy Research Working Paper
Report Number
WPS10964
Volume No
1
Country
World,
Region
Other,
Disclosure Date
2024/11/04
Disclosure Status
Disclosed
Doc Name
Yielding Insights : Machine Learning-Driven Imputations to Filling Agricultural Data Gaps

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Citation

Ismael Yacoubou Djima; Marco Tiberti; Talip Kilic.

Yielding Insights : Machine Learning-Driven Imputations to Filling Agricultural Data Gaps (English). Policy Research working paper;LSMS;PLANET Washington, D.C. : World Bank Group. http://documents.worldbank.org/curated/en/099853011042416192

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Yielding Insights : Machine Learning-Driven Imputations to Filling Agricultural Data Gaps (English)

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