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

Ismael Yacoubou Djima, Marco Tiberti and Talip Kilic

No 10964, Policy Research Working Paper Series from The World Bank

Abstract: 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 covers several crops, providing insights into the importance of different predictors, including farmer-reported yields and geo-spatial variables, and the conditions under which the approach is valid. The findings show that machine learning-based imputations can provide accurate yield estimates, especially for crops with low intercropping rates and higher commercialization. However, survey-to-survey imputations are less accurate than within-survey imputations, suggesting limitations in extrapolating data across different survey rounds. The study contributes valuable insights into improving cost-efficiency in agricultural surveys and the potential of imputation methods.

Date: 2024-11-04
New Economics Papers: this item is included in nep-agr and nep-cmp
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