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

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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تفاصيل

  • Ismael Yacoubou Djima;
    Marco Tiberti;
    Talip Kilic;

  • 2024/11/04

  • ورقة عمل خاصة ببحوث السياسات

  • WPS10964

  • 1

  • العالم,

  • أخرى,

  • 2024/11/04

  • Disclosed

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

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روابط ذات صلة

إشترك في التنبيهات الالكترونية/ تنبيهات البريد الالكتروني

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