Yielding Insights
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Washington, DC: World Bank
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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.
Palabras clave
SMALLHOLDER FARMING, AGRICULTURAL CROP YIELDS MEASUREMENTS, MACHINE LEARNING, MISSING DATA, MULTIPLE IMPUTATION, HOUSEHOLD SURVEYS