Using Machine Learning to Assess Yield Impacts of Crop Rotation : Combining Satellite and Statistical Data for Ukraine
To overcome the constraints for policy and practice posed by limited availability of data on crop rotation, this paper applies machine learning to freely available satellite imagery to identify the rotational practices of more than 7,000 villages i...
| Main Authors: | , , , , |
|---|---|
| Language: | English |
| Published: |
World Bank, Washington, DC
2020
|
| Subjects: | |
| Online Access: | http://documents.worldbank.org/curated/en/459481593442273789/Using-Machine-Learning-to-Assess-Yield-Impacts-of-Crop-Rotation-Combining-Satellite-and-Statistical-Data-for-Ukraine http://hdl.handle.net/10986/34021 |
| Summary: | To overcome the constraints for policy
and practice posed by limited availability of data on crop
rotation, this paper applies machine learning to freely
available satellite imagery to identify the rotational
practices of more than 7,000 villages in Ukraine. Rotation
effects estimated based on combining these data with
survey-based yield information point toward statistically
significant and economically meaningful effects that differ
from what has been reported in the literature, highlighting
the value of this approach. Independently derived indices of
vegetative development and soil water content produce
similar results, not only supporting the robustness of the
results, but also suggesting that the opportunities for
spatial and temporal disaggregation inherent in such data
offer tremendous unexploited opportunities for
policy-relevant analysis. |
|---|