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.
Details
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Author
Deininger,Klaus W., Ali,Daniel Ayalew, Kussul,Nataliia, Lavreniuk,Mykola, Nivievskyi,Oleg
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Document Date
2020/06/29
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Document Type
Policy Research Working Paper
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Report Number
WPS9306
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Volume No
1
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Total Volume(s)
1
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Country
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Region
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Disclosure Date
2020/06/29
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Disclosure Status
Disclosed
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Doc Name
Using Machine Learning to Assess Yield Impacts of Crop Rotation : Combining Satellite and Statistical Data for Ukraine
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Keywords
Crop; development research group; loss of soil fertility; crop rotation; soil water content
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Citation
Deininger,Klaus W. Ali,Daniel Ayalew Kussul,Nataliia Lavreniuk,Mykola Nivievskyi,Oleg
Using Machine Learning to Assess Yield Impacts of Crop Rotation : Combining Satellite and Statistical Data for Ukraine (English). Policy Research working paper,no. WPS 9306 Washington, D.C. : World Bank Group. http://documents.worldbank.org/curated/en/459481593442273789/Using-Machine-Learning-to-Assess-Yield-Impacts-of-Crop-Rotation-Combining-Satellite-and-Statistical-Data-for-Ukraine