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...
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| Format: | Working Paper |
| Language: | English |
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World Bank, Washington, DC
2020
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| 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 |
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okr-10986-340212022-09-20T00:11:53Z Using Machine Learning to Assess Yield Impacts of Crop Rotation : Combining Satellite and Statistical Data for Ukraine Deininger, Klaus Ali, Daniel Ayalew Kussul, Nataliia Lavreniuk, Mykola Nivievskyi, Oleg MACHINE LEARNING CROP ROTATION AGRICULTURAL PRODUCTIVITY SATELLITE IMAGERY 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. 2020-07-06T14:32:08Z 2020-07-06T14:32:08Z 2020-06 Working Paper 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 English Policy Research Working Paper;No. 9306 CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo World Bank World Bank, Washington, DC Publications & Research Publications & Research :: Policy Research Working Paper Europe and Central Asia Ukraine |
| repository_type |
Digital Repository |
| institution_category |
Foreign Institution |
| institution |
Digital Repositories |
| building |
World Bank Open Knowledge Repository |
| collection |
World Bank |
| language |
English |
| topic |
MACHINE LEARNING CROP ROTATION AGRICULTURAL PRODUCTIVITY SATELLITE IMAGERY |
| spellingShingle |
MACHINE LEARNING CROP ROTATION AGRICULTURAL PRODUCTIVITY SATELLITE IMAGERY Deininger, Klaus 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 |
| geographic_facet |
Europe and Central Asia Ukraine |
| relation |
Policy Research Working Paper;No. 9306 |
| description |
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. |
| format |
Working Paper |
| author |
Deininger, Klaus Ali, Daniel Ayalew Kussul, Nataliia Lavreniuk, Mykola Nivievskyi, Oleg |
| author_facet |
Deininger, Klaus Ali, Daniel Ayalew Kussul, Nataliia Lavreniuk, Mykola Nivievskyi, Oleg |
| author_sort |
Deininger, Klaus |
| title |
Using Machine Learning to Assess Yield Impacts of Crop Rotation : Combining Satellite and Statistical Data for Ukraine |
| title_short |
Using Machine Learning to Assess Yield Impacts of Crop Rotation : Combining Satellite and Statistical Data for Ukraine |
| title_full |
Using Machine Learning to Assess Yield Impacts of Crop Rotation : Combining Satellite and Statistical Data for Ukraine |
| title_fullStr |
Using Machine Learning to Assess Yield Impacts of Crop Rotation : Combining Satellite and Statistical Data for Ukraine |
| title_full_unstemmed |
Using Machine Learning to Assess Yield Impacts of Crop Rotation : Combining Satellite and Statistical Data for Ukraine |
| title_sort |
using machine learning to assess yield impacts of crop rotation : combining satellite and statistical data for ukraine |
| publisher |
World Bank, Washington, DC |
| publishDate |
2020 |
| url |
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 |
| _version_ |
1764480014908129280 |