Publication:
Using Machine Learning to Assess Yield Impacts of Crop Rotation: Combining Satellite and Statistical Data for Ukraine

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dc.contributor.authorKussul, Nataliia
dc.contributor.authorDeininger, Klaus
dc.contributor.authorLavreniuk, Mykola
dc.contributor.authorAli, Daniel Ayalew
dc.contributor.authorNivievskyi, Oleg
dc.date.accessioned2020-07-06T14:32:08Z
dc.date.available2020-07-06T14:32:08Z
dc.date.issued2020-06
dc.description.abstractTo 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.en
dc.identifierhttp://documents.worldbank.org/curated/en/459481593442273789/Using-Machine-Learning-to-Assess-Yield-Impacts-of-Crop-Rotation-Combining-Satellite-and-Statistical-Data-for-Ukraine
dc.identifier.doi10.1596/1813-9450-9306
dc.identifier.urihttps://hdl.handle.net/10986/34021
dc.languageEnglish
dc.publisherWorld Bank, Washington, DC
dc.relation.ispartofseriesPolicy Research Working Paper;No. 9306
dc.rightsCC BY 3.0 IGO
dc.rights.holderWorld Bank
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/igo
dc.subjectMACHINE LEARNING
dc.subjectCROP ROTATION
dc.subjectAGRICULTURAL PRODUCTIVITY
dc.subjectSATELLITE IMAGERY
dc.titleUsing Machine Learning to Assess Yield Impacts of Crop Rotationen
dc.title.subtitleCombining Satellite and Statistical Data for Ukraineen
dc.typeWorking Paperen
dc.typeDocument de travailfr
dc.typeDocumento de trabajoes
dspace.entity.typePublication
okr.crossref.titleUsing Machine Learning to Assess Yield Impacts of Crop Rotation: Combining Satellite and Statistical Data for Ukraine
okr.date.disclosure2020-06-29
okr.doctypePublications & Research
okr.doctypePublications & Research::Policy Research Working Paper
okr.docurlhttp://documents.worldbank.org/curated/en/459481593442273789/Using-Machine-Learning-to-Assess-Yield-Impacts-of-Crop-Rotation-Combining-Satellite-and-Statistical-Data-for-Ukraine
okr.guid459481593442273789
okr.identifier.doi10.1596/1813-9450-9306
okr.identifier.externaldocumentum090224b087b27161_2_0
okr.identifier.internaldocumentum32192033
okr.identifier.reportWPS9306
okr.importedtrueen
okr.language.supporteden
okr.pdfurlhttp://documents.worldbank.org/curated/en/459481593442273789/pdf/Using-Machine-Learning-to-Assess-Yield-Impacts-of-Crop-Rotation-Combining-Satellite-and-Statistical-Data-for-Ukraine.pdfen
okr.region.administrativeEurope and Central Asia
okr.region.countryUkraine
okr.statistics.combined1956
okr.statistics.dr459481593442273789
okr.statistics.drstats1319
okr.topicAgriculture::Agricultural Sector Economics
okr.topicAgriculture::Climate Change and Agriculture
okr.topicAgriculture::Crops & Crop Management Systems
okr.topicScience and Technology Development::Earth Sciences & GIS
okr.unitDevelopment Research Group, Development Economics
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relation.isAuthorOfPublication222ae66b-b10f-58d1-892a-56c27c73ba84
relation.isAuthorOfPublication.latestForDiscovery68546f99-84de-5466-be55-9170c38603e2
relation.isSeriesOfPublication26e071dc-b0bf-409c-b982-df2970295c87
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