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A crop-specific and time-variant spatial framework to characterize production environments: a case study for rainfed wheat in Ethiopia

dc.contributor.authorGelagay, H.S.
dc.contributor.authorLeroux, L.
dc.contributor.authorTamene, L.
dc.contributor.authorChernet, M.
dc.contributor.authorBlasch, G.
dc.contributor.authorTibebe, D.
dc.contributor.authorAbera, W.
dc.contributor.authorSida, T.
dc.contributor.authorTesfaye, K.
dc.contributor.authorCorbeels, M.
dc.contributor.authorSilva, J.V.
dc.date.accessioned2024-10-28T09:08:08Z
dc.date.available2024-10-28T09:08:08Z
dc.date.issued2024-08-03
dc.identifier.citationGelagay, H.S., Leroux, L., Tamene, L., Tariku, M., Abera, W., Tibebe, D., ... & Silva, J.V. (2024). A crop-specific and time-variant spatial framework to characterize production environments: a case study for rainfed wheat in Ethiopia. Available at SSRN 4915178, 1-35.
dc.identifier.urihttps://hdl.handle.net/20.500.12478/8614
dc.description.abstractAbstract1. CONTEXTAddressing the limitations of scaling agronomic recommendations, which are usually confined to small areas, requires a spatial framework for characterizing production environments in a timely and cost-effective manner.2.OBJECTIVEThis study aimed to introduce a data-driven framework to characterize rainfed wheat crop production environments in Ethiopia. The framework entails mapping of the annual rainfed wheat area and the delineation of crop-specific and dynamic agro-ecological spatial units (ASUs).3. METHODSAn ensemble machine learning approach built upon time-series satellite images and environmental data was used for crop type mapping while pixel- and object-based clustering algorithms were used to delineate dynamic ASUs from two temporal perspectives: annual ASUs for the 2021 and 2022 growing seasons to assess short-term dynamism, and ASUs from aggregated data (2016 – 2022) to capture long-term variations in the production environment.4. RESULTS AND CONCLUSIONSModel evaluation showed that the ensemble of random forest, gradient boosting, and classification and regression trees predicted wheat cropland in the 2021 and 2022 growing seasons with 88-90% accuracy. A concordance in defining ASUs between pixel- and object-based approaches was observed with consistency and dynamism in ASUs from 2021 to 2022 and between single-year and aggregated ASUs across approaches. This consistency and dynamism in ASUs highlight the spatial scalability and temporal flexibility of the framework, which allows for characterizing production environments across scales and analyzing trends and fluctuations, providing valuable insights for addressing food security and environmental challenges.5.SIGNIFICANCEThe developed spatial framework could facilitate future yield gap analysis and agronomic assessments for rainfed wheat in Ethiopia and be transfered to other crops and production environments.
dc.description.sponsorshipOne CGIAR Research Excellence in Agronomy Initiative
dc.format.extent1-35
dc.language.isoen
dc.subjectYield Gap
dc.subjectRemote Sensing
dc.subjectSpatial Units
dc.subjectCrop Production
dc.titleA crop-specific and time-variant spatial framework to characterize production environments: a case study for rainfed wheat in Ethiopia
dc.typeJournal Article
cg.contributor.affiliationUniversity of Montpellier
cg.contributor.affiliationCIRAD
cg.contributor.affiliationInternational Institute of Tropical Agriculture
cg.contributor.affiliationInternational Center for Tropical Agriculture
cg.contributor.affiliationAlliance of Bioversity International and the International Center for Tropical Agriculture
cg.contributor.affiliationInternational Maize and Wheat Improvement Center
cg.coverage.regionEast Africa
cg.coverage.countryEthiopia
cg.coverage.hubEastern Africa Hub
cg.identifier.bibtexciteidGELAGAY:2024
cg.authorship.typesCGIAR and advanced research institute
cg.iitasubjectAgronomy
cg.iitasubjectMeteorology and Climatology
cg.journalSSRN 4915178 (Journal Preprint)
cg.notesPre-print version
cg.accessibilitystatusOpen Access
cg.reviewstatusInternal Review
cg.usagerightslicenseCreative Commons Attribution 4.0 (CC BY 0.0)
cg.targetaudienceScientists
cg.iitaauthor.identifierWuletawu Abera: 0000-0002-3657-5223
cg.iitaauthor.identifierKindie Tesfaye: 0000-0002-7201-8053
cg.iitaauthor.identifierMarc Corbeels: 0000-0002-8084-9287
cg.iitaauthor.identifierJoão Vasco Silva: 0000-0002-3019-5895
cg.futureupdate.descriptionPublisher's version
cg.futureupdate.requiredNo
cg.futureupdate.duration3 Months


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