Data-driven similar response units for agricultural technology targeting: An example from Ethiopia

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cg.authorship.typesCGIAR and developing country instituteen
cg.authorship.typesCGIAR and advanced research instituteen
cg.contributor.affiliationInternational Center for Tropical Agricultureen
cg.contributor.affiliationDeutsche Gesellschaft Für Internationale Zusammenarbeiten
cg.contributor.affiliationGeospatial Information Instituteen
cg.contributor.affiliationAddis Ababa Universityen
cg.contributor.affiliationScuola Superiore Sant'Annaen
cg.contributor.affiliationAmhara Regional Agricultural Research Institute, Ethiopiaen
cg.contributor.affiliationInternational Maize and Wheat Improvement Centeren
cg.contributor.donorBill & Melinda Gates Foundationen
cg.contributor.donorCGIAR Trust Funden
cg.contributor.initiativeExcellence in Agronomy
cg.coverage.countryEthiopia
cg.coverage.iso3166-alpha2ET
cg.coverage.regionSub-Saharan Africa
cg.coverage.regionEastern Africa
cg.coverage.regionAfrica
cg.creator.identifierLulseged Tamene: 0000-0002-4846-2330en
cg.creator.identifierWuletawu Abera: 0000-0002-3657-5223en
cg.creator.identifierKindie Tesfaye: 0000-0002-7201-8053en
cg.howPublishedFormally Publisheden
cg.identifier.doihttps://doi.org/10.1017/s0014479722000126en
cg.isijournalISI Journalen
cg.issn0014-4797en
cg.issuee27en
cg.journalExperimental Agricultureen
cg.reviewStatusPeer Reviewen
cg.subject.alliancebiovciatCROP PRODUCTIONen
cg.subject.alliancebiovciatINFORMATICSen
cg.subject.alliancebiovciatMODELINGen
cg.subject.iitaAGRONOMYen
cg.subject.iitaCLIMATE CHANGEen
cg.subject.iitaFARMING SYSTEMSen
cg.subject.iitaPOST-HARVESTING TECHNOLOGYen
cg.subject.impactAreaClimate adaptation and mitigation
cg.subject.impactAreaEnvironmental health and biodiversity
cg.subject.impactAreaPoverty reduction, livelihoods and jobs
cg.subject.sdgSDG 1 - No povertyen
cg.subject.sdgSDG 2 - Zero hungeren
cg.volume58en
dc.contributor.authorTamene, Lulseged D.en
dc.contributor.authorAbera, Wuletawuen
dc.contributor.authorBendito, Eduardoen
dc.contributor.authorErkossa, Tekluen
dc.contributor.authorTariku, Mekliten
dc.contributor.authorSewnet, Habtamuen
dc.contributor.authorTibebe, Degefieen
dc.contributor.authorSied, Jemaen
dc.contributor.authorFeyisa, Gudinaen
dc.contributor.authorWondie, Menaleen
dc.contributor.authorTesfaye, Kindieen
dc.date.accessioned2022-08-24T13:43:38Zen
dc.date.available2022-08-24T13:43:38Zen
dc.identifier.urihttps://hdl.handle.net/10568/120934
dc.titleData-driven similar response units for agricultural technology targeting: An example from Ethiopiaen
dcterms.abstractEthiopia has heterogeneous topographic, climatic and socio-ecological systems. Recommendations of agricultural inputs and management practices based on coarse domains such as agro-ecological zones (AEZ) may not lead to accurate targeting, mainly due to large intra-zone variations. The lack of well-targeted recommendations may contribute to the underperformance of promising technologies. Therefore, there is a need to define units where similar environmental and biophysical features prevail, based on which specific recommendations can be made for similar response units (SRUs). We used unsupervised machine learning algorithms to identify areas of high similarity or homogeneous zones called ‘SRUs’ that can guide the targeting of agricultural technologies. SRUs are landscape entities defined by integrating relevant environmental covariates with the intention to identify areas of similar responses. Using environmental spatial data layers such as edaphic and ecological variables for delineation of the SRUs, we applied K- and X-means clustering techniques to generate various granular levels of zonation and define areas of high similarity. The results of the clustering were validated through expert consultation and by comparison with an existing operational AEZ map of Ethiopia. We also augmented validation of the heterogeneity of the SRUs by using field-based crop response to fertiliser application experimental data. The expert consultation highlighted that the SRUs can provide improved clustering of areas of high similarity for targeting interventions. Comparison with the AEZ map indicated that SRUs with the same number of AEZ units captured heterogeneity better with less within-cluster variability of the former. In addition, SRUs show lower within-cluster variability to optimal crop response to fertiliser application compared with AEZs with the same number of classes. This implies that the SRUs can be used for refined agricultural input and technology targeting. The work in this study also developed an operational framework that users can deploy to fetch data from the cloud and generate SRUs for their areas of interest.en
dcterms.accessRightsOpen Access
dcterms.audienceScientistsen
dcterms.available2022-07-25en
dcterms.bibliographicCitationTamene, L., Abera, W., Bendito, E., Erkossa, T., Tariku, M., Sewnet, H., Tibebe, D., Sied, J., Feyisa, G., Wondie, M., & Tesfaye, K. (2022). Data-driven similar response units for agricultural technology targeting: An example from Ethiopia. Experimental Agriculture, 58. https://doi.org/10.1017/s0014479722000126en
dcterms.extent17 p.en
dcterms.issued2022en
dcterms.languageen
dcterms.licenseCC-BY-4.0
dcterms.publisherCambridge University Pressen
dcterms.replaceshttps://hdl.handle.net/10568/126201en
dcterms.subjectappropriate technologyen
dcterms.subjectmachine learningen
dcterms.subjectpoliciesen
dcterms.subjectagricultureen
dcterms.subjectfarming systemsen
dcterms.subjecttecnología apropiadaen
dcterms.subjectaprendizaje electrónicoen
dcterms.subjectpolíticasen
dcterms.subjecttechnology transferen
dcterms.subjectfertilizer applicationsen
dcterms.subjectethiopiaen
dcterms.subjectagroecologyen
dcterms.typeJournal Article

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