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

dc.contributor.authorTamene, L.D.
dc.contributor.authorAbera, W.
dc.contributor.authorBendito, E.
dc.contributor.authorErkossa, T.
dc.contributor.authorTariku, M.
dc.contributor.authorSewnet, H.
dc.contributor.authorTibebe, D.
dc.contributor.authorSied, J.
dc.contributor.authorFeyisa, G.
dc.contributor.authorWondie, M.
dc.contributor.authorTesfaye, K.
dc.date.accessioned2022-12-21T14:37:12Z
dc.date.available2022-12-21T14:37:12Z
dc.date.issued2022-07-25
dc.identifier.citationTamene, L., Abera, W., Bendito, E., Erkossa, T., Tariku, M., Sewnet, H., ... & Tesfaye, K. (2022). Data-driven similar response units for agricultural technology targeting: an example from Ethiopia. Experimental Agriculture, 58: e27, 1-17.
dc.identifier.issn0014-4797
dc.identifier.urihttps://hdl.handle.net/20.500.12478/7991
dc.description.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.
dc.description.sponsorshipBill & Melinda Gates Foundation
dc.format.extent1-17
dc.language.isoen
dc.subjectAgriculture
dc.subjectMachine Learning
dc.subjectTechnology Transfer
dc.subjectFertilizer Applications
dc.subjectEthiopia
dc.subjectAgroecology
dc.titleData-driven similar response units for agricultural technology targeting: an example from Ethiopia
dc.typeJournal Article
cg.contributor.crpClimate Change, Agriculture and Food Security
cg.contributor.crpWater, Land and Ecosystems
cg.contributor.affiliationInternational Center for Tropical Agriculture
cg.contributor.affiliationInternational Institute of Tropical Agriculture
cg.contributor.affiliationDeutsche Gesellschaft für Internationale Zusammenarbeit
cg.contributor.affiliationGeospatial Information Institute, Ethiopia
cg.contributor.affiliationAddis Ababa University
cg.contributor.affiliationEthiopian Institute of Agricultural Research
cg.contributor.affiliationAmhara Agricultural Research Institute
cg.contributor.affiliationInternational Maize and Wheat Improvement Center
cg.contributor.affiliationScuola Superiore Sant’ Anna
cg.coverage.regionAfrica
cg.coverage.regionEast Africa
cg.coverage.countryEthiopia
cg.coverage.hubEastern Africa Hub
cg.identifier.bibtexciteidTAMENE:2022
cg.isijournalISI Journal
cg.authorship.typesCGIAR and developing country institute
cg.iitasubjectAgronomy
cg.iitasubjectClimate Change
cg.iitasubjectFarming Systems
cg.iitasubjectPost-Harvesting Technology
cg.journalExperimental Agriculture
cg.notesOpen Access Article; Published online: 25 Jul 2022
cg.accessibilitystatusOpen Access
cg.reviewstatusPeer Review
cg.usagerightslicenseCreative Commons Attribution 4.0 (CC BY 0.0)
cg.targetaudienceScientists
cg.identifier.doihttps://dx.doi.org/10.1017/s0014479722000126
cg.iitaauthor.identifierLulseged Tamene: 0000-0002-4846-2330
cg.iitaauthor.identifierWuletawu Abera: 0000-0002-3657-5223
cg.iitaauthor.identifierMeklit Chernet: 0000-0001-9246-5064
cg.iitaauthor.identifierKindie Tesfaye: 0000-0002-7201-8053
cg.futureupdate.requiredNo
cg.identifier.issuee27


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