dc.creatorTamene, L.
dc.creatorAbera, W.
dc.creatorBendito, E.
dc.creatorErkossa, T.
dc.creatorTariku, M.
dc.creatorSewnet, H.
dc.creatorTibebe, D.
dc.creatorSied, J.
dc.creatorFeyisa, G.
dc.creatorWondie, M.
dc.creatorFantaye, K. T.
dc.date2022-08-30T00:15:13Z
dc.date2022-08-30T00:15:13Z
dc.date2022
dc.date.accessioned2023-07-17T20:09:24Z
dc.date.available2023-07-17T20:09:24Z
dc.identifierhttps://hdl.handle.net/10883/22151
dc.identifier10.1017/S0014479722000126
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7513913
dc.descriptionEthiopia 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.languageEnglish
dc.publisherCambridge University Press
dc.rightsCIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact CIMMYT-Knowledge-Center@cgiar.org indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose
dc.rightsOpen Access
dc.source1
dc.source58
dc.source0014-4797
dc.sourceExperimental Agriculture
dc.sourcee27
dc.subjectAGRICULTURAL SCIENCES AND BIOTECHNOLOGY
dc.subjectClustering
dc.subjectTechnology Targeting
dc.subjectAGRICULTURE
dc.subjectMACHINE LEARNING
dc.subjectTECHNOLOGY
dc.subjectTARGETING
dc.titleData-driven similar response units for agricultural technology targeting: An example from Ethiopia
dc.typeArticle
dc.typePublished Version
dc.coverageEthiopia
dc.coverageUnited Kingdom


Este ítem pertenece a la siguiente institución