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

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    Journal Article (1.426Mb)
    Date
    2022-07-25
    Author
    Tamene, L.D.
    Abera, W.
    Bendito, E.
    Erkossa, T.
    Tariku, M.
    Sewnet, H.
    Tibebe, D.
    Sied, J.
    Feyisa, G.
    Wondie, M.
    Tesfaye, K.
    Type
    Journal Article
    Review Status
    Peer Review
    Target Audience
    Scientists
    Metadata
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    Abstract/Description
    Ethiopia 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.
    https://dx.doi.org/10.1017/s0014479722000126
    Multi standard citation
    Permanent link to this item
    https://hdl.handle.net/20.500.12478/7991
    IITA Authors ORCID
    Lulseged Tamenehttps://orcid.org/0000-0002-4846-2330
    Wuletawu Aberahttps://orcid.org/0000-0002-3657-5223
    Meklit Chernethttps://orcid.org/0000-0001-9246-5064
    Kindie Tesfayehttps://orcid.org/0000-0002-7201-8053
    Digital Object Identifier (DOI)
    https://dx.doi.org/10.1017/s0014479722000126
    IITA Subjects
    Agronomy; Climate Change; Farming Systems; Post-Harvesting Technology
    Agrovoc Terms
    Agriculture; Machine Learning; Technology Transfer; Fertilizer Applications; Ethiopia; Agroecology
    Regions
    Africa; East Africa
    Countries
    Ethiopia
    Hubs
    Eastern Africa Hub
    Journals
    Experimental Agriculture
    Collections
    • Journal and Journal Articles4836
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