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    Geospatial approach for delineating extrapolation domains for sustainable agricultural intensification technologies

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    U17ArtMuthoniGeospatialNothomDev.pdf (985.6Kb)
    Date
    2017-11-16
    Author
    Muthoni, Francis K.
    Baijukya, Frederick P.
    Sseguya, H.
    Bekunda, Mateete A.
    Hoeschle-Zeledon, Irmgard
    Ouko, E.
    Mubea, K.
    Type
    Journal Article
    Target Audience
    Scientists
    Metadata
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    Abstract/Description
    Sustainable intensification (SI) is a viable pathway to increase agricultural production and improve ecosystem health. Scaling SI technologies in locations with similar biophysical conditions enhance adoption. This paper employs novel extrapolation detection (ExeDet) algorithm and gridded bioclimatic layers to delineate extrapolation domains for improved maize variety (SC719) and inorganic fertilizers (YaraMila-CEREAL® and YaraBela-Sulfan®) in Tanzania. Suitability was based on grain yields recorded in on-farm trials. The ExeDet algorithm generated three maps: (1) the dissimilarity between bioclimatic conditions in the reference trial sites and the target extrapolation domain (Novelty type-1), (2) the magnitude of novel correlations between covariates in extrapolation domain (Novelty type-2) and (3) the most limiting covariate. The novelty type1 and 2 maps were intersected and reclassified into five suitability classes. These classes were cross-tabulated to generate extrapolation suitability index (ESI) for the candidate technology package. An impact based spatial targeting index (IBSTI) was used to identify areas within the zones earmarked as suitable using ESI where the potential impacts for out scaling interventions can be maximized. Application of ESI and IBSTI is expected to guide extension and development agencies to prioritize scaling intervention based on both biophysical suitability and potential impact of particular technology package. Annual precipitation was most limiting factor in largest area of the extrapolation domain. Identification of the spatial distribution of the limiting factor is useful for recommending remedial measures to address the limiting factor that hinder a technology to achieve its full potential. The method outlined in this paper is replicable to other technologies that require extrapolation provided that representative reference trial data and appropriate biophysical grids are available.
    https://dx.doi.org/10.5194/isprs-archives-XLII-3-W2-145-2017
    Multi standard citation
    Permanent link to this item
    https://hdl.handle.net/20.500.12478/2411
    Non-IITA Authors ORCID
    Irmgard Hoeschle-Zeledonhttps://orcid.org/0000-0002-2530-6554
    Digital Object Identifier (DOI)
    https://dx.doi.org/10.5194/isprs-archives-XLII-3-W2-145-2017
    Agrovoc Terms
    Intensification; Farming Systems; Maize
    Regions
    Africa; East Africa; Southern Africa
    Countries
    Tanzania
    Journals
    International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
    Collections
    • Journal and Journal Articles5283
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