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    A spatial framework for ex-ante impact assessment of agricultural technologies

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    U19ArtAndradeSpatialInthomNodev.pdf (2.942Mb)
    Date
    2019-03
    Author
    Andrade, J.F.
    Edreira, J.I.R.
    Farrow, A.
    Loon, Marloes P. van
    Craufurd, Peter Q.
    Rurinda, J.
    Zingore, S.
    Chamberlin, Jordan
    Claessens, Lieven
    Adewopo, J.
    Ittersum, Martin K. van
    Cassman, Kenneth G.
    Grassini, P.
    Type
    Journal Article
    Target Audience
    Scientists
    Metadata
    Show full item record
    Abstract/Description
    Traditional agricultural research and extension relies on replicated field experiments, on-farm trials, and demonstration plots to evaluate and adapt agronomic technologies that aim to increase productivity, reduce risk, and protect the environment for a given biophysical and socio-economic context. To date, these efforts lack a generic and robust spatial framework for ex-ante assessment that: (i) provides strategic insight to guide decisions about the number and location of testing sites, (ii) define the target domain for scaling-out a given technology or technology package, and (iii) estimate potential impact from widespread adoption of the technology(ies) being evaluated. In this study, we developed a data-rich spatial framework to guide agricultural research and development (AR&D) prioritization and to perform ex-ante impact assessment. The framework uses “technology extrapolation domains”, which delineate regions with similar climate and soil type combined with other biophysical and socio-economic factors that influence technology adoption. We provide proof of concept for the framework using a maize agronomy project in three sub-Saharan Africa countries (Ethiopia, Nigeria, and Tanzania) as a case study. We used maize area and rural population coverage as indicators to estimate potential project impact in each country. The project conducted 496 nutrient omission trials located at both on-farm and research station sites across these three countries. Reallocation of test sites towards domains with a larger proportion of national maize area could increase coverage of maize area by 79–134% and of rural population by 14–33% in Nigeria and Ethiopia. This study represents a first step in developing a generic, transparent, and scientifically robust framework to estimate ex-ante impact of AR&D programs that aim to increase food production and reduce poverty and hunger.
    https://dx.doi.org/10.1016/j.gfs.2018.12.006
    Multi standard citation
    Permanent link to this item
    https://hdl.handle.net/20.500.12478/5197
    Non-IITA Authors ORCID
    Julius Adewopohttps://orcid.org/0000-0003-4831-2823
    Lieven Claessenshttps://orcid.org/0000-0003-2961-8990
    Digital Object Identifier (DOI)
    https://dx.doi.org/10.1016/j.gfs.2018.12.006
    Research Themes
    NATURAL RESOURCE MANAGEMENT
    IITA Subjects
    Natural Resource Management
    Agrovoc Terms
    Agricultural Research For Development; Spatial; Framework; Impact Assessment; Climate Change; Agriculture; Food Security
    Regions
    Africa; East Africa; North Africa; West Africa
    Countries
    Ethiopia; Nigeria; Tanzania
    Journals
    Global Food Security
    Collections
    • Journal and Journal Articles4835
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