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

dc.creatorAndrade, J.F.
dc.creatorRattalino Edreira, J.I.
dc.creatorFarrow, A.
dc.creatorVan Loon, M.P.
dc.creatorCraufurd, P.
dc.creatorRurinda, J.
dc.creatorShamie Zingore
dc.creatorChamberlin, J.
dc.creatorClaessens, L.
dc.creatorAdewopo, J.
dc.creatorIttersum, M.K. van
dc.creatorCassman, K.G.
dc.creatorGrassini, P.
dc.date2019-03-16T01:25:11Z
dc.date2019-03-16T01:25:11Z
dc.date2019
dc.date.accessioned2023-07-17T20:04:03Z
dc.date.available2023-07-17T20:04:03Z
dc.identifierISSN: 2211-9124
dc.identifierhttps://hdl.handle.net/10883/20085
dc.identifier10.1016/j.gfs.2018.12.006
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7511912
dc.descriptionTraditional 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.
dc.description72-81
dc.formatPDF
dc.languageEnglish
dc.publisherElsevier
dc.relationhttps://ars.els-cdn.com/content/image/1-s2.0-S2211912418301378-mmc1.docx
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.source20
dc.sourceGlobal Food Security
dc.subjectAGRICULTURAL SCIENCES AND BIOTECHNOLOGY
dc.subjectAgricultural Research and Development
dc.subjectSpatial Framework
dc.subjectScaling Out
dc.subjectRESEARCH
dc.subjectIMPACT ASSESSMENT
dc.subjectSPATIAL ANALYSIS
dc.subjectAGRICULTURAL RESEARCH
dc.subjectAGRICULTURAL EXTENSION
dc.titleA spatial framework for ex-ante impact assessment of agricultural technologies
dc.typeArticle
dc.typePublished Version
dc.coverageNetherlands


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