Predictive mapping of wholesale grain prices for rural areas in Tanzania : A replicable modeling framework for predicting agricultural prices across time and space

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cg.contributor.affiliationInternational Maize and Wheat Improvement Centeren
cg.contributor.affiliationUniversity of Californiaen
cg.contributor.donorBill & Melinda Gates Foundationen
cg.contributor.donorCGIAR Trust Funden
cg.contributor.initiativeExcellence in Agronomy
cg.coverage.countryTanzania
cg.coverage.iso3166-alpha2TZ
cg.coverage.regionEastern Africa
cg.creator.identifierJordan Chamberlin: 0000-0001-9522-3001
cg.creator.identifierJoão Vasco Silva: 0000-0002-3019-5895
cg.creator.identifierRobert Hijmans: 0000-0001-5872-2872
cg.howPublishedFormally Publisheden
cg.reviewStatusInternal Reviewen
cg.subject.actionAreaSystems Transformation
cg.subject.actionAreaResilient Agrifood Systems
cg.subject.impactAreaPoverty reduction, livelihoods and jobs
dc.contributor.authorMadaga, Laviniaen
dc.contributor.authorChamberlin, Jordanen
dc.contributor.authorBisrat Gebrekidanen
dc.contributor.authorSilva, João Vascoen
dc.contributor.authorMkondiwa, Maxwellen
dc.contributor.authorHijmans, Robert J.en
dc.date.accessioned2024-11-25T16:23:27Zen
dc.date.available2024-11-25T16:23:27Zen
dc.identifier.urihttps://hdl.handle.net/10568/162729
dc.titlePredictive mapping of wholesale grain prices for rural areas in Tanzania : A replicable modeling framework for predicting agricultural prices across time and spaceen
dcterms.abstractOur understanding of small farm decision-making in developing countries is often critically constrained by sparse information about the input and output prices faced by farmers operating diverse landscapes with heterogeneous market and accessibility characteristics. We present a methodology for predicting local market prices over time and space, using relatively sparse pooled observations on crop commodity market prices at different locations and times. We show prediction results for wholesale prices for grains (six different cereals and beans) and potatoes in Tanzania. We find that pooling observations on prices for different commodities improves prediction for any given commodity, because of spatiotemporal covariance in observed prices. We discuss how our modeling framework could be used to design relatively low-cost monitoring systems for enabling regularly updated, national-scale spatial price maps.en
dcterms.accessRightsOpen Access
dcterms.bibliographicCitationMadaga, L., Chamberlin, J., Bisrat Gebrekidan., Silva, J. V., Mkondiwa, M. & Hijmans, R. J. (2024). Predictive mapping of wholesale grain prices for rural areas in Tanzania: A replicable modeling framework for predicting agricultural prices across time and space. EIA. https://hdl.handle.net/10883/35063en
dcterms.extent24 p.en
dcterms.hasVersionhttps://hdl.handle.net/10883/35063en
dcterms.issued2024-11
dcterms.languageen
dcterms.licenseOther
dcterms.publisherEiAen
dcterms.subjectwholesale pricesen
dcterms.subjectgrainen
dcterms.subjectrural areasen
dcterms.subjectagricultural pricesen
dcterms.subjectmarketsen
dcterms.subjectforecastingen
dcterms.typeReport

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