Targeting Drought-Tolerant Maize Varieties in Southern Africa: A Geospatial Crop Modeling Approach Using Big Data

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dc.creatorTesfaye, Kindie
dc.creatorSonder, Kai
dc.creatorCairns, Jill
dc.creatorMagorokosho, Cosmos
dc.creatorTarekegn, Amsal
dc.creatorKassie, Girma T.
dc.creatorGetaneh, Fite
dc.creatorAbdoulaye, Tahirou
dc.creatorAbate, Tsedeke
dc.creatorErenstein, Olaf
dc.date2017-04-01T14:11:17Z
dc.date.accessioned2026-07-09T10:39:17Z
dc.descriptionMaize is a major staple food crop in southern Africa and stress tolerant improved varieties have the potential to increase productivity, enhance livelihoods and reduce food insecurity. This study uses big data in refining the geospatial targeting of new drought-tolerant (DT) maize varieties in Malawi, Mozambique, Zambia, and Zimbabwe. Results indicate that more than 1.0 million hectares (Mha) of maize in the study countries is exposed to a seasonal drought frequency exceeding 20% while an additional 1.6 Mha experience a drought occurrence of 10–20%. Spatial modeling indicates that new DT varieties could give a yield advantage of 5–40% over the commercial check variety across drought environments while crop management and input costs are kept equal. Results indicate a huge potential for DT maize seed production and marketing in the study countries. The study demonstrates how big data and analytical tools enhance the targeting and uptake of new agricultural technologies for boosting rural livelihoods, agribusiness development and food security in developing countries.
dc.identifierOther:(ISSN #: 1559-2448)
dc.identifierdoi:10.22004/ag.econ.240697
dc.identifierhttps://ageconsearch.umn.edu/record/240697/files/420150114.pdf
dc.identifierhttp://ageconsearch.umn.edu/record/240697
dc.identifier.urihttp://hdl.handle.net/123456789/620746
dc.languageeng
dc.publisher
dc.sourcehttp://ageconsearch.umn.edu/record/240697
dc.titleTargeting Drought-Tolerant Maize Varieties in Southern Africa: A Geospatial Crop Modeling Approach Using Big Data
dc.typeText

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