Targeting drought-tolerant maize varieties in southern Africa: a geospatial crop modeling approach using big data
Maize 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 Malaw...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Journal Article |
| Language: | Inglés |
| Published: |
2016
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/76332 |
| _version_ | 1855526519232790528 |
|---|---|
| author | Tesfaye, K. Sonder, Kai Caims, J. Magorokosho, Cosmos Tarekegn, A. Kassie, Girma T. Getaneh, F. Abdoulaye, Tahirou Abate, T. Erenstein, Olaf |
| author_browse | Abate, T. Abdoulaye, Tahirou Caims, J. Erenstein, Olaf Getaneh, F. Kassie, Girma T. Magorokosho, Cosmos Sonder, Kai Tarekegn, A. Tesfaye, K. |
| author_facet | Tesfaye, K. Sonder, Kai Caims, J. Magorokosho, Cosmos Tarekegn, A. Kassie, Girma T. Getaneh, F. Abdoulaye, Tahirou Abate, T. Erenstein, Olaf |
| author_sort | Tesfaye, K. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Maize 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. |
| format | Journal Article |
| id | CGSpace76332 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2016 |
| publishDateRange | 2016 |
| publishDateSort | 2016 |
| record_format | dspace |
| spelling | CGSpace763322025-11-11T10:18:52Z Targeting drought-tolerant maize varieties in southern Africa: a geospatial crop modeling approach using big data Tesfaye, K. Sonder, Kai Caims, J. Magorokosho, Cosmos Tarekegn, A. Kassie, Girma T. Getaneh, F. Abdoulaye, Tahirou Abate, T. Erenstein, Olaf drought tolerance maize Maize 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. 2016 2016-08-04T07:45:50Z 2016-08-04T07:45:50Z Journal Article https://hdl.handle.net/10568/76332 en Open Access application/pdf Tesfaye, K., Sonder, K., Cairns, J., Magorokosho, C., Tarekegn, A., Kassie, G.T., ... & Erenstein, O. (2016). Targeting Drought-Tolerant Maize Varieties in Southern Africa: A Geospatial Crop Modeling Approach Using Big Data. International Food and Agribusiness Management Review, 19(A), 1-18 |
| spellingShingle | drought tolerance maize Tesfaye, K. Sonder, Kai Caims, J. Magorokosho, Cosmos Tarekegn, A. Kassie, Girma T. Getaneh, F. Abdoulaye, Tahirou Abate, T. Erenstein, Olaf Targeting drought-tolerant maize varieties in southern Africa: a geospatial crop modeling approach using big data |
| title | Targeting drought-tolerant maize varieties in southern Africa: a geospatial crop modeling approach using big data |
| title_full | Targeting drought-tolerant maize varieties in southern Africa: a geospatial crop modeling approach using big data |
| title_fullStr | Targeting drought-tolerant maize varieties in southern Africa: a geospatial crop modeling approach using big data |
| title_full_unstemmed | Targeting drought-tolerant maize varieties in southern Africa: a geospatial crop modeling approach using big data |
| title_short | Targeting drought-tolerant maize varieties in southern Africa: a geospatial crop modeling approach using big data |
| title_sort | targeting drought tolerant maize varieties in southern africa a geospatial crop modeling approach using big data |
| topic | drought tolerance maize |
| url | https://hdl.handle.net/10568/76332 |
| work_keys_str_mv | AT tesfayek targetingdroughttolerantmaizevarietiesinsouthernafricaageospatialcropmodelingapproachusingbigdata AT sonderkai targetingdroughttolerantmaizevarietiesinsouthernafricaageospatialcropmodelingapproachusingbigdata AT caimsj targetingdroughttolerantmaizevarietiesinsouthernafricaageospatialcropmodelingapproachusingbigdata AT magorokoshocosmos targetingdroughttolerantmaizevarietiesinsouthernafricaageospatialcropmodelingapproachusingbigdata AT tarekegna targetingdroughttolerantmaizevarietiesinsouthernafricaageospatialcropmodelingapproachusingbigdata AT kassiegirmat targetingdroughttolerantmaizevarietiesinsouthernafricaageospatialcropmodelingapproachusingbigdata AT getanehf targetingdroughttolerantmaizevarietiesinsouthernafricaageospatialcropmodelingapproachusingbigdata AT abdoulayetahirou targetingdroughttolerantmaizevarietiesinsouthernafricaageospatialcropmodelingapproachusingbigdata AT abatet targetingdroughttolerantmaizevarietiesinsouthernafricaageospatialcropmodelingapproachusingbigdata AT erensteinolaf targetingdroughttolerantmaizevarietiesinsouthernafricaageospatialcropmodelingapproachusingbigdata |