Space-time prediction of maize yields in Southern Africa
| cg.authorship.types | CGIAR multi-centre | en_US |
| cg.contributor.affiliation | International Institute of Tropical Agriculture | en_US |
| cg.contributor.affiliation | International Maize and Wheat Improvement Center | en_US |
| cg.contributor.donor | United States Agency for International Development | en_US |
| cg.contributor.donor | CGIAR Trust Fund | en_US |
| cg.contributor.initiative | Mixed Farming Systems | en_US |
| cg.coverage.country | Malawi | en_US |
| cg.coverage.country | Mozambique | en_US |
| cg.coverage.country | Zambia | en_US |
| cg.coverage.country | Zimbabwe | en_US |
| cg.coverage.iso3166-alpha2 | MW | en_US |
| cg.coverage.iso3166-alpha2 | MZ | en_US |
| cg.coverage.iso3166-alpha2 | ZM | en_US |
| cg.coverage.iso3166-alpha2 | ZW | en_US |
| cg.coverage.region | Africa | en_US |
| cg.coverage.region | Southern Africa | en_US |
| cg.coverage.region | Eastern Africa | en_US |
| cg.coverage.subregion | Southern Africa | en_US |
| cg.creator.identifier | Christian Thierfelder: 0000-0002-6306-7670 | en_US |
| cg.howPublished | Grey Literature | en_US |
| cg.place | Ibadan, Nigeria | en_US |
| cg.subject.actionArea | Resilient Agrifood Systems | en_US |
| cg.subject.impactArea | Nutrition, health and food security | en_US |
| cg.subject.impactPlatform | Nutrition, Health and Food Security | en_US |
| cg.subject.sdg | SDG 1 - No poverty | en_US |
| cg.subject.sdg | SDG 2 - Zero hunger | en_US |
| cg.subject.sdg | SDG 15 - Life on land | en_US |
| dc.contributor.author | Muthoni, Francis K. | en_US |
| dc.contributor.author | Parente, Leandro | en_US |
| dc.contributor.author | Thierfelder, Christian L. | en_US |
| dc.contributor.author | Diemen, Chris van | en_US |
| dc.contributor.author | Hengl, Tomislov | en_US |
| dc.date.accessioned | 2023-11-02T09:02:08Z | en_US |
| dc.date.available | 2023-11-02T09:02:08Z | en_US |
| dc.identifier.uri | https://hdl.handle.net/10568/132660 | en_US |
| dc.title | Space-time prediction of maize yields in Southern Africa | en_US |
| dcterms.accessRights | Open Access | en_US |
| dcterms.audience | CGIAR | en_US |
| dcterms.audience | Donors | en_US |
| dcterms.audience | Scientists | en_US |
| dcterms.bibliographicCitation | Muthoni, F., Thierfelder, C., Parente, L., van Diemen, C. and Hengl, T. 2023. Space-time prediction of maize yields in Southern Africa. Presented at the 39th International Symposium on Remote Sensing of Environment “From human needs to SDGs”, Antalya, Turkey, 24-28 April 2023. Ibadan, Nigeria: IITA. | en_US |
| dcterms.issued | 2023-04-24 | en_US |
| dcterms.language | en | en_US |
| dcterms.license | CC-BY-4.0 | en_US |
| dcterms.publisher | International Institute of Tropical Agriculture | en_US |
| dcterms.subject | mixed farming | en_US |
| dcterms.subject | conservation agriculture | en_US |
| dcterms.subject | climate-smart agriculture | en_US |
| dcterms.subject | crops | en_US |
| dcterms.subject | data | en_US |
| dcterms.subject | intensification | en_US |
| dcterms.type | Presentation | en_US |
Files
Original bundle
1 - 1 of 1
Loading...
- Name:
- Francis_Muthoni_Machine learning for predicting maize yields.pptx
- Size:
- 4.25 MB
- Format:
- Microsoft Powerpoint XML
- Description:
- Presentation
License bundle
1 - 1 of 1
Loading...
- Name:
- license.txt
- Size:
- 1.75 KB
- Format:
- Item-specific license agreed upon to submission
- Description: