Ecological niche modeling approach for assessing potential for expansion of irrigated rice under alternate wetting and drying method
The approach was developed and validated for irrigated schemes of Burkina Faso. The evaluation metrics of the machine learning approach showed values higher than 80%. The schemes and seasons suitable for AWD were validated against ground truth data. The validated approach can be applied to the whole...
| Autor principal: | |
|---|---|
| Formato: | Informe técnico |
| Lenguaje: | Inglés |
| Publicado: |
2020
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/123104 |
| _version_ | 1855540866779709440 |
|---|---|
| author | CGIAR Research Program on Climate Change, Agriculture and Food Security |
| author_browse | CGIAR Research Program on Climate Change, Agriculture and Food Security |
| author_facet | CGIAR Research Program on Climate Change, Agriculture and Food Security |
| author_sort | CGIAR Research Program on Climate Change, Agriculture and Food Security |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The approach was developed and validated for irrigated schemes of Burkina Faso. The evaluation metrics of the machine learning approach showed values higher than 80%. The schemes and seasons suitable for AWD were validated against ground truth data. The validated approach can be applied to the whole West African region. |
| format | Informe técnico |
| id | CGSpace123104 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| record_format | dspace |
| spelling | CGSpace1231042023-03-14T12:27:57Z Ecological niche modeling approach for assessing potential for expansion of irrigated rice under alternate wetting and drying method CGIAR Research Program on Climate Change, Agriculture and Food Security rice evaluation development rural development data learning seasons drying systems irrigated rice agrifood systems machine learning The approach was developed and validated for irrigated schemes of Burkina Faso. The evaluation metrics of the machine learning approach showed values higher than 80%. The schemes and seasons suitable for AWD were validated against ground truth data. The validated approach can be applied to the whole West African region. 2020-12-31 2022-10-06T14:21:23Z 2022-10-06T14:21:23Z Report https://hdl.handle.net/10568/123104 en Open Access application/pdf CGIAR Research Program on Climate Change, Agriculture and Food Security. 2020. Ecological niche modeling approach for assessing potential for expansion of irrigated rice under alternate wetting and drying method. Reported in Climate Change, Agriculture and Food Security Annual Report 2020. Innovations. |
| spellingShingle | rice evaluation development rural development data learning seasons drying systems irrigated rice agrifood systems machine learning CGIAR Research Program on Climate Change, Agriculture and Food Security Ecological niche modeling approach for assessing potential for expansion of irrigated rice under alternate wetting and drying method |
| title | Ecological niche modeling approach for assessing potential for expansion of irrigated rice under alternate wetting and drying method |
| title_full | Ecological niche modeling approach for assessing potential for expansion of irrigated rice under alternate wetting and drying method |
| title_fullStr | Ecological niche modeling approach for assessing potential for expansion of irrigated rice under alternate wetting and drying method |
| title_full_unstemmed | Ecological niche modeling approach for assessing potential for expansion of irrigated rice under alternate wetting and drying method |
| title_short | Ecological niche modeling approach for assessing potential for expansion of irrigated rice under alternate wetting and drying method |
| title_sort | ecological niche modeling approach for assessing potential for expansion of irrigated rice under alternate wetting and drying method |
| topic | rice evaluation development rural development data learning seasons drying systems irrigated rice agrifood systems machine learning |
| url | https://hdl.handle.net/10568/123104 |
| work_keys_str_mv | AT cgiarresearchprogramonclimatechangeagricultureandfoodsecurity ecologicalnichemodelingapproachforassessingpotentialforexpansionofirrigatedriceunderalternatewettinganddryingmethod |