Lessons from integrated seasonal forecast-crop modelling in Africa: a systematic review
Seasonal forecasts coupled with crop models can potentially enhance decision-making in smallholder farming in Africa. The study sought to inform future research through identifying and critiquing crop and climate models, and techniques for integrating seasonal forecast information and crop models. P...
| Autores principales: | , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Walter de Gruyter GmbH
2022
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/129892 |
| _version_ | 1855517936701145088 |
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| author | Mkuhlani, S. Zinyengere, Nkulumo Kumi, N. Crespo, O. |
| author_browse | Crespo, O. Kumi, N. Mkuhlani, S. Zinyengere, Nkulumo |
| author_facet | Mkuhlani, S. Zinyengere, Nkulumo Kumi, N. Crespo, O. |
| author_sort | Mkuhlani, S. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Seasonal forecasts coupled with crop models can potentially enhance decision-making in smallholder farming in Africa. The study sought to inform future research through identifying and critiquing crop and climate models, and techniques for integrating seasonal forecast information and crop models. Peer-reviewed articles related to crop modelling and seasonal forecasting were sourced from Google Scholar, Web of Science, AGRIS, and JSTOR. Nineteen articles were selected from a search outcome of 530. About 74% of the studies used mechanistic models, which are favored for climate risk management research as they account for crop management practices. European Centre for Medium-Range Weather Forecasts and European Centre for Medium-Range Weather Forecasts, Hamburg, are the predominant global climate models (GCMs) used across Africa. A range of approaches have been assessed to improve the effectiveness of the connection between seasonal forecast information and mechanistic crop models, which include GCMs, analogue, stochastic disaggregation, and statistical prediction through converting seasonal weather summaries into the daily weather. GCM outputs are produced in a format compatible with mechanistic crop models. Such outputs are critical for researchers to have information on the merits and demerits of tools and approaches on integrating seasonal forecast and crop models. There is however need to widen such research to other regions in Africa, crop, farming systems, and policy. |
| format | Journal Article |
| id | CGSpace129892 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Walter de Gruyter GmbH |
| publisherStr | Walter de Gruyter GmbH |
| record_format | dspace |
| spelling | CGSpace1298922025-11-11T10:06:52Z Lessons from integrated seasonal forecast-crop modelling in Africa: a systematic review Mkuhlani, S. Zinyengere, Nkulumo Kumi, N. Crespo, O. forecasting crop modelling smallholders climate change farm management Seasonal forecasts coupled with crop models can potentially enhance decision-making in smallholder farming in Africa. The study sought to inform future research through identifying and critiquing crop and climate models, and techniques for integrating seasonal forecast information and crop models. Peer-reviewed articles related to crop modelling and seasonal forecasting were sourced from Google Scholar, Web of Science, AGRIS, and JSTOR. Nineteen articles were selected from a search outcome of 530. About 74% of the studies used mechanistic models, which are favored for climate risk management research as they account for crop management practices. European Centre for Medium-Range Weather Forecasts and European Centre for Medium-Range Weather Forecasts, Hamburg, are the predominant global climate models (GCMs) used across Africa. A range of approaches have been assessed to improve the effectiveness of the connection between seasonal forecast information and mechanistic crop models, which include GCMs, analogue, stochastic disaggregation, and statistical prediction through converting seasonal weather summaries into the daily weather. GCM outputs are produced in a format compatible with mechanistic crop models. Such outputs are critical for researchers to have information on the merits and demerits of tools and approaches on integrating seasonal forecast and crop models. There is however need to widen such research to other regions in Africa, crop, farming systems, and policy. 2022-11-07 2023-04-04T08:08:55Z 2023-04-04T08:08:55Z Journal Article https://hdl.handle.net/10568/129892 en Open Access application/pdf Walter de Gruyter GmbH Mkuhlani, S., Zinyengere, N., Kumi, N. & Crespo, O. (2022). Lessons from integrated seasonal forecast-crop modelling in Africa: a systematic review. Open Life Sciences, 17(1), 1398-1417. |
| spellingShingle | forecasting crop modelling smallholders climate change farm management Mkuhlani, S. Zinyengere, Nkulumo Kumi, N. Crespo, O. Lessons from integrated seasonal forecast-crop modelling in Africa: a systematic review |
| title | Lessons from integrated seasonal forecast-crop modelling in Africa: a systematic review |
| title_full | Lessons from integrated seasonal forecast-crop modelling in Africa: a systematic review |
| title_fullStr | Lessons from integrated seasonal forecast-crop modelling in Africa: a systematic review |
| title_full_unstemmed | Lessons from integrated seasonal forecast-crop modelling in Africa: a systematic review |
| title_short | Lessons from integrated seasonal forecast-crop modelling in Africa: a systematic review |
| title_sort | lessons from integrated seasonal forecast crop modelling in africa a systematic review |
| topic | forecasting crop modelling smallholders climate change farm management |
| url | https://hdl.handle.net/10568/129892 |
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