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...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2022-11-01
|
| Series: | Open Life Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/biol-2022-0507 |
| _version_ | 1811320202143465472 |
|---|---|
| author | Mkuhlani Siyabusa Zinyengere Nkulumo Kumi Naomi Crespo Olivier |
| author_facet | Mkuhlani Siyabusa Zinyengere Nkulumo Kumi Naomi Crespo Olivier |
| author_sort | Mkuhlani Siyabusa |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-13T12:54:58Z |
| format | Article |
| id | doaj.art-861e6b83ea4f46b8b71a298eae681e9a |
| institution | Directory Open Access Journal |
| issn | 2391-5412 |
| language | English |
| last_indexed | 2024-04-13T12:54:58Z |
| publishDate | 2022-11-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Open Life Sciences |
| spelling | doaj.art-861e6b83ea4f46b8b71a298eae681e9a2022-12-22T02:46:05ZengDe GruyterOpen Life Sciences2391-54122022-11-011711398141710.1515/biol-2022-0507Lessons from integrated seasonal forecast-crop modelling in Africa: A systematic reviewMkuhlani Siyabusa0Zinyengere Nkulumo1Kumi Naomi2Crespo Olivier3Climate Systems Analysis Group, Department of Environmental and Geographical Science, University of Cape Town, Rondebosch, Cape Town 7700, South AfricaAgriculture and Food Global Practice, The World Bank Group, 1818H Str NW, Washington DC, 20433, USADepartment of Atmospheric and Climate Science, University of Energy and Natural Resources (UENR), Sunyani, GhanaClimate Systems Analysis Group, Department of Environmental and Geographical Science, University of Cape Town, Rondebosch, Cape Town 7700, South AfricaSeasonal 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.https://doi.org/10.1515/biol-2022-0507seasonal forecastcrop modelsmall scale farmerclimate risk managementfarm management practice |
| spellingShingle | Mkuhlani Siyabusa Zinyengere Nkulumo Kumi Naomi Crespo Olivier Lessons from integrated seasonal forecast-crop modelling in Africa: A systematic review Open Life Sciences seasonal forecast crop model small scale farmer climate risk management farm management practice |
| 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 | seasonal forecast crop model small scale farmer climate risk management farm management practice |
| url | https://doi.org/10.1515/biol-2022-0507 |
| work_keys_str_mv | AT mkuhlanisiyabusa lessonsfromintegratedseasonalforecastcropmodellinginafricaasystematicreview AT zinyengerenkulumo lessonsfromintegratedseasonalforecastcropmodellinginafricaasystematicreview AT kuminaomi lessonsfromintegratedseasonalforecastcropmodellinginafricaasystematicreview AT crespoolivier lessonsfromintegratedseasonalforecastcropmodellinginafricaasystematicreview |