| dc.contributor.author | Mkuhlani, S. |
| dc.contributor.author | Zinyengere, N. |
| dc.contributor.author | Kumi, N. |
| dc.contributor.author | Crespo, O. |
| dc.date.accessioned | 2023-04-03T08:55:49Z |
| dc.date.available | 2023-04-03T08:55:49Z |
| dc.date.issued | 2022-11-07 |
| dc.identifier.citation | 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. |
| dc.identifier.issn | 2391-5412 |
| dc.identifier.uri | https://hdl.handle.net/20.500.12478/8117 |
| dc.description.abstract | 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. |
| dc.description.sponsorship | Water Resource Commission |
| dc.description.sponsorship | International Institute of Tropical Agriculture |
| dc.format.extent | 1398-1417 |
| dc.language.iso | en |
| dc.subject | Forecasting |
| dc.subject | Crop Modelling |
| dc.subject | Smallholders |
| dc.subject | Climate Change |
| dc.subject | Farm Management |
| dc.title | Lessons from integrated seasonal forecast-crop modelling in Africa: a systematic review |
| dc.type | Journal Article |
| cg.contributor.affiliation | International Institute of Tropical Agriculture |
| cg.contributor.affiliation | University of Energy and Natural Resources, Ghana |
| cg.contributor.affiliation | University of Cape Town |
| cg.coverage.region | Africa |
| cg.coverage.region | East Africa |
| cg.coverage.hub | Eastern Africa Hub |
| cg.identifier.bibtexciteid | MKUHLANI:2022 |
| cg.isijournal | ISI Journal |
| cg.authorship.types | CGIAR and developing country institute |
| cg.iitasubject | Agronomy |
| cg.iitasubject | Climate Change |
| cg.iitasubject | Farming Systems |
| cg.iitasubject | Meteorology and Climatology |
| cg.iitasubject | Smallholder Farmers |
| cg.journal | Open Life Sciences |
| cg.notes | Open Access Journal; Published online: 07 Nov 2022 |
| cg.accessibilitystatus | Open Access |
| cg.reviewstatus | Peer Review |
| cg.usagerightslicense | Creative Commons Attribution 4.0 (CC BY 0.0) |
| cg.targetaudience | Scientists |
| cg.identifier.doi | https://doi.org/10.1515/biol-2022-0507 |
| cg.futureupdate.required | No |
| cg.identifier.issue | 1 |
| cg.identifier.volume | 17 |
