Mapping the spatial distribution of underutilised crop species under climate change using the MaxEnt model: a case of KwaZulu-Natal, South Africa
Knowing the spatial and temporal suitability of neglected and underutilised crop species (NUS) is important for fitting them into marginal production areas and cropping systems under climate change. The current study used climate change scenarios to map the future distribution of selected NUS, namel...
| Autores principales: | , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2022
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/125172 |
| _version_ | 1855523775762661376 |
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| author | Mugiyo, H. Chimonyo, Vimbayi Grace Petrova Kunz, R. Sibanda, M. Nhamo, L. Masemola, C. R. Modi, Albert Thembinkosi Mabhaudhi, Tafadzwanashe |
| author_browse | Chimonyo, Vimbayi Grace Petrova Kunz, R. Mabhaudhi, Tafadzwanashe Masemola, C. R. Modi, Albert Thembinkosi Mugiyo, H. Nhamo, L. Sibanda, M. |
| author_facet | Mugiyo, H. Chimonyo, Vimbayi Grace Petrova Kunz, R. Sibanda, M. Nhamo, L. Masemola, C. R. Modi, Albert Thembinkosi Mabhaudhi, Tafadzwanashe |
| author_sort | Mugiyo, H. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Knowing the spatial and temporal suitability of neglected and underutilised crop species (NUS) is important for fitting them into marginal production areas and cropping systems under climate change. The current study used climate change scenarios to map the future distribution of selected NUS, namely, sorghum (Sorghum bicolor), cowpea (Vigna unguiculata), amaranth (Amaranthus) and taro (Colocasia esculenta) in the KwaZulu-Natal (KZN) province, South Africa. The future distribution of NUS was simulated using a maximum entropy (MaxEnt) model using regional circulation models (RCMs) from the CORDEX archive, each driven by a different global circulation model (GCM), for the years 2030 to 2070. The study showed an increase of 0.1–11.8% under highly suitable (S1), moderately suitable (S2), and marginally suitable (S3) for sorghum, cowpea, and amaranth growing areas from 2030 to 2070 across all RCPs. In contrast, the total highly suitable area for taro production is projected to decrease by 0.3–9.78% across all RCPs. The jack-knife tests of the MaxEnt model performed efficiently, with areas under the curve being more significant than 0.8. The study identified annual precipitation, length of the growing period, and minimum and maximum temperature as variables contributing significantly to model predictions. The developed maps indicate possible changes in the future suitability of NUS within the KZN province. Understanding the future distribution of NUS is useful for developing transformative climate change adaptation strategies that consider future crop distribution. It is recommended to develop regionally differentiated climate-smart agriculture production guidelines matched to spatial and temporal variability in crop suitability. |
| format | Journal Article |
| id | CGSpace125172 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1251722025-12-08T09:54:28Z Mapping the spatial distribution of underutilised crop species under climate change using the MaxEnt model: a case of KwaZulu-Natal, South Africa Mugiyo, H. Chimonyo, Vimbayi Grace Petrova Kunz, R. Sibanda, M. Nhamo, L. Masemola, C. R. Modi, Albert Thembinkosi Mabhaudhi, Tafadzwanashe crop production underutilized species spatial distribution climate change adaptation food security nutrition security sorghum cowpeas amaranthus taro machine learning models forecasting Knowing the spatial and temporal suitability of neglected and underutilised crop species (NUS) is important for fitting them into marginal production areas and cropping systems under climate change. The current study used climate change scenarios to map the future distribution of selected NUS, namely, sorghum (Sorghum bicolor), cowpea (Vigna unguiculata), amaranth (Amaranthus) and taro (Colocasia esculenta) in the KwaZulu-Natal (KZN) province, South Africa. The future distribution of NUS was simulated using a maximum entropy (MaxEnt) model using regional circulation models (RCMs) from the CORDEX archive, each driven by a different global circulation model (GCM), for the years 2030 to 2070. The study showed an increase of 0.1–11.8% under highly suitable (S1), moderately suitable (S2), and marginally suitable (S3) for sorghum, cowpea, and amaranth growing areas from 2030 to 2070 across all RCPs. In contrast, the total highly suitable area for taro production is projected to decrease by 0.3–9.78% across all RCPs. The jack-knife tests of the MaxEnt model performed efficiently, with areas under the curve being more significant than 0.8. The study identified annual precipitation, length of the growing period, and minimum and maximum temperature as variables contributing significantly to model predictions. The developed maps indicate possible changes in the future suitability of NUS within the KZN province. Understanding the future distribution of NUS is useful for developing transformative climate change adaptation strategies that consider future crop distribution. It is recommended to develop regionally differentiated climate-smart agriculture production guidelines matched to spatial and temporal variability in crop suitability. 2022-12 2022-10-26T03:46:06Z 2022-10-26T03:46:06Z Journal Article https://hdl.handle.net/10568/125172 en Open Access Elsevier Mugiyo, H.; Chimonyo, V. G. P.; Kunz, R.; Sibanda, M.; Nhamo, L.; Masemola, C. R.; Modi, A. T.; Mabhaudhi, Tafadzwanashe. 2022. Mapping the spatial distribution of underutilised crop species under climate change using the MaxEnt model: a case of KwaZulu-Natal, South Africa. Climate Services, 28:100330. [doi: https://doi.org/10.1016/j.cliser.2022.100330] |
| spellingShingle | crop production underutilized species spatial distribution climate change adaptation food security nutrition security sorghum cowpeas amaranthus taro machine learning models forecasting Mugiyo, H. Chimonyo, Vimbayi Grace Petrova Kunz, R. Sibanda, M. Nhamo, L. Masemola, C. R. Modi, Albert Thembinkosi Mabhaudhi, Tafadzwanashe Mapping the spatial distribution of underutilised crop species under climate change using the MaxEnt model: a case of KwaZulu-Natal, South Africa |
| title | Mapping the spatial distribution of underutilised crop species under climate change using the MaxEnt model: a case of KwaZulu-Natal, South Africa |
| title_full | Mapping the spatial distribution of underutilised crop species under climate change using the MaxEnt model: a case of KwaZulu-Natal, South Africa |
| title_fullStr | Mapping the spatial distribution of underutilised crop species under climate change using the MaxEnt model: a case of KwaZulu-Natal, South Africa |
| title_full_unstemmed | Mapping the spatial distribution of underutilised crop species under climate change using the MaxEnt model: a case of KwaZulu-Natal, South Africa |
| title_short | Mapping the spatial distribution of underutilised crop species under climate change using the MaxEnt model: a case of KwaZulu-Natal, South Africa |
| title_sort | mapping the spatial distribution of underutilised crop species under climate change using the maxent model a case of kwazulu natal south africa |
| topic | crop production underutilized species spatial distribution climate change adaptation food security nutrition security sorghum cowpeas amaranthus taro machine learning models forecasting |
| url | https://hdl.handle.net/10568/125172 |
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