Empirical forecasting of slow-onset disasters for improved emergency response: An application to Kenya’s arid north
Mitigating the negative welfare consequences of crises such as droughts, floods, and disease outbreaks, is a major challenge in many areas of the world, especially in highly vulnerable areas insufficiently equipped to prevent food and livelihood security crisis in the face of adverse shocks. Given t...
| Autores principales: | , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2009
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| Acceso en línea: | https://hdl.handle.net/10568/371 |
| _version_ | 1855520959780356096 |
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| author | Mude, Andrew G. Barrett, Christopher B. McPeak, J.G. Kaitho, R. Kristjanson, Patricia M. |
| author_browse | Barrett, Christopher B. Kaitho, R. Kristjanson, Patricia M. McPeak, J.G. Mude, Andrew G. |
| author_facet | Mude, Andrew G. Barrett, Christopher B. McPeak, J.G. Kaitho, R. Kristjanson, Patricia M. |
| author_sort | Mude, Andrew G. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Mitigating the negative welfare consequences of crises such as droughts, floods, and disease outbreaks, is a major challenge in many areas of the world, especially in highly vulnerable areas insufficiently equipped to prevent food and livelihood security crisis in the face of adverse shocks. Given the finite resources allocated for emergency response, and the expected increase in incidences of humanitarian catastrophe due to changing climate patterns, there is a need for rigorous and efficient methods of early warning and emergency needs assessment. In this paper we develop an empirical model, based on a relatively parsimonious set of regularly measured variables from communities in Kenya’s arid north, that generates remarkably accurate forecasts of the likelihood of famine with at least 3 months lead time. Such a forecasting model is a potentially valuable tool for enhancing early warning capacity. |
| format | Journal Article |
| id | CGSpace371 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2009 |
| publishDateRange | 2009 |
| publishDateSort | 2009 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace3712023-12-08T19:36:04Z Empirical forecasting of slow-onset disasters for improved emergency response: An application to Kenya’s arid north Mude, Andrew G. Barrett, Christopher B. McPeak, J.G. Kaitho, R. Kristjanson, Patricia M. Mitigating the negative welfare consequences of crises such as droughts, floods, and disease outbreaks, is a major challenge in many areas of the world, especially in highly vulnerable areas insufficiently equipped to prevent food and livelihood security crisis in the face of adverse shocks. Given the finite resources allocated for emergency response, and the expected increase in incidences of humanitarian catastrophe due to changing climate patterns, there is a need for rigorous and efficient methods of early warning and emergency needs assessment. In this paper we develop an empirical model, based on a relatively parsimonious set of regularly measured variables from communities in Kenya’s arid north, that generates remarkably accurate forecasts of the likelihood of famine with at least 3 months lead time. Such a forecasting model is a potentially valuable tool for enhancing early warning capacity. 2009-08 2010-01-10T11:29:04Z 2010-01-10T11:29:04Z Journal Article https://hdl.handle.net/10568/371 en Limited Access Elsevier Mude, A.G., Barrett, C.B., McPeak, J.G., Kaitho, R. and Kristjanson, P. 2009. Empirical forecasting of slow-onset disasters for improved emergency response: An application to Kenya’s arid north. Food Policy 34(4):329-339. |
| spellingShingle | Mude, Andrew G. Barrett, Christopher B. McPeak, J.G. Kaitho, R. Kristjanson, Patricia M. Empirical forecasting of slow-onset disasters for improved emergency response: An application to Kenya’s arid north |
| title | Empirical forecasting of slow-onset disasters for improved emergency response: An application to Kenya’s arid north |
| title_full | Empirical forecasting of slow-onset disasters for improved emergency response: An application to Kenya’s arid north |
| title_fullStr | Empirical forecasting of slow-onset disasters for improved emergency response: An application to Kenya’s arid north |
| title_full_unstemmed | Empirical forecasting of slow-onset disasters for improved emergency response: An application to Kenya’s arid north |
| title_short | Empirical forecasting of slow-onset disasters for improved emergency response: An application to Kenya’s arid north |
| title_sort | empirical forecasting of slow onset disasters for improved emergency response an application to kenya s arid north |
| url | https://hdl.handle.net/10568/371 |
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