TY - JOUR
T1 - Empirical forecasting of slow-onset disasters for improved emergency response
T2 - An application to Kenya's arid north
AU - Mude, Andrew G.
AU - Barrett, Christopher B.
AU - McPeak, John G.
AU - Kaitho, Robert
AU - Kristjanson, Patti
N1 - Funding Information:
The authors thank Luc Christaensen, Allan Kute, Francesca Molinari, Nicholas Ndiwa, Kamau Ngamau, Abisalom Omolo, Maren Radney, Rob Rose, Julia Stone, Ben Watkins, Stephen Younger and seminar participants at Cornell University for all their helpful comments, insights and assistance. This research was partly funded by the United States Agency for International Development (USAID), through Grants DAN-1328-G-00-0046-00 and PCE-G-98-00036-00 to the Pastoral Risk Management (PARIMA) Project of the Global Livestock CRSP, and the Strategies and Analyses for Growth and Access (SAGA) cooperative agreement, Number HFM-A-00-01-00132-00, the International Livestock Research Institute, and the World Bank. We also acknowledge the contribution of the USAID Global Livestock Collaborative Research Support Programs (GL-CRSP), Livestock Early Warning System (LEWS), and its successor the Livestock Information Network and Knowledge System (LINKS). The views expressed here and any remaining errors are the authors’ and do not represent any official agency.
PY - 2009/8
Y1 - 2009/8
N2 - 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.
AB - 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.
KW - Early warning
KW - Emergency response
KW - Food aid
KW - Food security
KW - Forecasting famine
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U2 - 10.1016/j.foodpol.2009.05.003
DO - 10.1016/j.foodpol.2009.05.003
M3 - Article
AN - SCOPUS:67650685494
SN - 0306-9192
VL - 34
SP - 329
EP - 339
JO - Food Policy
JF - Food Policy
IS - 4
ER -