Agro-climatological Support of International Food Security Assessments by the Famine Early Warning Systems Network

Famine early warning requires agro-climatological assessment at multiple time scales, including changes at weekly, seasonal, inter-annual, and decadal intervals. The agro-climatological science partners of the USAID Famine Early Warning Systems Network (FEWS NET) are USGS, NOAA, NASA, and USDA. They are in regular and frequent communication with FEWS NET food security analysts, social scientists who simultaneously assess conditions of markets, trade, nutrition, and health for their impacts on vulnerable livelihood systems. Collectively, FEWS NET social and physical scientists provide humanitarian decision makers with an objective outlook for food insecurity in countries of Africa, Central Asia, Central America, and the Caribbean. Such outlooks inform the programming of limited aid resources for the times and places where they will be most needed in the next 3-6 months. FEWS NET's agro-climatological science partners (1) monitor current agro-climatological conditions, such as soil moisture, precipitation, temperature and crop health, (2) gather climate projections by different forecasting agencies to better understand the likely future conditions on weekly to seasonal time scales, and (3) research the drivers of regional agro-climatic variability on sub-seasonal to inter-annual time scales in the context of a changing climate. Key positive features of FEWS NET climate services include: (1) Direct communication of agro-climatological questions by food security analysts to the physical scientists. There is reduced need to speculate about what agro-climatological information might be relevant. (2) FEWS NET partners do not produce climate forecasts, affording them the freedom to examine and interpret the rich body of forecast information publicly available, to draw robust conclusions on likely future outcomes. (3) Frequent direct communication between FEWS NET physical scientists and food security analysts generates highly relevant research questions, and the resulting research findings consequently have a fast track to application and generation of societal benefits.