Abstract

<p>The world is confronted with the increasing threat of food insecurity which is driven by several shocks including droughts, floods, and conflict. The United Nations World Food Programme (WFP) is currently feeding over 95million people around the globe in urgent need of food including those in high emergency countries like Southern Madagascar, Haiti, Afghanistan, Northern Nigeria, South Sudan, Syria, and Yemen. The situation has been worsened by the impacts of COVID - 19 interrelated factors of movement restrictions and reduced economic activity, which together have caused income losses at the household level. Discussions with institutions like the Southern Africa Development Community (SADC), United Nations (UN) partners and respective governments in Southern Africa have clearly shown that the impacts of these shocks are more devastating in countries where early warning systems are weak. Over the years, USAID's Famine Early Warning Systems Network (FEWS NET) has invested in building the capacity of partners and governments to timely identify key shocks that are likely to cause food insecurity in different countries. Using a methodology called scenario development, FEWS NET has been able to develop understanding of the current situation, create informed assumptions about the future, compare their possible effects to food security and the likely responses of various actors. The ability to develop early warning systems helps to estimate future food security outcomes many months in advance, so that decision makers have adequate time to plan for and respond to potential humanitarian crises. This presentation seeks to (i) explore the different methods used to project the likely impacts of shocks on food security in different environments, (ii) highlight the strengths of collaborative partnerships in enhancing early warning systems to promote early action in food security response, and (iii) discuss the use of science products to improve forecasting of future food insecurity outcomes. The use of agrometeorological and remote sensing products including Water Requirement Satisfaction Index (WRSI), Normalized Difference Vegetation Index (NDVI) and CHIRPS Rainfall Estimates has proved useful in identifying hotspots of drought and have helped to facilitate projections in areas where physical access is impossible due to factors like conflict. Practical examples including those from southern Africa will be used to enrich discussions under this topic.</p>

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