Authors: Zhenong Jin*, University of Minnesota, Elinor Benami, University of California Davis
Topics: Agricultural Geography, Hazards, Risks, and Disasters, Africa
Keywords: remote sensing, crop yield, agriculture insurance
Session Type: Paper
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Advances in the science of detecting and predicting hazards such as droughts and floods offer the renewed promise of helping communities plan for adverse conditions. However, using these advances to effectively protect vulnerable people also requires innovations in the design and delivery of social protection programs. As one example, agricultural insurance has demonstrated potential to enable inclusive economic growth and prevent descents into poverty for vulnerable smallholder farmers. Well-designed agricultural insurance enhances farmer welfare through enabling investments in agricultural productivity and helping farmers recover from negative shocks. When insurance fails to match farmer needs, however, it can leave them even worse off than without insurance. Recent advances in remote sensing analysis -- including use of multiple sensors with finer spatial and temporal resolution and improved data processing techniques across the crop growth cycle -- coupled with innovations in contract design and implementation offer renewed promise of helping farmers survive and thrive amid unfavorable weather conditions. However, technical knowledge about the capabilities and limitations of these sensors has largely remained internal to the remote sensing community, while key features of successful insurance and social protection often remain elusive to the scientists developing these observation tools. This paper reviews recent advances in geospatial intelligence for predicting agricultural losses at individual field scale as they match the needs for the design and delivery of social protection programs. Several case studies from East Africa will be discussed to demonstrate the possibility as well as future research needs.
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