Uniting remote sensing, crop modelling and economics for agricultural risk management

Elinor Benami

Abstract: The expanding availability of satellite data at higher spatial, temporal, and spectral resolutions has spurred many applications in agriculture and economic development, including agricultural insurance. Yet, effectively using satellite data in this context requires blending technical knowledge about their capabilities and limitations with an understanding of how they influence the value of different risk reduction programs. This paper reviews how approaches to estimate agricultural losses for index insurance have evolved, starting with costly field-sampling based campaigns towards lower cost techniques from weather and now satellite data. We identify advances in satellite data and in crop modeling for estimating crop yield, but reliably and cheaply assessing yield remains a challenge in complex landscapes. A simple case study and diagnostic diagrams illustrate an economic framework to gauge and enhance the value of insurance based on earth observation data. As yield estimation techniques improve, much of their value for the insured depends on how well they capture low yield situations when people suffer most. Strategically improving the collection, archiving, and accessibility of reliable ground-reference data on crop types and production would facilitate this task. Audits to account for inevitable mis-estimation complement efforts to detect and protect against large losses.