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Notes from the Field

Supporting Quality Insurance for Farmers with Earth Observation Data

July 10th, 2019 by Sara Miller

I have been working with a project focused on drought in Kenya for months using NASA satellite data and was excited to get a ground-based perspective of the country and meet fellow Earth Scientists in Nairobi, Kenya. My colleague Eric Anderson and I attended a week long course on the Quality Index Insurance Certification (also known as QUIIC), which provides methods to evaluate the quality of satellite-based indices for use in agriculture/pastoralist insurance.

The left image shows average NDVI over Kenya while the right image shows average NDVI during the 2011 drought period.

I’ve been working with SERVIR since November 2018 to support the development of a lower-latency vegetation index, inspired by Kenya Livestock Insurance Program needs. A lower-latency product can enable programs like these to provide relief sooner, potentially before total losses. The Normalized Difference Vegetation Index (NDVI) is currently used in this insurance program and provides a satellite-based measure of vegetation health, which can show how much forage is available for livestock consumption. When conditions are bad, the program is intended to help people through the season without experiencing devastating losses. 

Indices, such as vegetation health, can be monitored using satellites and provide a low cost way to detect things like drought, especially where field data is scarce and pastoralists may otherwise be uninsurable under traditional contracts. Index insurance programs are meant to promote farmer and pastoralist resilience, but if they are designed poorly they can actually leave people worse off. For example, if conditions are bad one year but the index fails to trigger payouts, farmers would be worse off had they purchased insurance and not received a payout. The information-rich course, led by economics experts from UC Davis (Michael Carter and Elinor Belami), helped us understand a different side of applying remote sensing to real world problems. We learned about the economics of insurance and evaluated the quality of using different indices compared to traditional insurance for a focus region.

Here we are on the first day of the course playing a game to explain risk aversion. People who are more risk averse choose an option that has lower risk, even though the choice may have a lower average reward.

It was a great experience to be able to see a different culture and meet so many people from different backgrounds. Our next mission is to bring what we learned in the course back to the SERVIR hubs and explore ways to apply it. Using methods to measure quality of an insurance index we can decide if index based insurance is appropriate for different regions.

The course brought together representatives from UC Davis, SERVIR hubs and the Regional 
Centre for Mapping of Resources for Development (RCMRD).



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