Farmer Perception, Recollection, and Remote Sensing in Weather Index Insurance for Agriculture in the Developing World: An Ethiopia Case Study

Abstract

A major challenge in addressing climate risk in developing countries is that many regions do not have the necessary historical weather data to design and validate solutions using technologies such as remote sensing. Therefore, many projects are build using farmer’s reported perceptions and recollections of major climate risk events (such as drought). Although farmer perceptions are great potential value in the design and validation process, there are well known biases and limitations associated with farmer perceptions and recollections which could potentially lead to a problematic product. In order to better understand the value and validity of farmer perceptions this paper explores two related questions: 1) Is there evidence that farmer reporting data has any information about actual drought events and 2) Is there evidence that it is valuable to specifically address recollection and perception issues when using farmer reporting data? We investigate issues and strategies concerning farmer perceptions and remote sensing for risk protection by studying one of the most challenging climate risk applications of remote sensing, index insurance, for which remote sensing triggers payments to farmers during loss years. Our case study is of the largest participatory farmer remote sensing insurance projects in the developing world, the R4 Rural Resilience Initiative of the World Food Programme (WFP) and Oxfam America in Ethiopia. Our approach is to test the cross-consistency of farmer’s reported seasonal vulnerabilities against the years reported as droughts by independent satellite data sources. We find evidence that farmer reported events are reflected in multiple remote sensing datasets, and that utilizing strategies of repeated interviews over time, and to some extent, aggregating independent village reports over space lead to improved predictions. These findings are not only important in understanding the quality of and strategies for utilizing farmer perception information, but also for verifying the appropriate remote sensing approaches as remote sensing applications such as index insurance continue to scale.