Farmer Perception, Recollection, and Remote Sensing in Weather Index Insurance: An Ethiopia Case Study

Abstract

A challenge in addressing climate risk in developing countries is that many regions have extremely limited formal data sets, so for these regions, people must rely on technologies like remote sensing for solutions. However, this means the necessary formal weather data to design and validate remote sensing solutions do not exist. Therefore, many projects use farmers’ reported perceptions and recollections of climate risk events, such as drought. However, if these are used to design risk management interventions such as insurance, there may be biases and limitations which could potentially lead to a problematic product. To better understand the value and validity of farmer perceptions, this paper explores two related questions: (1) Is there evidence that farmers reporting data have any information about actual drought events, and (2) is there evidence that it is valuable to address recollection and perception issues when using farmer-reported data? We investigated these questions by analyzing index insurance, in which remote sensing products trigger payments to farmers during loss years. Our case study is perhaps the largest participatory farmer remote sensing insurance project in Ethiopia. We tested the cross-consistency of farmer-reported seasonal vulnerabilities against the years reported as droughts by independent satellite data sources. We found evidence that farmer-reported events are independently reflected in multiple remote sensing datasets, suggesting that there is legitimate information in farmer reporting. Repeated community-based meetings over time and aggregating independent village reports over space lead to improved predictions, suggesting that it may be important to utilize methods to address potential biases.

Designing Drought Indicators

“…While this approach is helpful, a stakeholder still must actually use the new drought indicator (Lemos et al 2012). Water, ecosystem, and resource managers perceive using new data as risky (Osgood et al 2018;Rayner et al 2005). Furthermore, sometimes other factors constrain the application of new data sources or prevent application of certain datasets or data sharing, such as internal organizational policy (Dilling et al 2015).…”

Reptile: Aggregation-level Explanations for Hierarchical Data

“…The Columbia University Financial Instruments Sector Team (FIST) group collects Ethiopian farmer-reported drought data to design drought insurance [42]. The data contains geography (Region, District, Village) and time (Year) hierarchies, and a severity measure from 1 (low severity) to 10 (high).…”

Why predict climate hazards if we need to understand impacts? Putting humans back into the drought equation

“…In a region where migration is one of the main coping mechanisms for drought, a targeted survey focusing on the early detection of migration movements would help mobilize the timely allocation of resources by humanitarian decision-makers or even the mitigation of drought impacts. Regular surveys, aiming to understand farmers' agricultural drought risk perception and related disaster management and agricultural coping mechanisms, could support the more efficient use of limited resources and the sustainable development of financial instruments (Osgood et al 2018). Figure 1 integrates these steps into the larger picture of high-frequency data collection, the joint analysis of climate and livelihood conditions to detect patterns or to predict hot spots, and the identification of climate-attributable impacts to strengthen impact forecasts.…”

“…In a region where migration is one of the main coping mechanisms for drought, a targeted survey focusing on the early detection of migration movements would help mobilize the timely allocation of resources by humanitarian decision-makers or even the mitigation of drought impacts. Regular surveys, aiming to understand farmers’ agricultural drought risk perception and related disaster management and agricultural coping mechanisms, could support the more efficient use of limited resources and the sustainable development of financial instruments (Osgood et al 2018 ).…”