Abstract
West Africa represents a wide gradient of climates, extending from tropical conditions along the Guinea Coast to the dry deserts of the south Sahara, and it has some of the lowest income, most vulnerable populations on the planet, which increases catastrophic impacts of low and high frequency climate variability. This paper investigates low and high frequency climate variability in West African monthly and seasonal precipitation and reference evapotranspiration from the early 1980s to 2016. We examine the impact of those trends and how they interact with payouts from index insurance products. Understanding low and high frequency variability in precipitation and reference evapotranspiration at these scales can provide insight into trends during periods critical to agricultural performance across the region. For index insurance, it is important to identify low-frequency variability, which can result in radical departures between designed/planned and actual insurance payouts, especially in the later part of a 30-year period, a common climate analysis period. We find that evaporative demand and precipitation are not perfect substitutes for monitoring crop deficits and that there may be space to use both for index insurance design. We also show that low yields—aligned with the need for insurance payouts—can be predicted using classification trees that include both precipitation and reference evapotranspiration.
Highlights
Index insurance is an insurance product that pays out based on the performance of a weather indicator and is used to mitigate the impact of poor cropping seasons, often through targeting drought years [1]
This paper explores the efficacy of two weather indicators, precipitation and reference evapotranspiration, that are subject to decadal trends in West Africa
We focus on patterns of water supply, atmospheric water demand, and the implications of these spatial and temporal patterns on index insurance
Summary
Index insurance is an insurance product that pays out based on the performance of a weather indicator and is used to mitigate the impact of poor cropping seasons, often through targeting drought years [1]. Our interest is in understanding how the choice of weather indicators may affect the robustness of index insurance programs. Using yield data as a direct measure of crop stress—the target of index insurance program intervention—we explore the degree to which the indicators are statistically associated with yields and can be used to predict the lowest yield quantile. Our goal is to highlight potential issues in index insurance programs related to the choice of indicator and to inform as to how those issues might be mitigated
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