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Calum G. Turvey and Megan K. Mclaurin

Abstract

Index insurance is becoming increasingly popular because of its ability to provide low-cost, relatively easy to implement agricultural insurance for vegetation types whose productivity has been notoriously difficult to measure and to farmers in less-developed nations where traditional crop insurance schemes are not reasonable to implement. This study examines if the remotely sensed normalized difference vegetation index (NDVI) can be an effective basis for index-based crop insurance over a diverse set of locations. To do this the authors compare Advanced Very High Resolution Radiometer (AVHRR) values to cumulative precipitation, extreme heat, and crop yields for 60 locations across the United States for the years 1982–2003. Quadratic regression equations are used to explore these relationships. The findings suggest that the relationship between NDVI, precipitation, extreme heat, and crop yields is highly variable and dependent on location-specific characteristics. Without site-specific calibration, NDVI should not be widely applied to index-based insurance product design. However, NDVI may still be a useful tool in insurance design under certain circumstances. This may be disappointing to proponents of NDVI as a risk transfer mechanism but the authors believe it important to report negative results as a caveat, and to give researchers and practitioners pause before investing time and money into the proposition.

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Calum G. Turvey, Apurba Shee, and Ana Marr

Abstract

Climate risk financing programs in agriculture have caught the attention of researchers and policy makers over the last decade. Weather index insurance has emerged as a promising market-based risk financing mechanism. However, to develop a suitable weather index insurance mechanism it is essential to incorporate the distribution of underlying weather and climate risks to a specific event model that can minimize intraseasonal basis risk. In this paper we investigate the erratic nature of rainfall patterns in Kenya using Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS) rainfall data from 1983 to 2017. We find that the patterns of rainfall are fractional, both erratic and persistent, which is consistent with the Noah and Joseph effects that are well known in mathematics. The erratic nature of rainfall emerges from the breakdown of the convergence to a normal distribution. Instead we find that the distribution about the average is approximately lognormal, with an almost 50% higher chance of deficit rainfall below the mean than adequate rainfall above the mean. We find that the rainfall patterns obey the Hurst law and that the measured Hurst coefficients for seasonal rainfall pattern across all years range from a low of 0.137 to a high above 0.685. To incorporate the erratic and persistent nature of seasonal rainfall, we develop a new approach to weather index insurance based upon the accumulated rainfall in any 21-day period falling below 60% of the long-term average for that same 21-day period. We argue that this approach is more satisfactory to matching drought conditions within and between various phenological stages of growth.

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