Improving agricultural microinsurance by applying universal kriging and generalised additive models for interpolation of mean daily temperature
Mitchell Roznik (),
C. Brock Porth,
Milton Boyd and
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Mitchell Roznik: University of Manitoba
C. Brock Porth: University of Manitoba
Lysa Porth: University of Manitoba
Milton Boyd: University of Manitoba
Katerina Roznik: University of Manitoba
The Geneva Papers on Risk and Insurance - Issues and Practice, 2019, vol. 44, issue 3, 446-480
Abstract Agricultural microinsurance has the potential to protect farmers against crop loss caused by extreme adverse weather conditions. Microinsurance policies for smallholder farmers are often designed on the basis of weather indices, whereby weather insurance variables are measured at ground weather stations and then interpolated to the location of the farm. However, a low density of weather stations causes interpolation error, which contributes to basis risk. The objective of this paper is to investigate whether agricultural microinsurance can be improved by reducing interpolation error through advanced interpolation methods, including universal kriging (UK) and generalised additive models (GAM) used with land surface temperature, elevation, and other covariates. Results indicate that for areas with a lower density of weather stations, UK with elevation substantially improves air temperature interpolation accuracy. The approach developed in this paper may help to improve interpolation and could therefore reduce basis risk for agricultural microinsurance in regions with a low density of weather stations, such as in developing countries.
Keywords: Weather insurance; Microinsurance; Basis risk; Remote sensing; Universal kriging; Generalised additive models (search for similar items in EconPapers)
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