The purpose of this paper is to present the development of cumulative rainfall deficit (CRD) indices for corn in Shandong Province, China, based on high-resolution weather (county, 1980-2011) and yield data (township, 1989-2010) for five counties in Tai’an prefecture.
A survey with farming households is undertaken to obtain local corn prices and production costs to compute the sum insured. CRD indices are developed for five corn-growth phases. Rainfall is spatially interpolated to derive indices for areas that are outside a 25 km radius from weather stations. To lower basis risk, triggers and exits of the payout functions are statistically determined rather than relying on water requirement levels.
The results show that rainfall deficits in the main corn-growth phases explain yield reductions to a satisfying degree, except for the emergence phase. Correlation coefficients between payouts of the CRD indices and yield reductions reach 0.86-0.96 and underline the performance of the indices with low basis risk. The exception is SA-Xintai (correlation 0.71) where a total rainfall deficit index performs better (0.87). Risk premium rates range from 5.6 percent (Daiyue) to 12.2 percent (SA-Xintai) and adequately reflect the drought risk.
This paper suggests that rainfall deficit indices can be used in the future to complement existing indemnity-based insurance products that do not cover drought for corn in Shandong or for CRD indices to operate as a new insurance product.
The authors would like to thank the Tai’an Meteorological Bureau for the weather data and the Tai’an Agriculture Bureau for the yield data. The authors are particularly grateful to the farmers in Daiyue county that participated and provided important information on the local corn production environment. This research was made possible from a grant of the School of Civil and Environmental Engineering at Nanyang Technological University in Singapore.
Chen, W., Hohl, R. and Tiong, L.K. (2017), "Rainfall index insurance for corn farmers in Shandong based on high-resolution weather and yield data", Agricultural Finance Review, Vol. 77 No. 2, pp. 337-354. https://doi.org/10.1108/AFR-10-2015-0042Download as .RIS
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