Exponential agricultural insurance product design method and product thereof

Abstract

The invention discloses an exponential agricultural insurance product design method and a product thereof. The method comprises the following steps: S1, acquiring data required for designing an exponential agricultural insurance product in a research area; S2, constructing an ideal scene and a simulation disaster scene; S3, localizing the site-level crop growth model, and then inputting the idealscene and the simulation disaster scene in the step S2 into the localized crop growth model to obtain output data corresponding to each scene of each research site; S4, calculating the premium rate ofthe agricultural insurance product according to the yield loss; S5, selecting a plurality of alternative indexes, forming a composite index, and constructing a vulnerability model reflecting the response between the yield loss rate and the composite index by utilizing the yield loss rate and the composite index of each simulation disaster scene; and S6, determining how to perform compensation according to the vulnerability model and the insurance premium rate.

Description

technical field [0001] The invention relates to the technical field of agricultural information, in particular to an index-type agricultural insurance product design method and its products. Background technique [0002] Climate risk is a serious problem facing agriculture worldwide, causing large fluctuations in production and unexpected losses. As a management method of disaster risk transfer, agricultural insurance is an important means to enhance the risk prevention ability of the grain planting industry and promote safe agricultural production. At present, my country's agricultural insurance is booming; from 2007 to 2012, the cumulative premium income of my country's agricultural insurance exceeded 60 billion yuan, ranking second in the world, with an average annual growth rate of 85%, and agricultural insurance paid a total of more than 70 million farmers. The insurance compensation exceeds 40 billion yuan, and the average household compensation is nearly 600 yuan, acc...

AI Technical Summary

Problems solved by technology

At present, there are two common data sources: the loss data obtained based on the Disaster Statistical Yearbook covers a wide range and lasts for a long time, but the data recording format is not uniform and there are some omissions in some years

The loss data obtained based on policy data is more specific and reliable, and can well reflect the unique loss response in different regions. However, for my country, the development time of index agricultural insurance is relatively short (less than 20 years) and each insurance company’s underwriting The type and scope are relatively limited, and the amount of loss data is not enough to support the establishment of a good regression relationship

Generally speaking, it is difficult to balance the quantity and quality of disaster loss data of crops

[0007] (2) Index selection dimension is low

[0009] (3) It is difficult to design insurance products commercially and automatically

Currently, there is no such insurance product

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