Method for designing composite-index agricultural insurance product and products therefrom

A method for designing a composite-index agricultural insurance product, comprising the following steps:

• S1:
• Obtaining the required data in a study area, including meteorological data, soil data, field trial data, remote sensing data, and disaster statistics data;
• S2:
• Determining disaster-free years, disaster years, and disaster events in disaster years according to meteorological data and disaster statistics data, generating the ideal climatic scenario as a benchmark by using the mean values of meteorological data during disasters-free years, and building climatic scenarios under disaster by using the Monte Carlo method based on the ideal climatic scenario;
• S3:
• Calibrating the site-level crop growth model by using the field trial data, then inputting the ideal climatic scenario and the climatic scenarios under disaster of S2 into the calibrated crop growth model to get the corresponding output data, and calculating the yield loss rates (Yloss) of each scenario in each site according to the following Eq.:
• Y
• l
• ⁢
• o
• ⁢
• s
• ⁢
• s
• ,
• k
• =
• Y
• d
• ,
• k
• -
• Y
• i
• Y
• i
• ×
• 1
• ⁢
• 0
• ⁢
• 0
• ⁢
• %
• where Yc,k is the yield under disaster scenario k, Yi is the yield under the ideal scenario, and Yloss,k is the corresponding yield loss rate under disaster scenario k;
• S4:
• Based on the distribution of the yield loss rates, determining the premium rates of agricultural insurance, in which the net rate (μ) is calculated according to the following Eq.:
• μ=E[LCR]=E[Yloss]
• where LCR is the loss cost ratio, and E[ . . . ] represents the process of averaging;
• S5:
• Choosing several candidate indicators to characterize the disaster, and calculating the correlation coefficients between each indicator and simulated Yloss based on the climatic scenarios under disaster and the output data, and based on the magnitude of the correlation, selecting 4 to 6 strong correlation indicators to form a composite index (C/);
• and
• S6:
• Building a vulnerability model that reflects the response between the yield loss rate and the composite index by using the composite index and the yield loss rate of the climatic scenarios under disaster, and using the vulnerability model to predict the yield loss due to disaster in a specific period and space and determine whether or not making a payout, if making a payout, the amount of payout is calculated based on the premium rate.