جستجوی مقالات مرتبط با کلیدواژه « بیمه درآمدی » در نشریات گروه « کشاورزی »

Reducing of the existing economic risk in agricultural activities and increasing security for investment is one of the important functions of agricultural insurance. This function is the main factor in creating tendency in private sector to improve investment in this sector. Insurance models and risk management methods of agricultural product, which are always designed and implemented by governments, are intended to compensate for any of these risks. In this study, first using a sample of 500 Rafsanjan pistachio producers, the willingness to pay for pistachio insurance was obtained by two methods of integration and proportional ratio, then the premium was determined and the factors affecting income insurance using Contingent Valuation Method and a one-dimensional dual questionnaire was investigated. The results of this study showed that there is a reverse relationship between the willingness to pay with the offers of premium rate, number of households, income, predictability of yield and increase in income compared to last year. But the education of farmers has a direct relationship with the willingness to pay. The average willingness to pay by farmers according to the income quarters derived from the completion of the questionnaire for the five income scenarios was 144, 447, 616, 729 and 907 million Rials per hectare. The average willingness to pay for income insurance for Rafsanjan pistachios was estimated 35734405 Rials per hectare. According to the results of the research, in order to reach the comprehensive plan of pistachio farmers' insurance, it is recommended that income insurance be considered.

The existence of a variety of natural and unnatural hazards in agricultural activities have caused farmers to face uncertain and vulnerable situations. In this regard, income protection insurance is one of the new insurance policies that covers the fluctuations of yield and price, simultaneously. The main objective of this research is to design a pattern of income protection insurance for strategic agricultural crops such as rain-fed barley and compare it with yield protection crop insurance in Hamadan province, for different insurance coverage levels in order to reduce the farmer's income fluctuations. Another objective of this research is to investigate the use of the time series analysis techniques in predicting the price and yield variables of the selected crop and to determine the minimum income, in order to pay the farmers for the damages. Also, determining the best model for simulating yield and price parameters, and finally estimating the income of the rain-fed barley, under the influence of the uncertainty are other objectives of this research.
In this research, the data were analyzed in order to extract the best predictive model. A fair premium offer for agricultural products requires, a prediction of the performance of various products as well as the price fluctuations in the future. In this study, this prediction was made through predicting the price and performance time series using the ARIMA various models test. The best prediction model based on the 22-year and 32-year statistics of price and performance variables was chosen using the Akaike information criterion (AIC). Then, the expected damages, fair premium and real premium of the product of interest, were calculated once by using the yield insurance method without applying the price-performance relationship and once by using the income insurance method by applying the price-performance relationship of 2017. In order to take into account the uncertainty of the deviation parameter from the mean of the yield data, about 10,000 random samples of the deviations (the residuals of errors) were generated by the Monte Carlo algorithm substitution and accordingly, the product performance was simulated. Each of these simulations could be the actual performance of the product in the upcoming year, according to which, the expected damages and subsequently the fair and real premiums will be estimated for the following year. All of these steps were done using coding in the MATLAB software.
The results of the time series analysis indicated that the price of barley was estimated to be about 10706 Rials in the upcoming year of 1396. The rain-fed barley’s yield was also predicted to be1476 kg/ha in the same year. By simulating the farmer’s actual income using the Monte Carlo method and by considering 10,000 iterations for implementing the forecasting model. The average payable compensation payments (fair premiums) for the upcoming year, for rain-fed barley and for 50, 60, 70, and 80 percent coverage levels, were obtained equal to 42371.9, 122972.9, 288375.8, and 580106.3 Rials per hectare, respectively. Using the results of time series analysis of the price of barley product, the forecast for next year of 1396 is about 10706 Rials. The yield of barley for the next year is expected to be 1476 kg ha-1. By simulating the actual earnings of farmers in the Monte Carlo method and considering 10,000 times the repeat of the implementation of the forecast model, the average payable compensation (fair premium) in the following year for the production of barley at the surface of the coatings 50, 60, 70 and 80 percent were calculated 42371.9, 2929.92, 8837.258, and 59.50106 Rials per hectare, respectively. Accordingly, the amount of the real premium of rain-fed barley, by using the operating insurance method and without applying the price- performance relationship was obtained equal to 48503.5, 141131, 331905.4 and 668566.4 Rials per hectare for 50, 60, 70, and 80 percent coverage levels, respectively. On the other hand, the amount of rain-fed barley’s real premium was obtained 47079.9, 136636.5, 320417.6, and 644562.6 Rials per hectare, by using the income insurance method and by applying the price-performance relationship, for 50, 60, 70, and 80 percent coverage levels, respectively. By deducting 71% government subsidy from this, the amount of premium for each farmer would cost 15536.4, 45090.1, 105738.8, and 212705.6 Rials, respectively.
The results ultimately indicated that for the selected product, the amount of the computed premiums in the income protection insurance model is lower than the yield protection crop insurance. Accordingly, considering the risk of planting rain-fed products, applying the income insurance model will encourage farmers more. Also, in this model, the insurer, having understood the simultaneous effect of price and performance, will offer computing insurances with more certainty in different coverage levels. According to the results of this research, it is suggested that income insurance, taking into account the uncertainties that were caused by price prediction and product performance, should be used instead of functional insurance. This policy will make farmers more lucrative and leads to a better risk management system by insuring companies.

This study developed a model of revenue insurance computing fair premiums for both maize and potatoes and applying bootstrapping simulation to time series of 1982-2008 in Kerman. Then, revenue insurance rates were compared taking into account maize, as one of the main products of the province, along with potatoes in a crop rotation with different ratios. The results showed that potato has better conditions than maize for revenue insurance due to having more income fluctuations and therefore has higher premium than maize especially for 50% and 75% coverage levels. Also the calculated premiums were less than current insurance schemes premiums for both products.

In this paper data were collected from 120 farmers in North Khorassan province for the two periods (2005-2007) in order to survey the role of rain-fed lentil risk profile for designing an insurance model. To evaluate looses of each risk, risk frequency, risk impact criteria’s as well as risk matrix were used. Results showed that low risk was the largest risk area. During study period, a decrease in risks diversity was not observed, and only the inputs price risk increased. Average rates of information, labor, production, price and credit risks were 30, 5.0, 4.5, 3.75 and 3.33%, respectively. Correlation between risk score and rain-fed lentil yield was positive (0.38). According to the results, developing a risk profile and use of risk profile in designing a revenue insurance model and coverage of risks and low-priced credit payments are suggested.