مدیریت ریسک محصول جو دیم بر اساس الگوی بیمه درآمدی در استان همدان

نوع مقاله : مقالات پژوهشی

نویسندگان

1 دانشگاه رازی

2 شرکت بیمه دانا

چکیده

بیمه درآمدی یکی از طرح‌های بیمه‌ای جدید است که نوسان‌های عملکرد و قیمت را همزمان پوشش می‌دهد. هدف از این تحقیق، طراحی الگوی بیمه درآمدی محصولات کشاورزی استراتژیک مانند جو دیم و مقایسه آن با بیمه عملکرد در استان همدان در سطوح پوشش مختلف بیمه‌ای است. در این راستا به پیش‌بینی متغیرهای قیمت و عملکرد محصول منتخب و تعیین حداقل درآمد به منظور پرداخت خسارت به کشاورزان پرداخته شد. برای استخراج بهترین مدل پیش‌بینی، داده‌های سالیانه قیمت و عملکرد محصول جو دیم به ترتیب به مدت 22 و 32 سال از آمارنامه‌های کشاورزی استان همدان استخراج شده و در سال 1395 بر اساس مدل ARIMA مورد تجزیه و تحلیل قرار گرفت. سپس میزان خسارت مورد انتظار، حق بیمه عادلانه و حق بیمه واقعی محصول مورد نظر محاسبه شد. برای در نظر گرفتن عدم قطعیت پارامتر انحراف از میانگین داده‌های عملکرد، حدود 10000 نمونه تصادفی از انحرافات (باقیمانده خطاها) با جایگزینی به روش الگوریتم مونت کارلو تولید و شبیه‌سازی گردید. در نهایت نتایج نشان داد حق بیمه جو دیم در مدل بیمه درآمدی در سطوح پوشش 50، 60، 70 و 80 درصد به ترتیب برابر 9/47079 ، 5/136636، 6/320417 و 6/644562 ریال در هکتار می‌باشد که در همه سطوح پوشش کمتر از حق بیمه عملکردی است. لذا با توجه به ریسک کشت محصولات دیم، اعمال الگوی بیمه درآمدی باعث استقبال بیشتر کشاورزان خواهد شد. همچنین شرکت بیمه‌گر نیز با درک اثر هم زمان قیمت و عملکرد، میزان حق بیمه محاسباتی را با اطمینان‌پذیری بیشتری در سطوح پوشش مختلف محاسبه خواهد کرد. بر این اساس پیشنهاد می‌شود الگوی بیمه درآمدی به عنوان راهکاری مؤثر برای کاهش خطرات ریسک و همچنین افزایش اطمینان در تعیین حق بیمه، جایگزین روش‌های سنتی مانند بیمه عملکردی شود.

کلیدواژه‌ها


عنوان مقاله [English]

Risk Management of Rain Fed Barley Based on the Pattern of Income Protection Insurance in Hamedan Province

نویسندگان [English]

  • Arash Azari 1
  • Ali Azari 2
1 Razi University
2 Dana Insurance Company
چکیده [English]

Introduction: 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 farmers' 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.
Materials and Methods: 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.
Results and Discussions: 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.
Conclusions: 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.

کلیدواژه‌ها [English]

  • Time Series
  • Uncertainty
  • Income protection insurance
  • Rain fed barley
  • Yield protection insurance
1- Abbaspour K. C., Johnson C. A., and Van Genuchten M. T. 2004. Estimating uncertain flow and transport parameters using a sequential uncertainty fitting procedure. Vadose Zone Journal, 3 (4):1340–1352.
2- Abbaspour K. C., Yang J., Maximov I., Siber R., Bogner K., Mieleitner J., Zobrist J., and Srinivasan R. 2007. Spatially-distributed modelling of hydrology and water quality in the Comparing uncertainty analysis techniques for a SWAT application to the Chaohe Basin in China 21 prealpine Thur watershed using SWAT. Journal of Hydrology, 333: 413–430.
3- Ahmadi K., Gholizadeh H., Ebadzadeh H., Hoseinpoor R., Abdeshah H., Kazemian A., and Rafie M. 2016. Agricultural Statistics Volume I: Crop products. Ministry of Agriculture, 125 P. (In Persian).
4- Atwood J. A., Watts M. J., and Baquet A. E. 1996. An examination of the effects of price supports and federal crop insurance upon the economic growth, capital structure, and financial survival of wheat growers in the northern High Plains. American Journal of Agricultural Economics, 78: 212–224.
5- Bates B. C., and Campbell E. P. 2001. A Markov chain Monte Carlo scheme for parameter estimation and inference in conceptual rainfall–runoff modeling. Water Resources Research, 37(4): 937–947.
6- Beven K., and Binley A. 1992. The future of distributed models – model calibration and uncertainty prediction. Hydrological Processes, 6 (3):279–298.
7- Coble K. H., Miller J. C., Barnett B. J., and Knight T. O. 2001. Potential for multicrop revenue insurance to serve the needs of Mississippi crop producers. Mississippi State University, 17 P.
8- Falco S. D., Adinolfi F., Bozzola M., and Capitanio F. 2014. Crop Insurance as a Strategy for Adapting to Climate Change. Journal of Agricultural Economics. 65(2): 485- 504.
9- Farajzadeh Z., and Shahvali O. 2009. Predicting the price of agricultural products, case study: Cotton, rice and saffron. Agricultural Economics and Development, 67: 43-71. (In Persian).
10- Farzin M., Torkamani J., and Mosavi S. N. 1391. Role of Income Insurance in Farmers Risk Management (Case Study: Cotton Suppliers in Darab City). Agricultural Economics Research, 4(3): 143-167.
11- Glauber J., and Mario M. 1994. Providing Catastrophic Yield Protection through a Targeted Revenue Program. Economics of Agricultural Crop Insurance: Theory and Evidence, 4: 349-369.
12- Janowicz-Lomott M., Lyskawa L., and Rozumek P. 2015. Farm Income Insurance as an Alternative for Traditional Crop Insurance. Procedia Economics and Finance, 33: 439-449.
13- Just R. E., and Zillberma D. 1986. Does the Law of Supply Hold under Uncertainty? The Economic Journal, 96:514-524.
14- Karamuz M., and Araghi Nejhad S. 2010. Advanced Hydrology. University of Technology (Tehran Polytechnic) Publishers, Third edition. 464 PP. (In Persian).
15- Kavetski D., Franks S.W., and Kuczera G. 2003. Confronting input uncertainty in environmental modelling. In: Duan, Q., Gupta, H.V., Sorooshian, S., Rousseau, A.N., Turcotte, R. (Eds.), Calibration of Watershed Models. American Geophysical Union, Washington, DC, 6: 49–68.
16- Kavetski D., Kuczera G., and Franks S.W. 2006a. Bayesian analysis of input uncertainty in hydrological modeling: 1. Theory. Water Resources Research, 42(3): 1-10.
17- Nelson C. H., and Loehman E.T. 1978. Further Toward a Theory of Agricultural Insurance. American Journal of Agricultural Economics. 1:523-531.
18- Laanemets O., Viira A. H., and Nurmet M. 2011. Price, Yield, and Revenue Risk in Wheat Production in Estonia. Agronomy Research, 9 (2): 421–426.
19- Radmehr F., and Shams-Gharneh N. 2012. Forecast of Tehran Stock Exchange index using fuzzy time series based on definition of rate of return. Journal of Investment Knowledge. 1(4): 175-193. (In Persian).
20- Salami H., and Mohtashami T. 2015. The Prediction Model of Iran's Crop Production Potential for the 1404 Horizon. Iranian Journal of Agricultural Economics and Development Research, 45(4): 585-599. (In Persian).
21- Santeramo F. G., Goodwin B. K., Adinolfi F., and Capitanio F. 2016. Farmer Participation, Entry and Exit Decisions in the Italian Crop Insurance Program. Journal of Agricultural Economics, 67(3): 639- 657.
22- Skees J. P., Black J. R., and Barnet B. J. 1997. Designing and Rating an Area Yield Crop Insurance Contract. American Journal of Agricultural Economics, 79: 430- 438.
23- Yang J., Reichert P., Abbaspour K. C., Xia J., and Yang H. 2008. Comparing uncertainty 692 analysis techniques for a SWAT application to the Chaohe Basin in China. Journal of Hydrology, 358: 1–23.
24- Yazdani S., and Kiani-Raz A. 2003. Income insurance: The New Pattern for Agricultural Product Risk Management. Agricultural Economics and Development, 47: 47-79. (In Persian).