بررسی ساختار وابستگی و شبیه‌سازی عملکرد گندم دیم شهرستان میانه و متغیرهای آب‌ و هوایی: کاربرد رهیافت تابع مفصل تاکی‌شکل کانونی (C-Vine)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه اقتصاد کشاورزی، دانشکده کشاورزی، دانشگاه تبریز

2 دانشجوی کارشناسی ارشد اقتصادکشاورزی، دانشکده کشاورزی، دانشگاه تبریز

3 استاد اقتصادکشاورزی، دانشکده کشاورزی، دانشگاه تبریز

4 استادیار مؤسسه پژوهش‌های برنامه‌ریزی، اقتصادکشاورزی و توسعة روستایی

چکیده

بیمه آب و هوا از بهترین سیستم‌های بیمه‌ای است که در آن به منظور محاسبه حق بیمه، به شبیه‌سازی عملکرد محصول با توجه به متغیرهای آب و هوایی با تبیین ساختار وابستگی‌ آن‌ها نیاز است. بررسی ساختار وابستگی با در نظر گرفتن تأثیرگذاری هم‌زمان متغیرها با استفاده از توابع مفصل تاکی‌‌شکل می‌تواند نتایج مناسب‌تری ارائه کند. بنابراین، در پژوهش حاضر، ساختار وابستگی بین متغیرهای آب و هوایی و عملکرد گندم دیم با استفاده از توابع مفصل تاکی‌شکل کانونی (C-vine) اندازه‌گیری شد، سپس عملکرد گندم دیم در شهرستان میانه شبیه‌سازی گردید. نتایج مطالعه حاضر نشان داد که اکثر متغیرهای مورد بررسی دارای وابستگی دنباله‌ای در دم پایین‌اند؛ به عبارتی، تأثیرپذیری آن‌ها در مقادیر کوچک بیشتر از مقادیر بزرگ است. به این ترتیب، مقدار متوسط شبیه‌سازی شده عملکرد گندم دیم 6839/871 کیلوگرم در هکتار به‌دست آمد که این نتایج می‌تواند در محاسبه خسارت مورد انتظار و حق بیمه مورد استفاده قرار گیرد. 

کلیدواژه‌ها

عنوان مقاله [English]

Investigation of Dependence Structure and Simulation of Rainfed Wheat Yield and Weather Variables in Miyaneh County: Application of C-Vine Copula Approach

نویسندگان [English]

  • Esmaeil Pishbahar 1
  • S. Abedi 2
  • Gh. Dashti 3
  • A. Kianirad 4
1 Department of Agricultural Economics, Faculty of Agriculture, University of Tabriz
2 MSc Student of Agricultural Economics, Department of Agricultural Economics, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
3 Professor of Agricultural Economics, Department of Agricultural Economics, Faculty of Agriculture, University of Tabriz, Tabriz, Iran
4 Assistant Professor and Scientific Board in Agricultural Planning, Economics and Rural Development Research Institute (APERDRI), Tehran, Iran.
چکیده [English]

Weather-based crop insurance is one of the best insurance systems in which it is required to simulate production yield with respect to weather variables and explaining of dependence structure to compute premium. The evaluation of dependence structure with consideration of simultaneous impact of variables using vine copula can provide more appropriate results. Therefore, in this study, the dependence structure between weather variables and rainfed wheat yield with utilization of canonical vine copula functions (C-vine) was measured and then yield of rainfed wheat in Miyaneh County has simulated. The result showed that most of the investigated variables have dependency in lower tail; in the other word, they are more impressible in small amounts than big amounts. Hence, the mean amount of simulated rainfed wheat yield was 871.6839 kg per hectare so that these results can be used in expected loss and premium calculation.

اصل مقاله

Weather-Based Crop Insurance, Simulation of Yield, Vine Copula Function, Rainfed Wheat, Miyaneh 

مراجع

  1. Aas, K., Czado, C., Frigessi, A. and Bakken, H. (2009). Pair-copula constructions of multiple dependence. Insurance: Mathematics and Economics, 44: 182–198.
  2. Azizi,Gh. And Yarahmadi, D. (2003). Investigation of climatic parameters and dry farmed wheat relationship using regression equation. Geographical Research Quarterly, 35:23-29. (Persian)
  3. Bedford, T. and Cooke, R.M. (2002). Vines: a new graphical model for dependent random variables. Annals of Statistics, 30: 1031–1068.
  4. Bedford, T. and Cooke, RM. (2001). Probability density decomposition for conditionally dependent random variables modeled by vines. Annals of Mathematics and Artificial Intelligence, 32: 245–268.
  5. Bokusheva, R. (2010). Measuring the dependence structure between yield and weather variables. ETH Zurich, Institute for Environmental Decisions.
  6. Brechmann, E.C. and Czado, C. (2011). Risk management with high-dimensional vine copulas: an analysis of the Euro Stoxx 50. Submitted for publication.
  7. Brechmann, E.C. and Schepsmeier, U. (2012). Modeling dependence with C- and D-vine copulas: the R- package CDVine. To appear in the Journal of Statistical Software.
  8. Brechmann, E.C., Czado, C. and Aas, K. (2010). Truncated regular vines and their applications. Canadian Journal of Statistics, 40(1): 68–85.
  9. Chen, S., Wilson, W.W., Larsen, R. and Dahl, B. (2013). Investing in agriculture as an asset class. Department of Agribusiness and Applied Economics Agricultural Experiment Station North Dakota State University.
  10. Cooke, R.M., Morales, O. and Kurowicka, D. (2007). Vines in overview. Invited Paper Third Brazilian Conference on Statistical Modeling in Insurance and Finance Maresias.
  11. Czado, C., Brechmann, E.C. and Gruber, L. (2014). Selection of vine copulas. Technische Universitat Munchen.
  12. Dibmann, J., Brechmann, E.C., Czado, C. and Kurowicka, D. (2013). Selecting and estimating regular vine copulae and application to financial returns. Computational Statistics & Data Analysis. 59: 52–69.
  13. Farajzadeh, M. and Zarrin, A. (2002). Modeling the amount of dry wheat yield with respect to agricultural climatic criteria in West Azerbaijan Province. Tarbiat Modares University Press, 25: 71-96. (Persian)
  14. Farajzadeh, M., Khoorani, A., Bazgeer, S. and Zeaeian, P. (2011). Modeling and predicting of rainfed wheat yield in attention to phenological phases of plant growth (A case study for Kurdistan Province). Physical Geography ResearchQuarterly, 76: 21-34. (Persian)
  15. Fischer, M. (2002). Tailoring copula-based multivariate generalized hyperbolic secant distributions to financial return data: an empirical investigation. Institute of Statistics and Econometrics University of Erlangen- Nurnberg, Lange Gasse 20, D-90403 Nurnberg, Germany.
  16. Goodwin, B.K., Holt, M.T., Onel, G. and Prestemon, J.P. (2011). Copula-based nonlinear models of spatial market linkages. American Journal of Agricultural Economics, in press, 2011.
  17. Goodwin, BK. (2012). Copula-based models of systemic risk in US. agriculture: implications for crop insurance and reinsurance contracts. The NBER conference on Insurance Markets and Catastrophe Risk in Boston.
  18. Joe, H. (1997). Multivariate models and dependence concepts. Chapman and Hall, London
  19. Kamali, Gh. And Bazgir, S. (2008). Dry wheat yield prediction using meteorological indices in some parts of Iran western. Journal of Agricultural Science and Natural Resources, 2(64): 113-121. (Persian)
  20. Kurowicka, D. and Cooke, R.M. (2006). Uncertainty analysis with high dimensional dependence Modeling. John Wiley & Sons Ltd.
  21. Kurowicka, D. and Joe, H. (2011). Dependence modeling: vine copula handbook. World Scientific Publishing Co, Singapore.
  22. Ministry of agriculture-Jahad. (2015). Center for Statistics and Information, production cost of crops Available at: http://www.maj.ir/Index.aspx?page_= form&lang=1&PageID=11583&tempname=amar&sub=65&methodName=ShowModuleContent.
  23. Nelsen, R.B. (2005). An introduction to copulas. Second Edition. Springer-Verlag, Berlin.
  24. Scholzel, C. and Friederichs, P. (2008). Multivariate non-normally distributed random variables in climate research introduction to the copula approach. Nonlinear Processes in Geophysics, 15: 761–772.
  25. Schulte, G.M. and Berg, E. (2011). Modeling farm production risk with copula instead of correlations. Institute of Food and Resource Economics, University of Bonn, Germany.
  26. Zare abyaneh, H. (2013). Evaluating roles of drought and climatic factors on variability of four dry farming yields in Mashhad and Birjand. Water and Soil Science, 23(1):39-56. (Persian)
  27. Zhu, Y., Ghosh, S. and Goodwin, B. (2008). Modeling dependence in the design of whole farm insurance contract a copula-based approach. Contributed paper at the Annual Meeting of the AAEA,Orlando, USA, July 27-29. 

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