کاربرد تابع‌های مفصل در تعیین حق بیمۀ مبتنی بر شاخص تنش گرمایی گاوهای شیری (بررسی: شهرستان دماوند)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار بخش کشاورزی، دانشگاه پیام نور، تهران، ایران

2 دانشجوی دکتری اقتصاد کشاورزی، پردیس بین الملل دانشگاه فردوسی مشهد

چکیده

طراحی و اجرای بیمۀ آب و هوایی شاخص دما-رطوبت یک ابزار مناسب مدیریت خطرپذیری است که می‌تواند آسیب و زیان دامداران را در برابر تنش گرمایی کاهش داده و به تثبیت درآمد آن‌ها منجر شود. با توجه به این مهم در این پژوهش، این نظام بیمه‌ای برای فعالیت پرورش گاو شیری در شهرستان دماوند طراحی شده است. داده‌های مورد نیاز به‌صورت ماهانه و از مدیریت جهاد کشاورزی و ایستگاه هواشناسی شهرستان دماوند برای دورۀ 95-1391 گردآوری شد. به دلیل انعطاف‌پذیری رهیافت مفصل و دقت بالا در اندازه‌گیری ساختار وابستگی، از این روش برای تبیین تابع توزیع همزمان و اندازه‌گیری آسیب مورد انتظار استفاده شد. نتایج نشان داد، بین عملکرد گاو شیری (شیر) و شاخص دما-رطوبت همبستگی منفی قوی وجود دارد که توسط مفصل کلایتون چرخشی منفی بهتر از دیگر مفصل‌های تبیین می‌شود. حق بیمۀ شاخص هوایی برای گاو شیری 65/610 هزار ریال در سطح پوشش 100 درصد محاسبه شد. همچنین آسیب مورد انتظار ناشی از تنش گرمایی در سطح پوشش 100 درصد برابر 42 کیلوگرم در ماه برای هر رأس گاو شیری به دست آمد که با توجه به شمار کل گاوهای شیری در شهرستان (2207 رأس) آسیب و زیان کل ناشی از تنش گرمایی برابر با 92694 کیلوگرم در ماه و رقمی بالغ بر 2/1 میلیارد ریال در ماه محاسبه شد. با توجه به بزرگ بودن آسیب و زیان ناشی از تنش گرمایی و اهمیت شیر در سلامت جامعه، توصیه می‌شود مسئولان و سیاست‌گذاران به طراحی این نظام بیمه‌ای در کل کشور توجه خاص و ویژه کنند.

کلیدواژه‌ها

عنوان مقاله [English]

The application of copulas approach in determining heat stress based index insurance premium for dairy cows in Damavand county

نویسندگان [English]

  • Afsaneh Nikoukar 1
  • Sasan Torabi 2
1 Assistant Professor, Department of Agriculture, Payame Noor University, Tehran, Iran
2 Ph.D. Student of Agricultural Economics, International Campus of Ferdowsi University of Mashhad, Mashhad, Iran
چکیده [English]

Designing and implementing temperature-humidity index insurance is a proper risk management tool that can lower animal breeder loss against heat stress and can lead to their income stability. Given this important issue in the present study, this insurance system has been designed for dairy cattle production in Damavand County. The required data were collected monthly from Damavand County’s agricultural and meteorological organizations in a time span ranging between 2012 and 2016. Given the flexibility of copula approach and its high accuracy in measuring dependency structure, this method was employed to account for joint distribution function and to measure expected loss. The results indicated that a strong dependency exists between dairy cow yield and temperature-humidity index, and this dependency can be better explicated through negative rotating Clayton index than any other copulas. The premium amount for each dairy cow has been calculated as 610650 Rials at 100 percent coverage level. Additionally, the expected loss resulted from heat stress at the 100 percent coverage level has been calculated as 42 kilograms within a month for each dairy cow. Considering the total number of dairy cows in the town, i.e. 2207 cows, the total loss resulted from heat stress has been calculated as 92694 kilograms and over 1.2 billion Rials within a month. With regard to the great loss resulted from heat stress and the importance of milk in the health of societies, it is recommended that the officials and policy-makers devote a particular attention to designing this insurance system throughout country.

کلیدواژه‌ها [English]

  • Dairy Cows
  • Damavand
  • Temperature-humidity index
  • Weather-based index insurance

مراجع

  1. Agriculture Statistics. (2015). Agriculture Ministry. Department of planning and economy, IT center. Volume 2, 1st edition. (in Farsi)
  2. Ahangaran, J. (2013). Investigating temperature humidity index (THI) and its impact on the performance of dairy cows in Isfahan province. MA thesis in Animal Sciences, Isfahan University, department of agriculture. (in Farsi)
  3. Akyuz, A., Boyaci, S. & Cayli, A. (2010). Determination of critical period for dairy cows using temperature humidity index. Journal of Animal and Veterinary Advances, 9(13), 1824-1827.
  4. Berman, A. (2006(. Estimate of heat stress relief needs for Holstein dairy cows. Journal Animal Science, 83, 1377-1384.
  5. Bickert, W. G. (2016). Stress in dairy animals: Cold stress: Management considerations. Reference Module in Food Science, 2, 555-560
  6. Bohmanova, J., Misztal, I. & Cole, J. B. (2007). Temperature-humidity indices as indicators of milk production losses due to heat stress. Journal of Dairy Science, 90, 1947-1956.
  7. Bokusheva, R. (2010). Measuring the dependence structure between yield and weather variables. ETH Zurich, Institute for Environmental Decisions.
  8. Bravo-Ureta, L. Q. B. E. & Cabrera, V. E. (2015). From cold to hot: Climatic effects and productivity in Wisconsin dairy farms. Journal of Dairy Science, 98(12), 8664-8677.
  9. Brechmann, E. C. & Schepsmeier, U. (2012). Modeling dependence with C- and D-vine
    copulas: The R-package CDVine. To appear in the Journal of Statistical Software.
  10. Chen, G., Roberts, M. C. & Thraen, C. (2004). Weather derivatives in the presence of index & geographical basis risk: hedging dairy profit risk. Paper presented at the NCR-134 conference on applied commodity price analysis, forecasting, and market risk management, St. Louis, Missouri, April.
  11. Daron, J. D. & Stainforth, D. A. (2014). Assessing pricing assumptions for weather index insurance in a changing climate. Climate Risk Management, 1, 76-91.
  12. Dashti, H., Riyasi, A., Edris, M.A., Ghorbani, G. & Omidimirzaei, H. (2016). The impact of Temperature-Humidity Index in winter and summer on the performance of some of the fertility characteristics of dairy cows. Iranian Journal of Animal Sciences, 47(2), 231-327. (In Farsi)
  13. Deng, X., Barnett, J. B., Vedenov, D. V. & West, J. W. (2007). Hedging dairy production losses using weather-based index insurance. Agricultural Economics, 36, 271-280.
  14. Dikmen, S. & Hansen, P. J. (2009). Is the temperature-humidity index the best indicator of heat stress in lactating dairy cows in a subtropical environment? Journal of Dairy Science, 92, 109–116.
  15. Dourandish, A. & Nikoukar, A. (2008). Comparative Study of Agriculture Insurance Policies in Other Countries. Iran Agriculture Insurance Fund. (in Farsi)
  16. 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
  17. Fox, D. G. & Tylutki, T. P. (1998). Accounting for the effects of environment on the nutrient requirements of dairy cattle. Journal of Dairy Science, 81, 3085-3095.
  18. Galambos, J. (1987). The asymptotic theory of extreme order statistics. Malabar FL: Kreiger
    Publishing Co.
  19. Gaughan, J. B., Mader, T. L., Holt, S. M. & Lisle, A. (2008). A new heat load index for feedlot cattle. Journal of Animal Science, 86, 226-234.
  20. Gumbel, E. J. (1960). Bivariate exponential distributions. Journal of the American Statistical Association, 55, 698-707.
  21. Hansen, P. J. (2007). Exploitation of genetic and physiological determinants of embryonic resistance to elevated temperature to improve embryonic survival in dairy cattle during heat stress. Theriogenology, 68, 242-249.
  22. Hayati, B., Ghahremanzadeh, M., Khodaverdizadeh, M. & Najafi, N. (2010). Investigating the effective factors on the acceptability of the rural livestock insurance (mixed cows):  A case study of Salmas. The Journal of Animal Sciences (The Agriculture Science), 20 (2), 27-38. (In Farsi)
  23. Hayati, B., Najafi, N. & Khodaverdizadeh, M. (2010). Investigating the effective factors on the acceptability of the rural livestock insurance (mixed cows):  A case study of Salmas. The 6th conference of Iran’s economy of agriculture, Karaj, Iran.Iranian association of Agricultural economy, Agriculture and Natural Resources Campus of Tehran University. http://www.civilica.com/Paper-IAEC07-IAEC07_040.html. (in Farsi)
  24. Hougaard, P. (1986). A class of multivariate failure time distributions. Biometrika, 73, 671-678. http://www.vosesoftware.com/Model-Risk.
  25. Husler, P. & Reiss, R. D. (1989). Maxima of normal random vectors: between independence and complete dependence. Statistics & Probability Letters, 7, 283-286.
  26. Ingraham, R. H., Stanley, R. W. & Wagner, W. C. (1979). Seasonal effects of tropical climate on shaded and nonshaded cows as measured by rectal temperature, adrenal cortex hormones, thyroid hormone, and milk production. American Journal Vet. Research, 40, 1792–1797.
  27. Jensen, N. D., Barrett, C. B. & Mude, A. G. (2014). Basis risk and the welfare gains from index insurance: evidence from Northern Kenya. Munich Personal RePEc Archive. 1-50. Online at http://mpra.ub.uni-muenchen.de/59153/.
  28. Jie, C., Li, Y. & Sijia, L. (2013). Design of Wheat drought index insurance in Shandong province. International Journal of Hybrid Information Technology, 6(4), 95-104.
  29. Joe, H. (1997). Multivariate models and dependence concepts. Chapman and Hall, London.
  30. Kazdere, C. T., Murphy, M. R., Silanikove, N. & Maltz, E. (2002). Heat stress in lactating dairy cows: A review. Livest. Production Science, 77, 59-91.
  31. Keshavarzi Bank. (2015). Tehran Province Headquarter. (in Farsi)
  32. Kimeldorf, G. & Sampson, A. (1975). Uniform representations of bivariate distributions. Common Statist a Theory Methods, 4, 617-627. Statistical University Paris, 8, 229-231.
  33. Lekzashkor, Gh. (2015). The impact of global warming on the changes in the thresholds of biological climates dairy cows: a case study of Khorasan Razavi, The 1st international conference of the Earth, Space and Clean Energy, Ardabil, University of MohagheghArdabili, com/Paper-ATTITTDE01-ATTITTDE01_227.html. (In Farsi)
  34. Mahul, O. & Skees, J. (2007). Managing agriculture risk at the country level: The case of livestock mortality in mongola. World Bank, Washington, D.C.
  35. Moeini, M.M., Sanjabi, M. & Alibeigi, A.H. (2011). Analyzing the effective factors on the acceptability of insurance and risk level in traditional dairy farms in Isfahan province. Livestock Sciences (Research and Construction), 90,13-21. (In Farsi)
  36. Nasr. M. A. F. & El-Tarabany, M. S. (2017). Impact of three THI levels on somatic cell count, milk yield and composition of multiparous Holstein cows in a subtropical region. Journal of Thermal Biology, 64,73-77.
  37. Nelsen, R. B. (2005). An introduction to copulas. Second Edition.
  38. OCHA. (2011). Eastern Africa: Drought - Humanitarian Snapshot. http://www.fews.net/docs/Publications/Horn_of_africa_drought_2011_06.pdf.
  39. Pishbaghar, A., Abedi, S., Dashti, Gh. & Kianirad, A. (2015). Investigating weather-based insurance index of the rain-fed wheat: D-vine copula approach. The Journal of Agricultural Economy, 9(3), 37-62. (In Farsi)
  40. Rahbar, R. (2014). The impact of global warming on the performance of dairy cows. The National Conference of the Climate and Engineering Changes in the Sustainable Development of Agriculture and Natural Resources, Tehran, TolueFarzin Science and Industry Company. http://www.civilica.com/Paper-CCASD01-CCASD01_245.html. (in Farsi)
  41. Ravagnolo, O., Misztal, I. & Hoogenboom, G. (2000). Genetic component of heat stress in dairy cattle, development of heat index function. Journal Animal Science, 83, 2120-2125.
  42. Rensis, F. D., Lopez-Gatius, F., Garcia-Ispierto, I., Morini, G. & Scaramuzzi, R. J. (2017). Causes of declining fertility in dairy cows during the warm season. Theriogenology, 91, 145-153.
  43. Roshan, G. & Lekzashkor, G. (2015). The impact of global warming on the changes in the thresholds of biological climates of dairy cows in Northern Iran. The 2nd National Conference of Sustainable Development of Agriculture and Natural Resources, Tehran, Shahid Beheshti University. (In Farsi)
  44. Schepsmeier, U. & Stober, J. (2012). Derivatives and fisher information of bivariate copulas. Statistical Papers manuscript.
  45. Schulte, G. M. & Berg, E. (2011). Modelling farm production risk with copula instead of
    correlations. Institute of Food and Resource Economics. University of Bonn, Germany.
  46. Skees, J. R., Hazell, P. & Miranda, M. (1999). New approaches to crop yield insurance in developing countries. International Food Policy Research Institute, EPTD discussion.
  47. Taghavi, H., Naserian, A. & Valizadeh, R. (2014). Determination of critical weather periods in management of dairy cows in northeast of Iran using Temprature Humidity Index. Iranian Quarterly of Animal Sciences, 6(4), 295-303. (in Farsi)
  48. Tawn, J. A. (1988). Bivariate extreme value theory: Models and estimation. Biometrika, 75, 397-415.
  49. Thom, E. C. (1959). The discomfort index. Weatherwise, 12, 57-59.
  50. Torabi, S. & Ghorbani, M. (2015). The efficiency of traditional dairy farms in Mazandaran province: implications and recommendations for improvement (fuzzy data envelopment analysis). Iraninan Journal of Animal Science, 46(4), 445-456. (in Farsi)
  51. USAID/OFDA. (2011). Horn of Africa-Drought, Fact Sheet #1, Fiscal Year 2011.
  52. West, J. W., Mullinix, B. G. & Bernard, J. K. (2003). Effects of hot, humid weather on milk temperature, dry matter intake, and milk yield of lactating dairy cows. Journal of Dairy Science, 86, 232-242.
  53. Zwaagstra, L., Sharif, Z., Wambile, A., Leeuw, J. D., Said, M. Y., Johnson, N., Njuki, J., Said, M., Ericksen, P. & Herrero, M. (2010). An assessment of the response to the 2008-2009 drought in Kenya. International Livestock Research Institute (ILRI).

فایل ها

سابقه مقاله

  • تاریخ دریافت: 16 خرداد 1396
  • تاریخ بازنگری: 29 مهر 1396
  • تاریخ پذیرش: 21 آذر 1396

هم رسانی

ارجاع به این مقاله

آمار