Natural Hazards

, Volume 91, Issue 1, pp 69–88 | Cite as

Pricing weather index insurance based on artificial controlled experiment: a case study of cold temperature for early rice in Jiangxi, China

  • Qing Sun
  • Zaiqiang Yang
  • Xianghong Che
  • Wei Han
  • Fangmin Zhang
  • Fang Xiao
Original Paper


The growth of early rice is often threatened by a phenomenon known as Grain Buds Cold, a period of anomalously cold temperatures during the booting and flowering stage. As a high yield loss due to Grain Buds Cold will lead to increasing insurance premiums, quantifying the impact of weather on crop yield is crucial to the design of weather index insurance. In this study, we propose a new approach to the estimation of premium rates of Grain Buds Cold weather index insurance. A 2-year artificial controlled experiment was utilized to develop logarithmic and linear yield loss models. Additionally, incorporating 51 years of meteorological data, an information diffusion model was used to calculate the probability of different durations of Grain Buds Cold, ranging from 3 to 20 days. The results show that the pure premium rates determined by a logarithmic yield loss model exhibit lower risk and greater efficiency than those determined by a linear yield loss model. The premium rates of Grain Buds Cold weather index insurance were found to fluctuate between 7.085 and 10.151% at the county level in Jiangxi Province, while the premium rates based on the linear yield loss model were higher (ranging from 7.787 to 11.672%). Compared with common statistical methods, the artificial controlled experiment presented below provides a more robust, reliable and accurate way of analyzing the relationship between yield and a single meteorological factor. At the same time, the minimal data requirements of this experimental approach indicate that this method could be very important in regions lacking historical yield and climate data. Estimating weather index insurance accurately will help farmers address extreme cold weather risk under changing climatic conditions.


Early rice Weather index insurance Artificial controlled experiment Grain Buds Cold 



We thank the programs of the National Natural Science Foundation of China (Grant No. 41475107, 31300420), Natural Science Foundation of Jiangsu (BK20130987), National Science and Technology Support Program during the Twelfth Five Year Plan Period (Grant No. 2014BAD10B07), College and University’s Graduate Student Innovative Scientific Research Projects of Jiangsu Province, China Scholarship Council, and also thank Jifu Yin, Yufeng He, Junyu Qi and Jennifer Kennedy in ESSIC to review this paper. The authors would like to thank anonymous reviewers for their helpful suggestions.


  1. Alan PK, Barry KG, (2000) Nonparametric estimation of crop insurance rates revisited. Am J Agric Econ 82(2):463–478CrossRefGoogle Scholar
  2. Alaton P, Djehiche B, Stillberger D (2002) On modelling and pricing weather derivatives. Appl Math Finance 9(1):1–20CrossRefGoogle Scholar
  3. Allen L, Jagtiani J (2000) The risk effects of combining banking, securities, and insurance activities. J Econ Bus 52(6):485–497CrossRefGoogle Scholar
  4. Arino O, Ramos P, Jose J, Kalogirou V, Bontemps S, Defourny P, Van BE (2012) Global land cover map for 2009 (GlobCover 2009) European Space Agency (ESA). Université catholique de Louvain (UCL), PANGAEA. doi:
  5. Barnett BJ, Mahul O (2007) Weather index insurance for agriculture and rural areas in lower-income countries. Am J Agric Econ 89(5):1241–1247CrossRefGoogle Scholar
  6. Benth FE, Šaltytė Benth JŪRATĖ, Koekebakker S (2007) Putting a price on temperature. Scand J Stat 34(4):746–767Google Scholar
  7. Breustedt G, Bokusheva R, Heidelbach O (2008) Evaluating the potential of index insurance schemes to reduce crop yield risk in an arid region. J Agric Econ 59:312–328CrossRefGoogle Scholar
  8. Caballero R, Jewson S, Brix A (2002) Long memory in surface air temperature: detection, modeling, and application to weather derivative valuation. Clim Res 21(2):127–140CrossRefGoogle Scholar
  9. Chen F, Yang SB, Shen SH, Jiang XD, Hu JC, Hu N (2014) Simulation model of relative meteorological yield of double-cropping early rice in middle and lower reaches of Yangtze river based on principal component regression. Chin J Agrometeorol 35(5):522–528Google Scholar
  10. DB36/T511-2007 (2007) Meteorological disaster indicators of double cropping rice in Jiangxi Province. Jiangxi provincial meteorological bureau. Jiangxi province quality and technical supervision, NanchangGoogle Scholar
  11. Deng NX (1986) Grain Buds Cold for early rice and defensive. Jiangxi Agric Sci Technol 05:7–8Google Scholar
  12. Deng X, Barnett BJ, Vedenov DV, West JW (2007) Hedging dairy production losses using weather-based index insurance. Agric Econ 36(2):271–280CrossRefGoogle Scholar
  13. Ding SQ, Wang X (2011) The mechanism of technical obstacles and catastrophe risk diversification research on policy-based agricultural insurance. Insur Stud 6:56–62Google Scholar
  14. Du XY, Huang SL, Zhao QX (2009) Drought and water logging hazard assessment in Tianjing based on information diffusion model. J Catastrophol 24(1):22Google Scholar
  15. Erec Heimfarth L, Musshoff O (2011) Weather index-based insurances for farmers in the North China Plain: an analysis of risk reduction potential and basis risk. Agric Finance Rev 71(2):218–239CrossRefGoogle Scholar
  16. Fuchs A, Wolff H (2011) Concept and unintended consequences of weather index insurance: the case of Mexico. Am J Agric Econ 93(2):505–511CrossRefGoogle Scholar
  17. Hess U, Richter K, Stoppa A (2002) Weather risk management for agriculture and agri-business in developing countries. Climate risk and the weather market, financial risk management with weather hedges. Risk Books, LondonGoogle Scholar
  18. Huang CF (1995) Fuzziness of incompleteness and information diffusion principle. In: Proceedings of FUZZ-IEEE/IFES, Yokohama, Japan, pp 1605–1612Google Scholar
  19. Huang CF, Da R (1996) Information diffusion principle and application in fuzzy neuron. Fuzzy logic foundations and industrial applications. Springer, Berlin, pp 165–198Google Scholar
  20. Jacobs BC, Pearson CJ (1999) Growth, development and yield of rice in response to cold temperature. J Agron Crop Sci 182(2):79–88CrossRefGoogle Scholar
  21. Jolliffe IT (1986) Principal component analysis. Springer, Berlin, p 487CrossRefGoogle Scholar
  22. Lawas CP (2005) Crop insurance premium rate impacts of flexible parametric yield distributions: an evaluation of johnson family of distributions. Dissertation, Texas Tech UniversityGoogle Scholar
  23. Li Y, Yang XG, Ye Q, Qing F (2013) The possible effects of global warming on cropping systems in China IX. The risk of high and low temperature disasters for single and double rice and its impacts on rice yield in the middle-lower Yangtze plain. Sci Agric Sin 46(19):3997–4006Google Scholar
  24. Li JL, Huo ZG, Wu LJ, Zhu QH, Hu F (2014) Effects of low temperature on grain yield of rice and its physiological mechanism at the booting stage. Chin J Rice Sci 28(3):277–288Google Scholar
  25. Liao F, Huang SQ (2011) Research on the current status and implementation of policy-based rice insurance—a case in Jiangxi. Contemp Econ 19:84–85Google Scholar
  26. Liu B, Li M, Guo Y, Shan K (2010a) Analysis of the demand for weather index agricultural insurance on household level in Anhui, China. Agric Agric Sci Proc 1:179–186Google Scholar
  27. Liu YB, Liu LM, Xu D, Zhang SH (2010b) Risk assessment of flood and drought in major grain-producing areas based on information diffusion theory. Trans CSAE 26(8):1–7Google Scholar
  28. Liu YN, He LW, Li YL, Bai QF, Liang Y, Zhang T (2010c) A study on the risk index design of agricultural insurance on apple florescence freezing injury in Shaanxi fruit zone. Chin J Agrometeorol 31(1):125–129Google Scholar
  29. Liu JF, Zhan WF, Liang YH (2011) A comparative study of risk estimation mode based on P-III distribution and information diffusion theory. J Beijing Norm Univ Nat Sci 47(3):300–303Google Scholar
  30. Liu XZ, Su Q, Li JZ, Quan B, Li CK, Zhang Y, Wang ZQ, Wang GA (2015) Response of carbon isotopic composition of C3 and C4 herbaceous plants to temperature under controlled temperature conditions. Acta Ecol Sin 35(10):3278–3287Google Scholar
  31. Lou WP, Sun S (2013) Design of agricultural insurance policy for tea tree freezing damage in Zhejiang Province, China. Theor Appl Climatol 111(3–4):713–728CrossRefGoogle Scholar
  32. Lou WP, Wu LH, Ni HP, Tang QY, Mao YD (2009) Design of Weather Claiming Index for Citrus Freezing Damage Insurance. Sci Agric Sin 42(4):1339–1347Google Scholar
  33. Lou WP, Wu LH, Yao YP (2010) Design of weather-based indemnity indices for paddy rice heavy rain damage insurance. Sci Agric Sin 43(3):632–639Google Scholar
  34. Ma SD, Li ZD (2016) Pricing rice weather index insurance—a case study of cold temperature weather index in Guizhou Province. Rural Econ Technol 27(3):1–4Google Scholar
  35. Mao YD, Wu LH, Miao CM, Yao YP, Su GL (2007) A reference design for Citrus freeze damage insurance by using meteorological index in Zhejiang Province. Chin J Agrometeorol 28(2):226–230Google Scholar
  36. Mahul O, Skees J (2007) Managing Agricultural Risk at the Country Level: The Case of Index-Based Livestock Insurance in Mongolia, Working Paper No 4325. The World Bank Policy Research: Washington, D.CGoogle Scholar
  37. Mueller B, Quaas MF, Frank K, Baumgaertner S (2011) Pitfalls and potential of institutional change: Rain-index insurance and the sustainability of rangeland management. Ecol Econ 70:2137–2144CrossRefGoogle Scholar
  38. National Bureau of Statistics of China (2012) China statistical yearbooks. China Statistics Press, BeijingGoogle Scholar
  39. National Bureau of Statistics of Jiangxi, China (2012) Jiangxi Statistical Yearbooks. China Statistics Press, BeijingGoogle Scholar
  40. Osgood D, McLaurin M, Carriquiri M, Mishra A, Fiondella F, Hansen J, Peterson N, Ward N (2007) Designing Weather Insurance Contracts for Farmers in Malawi, Tanzania and Kenya. Report to the Commodity Risk Management Group, ARD, World Bank International Research Institute for Climate and Society, Columbia University: New YorkGoogle Scholar
  41. Ozaki VA, Ghosh SK, Goodwin BK, Shirota R (2008) Spatio-temporal modeling of agricultural yield data with an application to pricing crop insurance contracts. Am J Agric Econ 90(4):951–961CrossRefGoogle Scholar
  42. Peng CR, Liu XL, Li MD (2005) Main weather disasters of rice and defensive counter measures in Jiangxi. Acta Agric Jiangxi 17(4):127–130Google Scholar
  43. Ramírez OA, Misra SK, Nelson J (2003) Efficient estimation of agricultural time series models with nonnormal dependent variables. Am J Agric Econ 85(4):1029–1040CrossRefGoogle Scholar
  44. Schlenker W, Hanemann WM, Fisher AC (2006) The impact of global warming on US agriculture: an econometric analysis of optimal growing conditions. Rev Econ Stat 88(1):113–125Google Scholar
  45. Sherrick BJ, Zanini FC, Schnitkey GD, Irwin SH (2004) Crop insurance valuation under alternative yield distributions. Am J Agric Econ 86(2):406–419CrossRefGoogle Scholar
  46. Shi H, Jiang Z (2016) The efficiency of composite weather index insurance in hedging rice yield risk: evidence from China. Agric Econ 47(3):319–328CrossRefGoogle Scholar
  47. Skees J, Gober S, Varangis P, Lester R, Kalavakonda V (2001) Developing Rainfall-Based Index Insurance in Morocco, Working Paper No. 2577. The World Bank, Policy Research: Washington, DCGoogle Scholar
  48. Sun Q, Yang ZQ, Yin JM, Yu KJ, Yuan XK, Yu YW, Gao LN (2014) Estimation of premium rates of high temperature disaster for early rice in Jiangxi. Chin J Agrometeorol 35(5):561–566Google Scholar
  49. Taib CM, Benth FE (2012) Pricing of temperature index insurance. Rev Dev Finance 2(1):22–31CrossRefGoogle Scholar
  50. Vedenov DV, Barnett BJ (2004) Efficiency of weather derivatives as primary crop insurance instruments. J Agric Resour Econ 29(3):387–403Google Scholar
  51. Wang CY, Zhang YJ, Zhang JH, Cai DX, Che XF (2016a) Determine of the premium rate based on the weather indices of chilling injury in mangos and contract design in Hainan Province. Chin J Agrometeorol 39(1):108–133Google Scholar
  52. Wang SQ, Song XH, Zhao HH, Sun MM, Xiao CL, Gu CM, Na YG, Xie BS, Cao LY, Cheng SH (2016b) Effect of cold stress at booting stage on rice yield and quality in the cold region. Res Agric Mod 37(3):568–579Google Scholar
  53. Wei L (1991) Impact on yield index of early rice of high temperature forced maturity and Grain Buds Cold in Jiangxi. Meteorol Mon 10:47–49Google Scholar
  54. Woodard JD, Garcia P (2008a) Basis risk and weather hedging effectiveness. Agric Finance Rev 68(1):99–117CrossRefGoogle Scholar
  55. Woodard JD, Garcia P (2008b) Weather derivatives, spatial aggregation, and systemic risk: implications for reinsurance hedging. J Agric Resour Econ 4:34–51Google Scholar
  56. Wu X, Yang J (2002) Analysis on rainstorm risk in Zhejiang Province by Use of Information Proliferate model. J Catastrophol 17(4):7–10Google Scholar
  57. Wu L, Huo ZG, Jiang Y, Zhang L, Yu CX (2016) Trends and risk of spring low-temperature damage to early rice in southern China against the background of global warming. Acta Ecol Sin 36(5):1263–1271Google Scholar
  58. Yang WY, Tu NM (2003) Various crop cultivation. China Science Press, BeijingGoogle Scholar
  59. Yang TM, Liu BC, Sun XB, Li D, Xun SP (2013) Design and application of the weather indices of winter wheat planting insurance in Anhui China. Chin J Agrometeorol 34(2):229–235Google Scholar
  60. Zeng H, Yang SX (2014) Applying nonparametric and parametric methods in regional cropland premium determination. Stat Decis 400(4):74–77Google Scholar
  61. Zhang GC (2010) Meteorological disasters risk assessments and regionalization methods. China Meteorological Press, BeijingGoogle Scholar
  62. Zhang R, Xu ZS, Huang ZS, Zeng G, Shen SH (2012) Uneven information diffusion model and its application in inadequate samples disaster events evaluation. Adv Earth Sci 27(11):1229–1235Google Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2017

Authors and Affiliations

  1. 1.Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)Nanjing University of Information Science and TechnologyNanjingChina
  2. 2.Earth System Science Interdisciplinary Center (ESSIC)University of MarylandCollege ParkUSA
  3. 3.State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina

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