Theoretical and Applied Climatology

, Volume 131, Issue 1–2, pp 653–659 | Cite as

A quantitative method for risk assessment of agriculture due to climate change

  • Zhiqiang Dong
  • Zhihua Pan
  • Pingli An
  • Jingting Zhang
  • Jun Zhang
  • Yuying Pan
  • Lei Huang
  • Hui Zhao
  • Guolin Han
  • Dong Wu
  • Jialin Wang
  • Dongliang Fan
  • Lin Gao
  • Xuebiao Pan
Original Paper

Abstract

Climate change has greatly affected agriculture. Agriculture is facing increasing risks as its sensitivity and vulnerability to climate change. Scientific assessment of climate change-induced agricultural risks could help to actively deal with climate change and ensure food security. However, quantitative assessment of risk is a difficult issue. Here, based on the IPCC assessment reports, a quantitative method for risk assessment of agriculture due to climate change is proposed. Risk is described as the product of the degree of loss and its probability of occurrence. The degree of loss can be expressed by the yield change amplitude. The probability of occurrence can be calculated by the new concept of climate change effect-accumulated frequency (CCEAF). Specific steps of this assessment method are suggested. This method is determined feasible and practical by using the spring wheat in Wuchuan County of Inner Mongolia as a test example. The results show that the fluctuation of spring wheat yield increased with the warming and drying climatic trend in Wuchuan County. The maximum yield decrease and its probability were 3.5 and 64.6%, respectively, for the temperature maximum increase 88.3%, and its risk was 2.2%. The maximum yield decrease and its probability were 14.1 and 56.1%, respectively, for the precipitation maximum decrease 35.2%, and its risk was 7.9%. For the comprehensive impacts of temperature and precipitation, the maximum yield decrease and its probability were 17.6 and 53.4%, respectively, and its risk increased to 9.4%. If we do not adopt appropriate adaptation strategies, the degree of loss from the negative impacts of multiclimatic factors and its probability of occurrence will both increase accordingly, and the risk will also grow obviously.

Notes

Acknowledgments

This study was supported by the Non-profit Research Foundation for Meteorology of China (No. GYHY201506016), the National Basic Research Program of China (No. 2012CB956204), the National Natural Science Foundation of China (Grant Nos. 41371232 and 41271110), the Non-profit Research Foundation for Agriculture of China (No. 201103039), and the National Science and Technology Support Program of China (No. 2012BAD09B02).

References

  1. Bai HX, Li DY, Ge Y, Wang JF (2016) Detecting nominal variables’ spatial associations using conditional probabilities of neighboring surface objects’ categories. Inf Sci 329:701–718CrossRefGoogle Scholar
  2. Botts RR, Boles JN (1958) Use of normal curve theory in crop insurance rate making. J Farm Econ 40:733–740CrossRefGoogle Scholar
  3. Bruce JS, Fabio CZ, Gary DS, Scott HI (2004) Crop insurance valuation under alternative yield distributions. Am J Agric Econ 86(2):406–419CrossRefGoogle Scholar
  4. Dong ZH (1999) Establishing agricultural risk management system in China. J Zhongnan Univ Finance Econ 2:69–71Google Scholar
  5. Gallagher PUS (1987) Soybean yields: estimation and forecasting with nonsymmetric disturbances. Am J Agric Econ 69:796–803CrossRefGoogle Scholar
  6. Goodwin BK, Mahul O (2004) Modeling concepts relating to the design and rating of Agricultural Insurance Contracts. Working Paper, World BankGoogle Scholar
  7. Goodwin BK, Roberts MC, Coble KH (2000) Measurement of price risk in revenue insurance: implications of distributional assumptions. J Agric Resour Econ 25:195–214Google Scholar
  8. Hubig M, Muggenthaler H, Mall G (2014) Conditional probability distribution (CPD) method in temperature based death time estimation: error propagation analysis. Forensic Sci Int 238:53–58CrossRefGoogle Scholar
  9. Huo ZG, Li K, Wang SY, Liu JL, Xue CY (2003) Study on the risk evaluation technologies of main agrometeorological disasters and their application. J Nat Resour 11(6):692–703Google Scholar
  10. IPCC (2001) Technical summary. In: White KS, Ahmad QK, Anisimov O et al (eds) Climate change 2001: impacts, adaptation, and vulnerability. Contribution of Working Group II to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, CambridgeGoogle Scholar
  11. IPCC (2007) Technical summary. In: Parry ML, Canziani OF, Palutikof JP et al (eds) Climate change 2007: impacts, adaptation, and vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, CambridgeGoogle Scholar
  12. IPCC (2012) In: Field CB, Barros V, Stocker TF et al (eds) Managing the risks of extreme events and disasters to advance climate change adaptation: a special report of Working Groups I and II of the Intergovernmental Panel on Climate Change. Cambridge University Press, CambridgeGoogle Scholar
  13. IPCC (2014) Impacts, adaptation, and vulnerability. Part a: global and sectoral aspects. In: Field CB, Barros VR, Dokken DJ et al (eds) Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, CambridgeGoogle Scholar
  14. ISO (2009) ISO guide 73–2009. International Standards Organization, GenevaGoogle Scholar
  15. Jones RN, Preston BL (2010) Adaptation and risk management: climate change working paper no. 15. Centre for Strategic Economic Studies, Victoria University, Melbourne, pp. 1–18Google Scholar
  16. Li Y, Gao G, Song LC (2014) Understanding of disaster risk and the management associated with climate change in IPCC AR5. Progressus Inquisitiones De Mutatione Climatis 10(4):260–267Google Scholar
  17. Lu L, Ding D, Deng HB (2012) Climate change: risk assessment and coping strategies. Rev Econ Res 20:19–22Google Scholar
  18. Nelson CH, Preckel PV (1989) The conditional beta distribution as a stochastic production function. Am J Agric Econ 71:370–378CrossRefGoogle Scholar
  19. Qu ML (1991) Practice supervision of agricultural climate. Beijing Agricultural University Press, BeijingGoogle Scholar
  20. Renn O (2005) Whitepaper on risk governance: towards an integrative approach Whitepaper No. 1 of the International Risk Governance CouncilGoogle Scholar
  21. Wang K, Zhang Q (2013) The review and prospect of risk assessment methods on agricultural production. Agric Econ Outlook 2:38–43Google Scholar
  22. Wu SH, Pan T, He SF (2011) Primary study on the theories and methods of research on climate change risk. Adv Clim Chang Res 7(5):363–368Google Scholar
  23. Zhang JQ, Li N (2007) The risk assessment and quantitative management method of the main meteorological disaster and its application. Beijing Normal University Press, BeijingGoogle Scholar
  24. Zhao SJ (2010) The risk assessment of rice flood disaster in Huai River basin driven by scenario. Proceedings of the 4th Annual Meeting of the Professional Committee on Risk Analysis of China Disaster Prevention Association. 392–399Google Scholar

Copyright information

© Springer-Verlag Wien 2016

Authors and Affiliations

  • Zhiqiang Dong
    • 1
    • 2
    • 3
  • Zhihua Pan
    • 1
    • 3
  • Pingli An
    • 1
    • 3
  • Jingting Zhang
    • 1
    • 3
  • Jun Zhang
    • 1
    • 3
  • Yuying Pan
    • 1
    • 3
  • Lei Huang
    • 1
    • 3
  • Hui Zhao
    • 1
    • 3
  • Guolin Han
    • 1
    • 3
  • Dong Wu
    • 1
    • 3
  • Jialin Wang
    • 1
    • 3
  • Dongliang Fan
    • 1
    • 3
  • Lin Gao
    • 3
  • Xuebiao Pan
    • 1
    • 3
  1. 1.College of Resources and Environmental ScienceChina Agricultural UniversityBeijingChina
  2. 2.Shandong Provincial Climate CenterJinanChina
  3. 3.Key Ecology and Environment Experimental Station of Ministry of Agriculture for Field Scientific Observation in HohhotHohhotChina

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