Dependency of Crop Production between Global Breadbaskets: A Copula Approach for the Assessment of Global and Regional Risk Pools

Risk Analysis

Volumn 37, Issue 11, 2017, Pages 2212-2228

  Gaupp, Franziska ^a   Pflug, Georg ^b   Hochrainer Stigler, Stefan ^b   Hall, Jim ^a   Dadson, Simon ^c  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b INTERNATIONAL INSTITUTE FOR APPLIED SYSTEMS ANALYSIS   (Austria)

^c UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

Copula approach; risk pooling; wheat yield losses

Indexed keywords

CROPS; CULTIVATION; FOOD SUPPLY; RISK MANAGEMENT; RISKS;

COPULA APPROACH; GLOBAL FOOD SECURITY; HIERARCHICAL STRUCTURING; INTERNATIONAL DONORS; MINIMAX APPROACH; RISK POOLING; SPATIAL DEPENDENCIES; WHEAT YIELD;

RISK ASSESSMENT;

CROP PRODUCTION; CROP YIELD; EXTREME EVENT; FOOD SECURITY; GLOBAL PERSPECTIVE; GOVERNANCE APPROACH; MULTIVARIATE ANALYSIS; RISK ASSESSMENT; VULNERABILITY; WHEAT; YIELD RESPONSE;

ARGENTINA; AUSTRALIA; CHINA; INDIA; UNITED STATES;

TRITICUM AESTIVUM;

EID: 85007440119     PISSN: 02724332     EISSN: 15396924     Source Type: Journal    
DOI: 10.1111/risa.12761     Document Type: Article

References (82)

1. Field CB, Intergovernmental Panel on Climate Change. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaption: Special Report of the Intergovernmental Panel on Climate Change. New York, NY: Cambridge University Press, 2012.
2. Gbegbelegbe S, Chung U, Shiferaw B, Msangi S, Tesfaye K. Quantifying the impact of weather extremes on global food security: A spatial bio-economic approach. Weather and Climate Extremes, 2014; 4:96–108.
3. Von Braun J. The food crisis isn't over. Nature, 2008; 456(7223):701–701.
4. United Nations Conference on Trade and Development. Growth, Poverty and the Terms of Development Partnership. New York, NY: United Nations, 2008.
5. Mittal A. The 2008 Food Price Crisis: Rethinking Food Security Policies. UN, 2009.
6. Mueller SA, Anderson JE, Wallington TJ. Impact of biofuel production and other supply and demand factors on food price increases in 2008. Biomass and Bioenergy, 2011; 35(5):1623–1632.
7. Lazear E. Testimony of Edward P. Lazear Chairman, Council of Economic Advisers, 2008.
8. Grey D, Garrick D, Blackmore D, Kelman J, Muller M, Sadoff C. Water security in one blue planet: Twenty-first century policy challenges for science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2013; 371(2002):20120406.
9. Wegren SK. Food security and Russia's 2010 drought. Eurasian Geography and Economics, 2011; 52(1):140–156.
10. Paterson J, Wilkinson I. Western Australian wheat industry. Available at: https://www.agric.wa.gov.au/grains-research-development/western-australian-wheat-industry. Published 2015.
11. Yang J, Gong D, Wang W, Hu M, Mao R. Extreme drought event of 2009/2010 over southwestern China. Meteorology and Atmospheric Physics, 2012; 115(3–4):173–184.
12. Zhang L, Xiao J, Li J, Wang K, Lei L, Guo H. The 2010 spring drought reduced primary productivity in southwestern China. Environmental Research Letters, 2012; 7(4):045706.
13. World Bank. Food Price Watch, 2011.
14. Johnstone S, Mazo J. Global warming and the Arab Spring. Survival, 2011; 53(2):11–17.
15. Extreme weather and resilience of the global food system. Final Project Report from the UK-US Taskforce on Extreme Weather and Global Food System Resilience. Global Food Security Programme, UK, 2015.
16. Embrechts P, Klüppelberg C, Mikosch T. Modelling Extremal Events. Berlin, Heidelberg: Springer Science & Business Media, 1997.
17. Freeman PK, Scott K. Comparative analysis of large scale catastrophe compensation schemes. Pp. 187–234 in Messey FA (ed). Catastrophic Risks and Insurance, Paris: Organization for Economic Cooperation and Development, 2005.
18. Jongman B, Hochrainer-Stigler S, Feyen L, Aerts JC, Mechler R, Botzen WW, Bouwer LM, Pflug G, Rojas R, Ward PJ. Increasing stress on disaster-risk finance due to large floods. Nature Climate Change, 2014; 4(4):264–268.
19. Ward PJ, Jongman B, Kummu M, Dettinger MD, Weiland FCS, Winsemius HC. Strong influence of El Niño Southern Oscillation on flood risk around the world. Proceedings of the National Academy of Sciences, 2014; 111(44):15659–15664.
20. Petoukhov V, Rahmstorf S, Petri S, Schellnhuber HJ. Quasi-resonant amplification of planetary waves and recent Northern Hemisphere weather extremes. Proceedings of the National Academy of Sciences, 2013; 110(14):5336–5341.
21. Power S, Delage F, Chung C, Kociuba G, Keay K. Robust twenty-first-century projections of El Nino and related precipitation variability. Nature, 2013; 502(7472):541–545.
22. Lau WK, Kim K-M. The 2010 Pakistan flood and Russian heat wave: Teleconnection of hydrometeorological extremes. Journal of Hydrometeorology, 2012; 13(1):392–403.
23. Cai W, Borlace S, Lengaigne M, Van Rensch P, Collins M, Vecchi G, Timmermann A, Santoso A, McPhaden MJ, Wu L, England MH, Wang G, Guilyardi E, Jin F-F. Increasing frequency of extreme El Niño events due to greenhouse warming. Nature Climate Change, 2014; 4(2):111–116.
24. Huntingford C, Marsh T, Scaife AA, Kendon EJ, Hannaford J, Kay AL, Lockwood M, Prudhomme C, Reynard NS, Parry S, Lowe JA, Screen JA, Ward HC, Roberts M, Stott PA, Bell VA, Bailey M, Jenkins A, Legg T, Otto FEL, Massey N, Schaller N, Slingo J, Allen MR. Potential influences on the United Kingdom's floods of winter 2013/14. Nature Climate Change, 2014; 4(9):769–777.
25. Rasmijn LM, Schrier G, Barkmeijer J, Sterl A, Hazeleger W. Simulating the extreme 2013/2014 winter in a future climate. Journal of Geophysical Research: Atmospheres, 2016; 121(10):5680–5698.
26. Jaworski P, Durante F, Härdle WK, Rychlik T (eds). Copula Theory and Its Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010.
27. Vedenov D. Application of copulas to estimation of joint crop yield distributions. Pp. 27–29 in American Agricultural Economics Association Annual Meeting, Orlando, FL, 2008.
28. Zhu Y, Ghosh SK, Goodwin BK. Modeling dependence in the design of whole-farm insurance contract: A copula-based model approach. Pp. 27–29 in Annual Meetings of the American Agricultural Economics Association, Orlando, FL, 2008.
29. Xu W, Filler G, Odening M, Okhrin O. On the systemic nature of weather risk. Agricultural Finance Review, 2010; 70(2):267–284.
30. Bokusheva R. Measuring dependence in joint distributions of yield and weather variables. Agricultural Finance Review, 2011; 71(1):120–141.
31. Okhrin O, Odening M, Xu W. Systemic weather risk and crop insurance: The case of China. Journal of Risk and Insurance, 2013; 80(2):351–372.
32. Larsen R, Mjelde JW, Klinefelter D, Wolfley J. The use of copulas in explaining crop yield dependence structures for use in geographic diversification. Agricultural Finance Review, 2013; 73(3):469–492.
33. Goodwin BK, Hungerford A. Copula-based models of systemic risk in U.S. agriculture: Implications for crop insurance and reinsurance contracts. American Journal of Agricultural Economics, 2015; 97(3):879–896.
34. Ahmed O, Serra T. Economic analysis of the introduction of agricultural revenue insurance contracts in Spain using statistical copulas. Agricultural Economics, 2015; 46(1):69–79.
35. FAO. Statistical Yearbook 2013: World Food and Agriculture, Vol. 289. Rome: Food and Agriculture Organization of the United Nations, 2013.
36. Harvest Choice. Crop Production: SPAM, 2014.
37. FAO. Statistical database, 2015.
38. Ministerio de Agricultura, Ganaderia y Pesca de Argentina. Statistical database, 2015.
39. USDA/NOAA. Joint agricultural weather facility, 2015.
40. USDA. Economics, Statistics and Market Information System, 2015.
41. United States Department of Agriculture. National Agricultural Statistics Service, 2015.
42. Li S, Wheeler T, Challinor A, Lin E, Ju H, Xu Y. The observed relationships between wheat and climate in China. Agricultural and Forest Meteorology, 2010; 150(11):1412–1419.
43. National Bureau of Statistics of China. China Statistical Yearbook 2014, 2015.
44. Ministry of Agriculture and Farmers Welfare, Govt. of India. Crop Production Statistics, 2015.
45. Australian Bureau of Statistics. Historical Selected Agriculture Commodities, 2015.
46. Sklar M. Fonctions de Répartition à N Dimensions et Leurs Marges. Université Paris 8, 1959.
47. Khedun CP, Chowdhary H, Giardino JR, Mishra AK, Singh VP. Analysis of drought severity and duration based on runoff derived from the NOAH land surface model, 2011.
48. Aas K. Statistical modelling of financial risk, 2007.
49. Liu X, Xu W, Odening M. Can crop yield risk be globally diversified? 2011.
50. Savu C, Trede M. Hierarchies of Archimedean copulas. Quantitative Finance, 2010; 10(3):295–304.
51. Aas K, Czado C, Frigessi A, Bakken H. Pair-copula constructions of multiple dependence. Insurance: Mathematics and Economics, 2009; 44(2):182–198.
52. Brechmann EC, Schepsmeier U. Modeling dependence with C-and D-vine copulas: The R-package CDVine. Journal of Statistical Software, 2013; 52(3):1–27.
53. Czado C, Brechmann EC, Gruber L. Selection of vine copulas. Pp. 17–37 in Jaworski P, Durante F, Härdle WK (eds). Copulae in Mathematical and Quantitative Finance. Berlin, Heidelberg: Springer, 2013.
54. Timonina A, Hochrainer-Stigler S, Pflug G, Jongman B, Rojas R. Structured coupling of probability loss distributions: Assessing joint flood risk in multiple river basins. Risk Analysis, 2015; 35(11):2102–2119.
55. Genest C, Rivest L-P. Statistical inference procedures for bivariate Archimedean copulas. Journal of the American Statistical Association, 1993; 88(423):1034–1043.
56. Clarke KA. A simple distribution-free test for nonnested model selection. Political Analysis, 2007; 15(3):347–363.
57. Vuong QH. Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica: Journal of the Econometric Society, 1989; 57:307–333.
58. Akaike H. Information theory and an extension of the maximum likelihood principle. Pp. 267–281 in Petran BN, Csáki F (eds.). Proceedings of the International Symposium on Information Theory, Akadémiai Kiadi, Budapest, Hungary, 1973.
59. Schwarz G. Estimating the dimension of a model. Annals of Statistics, 1978; 6(2):461–464.
60. Bedford T, Cooke RM. Vines: A new graphical model for dependent random variables. Annals of Statistics, 2002; 30:1031–1068.
61. Kurowicka D, Cooke RM. Uncertainty Analysis with High Dimensional Dependence Modelling. Chichester, UK: John Wiley & Sons, 2006.
62. Dißmann J, Brechmann EC, Czado C, Kurowicka D. Selecting and estimating regular vine copulae and application to financial returns. Computational Statistics & Data Analysis, 2013; 59:52–69.
63. Akaike H. Information theory and an extension of the likelihood ratio principle. Pp. 267–281 in Petrov BN, Csaki F(eds). Proceedings of the Second International Symposium of Information Theory, Vol. 257, 1973.
64. FAOSTAT, 2015.
65. Ben-Ari T, Adrian J, Klein T, Calanca P, Van der Velde M, Makowski D. Identifying indicators for extreme wheat and maize yield losses. Agricultural and Forest Meteorology, 2016; 220:130–140.
66. Miranda MJ, Glauber JW. Systemic risk, reinsurance, and the failure of crop insurance markets. American Journal of Agricultural Economics, 1997; 79(1):206–215.
67. Quiggin J. The optimal design of crop insurance. Pp. 115–134 in Hueth DL, Furtan WH (eds). Economics of Agricultural Crop Insurance: Theory and Evidence. The Netherlands: Springer, 1994.
68. Linnerooth-Bayer J, Hochrainer-Stigler S. Financial instruments for disaster risk management and climate change adaptation. Climatic Change, 2014; 1(133):85–100.
69. World Bank. The Caribbean Catastrophe Risk Insurance Initiative; Results of Preparation Work on the Design of a Caribbean Catastrophe Risk Insurance Facility. Washington, DC:World Bank, 2007.
70. Lobell DB, Field CB. Global scale climate–crop yield relationships and the impacts of recent warming. Environmental Research Letters, 2007; 2(1):014002.
71. Williams JR. The EPIC model. In Singh VP (ed). Computer Models in Watershed Hydrology. Highlands Ranch, CO: Water Resources Publication, 1995.
72. Bondeau A, Smith PC, Zaehle S, Schaphoff S, Lucht W, Cramer W, Gerten D, Lotze-Campen H, Müller C, Reichstein M, Smith B. Modelling the role of agriculture for the 20th century global terrestrial carbon balance. Global Change Biology, 2007; 13(3):679–706.
73. Sitch S, Smith B, Prentice IC, Arneth A, Bondeau A, Cramer W, Kaplan JO, Levis S, Lucht W, Sykes MT, Thonicke K, Venevsky S. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Global Change Biology, 2003; 9(2):161–185.
74. Tao F, Yokozawa M, Xu Y, Hayashi Y, Zhang Z. Climate changes and trends in phenology and yields of field crops in China, 1981–2000. Agricultural and Forest Meteorology, 2006; 138(1):82–92.
75. Teixeira EI, Fischer G, van Velthuizen H, Walter C, Ewert F. Global hot-spots of heat stress on agricultural crops due to climate change. Agricultural and Forest Meteorology, 2013; 170:206–215.
76. Wang J, Mendelsohn R, Dinar A, Huang J, Rozelle S, Zhang L. The impact of climate change on China's agriculture. Agricultural Economics, 2009; 40(3):323–337.
77. Mechler R, Bouwer LM, Linnerooth-Bayer J, Hochrainer-Stigler S, Aerts JC, Surminski S, Williges K. Managing unnatural disaster risk from climate extremes. Nature Climate Change, 2014; 4(4):235–237.
78. Borgomeo E, Pflug G, Hall JW, Hochrainer-Stigler S. Assessing water resource system vulnerability to unprecedented hydrological drought using copulas to characterize drought duration and deficit. Water Resources Research, 2015; 51(11):8927–8948.
79. L'Heureux ML, Collins DC, Hu Z-Z. Linear trends in sea surface temperature of the tropical Pacific Ocean and implications for the El Niño-Southern Oscillation. Climate Dynamics, 2013; 40(5–6):1223–1236.
80. Ashok K, Behera SK, Rao SA, Weng H, Yamagata T. El Niño Modoki and its possible teleconnection. Journal of Geophysical Research: Oceans, 2007; 112(C11):1–27.
81. Iizumi T, Luo J-J, Challinor AJ, Sakurai G, Yokozawa M, Sakuma H, Brown ME, Yamagata T. Impacts of El Niño Southern Oscillation on the global yields of major crops. Nature Communications, 2014; 5(3712):1–7.
82. Centeno MA, Nag M, Patterson TS, Shaver A, Windawi AJ. The emergence of global systemic risk. Annual Review of Sociology, 2015; 41:65–85.