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Changing risks of simultaneous global breadbasket failure

Abstract

The risk of extreme climatic conditions leading to unusually low global agricultural production is exacerbated if more than one global ‘breadbasket’ is exposed at the same time. Such shocks can pose a risk to the global food system, amplifying threats to food security, and could potentially trigger other systemic risks1,2. While the possibility of climatic extremes hitting more than one breadbasket has been postulated3,4, little is known about the actual risk. Here we combine region-specific data on agricultural production with spatial statistics of climatic extremes to quantify the changing risk of low production for the major food-producing regions (breadbaskets) over time. We show an increasing risk of simultaneous failure of wheat, maize and soybean crops across the breadbaskets analysed. For rice, risks of simultaneous adverse climate conditions have decreased in the recent past, mostly owing to solar radiation changes favouring rice growth. Depending on the correlation structure between the breadbaskets, spatial dependence between climatic extremes globally can mitigate or aggravate the risks for the global food production. Our analysis can provide the basis for more efficient allocation of resources to contingency plans and/or strategic crop reserves that would enhance the resilience of the global food system.

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Fig. 1: Likelihood of simultaneous climate risks in the nine most important soybean-producing provinces in China threatening agricultural production.
Fig. 2: Likelihood of climatic conditions simultaneously threatening crop losses in the global breadbaskets.

Data availability

Crop yield data are taken from official governmental databases: data from Argentina can be accessed at http://www.siia.gov.ar/; from Australia at https://www.abs.gov.au; from Brazil at http://www.conab.gov.br; from China at http://data.stats.gov.cn/; from Europe and the Ukraine at http://www.fao.org/faostat/en; from India at http://eands.dacnet.nic.in/; from Indonesia at https://www.bps.go.id/linkTableDinamis/view/id/865; from Russia at http://cbsd.gks.ru/ and from the United States at http://quickstats.nass.usda.gov. Climate re-analysis data used in our analysis can be accessed at http://hydrology.princeton.edu/data/pgf/.

Code availability

The R script written to read and analyse data and generate figures can be accessed at https://github.com/FranziskaGaupp/Simultaneous_BB_failure

References

  1. 1.

    Johnstone, S. & Mazo, J. Global warming and the Arab Spring. Survival 53, 11–17 (2011).

    Google Scholar 

  2. 2.

    Von Braun, J. & Tadesse, G. Global Food Price Volatility and Spikes: An Overview of Costs, Causes, and Colutions Discussion Papers on Development Policy No. 161 (ZEF, 2012).

  3. 3.

    Schaffnit-Chatterjee, C., Schneider, S., Peter, M. & Mayer, T. Risk Management in Agriculture (Deutsche Bank Research, 2010).

  4. 4.

    UK–US Taskforce on Extreme Weather and Global Food System Resilience Extreme Weather and Resilience of the Global Food System Final Project Report (The Global Food Security Programme, 2015).

  5. 5.

    Lobell, D. B. & Field, C. B. Global scale climate–crop yield relationships and the impacts of recent warming. Environ. Res. Lett. 2, 014002 (2007).

    Google Scholar 

  6. 6.

    Bren d’Amour, C., Wenz, L., Kalkuhl, M., Christoph Steckel, J. & Creutzig, F. Teleconnected food supply shocks. Environ. Res. Lett. 11, 035007 (2016).

    Google Scholar 

  7. 7.

    Fraser, E. D. G., Simelton, E., Termansen, M., Gosling, S. N. & South, A. “Vulnerability hotspots”: integrating socio-economic and hydrological models to identify where cereal production may decline in the future due to climate change induced drought. Agric. Meteorol. 170, 195–205 (2013).

    Google Scholar 

  8. 8.

    Puma, M. J., Bose, S., Chon, S. Y. & Cook, B. I. Assessing the evolving fragility of the global food system. Environ. Res. Lett. 10, 024007 (2015).

    Article  Google Scholar 

  9. 9.

    Von Braun, J. The food crisis isn’t over. Nature 456, 701–701 (2008).

    Google Scholar 

  10. 10.

    Maxwell, D. & Fitzpatrick, M. The 2011 Somalia famine: context, causes, and complications. Glob. Food Secur. 1, 5–12 (2012).

    Google Scholar 

  11. 11.

    Ratnam, J. V., Behera, S. K., Ratna, S. B., Rajeevan, M. & Yamagata, T. Anatomy of Indian heatwaves. Sci. Rep. 6, 24395 (2016).

    CAS  Google Scholar 

  12. 12.

    Ward, P. J. et al. Strong influence of El Niño Southern Oscillation on flood risk around the world. Proc. Natl Acad. Sci. USA 111, 15659–15664 (2014).

    CAS  Google Scholar 

  13. 13.

    Anderson, W. B., Seager, R., Baethgen, W., Cane, M. & You, L. Synchronous crop failures and climate-forced production variability. Sci. Adv. 5, eaaw1976 (2019).

    CAS  Google Scholar 

  14. 14.

    Tigchelaar, M., Battisti, D. S., Naylor, R. L. & Ray, D. K. Future warming increases probability of globally synchronized maize production shocks. Proc. Natl Acad. Sci. USA 115, 6644–6649 (2018).

    Google Scholar 

  15. 15.

    Mehrabi, Z. & Ramankutty, N. Synchronized failure of global crop production. Nat. Ecol. Evol. 3, 780–786 (2019).

    Google Scholar 

  16. 16.

    Gaupp, F., Pflug, G., Hochrainer-Stigler, S., Hall, J. & Dadson, S. Dependency of crop production between global breadbaskets: a copula approach for the assessment of global and regional risk pools. Risk Anal. 37, 2212–2228 (2016).

    Google Scholar 

  17. 17.

    Sarhadi, A., Ausín, M. C., Wiper, M. P., Touma, D. & Diffenbaugh, N. S. Multidimensional risk in a nonstationary climate: joint probability of increasingly severe warm and dry conditions. Sci. Adv. 4, eaau3487 (2018).

    Google Scholar 

  18. 18.

    Zheng, H. F. et al. Phosphorus control as an effective strategy to adapt soybean to drought at the reproductive stage: evidence from field experiments across northeast China. Soil Use Manag. 31, 19–28 (2015).

    CAS  Google Scholar 

  19. 19.

    Yin, X. G., Olesen, J. E., Wang, M., ÖztürkI. & Chen, F. Climate effects on crop yields in the northeast farming region of China during 1961–2010. J. Agric. Sci. 154, 1190–1208 (2016).

    Google Scholar 

  20. 20.

    Lobell, D. B., Schlenker, W. & Costa-Roberts, J. Climate trends and global crop production since 1980. Science 333, 616–620 (2011).

    CAS  Google Scholar 

  21. 21.

    Duncan, J. M. A., Dash, J. & Tompkins, E. L. Observing adaptive capacity in Indian rice production systems. AIMS Agric. Food 2, 165–182 (2017).

    Google Scholar 

  22. 22.

    Fishman, R. M. Climate Change, Rainfall Variability, and Adaptation through Irrigation: Evidence from Indian Agriculture (Columbia Univ., 2011).

  23. 23.

    Tao, F., Yokozawa, M., Liu, J. & Zhang, Z. Climate–crop yield relationships at provincial scales in China and the impacts of recent climate trends. Clim. Res. 38, 83–94 (2008).

    Google Scholar 

  24. 24.

    Wassmann, R. et al. Regional vulnerability of climate change impacts on Asian rice production and scope for adaptation. Adv. Agron. 102, 91–133 (2009).

    Google Scholar 

  25. 25.

    Zhang, T., Zhu, J. & Wassmann, R. Responses of rice yields to recent climate change in China: an empirical assessment based on long-term observations at different spatial scales (1981–2005). Agric. Meteorol. 150, 1128–1137 (2010).

    Google Scholar 

  26. 26.

    Chatham House Resource Trade Database (CHRTD) (Chatham House, 2017); https://resourcetrade.earth/data?year=2012&units=value

  27. 27.

    Rosenzweig, C. et al. Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc. Natl Acad. Sci. USA 111, 3268–3273 (2014).

    CAS  Google Scholar 

  28. 28.

    Stehfest, E., Heistermann, M., Priess, J. A., Ojima, D. S. & Alcamo, J. Simulation of global crop production with the ecosystem model DayCent. Ecol. Model. 209, 203–219 (2007).

    Google Scholar 

  29. 29.

    Auffhammer, M., Ramanathan, V. & Vincent, J. R. Climate change, the monsoon, and rice yield in India. Climatic Change 111, 411–424 (2012).

    Google Scholar 

  30. 30.

    Masutomi, Y., Takahashi, K., Harasawa, H. & Matsuoka, Y. Impact assessment of climate change on rice production in Asia in comprehensive consideration of process/parameter uncertainty in general circulation models. Agric. Ecosyst. Environ. 131, 281–291 (2009).

    Google Scholar 

  31. 31.

    Avnery, S., Mauzerall, D. L., Liu, J. & Horowitz, L. W. Global crop yield reductions due to surface ozone exposure: 1. Year 2000 crop production losses and economic damage. Atmos. Environ. 45, 2284–2296 (2011).

    CAS  Google Scholar 

  32. 32.

    Kersebaum, K. C. & Nendel, C. Site-specific impacts of climate change on wheat production across regions of Germany using different CO2 response functions. Eur. J. Agron. 52, 22–32 (2014).

    CAS  Google Scholar 

  33. 33.

    Statistical Database (Ministerio de Agricultura, Ganaderia y Pesca de Argentina, 2015); http://www.siia.gov.ar

  34. 34.

    Crop Production Statistics (Ministry of Agriculture and Farmers Welfare, 2015); http://eands.dacnet.nic.in/

  35. 35.

    Economics, Statistics and Market Information System (USDA, 2015); http://quickstats.nass.usda.gov

  36. 36.

    Regional Data (National Bureau of Statistics of China); http://data.stats.gov.cn/

  37. 37.

    Séries Históricas (Companhia Nacional de Abastecimento Brazil, 2015); http://www.conab.gov.br

  38. 38.

    Historical Selected Agriculture Commodities (Australian Bureau of Statistics, 2015); https://www.abs.gov.au

  39. 39.

    Statistical Database (Russian Federal State Statistics Service, 2018); http://cbsd.gks.ru/

  40. 40.

    Statistical Database (Statistics Indonesia, 2018); https://www.bps.go.id/linkTableDinamis/view/id/865

  41. 41.

    FAOSTAT (FAO, 2015); http://www.fao.org/faostat/en

  42. 42.

    Sheffield, J., Goteti, G. & Wood, E. F. Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling. J. Clim. 19, 3088–3111 (2006).

    Google Scholar 

  43. 43.

    Sheffield, J., Wood, E. F. & Roderick, M. L. Little change in global drought over the past 60 years. Nature 491, 435–438 (2012).

    Google Scholar 

  44. 44.

    Auffhammer, M., Ramanathan, V. & Vincent, J. R. Integrated model shows that atmospheric brown clouds and greenhouse gases have reduced rice harvests in India. Proc. Natl Acad. Sci. USA 103, 19668–19672 (2006).

    CAS  Google Scholar 

  45. 45.

    Ray, D. K., Gerber, J. S., MacDonald, G. K. & West, P. C. Climate variation explains a third of global crop yield variability. Nat. Commun. 6, 5989 (2015).

    CAS  Google Scholar 

  46. 46.

    Osborne, T. M. & Wheeler, T. R. Evidence for a climate signal in trends of global crop yield variability over the past 50 years. Environ. Res. Lett. 8, 024001 (2013).

    Google Scholar 

  47. 47.

    Schlenker, W. & Roberts, M. J. Nonlinear temperature effects indicate severe damages to US crop yields under climate change. Proc. Natl Acad. Sci. USA 106, 15594–15598 (2009).

    CAS  Google Scholar 

  48. 48.

    McKee, T. B., Doesken, N. J. & Kleist, J. The relationship of drought frequency and duration to time scales. In Proc. 8th Conference on Applied Climatology 179–184 (American Meteorological Society, 1993).

  49. 49.

    Mueller, N. D. et al. Cooling of US Midwest summer temperature extremes from cropland intensification. Nat. Clim. Change 6, 317–322 (2016).

    Google Scholar 

  50. 50.

    Mueller, N. D. et al. Global relationships between cropland intensification and summer temperature extremes over the last 50 years. J. Clim. 30, 7505–7528 (2017).

    Google Scholar 

  51. 51.

    Lobell, D. B. & Asseng, S. Comparing estimates of climate change impacts from process-based and statistical crop models. Environ. Res. Lett. 12, 015001 (2017).

    Google Scholar 

  52. 52.

    Luo, Q. Temperature thresholds and crop production: a review. Climatic Change 109, 583–598 (2011).

    Google Scholar 

  53. 53.

    Doorenbos, J. & Kassam, A. H. Yield Response to Water Irrigation and Drainage Paper No. 33 (FAO, 1979).

  54. 54.

    Shepherd, T. G. Atmospheric circulation as a source of uncertainty in climate change projections. Nat. Geosci. 7, 703–708 (2014).

    CAS  Google Scholar 

  55. 55.

    Welch, J. R. et al. Rice yields in tropical/subtropical Asia exhibit large but opposing sensitivities to minimum and maximum temperatures. Proc. Natl Acad. Sci. USA 107, 14562–14567 (2010).

    CAS  Google Scholar 

  56. 56.

    Burton, I. et al. 2009 UNISDR Terminology on Disaster Risk Reduction (UNISDR, 2015).

  57. 57.

    IPCC Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special Report (eds Field, C. B. et al.) (Cambridge Univ. Press, 2012).

  58. 58.

    Richter, G. M. & Semenov, M. A. Modelling impacts of climate change on wheat yields in England and Wales: assessing drought risks. Agric. Syst. 84, 77–97 (2005).

    Google Scholar 

  59. 59.

    Sklar, M. Fonctions de répartition à n dimensions et leurs marges (Université Paris, 1959).

  60. 60.

    Aas, K., Czado, C., Frigessi, A. & Bakken, H. Pair-copula constructions of multiple dependence. Insur. Math. Econ. 44, 182–198 (2009).

    Google Scholar 

  61. 61.

    Kurowicka, D. & Cooke, R. M. Uncertainty Analysis with High Dimensional Dependence Modelling (Wiley, 2006).

  62. 62.

    Bedford, T. & Cooke, R. M. Vines: a new graphical model for dependent random variables. Ann. Stat. 30, 1031–1068 (2002).

    Google Scholar 

  63. 63.

    Dißmann, J., Brechmann, E. C., Czado, C. & Kurowicka, D. Selecting and estimating regular vine copulae and application to financial returns. Comput. Stat. Data Anal. 59, 52–69 (2013).

    Google Scholar 

  64. 64.

    Akaike, H. Information theory and an extension of the maximum likelihood principle. In Proc. 2nd International Symposium on Information Theory 2nd edn (eds Petrov, B. N. & Csáki, F.) 267–281 (Akadémiai Kiadi, 1973).

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Acknowledgements

This research was supported by the International Institute for Applied Systems Analysis and the ECOCEP project, funded by the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7-PEOPLE-2013-IRSES, grant agreement no. 609642. Part of the research by S.H.-S. received funding from the Austrian Climate Research Program (KR15AC8K12597).

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The analysis was conceived by F.G., J.H. and S.H.-S. and conducted by F.G. All authors contributed to writing the paper.

Corresponding author

Correspondence to Franziska Gaupp.

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Peer review information Nature Climate Change thanks Christopher Bren d’Amour and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–8, Table 1 and references.

Supplementary Data 1

Calculations of expected crop losses in case of a breadbasket failure.

Supplementary Data 2

Pearson correlation coefficient (r) between Princeton re-analysis climatological data and detrended, observed historical subnational crop yield data.

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Gaupp, F., Hall, J., Hochrainer-Stigler, S. et al. Changing risks of simultaneous global breadbasket failure. Nat. Clim. Chang. 10, 54–57 (2020). https://doi.org/10.1038/s41558-019-0600-z

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