Beer is the most popular alcoholic beverage in the world by volume consumed, and yields of its main ingredient, barley, decline sharply in periods of extreme drought and heat. Although the frequency and severity of drought and heat extremes increase substantially in range of future climate scenarios by five Earth System Models, the vulnerability of beer supply to such extremes has never been assessed. We couple a process-based crop model (decision support system for agrotechnology transfer) and a global economic model (Global Trade Analysis Project model) to evaluate the effects of concurrent drought and heat extremes projected under a range of future climate scenarios. We find that these extreme events may cause substantial decreases in barley yields worldwide. Average yield losses range from 3% to 17% depending on the severity of the conditions. Decreases in the global supply of barley lead to proportionally larger decreases in barley used to make beer and ultimately result in dramatic regional decreases in beer consumption (for example, −32% in Argentina) and increases in beer prices (for example, +193% in Ireland). Although not the most concerning impact of future climate change, climate-related weather extremes may threaten the availability and economic accessibility of beer.

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Data availability

The historical weather data (1981–2010) that support the analysis with ESMs in this study are publicly available online at https://data.giss.nasa.gov/impacts/agmipcf/; the future climate scenario data (2010–2099) that support the analysis with ESMs in this study are publicly available online at https://pcmdi.llnl.gov/?cmip5 and https://esgf-node.llnl.gov/projects/esgf-llnl/. The spatial data of harvest area, yield, crop calendar, irrigation portion and chemical N input for barley that support the simulation with crop model (DSSAT) in this study are publicly available at http://mapspam.info/ (SPAM) and http://www.sage.wisc.edu (SAGE); the soil data that support the simulation with crop model (DSSAT) in this study are publicly available from the WISE database (https://www.isric.online/index.php/) and the Digital Soil Map of the World (DSMW) (http://www.fao.org/land-water/land/land-governance/land-resources-planning-toolbox/category/details/en/c/1026564/). The data and parameters that support the economic model in this study are available from the GTAP 9 database (https://www.gtap.agecon.purdue.edu/databases/v9/default.asp), which was used under license for the current study. Data are available with permission from the GTAP Center. The other data that support splitting barley and beer from the original database GTAP 9 are publicly available at FAOSTAT (http://www.fao.org/faostat/en/#data) and from the UN Comtrade Database (https://comtrade.un.org/data). All other relevant data are available from the corresponding authors.

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  1. 1.

    Gandhi, V. P. & Zhou, Z. Y. Food demand and the food security challenge with rapid economic growth in the emerging economies of India and China. Food Res. Int. 63, 108–124 (2014).

  2. 2.

    Tilman, D. & Clark, M. Global diets link environmental sustainability and human health. Nature 515, 518–522 (2014).

  3. 3.

    Monteiro, C. A., Moubarac, J. C., Cannon, G., Ng, S. W. & Popkin, B. Ultra-processed products are becoming dominant in the global food system. Obes. Rev. 14, 21–28 (2013).

  4. 4.

    Colen, L. & Swinnen, J. Economic growth, globalisation and beer consumption. J. Agricult. Econ. 67, 186–207 (2016).

  5. 5.

    Mueller, N. D. et al. Closing yield gaps through nutrient and water management. Nature 490, 254–257 (2012).

  6. 6.

    Stuckler, D., McKee, M., Ebrahim, S. & Basu, S. Manufacturing epidemics: the role of global producers in increased consumption of unhealthy commodities including processed foods, alcohol, and tobacco. PLoS. Med. 9, e1001235 (2012).

  7. 7.

    Valin, H. et al. The future of food demand: understanding differences in global economic models. Agr. Econ.-Blackwell 45, 51–67 (2014).

  8. 8.

    Wheeler, T. & von Braun, J. Climate change impacts on global food security. Science 341, 508–513 (2013).

  9. 9.

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

  10. 10.

    Schmidhuber, J. & Tubiello, F. N. Global food security under climate change. Proc. Natl Acad. Sci. USA 104, 19703–19708 (2007).

  11. 11.

    Dawson, T. P., Perryman, A. H. & Osborne, T. M. Modelling impacts of climate change on global food security. Climatic Change 134, 429–440 (2016).

  12. 12.

    Schlenker, W. & Lobell, D. B. Robust negative impacts of climate change on African agriculture. Environ. Res. Lett. 5, 014010 (2010).

  13. 13.

    Asseng, S. et al. Uncertainty in simulating wheat yields under climate change. Nat. Clim. Change 3, 827–832 (2013).

  14. 14.

    Rosenzweig, C. et al. The Agricultural Model Intercomparison and Improvement Project (AgMIP): protocols and pilot studies. Agr. Forest. Meteorol. 170, 166–182 (2013).

  15. 15.

    Ruane, A. C. et al. Climate change impact uncertainties for maize in Panama: farm information, climate projections, and yield sensitivities. Agr. Forest. Meteorol. 170, 132–145 (2013).

  16. 16.

    Bassu, S. et al. How do various maize crop models vary in their responses to climate change factors? Glob. Change Biol. 20, 2301–2320 (2014).

  17. 17.

    Kucharik, C. J. & Serbin, S. P. Impacts of recent climate change on Wisconsin corn and soybean yield trends. Environ. Res. Lett. 3, 034003 (2008).

  18. 18.

    Sakurai, G., Iizumi, T. & Yokozawa, M. Varying temporal and spatial effects of climate on maize and soybean affect yield prediction. Clim. Res. 49, 143–154 (2011).

  19. 19.

    Sanchez, B., Rasmussen, A. & Porter, J. R. Temperatures and the growth and development of maize and rice: a review. Glob. Change Biol. 20, 408–417 (2014).

  20. 20.

    Krishnan, P., Swain, D. K., Bhaskar, B. C., Nayak, S. K. & Dash, R. N. Impact of elevated CO2 and temperature on rice yield and methods of adaptation as evaluated by crop simulation studies. Agr. Ecosyst. Environ. 122, 233–242 (2007).

  21. 21.

    Hannah, L. et al. Climate change, wine, and conservation. Proc. Natl Acad. Sci. USA 110, 6907–6912 (2013).

  22. 22.

    van Leeuwen, C. & Darriet, P. The impact of climate change on viticulture and wine quality. J. Wine Econ. 11, 150–167 (2016).

  23. 23.

    Davis, A. P., Gole, T. W., Baena, S. & Moat, J. The impact of climate change on indigenous Arabica coffee (Coffea arabica): predicting future trends and identifying priorities. PLoS ONE 7, e47981 (2012).

  24. 24.

    Lobell, D. B. et al. The critical role of extreme heat for maize production in the United States. Nat. Clim. Change 3, 497–501 (2013).

  25. 25.

    Lesk, C., Rowhani, P. & Ramankutty, N. Influence of extreme weather disasters on global crop production. Nature 529, 84–87 (2016).

  26. 26.

    Division, F. I. C. Agribusiness Handbook: Barley, Malt, Beer (FAO, Rome, 2009).

  27. 27.

    Hawkins, E. & Sutton, R. The potential to narrow uncertainty in regional climate predictions. Bull. Am. Meteorol. Soc. 90, 1095–1107 (2009).

  28. 28.

    Nelson, G. C. et al. Climate change effects on agriculture: economic responses to biophysical shocks. Proc. Natl Acad. Sci. USA 111, 3274–3279 (2014).

  29. 29.

    Iglesias, A., Garrote, L., Quiroga, S. & Moneo, M. A regional comparison of the effects of climate change on agricultural crops in Europe. Climatic Change 112, 29–46 (2012).

  30. 30.

    Lobell, D. B. et al. Climate change adaptation in crop production: beware of illusions. Global Food Secur. 3, 72–76 (2014).

  31. 31.

    Liu, B. et al. Testing the responses of four wheat crop models to heat stress at anthesis and grain filling. Glob. Change Biol. 22, 1890–1903 (2016).

  32. 32.

    Nacke, S., Ritchie, J. T., Godwin, D. W., Singh, U. & Otter, S. A User’s Guide to CERES Barley-V2.10 (International Fertilizer Development Centre, Muscle Shoals, 1991).

  33. 33.

    Elad, Y. & Pertot, I. Climate change impacts on plant pathogens and plant diseases. J. Crop Improve. 28, 99–139 (2014).

  34. 34.

    Trnka, M., Dubrovsky, M. & Zalud, Z. Climate change impacts and adaptation strategies in spring barley production in the Czech Republic. Climatic Change 64, 227–255 (2004).

  35. 35.

    Hlavinka, P. et al. The performance of CERES-Barley and CERES-Wheat under various soil conditions and tillage practices in Central Europe. Die Bodenkultur 61, 5–16 (2010).

  36. 36.

    Holden, N. M., Brereton, A. J., Fealy, R. & Sweeney, J. Possible change in Irish climate and its impact on barley and potato yields. Agri. Forest. Meteorol. 116, 181–196 (2003).

  37. 37.

    Fatemi, R. Z., Paknejad, F., Amiri, E., Nabi, I. M. & Mehdi, M. S. Investigation of barley productivity responses to different water consumption by using the CERES-Barley model. J. Biol. Environ. Sci. 9, 119–126 (2015).

  38. 38.

    Travasso, M. I. & Magrin, G. O. Utility of CERES-Barley under Argentine condition. Field Crops Res. 57, 329–333 (1998).

  39. 39.

    Rotter, R. P. et al. Simulation of spring barley yield in different climatic zones of Northern and Central Europe: a comparison of nine crop models. Field Crops Res. 133, 23–26 (2012).

  40. 40.

    Ciscar, J. C. et al. Physical and economic consequences of climate change in Europe. Proc. Natl Acad. Sci. USA 108, 2678–2683 (2011).

  41. 41.

    Hsiang, S. et al. Estimating economic damage from climate change in the United States. Science 356, 1362–1369 (2017).

  42. 42.

    Swinnen, J. The Economics of Beer (Oxford Univ. Press, Oxford, 2011).

  43. 43.

    van Vuuren, D. P., Kok, M. T. J., Girod, B., Lucas, P. L. & de Vries, B. Scenarios in global environmental assessments: key characteristics and lessons for future use. Glob. Environ. Change 22, 884–895 (2012).

  44. 44.

    Kriegler, E. et al. The need for and use of socio-economic scenarios for climate change analysis: A new approach based on shared socio-economic pathways. Global Environ. Change 22, 807–822 (2012).

  45. 45.

    Eßlinger, H. M. Handbook of Brewing: Processes, Technology, Markets (Wiley, Weinheim, 2009).

  46. 46.

    Hayden, B., Canuel, N. & Shanse, J. What was brewing in the Natufian? An archaeological assessment of brewing technology in the Epipaleolithic. J. Archaeol. Method Theory 20, 102–150 (2012).

  47. 47.

    Wei, Y. M. et al. An integrated assessment of INDCs under shared socioeconomic pathways: an implementation of C3IAM. Nat. Hazards 92, 585–618 (2018).

  48. 48.

    Ruane, A. C., Goldberg, R. & Chryssanthacopoulos, J. Climate forcing datasets for agricultural modeling: Merged products for gap-filling and historical climate series estimation. Agr. Forest. Meteorol. 200, 233–248 (2015).

  49. 49.

    Hempel, S., Frieler, K., Warszawski, L., Schewe, J. & Piontek, F. A trend-preserving bias correction – the ISI-MIP approach. Earth Syst. Dynam. 4, 219–236 (2013).

  50. 50.

    Sacks, W. J., Deryng, D., Foley, J. A. & Ramankutty, N. Crop planting dates: an analysis of global patterns. Glob. Ecol. Biogeogr. 19, 607–620 (2010).

  51. 51.

    Monfreda, C., Ramankutty, N. & Foley, J. A. Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Glob. Biogeochem. Cycles 22, GB1022 (2008).

  52. 52.

    You, L. et al. Spatial Production Allocation Model (SPAM) 2000 version 3.2 (2009); http://mapspam.info

  53. 53.

    McKee, T. B., Doesken, N. J. & Kleist, J. in Eighth Conf. on Applied Climatology. 179–186 (American Meteorological Society, Anaheim, 1993).

  54. 54.

    Sakata, T., Takahashi, H. & Nishiyama, I. Effects of high temperature on the development of pollen mother cells and microspores in barley Hordeum vulgare L. J. Plant. Res. 113, 395–402 (2000).

  55. 55.

    Abiko, M. et al. High-temperature induction of male sterility during barley (Hordeum vulgare L.) anther development is mediated by transcriptional inhibition. Sex. Plant. Reprod. 18, 91–100 (2005).

  56. 56.

    Oshino, T. et al. Premature progression of anther early developmental programs accompanied by comprehensive alterations in transcription during high-temperature injury in barley plants. Mol. Genet. Genom. 278, 31–42 (2007).

  57. 57.

    Guttman, N. B. Accepting the standardized precipitation index: a calculation algorithm. J. Am. Water Res. Assoc. 35, 311–322 (1999).

  58. 58.

    Hoogenboom, G. et al. Decision Support System for Agrotechnology Transfer (DSSAT) Version 4.6 (DSSAT Foundation, Prosser, Washington, 2015); http://dssat.net

  59. 59.

    Batjes, H. N. A Homogenized Soil Data File for Global Environmental Research: A Subset of FAO. ISRIC and NRCS Profiles (Version 1.0). Working Paper and Preprint 95/10b, (International Soil Reference and Information Centre, Wageningen, 1995).

  60. 60.

    FAO. Digital Soil Map of the World And Derived Soil Properties. Derived from the FAO/UNESCO Soil Map of the World (FAO, Rome, 1996).

  61. 61.

    Schaap, M. G. & Bouten, W. Modeling water retention curves of sandy soils using neural networks. Water Resour. Res. 32, 3033–3040 (1996).

  62. 62.

    Boogaart, H. L. et al. User’s Guide for the WOFOST 7.1 Crop Growth Simulation Model and WOFOST Control Center 1.5 (DLO Winand Staring Centre for Integrated Land, Soil and Water Research (SC-DLO), Wageningen, 1998).

  63. 63.

    Elliott, J. et al. Constraints and potentials of future irrigation water availability on agricultural production under climate change. Proc. Natl Acad. Sci. USA 111, 3239–3244 (2014).

  64. 64.

    Elliott, J. et al. The Global Gridded Crop Model intercomparison: data and modeling protocols for Phase I (v1.0). Geosci. Model Dev. 2, 261–277 (2015).

  65. 65.

    Xiong, W. et al. Can climate-smart agriculture reverse the recent slowing of rice yield growth in China? Agric. Ecosyst. Environ. 196, 125–136 (2014).

  66. 66.

    Hertel, T. W. Global Trade Analysis: Modeling and Applications (Cambridge Univ. Press, New York, 1997).

  67. 67.

    Corong, E. L., Hertel, T. W., McDougall, R., Tsigas, M. E. & van der Mensbrugghe, D. The Standard GTAP Model, Version 7. J. Glob. Econ. Anal. 2, 1–119 (2017).

  68. 68.

    Horridge, M. SplitCom (Victoria University, Melbourne, 2005); http://www.copsmodels.com/splitcom.html

  69. 69.

    FAOSTAT (FAO, 2017); http://www.fao.org/faostat/en/#data

  70. 70.

    DESA/UNSD (Comtrade, 2016); https://comtrade.un.org/data

  71. 71.

    ​Nelson, J. P. Estimating the price elasticity of beer: Meta-analysis of data with heterogeneity, dependence, and publication bias. J. Health Econom. 33, 180–187 (2014).

  72. 72.

    Palatnik, R. R. & Roson, R. Climate change and agriculture in computable general equilibrium models: alternative modeling strategies and data needs. Climatic Change 112, 1085–1100 (2012).

  73. 73.

    ​Rose A. & Liao S.Y. Modeling regional economic resilience to disasters: a computable general equilibrium analysis of water service disruptions. J. Regional Sci. 45, 75–112 (2005).

  74. 74.

    ​Rose A., Oladosu G. & Liao S.Y. Business interruption impacts of a terrorist attack on the electric power system of Los Angeles: customer resilience to a total blackout. Risk Analysis 27, 513–531 (2007).

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There was equal financial support from Peking University and Chinese Academy of Agricultural Sciences (CASS) to this study. W.Xie and D.G. thank the National Key R&D Program of China (grant no. 2016YFA0602604) for financial support; W.Xie thanks the National Natural Science Foundation of China (grant nos. 71503243, 71333013 and 71873009) and Ministry of Science and Technology (grant no. 2012CB955700) for financial support; D.G. aknowledges support of the National Natural Science Foundation of China (grant no. 41629501, 71533005), Chinese Academy of Engineering (grant no. 2017-ZD-15-07), the UK Natural Environment Research Council (grant no. NE/N00714X/1 and NE/P019900/1), the Economic and Social Research Council (gant no. ES/L016028/1), a British Academy Grant (grant no. AF150310) and the Philip Leverhulme Prize. E.L., J.P. and W.Xiong thank the National Natural Science Foundation of China (grant no. 41675115, 41471074, 41171093), National Key Research and Development Program of China (grant nos. 2017YFD0300301 and 2017YFD0200106) and the Agricultural Science and Technology Innovation Program of Chinese Academy of Agricultural Sciences for financial support; S.J.D. acknowledges support of the U.S. National Science Foundation (INFEWS grant EAR 1639318). We also thank Y. He, K. Li, X. Han, Y. Li and others from the CAAS team for discussion on the method framework of this study, and Z. Zhang, Q. Deng and Y. Zhang for their assistance in producing the graphical representation of the results.

Author information


  1. China Center for Agricultural Policy, School of Advanced Agricultural Sciences, Peking University, Beijing, China

    • Wei Xie
    •  & Tariq Ali
  2. Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing, China

    • Wei Xiong
    • , Jie Pan
    •  & Erda Lin
  3. International Maize and Wheat Improvement Center, Texcoco, Mexico

    • Wei Xiong
  4. College of Agronomy, Henan Agricultural University, Zhengzhou, Henan, China

    • Wei Xiong
  5. School of Economics and Resource Management, Beijing Normal University, Beijing, China

    • Qi Cui
  6. Department of Earth System Science, Tsinghua University, Beijing, China

    • Dabo Guan
  7. School of International Development, University of East Anglia, Norwich, UK

    • Dabo Guan
  8. Department of Politics and International Studies, University of Cambridge, Cambridge, UK

    • Jing Meng
  9. Department of Earth System Science, University of California, Irvine, CA, USA

    • Nathaniel D. Mueller
    •  & Steven J. Davis
  10. Department of Civil and Environmental Engineering, University of California, Irvine, CA, USA

    • Steven J. Davis


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W.Xie coordinated the study. W.Xie, D.G. and E.L. conceived the study. J.P. and E.L. conducted the ESMs analysis. W.Xiong and E.L. conducted the crop model simulations. W.Xie, T.A. and Q.C. conducted the economic analysis. W.Xie, D.G., S.J.D. and N.D.M. interpreted the final results. W.Xie, S.J.D., W.Xiong, J.P. and D.G. wrote the paper. N.D.M., T.A., Q.C., J.M. and E.L. contributed to revising the paper.

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The authors declare no competing interests.

Corresponding authors

Correspondence to Wei Xie or Dabo Guan or Erda Lin.

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