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Decreases in global beer supply due to extreme drought and heat

Nature Plants volume 4, pages 964–973 (2018)Cite this article

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Abstract

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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Fig. 1: Extreme events severity and frequency in barley-growing regions and during the barley-growing season under future climate change.
Fig. 2: Average barley yield shocks during extreme events years.
Fig. 3: Barley consumption by country and globally under future climate change.
Fig. 4: Changes in beer consumption and price under increasingly severe drought–heat events.
Fig. 5: Beer consumption and price in recent years.

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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.

References

  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).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  Google Scholar 

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

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Google Scholar 

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

    Article  Google Scholar 

  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).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Book  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

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

  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).

    Article  Google Scholar 

  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. 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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Google Scholar 

  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).

    Article  Google Scholar 

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

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

  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).

    Article  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

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

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

    Google Scholar 

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

    Article  CAS  Google Scholar 

  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. 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).

    Article  CAS  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

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

    Google Scholar 

  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).

    Google Scholar 

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

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

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

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

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Acknowledgements

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.

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Authors and Affiliations

  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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Contributions

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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Correspondence to Wei Xie, Dabo Guan or Erda Lin.

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Xie, W., Xiong, W., Pan, J. et al. Decreases in global beer supply due to extreme drought and heat. Nature Plants 4, 964–973 (2018). https://doi.org/10.1038/s41477-018-0263-1

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