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The impact of 1.5 °C and 2.0 °C global warming on global maize production and trade | Scientific Reports – Nature.com

  • Angélil, O. et al. An independent assessment of anthropogenic attribution statements for recent extreme temperature and rainfall events. J. Clim. 30(1), 5–16 (2017).

    ADS  Google Scholar 

  • Rosenzweig, C. et al. Coordinating AgMIP data and models across global and regional scales for 1.5°C and 2.0°C assessments. Philos. Trans. R. Soc. A. 376, 20160455 (2018).

    ADS  Google Scholar 

  • Mitchell, D. et al. Half a degree additional warming, prognosis and projected impacts (HAPPI): Background and experimental design. Geosci. Model Dev. 10, 571–583 (2017).

    ADS  CAS  Google Scholar 

  • Coumou, D. & Rahmstorf, S. A decade of weather extremes. Nat. Clim. Change 2, 491–496 (2012).

    ADS  Google Scholar 

  • IPCC: Summary for Policymakers. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change 4–6 (Cambridge University Press, 2013).

  • Diffenbaugh, N. S. et al. Quantifying the influence of global warming on unprecedented extreme climate events. PNAS 114(19), 4881–4886 (2016).

    ADS  Google Scholar 

  • Tai, A. P. K., Martin, M. V. & Heald, C. L. Threat to future global food security from climate change and ozone air pollution. Nat. Clim. Change 4, 817–821 (2014).

    ADS  CAS  Google Scholar 

  • Román-Palacios, C. & Wiens, J. J. Recent responses to climate change reveal the drivers of species extinction and survival. PNAS 117(8), 4211–4217 (2020).

    ADS  PubMed  PubMed Central  Google Scholar 

  • Dong, W. H., Liu, Z., Liao, H., Tang, Q. H. & Li, X. E. New climate and socio-economic scenarios for assessing global human health challenges due to heat risk. Clim. Change 130(4), 505–518 (2015).

    ADS  Google Scholar 

  • Brown, S. C., Wigley, T. M. L., Otto-Bliesner, B. L., Rahbek, C. & Fordham, D. A. Persistent Quaternary climate refugia are hospices for biodiversity in the Anthropocene. Nat. Clim. Change 10, 244–248 (2020).

    ADS  Google Scholar 

  • Fischer, H., Amelung, D. & Said, N. The accuracy of German citizens’ confidence in their climate change knowledge. Nat. Clim. Change 9, 776–780 (2020).

    ADS  Google Scholar 

  • Hasegawa, T. et al. Risk of increased food insecurity under stringent global climate change mitigation policy. Nat. Clim. Change 8, 699–703 (2018).

    ADS  Google Scholar 

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

    ADS  CAS  PubMed  Google Scholar 

  • UNFCCC. The Paris Agreement. 2015, https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris-agreement.

  • Roche, K. R., Müller-Itten, M., Dralle, D. N., Bolster, D. & Müller, M. F. Climate change and the opportunity cost of conflict. PNAS 117(4), 1935–1940 (2020).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  • Challinor, A. J. et al. A meta-analysis of crop yield under climate change and adaptation. Nat. Clim. Change 4, 287–291 (2014).

    ADS  Google Scholar 

  • Lobell, D. B. et al. Prioritizing climate change adaptation needs for food security in 2030. Science 319, 607–610 (2017).

    Google Scholar 

  • Lv, S. et al. Yield gap simulations using ten maize cultivars commonly planted in Northeast China during the past five decades. Agric. For. Meteorol. 205, 1–10 (2015).

    ADS  Google Scholar 

  • Chao, W., Kehui, C. & Shah, F. Heat stress decreases rice grain weight: Evidence and physiological mechanisms of heat effects prior to flowering. Int. J. Mol. Sci. 23(18), 10922 (2022).

    Google Scholar 

  • Chao, W. et al. Estimating the yield stability of heat-tolerant rice genotypes under various heat conditions across reproductive stages: A 5-year case study. Sci. Rep. 11, 13604 (2021).

    ADS  Google Scholar 

  • IPCC. Food security and food production systems. In Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel of Climate Change 485–533 (Cambridge University Press, 2014).

  • Tigchelaar, M., Battisti, D. S., Naylor, R. L. & Ray, D. K. Future warming increases probability of globally synchronized maize production shocks. PNAS 115(26), 6644–6649 (2018).

    ADS  PubMed  PubMed Central  Google Scholar 

  • Zhao, C. et al. Temperature increase reduces global yields of major crops in four independent estimates. PNAS 114, 9326–9331 (2017).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  • Diffenbaugh, N. S., Hertel, T. W., Scherer, M. & Verma, M. Response of corn markets to climate volatility under alternative energy futures. Nat. Clim. Change 2, 514–518 (2012).

    ADS  Google Scholar 

  • Jensen, H. G. & Anderson, K. Grain price spikes and beggar-thy-neighbor policy responses: A global economywide analysis. World Bank Econ. Rev. 31, 158–175 (2017).

    Google Scholar 

  • 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. For. Meteorol. 170, 195–205 (2013).

    ADS  Google Scholar 

  • 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).

    ADS  Google Scholar 

  • Wheeler, T. & Braun, J. V. Climate change impacts on global food security. Science 341(6145), 508–513 (2013).

    ADS  CAS  PubMed  Google Scholar 

  • Lunt, T., Jones, A. W., Mulhern, W. S., Lezaks, D. P. M. & Jahn, M. M. Vulnerabilities to agricultural production shocks: An extreme, plausible scenario for assessment of risk for the insurance sector. Clim. Risk Manag. 13, 1–9 (2016).

    Google Scholar 

  • Jägermeyr, J. & Frieler, K. Spatial variations in crop growing seasons pivotal to reproduce global fluctuations in maize and wheat yields. Sci. Adv. 4(11), eaat4517 (2018).

    ADS  PubMed  PubMed Central  Google Scholar 

  • Elliott, J. et al. Characterizing agricultural impacts of recent large-scale US droughts and changing technology and management. Agric. Syst. 159, 275–281 (2017).

    Google Scholar 

  • Tack, J., Barkley, A. & Nalley, L. L. Effect of warming temperatures on US wheat yields. Proc. Natl. Acad. Sci. 112, 6931–6936 (2015).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  • Tao, F., Zhang, Z., Liu, J. & Yokozawa, M. Modelling the impacts of weather and climate variability on crop productivity over a large area: A new super-ensemblebased probabilistic projection. Agric. For. Meteorol. 149, 1266–1278 (2009).

    ADS  Google Scholar 

  • Parent, B. et al. Maize yields over Europe may increase in spite of climate change, with an appropriate use of the genetic variability of flowering time. PNAS 115(42), 10642–10647 (2018).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  • Yang, C. Y., Fraga, H., Ieperen, W. V. & Santos, J. A. Assessment of irrigated maize yield response to climate change scenarios in Portugal. Agric. Water Manag. 184, 178–190 (2017).

    Google Scholar 

  • Miller, S. A. & Moore, F. C. Climate and health damages from global concrete production. Nat. Clim. Change https://doi.org/10.1038/s41558-020-0733-0 (2020).

    Article  Google Scholar 

  • Kassie, B. T. et al. Exploring climate change impacts and adaptation options for maize production in the Central Rift Valley of Ethiopia using different climate change scenarios and crop models. Clim. Change 129, 145–158 (2015).

    ADS  Google Scholar 

  • Tao, F. & Zhang, Z. Climate change, high-temperature stress, rice productivity, and water use in Eastern China: A new superensemble-based probabilistic projection. J. Appl. Meteorol. Climatol. 52, 531–551 (2013).

    ADS  Google Scholar 

  • Glotter, M. & Elliott, J. Simulating US agriculture in a modern Dust Bowl drought. Nat. Plants 3, 16193 (2016).

    PubMed  Google Scholar 

  • Challinor, A. J., Koehler, A. K., Ramirez-Villegas, J., Whitfield, S. & Das, B. Current warming will reduce yields unless maize breeding and seed systems adapt immediately. Nat. Clim. Change 6, 954–958 (2016).

    ADS  Google Scholar 

  • Cammarano, D. et al. Using historical climate observations to understand future climate change crop yield impacts in the Southeastern US. Clim. Change 134, 311–326 (2016).

    ADS  Google Scholar 

  • Etten, J. V. et al. Crop variety management for climate adaptation supported by citizen science. PNAS 116(10), 4194–4199 (2019).

    ADS  PubMed  PubMed Central  Google Scholar 

  • Urban, D. W., Sheffield, J. & Lobell, D. B. The impacts of future climate and carbon dioxide changes on the average and variability of US maize yields under two emission scenarios. Environ. Res. Lett. 10, 045003 (2015).

    ADS  Google Scholar 

  • IPCC. Summary for policymakers. In Global Warming of 1.5°C. An IPCC Special Report on the Impacts of Global Warming of 1.5°C Above Pre-industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate Change, Sustainable Development, and Efforts to Eradicate Poverty 32 (World Meteorological Organization, 2018).

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

    Google Scholar 

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

    ADS  Google Scholar 

  • 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, 1022 (2008).

    ADS  Google Scholar 

  • You, L.Z., et al. Spatial Production Allocation Model (SPAM) 2000 Version 3.2. http://mapspam.info (2015).

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

  • 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 

  • 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, 1995).

  • 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).

    Google Scholar 

  • Hertel, T. W. Global Trade Analysis: Modeling and Applications 5–30 (Cambridge University Press, 1997).

    Google Scholar 

  • Corong, E. L., Hertel, T. W., McDougall, R., Tsigas, M. E. & Mensbrugghe, D. V. The standard GTAP model, version 7. J. Glob. Econ. Anal. 2(1), 1–119 (2017).

    Google Scholar 

  • Ciscar, J. C. et al. Physical and economic consequences of climate change in Europe. PNAS 108, 2678–2683 (2011).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  • Hsiang, S. et al. Estimating economic damage from climate change in the United States. Science 356(6345), 1362–1369 (2017).

    ADS  CAS  PubMed  Google Scholar 

  • Taheripour, F., Hertel, T. W. & Liu, J. The role of irrigation in determining the global land use impacts of biofuels. Energy Sustain. Soc. 3(1), 4 (2013).

    Google Scholar 

  • Ali, T., Huang, J. K. & Yang, J. Impact assessment of global and national biofuels developments on agriculture in Pakistan. Appl. Energy 104, 466–474 (2013).

    Google Scholar 

  • Yang, J., Huang, J. K., Qiu, H. G., Rozelle, S. & Sombilla, M. A. Biofuels and the greater Mekong Subregion: Assessing the impact on prices, production and trade. Appl. Energy 86, S37–S46 (2009).

    Google Scholar 

  • Horridge, M. SplitCom, programs to disaggregate a GTAP sector (Centre of Policy Studies, Vitorial University). https://www.copsmodels.com/splitcom.htm (2005).

  • Taylor, K. E., Stouffer, B. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).

    ADS  Google Scholar 

  • Zhou, B. T., Wen, H. Q. Z., Xu, Y., Song, L. C. & Zhang, X. B. Projected changes in temperature and precipitation extremes in China by the CMIP5 multimodel ensembles. J. Clim. 27, 6591–6611 (2014).

    ADS  Google Scholar 

  • Knutti, R., Rogelj, J., Sedláček, J. & Ficher, E. M. A scientific critique of the two-degree climate change target. Nat. Geosci. 9(1), 1–6 (2015).

    Google Scholar 

  • Rogelj, J. et al. Energy system transformations for limiting end-of-century warming to below 1.5°C. Nat. Clim. Change 5(6), 519–527 (2015).

    ADS  Google Scholar 

  • Friedlingstein, P. et al. Persistent growth of CO2 emissions and implications for reaching climate targets. Nat. Geosci. 7(10), 709–715 (2014).

    ADS  CAS  Google Scholar 

  • Azar, C., Johansson, D. J. A. & Mattsson, N. Meeting global temperature targets the role of bioenergy with carbon capture and storage. Environ. Res. Lett. 8(3), 1345–1346 (2013).

    Google Scholar 

  • 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).

    ADS  Google Scholar 

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

    CAS  Google Scholar 

  • Challinora, A. J. et al. Improving the use of crop models for risk assessment and climate change adaptation. Agric. Syst. 159, 296–306 (2018).

    Google Scholar 

  • 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).

    ADS  Google Scholar 

  • Wang, N. et al. Increased uncertainty in simulated maize phenology with more frequent supra-optimal temperature under climate warming. Eur. J. Agron. 71, 19–33 (2015).

    Google Scholar 

  • Rosenzweig, C. et al. Assessing agricultural risks of climate change in the twenty-first century in a global gridded crop model intercomparison. PNAS 111, 3268–3273 (2014).

    ADS  CAS  PubMed  Google Scholar 

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