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Global irrigation contribution to wheat and maize yield

  • 1.

    Tilman, D., Balzer, C., Hill, J. & Befort, B. L. Global food demand and the sustainable intensification of agriculture. Proc. Natl Acad. Sci. USA 108, 20260–20264 (2011).

    ADS  CAS  PubMed  Article  Google Scholar 

  • 2.

    Alexandratos, N. & Bruinsma, J. World Agriculture Towards 2030/2050: The 2012 Revision (FAO, ESA Working paper, Rome, 2012).

  • 3.

    Carlson, K. M. et al. Greenhouse gas emissions intensity of global croplands. Nat. Clim. Change 7, 63–68 (2017).

    ADS  CAS  Article  Google Scholar 

  • 4.

    Houghton, R. A. et al. Carbon emissions from land use and land-cover change. Biogeosciences 9, 5125–5142 (2012).

    ADS  CAS  Article  Google Scholar 

  • 5.

    Cassman, K. G., Dobermann, A., Walters, D. T. & Yang, H. Meeting cereal demand while protecting natural resources and improving environmental quality. Annu. Rev. Environ. Resour. 28, 315–358 (2003).

    Article  Google Scholar 

  • 6.

    Laurance, W. F., Sayer, J. & Cassman, K. G. Agricultural expansion and its impacts on tropical nature. Trends Ecol. Evol. 29, 107–116 (2014).

    PubMed  Article  Google Scholar 

  • 7.

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

    ADS  CAS  Article  Google Scholar 

  • 8.

    Asseng, S. et al. Rising temperatures reduce global wheat production. Nat. Clim. Change 5, 143–147 (2015).

    ADS  Article  Google Scholar 

  • 9.

    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).

    ADS  CAS  Article  Google Scholar 

  • 10.

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

    ADS  CAS  PubMed  Article  Google Scholar 

  • 11.

    Jägermeyr, J., Pastor, A., Biemans, H. & Gerten, D. Reconciling irrigated food production with environmental flows for Sustainable Development Goals implementation. Nat. Commun. 8, 15900 (2017).

    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

  • 12.

    Schauberger, B. et al. Consistent negative response of US crops to high temperatures in observations and crop models. Nat. Commun. 8, 13931 (2017).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  • 13.

    Tack, J., Barkley, A. & Hendricks, N. Irrigation offsets wheat yield reductions from warming temperatures. Environ. Res. Lett. 12, 114027 (2017).

    ADS  Article  Google Scholar 

  • 14.

    Troy, T. J., Kipgen, C. & Pal, I. The impact of climate extremes and irrigation on US crop yields. Environ. Res. Lett. 10, 054013 (2015).

    ADS  Article  Google Scholar 

  • 15.

    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).

    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

  • 16.

    Jägermeyr, J. et al. Integrated crop water management might sustainably halve the global food gap. Environ. Res. Lett. 11, 025002 (2016).

    ADS  Article  Google Scholar 

  • 17.

    Rosegrant, M. W., Ringler, C. & Zhu, T. J. Water for agriculture: maintaining food security under growing scarcity. Annu. Rev. Environ. Resour. 34, 205–222 (2009).

    Article  Google Scholar 

  • 18.

    Siebert, S. & Döll, P. Quantifying blue and green virtual water contents in global crop production as well as potential production losses without irrigation. J. Hydrol. 384, 198–217 (2010).

    ADS  Article  Google Scholar 

  • 19.

    Li, X. & Troy, T. J. Changes in rainfed and irrigated crop yield response to climate in the western US. Environ. Res. Lett. 13, 064031 (2018).

    ADS  Article  Google Scholar 

  • 20.

    Neverre, N., Dumas, P., Nassopoulos, H. Large-scale water scarcity assessment under global changes: insights from a hydroeconomic framework. Hydrol. Earth Syst. Sci. Discuss. Preprint at https://doi.org/10.5194/hess-2015-502 (2016).

  • 21.

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

    ADS  CAS  PubMed  Article  Google Scholar 

  • 22.

    Lobell, D. B., Sibley, A. & Ortiz-Monasterio, J. I. Extreme heat effects on wheat senescence in India. Nat. Clim. Change 2, 186–189 (2012).

    ADS  Article  Google Scholar 

  • 23.

    Gourdji, S. M., Sibley, A. M. & Lobell, D. B. Global crop exposure to critical high temperatures in the reproductive period: historical trends and future projections. Environ. Res. Lett. 8, 024041 (2013).

    ADS  Article  Google Scholar 

  • 24.

    Espe, M. B. et al. Point stresses during reproductive stage rather than warming seasonal temperature determine yield in temperate rice. Glob. Change Biol. 23, 4386–4395 (2017).

    ADS  Article  Google Scholar 

  • 25.

    Martre, P. et al. Multimodel ensembles of wheat growth: many models are better than one. Glob. Change Biol. 21, 911–925 (2015).

    ADS  Article  Google Scholar 

  • 26.

    Li, T. et al. Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions. Glob. Change Biol. 21, 1328–1341 (2015).

    ADS  CAS  Article  Google Scholar 

  • 27.

    Müller, C. et al. Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications. Geosci. Model Dev. 10, 1403–1422 (2017).

    ADS  Article  Google Scholar 

  • 28.

    Wang, X. et al. Emergent constraint on crop yield response to warmer temperature from field experiments. Nat. Sustain. https://doi.org/10.1038/s41893-020-0569-7 (2020).

  • 29.

    Folberth, C. et al. Uncertainty in soil data can outweigh climate impact signals in global crop yield simulations. Nat. Commun. 7 https://doi.org/10.1038/ncomms11872 (2016).

  • 30.

    Elliott, J. et al. The Global Gridded Crop Model Intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261–277 (2015).

    ADS  Article  Google Scholar 

  • 31.

    Roberts, M. J. et al. Comparing and combining process-based crop models and statistical models with some implications for climate change. Environ. Res. Lett. 12, 095010 (2017).

    ADS  Article  Google Scholar 

  • 32.

    Raftery, A. E., Gneiting, T., Balabdaoui, F. & Polakowski, M. Using Bayesian model averaging to calibrate forecast ensembles. Mon. Weather Rev. 133, 1155–1174 (2005).

    ADS  Article  Google Scholar 

  • 33.

    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).

    ADS  Article  CAS  Google Scholar 

  • 34.

    Fekete, B. M., Vorosmarty, C. J., & Grabs, W. Global, composite runoff fields based on observed river discharge and simulated water balances. Report No. 22. (World Meteorological Organization–Global Runoff Data Center, Koblenz, Germany, 1999).

  • 35.

    Wang, X. X. et al. Taking account of governance: Implications for land-use dynamics, food prices, and trade patterns. Ecol. Econ. 122, 12–24 (2016).

    Article  Google Scholar 

  • 36.

    Neumann, K. et al. Exploring global irrigation patterns: a multilevel modelling approach. Agric. Syst. 104, 703–713 (2011).

    Article  Google Scholar 

  • 37.

    Fekete, B. M. & Vörösmarty, C. J. The current status of global river discharge monitoring and potential new technologies complementing traditional discharge measurements. IAHS Publ. 309, 129–136 (2007).

    Google Scholar 

  • 38.

    Hanasaki, N., Yoshikawa, S., Pokhrel, Y. & Kanae, S. A global hydrological simulation to specify the sources of water used by humans. Hydrol. Earth Syst. Sci. 22, 789–817 (2018).

    ADS  Article  Google Scholar 

  • 39.

    Siebert, S., Henrich, V., Frenken, K., & Burke, J. Update of the digital global map of irrigation areas to version 5 (Rheinische Friedrich-Wilhelms-Universität, Bonn, Germany, Food and Agriculture Organization of the United Nations, Rome, Italy, 2013).

  • 40.

    Rosa, L. et al. Potential for sustainable irrigation expansion in a 3 °C warmer climate. Proc. Natl Acad. Sci. USA 117, 29526–29534 (2020).

    ADS  CAS  PubMed  Article  Google Scholar 

  • 41.

    Rosa L., Chiarelli D. D., Rulli M. C., Dell’Angelo J. & D’Odorico P. Global agricultural economic water scarcity. Sci. Adv. 6, eaaz6031 (2020).

  • 42.

    Rosegrant, M. W. & Cai, X. Global water demand and supply projections. Water Int. 27, 170–182 (2002).

    Article  Google Scholar 

  • 43.

    Berkoff, J. China: the South–North Water Transfer Project—is it justified? Water Policy 5, 1–28 (2003).

    Article  Google Scholar 

  • 44.

    Schmitz, C. et al. Blue water scarcity and the economic impacts of future agricultural trade and demand. Water Resour. Res. 49, 3601–3617 (2013).

    ADS  Article  Google Scholar 

  • 45.

    Biewald, A., Rolinski, S., Lotze-Campen, H., Schmitz, C. & Dietrich, J. P. Valuing the impact of trade on local blue water. Ecol. Econ. 101, 43–53 (2014).

    Article  Google Scholar 

  • 46.

    Portmann, F. T., Siebert, S. & Döll, P. MIRCA2000—Global monthly irrigated and rainfed crop areas around the year 2000: a new high-resolution data set for agricultural and hydrological modeling. Glob. Biogeochem. Cycles 24, GB1011 (2010).

    ADS  Article  CAS  Google Scholar 

  • 47.

    Muller, C. et al. The Global Gridded Crop Model Intercomparison phase 1 simulation dataset. Sci. Data 6, 50 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  • 48.

    Kobayashi, K. & Salam, M. U. Comparing simulated and measured values using mean squared deviation and its components. Agron. J. 92, 345–352 (2000).

    Article  Google Scholar 

  • 49.

    Deutscher Wetterdienst Frankfurt M. Mitteilungen des Deutschen Wetterdienstes: Windschutzanlagen auf der hohen Rhön (Dt. Wetterdienst P.11, 1954).

  • 50.

    Gornott, C. & Wechsung, F. Statistical regression models for assessing climate impacts on crop yields: a validation study for winter wheat and silage maize in Germany. Agric. Meteorol. 217, 89–100 (2016).

    Article  Google Scholar 

  • 51.

    Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations – the CRU TS3.10 Dataset. Int J. Climatol. 34, 623–642 (2014).

    Article  Google Scholar 

  • 52.

    Jägermeyr, J. et al. Water savings potentials of irrigation systems: global simulation of processes and linkages. Hydrol. Earth Syst. Sci. 19, 3073–3091 (2015).

    ADS  Article  Google Scholar 

  • 53.

    Pastor, A. V., Ludwig, F., Biemans, H., Hoff, H. & Kabat, P. Accounting for environmental flow requirements in global water assessments. Hydrol. Earth Syst. Sci. 18, 5041–5059 (2014).

    ADS  Article  Google Scholar 

  • 54.

    Poff, N. L. et al. The natural flow regime: a paradigm for river conservation and restoration. BioScience 47, 769–784 (1997).

    Article  Google Scholar 


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