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Using statistical model to simulate the impact of climate change on maize yield with climate and crop uncertainties

Abstract

Assessment of the impact of climate change on crop productions with considering uncertainties is essential for properly identifying and decision-making agricultural practices that are sustainable. In this study, we employed 24 climate projections consisting of the combinations of eight GCMs and three emission scenarios representing the climate projections uncertainty, and two crop statistical models with 100 sets of parameters in each model representing parameter uncertainty within the crop models. The goal of this study was to evaluate the impact of climate change on maize (Zea mays L.) yield at three locations (Benxi, Changling, and Hailun) across Northeast China (NEC) in periods 2010–2039 and 2040–2069, taking 1976–2005 as the baseline period. The multi-models ensembles method is an effective way to deal with the uncertainties. The results of ensemble simulations showed that maize yield reductions were less than 5 % in both future periods relative to the baseline. To further understand the contributions of individual sources of uncertainty, such as climate projections and crop model parameters, in ensemble yield simulations, variance decomposition was performed. The results indicated that the uncertainty from climate projections was much larger than that contributed by crop model parameters. Increased ensemble yield variance revealed the increasing uncertainty in the yield simulation in the future periods.

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References

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  4. Ceglar A, Kajfež-Bogataj L (2012) Simulation of maize yield in current and changed climatic conditions: addressing modelling uncertainties and the importance of bias correction in climate model simulations. Eur J Agron 37:83–95

    Article  Google Scholar 

  5. Ceglar A, Črepinšek Z, Kajfež-Bogataj L, Pogačar T (2011) The simulation of phenological development in dynamic crop model: the Bayesian comparison of different methods. Agric Forest Meteor 151:101–115

    Article  Google Scholar 

  6. Challinor AJ, Watson J, Lobell DB, Howden SM, Smith DR, Chhetri N (2014) A meta-analysis of crop yield under climate change and adaptation. Nat Clim Chang 4:287–291

    Article  Google Scholar 

  7. Diniz-Filho JAF, Bini LM, Rangel TF, Loyola RD, Hof C, Noguės-Bravo D, Araứjo MB (2009) Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change. Ecography 32:897–906

    Article  Google Scholar 

  8. Efron B, Gong G (1983) A leisurely look at the bootstrap; the jackknife; and cross-validation. Am Stat 37:36–48

    Google Scholar 

  9. Efron B, Tibshirani R (1986) Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Stat Sci 1:54–57

    Article  Google Scholar 

  10. Iizumi T, Yokozawa M, Nishinori M (2009) Parameter estimation and uncertainty analysis of a large-scale crop model for paddy rice: application of a Bayesian approach. Agric For Meteor 149:333–348

    Article  Google Scholar 

  11. IPCC (2013) Climate change 2013: the physical science basis. Cambridge University Press, Cambridge

    Google Scholar 

  12. Knutti R, Sedláček J (2013) Robustness and uncertainties in the new CMIP5 climate model projections. Nat Clim Chang 3:369–373

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. Lobell DB, Burke MB (2010) On the use of statistical models to predict crop yield responses to climate change. Agric Forest Meteor 150:1443–1452

    Article  Google Scholar 

  15. Lobell DB, Field CB, Cahill KN, Bonfils C (2006) On the use of statistical models to predict crop yield responses to climate change. Agric Forest Meteor 141:208–218

    Article  Google Scholar 

  16. Lobell DB, Banziger M, Magorokosho C, Vivek B (2011) Nonlinear heat effects on African maize as evidenced by historical yield trials. Nat Clim Chang 1(1):42–45

    Article  Google Scholar 

  17. Masutomi Y, Takahashi K, Harasawa H, Matsuoka Y (2009) 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

    Article  Google Scholar 

  18. Ray DK, Mueller ND, West PC, Foley JA (2013) Yield trends are insufficient to double global crop production by 2050. PLoS One 8:e66428

    Article  Google Scholar 

  19. Ruiz-Ramos M, Mínguez MI (2010) Evaluating uncertainty in climate change impacts on crop productivity in the Iberian Peninsula. Clim Res 44:69–82

    Article  Google Scholar 

  20. Semenov MA, Stratonovitch P (2010) Use of multi-model ensembles from global climate models for assessment of climate change impacts. Clim Res 41:1–14

    Article  Google Scholar 

  21. Shi W, Tao F, Zhang Z (2012) Identifying contributions of climate change to crop yields based on statistical models: a review. Acta Geogr Sin 67(9):1213–1222 in Chinese

    Google Scholar 

  22. Tao F, Yokozawa M, Zhang Z (2009) Modelling the impacts of weather and climate variability on crop productivity over a large area: a new super-ensemble-based probabilistic projection. Agric Forest Meteor 149:1266–1278

    Article  Google Scholar 

  23. Tao F et al. (2013) Single rice growth period was prolonged by cultivars shifts, but yield was damaged by climate change during 1981–2009 in China, and late rice was just opposite. Glob Chang Biol 19(10):3200–3209

    Article  Google Scholar 

  24. Tebaldi C, Lobell DB (2008) Towards probabilistic projections of climate change impacts on global crop yields. Geophys Res Lett 35:L08705

    Article  Google Scholar 

  25. Wang L, Chen W (2014) A CMIP5 multimodel projection of future temperature, precipitation, and climatological drought in China. Int J Climatol 34:2059–2078

    Article  Google Scholar 

  26. White JW, Hoogenboom G, Kimball BA, Wall GW (2011) Methodologies for simulating impact of climate change on crop production. Field Crops Res 124:357–368

    Article  Google Scholar 

  27. Xu C, Xu Y (2012) The projection of temperature and precipitation over China under RCP scenarios using a CMIP5 multi-model ensemble. Atmos Oceanic Sci Lett 5:527–533

    Google Scholar 

  28. Zhang Y, Zhao Y, Chen S, Guo J, Wang E (2015) Prediction of maize yield response to climate change with climate and crop model uncertainties. J Appl Meteor Climatol 54:785–794

    Article  Google Scholar 

  29. Zhou M, Wang H (2015) Potential impact of future climate change on crop yield in northeastern China. Adv Atmos Sci 32:889–897

    Article  Google Scholar 

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Acknowledgments

This work was supported by the National Science Foundation of China (41505097) and Basic Research Funds—regular at the Chinese Academy of Meteorological Sciences (Grant 2013Z008). We gratefully acknowledge Prof. Ying Xu from the National Climate Center of China for providing the climate projections data.

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Correspondence to Yanxia Zhao.

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Zhang, Y., Zhao, Y., Wang, C. et al. Using statistical model to simulate the impact of climate change on maize yield with climate and crop uncertainties. Theor Appl Climatol 130, 1065–1071 (2017). https://doi.org/10.1007/s00704-016-1935-2

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Keywords

  • Emission Scenario
  • Climate Projection
  • Maize Yield
  • Future Period
  • Crop Model