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Theoretical and Applied Climatology

, Volume 130, Issue 3–4, pp 1065–1071 | Cite as

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

  • Yi Zhang
  • Yanxia ZhaoEmail author
  • Chunyi Wang
  • Sining Chen
Original Paper

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.

Keywords

Emission Scenario Climate Projection Maize Yield Future Period Crop Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

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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Copyright information

© Springer-Verlag Wien 2016

Authors and Affiliations

  • Yi Zhang
    • 1
  • Yanxia Zhao
    • 1
    • 2
    Email author
  • Chunyi Wang
    • 3
  • Sining Chen
    • 4
  1. 1.State Key Laboratory of Severe Weather, Chinese Academy of Meteorological SciencesBeijingChina
  2. 2.Shanghai Institute of Meteorological SciencesShanghaiChina
  3. 3.Hainan Meteorological ServiceHaikouChina
  4. 4.Tianjin Climate CenterTianjinChina

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