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Simulating climate change impacts and potential adaptations on rice yields in the Sichuan Basin, China

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  • Che-Chen Xu

    (Chinese Academy of Sciences
    Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Wen-Xiang Wu

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Quan-Sheng Ge

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Yang Zhou

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Yu-Mei Lin

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Ya-Mei Li

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

Abstract

Rice (Oryza) is a staple food in China, and rice yield is inherently sensitive to climate change. It is of great regional and global importance to understand how and to what degree climate change will impact rice yields and to determine the adaptation options effectiveness for mitigating possible adverse impacts or for taking advantage of beneficial changes. The objectives of this study are to assess the climate change impact, the carbon dioxide (CO2) fertilization effect, and the adaptation strategy effectiveness on rice yields during future periods (2011–2099) under the newly released Representative Concentration Pathway (RCP) 4.5 scenario in the Sichuan Basin, one of the most important rice production areas of China. For this purpose, the Crop Estimation through Resource and Environment Synthesis (CERES)-Rice model was applied to conduct simulation, based on high-quality meteorological, soil and agricultural experimental data. The modeling results indicated a continuing rice reduction in the future periods. Compared to that without incorporating of increased CO2 concentration, a CO2 fertilization effect could mitigate but still not totally offset the negative climate change impacts on rice yields. Three adaptive measures, including advancing planting dates, switching to current high temperature tolerant varieties, and breeding new varieties, could effectively offset the negative climate change impacts with various degrees. Our results will not only contribute to inform regional future agricultural adaptation decisions in the Sichuan Basin but also gain insight into the mechanism of regional rice yield response to global climate change and the effectiveness of widely practiced global thereby assisting with appropriate adaptive strategies.

Suggested Citation

  • Che-Chen Xu & Wen-Xiang Wu & Quan-Sheng Ge & Yang Zhou & Yu-Mei Lin & Ya-Mei Li, 2017. "Simulating climate change impacts and potential adaptations on rice yields in the Sichuan Basin, China," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 22(4), pages 565-594, April.
    • Handle: RePEc:spr:masfgc:v:22:y:2017:i:4:d:10.1007_s11027-015-9688-2
    DOI: 10.1007/s11027-015-9688-2
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    References listed on IDEAS

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    1. Frances C. Moore & David B. Lobell, 2014. "Adaptation potential of European agriculture in response to climate change," Nature Climate Change, Nature, vol. 4(7), pages 610-614, July.
    2. He, Jianqiang & Jones, James W. & Graham, Wendy D. & Dukes, Michael D., 2010. "Influence of likelihood function choice for estimating crop model parameters using the generalized likelihood uncertainty estimation method," Agricultural Systems, Elsevier, vol. 103(5), pages 256-264, June.
    3. Roman-Paoli, E. & Welch, S. M. & Vanderlip, R. L., 2000. "Comparing genetic coefficient estimation methods using the CERES-Maize model," Agricultural Systems, Elsevier, vol. 65(1), pages 29-41, July.
    4. Rinaldi, Michele & Losavio, Nicola & Flagella, Zina, 2003. "Evaluation and application of the OILCROP-SUN model for sunflower in southern Italy," Agricultural Systems, Elsevier, vol. 78(1), pages 17-30, October.
    5. Toshihiko Masui & Kenichi Matsumoto & Yasuaki Hijioka & Tsuguki Kinoshita & Toru Nozawa & Sawako Ishiwatari & Etsushi Kato & P. Shukla & Yoshiki Yamagata & Mikiko Kainuma, 2011. "An emission pathway for stabilization at 6 Wm −2 radiative forcing," Climatic Change, Springer, vol. 109(1), pages 59-76, November.
    6. Hansen, J. W. & Jones, J. W., 2000. "Scaling-up crop models for climate variability applications," Agricultural Systems, Elsevier, vol. 65(1), pages 43-72, July.
    7. Hunt, L. A. & White, J. W. & Hoogenboom, G., 2001. "Agronomic data: advances in documentation and protocols for exchange and use," Agricultural Systems, Elsevier, vol. 70(2-3), pages 477-492.
    8. G. Kapetanaki & C. Rosenzweig, 1997. "Impact of climate change on maize yield in central and northern Greece: A simulation study with CERES-Maize," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 1(3), pages 251-271, September.
    9. Bocchiola, D., 2015. "Impact of potential climate change on crop yield and water footprint of rice in the Po valley of Italy," Agricultural Systems, Elsevier, vol. 139(C), pages 223-237.
    10. Kattarkandi Byjesh & Soora Kumar & Pramod Aggarwal, 2010. "Simulating impacts, potential adaptation and vulnerability of maize to climate change in India," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 15(5), pages 413-431, June.
    11. A. J. Challinor & J. Watson & D. B. Lobell & S. M. Howden & D. R. Smith & N. Chhetri, 2014. "A meta-analysis of crop yield under climate change and adaptation," Nature Climate Change, Nature, vol. 4(4), pages 287-291, April.
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    Cited by:

    1. Xin Dong & Tianyi Zhang & Xiaoguang Yang & Tao Li, 2023. "Breeding priorities for rice adaptation to climate change in Northeast China," Climatic Change, Springer, vol. 176(6), pages 1-19, June.
    2. Yahui Guo & Wenxiang Wu & Yumei Liu & Zhaofei Wu & Xiaojun Geng & Yaru Zhang & Christopher Robin Bryant & Yongshuo Fu, 2020. "Impacts of Climate and Phenology on the Yields of Early Mature Rice in China," Sustainability, MDPI, vol. 12(23), pages 1-16, December.
    3. Zongying Yang & Bo Zhang & Jie Chen & Yule Hou & Yan Wu & Hong Xie, 2024. "Characteristics of Spatial and Temporal Variation in Drought in the Sichuan Basin from 1963 to 2022," Sustainability, MDPI, vol. 16(19), pages 1-23, September.

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