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Projecting future changes in extreme climate for maize production in the North China Plain and the role of adjusting the sowing date

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  • Dengpan Xiao

    (College of Geography Science, Hebei Normal University
    Hebei Laboratory of Environmental Evolution and Ecological Construction
    Engineering Technology Research Center, Geographic Information Development and Application of Hebei, Institute of Geographical Sciences, Hebei Academy of Sciences)

  • Huizi Bai

    (Engineering Technology Research Center, Geographic Information Development and Application of Hebei, Institute of Geographical Sciences, Hebei Academy of Sciences)

  • De Li Liu

    (NSW Department of Primary Industries, Wagga Wagga Agricultural Institute
    University of New South Wales)

  • Jianzhao Tang

    (Engineering Technology Research Center, Geographic Information Development and Application of Hebei, Institute of Geographical Sciences, Hebei Academy of Sciences)

  • Bin Wang

    (NSW Department of Primary Industries, Wagga Wagga Agricultural Institute)

  • Yanjun Shen

    (Key Laboratory for Agricultural Water Resources, Hebei Key Laboratory for Agricultural Water-Saving, Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences
    University of the Chinese Academy of Sciences)

  • Jiansheng Cao

    (Key Laboratory for Agricultural Water Resources, Hebei Key Laboratory for Agricultural Water-Saving, Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences)

  • Puyu Feng

    (China Agricultural University)

Abstract

The increase of extreme climate events under a warming climate has and will continue to threaten the growth and development of maize across the North China Plain (NCP). Understanding and assessing the spatiotemporal changes of future extreme climate events during the maize growth period are essential for developing adaptation strategies to reduce the risks of climate to maize productivity under future climate change. In this study, we applied statistically downscaled climate data from 20 global climate models (GCMs) and two Shared Socioeconomic Pathways (SSP245 and SSP585) for 52 stations in the NCP and investigated the future changes of 6 extreme climate indices (ECIs) during different maize growth periods that are sensitive to maize yield. The change in maize phenology under future climate scenarios was simulated by the well-validated APSIM-maize model. Moreover, we selected the independence weighted mean (IWM) method to evaluate the performance of 20 GCMs in reproducing historical changes in ECIs. The results from IWM could better reproduce historical changes of ECIs than any individual GCM and multi-model arithmetic mean. We found that the intensity and frequency of extreme high temperature indices during the maize growth period were projected to increase over the twenty-first century for both SSP245 and SSP585 across the NCP. There was no significant change in extreme precipitation index (R20). The consecutive wet days (CWD) significantly increased, while the consecutive dry days (CDD) slightly decreased over the twenty-first century. To mitigate and adapt the impacts of future extreme climate on maize growth, we found adjustment of sowing date (SD) had important effects on ECIs, especially on the extreme high temperature indices. Overall, a proper delay of SD could greatly reduce the occurrence of extreme heat stress on maize production under both scenarios. We expect these climate extreme projections will provide helpful information to optimize climate resources in the NCP to better adapt future climate change.

Suggested Citation

  • Dengpan Xiao & Huizi Bai & De Li Liu & Jianzhao Tang & Bin Wang & Yanjun Shen & Jiansheng Cao & Puyu Feng, 2022. "Projecting future changes in extreme climate for maize production in the North China Plain and the role of adjusting the sowing date," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 27(3), pages 1-21, March.
    • Handle: RePEc:spr:masfgc:v:27:y:2022:i:3:d:10.1007_s11027-022-09995-4
    DOI: 10.1007/s11027-022-09995-4
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    References listed on IDEAS

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    Cited by:

    1. Yanxi Zhao & Dengpan Xiao & Huizi Bai & Jianzhao Tang & De Li Liu & Yongqing Qi & Yanjun Shen, 2022. "The Prediction of Wheat Yield in the North China Plain by Coupling Crop Model with Machine Learning Algorithms," Agriculture, MDPI, vol. 13(1), pages 1-19, December.
    2. Yanxi Zhao & Dengpan Xiao & Huizi Bai & Jianzhao Tang & Deli Liu, 2022. "Future Projection for Climate Suitability of Summer Maize in the North China Plain," Agriculture, MDPI, vol. 12(3), pages 1-20, February.
    3. Zhang, Qi & Yu, Xin & Qiu, Rangjian & Liu, Zhongxian & Yang, Zaiqiang, 2022. "Evolution, severity, and spatial extent of compound drought and heat events in north China based on copula model," Agricultural Water Management, Elsevier, vol. 273(C).

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