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Future Projection for Climate Suitability of Summer Maize in the North China Plain

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  • Yanxi Zhao

    (College of Geography Science, Hebei Normal University, Shijiazhuang 050024, China
    Hebei Laboratory of Environmental Evolution and Ecological Construction, Shijiazhuang 050024, China
    Engineering Technology Research Center, Geographic Information Development and Application of Hebei, Institute of Geographical Science, Hebei Academy of Sciences, Shijiazhuang 050011, China)

  • Dengpan Xiao

    (College of Geography Science, Hebei Normal University, Shijiazhuang 050024, China
    Hebei Laboratory of Environmental Evolution and Ecological Construction, Shijiazhuang 050024, China
    Engineering Technology Research Center, Geographic Information Development and Application of Hebei, Institute of Geographical Science, Hebei Academy of Sciences, Shijiazhuang 050011, China)

  • Huizi Bai

    (Engineering Technology Research Center, Geographic Information Development and Application of Hebei, Institute of Geographical Science, Hebei Academy of Sciences, Shijiazhuang 050011, China)

  • Jianzhao Tang

    (Engineering Technology Research Center, Geographic Information Development and Application of Hebei, Institute of Geographical Science, Hebei Academy of Sciences, Shijiazhuang 050011, China)

  • Deli Liu

    (NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW 2650, Australia)

Abstract

Climate change has and will continue to exert significant effects on social economy, natural environment, and human life. Research on the climatic suitability of crops is critical for mitigating and adapting to the negative impacts of climate change on crop production. In the study, we developed the climate suitability model of maize and investigated the climate suitability of summer maize during the base period (1981–2010) and two future periods of 2031–2060 (2040s) and 2071–2100 (2080s) in the North China Plain (NCP) based on BCC-CSM2-MR model (BCC) from the Coupled Model Comparison Program (CMIP6) under two Shared Socioeconomic Pathways (SSP) 245 and SSP585. The phenological shift of maize under future climate scenarios was simulated by the Agricultural Production Systems Simulator (APSIM). The results showed that the root mean square errors ( RMSE ) between observations and projections for sunshine suitability ( S S ), temperature suitability ( S T ), precipitation suitability ( S P ), and integrated climate suitability ( S Z ) during the whole growth period were 0.069, 0.072, 0.057, and 0.040, respectively. Overall, the BCC projections for climate suitability were in suitable consistency with the observations in the NCP. During 1981–2010, the S P , S T, and S Z were high in the north of the NCP and low in the south. The S P , S T, and S Z showed a downward trend under all the future climate scenarios in most areas of NCP while the S S increased. Therein, the change range of S P and S S was 0–0.1 under all the future climate scenarios. The S T declined by 0.1–0.2 in the future except for the decrease of more than 0.3 under the SSP585 scenario in the 2080s. The decrease in S Z in the 2040s and 2080s under both SSP scenarios varied from 0 to 0.2. Moreover, the optimum area decreases greatly under future scenarios while the suitable area increases significantly. Adjusting sowing data (SD) would have essential impacts on climate suitability. To some extent, delaying SD was beneficial to improve the climate suitability of summer maize in the NCP, especially under the SSP585 scenario in the 2080s. Our findings can not only provide data support for summer maize production to adapt to climate change but also help to propose agricultural management measures to cope with future climate change.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:3:p:348-:d:761024
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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. Kun Jia & Wei Zhang & Bingyan Xie & Xitong Xue & Feng Zhang & Dongrui Han, 2022. "Does Climate Change Increase Crop Water Requirements of Winter Wheat and Summer Maize in the Lower Reaches of the Yellow River Basin?," IJERPH, MDPI, vol. 19(24), pages 1-12, December.
    3. Dengpan Xiao & Wenjiao Shi, 2023. "Modeling the Adaptation of Agricultural Production to Climate Change," Agriculture, MDPI, vol. 13(2), pages 1-4, February.

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