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Identifying climate risk causing maize (Zea mays L.) yield fluctuation by time-series data

Author

Listed:
  • Yuhe Ji

    (Chinese Academy of Meteorological Science)

  • Guangsheng Zhou

    (Chinese Academy of Meteorological Science)

  • Lixia Wang

    (Satellite Environment Center, Ministry of Ecology and Environment of the People’s Republic of China)

  • Shudong Wang

    (Institute of Remote Sensing and Digital Earth, Chinese Academy of Science)

  • Zongshan Li

    (Research Center for Eco-environmental Sciences, Chinese Academy of Sciences)

Abstract

A long time series in crop yield is usually expressed as a long-term trend and a short-term fluctuation due to agricultural technological advance and climatic anomaly. The real climate risk is related to the short-term fluctuation in crop yield. In the paper, the climate risk of maize yield response to long-term climate variables is tested with the long time series (1961–2015) by a trend base line method. The long time series of maize yield is divided into short-term fluctuating meteorological yield and long-term trend yield. The long time series of climate variables are also divided into fluctuating variables and trend variables. After that, Pearson correlation analysis between fluctuating maize yield and fluctuating climate variables is used to identify risk factor causing maize yield fluctuation. Our results reveal that the main risk factors are night-time precipitation and extreme high temperature in growing season. Comparing climate risks in maize-producing provinces, much more climate risks are identified in some regions such as Liaoning province. The results provide useful information for reducing maize yield loss under climatic change.

Suggested Citation

  • Yuhe Ji & Guangsheng Zhou & Lixia Wang & Shudong Wang & Zongshan Li, 2019. "Identifying climate risk causing maize (Zea mays L.) yield fluctuation by time-series data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 96(3), pages 1213-1222, April.
    • Handle: RePEc:spr:nathaz:v:96:y:2019:i:3:d:10.1007_s11069-019-03605-4
    DOI: 10.1007/s11069-019-03605-4
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    References listed on IDEAS

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    1. Deepak K. Ray & James S. Gerber & Graham K. MacDonald & Paul C. West, 2015. "Climate variation explains a third of global crop yield variability," Nature Communications, Nature, vol. 6(1), pages 1-9, May.
    2. Qingfeng Meng & Peng Hou & David Lobell & Hongfei Wang & Zhenling Cui & Fusuo Zhang & Xinping Chen, 2014. "The benefits of recent warming for maize production in high latitude China," Climatic Change, Springer, vol. 122(1), pages 341-349, January.
    3. Enliang Guo & Jiquan Zhang & Yongfang Wang & Ha Si & Feng Zhang, 2016. "Dynamic risk assessment of waterlogging disaster for maize based on CERES-Maize model in Midwest of Jilin Province, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 83(3), pages 1747-1761, September.
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    Cited by:

    1. Junjun Cao & Guoyong Leng & Peng Yang & Qingbo Zhou & Wenbin Wu, 2022. "Variability in Crop Response to Spatiotemporal Variation in Climate in China, 1980–2014," Land, MDPI, vol. 11(8), pages 1-13, July.
    2. László Huzsvai & Csaba Juhász & Loujaine Seddik & Györgyi Kovács & József Zsembeli, 2024. "The Future Probability of Winter Wheat and Maize Yield Failure in Hungary Based on Long-Term Temporal Patterns," Sustainability, MDPI, vol. 16(10), pages 1-17, May.

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