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Improving the forecasting of winter crop yields in northern China with machine learning-dynamical hybrid subseasonal-to-seasonal ensemble prediction
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  • Huijing Wang,
  • Junjun Cao,
  • Jinxiao Li,
  • Qun Tian,
  • Dev Niyogi
Huijing Wang
Central China Normal University
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Junjun Cao
Central China Normal University

Corresponding Author:caojun@ccnu.edu.cn

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Jinxiao Li
Institute of Atmospheric Physics
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Qun Tian
Guangzhou Institute of Tropical and Marine Meteorology
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Dev Niyogi
University of Texas at Austin
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Subseasonal-to-seasonal (S2S) prediction of winter wheat yields is crucial for farmers and decision-makers to reduce yield losses and ensure food security. Recently, numerous researchers have utilized machine learning (ML) methods to predict crop yield using observational climate variables and satellite data. Meanwhile, some studies also illustrate the potential of state-of-the-art dynamical atmospheric prediction in crop yield forecasting. However, the potential of coupling both methods has not been fully exploited. Herein, we aim to establish a skilled ML–dynamical hybrid model for crop yield forecasting (MHCF v1.0), which hybridizes ML and a global dynamical atmospheric prediction system, and apply it to northern China at the S2S time scale. MHCF v1.0 demonstrates that crop yield forecasting with S2S dynamical predictions generally outperforms that with observational climate data. The coupling of ML and S2S dynamical atmospheric prediction provides a useful tool for yield forecasting, which could guide agricultural practices, policy-making and agricultural insurance.