Improving the forecasting of winter crop yields in northern China with machine learning-dynamical hybrid subseasonal-to-seasonal ensemble prediction

Huijing Wang, Junjun Cao, Jinxiao Li, Qun Tian, Dev Niyogi

Preprint · 20 January 2022 · China Meteorological Administration (CMA), Institute of Atmospheric Physics, Chinese Academy of Sciences

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In one sentence. Hybrid machine learning and S2S dynamical forecasts improve northern China winter wheat yield prediction over observation-based inputs.

Abstract

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.

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