Early-season estimation of winter wheat yield: A hybrid machine learning-enabled approach.

Accurate crop yield forecasting can help stakeholders take effective measures in advance to avoid potential grain supply risks. However, currently, yield forecasts are mostly made close to harvest (e.g. 1–3 months before harvest for Chinese winter wheat), which gives stakeholders a relatively short time to react, decide, and intervene. To satisfy stakeholders' requirements for timely and precise yield forecasting, we propose a hybrid machine learning-enabled early-season yield forecasting method integrated with an intermediate climate forecast process. The results show that: (1) Compared with the baseline model, our proposed method advances winter wheat yield prediction up to 8 months before harvest with satisfactory accuracy. (2) The climate forecast process incorporated is effective and consistently optimized in various model combinations and controlled experiments. (3) The proposed method performs robustly over different spatial scales (e.g., in the first month of Chinese winter wheat, the yield predictive accuracy is improved in 183 out of 233 counties). In summary, our work provides an effective and robust approach for early-season yield forecasting that gives stakeholders more time to take appropriate actions to cope with crop yield volatility risks. • Timely forecasting of crop yields is critical to regional and global food supplies. • An early-season yield forecast framework with hybrid machine learning is proposed. • Our proposed methodology outperforms the benchmark models in the early season. • Our methodology exhibits strong robustness across heterogeneous regions in China. [ABSTRACT FROM AUTHOR]