Apr 1, 2022 · Four types of data were used in this study: crop yield data, satellite data, observational climate data, and S2S atmospheric prediction data.

Jul 28, 2024 · Our findings highlighted that the coupling of ML and S2S dynamical atmospheric prediction provided a useful tool for yield forecasting, which ...

Jan 20, 2022 · Subseasonal-to-seasonal (S2S) prediction of winter wheat yields is crucial for farmers and decision-makers to reduce yield losses and ensure ...

Improving the Forecasting of Winter Wheat Yields in Northern China with Machine Learning–Dynamical Hybrid Subseasonal-to-Seasonal Ensemble Prediction.

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

Subseasonal-to-seasonal (S2S) prediction of winter wheat yields is crucial for farmers and decision-makers to reduce yield losses and ensure food security.

Subseasonal-to-seasonal (S2S) prediction of winter wheat yields is crucial for farmers and decision-makers to reduce yield losses and ensure food security.

Cao, J. J., Wang, H. J., Li, J. X., Tian, Q., Niyogi, D. 2022, Improving the forecasting of winter wheat yields in northern China with ...

Aug 19, 2024 · Improving the Forecasting of Winter #Wheat Yields in Northern China with Machine Learning–Dynamical Hybrid Subseasonal-to-Seasonal Ensemble ...

Improving the Forecasting of Winter Wheat Yields in Northern China with Machine Learning–Dynamical Hybrid Subseasonal-to-Seasonal Ensemble Prediction.