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 Wheat 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. Recently, numerous researchers have utilized machine learning (ML) methods to predict crop yield, using observational climate variables and sate...

Szczegółowa specyfikacja

Opis bibliograficzny
Główni autorzy: Junjun Cao, Huijing Wang, Jinxiao Li, Qun Tian, Dev Niyogi
Format: Artykuł
Język:English
Wydane: MDPI AG 2022-04-01
Seria:Remote Sensing
Hasła przedmiotowe:
yield forecasting
climate variables
subseasonal-to-seasonal prediction
machine learning
winter wheat
Dostęp online:https://www.mdpi.com/2072-4292/14/7/1707

Internet

https://www.mdpi.com/2072-4292/14/7/1707

Podobne zapisy