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...

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Bibliographic Details
Main Authors: Junjun Cao, Huijing Wang, Jinxiao Li, Qun Tian, Dev Niyogi
Format: Article
Language:English
Published: MDPI AG 2022-04-01
Series:Remote Sensing
Subjects:
yield forecasting
climate variables
subseasonal-to-seasonal prediction
machine learning
winter wheat
Online Access:https://www.mdpi.com/2072-4292/14/7/1707

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