Coupling Process-Based Crop Model and Extreme Climate Indicators with Machine Learning Can Improve the Predictions and Reduce Uncertainties of Global Soybean Yields

Coupling Process-Based Crop Model and Extreme Climate Indicators with Machine Learning Can Improve the Predictions and Reduce Uncertainties of Global Soybean Yields

Soybean is one of the most important agricultural commodities in the world, thus making it important for global food security. However, widely used process-based crop models, such as the GIS-based Environmental Policy Integrated Climate (GEPIC) model, tend to underestimate the impacts of extreme cli...

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Bibliographic Details
Main Authors: Qing Sun, Yi Zhang, Xianghong Che, Sining Chen, Qing Ying, Xiaohui Zheng, Aixia Feng
Format: Article
Language:English
Published: MDPI AG 2022-10-01
Series:Agriculture
Subjects:
soybean yield
extreme climate events
machine learning
crop model
uncertainty
Online Access:https://www.mdpi.com/2077-0472/12/11/1791

Internet

https://www.mdpi.com/2077-0472/12/11/1791

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