Modeling wheat development under extreme weather with WOFOST-EW v1

Zheng, Jinhui; Yu, Le; Du, Zhenrong; Xiao, Liujun; Huang, Xiaomeng

doi:10.5194/gmd-18-8379-2025

Articles | Volume 18, issue 21

https://doi.org/10.5194/gmd-18-8379-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-18-8379-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 18, issue 21

Development and technical paper

|

10 Nov 2025

Development and technical paper |

| 10 Nov 2025

Modeling wheat development under extreme weather with WOFOST-EW v1

Jinhui Zheng, Le Yu, Zhenrong Du, Liujun Xiao, and Xiaomeng Huang

Supplement

https://doi.org/10.5194/gmd-18-8379-2025-supplement

Data sets

SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty L. Poggio et al. https://doi.org/10.5194/soil-7-217-2021

Model code and software

WOFOST-EW v1: Enhanced WOFOST for Extreme Weather Jinhui Zheng https://doi.org/10.5281/zenodo.15698320

Short summary

This study integrates the extreme weather index and deep learning algorithms with the World Food Studies Simulation Model (WOFOST), proposing the WOFOST-EW v1. WOFOST-EW significantly improves the simulation of winter wheat growth under extreme weather conditions, providing more accurate predictions of phenology and yield. As extreme weather events become more frequent, WOFOST-EW provides a key tool for agricultural development.