Articles | Volume 18, issue 21
https://doi.org/10.5194/gmd-18-8379-2025
https://doi.org/10.5194/gmd-18-8379-2025
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

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on egusphere-2024-4010', Theodoros Mavrommatis, 09 Apr 2025
    • AC3: 'Reply on CC1', Le Yu, 26 Jun 2025
  • RC1: 'Comment on egusphere-2024-4010', Theodoros Mavrommatis, 09 Apr 2025
    • AC1: 'Reply on RC1', Le Yu, 26 Jun 2025
  • RC2: 'Comment on egusphere-2024-4010', Anonymous Referee #2, 27 May 2025
    • AC2: 'Reply on RC2', Le Yu, 26 Jun 2025

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Le Yu on behalf of the Authors (26 Jun 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (09 Jul 2025) by Christian Folberth
RR by Anonymous Referee #2 (06 Aug 2025)
ED: Reconsider after major revisions (12 Aug 2025) by Christian Folberth
AR by Le Yu on behalf of the Authors (25 Aug 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (01 Sep 2025) by Christian Folberth
RR by Anonymous Referee #2 (28 Sep 2025)
ED: Publish subject to minor revisions (review by editor) (02 Oct 2025) by Christian Folberth
AR by Le Yu on behalf of the Authors (03 Oct 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to technical corrections (10 Oct 2025) by Christian Folberth
AR by Le Yu on behalf of the Authors (10 Oct 2025)  Author's response   Manuscript 
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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.
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