Articles | Volume 25, issue 12
Research article
22 Dec 2021
Research article |  | 22 Dec 2021

Machine-learning methods to assess the effects of a non-linear damage spectrum taking into account soil moisture on winter wheat yields in Germany

Michael Peichl, Stephan Thober, Luis Samaniego, Bernd Hansjürgens, and Andreas Marx


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-9', Anonymous Referee #1, 30 Jan 2021
    • AC1: 'Reply on RC1', Michael Peichl, 30 Apr 2021
  • RC2: 'Comment on hess-2021-9', Anonymous Referee #2, 06 Feb 2021
    • AC2: 'Reply on RC2', Michael Peichl, 30 Apr 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (further review by editor and referees) (06 May 2021) by Bart van den Hurk
AR by Michael Peichl on behalf of the Authors (02 Jul 2021)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (06 Jul 2021) by Bart van den Hurk
RR by Anonymous Referee #1 (08 Jul 2021)
RR by Anonymous Referee #2 (09 Aug 2021)
ED: Publish subject to minor revisions (review by editor) (29 Sep 2021) by Bart van den Hurk
AR by Manal Becker on behalf of the Authors (11 Oct 2021)  Author's response
ED: Publish as is (21 Oct 2021) by Bart van den Hurk
Short summary
Using a statistical model that can also take complex systems into account, the most important factors affecting wheat yield in Germany are determined. Different spatial damage potentials are taken into account. In many parts of Germany, yield losses are caused by too much soil water in spring. Negative heat effects as well as damaging soil drought are identified especially for north-eastern Germany. The model is able to explain years with exceptionally high yields (2014) and losses (2003, 2018).