Articles | Volume 8, issue 2
SOIL, 8, 587–604, 2022
https://doi.org/10.5194/soil-8-587-2022
SOIL, 8, 587–604, 2022
https://doi.org/10.5194/soil-8-587-2022
Original research article
22 Sep 2022
Original research article | 22 Sep 2022

Spatial prediction of organic carbon in German agricultural topsoil using machine learning algorithms

Ali Sakhaee et al.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on soil-2021-107', Anonymous Referee #1, 27 Dec 2021
    • AC1: 'Reply on RC1', Ali Sakhaee, 20 Feb 2022
  • RC2: 'Comment on soil-2021-107', Anonymous Referee #2, 13 Jan 2022
    • AC2: 'Reply on RC2', Ali Sakhaee, 20 Feb 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (22 Feb 2022) by Olivier Evrard
AR by Ali Sakhaee on behalf of the Authors (03 Apr 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (05 Apr 2022) by Olivier Evrard
RR by Anonymous Referee #1 (05 Apr 2022)
RR by Anonymous Referee #2 (16 May 2022)
ED: Publish subject to minor revisions (review by editor) (16 May 2022) by Olivier Evrard
AR by Ali Sakhaee on behalf of the Authors (09 Jun 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (09 Jun 2022) by Olivier Evrard
ED: Publish as is (15 Jul 2022) by John Quinton(Executive Editor)
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Short summary
As soil carbon has become a key component of climate-smart agriculture, the demand for high-resolution maps has increased drastically. Meanwhile, machine learning algorithms are becoming more widely used and are opening up new solutions in soil mapping. This paper shows which algorithms perform best, how soil inventory data can be most efficiently used for digital soil mapping, and the different available options and methods to derive high-resolution soil carbon data at the large regional scale.