SOIL
SOIL
SOIL
Articles | Volume 10, issue 2
Articles | Volume 10, issue 2
https://doi.org/10.5194/soil-10-619-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-10-619-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 10, issue 2
Original research article
 | 
10 Sep 2024
Original research article |  | 10 Sep 2024

An ensemble estimate of Australian soil organic carbon using machine learning and process-based modelling

Lingfei Wang, Gab Abramowitz, Ying-Ping Wang, Andy Pitman, and Raphael A. Viscarra Rossel

Viewed

Total article views: 1,229 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
959 208 62 1,229 24 23
  • HTML: 959
  • PDF: 208
  • XML: 62
  • Total: 1,229
  • BibTeX: 24
  • EndNote: 23
Views and downloads (calculated since 22 Jan 2024)
Cumulative views and downloads (calculated since 22 Jan 2024)

Viewed (geographical distribution)

Total article views: 1,229 (including HTML, PDF, and XML) Thereof 1,204 with geography defined and 25 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 20 Nov 2024
Download
Short summary
Effective management of soil organic carbon (SOC) requires accurate knowledge of its distribution and factors influencing its dynamics. We identify the importance of variables in spatial SOC variation and estimate SOC stocks in Australia using various models. We find there are significant disparities in SOC estimates when different models are used, highlighting the need for a critical re-evaluation of land management strategies that rely on the SOC distribution derived from a single approach.
Read more