SOIL
SOIL
SOIL
Articles | Volume 8, issue 1
Articles | Volume 8, issue 1
https://doi.org/10.5194/soil-8-223-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-8-223-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 8, issue 1
Original research article
 | 
25 Mar 2022
Original research article |  | 25 Mar 2022

Estimating soil fungal abundance and diversity at a macroecological scale with deep learning spectrotransfer functions

Yuanyuan Yang, Zefang Shen, Andrew Bissett, and Raphael A. Viscarra Rossel

Viewed

Total article views: 3,311 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,509 735 67 3,311 205 45 43
  • HTML: 2,509
  • PDF: 735
  • XML: 67
  • Total: 3,311
  • Supplement: 205
  • BibTeX: 45
  • EndNote: 43
Views and downloads (calculated since 13 Sep 2021)
Cumulative views and downloads (calculated since 13 Sep 2021)

Viewed (geographical distribution)

Total article views: 3,311 (including HTML, PDF, and XML) Thereof 3,073 with geography defined and 238 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 11 Dec 2024
Download
Short summary
We present a new method to estimate the relative abundance of the dominant phyla and diversity of fungi in Australian soil. It uses state-of-the-art machine learning with publicly available data on soil and environmental proxies for edaphic, climatic, biotic and topographic factors, and visible–near infrared wavelengths. The estimates could serve to supplement the more expensive molecular approaches towards a better understanding of soil fungal abundance and diversity in agronomy and ecology.
Read more