SOIL
SOIL
SOIL
Articles | Volume 3, issue 1
Articles | Volume 3, issue 1
https://doi.org/10.5194/soil-3-67-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/soil-3-67-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Articles | Volume 3, issue 1
Original research article
 | 
30 Mar 2017
Original research article |  | 30 Mar 2017

A probabilistic approach to quantifying soil physical properties via time-integrated energy and mass input

Christopher Shepard, Marcel G. Schaap, Jon D. Pelletier, and Craig Rasmussen

Viewed

Total article views: 2,778 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,464 1,093 221 2,778 486 162 186
  • HTML: 1,464
  • PDF: 1,093
  • XML: 221
  • Total: 2,778
  • Supplement: 486
  • BibTeX: 162
  • EndNote: 186
Views and downloads (calculated since 18 Oct 2016)
Cumulative views and downloads (calculated since 18 Oct 2016)

Viewed (geographical distribution)

Total article views: 2,778 (including HTML, PDF, and XML) Thereof 2,556 with geography defined and 222 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 14 Dec 2024
Download
Short summary
Here we demonstrate the use of a probabilistic approach for quantifying soil physical properties and variability using time and environmental input. We applied this approach to a synthesis of soil chronosequences, i.e., soils that change with time. The model effectively predicted clay content across the soil chronosequences and for soils in complex terrain using soil depth as a proxy for hill slope. This model represents the first attempt to model soils from a probabilistic viewpoint.
Read more