Articles | Volume 6, issue 2
SOIL, 6, 359–369, 2020
https://doi.org/10.5194/soil-6-359-2020
SOIL, 6, 359–369, 2020
https://doi.org/10.5194/soil-6-359-2020
Original research article
06 Aug 2020
Original research article | 06 Aug 2020

Disaggregating a regional-extent digital soil map using Bayesian area-to-point regression kriging for farm-scale soil carbon assessment

Sanjeewani Nimalka Somarathna Pallegedara Dewage et al.

Viewed

Total article views: 1,629 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,103 461 65 1,629 64 60
  • HTML: 1,103
  • PDF: 461
  • XML: 65
  • Total: 1,629
  • BibTeX: 64
  • EndNote: 60
Views and downloads (calculated since 30 Jan 2020)
Cumulative views and downloads (calculated since 30 Jan 2020)

Viewed (geographical distribution)

Total article views: 1,368 (including HTML, PDF, and XML) Thereof 1,317 with geography defined and 51 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 06 Jul 2022
Download
Short summary
Most soil management activities are implemented at farm scale, yet digital soil maps are commonly available at regional/national scales. This study proposes Bayesian area-to-point kriging to downscale regional-/national-scale soil property maps to farm scale. A regional soil carbon map with a resolution of 100 m (block support) was disaggregated to 10 m (point support) information for a farm in northern NSW, Australia. Results are presented with the uncertainty of the downscaling process.