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.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (review by editor) (02 Jun 2020) by Bas van Wesemael
AR by Lorena Grabowski on behalf of the Authors (19 Jun 2020)  Author's response
ED: Publish as is (22 Jun 2020) by Bas van Wesemael
ED: Publish as is (25 Jun 2020) by Kristof Van Oost(Executive Editor)
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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.