Articles | Volume 3, issue 4
https://doi.org/10.5194/soil-3-235-2017
https://doi.org/10.5194/soil-3-235-2017
Original research article
 | 
13 Dec 2017
Original research article |  | 13 Dec 2017

Planning spatial sampling of the soil from an uncertain reconnaissance variogram

R. Murray Lark, Elliott M. Hamilton, Belinda Kaninga, Kakoma K. Maseka, Moola Mutondo, Godfrey M. Sakala, and Michael J. Watts

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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (review by editor) (27 Oct 2017) by Paul Hallett
AR by R M Lark on behalf of the Authors (27 Oct 2017)  Author's response   Manuscript 
ED: Publish as is (28 Oct 2017) by Paul Hallett
ED: Publish subject to technical corrections (07 Nov 2017) by Kristof Van Oost (Executive editor)
AR by R M Lark on behalf of the Authors (09 Nov 2017)  Manuscript 
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Short summary
An advantage of geostatistics for mapping soil properties is that, given a statistical model of the variable of interest, we can make a rational decision about how densely to sample so that the map is sufficiently precise. However, uncertainty about the statistical model affects this process. In this paper we show how Bayesian methods can be used to support decision making on sampling with an uncertain model, ensuring that the probability of meeting certain levels of precision is high enough.