Articles | Volume 3, issue 4
SOIL, 3, 235–244, 2017
https://doi.org/10.5194/soil-3-235-2017
SOIL, 3, 235–244, 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 et al.

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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)
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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.