Articles | Volume 3, issue 4
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


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Peer-review completion

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)
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.