Articles | Volume 3, issue 4
SOIL, 3, 235–244, 2017
SOIL, 3, 235–244, 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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Cited articles

de Gruijter, J. J., Brus, D. J., Biekens, M. F. P., and Knotters, M.: Sampling for Natural Resource Monitoring, Springer, Berlin, 2006.
Di, H. J., Trangmar, B. B., and Kemp, R. A.: Use of geostatistics in designing sampling strategies for soil survey, Soil Sci. Soci. Am. J., 53, 1163–1167, 1989.
Diggle, P. J. and Ribeiro, P. J.: Model-Based Geostatistics, Springer, New York, 2007.
Dobson, A. J.: An Introduction to Generalized Linear Models, Chapman & Hall, London, 1990.
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.