A geostatistical method to account for the number of aliquots in composite samples for normal and lognormal random variables

Orton, T. G., Pringle, M. J., Allen, D. E., Dalal, R. C. and Bishop, T. F. A. (2015) A geostatistical method to account for the number of aliquots in composite samples for normal and lognormal random variables. European Journal of Soil Science, 66 6: 1023-1032. doi:10.1111/ejss.12297


Author Orton, T. G.
Pringle, M. J.
Allen, D. E.
Dalal, R. C.
Bishop, T. F. A.
Title A geostatistical method to account for the number of aliquots in composite samples for normal and lognormal random variables
Journal name European Journal of Soil Science   Check publisher's open access policy
ISSN 1365-2389
1351-0754
Publication date 2015-11-01
Year available 2015
Sub-type Article (original research)
DOI 10.1111/ejss.12297
Open Access Status Not Open Access
Volume 66
Issue 6
Start page 1023
End page 1032
Total pages 10
Place of publication Chichester, West Sussex United Kingdom
Publisher Wiley-Blackwell Publishing
Language eng
Formatted abstract
Geostatistical methods can be used to calculate predictions of soil variables at unsampled locations, but the methodology is typically based on samples collected on identical sample supports. In this paper, we provide and test theory that allows the inclusion of data from mixed sample supports in a single analysis. In particular, we consider composite sample supports that are defined by the number of aliquots used to form a single composite sample, ni, and the set of locations, xi, from which the aliquots were collected. We allow both ni and xi to vary between samples (xi can vary in the extent and geometry of the aliquot locations), and thereby show how point data (a special case of composite data, defined by ni = 1 and xi as the known sample point) can be included in the same geostatistical analysis as composite data. A further complication arises when data are not normally distributed, rather their logarithm is. When composite sampling is used for such lognormal data, the sample support affects not only the variance but also the mean. We give the theory for normally distributed variables, and also derive an approximation that can be used when the point-support variable is lognormal. We focus on this latter case, and test the approach with a series of simulation experiments. Finally, we illustrate the approach on a dataset of soil organic carbon (SOC) values from a grazing property in Queensland, Australia, where soil information from two measurement phases was obtained on different supports.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Agriculture and Food Sciences
Official 2016 Collection
 
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