The value of nested sampling for exploring the spatial structure of univariate variation of the soil has been demonstrated in several studies and applied to practical problems. This paper shows how the method can be extended to the multivariate case. While the extension is simple in theory, in practice the direct estimation of covariance components by equating mean-square matrices with their expectation will often lead to estimates that are not positive semidefinite. This paper discusses solutions to this problem for balanced and unbalanced sample designs. In the balanced case there is a residual maximum likelihood (REML) estimator that will find estimates of covariance components that maximize an overall likelihood on the condition that all components are positive semidefinite (p.s.d.). This is possible because the condition is met if the differences of successive mean-square matrices are positive semidefinite, and this constraint can be incorporated into an algorithm. This does not hold for unbalanced designs. In this paper the problem was solved for unbalanced designs by scaling covariance components that were not p.s.d. to the nearest p.s.d. matrix according to a Euclidean distance.

These methods were applied to data from three surveys, two with balanced and one with unbalanced sampling. Different patterns of scale-dependence of the correlation of soil properties were found. For example, at Ginninderra Experimental Station in Australia the soil water content and bulk density were correlated significantly, with the correlation increasing with distance to 56 m, but at longer distances the properties were not significantly correlated. By contrast, the pH of the soil and the available P content showed correlation that increased with distance. The implications of these results for planning more detailed sampling, both for prediction and for investigation of processes, are discussed.