How should a spatial-coverage sample design for a geostatistical soil survey be supplemented to support estimation of spatial covariance parameters?

We use an expression for the error variance of geostatistical predictions, which includes the effect of uncertainty in the spatial covariance parameters, to examine the performance of sample designs in which a proportion of the total number of observations are distributed according to a spatial coverage design, and the remaining observations are added at supplementary close locations. This expression has been used in previous studies on numerical optimization of spatial sampling, the objective of this study was to use it to discover simple rules of thumb for practical geostatistical sampling. Results for a range of sample sizes and contrasting properties of the underlying random variables show that there is an improvement on adding just a few sample points and close pairs, and a rather slower increase in the prediction error variance as the proportion of sample points allocated in this way is increased above 10 to 20% of the total sample size. One may therefore propose a rule of thumb that, for a fixed sample size, 90% of sample sites are distributed according to a spatial coverage design, and 10% are then added at short distances from sites in the larger subset to support estimation of spatial covariance parameters.