You can create a subset of your data (from a randomly selected intact region) in a separate DATA step and then analyze this data set. Or you could randomly sample the locations from the entire data set, possibly using SURVEYSELECT to do the selections. I can see both approaches. The problem with limiting your analysis to an intact section of the field is that you will not have any information on spatial associations over large distances (by definition, you will not be considering the largest distance lags if you are only looking at a section). Of course, it is possible that you will find no spatial correlations at the largest distances. The problem with a random sample of all the locations is that you may not have sufficient numbers of locations at a given distance to obtain precise parameter estimates. I would consider doing the analysis on multiple sections or multiple samplings to see if your results are consistent. You could thus do most of your analysis with VARIOGRAM (you don't need to do this with residuals when your model is only a constant for the fixed effects). I am guessing that you can have much larger data sets with VARIOGRAM. Plus, you have to be careful in using REML or ML (the MIXED approach for the spatial parameters) for fitting spatial covariances (or semi-variances). Those large spatial lags with few observations can be too influential (the spatial covariance parameter estimates may be poor, unless you have good starting values). Note that MIXED uses GLS for the fixed effects, but REML/ML for the random effects (which includes the terms in a REPEATED statement). This is all very nicely described in the spatial analysis chapter in Littell et al. (2006), SAS for Mixed Models, 2nd edition. Weighted least squares (as in VARIOGRAM) is useful for this analysis. There is trial and error in getting good starting values for covariance/semi-variance models (sills, etc.). Scabenberger & Pierce (2002), Contemporary Statistical Models for the Plant and Soil Sciences, has a very good description of this. But also check out the VARIOGRAM User's Guide; recent versions of the proc have very good modeling capabilities.
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