nested Kruskal Wallis

Hi,

I'm trying to analyse a dataset on the size of beetles collected in
different crop types.  Crop type is the fixed effect, and field is a
random factor, nested in crop.  Although my data is normal after
transformation, it doesn't meet the assumption of homogeneity of
variances.  In addition, sample size within fields is small and
unequal (n=2-10).  So, I'm trying to figure out if I can run a nested
Kruskal Wallis type analysis, since this is more robust to both
problems than an ANOVA, even with permutation.  I've been told it
might be possible to run such an analysis within the coin package, but
I can't figure out how to do this -methods for blocked designs are
mentioned in the documentation, but not nested designs.

I'd appreciate any help or thoughts!

Cheers, Jude Phillips