If you have access to Milliken and Johnson's Analysis of Messy Data, vol. 1, you could see their approach (termed a "means model") where they fit the response as the highest level interaction, and then create tests and contrasts to fit the research questions. A google search on "means model" turned up a lot of hits.
. Here is a link to an example, which would cover your whole plot analysis:
http://www.ats.ucla.edu/stat/sas/faq/cell_mean_coding_contrast.htm
Now, you have the additional design element of a repeated measures part, so you would need to port the code to PROC MIXED to correctly accommodate the covariance between the repeat measures. Rather than ESTIMATE statments, LSMESTIMATE statements might be more useful.
I would suggest getting a whole plot analysis, and then sharing that code. From there, we could craft PROC MIXED code to correctly handle the repeated part.
Steve Denham
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