First of all, I explained that the overall ANOVA and the individual effect tests are based on adding a set of terms or a term, respectively, and are not a statement about any pair of levels. The Tukey comparisons, on the other hand, are about the levels. Even though Tukey adjusts for the number of comparisons so that the type I error rate is experiment-wise instead of comparison-wise, it is still (apparently) 0.05 so when you make enough tests like your example so far, you are likely to get a false positive.
It also seems that you are trying lots of different comparisons. Your experimental design is inadequate to support so many tests. The design assumes that you are making specific inferences. Be careful to avoid "cherry picking" until you get a positive result.
In either case, if you believe the comparisons found by the Tukey method, then you need to confirm them with new, independent observations.
Finally, before statman gets a chance to ask, are the differences meaningful or practically important, regardless of the statistical significance?