To clarify, you have three replicates ("strips") in one year and three different replicates in the second year, yes? If so, then year is a fixed effects factor that is assigned (although probably not randomly assigned) to replicates; consequently, year is not a repeated measures factor.
I'll assume that each replicate contains 16 plots, and that the 16 combinations of 4 grass species, 2 brassica species, and 2 legume species are randomly assigned to plots. I'll also assume that you measured yield on each plot three times (days 45, 70, and 90).
Because each level of day is measured on the same plot, day is a repeated measures factor. It is important to note that the levels of day (45, 70, 90) are not evenly spaced; this is a consideration for the choice of covariance structure type, most of which are not appropriate for repeated measures factors with unequally spaced levels. Perhaps in your experimental system, there is not much actual difference between 25 days (70 - 45) and 20 days (90 - 70); that's something you would have to decide.
Do you want to assess whether the brassica species or the legume species potentially affect the grass yield, in addition to a possible effect of grass species? If so, then brassica species and legume species would be included in the MODEL statement, giving you a five-factor treatment structure. A full five-way factorial (with one 5-way interaction, five 4-way interactions, ten 3-way interactions, ten 2-way interactions, and five main effects) can be difficult to interpret, and you will want to ponder how your research objectives relate to the complexity of the model.
An additional complication is that you have three responses: grass yield, brassica yield, and legume yield. These 3 variables are yield components measured on the same plot and will be correlated. You could start by analyzing each yield component separately, but at some point you will probably need to address the interdependence of these 3 yield metrics.
Are you using MIXED or GLIMMIX? With either, an excellent resource for building hierarchical models and implementing repeated measures is https://www.sas.com/store/books/categories/usage-and-reference/sas-for-mixed-models-second-edition/prodBK_59882_en.html . The update to this text (SAS for Mixed Models: An Introduction by W. W. Stroup et al.) is supposed to be available this fall; I expect it will provide more details about the use of GLIMMIX than does the 2nd ed (which is largely focused on MIXED).
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