I don't know the standard deviation of your response or the minimum effect that you are trying to detect but this comparison is fair because I use 1 for the standard deviation (Anticipated RMSE) and 1 for the parameter value (with coding of factor levels, the value is equivalent to an effect that is twice the standard deviation) in both analyses. Here is the power analysis using the random block effect in the model:
(RCBD with random block effects in the model)
Here is the power analysis using your model:
(RCBD with fixed block effects in the model)
So you can see that the power is essentially the same for the effects of interest. Your choice should come down to how you think about the block effect. A random effect seems appropriate to me. Why would you want to model Block as a fixed effect? I do not mean that question as a challenge. It is simply something to think about.
Thank you for comparing the power of detection for me. I did't want to treat the nested Block (Year*Location) effect as fixed, effect. I wanted to treat it as random effect, but just didn't know how to run the test by nesting the Block within the interactions of Year and Location. But now since the power of detection was not affected by simply naming the Blocks 1~12 throughhout the 2 Locations and 2 Years, I would do the analysis this way. Thank you very cmuh again!!