Repeated Measures ANOVA vs multiple simple ANOVAs I have calculated plant growth rates at 2, 4, and 6 weeks of growth and would like to answer the question, "at each of these time points, do the soil treatments affect plant growth rates?". I have been advised that I need to run a related measure ANOVA as I have sampled the same individuals multiple times, however, I am not convinced that my data will meet the assumptions or that this is the correct choice of test.
The plant growth rate for each time was calculated by taking the change in plant growth (from the start to end of that fortnight) and dividing by 14 days.  So the week 2 growth rate was calculated by taking each individuals day 0 height from its day 14 height and then dividing by 14 days. The same was done for week 4 heights but using day 14 to day 28. And so on.
My issues are:

*

*I do not have the same number of observations per treatment (some treatments resulted in higher germination success than others), nor are they consistent through time (some plants died throughout the course of measurements). As such, it appears that my I may be violating the need of a balanced design (or is this simply a missing values issue? I can't get my r {anova_test} function to run using my data for this reason)

*I am not so interested in if plant growth rates change between times (this is to be expected), but more so if treatments have an affect at each time (three seperate questions; that is to say, at week 2, was there a sig difference?; and week 4, was there a sig difference?...), hence why I am more inclined to do multiple simple ANOVA instead (one for each time). Would this be suitable, or is it still improper to do so given the same individual was measured multiple times for the same variable (despite one one measurement per individual being used per ANOVA)?


It is very difficult to understand plant growth conditional on the plant not dying.  I would opt for an unconditional analysis of an ordinal outcome Y where the lowest level of Y codes for death and all other levels are actual plant heights.  That way you don't need to use a hard-to-interpret "missing" data imputation.  Consider a longitudinal ordinal model as discussed here - either a first-order Markov model or using random effects.
It is generally not a good idea to model differences, because in doing so one must have perfectly transformed Y before the subtraction.  It's usually better to model trajectories, then after fitting the model to compute any contrast of interest.  But the simplest thing to assess, from the fitted longitudinal model, is the plant height at the final time point, adjusted for the baseline height.  This captures growth.
