PERMANOVA is more flexible than ANOVA, since it takes into account non parametric data. Also, a general additive model (GAM) is recommended, since it is flexible and you can continue to add variables :)

Remember, statistical analysis can be complex and it’s always a good idea to consult with a statistician if you’re unsure. Software like SPSS, R, or Python’s statsmodels package can be used to perform mixed design ANOVA1.

As for the growth performance of Melia seedlings, there are studies available that you might find useful34567.

Yes, a Mixed Design ANOVA could be a suitable approach for analyzing the growth performance of Melia seedlings under different treatments over time. This design is appropriate when you have both repeated measures (quarters) and between-subjects factors (treatments: VC, FYM, Control).Here's a general guide on how to proceed

Define Factors:Between-Subjects Factor: Treatment (VC, FYM, Control)

Within-Subjects Factor: Time (Quarterly data)

Organize Data:Arrange your data table with columns for Treatment, Time, and the measured variables (height and girth).

Assumptions Check:Check assumptions like normality and homogeneity of variance for the ANOVA.

Conduct Mixed Design ANOVA:Perform a mixed-design ANOVA, considering Treatment as a between-subjects factor and Time as a within-subjects factor.

Explore interaction effects to see if the treatment effects vary over time.

Post-hoc Tests:If you find significant effects, conduct post-hoc tests (e.g., Tukey HSD) to identify specific differences between treatments or time points.

Data Visualization:Create graphs to visually represent the growth patterns over time for each treatment.

Interpretation:Interpret the results, considering both main effects and interactions between factors.