Selection of the residual variance-covariance matrix in the analysis of varietal trials with repeated measures in sugarcane

This study aimed to evaluate different residual structures of variance-covariance matrix (Sigma), regarding the fitting of longitudinal data via mixed models in variety trials of sugarcane. The adequate choice of this matrix provides most representative models to the data. In each model was also evaluated the effects of treatments (varieties), either as fixed or as random. Four trials were carried out in three locations in the Goias State, Brazil, from 2005 to 2009. Each experiment was designed in randomized complete block with three or four repetitions. The response variable analyzed was tons of stalks per hectare (TCH). The goodness of fitting of the different models to the data was assessed by Akaike information criterion (AIC) and by likelihood ratio test (LRT). This last statistic was used only to compare nested models, two by two. It was observed that classic model in split-plot design ranged among the worst or with just median adjustments. The structures of Sigma matrix with the best fittings to the data varied among trials, with outstanding for the unstructured matrix. These results show that the structure of independent errors, in general, is not adequate for these analyses, and a prior definition of the co-variance structure can lead to unreliable results for these trials. Small changes were observed in the ranking of these structures by assuming the treatment effects as fixed or random, however, without significant effects on the ranking of the best structures in each trial.