Comparison between multivariate methods applied for the evaluation of genetic divergence in cacao (Theobroma cacao L.)

Several multivariate methods have been used in divergence analyses of populations. Consistency and relative association among four methods were assessed using a 5 x 5 complete-diallel data involving cacao cultivars. Over a 5-year period, five cultivars were analyzed based upon five yield components. In assessing the divergence of parents only the data obtained from five cacao cultivars were analyzed. Four multivariate statistics presented close association when considered in pairs, in this case the Mahalanobis' (D-2) With the mean Euclidean distance obtained SI am canonical variates (d(cv)), and mean Euclidean distance (d(e)) with the mean Euclidean distance obtained from principal components (d(pc)). In both cases, high correlations (r > 0.95) were obtained. However, a weak association was detected between D-2 and d(e) and between d(pc) and d(cv) (0.50 and 0.66, respectively). Thus, in studies on genetic divergence, statistics considering the error variance-covariance matrix should be preferred whenever its estimate is possible.