The principal component analysis is a multivariate analysis technique that has not been frequently used in the interpretation of research data on forage plants. Thus, the aim of this study was to use data already published and interpreted according to univariate analysis and verify if their hypotheses could also be validated through the principal component analysis. Two principal components analysis were performed. For the first one, the following variables were considered: tiller population density, individual tillers mass, leaf area index, leaf area/tiller volume ratio and tiller appearance and survival rates. In the first analysis more than 80% of of the data set variation was explained by the first three main components, which, basically, showed patterns of tiller size / density compensation mechanisms and revealed discrepancies in the way of evaluating tiller shape (leaf : stem ratio or leaf area: volume ratio per tiller). In the second analysis, the first three principal components explained 91.4% of the total variation, which was related basically to the process of resources economy and seasonal allocation of assimilates for different plant structures as a mean of ensuring survival and persistence of plants. The results show the potential of using the principal component analysis in the interpretation of research data on forage plants and corroborates conclusions obtained using univariate methods, with the advantage of reducing the number of global variables
multivariate analysis techniques; Brachiaria brizantha; continuous grazing method
