The use of multivariate analysis on classification of the Leucaena leucocephala (Lam.) de Wit. growth phases.

Abstract

The aim of this work was to identify the growth phases of leucena (Leucaena leucocephala) in presence or absence of organic compound residue, by using of the multivariate analysis techniques. The data set was obtained through an experiment performed at the Experimental Station of Caruaru-PE of the Institute of Agricultural Research - IPA. Besides 19 actual measures of height, additional 18 values were used, obtained by interpolation of best fit curves to the Weibull model, as well as by the linear interpolation, for two groups: according to presence or absence of organic compound residue. The factor analysis was used to reduce the dimensionality of measured data to three factors with explaining 94.60% and 94.30% of the total variation for the treatments with and without organic compound, respectively. The resulting factor scores were subjected to k-means cluster analysis, using previously selected, number of groups k from 3 to 10. The discriminant analysis was then employed to verify the efficiency of clustering of the best classification groups, which was found to be 95.2% and 95.1% of correct classification for the two treatments. The study showed that the leucena trees fertilized with the organic compound presented greater heights. The graphical analysis allowed the comparison between the data treatment with and without organic compound treatment, according to groups, in growth phase. With the fatorial analysis was possible to represent the phases of growth of the leucena in the presence or not of composed organic.