Path analysis under multicollinearity in S˳ x S˳ maize hybrids

Abstract

The goal of this study was to partition genotypic correlations into direct and indirect yield components effects of grain weight of S˳ x S˳ maizehybrids by means of path analysis. Since multicollinearity was expressed in the independent variable correlation matrix, data adequacy to the analysis was evaluated. Least squares alternative methods were used to avoid the adverse effects of this phenomenon. The experimental design was randomized complete blocks with two replications. The associations among traits obtained from the path analysis under multicollinearity, using least squares solutions, showed little coherence. When using the ridge path analysis or the analysis with trait culling, the traits ear number and 50-kernel weight had the highest direct effects on grain weight/plant. These methods were efficient to reduce the multicollinearity effects.