Path analysis under varying degrees of multicollinearity identifies key traits for indirect grain yield selection in common bean

Path analysis of agronomic traits has been conducted under varying degrees of multicollinearity, which can hinder the effectiveness of indirect selection for high grain yield in common bean (Phaseolus vulgaris L.). This study aimed to examine the outcomes of path analysis for multiple agronomic traits across different degrees of multicollinearity and to identify the most promising traits for the indirect selection of common bean cultivars with high grain yields. A total of 25 common bean cultivars were evaluated for multiple agronomic traits in four experiments. Path analysis was carried out under three levels of multicollinearity: severe, moderate to strong, and weak. Significant effects of cultivar, environment, and genotype × environment interaction were observed for all traits, facilitating the use of indirect selection. Under conditions of severe or moderate to strong multicollinearity, high regression coefficients and/or coefficients with signs contrary to the anticipated direction of selection were observed, resulting in interpretative errors in path analysis. Conversely, under weak multicollinearity, the regression coefficients were more consistent with the biological phenomena under investigation in terms of both magnitude and sign. This alignment enhances the ability to identify promising agronomic traits for indirect selection. Therefore, path analysis under weak multicollinearity helps choose the most effective agronomic traits for indirect grain yield selection in common bean programs. It is recommended that indirect selection targets the highest numbers of grains per pod and pods per plant in the development of new common bean cultivars aimed at achieving high grain yields.

Keywords:
Phaseolus vulgaris L; multicollinearity diagnostics; indirect selection