Sequential Path Model for Determining Interrelationships among Grain Yield and Related Characters in Maize

Abstract

Knowledge of interrelationships between grain yield and its contributing components will improve the efficiency of breeding programs through the use of appropriate selection indices. Previous path analyses studies in maize (Zea mays L.) treated yield components as first‐order variables. The present study, based on evaluation of 90 experimental maize hybrids (comprising one diallel and one line × tester set) at two locations in India, utilizes a sequential path model for analysis of genetic associations among grain yield and its related traits by ordering the various variables in first‐, second‐, and third‐order paths on the basis of their maximum direct effects and minimal collinearity. The sequential path model showed distinct advantages over the conventional path model in discerning the actual effects of different predictor variables. Two first‐order variables, namely 100‐grain weight and total number of kernels per ear, revealed highest direct effects on total grain weight (p = 0.74 and p = 0.78, respectively), while ear length, ear diameter, number of kernel rows, and number of kernels per row were found to fit as second‐order variables. All direct effects were found to be significant, as indicated by bootstrap analysis. Test for the goodness‐of‐fit revealed that the sequential path model provided better fit to various datasets analyzed in the study. Correlations between the predicted values of various response variables in the second season dataset based on the path coefficients of the first season were high, except for ear length and number of kernels per row. The applicability of the model has been confirmed through analysis of two additional datasets during 2000. The results indicated the utility of the sequential path model for determining the interrelationships among grain yield and related traits in maize.