Path analysis and multivariate factorial analyses for determining interrelationships between grain yield and related characters in maize hybrids

In order to study the association among yield components and their direct and indirect effects on the grain yield, 144 experimental maize hybrids in Kermanshah, Iran were evaluated in a lattice design with two replications. Analysis of variance, factor and sequential path analyses were carried out for the studied traits. Results of path analysis showed that two first-order variables, namely; 100 grain weight (100-GW) and total number of kernels per ear (TNK) revealed highest direct effects on total grain weight (TGW), while ear length (EL), ear diameter (ED), number of kernel rows (NR) and number of kernels per row (NKR) were found to fit as second-order variables. Multivariate factorial analyses showed that six independent factors justified 77.852% of total data variations. The first and third justifications that explain 33.155% of the data variation was called yield and yield components. The second factor (14.121%) called traits was related to kernel depth. Other factors were phenology (11.060%), plant growth (11.038%) and tassel (8.478%). We concluded that hybrid number 69 (860002-2) has the highest grain yield. The traits 100 GW and TNK were shown to be the two important factors that affected the maize performance. Moreover, NKR showed the highest direct effect on TNK which showed the highest direct effect on TGW. Therefore, NKR could be used as a suitable index to improve grain yield too.   Key words: Correlation, sequential path analysis, indirect selection.