Factor Analysis for Quantitative Traits and Path Analysis for Grain Yield in Wheat

In this study, factor analysis was conducted to determine the factors which contributed to the variation of quantitative traits and path analysis was performed to find the direct and indirect effects of yield components on grain yield in bread wheat. A doubled haploid population of 157 lines of wheat (Triticum aestivum L.) was evaluated for agronomic and morphological traits, using a randomized complete block design with three replications in 2003 and 2004. The results of factor analysis based on maximum likelihood indicated five factors explaining 80.4% and 73.9% of total variation in 2003 and 2004, respectively. The first factor in 2003 had 30.5% contribution to the total variation, strongly influenced by the traits of pollination date, heading date, flag leaf length and days to maturity. This factor also indicated the negative relationship among the yield components and the importance of relationship between grain yield and some morphological traits. The first factor in 2004 was more affected by grain weight/spike, grains/spike and 1000-grain weight, thus it was named as grain yield factor. The second and third factors in 2003 were considered as plant height and grain yield and in 2004 as maturity and plant height, respectively. The results of path analysis showed that grains/spike had the most direct and positive effects on grain yield in 2003 (1.33) and 2004 (0.87). Because of the negative and high indirect effects of grains/spike via fertile spikes/m2 and 1000-grains weight on grain yield, the correlation coefficient between grain yield and grains/spike was very low. There was not much difference between the phenotypic and genetic direct effects of spike/m2 on grain yield, indicating that their relationship was less affected by environmental conditions. In general, the results showed that grains/spike and spikes/m2 can be more efficient compared to 1000- grains weight for increasing grain yield and can be used as selection indices in breeding programs. Also, according to the results of factor analysis, selection based on the fourth factor including biological yield, spike/m2 and grain yield as selection index can be effective to improve grain yield in breeding programs.