Detection and Selection of Effective Traits on Grain Yield in Chickpea (Cicer arientinum L.) Under Normal Moisture Condition

Inequality of increasing population and food will be worse as soon as possible, because the human population is expected to increase to over 8 billion by the year 2020 (Ashraf and Harris, 2006). Therefore, plant breeding is a permanent and economic method to solving the problem. Detection of effective traits on grain yield is an efficient approach to increase crop production. Among agronomy crops, chickpea plays vary important role in human life. KHalighi et al. (2011) showed that grain yield had a significant correlation with pod number per plant, seed number per plant, harvest index and biomass. In another research (Fayyaz and Talebi, 2009), the traits of number of pod and number of seed per plant were proposed as the most important traits for yield increasing in chickpea. Yucel et al. (2006) suggested that seed number per plant and pod number per plant have maximum direct effect on grain yield. Stepwise regression showed that the traits of biomass, harvest index and 100-kernel weight had great effect on chickpea grain yield, so the traits explained the main part of yield variance (Mardi et al, 2003). Detection and identifying the most effective traits on chickpea grain yield was the main objective of the current research.
The experiment was conducted based on randomized complete block design with three replications on 18 chickpea promising lines and two cultivars (as control) under normal moisture condition in 2011-2012. The experiment was conducted in agricultural and natural resources research center of Kurdestan, Iran. The traits of day to 50 percent flowering, day to 50 percent podding, day to maturity, relative water content, number of sub-branches, number of main branches, seed number per plant, pod number per plant, plant height, 100- kernel weight, grain yield, harvest index and biomass were measured. Correlation analysis, stepwise regression analysis, and path analysis were used for identifying the best genotypes and traits in the current experiment. Phenotypic coefficient variation, genotypic coefficient variation and broad sense heritability were estimated for all traits.
The results showed that the traits of number of sub-branch per plant, number of pod per plant, number of main-branch per plant, seed number per plant, harvest index and biomass had the maximum, positive and significant correlation with grain yield, while the trait of day to 50% flowering had the maximum, negative and significant correlation with grain yield. Stepwise regression results indicated that the traits of number of sub-branch, biomass, harvest index and day to 50% flowering (with negative beta) were the most important effective traits on the mean of grain yield and explained 97.98% of yield variance. Path analysis results confirmed the obtained results. The path analysis results showed that the traits of number of sun-branch and biomass had the maximum positive direct effect on grain yield. 100-kerenl weight and plant height had the highest and lowest heritability respectively. The maximum genotypic coefficient of variance belongs to 100-kernel weight, number of sub and main branches respectively. Meanwhile, the trait of harvest index and biomass (by increasing the number of sub-branch) had the maximum indirect positive effect on yield. As a result, increasing the sub-branch number and decreasing of day to 50% flowering may be proposed as a useful strategy for yield increase under the current experiment condition.
Grain yield is a quantitative trait with low broad sense heritability. Therefore, identifying the traits correlated with grain yield is a suitable strategy for improvement and indirect selection of grain yield. Stepwise regression and path analysis are an efficient and useful statistical approach for reaching were the above aim. According to results, the traits of number of sun-branch, number of main-branch and biomass recognized as the best suitable traits for indirect grain yield selection under normal moisture stress condition.