Assess Correlation, Stepwise Regression and Path Coefficient Analysis of Characteris-tics Affecting Seed Yield of Canola (Brassica napus L.) Genotypes
محورهای موضوعی : Journal of Crop Nutrition ScienceSomayeh Ghalandari 1 , Tayeb Sakinezhad 2 , Mani Mojaddam 3 , Shahram Lak 4 , Mojtaba Alavifazel 5
1 - PhD. Student, Department of Agronomy, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran.
2 - Assistant Professor, Department of Agronomy, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran.
3 - Associate Professor, Department of Agronomy, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran.
4 - Professor, Department of Agronomy, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran.
5 - Department of Agronomy, Ahvaz Branch, Islamic Azad University, Iran.
کلید واژه: Gibberellin, Morphology, Nitrogen, Oil, Protein, Rapeseed,
چکیده مقاله :
BACKGROUND: Correlation and path coefficient analysis could be used as an important tool to evaluate relation between traits, propose most effective trait on seed yield and determine direct and indirect effects of selected traits on dependent variable. OBJECTIVES: Evaluate correlation between traits and selected effective traits those had the highest effect on seed yield by stepwise regression and determine direct and indirect effect of selected traits on seed yield by path analysis. METHODS: This research was done via combined analysis split plot factorial experiment based on randomized complete blocks design (RCBD) with three replications along 2015-16 and 2016-17. The main factor included different level of canola genotype (Hyola401, RGS003, Jerry) and sub factors consisted different concentration of gibberellin hormone (0, 50 and 100 mg.l-1) and different times of application of gibberellin hormone (Planting, vegetative phase before flowering, flowering until pod emergence). RESULT: Correlation between traits revealed the most positive and significant relation between biologic yield (r=0.960**), number of pod per plant (r=0.931**), number of seed per pod (r=0.905**), 1000-seed weight (r= 0.834**), pod length (r= 0.824**), harvest index (r= 0.690**) and seed yield was observed. Also the traits of leaf dry weight (r=0.550*), stem dry weight (r=0.530*) and plant height (r=0.510*) had significant correlation with the seed yield at 5 percent probability level. Stepwise regression analysis introduced five selected traits (biologic yield, number of pod per plant, number of seed per pod, 1000-seed weight and pod length determines) that covered 93.5 percent of variation related to seed yield. Variable of the biological yield with a positive direct effect (0.785) on seed yield with indirect effects of number of pods per plant (0.130), number of seed per pods (0.025), 1000-seed weight (-0.030) and pod length (0.05) caused a positive correlation between this trait and seed yield. So breeders can use selected traits for achieve optimum genotypes instead of direct selection for seed yield. CONCLUSION: Among studied traits, biologic yield had the most direct positive effect (0.785) on seed yield and had an important role to explain seed yield variation.
BACKGROUND: Correlation and path coefficient analysis could be used as an important tool to evaluate relation between traits, propose most effective trait on seed yield and determine direct and indirect effects of selected traits on dependent variable. OBJECTIVES: Evaluate correlation between traits and selected effective traits those had the highest effect on seed yield by stepwise regression and determine direct and indirect effect of selected traits on seed yield by path analysis. METHODS: This research was done via combined analysis split plot factorial experiment based on randomized complete blocks design (RCBD) with three replications along 2015-16 and 2016-17. The main factor included different level of canola genotype (Hyola401, RGS003, Jerry) and sub factors consisted different concentration of gibberellin hormone (0, 50 and 100 mg.l-1) and different times of application of gibberellin hormone (Planting, vegetative phase before flowering, flowering until pod emergence). RESULT: Correlation between traits revealed the most positive and significant relation between biologic yield (r=0.960**), number of pod per plant (r=0.931**), number of seed per pod (r=0.905**), 1000-seed weight (r= 0.834**), pod length (r= 0.824**), harvest index (r= 0.690**) and seed yield was observed. Also the traits of leaf dry weight (r=0.550*), stem dry weight (r=0.530*) and plant height (r=0.510*) had significant correlation with the seed yield at 5 percent probability level. Stepwise regression analysis introduced five selected traits (biologic yield, number of pod per plant, number of seed per pod, 1000-seed weight and pod length determines) that covered 93.5 percent of variation related to seed yield. Variable of the biological yield with a positive direct effect (0.785) on seed yield with indirect effects of number of pods per plant (0.130), number of seed per pods (0.025), 1000-seed weight (-0.030) and pod length (0.05) caused a positive correlation between this trait and seed yield. So breeders can use selected traits for achieve optimum genotypes instead of direct selection for seed yield. CONCLUSION: Among studied traits, biologic yield had the most direct positive effect (0.785) on seed yield and had an important role to explain seed yield variation.
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