Study the Correlation and Path Analysis of Yield and its Related Traits of Maize under Different Water and Nitrogen Conditions

پژوهشهای زراعی ایران

Article 7, Volume 17, Issue 2 - Serial Number 54, July 2019, Pages 275-289   

Document Type: Research Article

DOI: 10.22067/gsc.v17i2.71772

Authors

R Ghobadi; M Ghobadi^* ; S Jalali Honarmand; B Farhadi; F Mondani

Razi University

Abstract

Introduction
Grain yield depends on genetic potential of plant. Environmental factors play an important role to achieve this potential. Inappropriate management of irrigation and nitrogen are main factors to reduce the maize yield. Better control of environmental effects in breeding programs can be achieved through indirect selection for traits that in addition to high heritability have a good correlation with yield and be less affected by environmental changes. This study was conducted to investigate the correlation and cause and effect relationships between some traits with yield of maize in different irrigation and nitrogen conditions.
 Materials and Methods
This experiment was done during 2014-15 at Razi University, Kermanshah, Iran. The experiment was conducted as split plot. Main plot factor was four irrigation levels included supplying 120, 100, 80 and 60% water requirement (I120%, I100%, I80% and I60%, respectively), and sub-plot factor included four nitrogen levels 40, 70, 100 and 140% (N40%, N70%, N100% and N140%, respectively) of recommended amount based on the soil test. During growth period, the time of occurrence of growth stages were recorded and at harvest stage, yield, its components, ear size and grain dimensions were measured. Pearson correlation coefficients and stepwise regression analysis were determined using SPSS software. Direct and indirect effects of traits on grain yield were determined by path analysis using PATH2 software.
 Results and Discussion
Results of correlation analysis in all irrigation conditions showed that the higher total dry weight and less interval of anthesis until silking are desirable. In addition, selection for 100 grain weight, number of days from planting until physiological maturity and harvest index in I120%, I100% and I80% would improve grain yield. Under I60%, negative selection for grain depth and number rows per ear can improve grain yield. Positive correlation between ear size, yield components, plant height, grain length, number of days from planting until physiological maturity and harvest index with grain yield and negative selection for cob percent, grain thickness and interval of anthesis until silking in all nitrogen levels would improve grain yield. Results of regression analysis under I120%, I100% and I80% showed that 100 grain weight explained a high percentage of grain yield changes. Under I60%, grain depth had the highest contribution to explaining grain yield changes. Under N70%, N100% and N140%, harvest index and total dry weight that explain a significant percentage of total grain yield changes. In N40%, number of grain per row will be effective on grain yield. In I120% and I100%, 100 grain weight and number grains per row, in addition to high direct effects on grain yield, had a significant indirect effect on grain yield through each other. In I80%direct effect of 100 grain weight and indirect effect of plant height through it on grain yield were observed. In I60% selection for grain depth, interval of anthesis until silking and days from planting until anthesis would be more appropriate. Due to negative correlation between grain number per row with grain yield and positive direct effect of it in I60%, the indirect effects of grain number per row should be considered. Regarding nitrogen levels, in N40%, grain number per row had a direct effect on grain yield. In N70%, N100% and N140%, total dry weight and harvest index in addition to relatively high direct effects on grain yield, had an indirect effect on each other.
 Conclusions
In all environmental conditions, traits entered to regression models explained more than 95% of grain yield changes. Based on the results of path analysis, under I120% and I100% conditions, 100 grain weight and number of grains per row, in I80%, 100 grain weight and plant height, under severe deficit irrigation, grain depth and interval of anthesis until silking due to considerable direct and indirect effects on yield introduced as proper indices to improve grain yield. At all nitrogen levels, selection based on total dry weight and harvest index will be helpful. Number of grains per row and 100 grain weight should also be considered due to high indirect effects on grain yield at some nitrogen levels.

Keywords

grain yield; Independent variable; Stepwise regression; Water deficit

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