Path and Biplot analysis of agronomic traits of maize hybrids under normal irrigation and water-deficit stress conditions

Document Type : Research Paper

Authors

1 Dept. of Plant Breeding & Biotechnology, Faculty of Agriculture, University of Tabriz,Tabriz,, Iran

2 Department of Plant breeding and Biotechnology, Faculty of Agriculture, University of Tabriz, Tabriz, Iran

Abstract

Multiple regression analysis and path analysis of maize grain yield with other agronomic traits to determine the most effective traits of the grain yield as well as analysis of genotype×trait interaction using the GGE biplot method, to evaluate the response of the hybrids to normal and water-deficit stress conditions based on the measured traits for determining high-yielding hybrids for both conditions, were carried out. In order to determine superior hybrids under normal and drought stress conditions based on agronomic traits and with the help of genotype×trait biplot analysis and also to determine traits affecting grain yield through multiple regression and path analysis, 18 maize hybrids were evaluated in a split-plot design based on the randomized complete block design in three replications and in two consecutive years.Two levels of irrigation were allocated in the main plots and maize hybrids in the subplots.In the path analysis of the grain yield with other studied traits, the number of kernel rows, the 300-grain weight and the number of grains per row under normal conditions, and the number of kernel rows and the 300-grain weight in the drought stress conditions had significant direct effects on the grain yield.The results of GGE biplot analysis of genotype×trait in both conditions showed that in normal conditions the two first components explained 72/28% and in water-deficit stress conditions 83/60% of the total variance.In both conditions hybrid SC704 was recognized as the ideal genotype.Hybrid SC704 ranked first as the most high-yielding hybrid in both conditions, and hybrid SC647 ranked next.

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References

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JOURNAL OF AGRICULTURAL SCIENCE AND SUSTAINABLE PRODUCTION
Volume 34, Issue 1
April 2024
Pages 221-232

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