Multivariate and Association Analysis for Yield and Yield Attributing Traits in Quality Protein Maize (QPM) Inbred Lines
Vaskar Subba, Anirban Nath, Aditi Ghosh, Amitava Ghosh, Sabyasachi Kundagrami
Current Journal of Applied Science and Technology · pp. 11–20 · Published 16 Dec 2021
10.9734/cjast/2021/v40i3931592Abstract
The present investigation reveals the diversity existing among thirty inbred lines of Quality Protein Maize (QPM) in terms of yield and yield attributing traits. The study further elucidates the mutual association among the various morphological traits recorded among the inbred lines. The inbred lines were evaluated during the Rabi seasons of 2016-17, 2017-18 and 2018-19. The analysis of variance calculated over the mean performances of the inbred lines across three rabi seasons revealed significant differences among the inbred lines in terms of yield and yield attributing traits. The diversity among the inbred lines were further determined using cluster analysis which classified the inbred lines into 3 phylogenetically distinct groups. Additionally, a principal component analysis was performed which revealed three principal components (i.e., PC I, II and III) elucidating eighty six percent of the total observable variance among the inbred lines, with traits like grain yield, cob length, cob diameter, number of grain rows per cob, number of grains per row and number of grains per cob contributing to nearly half of the total variance explained by the Principal Component Analysis (PCA). The correlation as well as path coefficient analysis performed for the various traits further indicated significant influence of morphological traits like cob length, cob diameter, number of grain rows per cob and number of grains per cob over the observable grain yield per plant. Overall, the observations from the current investigation can be helpful in identifying superior parental lines to be used in future hybrid maize development programs.
Cited by 2
Rajendra Vidadala, Vinay Kumar, Sanghamitra Rout · Cereal Research Communications · 2024
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