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Quantitative Studies in Upland Cotton (Gossypium hirsutum L.) using Multivariate Techniques

Muhammad Rizwan1*, Jehanzeb Farooq1, Muhammad Farooq1,2, Aqeel Sarwar4, Abid Ali3, Farrukh Ilahi1, Muhammad Asif1 and Ghulam Sarwar1

1Cotton Research Station, Ayub Agricultural Research Institute, Faisalabad, Pakistan; 2School of Biology and Environmental Science, Science and Engineering Faculty, Queensland University of Technology, Brisbane, Australia; 3Reigonal Agricultural Research Institute, Bahawalpur, Pakistan; 4Crop Sciences Institute, National Agricultural Research Centre, Islamabad, Pakistan.

 
*Correspondence | Muhammad Rizwan, Cotton Research Station, Ayub Agricultural Research Institute, Faisalabad, Pakistan; Email: muhammadrizwan29@gmail.com 

ABSTRACT

Sustainable production of cotton be contingent upon development of genotypes having better yield, tolerance with respect to abiotic and biotic stresses and enhanced fiber quality. Forty five elite lines of cotton were used to evaluate the genetic variability for fifteen parameters viz., plant height (cms), days taken in 50% flowering, monopodia plant-1, sympodia plant-1, boll weight (g), No. of bolls plant-1, ginning out turn (%), lint index(g), seed index(g), 2.5 percent span length(mm), bundle strength(g/tex), micronaire (µg/in), fibre elongation (%), uniformity ratio and yield plant-1(g). Using Mahalanobis D2 analysis, these parameters were assembled into seven clusters. Among these clusters, cluster I and VII were largest each having nine and eight genotypes respectively, followed by cluster VI having seven genotypes. According to the illustrations by using hierarchical cluster analysis, total genotypes were grouped in seven clusters with 11 genotypes in cluster VI after that cluster II comprising 9 genotypes. The random distribution among genotypes showed that no parallelism exists amongst genetic and geographical diversity. First seven components in principal component analysis (PCA) having eigenvalue more than 1, showed 91.131 % the cumulative variance, while PC-1 alone showed 32.47 % variance. Hierarchical cluster analysis and PCA provided an opportunity to identify subgroups of clusters at different stages, so that every single subgroup may be analyzed critically and it will be helpful for incorporation of desirable characters in future breeding programmes.

 

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Pakistan Journal of Agricultural Research

June

Vol. 34, Iss. 2, Pages 254-493

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