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Asian Research Journal of Agriculture, 2456-561X,Vol.: 3, Issue.: 4

Original-research-article

Genetic Divergence of Quantitative Traits in Brassica juncea L. Genotypes Based on Multivariate Analysis

 

Tahira Bibi1, Awais Riaz1*, Tariq Mahmood2, Muhammad Akhter1, Zulqarnain Haider1 and Misbah Riaz1

1Rice Research Institute, Kala Shah Kaku, Lahore, Pakistan.

2Oilseeds Research Institute, AARI, Faisalabad, Pakistan.

Article Information
Editor(s):
(1) Afroz Alam, Department of Bioscience & Biotechnology, Banasthali University,Rajasthan, India.
Reviewers:
(1) Rahal-Bouziane Hafida, National Institute of Agronomic Research of Algeria (INRAA), Algeria.
(2) Essam Fathi Mohamed El-Hashash, Al-Azhar University, Cairo, Egypt.
(3) Leyla Acık, Gazi University, Turkey.
Complete Peer review History: http://www.sciencedomain.org/review-history/18227

Abstracts

Edible oil requirement of Pakistan is increasing every year due to growing population and per capita consumption. After cotton, rapeseed-mustard is the second most important source of edible oil in Pakistan, where it is cultivated under diverse agro-climatic regions. The present research was conducted at Oilseeds Research Institute, Faisalabad (Pakistan) to find out the genetic divergence among 10 genotypes of mustard (Brassica juncea L.) with three replications in RCBD design. Eight morphological characters were measured to find out superior genotype use as a donor parent in hybridization programs. The cluster analysis revealed to two clusters of genotypes on based similarity and difference in morphological trait. The first cluster comprises seven genotypes and second group consist of three genotypes. The result of dendrogram indicated that high genetic distance among the genotype in terms of studied traits. Day to maturity had significant highly positive correlation with plant height and days to flowering. Pod length had highly significant positive correlation with seeds per pods. Plant height had correlate significant positive with days of maturity and days to flowering that would have brought simultaneous improvement for these traits as a result of correlated response. The principal component analysis shows that PC1, PC2 and PC3 contribute 52.1%, 19.9% and 15.3% of total variation, respectively. Therefore, it is possible to utilize that genetic variation in selection and hybridization program to develop new or more productive Brassica varieties and to optimize donor parental lines.

Keywords :

Brassica juncea L.; genetic variation; principal component analysis; cluster analysis.

Full Article - PDF    Page 1-8

DOI : 10.9734/ARJA/2017/31449

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