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Current IssueEditorial BoardOnline First Articles
Legume Research
Print ISSN: 0250-5371
Online ISSN: 0976-0571
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6.80
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0.32
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0.906

Chief Editor:
J. S. Sandhu
Vice Chancellor, SKN Agriculture, University, Jobner, VC, NDUAT, Faizabad, Deputy Director General (Crop Science), Indian Council of Agricultural Research (ICAR), New Delhi
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Research Article

Legume Research, volume 43 issue 6 (december 2020) : 764-769

Selection of promising genotypes of lentil (Lens culinaris Medik.) by deciphering genetic diversity and trait association

S.R. Sharma, Sarvjeet Singh, R.K. Gill, R. Kumar, A.K. Parihar
1Department of Plant Breeding & Genetics, Punjab Agricultural University (PAU), Ludhiana-141 004, Punjab, India.

  • Submitted26-06-2018|

  • Accepted13-08-2018|

  • First Online 03-01-2019|

  • doi 10.18805/LR-4056

Cite article:- Sharma S.R., Singh Sarvjeet, Gill R.K., Kumar R., Parihar A.K. (2018). Selection of promising genotypes of lentil (Lens culinaris Medik.) by deciphering genetic diversity and trait association. Legume Research. 43(6): 764-769. doi: 10.18805/LR-4056.

ABSTRACT

A panel of fifty diverse genotypes of lentil were evaluated to assess the genetic diversity for 11 morpho-physiological attributes. The results revealed wide range of variability among the genotypes for the examined characteristics. The mean performance demonstrated that the best genotype for more than one trait viz., plot yield, yield/plant, Leaf Area Index (LAI), Crop Growth Rate (CGR), biomass and plant height was LL1384. Traits association approximation witnessed a positive and significant relationship of seed yield with most of the studied traits. Cluster analysis distributed genotypes into seven distinct clusters. Cluster III happened to be the largest comprising of 20 genotypes, followed by Cluster VII comprising of 9 genotypes. The maximum inter-cluster distance was perceived between clusters II and IV which suggests the possibility of high heterotic effect if cross will be made between the individuals of these clusters. The principal component analysis developed 11 Eigen vectors and, of these three principal components had explained about 82.50% of variations. The characters that had the highest weight in component first were plot yield, yield/plant, pods/plant, CGR, biomass/plant, LAI and plant height, which explained 57.03% of the total variance. The biplot results were in accordance with correlation estimation, indicating that indirect selection based on component traits would help in improving the seed yield in lentil. Hybridization among genetically diverse genotypes of different clusters would be helpful in broadening the genetic base as well as in the creation of desirable recombinants for developing new improved lentil varieties.  

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