The richness of diversity in a core collection of bread wheat (Triticum aestivum L.)

Diversity evaluation provides opportunity to assess genetically important distinct traits that will effectively contribute to improvement of genotypes. Assessing genetic diversity in a core collection is key to find out the ways to efficient utilization of genetic resource. Wheat cultivars spinnin...

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Main Authors: Vivek Sharma, Sumita Kumari, Ashwani kumar, Sheetalraj Sharma and, Chhagan lal
Format: Article
Language:English
Published: Indian Society of Plant Breeders 2018-06-01
Series:Electronic Journal of Plant Breeding
Subjects:
Online Access:http://ejplantbreeding.org/index.php/EJPB/article/view/2405
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spelling doaj-7ff4c0dc63294b529fabdb3c7697ef752020-11-25T01:17:19ZengIndian Society of Plant BreedersElectronic Journal of Plant Breeding0975-928X2018-06-019248248910.5958/0975-928X.2018.00059.5The richness of diversity in a core collection of bread wheat (Triticum aestivum L.)Vivek SharmaSumita KumariAshwani kumarSheetalraj Sharma andChhagan lalDiversity evaluation provides opportunity to assess genetically important distinct traits that will effectively contribute to improvement of genotypes. Assessing genetic diversity in a core collection is key to find out the ways to efficient utilization of genetic resource. Wheat cultivars spinning over a century were collected from the Indian Institute of Wheat & Barely Research (IIWBR), Karnal, made up of a core collection. The core set of data was analysed by multivariate methods. The experimental material consisted of ~100 genotypes which were evaluated in an Augmented Randomized Block Design. Quantitative characters like no. of grain per spike, no. of spikelet per spike, test weight and spike length were found to be the key yield contributing traits. Principal component analysis (PCA) and cluster analysis of eight quantitative characters and genotypes fall into three principal component and three cluster respectively. Based on these experiment first and third cluster genotypes have high associated with PCI and PCIII. These principal components were made by grouping them high yield contributing traits. Genotypes in these clusters have higher values for yield contributing traits then the total average of traits. Genotypes belonging to superior clusters could be considered to very useful to developing high yielding varieties and other breeding activities.http://ejplantbreeding.org/index.php/EJPB/article/view/2405principal component analysis (pca)cluster analysiswheat
collection DOAJ
language English
format Article
sources DOAJ
author Vivek Sharma
Sumita Kumari
Ashwani kumar
Sheetalraj Sharma and
Chhagan lal
spellingShingle Vivek Sharma
Sumita Kumari
Ashwani kumar
Sheetalraj Sharma and
Chhagan lal
The richness of diversity in a core collection of bread wheat (Triticum aestivum L.)
Electronic Journal of Plant Breeding
principal component analysis (pca)
cluster analysis
wheat
author_facet Vivek Sharma
Sumita Kumari
Ashwani kumar
Sheetalraj Sharma and
Chhagan lal
author_sort Vivek Sharma
title The richness of diversity in a core collection of bread wheat (Triticum aestivum L.)
title_short The richness of diversity in a core collection of bread wheat (Triticum aestivum L.)
title_full The richness of diversity in a core collection of bread wheat (Triticum aestivum L.)
title_fullStr The richness of diversity in a core collection of bread wheat (Triticum aestivum L.)
title_full_unstemmed The richness of diversity in a core collection of bread wheat (Triticum aestivum L.)
title_sort richness of diversity in a core collection of bread wheat (triticum aestivum l.)
publisher Indian Society of Plant Breeders
series Electronic Journal of Plant Breeding
issn 0975-928X
publishDate 2018-06-01
description Diversity evaluation provides opportunity to assess genetically important distinct traits that will effectively contribute to improvement of genotypes. Assessing genetic diversity in a core collection is key to find out the ways to efficient utilization of genetic resource. Wheat cultivars spinning over a century were collected from the Indian Institute of Wheat & Barely Research (IIWBR), Karnal, made up of a core collection. The core set of data was analysed by multivariate methods. The experimental material consisted of ~100 genotypes which were evaluated in an Augmented Randomized Block Design. Quantitative characters like no. of grain per spike, no. of spikelet per spike, test weight and spike length were found to be the key yield contributing traits. Principal component analysis (PCA) and cluster analysis of eight quantitative characters and genotypes fall into three principal component and three cluster respectively. Based on these experiment first and third cluster genotypes have high associated with PCI and PCIII. These principal components were made by grouping them high yield contributing traits. Genotypes in these clusters have higher values for yield contributing traits then the total average of traits. Genotypes belonging to superior clusters could be considered to very useful to developing high yielding varieties and other breeding activities.
topic principal component analysis (pca)
cluster analysis
wheat
url http://ejplantbreeding.org/index.php/EJPB/article/view/2405
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