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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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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