Multivariate analysis for evaluation and classification of toria germplasm accessions

Avtar, Ram; Singh, Dhirendra Kumar; Thakral, N. K.; Singh, Amit Kumar; Sangwan, O.; Rani, Babita; Kumari, Nisha

doi:10.5958/j.2348-7542.15.1.018

Cited by 5 publications

(6 citation statements)

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“…The first one absorbed and accounted for maximum proportion of total variability in the set of all PCs and the remaining ones accounted for progressively lesser and lesser amount of variation. Similar results have also been reported earlier by Zada et al (2013) in Ethiopian mustard; Avtar et al (2014) in toria and Neeru et al (2015) in Indian mustard. The analysis without rotation of axes failed to load all the variables signifying that it could not offer much information regarding the idea of correlation between the variables and the principal components.…”

Section: Resultssupporting

confidence: 92%

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Evaluation and diversity analysis in Indian mustard [Brassica juncea (L.) Czern & Coss.] germplasm accessions on the basis of principal component analysis

Avtar

Manmohan

Jattan

et al. 2017

JANS

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Principal component analysis was carried out with 20 morphological traits (including quantitative as well as qualitative) among 96 germplasm lines of Indian mustard [Brassica juncea (L.) Czern & Coss.]. Principal factor analysis led to the identification of eight principal components (PCs) which explained about 70.41% variability. The first principal component (PC1) explained 16.21% of the total variation. The remaining PC's explained progressively lesser and lesser of the total variation. Varimax Rotation enabled loading of similar type of variables on a common principal factor (PF) permitting to designate them as yield factor, maturity factor and oil factor etc. Based on PF scores and cluster mean values the germplasm accessions viz., RC2, RC32 and RC51 (cluster I), RC95 and RC96 (cluster X) were found superior for seed yield/plant and yield related factors like primary and secondary branches/plant; while the accessions RC34, RC185 and RC195 (cluster III) and RC53 (cluster VIII) were found superior for oil content. These accessions may further be utilized in breeding programmes for evolving mustard varieties having high seed yield and oil content. Hierarchical cluster analysis resulted into ten clusters containing two to 26 accessions. The results of cluster and principal factor analyses were in confirmation of each other.

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Section: Resultssupporting

confidence: 92%

“…leaf angle and siliqua angle and could be designated as angle factor. Avtar et al (2014) in toria and Singh et al (2014) in Indian mustard also reported loading of similar type of variables on a common principal factor (PF).…”

Section: Resultsmentioning

confidence: 93%

Evaluation and diversity analysis in Indian mustard [Brassica juncea (L.) Czern & Coss.] germplasm accessions on the basis of principal component analysis

Avtar

Manmohan

Jattan

et al. 2017

JANS

Self Cite

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“…PC-II had positive correlation (P < 0.05) with two variables (secondary branches, seed yield/plant) whereas, PC-III had positive correlation (P<0.05) with three variables (plant height, silique on main shoot, seed yield/plant). Similar results have also been reported earlier by Zada et al, (2013) in Ethiopian mustard; Avtar et al, (2014) in toria and Neeru et al, (2015) in Indian mustard.…”

Section: Principal Component Analysissupporting

confidence: 92%

Variability, Genetic Diversity and Principal Component Analysis in Indian Mustard (Brassica juncea (L.) Czern.& Coss.) for Seed Yield and Attributing Traits

Sharma¹,

Ram²

2021

Int.J.Curr.Microbiol.App.Sci

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Indian Mustard (Brassica juncea L. czern.& coss.2n=36, AABB genome), is an allopolyploid brassica species. It contributes more than 80% to the total rapeseed-mustard production in the country and is an important component in the oilseed sector. It is known to be more drought tolerant and shattering resistant than B. napus and B. rapa, therefore, has an enormous cultivation potential in semiarid areas. It is mainly self-pollinating species,

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“…The first principal component absorbed and accounted for maximum proportion of total variability in the set of all PCs and the remaining ones accounted for progressively lesser and lesser amount of variation. Similar results have also been reported earlier by Zada et al, (2013), Avtar et al, (2014Avtar et al, ( , 2017, Ray et al, (2014), Neeru et al, (2015) and Verma et al, (2016). The Varimax Rotation was applied to estimate correlation between the variables and the principal components.…”

Section: Principal Component Analysis Indicated That Only First Four Principal Components (Pcs)supporting

confidence: 90%

Assessment of Genetic Diversity in Thirty-Five Genotypes of Oilseed Brassica Species using Principal Component Analysis

Gupta¹,

Sharma²,

Singh³

et al. 2019

Int.J.Curr.Microbiol.App.Sci

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Multivariate analysis for evaluation and classification of toria germplasm accessions

Cited by 5 publications

References 0 publications

Evaluation and diversity analysis in Indian mustard [Brassica juncea (L.) Czern & Coss.] germplasm accessions on the basis of principal component analysis

Evaluation and diversity analysis in Indian mustard [Brassica juncea (L.) Czern & Coss.] germplasm accessions on the basis of principal component analysis

Variability, Genetic Diversity and Principal Component Analysis in Indian Mustard (Brassica juncea (L.) Czern.& Coss.) for Seed Yield and Attributing Traits

Assessment of Genetic Diversity in Thirty-Five Genotypes of Oilseed Brassica Species using Principal Component Analysis

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