Principal Component and Cluster Analyses for Quantitative Traits in Black Cumin (Nigella Sativa)

Yewubdinber Lemma; Wassu Mohammed; Sintayehu Adimas

doi:doi:10.11648/j.aff.20221102.11

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Principal Component and Cluster Analyses for Quantitative Traits in Black Cumin (Nigella Sativa)

Yewubdinber Lemma, Wassu Mohammed, Sintayehu Adimas

Published in Agriculture, Forestry and Fisheries (Volume 11, Issue 2)

Received: 28 January 2022 Accepted: 8 March 2022 Published: 18 March 2022

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Abstract

Breeding for high yield crop needs information on the nature and magnitude of variation in the available materials, relationship of yield with other agronomic characters and the degree of environmental influence on the expression of these components characters. Multivariate analyses such as cluster and principal component analysis measures the amount of genetic variability in respect of several characters and assesses the relative contribution of different traits to the total variation. This study was conducted with the aim of identifying better performing black cumin genotypes and related traits with the help of principal component analysis and cluster analysis of major quantitative traits of the crop In principal component analysis, The first four principal component axes (PCAs) accounted 70.62% of the total variability in which PCA1 contributed 28.43%, and PCA2, PCA3, and PCA4 exhibited 18.91%, 13.30%, and 9.98% contribution to the total variability, respectively. Cluster analysis based on Euclidian distance grouped the genotypes into 10 distinct clusters. Some of the genotypes that have narrow genetic base were grouped into a similar cluster. Based on these results, it may be concluded that some of the genotypes are highly diverse while most of the genotypes are similar in nature. Genotypes from the distinct cluster should be used for obtaining diverse recombinants in segregating generations, exploiting variety, and broaden the genetic base of the black cumin germplasm.

Published in	Agriculture, Forestry and Fisheries (Volume 11, Issue 2)
DOI	10.11648/j.aff.20221102.11
Page(s)	67-75
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Principal Component, Cluster Analyses, Nigella Sativa, Genetic Divergence

References

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[16]	Gerrano AS, Jansen van Rensburg WS, Adebola PO. “Nutritional composition of immature pods in selected cowpea [Vigna unguiculata (L.) genotypes in South Africa’’ Australian Journal of Crop Science, 11: 134-141, 2017.
[17]	Yan and Frégeau-Reid, 2018). Yan, W. and Frégeau-Reid, J. “Genotype by Yield Trait (GYT) Biplot,’’ a novel approach for genotype selection based on multiple traits. Scientific reports, vol. 8, no. 1, p. 8242, 2018.
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APA Style

Yewubdinber Lemma, Wassu Mohammed, Sintayehu Adimas. (2022). Principal Component and Cluster Analyses for Quantitative Traits in Black Cumin (Nigella Sativa). Agriculture, Forestry and Fisheries, 11(2), 67-75. https://doi.org/10.11648/j.aff.20221102.11

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ACS Style

Yewubdinber Lemma; Wassu Mohammed; Sintayehu Adimas. Principal Component and Cluster Analyses for Quantitative Traits in Black Cumin (Nigella Sativa). Agric. For. Fish. 2022, 11(2), 67-75. doi: 10.11648/j.aff.20221102.11

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AMA Style

Yewubdinber Lemma, Wassu Mohammed, Sintayehu Adimas. Principal Component and Cluster Analyses for Quantitative Traits in Black Cumin (Nigella Sativa). Agric For Fish. 2022;11(2):67-75. doi: 10.11648/j.aff.20221102.11

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@article{10.11648/j.aff.20221102.11,
  author = {Yewubdinber Lemma and Wassu Mohammed and Sintayehu Adimas},
  title = {Principal Component and Cluster Analyses for Quantitative Traits in Black Cumin (Nigella Sativa)},
  journal = {Agriculture, Forestry and Fisheries},
  volume = {11},
  number = {2},
  pages = {67-75},
  doi = {10.11648/j.aff.20221102.11},
  url = {https://doi.org/10.11648/j.aff.20221102.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.aff.20221102.11},
  abstract = {Breeding for high yield crop needs information on the nature and magnitude of variation in the available materials, relationship of yield with other agronomic characters and the degree of environmental influence on the expression of these components characters. Multivariate analyses such as cluster and principal component analysis measures the amount of genetic variability in respect of several characters and assesses the relative contribution of different traits to the total variation. This study was conducted with the aim of identifying better performing black cumin genotypes and related traits with the help of principal component analysis and cluster analysis of major quantitative traits of the crop In principal component analysis, The first four principal component axes (PCAs) accounted 70.62% of the total variability in which PCA1 contributed 28.43%, and PCA2, PCA3, and PCA4 exhibited 18.91%, 13.30%, and 9.98% contribution to the total variability, respectively. Cluster analysis based on Euclidian distance grouped the genotypes into 10 distinct clusters. Some of the genotypes that have narrow genetic base were grouped into a similar cluster. Based on these results, it may be concluded that some of the genotypes are highly diverse while most of the genotypes are similar in nature. Genotypes from the distinct cluster should be used for obtaining diverse recombinants in segregating generations, exploiting variety, and broaden the genetic base of the black cumin germplasm.},
 year = {2022}
}

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TY  - JOUR
T1  - Principal Component and Cluster Analyses for Quantitative Traits in Black Cumin (Nigella Sativa)
AU  - Yewubdinber Lemma
AU  - Wassu Mohammed
AU  - Sintayehu Adimas
Y1  - 2022/03/18
PY  - 2022
N1  - https://doi.org/10.11648/j.aff.20221102.11
DO  - 10.11648/j.aff.20221102.11
T2  - Agriculture, Forestry and Fisheries
JF  - Agriculture, Forestry and Fisheries
JO  - Agriculture, Forestry and Fisheries
SP  - 67
EP  - 75
PB  - Science Publishing Group
SN  - 2328-5648
UR  - https://doi.org/10.11648/j.aff.20221102.11
AB  - Breeding for high yield crop needs information on the nature and magnitude of variation in the available materials, relationship of yield with other agronomic characters and the degree of environmental influence on the expression of these components characters. Multivariate analyses such as cluster and principal component analysis measures the amount of genetic variability in respect of several characters and assesses the relative contribution of different traits to the total variation. This study was conducted with the aim of identifying better performing black cumin genotypes and related traits with the help of principal component analysis and cluster analysis of major quantitative traits of the crop In principal component analysis, The first four principal component axes (PCAs) accounted 70.62% of the total variability in which PCA1 contributed 28.43%, and PCA2, PCA3, and PCA4 exhibited 18.91%, 13.30%, and 9.98% contribution to the total variability, respectively. Cluster analysis based on Euclidian distance grouped the genotypes into 10 distinct clusters. Some of the genotypes that have narrow genetic base were grouped into a similar cluster. Based on these results, it may be concluded that some of the genotypes are highly diverse while most of the genotypes are similar in nature. Genotypes from the distinct cluster should be used for obtaining diverse recombinants in segregating generations, exploiting variety, and broaden the genetic base of the black cumin germplasm.
VL  - 11
IS  - 2
ER  -

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Author Information

Yewubdinber Lemma

Crop and Horticulture Directorate, Ethiopian Biodiversity Institute, Addis Ababa, Ethiopia
Wassu Mohammed

School of Plant Sciences, Haramaya University, Dire Dawa, Ethiopia
Sintayehu Adimas

Crop and Horticulture Directorate, Ethiopian Biodiversity Institute, Addis Ababa, Ethiopia

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Plain Text BibTeX RIS

APA Style

Yewubdinber Lemma, Wassu Mohammed, Sintayehu Adimas. (2022). Principal Component and Cluster Analyses for Quantitative Traits in Black Cumin (Nigella Sativa). Agriculture, Forestry and Fisheries, 11(2), 67-75. https://doi.org/10.11648/j.aff.20221102.11

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ACS Style

Yewubdinber Lemma; Wassu Mohammed; Sintayehu Adimas. Principal Component and Cluster Analyses for Quantitative Traits in Black Cumin (Nigella Sativa). Agric. For. Fish. 2022, 11(2), 67-75. doi: 10.11648/j.aff.20221102.11

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AMA Style

Yewubdinber Lemma, Wassu Mohammed, Sintayehu Adimas. Principal Component and Cluster Analyses for Quantitative Traits in Black Cumin (Nigella Sativa). Agric For Fish. 2022;11(2):67-75. doi: 10.11648/j.aff.20221102.11

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@article{10.11648/j.aff.20221102.11,
  author = {Yewubdinber Lemma and Wassu Mohammed and Sintayehu Adimas},
  title = {Principal Component and Cluster Analyses for Quantitative Traits in Black Cumin (Nigella Sativa)},
  journal = {Agriculture, Forestry and Fisheries},
  volume = {11},
  number = {2},
  pages = {67-75},
  doi = {10.11648/j.aff.20221102.11},
  url = {https://doi.org/10.11648/j.aff.20221102.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.aff.20221102.11},
  abstract = {Breeding for high yield crop needs information on the nature and magnitude of variation in the available materials, relationship of yield with other agronomic characters and the degree of environmental influence on the expression of these components characters. Multivariate analyses such as cluster and principal component analysis measures the amount of genetic variability in respect of several characters and assesses the relative contribution of different traits to the total variation. This study was conducted with the aim of identifying better performing black cumin genotypes and related traits with the help of principal component analysis and cluster analysis of major quantitative traits of the crop In principal component analysis, The first four principal component axes (PCAs) accounted 70.62% of the total variability in which PCA1 contributed 28.43%, and PCA2, PCA3, and PCA4 exhibited 18.91%, 13.30%, and 9.98% contribution to the total variability, respectively. Cluster analysis based on Euclidian distance grouped the genotypes into 10 distinct clusters. Some of the genotypes that have narrow genetic base were grouped into a similar cluster. Based on these results, it may be concluded that some of the genotypes are highly diverse while most of the genotypes are similar in nature. Genotypes from the distinct cluster should be used for obtaining diverse recombinants in segregating generations, exploiting variety, and broaden the genetic base of the black cumin germplasm.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - Principal Component and Cluster Analyses for Quantitative Traits in Black Cumin (Nigella Sativa)
AU  - Yewubdinber Lemma
AU  - Wassu Mohammed
AU  - Sintayehu Adimas
Y1  - 2022/03/18
PY  - 2022
N1  - https://doi.org/10.11648/j.aff.20221102.11
DO  - 10.11648/j.aff.20221102.11
T2  - Agriculture, Forestry and Fisheries
JF  - Agriculture, Forestry and Fisheries
JO  - Agriculture, Forestry and Fisheries
SP  - 67
EP  - 75
PB  - Science Publishing Group
SN  - 2328-5648
UR  - https://doi.org/10.11648/j.aff.20221102.11
AB  - Breeding for high yield crop needs information on the nature and magnitude of variation in the available materials, relationship of yield with other agronomic characters and the degree of environmental influence on the expression of these components characters. Multivariate analyses such as cluster and principal component analysis measures the amount of genetic variability in respect of several characters and assesses the relative contribution of different traits to the total variation. This study was conducted with the aim of identifying better performing black cumin genotypes and related traits with the help of principal component analysis and cluster analysis of major quantitative traits of the crop In principal component analysis, The first four principal component axes (PCAs) accounted 70.62% of the total variability in which PCA1 contributed 28.43%, and PCA2, PCA3, and PCA4 exhibited 18.91%, 13.30%, and 9.98% contribution to the total variability, respectively. Cluster analysis based on Euclidian distance grouped the genotypes into 10 distinct clusters. Some of the genotypes that have narrow genetic base were grouped into a similar cluster. Based on these results, it may be concluded that some of the genotypes are highly diverse while most of the genotypes are similar in nature. Genotypes from the distinct cluster should be used for obtaining diverse recombinants in segregating generations, exploiting variety, and broaden the genetic base of the black cumin germplasm.
VL  - 11
IS  - 2
ER  -

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