Variability, Mean Performance Evaluation, Trait Relationship and Principal Component Analysis of Sunflower Genotypes

Alemu Doda Gemeda; Mohammed Abu

doi:doi:10.11648/j.ajpb.20240903.12

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Variability, Mean Performance Evaluation, Trait Relationship and Principal Component Analysis of Sunflower Genotypes

Alemu Doda Gemeda^*

, Mohammed Abu

Published in American Journal of Plant Biology (Volume 9, Issue 3)

Received: 6 June 2024 Accepted: 8 July 2024 Published: 20 August 2024

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Abstract

Forty nine sunflower genotypes evaluated for mean performance and Variability parameters of yield contributing traits at Kulumsa in simple lattice design. The aim is to identify desired characters of the crop, information of nature and genetic variability for seed yield improvement. The traits revealed presence of highly significant genotypic differences at P≤0.01 for yield contrbuting traits: head diameter, number of seed head^-1, thousand seed weight and seed yield ton ha^-1. Among the studied genotypes mean performance evaluation indicates that the highest seed yield ton ha^-1recorded for genotypes SHRS-2020#18 (3.06ton ha^-1), followed by SHRS-2020#4 (2.95tonha^-1) and SHRS-2020#16 (2.84t ha^-1) and the lowest average seed yield ton ha^-1recorded for genotype SHRS-2020#13 (1.15tonha^-1). Genotypes SHRS-2020#46 (83.5) and SHRS-2020#38 (84.5) the early flowered whereas, the late flowered recorded for the genotype SHRS-2020#43 (107.5) after the date of sowing. Seed yield ton ha^-1 (YTPH), is the most economic trait, was positively and significantly associated with number seed head^-1 and plant height. The characters indicating significantly positively correlation among seed yield and important traits would be highly effective and efficient improving respective traits. Higher estimates of heritability coupled with higher genetic advance were observed for seed yieldtonha^-1 (46.49) and number of seed head^-1 (42.46). This indicated that heritability of the trait is mainly due to additive gene effect and selection is effective for such traits. Principle component analysis (PCA) is usually used to identify the most significant variables in the data. In this study the principle component analysis result showed that accumulative variability original data accounted about 100% for the traits. The first Principal component which accounted for 38.5% total variation were observed through agronomic traits such as: SD, DFF, HD, days to maturity, number of seed head^-1. Similarily the second principal components which accounted for 17.4% of the total variations among the genoypes were attributed to differently from traits such as: yield ton ha^-1, number of seed head^-1and head diameter were the most important of seed yield positive contributors in the second Principal component. Whereas the third and fourth PCA accounted 14.4% and 14% of variations for agronomic traits such as: TSW, HD and SD in PCA 3 and for PCA 4 TSW, seed yield ton ha^-1, PH and DNM were the most important positive contributors traits for seed yield. Thus, these variation of traits observed in this experiment can help further as a selection index in genetic improvement of sunflower seed yield and its components.

Published in	American Journal of Plant Biology (Volume 9, Issue 3)
DOI	10.11648/j.ajpb.20240903.12
Page(s)	56-66
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

Sunflower Genotypes, Evaluation, Correlation, Phonetypic Variance, Genotypic Variance, Genetic Advance, Heritability, Principal Component Analysis

References

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Gemeda, A. D., Abu, M. (2024). Variability, Mean Performance Evaluation, Trait Relationship and Principal Component Analysis of Sunflower Genotypes. American Journal of Plant Biology, 9(3), 56-66. https://doi.org/10.11648/j.ajpb.20240903.12

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Gemeda, A. D.; Abu, M. Variability, Mean Performance Evaluation, Trait Relationship and Principal Component Analysis of Sunflower Genotypes. Am. J. Plant Biol. 2024, 9(3), 56-66. doi: 10.11648/j.ajpb.20240903.12

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AMA Style

Gemeda AD, Abu M. Variability, Mean Performance Evaluation, Trait Relationship and Principal Component Analysis of Sunflower Genotypes. Am J Plant Biol. 2024;9(3):56-66. doi: 10.11648/j.ajpb.20240903.12

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@article{10.11648/j.ajpb.20240903.12,
  author = {Alemu Doda Gemeda and Mohammed Abu},
  title = {Variability, Mean Performance Evaluation, Trait Relationship and Principal Component Analysis of Sunflower Genotypes
},
  journal = {American Journal of Plant Biology},
  volume = {9},
  number = {3},
  pages = {56-66},
  doi = {10.11648/j.ajpb.20240903.12},
  url = {https://doi.org/10.11648/j.ajpb.20240903.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajpb.20240903.12},
  abstract = {Forty nine sunflower genotypes evaluated for mean performance and Variability parameters of yield contributing traits at Kulumsa in simple lattice design. The aim is to identify desired characters of the crop, information of nature and genetic variability for seed yield improvement. The traits revealed presence of highly significant genotypic differences at P≤0.01 for yield contrbuting traits: head diameter, number of seed head-1, thousand seed weight and seed yield ton ha-1. Among the studied genotypes mean performance evaluation indicates that the highest seed yield ton ha-1 recorded for genotypes SHRS-2020#18 (3.06ton ha-1), followed by SHRS-2020#4 (2.95tonha-1) and SHRS-2020#16 (2.84t ha-1) and the lowest average seed yield ton ha-1 recorded for genotype SHRS-2020#13 (1.15tonha-1). Genotypes SHRS-2020#46 (83.5) and SHRS-2020#38 (84.5) the early flowered whereas, the late flowered recorded for the genotype SHRS-2020#43 (107.5) after the date of sowing. Seed yield ton ha-1 (YTPH), is the most economic trait, was positively and significantly associated with number seed head-1 and plant height. The characters indicating significantly positively correlation among seed yield and important traits would be highly effective and efficient improving respective traits. Higher estimates of heritability coupled with higher genetic advance were observed for seed yieldtonha-1 (46.49) and number of seed head-1 (42.46). This indicated that heritability of the trait is mainly due to additive gene effect and selection is effective for such traits. Principle component analysis (PCA) is usually used to identify the most significant variables in the data. In this study the principle component analysis result showed that accumulative variability original data accounted about 100% for the traits. The first Principal component which accounted for 38.5% total variation were observed through agronomic traits such as: SD, DFF, HD, days to maturity, number of seed head-1. Similarily the second principal components which accounted for 17.4% of the total variations among the genoypes were attributed to differently from traits such as: yield ton ha-1, number of seed head-1 and head diameter were the most important of seed yield positive contributors in the second Principal component. Whereas the third and fourth PCA accounted 14.4% and 14% of variations for agronomic traits such as: TSW, HD and SD in PCA 3 and for PCA 4 TSW, seed yield ton ha-1, PH and DNM were the most important positive contributors traits for seed yield. Thus, these variation of traits observed in this experiment can help further as a selection index in genetic improvement of sunflower seed yield and its components.
},
 year = {2024}
}

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TY  - JOUR
T1  - Variability, Mean Performance Evaluation, Trait Relationship and Principal Component Analysis of Sunflower Genotypes

AU  - Alemu Doda Gemeda
AU  - Mohammed Abu
Y1  - 2024/08/20
PY  - 2024
N1  - https://doi.org/10.11648/j.ajpb.20240903.12
DO  - 10.11648/j.ajpb.20240903.12
T2  - American Journal of Plant Biology
JF  - American Journal of Plant Biology
JO  - American Journal of Plant Biology
SP  - 56
EP  - 66
PB  - Science Publishing Group
SN  - 2578-8337
UR  - https://doi.org/10.11648/j.ajpb.20240903.12
AB  - Forty nine sunflower genotypes evaluated for mean performance and Variability parameters of yield contributing traits at Kulumsa in simple lattice design. The aim is to identify desired characters of the crop, information of nature and genetic variability for seed yield improvement. The traits revealed presence of highly significant genotypic differences at P≤0.01 for yield contrbuting traits: head diameter, number of seed head-1, thousand seed weight and seed yield ton ha-1. Among the studied genotypes mean performance evaluation indicates that the highest seed yield ton ha-1 recorded for genotypes SHRS-2020#18 (3.06ton ha-1), followed by SHRS-2020#4 (2.95tonha-1) and SHRS-2020#16 (2.84t ha-1) and the lowest average seed yield ton ha-1 recorded for genotype SHRS-2020#13 (1.15tonha-1). Genotypes SHRS-2020#46 (83.5) and SHRS-2020#38 (84.5) the early flowered whereas, the late flowered recorded for the genotype SHRS-2020#43 (107.5) after the date of sowing. Seed yield ton ha-1 (YTPH), is the most economic trait, was positively and significantly associated with number seed head-1 and plant height. The characters indicating significantly positively correlation among seed yield and important traits would be highly effective and efficient improving respective traits. Higher estimates of heritability coupled with higher genetic advance were observed for seed yieldtonha-1 (46.49) and number of seed head-1 (42.46). This indicated that heritability of the trait is mainly due to additive gene effect and selection is effective for such traits. Principle component analysis (PCA) is usually used to identify the most significant variables in the data. In this study the principle component analysis result showed that accumulative variability original data accounted about 100% for the traits. The first Principal component which accounted for 38.5% total variation were observed through agronomic traits such as: SD, DFF, HD, days to maturity, number of seed head-1. Similarily the second principal components which accounted for 17.4% of the total variations among the genoypes were attributed to differently from traits such as: yield ton ha-1, number of seed head-1 and head diameter were the most important of seed yield positive contributors in the second Principal component. Whereas the third and fourth PCA accounted 14.4% and 14% of variations for agronomic traits such as: TSW, HD and SD in PCA 3 and for PCA 4 TSW, seed yield ton ha-1, PH and DNM were the most important positive contributors traits for seed yield. Thus, these variation of traits observed in this experiment can help further as a selection index in genetic improvement of sunflower seed yield and its components.

VL  - 9
IS  - 3
ER  -

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Author Information

Alemu Doda Gemeda

Kulumsa Agricultural Research Center, Asella, Ethiopia

Contact Email

http://orcid.org/0009-0004-9692-1740
Mohammed Abu

Holeta Agricultural Research Center Holeta, Holeta, Ethiopia

http://orcid.org/0000-0003-1730-7829

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APA Style

Gemeda, A. D., Abu, M. (2024). Variability, Mean Performance Evaluation, Trait Relationship and Principal Component Analysis of Sunflower Genotypes. American Journal of Plant Biology, 9(3), 56-66. https://doi.org/10.11648/j.ajpb.20240903.12

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ACS Style

Gemeda, A. D.; Abu, M. Variability, Mean Performance Evaluation, Trait Relationship and Principal Component Analysis of Sunflower Genotypes. Am. J. Plant Biol. 2024, 9(3), 56-66. doi: 10.11648/j.ajpb.20240903.12

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AMA Style

Gemeda AD, Abu M. Variability, Mean Performance Evaluation, Trait Relationship and Principal Component Analysis of Sunflower Genotypes. Am J Plant Biol. 2024;9(3):56-66. doi: 10.11648/j.ajpb.20240903.12

Copy | Download

@article{10.11648/j.ajpb.20240903.12,
  author = {Alemu Doda Gemeda and Mohammed Abu},
  title = {Variability, Mean Performance Evaluation, Trait Relationship and Principal Component Analysis of Sunflower Genotypes
},
  journal = {American Journal of Plant Biology},
  volume = {9},
  number = {3},
  pages = {56-66},
  doi = {10.11648/j.ajpb.20240903.12},
  url = {https://doi.org/10.11648/j.ajpb.20240903.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajpb.20240903.12},
  abstract = {Forty nine sunflower genotypes evaluated for mean performance and Variability parameters of yield contributing traits at Kulumsa in simple lattice design. The aim is to identify desired characters of the crop, information of nature and genetic variability for seed yield improvement. The traits revealed presence of highly significant genotypic differences at P≤0.01 for yield contrbuting traits: head diameter, number of seed head-1, thousand seed weight and seed yield ton ha-1. Among the studied genotypes mean performance evaluation indicates that the highest seed yield ton ha-1 recorded for genotypes SHRS-2020#18 (3.06ton ha-1), followed by SHRS-2020#4 (2.95tonha-1) and SHRS-2020#16 (2.84t ha-1) and the lowest average seed yield ton ha-1 recorded for genotype SHRS-2020#13 (1.15tonha-1). Genotypes SHRS-2020#46 (83.5) and SHRS-2020#38 (84.5) the early flowered whereas, the late flowered recorded for the genotype SHRS-2020#43 (107.5) after the date of sowing. Seed yield ton ha-1 (YTPH), is the most economic trait, was positively and significantly associated with number seed head-1 and plant height. The characters indicating significantly positively correlation among seed yield and important traits would be highly effective and efficient improving respective traits. Higher estimates of heritability coupled with higher genetic advance were observed for seed yieldtonha-1 (46.49) and number of seed head-1 (42.46). This indicated that heritability of the trait is mainly due to additive gene effect and selection is effective for such traits. Principle component analysis (PCA) is usually used to identify the most significant variables in the data. In this study the principle component analysis result showed that accumulative variability original data accounted about 100% for the traits. The first Principal component which accounted for 38.5% total variation were observed through agronomic traits such as: SD, DFF, HD, days to maturity, number of seed head-1. Similarily the second principal components which accounted for 17.4% of the total variations among the genoypes were attributed to differently from traits such as: yield ton ha-1, number of seed head-1 and head diameter were the most important of seed yield positive contributors in the second Principal component. Whereas the third and fourth PCA accounted 14.4% and 14% of variations for agronomic traits such as: TSW, HD and SD in PCA 3 and for PCA 4 TSW, seed yield ton ha-1, PH and DNM were the most important positive contributors traits for seed yield. Thus, these variation of traits observed in this experiment can help further as a selection index in genetic improvement of sunflower seed yield and its components.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - Variability, Mean Performance Evaluation, Trait Relationship and Principal Component Analysis of Sunflower Genotypes

AU  - Alemu Doda Gemeda
AU  - Mohammed Abu
Y1  - 2024/08/20
PY  - 2024
N1  - https://doi.org/10.11648/j.ajpb.20240903.12
DO  - 10.11648/j.ajpb.20240903.12
T2  - American Journal of Plant Biology
JF  - American Journal of Plant Biology
JO  - American Journal of Plant Biology
SP  - 56
EP  - 66
PB  - Science Publishing Group
SN  - 2578-8337
UR  - https://doi.org/10.11648/j.ajpb.20240903.12
AB  - Forty nine sunflower genotypes evaluated for mean performance and Variability parameters of yield contributing traits at Kulumsa in simple lattice design. The aim is to identify desired characters of the crop, information of nature and genetic variability for seed yield improvement. The traits revealed presence of highly significant genotypic differences at P≤0.01 for yield contrbuting traits: head diameter, number of seed head-1, thousand seed weight and seed yield ton ha-1. Among the studied genotypes mean performance evaluation indicates that the highest seed yield ton ha-1 recorded for genotypes SHRS-2020#18 (3.06ton ha-1), followed by SHRS-2020#4 (2.95tonha-1) and SHRS-2020#16 (2.84t ha-1) and the lowest average seed yield ton ha-1 recorded for genotype SHRS-2020#13 (1.15tonha-1). Genotypes SHRS-2020#46 (83.5) and SHRS-2020#38 (84.5) the early flowered whereas, the late flowered recorded for the genotype SHRS-2020#43 (107.5) after the date of sowing. Seed yield ton ha-1 (YTPH), is the most economic trait, was positively and significantly associated with number seed head-1 and plant height. The characters indicating significantly positively correlation among seed yield and important traits would be highly effective and efficient improving respective traits. Higher estimates of heritability coupled with higher genetic advance were observed for seed yieldtonha-1 (46.49) and number of seed head-1 (42.46). This indicated that heritability of the trait is mainly due to additive gene effect and selection is effective for such traits. Principle component analysis (PCA) is usually used to identify the most significant variables in the data. In this study the principle component analysis result showed that accumulative variability original data accounted about 100% for the traits. The first Principal component which accounted for 38.5% total variation were observed through agronomic traits such as: SD, DFF, HD, days to maturity, number of seed head-1. Similarily the second principal components which accounted for 17.4% of the total variations among the genoypes were attributed to differently from traits such as: yield ton ha-1, number of seed head-1 and head diameter were the most important of seed yield positive contributors in the second Principal component. Whereas the third and fourth PCA accounted 14.4% and 14% of variations for agronomic traits such as: TSW, HD and SD in PCA 3 and for PCA 4 TSW, seed yield ton ha-1, PH and DNM were the most important positive contributors traits for seed yield. Thus, these variation of traits observed in this experiment can help further as a selection index in genetic improvement of sunflower seed yield and its components.

VL  - 9
IS  - 3
ER  -

Copy | Download