Maize Germplasm Characterization Using Principal Component and Cluster Analysis

Solomon Mengistu

doi:doi:10.11648/j.ajbio.20210904.12

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Maize Germplasm Characterization Using Principal Component and Cluster Analysis

Solomon Mengistu

Published in American Journal of BioScience (Volume 9, Issue 4)

Received: 25 May 2021 Accepted: 5 July 2021 Published: 16 July 2021

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Abstract

In Ethiopian Biodiversity Institute Gene bank, large collections of maize germplasm are not yet characterized for the magnitude of genetic variability from each other. Although, knowing the contribution of individual a character is essential to focus on particular characters in cultivar development. Hence, this experiment was conducted on 92 maize accessions which were not yet characterized and 2 local checks to estimate the magnitude of genetic diversity among the genotypes and to identify the major agro-morphological characters contributing for the observed variations. The experiment was arranged in an Augmented Design in seven blocks at Arsi Negele in the 2016 main cropping season. The characters used for analysis were days to flowering, plant height, ear height, ear per plant, days to maturity, ear length, kernel rows per ear, a thousand grain weight and yield per plot. The 94 genotypes were grouped into four clusters where cluster I, II, III, and IV comprised 30, 21, 23, and 20 genotypes, respectively. Early matured and short genotypes were grouped in cluster IV, late matured in cluster II, and high yielding and tall genotypes in cluster I. The principal component analysis indicated that the first principal component (PC1) had an eigenvalue of 4.4 and reflects 48.85% of the total variation, this represents the equivalent of two individual variables and the two variables that weighted higher than the other variables are plant height and ear length. The second principal component (PC2) was a recorded eigenvalue of 1.63 and maintained 18.11% of the total variation and related to diversity among genotypes due to ear per plant (EPP). Moreover, principal components 3 to 9 were shown to have more than one eigenvalue, thus they represent equivalent of one individual variable each accounted for 0.98%, 0.78%, 0.68%, 0.35%, 0.15%, 0.03% and 0% respectively toward the variation observed among genotypes. The result ensures the existence of high genetic divergence among the studied maize genotypes.

Published in	American Journal of BioScience (Volume 9, Issue 4)
DOI	10.11648/j.ajbio.20210904.12
Page(s)	122-127
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), 2021. Published by Science Publishing Group

Keywords

Maize, Germplasm, Quantitative Characters, Variability, Principal Component Analysis, Cluster Analysis

References

[1]	Vasal SK (2000) Quality maize story. In: Improving Human Nutrition through Agriculture. The Roll of International Agricultural Research. A workshop hosted by IRRI, Philippines, Organized by International Food Policy Institute, Los Banos, USA.
[2]	Bello OB, Abdul Malik SY, Afolabi MS, Lge SA (2010) Correlation and path coefficient hybrids analysis of yield and agronomic characters among open pollinated maize varieties and their F1 in a diallel cross. AfrJBiotech 9 (18): 2633-2639.
[3]	Central Statistical Authority (2015) Agriculture sample survey report on area and production for major crops for 2014/015. The FDRE Statistical Bulletin, CSA, Addis Ababa, Ethiopia.
[4]	Mohammadi SA, Prasanna BM (2003). Analysis of genetic diversity in crop plants-salient statistical tools and considerations. Crop Sci. 43: 1235-1248.
[5]	Adams MW (1995). An estimate of homogeneity in crop plants with special reference to genetic vulnerability in dry season. Phaseolus vulgaris. Euphytica, 26: 665-679.
[6]	Rincon, F., Johnson, B., Crossa, J., & Taba, S. (1996). Cluster analysis. An approach to sampling variability in maize [Zea mays L.] accessions. Maydica (Italy).
[7]	Levesque, R. (2007). SPSS. Statistical package for the social sciences, version 16.0. SPSS Programming and data management. A guide for SPSS and SAS users, fourth edition, SPSS inc., Chicago III.
[8]	Aliu, S., I. Rusinvoci, S. Fetahu and K. Bislimi. (2013). Morpho-physiological Traits and Mineral Composition on Local Maize Population Growing in Agro Ecological Conditions in Kosovo. Notulae Scientia Biol., 5 (2): 232-237.
[9]	Mujaju, C., Chakuya, E. (2008): Morphological variation of sorghum landrace accessions on-farm in Semi-arid areas of Zimbabwe. International Journal of Botany 4: 376-382.
[10]	Ali., M. A., Jabran, K., Awan, S. I., Abbas, A., Ehsanullah, Zulkifal, M., Acet, T., Farooq, J., Rehman, A. (2011): Morpho-physiological diversity and its implications for improving drought tolerance in grain sorghum at different growth stages. Australian Journal of Crop Science 5 (3): 311-320.
[11]	Van Hintum, T. J. L. (1995): Hierarchical approaches to the analysis of genetic diversity in crop plants. In: Hodgkin, T., Brown, A. H. D., Van Hintum T. J. L., Morales, E. A. V. (Eds.) Core collection of plant genetic resources. John Wiley and Sons, pp. 23-34.
[12]	Crossa, J., Delacy, I. H., Taba, S. (1995): The use of multivariate methods in developing a core collection. p. 77-92. In: Hodgkin, T., Brown, A. H. D., Van Hintum, Th. J. L., Morales, E. A. V. (Eds.) Core collections of plant genetic resources. John Wiley & Sons, Chichester, UK.
[13]	Ali, M. A., Nawab, N. N., Abbas, A., Zulkiffal, M., Sajjad, M. (2009): Evaluation of selection criteria in Cicerarietinum L. Using correlation coefficients and path analysis. Australian Journal of Crop Science 3: 65-70.

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Solomon Mengistu. (2021). Maize Germplasm Characterization Using Principal Component and Cluster Analysis. American Journal of BioScience, 9(4), 122-127. https://doi.org/10.11648/j.ajbio.20210904.12

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Solomon Mengistu. Maize Germplasm Characterization Using Principal Component and Cluster Analysis. Am. J. BioScience 2021, 9(4), 122-127. doi: 10.11648/j.ajbio.20210904.12

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Solomon Mengistu. Maize Germplasm Characterization Using Principal Component and Cluster Analysis. Am J BioScience. 2021;9(4):122-127. doi: 10.11648/j.ajbio.20210904.12

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@article{10.11648/j.ajbio.20210904.12,
  author = {Solomon Mengistu},
  title = {Maize Germplasm Characterization Using Principal Component and Cluster Analysis},
  journal = {American Journal of BioScience},
  volume = {9},
  number = {4},
  pages = {122-127},
  doi = {10.11648/j.ajbio.20210904.12},
  url = {https://doi.org/10.11648/j.ajbio.20210904.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajbio.20210904.12},
  abstract = {In Ethiopian Biodiversity Institute Gene bank, large collections of maize germplasm are not yet characterized for the magnitude of genetic variability from each other. Although, knowing the contribution of individual a character is essential to focus on particular characters in cultivar development. Hence, this experiment was conducted on 92 maize accessions which were not yet characterized and 2 local checks to estimate the magnitude of genetic diversity among the genotypes and to identify the major agro-morphological characters contributing for the observed variations. The experiment was arranged in an Augmented Design in seven blocks at Arsi Negele in the 2016 main cropping season. The characters used for analysis were days to flowering, plant height, ear height, ear per plant, days to maturity, ear length, kernel rows per ear, a thousand grain weight and yield per plot. The 94 genotypes were grouped into four clusters where cluster I, II, III, and IV comprised 30, 21, 23, and 20 genotypes, respectively. Early matured and short genotypes were grouped in cluster IV, late matured in cluster II, and high yielding and tall genotypes in cluster I. The principal component analysis indicated that the first principal component (PC1) had an eigenvalue of 4.4 and reflects 48.85% of the total variation, this represents the equivalent of two individual variables and the two variables that weighted higher than the other variables are plant height and ear length. The second principal component (PC2) was a recorded eigenvalue of 1.63 and maintained 18.11% of the total variation and related to diversity among genotypes due to ear per plant (EPP). Moreover, principal components 3 to 9 were shown to have more than one eigenvalue, thus they represent equivalent of one individual variable each accounted for 0.98%, 0.78%, 0.68%, 0.35%, 0.15%, 0.03% and 0% respectively toward the variation observed among genotypes. The result ensures the existence of high genetic divergence among the studied maize genotypes.},
 year = {2021}
}

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TY  - JOUR
T1  - Maize Germplasm Characterization Using Principal Component and Cluster Analysis
AU  - Solomon Mengistu
Y1  - 2021/07/16
PY  - 2021
N1  - https://doi.org/10.11648/j.ajbio.20210904.12
DO  - 10.11648/j.ajbio.20210904.12
T2  - American Journal of BioScience
JF  - American Journal of BioScience
JO  - American Journal of BioScience
SP  - 122
EP  - 127
PB  - Science Publishing Group
SN  - 2330-0167
UR  - https://doi.org/10.11648/j.ajbio.20210904.12
AB  - In Ethiopian Biodiversity Institute Gene bank, large collections of maize germplasm are not yet characterized for the magnitude of genetic variability from each other. Although, knowing the contribution of individual a character is essential to focus on particular characters in cultivar development. Hence, this experiment was conducted on 92 maize accessions which were not yet characterized and 2 local checks to estimate the magnitude of genetic diversity among the genotypes and to identify the major agro-morphological characters contributing for the observed variations. The experiment was arranged in an Augmented Design in seven blocks at Arsi Negele in the 2016 main cropping season. The characters used for analysis were days to flowering, plant height, ear height, ear per plant, days to maturity, ear length, kernel rows per ear, a thousand grain weight and yield per plot. The 94 genotypes were grouped into four clusters where cluster I, II, III, and IV comprised 30, 21, 23, and 20 genotypes, respectively. Early matured and short genotypes were grouped in cluster IV, late matured in cluster II, and high yielding and tall genotypes in cluster I. The principal component analysis indicated that the first principal component (PC1) had an eigenvalue of 4.4 and reflects 48.85% of the total variation, this represents the equivalent of two individual variables and the two variables that weighted higher than the other variables are plant height and ear length. The second principal component (PC2) was a recorded eigenvalue of 1.63 and maintained 18.11% of the total variation and related to diversity among genotypes due to ear per plant (EPP). Moreover, principal components 3 to 9 were shown to have more than one eigenvalue, thus they represent equivalent of one individual variable each accounted for 0.98%, 0.78%, 0.68%, 0.35%, 0.15%, 0.03% and 0% respectively toward the variation observed among genotypes. The result ensures the existence of high genetic divergence among the studied maize genotypes.
VL  - 9
IS  - 4
ER  -

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Author Information

Solomon Mengistu

Harar Biodiversity Center, Ethiopian Biodiversity Institute, Addis Ababa, Ethiopia

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APA Style

Solomon Mengistu. (2021). Maize Germplasm Characterization Using Principal Component and Cluster Analysis. American Journal of BioScience, 9(4), 122-127. https://doi.org/10.11648/j.ajbio.20210904.12

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ACS Style

Solomon Mengistu. Maize Germplasm Characterization Using Principal Component and Cluster Analysis. Am. J. BioScience 2021, 9(4), 122-127. doi: 10.11648/j.ajbio.20210904.12

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AMA Style

Solomon Mengistu. Maize Germplasm Characterization Using Principal Component and Cluster Analysis. Am J BioScience. 2021;9(4):122-127. doi: 10.11648/j.ajbio.20210904.12

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@article{10.11648/j.ajbio.20210904.12,
  author = {Solomon Mengistu},
  title = {Maize Germplasm Characterization Using Principal Component and Cluster Analysis},
  journal = {American Journal of BioScience},
  volume = {9},
  number = {4},
  pages = {122-127},
  doi = {10.11648/j.ajbio.20210904.12},
  url = {https://doi.org/10.11648/j.ajbio.20210904.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajbio.20210904.12},
  abstract = {In Ethiopian Biodiversity Institute Gene bank, large collections of maize germplasm are not yet characterized for the magnitude of genetic variability from each other. Although, knowing the contribution of individual a character is essential to focus on particular characters in cultivar development. Hence, this experiment was conducted on 92 maize accessions which were not yet characterized and 2 local checks to estimate the magnitude of genetic diversity among the genotypes and to identify the major agro-morphological characters contributing for the observed variations. The experiment was arranged in an Augmented Design in seven blocks at Arsi Negele in the 2016 main cropping season. The characters used for analysis were days to flowering, plant height, ear height, ear per plant, days to maturity, ear length, kernel rows per ear, a thousand grain weight and yield per plot. The 94 genotypes were grouped into four clusters where cluster I, II, III, and IV comprised 30, 21, 23, and 20 genotypes, respectively. Early matured and short genotypes were grouped in cluster IV, late matured in cluster II, and high yielding and tall genotypes in cluster I. The principal component analysis indicated that the first principal component (PC1) had an eigenvalue of 4.4 and reflects 48.85% of the total variation, this represents the equivalent of two individual variables and the two variables that weighted higher than the other variables are plant height and ear length. The second principal component (PC2) was a recorded eigenvalue of 1.63 and maintained 18.11% of the total variation and related to diversity among genotypes due to ear per plant (EPP). Moreover, principal components 3 to 9 were shown to have more than one eigenvalue, thus they represent equivalent of one individual variable each accounted for 0.98%, 0.78%, 0.68%, 0.35%, 0.15%, 0.03% and 0% respectively toward the variation observed among genotypes. The result ensures the existence of high genetic divergence among the studied maize genotypes.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Maize Germplasm Characterization Using Principal Component and Cluster Analysis
AU  - Solomon Mengistu
Y1  - 2021/07/16
PY  - 2021
N1  - https://doi.org/10.11648/j.ajbio.20210904.12
DO  - 10.11648/j.ajbio.20210904.12
T2  - American Journal of BioScience
JF  - American Journal of BioScience
JO  - American Journal of BioScience
SP  - 122
EP  - 127
PB  - Science Publishing Group
SN  - 2330-0167
UR  - https://doi.org/10.11648/j.ajbio.20210904.12
AB  - In Ethiopian Biodiversity Institute Gene bank, large collections of maize germplasm are not yet characterized for the magnitude of genetic variability from each other. Although, knowing the contribution of individual a character is essential to focus on particular characters in cultivar development. Hence, this experiment was conducted on 92 maize accessions which were not yet characterized and 2 local checks to estimate the magnitude of genetic diversity among the genotypes and to identify the major agro-morphological characters contributing for the observed variations. The experiment was arranged in an Augmented Design in seven blocks at Arsi Negele in the 2016 main cropping season. The characters used for analysis were days to flowering, plant height, ear height, ear per plant, days to maturity, ear length, kernel rows per ear, a thousand grain weight and yield per plot. The 94 genotypes were grouped into four clusters where cluster I, II, III, and IV comprised 30, 21, 23, and 20 genotypes, respectively. Early matured and short genotypes were grouped in cluster IV, late matured in cluster II, and high yielding and tall genotypes in cluster I. The principal component analysis indicated that the first principal component (PC1) had an eigenvalue of 4.4 and reflects 48.85% of the total variation, this represents the equivalent of two individual variables and the two variables that weighted higher than the other variables are plant height and ear length. The second principal component (PC2) was a recorded eigenvalue of 1.63 and maintained 18.11% of the total variation and related to diversity among genotypes due to ear per plant (EPP). Moreover, principal components 3 to 9 were shown to have more than one eigenvalue, thus they represent equivalent of one individual variable each accounted for 0.98%, 0.78%, 0.68%, 0.35%, 0.15%, 0.03% and 0% respectively toward the variation observed among genotypes. The result ensures the existence of high genetic divergence among the studied maize genotypes.
VL  - 9
IS  - 4
ER  -

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