In order to group winter rapeseed cultivars according to evaluated traits, an experiment was conducted in the Research Greenhouse of Agriculture Faculty, University of Tabriz – IRAN. In the experiment were included 12 cultivars of winter rapeseed and 3 levels of water deficit stress. Gypsum blocks were used to monitor soil moisture. Water deficit stress was imposed from stem elongation to physiological maturity. According to the principal component analysis, five principal components were chosen with greater eigenvalue (more than 0.7) that are including 81.34% of the primeval variance of variables. The first component that explained the 48.02% of total variance had the high eigenvalue. The second component could justify about 13.64% of total variance and had positive association with leaf water potential and proline content and had negative relationship with leaf stomatal conductivity. The third, fourth and fifth components expressed around, 10.18, 4.83 and 4.68% of the total variance respectively. The third component had the high eigenvalue for plant dry weight. The fourth component put 1000-seed weight, seed yield, Silique per Plant and root dry weight against plant dry weight, chlorophyll fluorescence and leaf water potential. The fifth component had the high eigenvalue for root dry weight, root volume and 1000-seed weight.
| Published in | American Journal of Bioscience and Bioengineering (Volume 2, Issue 1) |
| DOI | 10.11648/j.bio.20140201.13 |
| Page(s) | 15-17 |
| 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), 2014. Published by Science Publishing Group |
Winter Rapeseed, Water deficit Stress, Principal Component Analysis
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APA Style
Gader Ghaffari, Farhad Baghbani. (2014). The Study of Important Agronomic Traits by Multivariate Analysis in Winter Rapeseed Cultivars. American Journal of Bioscience and Bioengineering, 2(1), 15-17. https://doi.org/10.11648/j.bio.20140201.13
ACS Style
Gader Ghaffari; Farhad Baghbani. The Study of Important Agronomic Traits by Multivariate Analysis in Winter Rapeseed Cultivars. Am. J. BioSci. Bioeng. 2014, 2(1), 15-17. doi: 10.11648/j.bio.20140201.13
AMA Style
Gader Ghaffari, Farhad Baghbani. The Study of Important Agronomic Traits by Multivariate Analysis in Winter Rapeseed Cultivars. Am J BioSci Bioeng. 2014;2(1):15-17. doi: 10.11648/j.bio.20140201.13
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Download@article{10.11648/j.bio.20140201.13,
author = {Gader Ghaffari and Farhad Baghbani},
title = {The Study of Important Agronomic Traits by Multivariate Analysis in Winter Rapeseed Cultivars},
journal = {American Journal of Bioscience and Bioengineering},
volume = {2},
number = {1},
pages = {15-17},
doi = {10.11648/j.bio.20140201.13},
url = {https://doi.org/10.11648/j.bio.20140201.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bio.20140201.13},
abstract = {In order to group winter rapeseed cultivars according to evaluated traits, an experiment was conducted in the Research Greenhouse of Agriculture Faculty, University of Tabriz – IRAN. In the experiment were included 12 cultivars of winter rapeseed and 3 levels of water deficit stress. Gypsum blocks were used to monitor soil moisture. Water deficit stress was imposed from stem elongation to physiological maturity. According to the principal component analysis, five principal components were chosen with greater eigenvalue (more than 0.7) that are including 81.34% of the primeval variance of variables. The first component that explained the 48.02% of total variance had the high eigenvalue. The second component could justify about 13.64% of total variance and had positive association with leaf water potential and proline content and had negative relationship with leaf stomatal conductivity. The third, fourth and fifth components expressed around, 10.18, 4.83 and 4.68% of the total variance respectively. The third component had the high eigenvalue for plant dry weight. The fourth component put 1000-seed weight, seed yield, Silique per Plant and root dry weight against plant dry weight, chlorophyll fluorescence and leaf water potential. The fifth component had the high eigenvalue for root dry weight, root volume and 1000-seed weight.},
year = {2014}
}
TY - JOUR T1 - The Study of Important Agronomic Traits by Multivariate Analysis in Winter Rapeseed Cultivars AU - Gader Ghaffari AU - Farhad Baghbani Y1 - 2014/03/10 PY - 2014 N1 - https://doi.org/10.11648/j.bio.20140201.13 DO - 10.11648/j.bio.20140201.13 T2 - American Journal of Bioscience and Bioengineering JF - American Journal of Bioscience and Bioengineering JO - American Journal of Bioscience and Bioengineering SP - 15 EP - 17 PB - Science Publishing Group SN - 2328-5893 UR - https://doi.org/10.11648/j.bio.20140201.13 AB - In order to group winter rapeseed cultivars according to evaluated traits, an experiment was conducted in the Research Greenhouse of Agriculture Faculty, University of Tabriz – IRAN. In the experiment were included 12 cultivars of winter rapeseed and 3 levels of water deficit stress. Gypsum blocks were used to monitor soil moisture. Water deficit stress was imposed from stem elongation to physiological maturity. According to the principal component analysis, five principal components were chosen with greater eigenvalue (more than 0.7) that are including 81.34% of the primeval variance of variables. The first component that explained the 48.02% of total variance had the high eigenvalue. The second component could justify about 13.64% of total variance and had positive association with leaf water potential and proline content and had negative relationship with leaf stomatal conductivity. The third, fourth and fifth components expressed around, 10.18, 4.83 and 4.68% of the total variance respectively. The third component had the high eigenvalue for plant dry weight. The fourth component put 1000-seed weight, seed yield, Silique per Plant and root dry weight against plant dry weight, chlorophyll fluorescence and leaf water potential. The fifth component had the high eigenvalue for root dry weight, root volume and 1000-seed weight. VL - 2 IS - 1 ER -
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