Volume 10, Issue 28 (12-2018)                   jcb 2018, 10(28): 162-170 | Back to browse issues page


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Mirabadi A, Haghpanah M, Foroozan K, talaeei S. Multivariate Analysis of Some Quantitative Traits in Introduced Safflower (Carthamus tinctorius L.) Genotypes in Sari. jcb. 2018; 10 (28) :162-170
URL: http://jcb.sanru.ac.ir/article-1-818-en.html
Oilseeds Research and Development Company, Tehran, Iran
Abstract:   (737 Views)
Development of germplasm resources through importing new genotypes could improve the efficiency of safflower (Carthamus tinctorius L.) breeding programs. During 2014-2015 season, 82 imported safflower genotypes were evaluated in randomized complete block design at the research station of ORDC (Oilseeds Research & Development Company) in Sari. In this plan, six important quantitative traits related to grain yield were recorded for all genotypes. For modeling of stepwise regression PCA (Principal components analysis) were analyzed by PROC MIXED, PROC GLM and PROC IML methods, respectively. The results of PCA showed that grain yield and thousand grain weight components were correlation in linear combination. It was found 68% of the total variation justified with the three first components. The regression coefficients were significant for grain number per head and thousand grain weights at 1% and 5%, respectively indicating that these two mentioned traits had an effective role on increasing grain yield. The number of grain per pod and thousand grain weights remained in the model of Stepwise regression and for showed a signification with grain yield. Therefore, for increasing grain yield in breeding programs and producing high potential genotypes, the studied traits would be of great importance.
 
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Type of Study: Research | Subject: اصلاح نباتات، بیومتری
Received: 2017/08/22 | Revised: 2019/03/2 | Accepted: 2017/12/12 | Published: 2019/03/2

References
1. Ahmadzadeh, A.R. 2007. Analysis of genetic diversity in spring safflower (Carthamus tinctorius L.) cultivars using morphological characters and RAPD markers. Ph.D. thesis, Tehran Azad Islamic University, Iran.
2. Alyari, H. and F. Shekary. 1990. Oil Seeds Physiology and Agronomy. Amidi (In Persian).
3. Aziz, H. and B. Abdollahi. 2016. Assessment of genetic variation in alfalfa (Medicago sativa L.) populations using Canonical Discriminant Analysis. Agronomy Journal, 107: 183-189 (In Persian).
4. Collins, C.A. and F.M. Seeney. 1999. Statistical experiment design and interpretation: an introduction with agricultural examples. John Wiley and Sons Ltd.
5. Coşge, B., B. Gurbuz and M. Kiralan. 2007. Oil content and fatty acid composition of some safflower (Carthamus tinctorius L.) varieties sown in spring and winter. International Journal of Natural and Engineering Sciences, 1(3): 11-15.
6. Dettweiler, E. and R. Eibach. 2002. The two Vitis databases as tools for germplasm management Vitis international variety catalogue. In VIII International Conference on Grape Genetics and Breeding, 603 pp: 505-509. [DOI:10.17660/ActaHortic.2003.603.66]
7. Dwivedi, S.L., H.D. Upadhyaya and D.M. Hegde. 2005. Development of core collection using geographic information and morphological descriptors in safflower (Carthamus tinctorius L.) germplasm. Genetic Resources and Crop Evolution, 52(7): 821-830. [DOI:10.1007/s10722-003-6111-8]
8. FAO .2014. FAOstat. Retrieved Feb, 2014.
9. Jaradat, A.A. and M. Shahid. 2006. Patterns of phenotypic variation in a germplasm collection of Carthamus tinctorius L. from the Middle East. Genetic Resources and Crop Evolution, 53(2): 225-244. [DOI:10.1007/s10722-004-6150-9]
10. Johnson, R.C., T.J. Kisha and M.A. Evans. 2007. Characterizing safflower germplasm with AFLP molecular markers. Crop science, 47(4): 1728-1736. [DOI:10.2135/cropsci2006.12.0757]
11. Khan, M.A., S. Von Witzke-Ehbrecht, B.L. Maass and H.C. Becker. 2009. Relationships among different geographical groups, agro-morphology, fatty acid composition and RAPD marker diversity in safflower (Carthamus tinctorius). Genetic Resources and Crop Evolution, 56(1): 19-30. [DOI:10.1007/s10722-008-9338-6]
12. Khan, M.A., S. Von Witzke-Ehbrecht, B.L. Maass and H.C. Becker. 2004. Evaluation of a worldwide collection of safflower for morphological diversity and fatty acid composition. Vorträge für Pflanzenzüchtung, 62: 104-106.
13. Knudsen, S. 2005. Guide to analysis of DNA microarray data. John Wiley and Sons. [DOI:10.1002/0471670278]
14. Maleki Nejad, R. and M.M. Majidi. 2015. Association of Seed Yield, Oil and Related Traits in Safflower Genotypes under Normal and Drought Stress. Iranian Journal of Field Crops Research, 13(1): 109-119.
15. McPherson, M.A., A.G. Good, A.K.C. Topinka and L.M. Hall. 2004. Theoretical hybridization potential of transgenic safflower (Carthamus tinctorius L.) with weedy relatives in the New World. Canadian Journal of Plant Science, 84(3): 923-934. [DOI:10.4141/P03-150]
16. Mohammadi, S.A. and B.M. Prasanna. 2003. Analysis of genetic diversity in crop plants- Salient statistical tools and considerations. Crop Science, 43: 1235-1248. [DOI:10.2135/cropsci2003.1235]
17. Mohammadi, R., H. Dehghani and G. Karimzadeh. 2014. Graphic analysis of trait relations of cantaloupe using the Biplot method. Plant production research, 21(4): 43-62.
18. Mozaffari, K. and A.A. Asadi. 2006. Relationships among traits using correlation, principal components and path analysis in safflower mutants sown in irrigated and drought stress condition. Asian Journal Plant Science, 5: 972-983. [DOI:10.3923/ajps.2006.977.983]
19. Rezaei, A. and A. Soltani. 2013. An introduction to applied regression analysis (7th edition). Isfahan University of Technology publications center (In Persian).
20. Ringner, M. 2008. What is principal component analysis?. Nature Biotechnology, 26: 303-304. [DOI:10.1038/nbt0308-303]
21. SAS Institute. 2015. SAS/STAT user's guide. (4nd edition). SAS institute Inc.
22. Singh, R.J. 2006. Genetic resources, chromosome engineering and crop improvement: vegetable crops. CRC press. [DOI:10.1201/9781420009569]
23. Vollmann, J. and I. Rajcan. 2009. Oil crop breeding and genetics. In: Oil Crops (pp: 1-30). Springer. [DOI:10.1007/978-0-387-77594-4_1]
24. Wall, M.E., A. Rechtsteiner and L.M. Rocha. 2009. Singular value decomposition and principal component analysis. In A practical approach to microarray data analysis (pp: 91-109). Springer US. [DOI:10.1007/0-306-47815-3_5]
25. Weiss, E.A. 2000. Oilseed crops. Blackwell Science.
26. Wooldridge, J.M. 2015. Introductory econometrics: A modern approach. Nelson Education.
27. Yan, W. and M.S. Kang. 2002. GGE biplot analysis: A graphical tool for breeders, geneticists and agronomists. CRC press. [DOI:10.1201/9781420040371]
28. Yan, W. 2002. Singular-value partitioning in biplot analysis of multienvironment trial data. Agronomy Journal, 94: 990-996. [DOI:10.2134/agronj2002.0990]
29. Yan, W., L.A. Hunt, Q. Sheng and Z. Szlavnics. 2000. Cultivar evaluation and megaenvironment investigation based on the GGE biplot. Crop Science, 40: 597-605. [DOI:10.2135/cropsci2000.403597x]
30. Yan, W. and Kang, M.S. 2002. GGE biplot analysis: A graphical tool for breeders, geneticists, and agronomists. CRC Press, Boca Raton, FL. 273 pp. [DOI:10.1201/9781420040371]
31. Yari, P., A. Keshtkar and A. Sepehri. 2015. Evaluation of Water Stress Effect on Growth and Yield of Spring Safflower. Plant Product Technology, 4: 101-117 (In Persian).

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