Multivariate analysis for yield contributing traits in wheat-Thinopyrum bessarabicum addition and translocation lines

Paper Details

Research Paper 01/07/2016
Views (287) Download (10)
current_issue_feature_image
publication_file

Multivariate analysis for yield contributing traits in wheat-Thinopyrum bessarabicum addition and translocation lines

Noshin Shafqat, Habib Ahmed, Armghan Shehzad, Faisal Ali, Shafqat Ullah, Ghulam Muhammad Ali, Jalal-Ud-Din
Int. J. Biosci.9( 1), 34-44, July 2016.
Certificate: IJB 2016 [Generate Certificate]

Abstract

For world food security development of high yielding cultivars through identification of genetic diversity is also an important tool. The objective of the present experiment was to get complementary information about different yield contributing characters and to identify the diversity among addition and translocation lines which would assist the plant breeders in making their selection for developing high yielding cultivars. Twenty one wheat genotype including seven wheat- T bessarabicum addition and nine translocation lines along with amphiploid, CS, Genaro and two BC1 self fertile lines were grown to study thirteen phenotypic characteristics in two year field experiment, using a randomized complete block design (RCBD) with three replications. Multivariate statistical analysis was used to understand the data structure and trait relations. Simple correlation coefficients depicted highly significant correlation of grain yield per plant with the number of seeds, seed weight per spike, and significant association with spike weight and days to heading. Principal component analysis revealed that five components explained 87% of the total variation among traits. The first PCA contributes maximum portion of total variability (29%) and was more related to spike weight, number of seeds per spike, seed weight per spike, days to heading and grain yield per plant. Cluster analysis assigned 21 genotypes into four clusters. Multivariate statistical analysis revealed that this genetic stock has potential to enhance yield of wheat cultivars and traits like spike length, spike weight, number of spike per plant, number of grain per spike, grain weight per spike, and 1000 grain- weight should be used as selection criteria.

VIEWS 5

Aharizad S, Sabzi M, Mohammadi SA, Khodadadi E. 2012. Multivariate analysis of genetic diversity in wheat (Titicum aestivum L.) recombinant inbred lines using agronomic traits. Annals of Biological Research 5, 2118-2126.

Ahmad Z, Ajmal SU, Munir M, Zubair M, Masood MS. 2008. Genetic diversity for morpho-genetic traits in barley germplasm. Pakistan Journal of Botany 40, 1217-1224.

Ali Y, Atta MB, Akhter J, Monneveux P, Lateef Z. 2008. Genetic variability, association and diversity studies in wheat (Triticum aestivum L.) germplasm. Pakistan Journal of Botany 40, 2087-2097.

Asseng S, Turnera NC, Rayb JD, Keatingc BA. 2002. A simulation analysis that predicts the influence of physiological traits on the potential yield of wheat. European Journal of Agronomy 17(2), 123–141 http://dx.doi.org/10.1016/S1161-0301(01)001496

Bramel PJ, Hinnze PN, Green DE, Shibles RM. 1984. Uses of principal factor analysis in the study of three stem termination types of soybean. Euphytica 33, 387–400. http://dx.doi.org/10.1007/BF0002113.6

Collaku A. 1989. Analysis of the structure of correlations between yield and some quantitative traits in bread wheat. Buletini i Shkencave Bujqësore 28, 137-144.

Deyong Z. 2011. Analysis among main agronomic traits of spring wheat (Triticum aestivum) in Qinghai Tibet plateau. Bulgarian Journal of Agricultural Science 17, 615-622.

Eisen MB, Spellman PT, Brown PO, Botstein D. 1998. Cluster analysis and display of genome-wide expression ilatteriis. Proceedings of the National Academy of Science, USA 95, 14863–14868. http://dx.doi.org/10.1073/pnas.95.25.1486.3

El- Deeb AA, Mohamed NA. 1999. Factor and cluster analysis for some quantitative characters in sesame (Sesamum indicum L.). The Annual Conference ISSR, Cairo university, 4–6 December, 34, Part (II).

Everitt BS, Dunn G. 1992. Applied multivariate data analysis. Oxford University Press, New York, USA.

Everitt BS. 1993. Cluster Analysis. Wiley, New York, NY.

Fellahi Z, Hannachi A, Bouzerzour H, Boutekrabt A. 2013. Study of interrelationships among yield and yield related attributes by using various statistical methods in bread wheat (Triticum aestivum L.em Thell.). International journal of Agriculture & plant Protection 4, 1256-1266.

Fufa H, Baenizger PS, Beecher BS, Dweikat I, Graybosch RA, Eskridge KM. 2005. Comparison of phenotypic and molecular-based classifications of hard red winter wheat cultivars. Euphytica 145, 133-146. http://dx.doi.org/10.1007/s10681-005-0626-3

Jaynes DB, Kaspar TC, Colvin TS, James DE. 2003. Cluster analysis of spatio temporal corn yield pattern in an Iowa field. Agronomy Journal 95, 574– 586. http://dx.doi.org/10.2134/agronj2003.057.4

Khokhar MI, Hussain M, Zulkiffal M, Ahmad N, Sabar W. 2010. Correlation and path analysis for yield and yield contributing characters in wheat (Triticum aestivum L.). African Journal of Plant Science. 4, 464–466.

Kumbhar MB, Larik AS, Hafiz HM, Rind MJ. 1983. Wheat Information Services 57, 42–45.

Leilah AA, Al-Khateeb SA. 2005. Statistical analysis of wheat yield under drought conditions. Journal of Arid Environments 61, 483–496. http://dx.doi.org/10.1016/j.jaridenv.2004.10.01.1

Lewis GJ, Lisle AT. 1998. Towards better canola yield; a principal components analysis approach. Proceedings of the 9th Australian Agronomy Conference, Wagga Wagga, New South Wales, Australia.

Moghaddam M, Ehdaie B, Waines JG. 1998. Genetic variation for and interrelationships among agronomic traits in landraces of bread wheat from south western Iran. Journal of Genetics and Breeding 52, 73–81.

Mohamed NA. 1999. Some statistical procedures for evaluation of the relative contribution for yield components in wheat. Zagazig Journal of Agricultural Research 26, 281-290.

Mohammadi SA, Prasanna BM. 2003. Analysis of genetic diversity in crop plants- Salient statistical tools and considerations. Crop Science 43, 1234-1248..

Mostafa K, Fotokian MH, Miransari M. 2011. Genetic diversity of wheat (Triticum estivum. L) genotypes based on cluster and principal component analyses for breeding strategies. Australian Journal of Crop Sciences 5, 17-24.

Nazli HI, Hamza H, Asjad T. 2012. Supply and Demand for Cereals in Pakistan, 2010-2030. IFPRI Discussion Paper 01222.

Rymuza K, Turska E, Wielogórska G, Bombik A. 2012. Use of principal component analysis for the assessment of spring wheat characteristics. Acta Scientiarum Polonorum – Agricultura 11, 79-90.

Snedecor GW. 1956. Statistical Methods. 5 th edn. Iowa State University Press, Ames, Iowa, U.S.A.

Yin X, Chasalow SD, Stam PM, Kropff J, Dourleijn CJ, Bos I, Bindraban PS. 2002. Use of component analysis in QTL mapping of complex crop traits: a case study on yield in barley. Plant Breeding 121, 314–319.

Zubair M, Ajmal SU, Anwar M, Haqqani M. 2007. Multivariate analysis for quantitative traits in mungbean [Vigna radiate(L.) Wilczek]. Pakistan Journal of Botany 39, 103-113.

Zarei L, Farshadfar A, Choghamirza K. 2010. The 11th Iranian Congress of Agronomy and Plant Breeding Science, 24-26 July. Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran, 196-199.