The present investigation was carried out during 2011-12 in a randomized block design (RBD) with 35 diverse wheat genotypes to assess the genetic diversity for various morphological and quality traits. The analysis of variance for grain yield and its contributing components namely days to 50% flowering, days to maturity, productive tillers, plant height, spike length, spikelets pet spike, grains per spikelet, biological yield, harvest index, 1000 grain weight, grain yield and gluten content showed highly significant differences (at <1% level of significance) among the genotypes under present study. High heritability along with high genetic advance and high phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV) for grain yield (g), biological yield (g), harvest index (%), spike length (cm) and 1000 grain weight (g) indicated substantial contribution of additive gene action in the expression and thus selection would be effective for genetic improvement of these traits for improving grain yield in wheat. On the basis of multivariate analysis, 35 genotypes were grouped into â€˜6â€™ clusters based on genetic divergence (D2 ) value. The compositions of clusters revealed that the Cluster IV contained the highest number of genotypes (9) followed by Cluster II (8), Cluster VI (8) and Cluster III (7). The highest inter cluster values were recorded between cluster III and V (8357.19) followed by cluster IV and V (7513.88), cluster IV and VI (6009.44) and cluster III and VI (5530.40) exhibiting wide genetic diversity. Among different traits, biological yield (32.12%), productive tillers (28.74%), harvest index (26.71%), plant height (24.20%), grain yield (19.23%) and grains per spikelets (14.89%) had maximum contribution to total genetic divergence, therefore may be used as selection parameters in transgressive segregants. Selection of genotypes from the clusters may be used as potential donors for further hybridization programme to develop genotypes with high yield potential in wheat crop.
Bread wheat, Genetic diversity, Genetic parameters, Gluten content, Yield traits
Burton, G.W. and Vane de, E.H. (1953). Estimating heritability in tall fescue (Festuca arundinacea L.) from replicated clonal material. Agronomy Journal, 45: 478-481.
Johnson, H.W., Robinson, H.F. and Comstock, R. E. (1955). Estimates of genetic and environmental variability in soybeans. Agronomy Journal, 47: 314-318.
Kumar Pradeep, Singh Gyanendra, Kumar Sarvan, Kumar Anuj and Ojha Ashish. (2016a). Genetic analysis of grain yield and its contributing traits for their implications in improvement of bread wheat cultivars. Journal of Applied and Natural Science, 8: 350 -357.
Kumar Sandeep, Pradeep Kumar and Kerkhi, S.A. (2017). Genetic analysis for various yield components and gluten content in bread wheat (Triticum aestivum L.). Journal of Applied and Natural Science, 9(2): 879-882.
Kumar, J., Kumar, A., Singh, S.K., Singh, L., Kumar, A., Chaudhary, M., Kumar, S. and Singh, S.K. (2016b). Principal component analysis for yield and its contributing traits in bread wheat (Triticum aestivum) genotypes under late. Current Advances in Agricultural Sciences, 8: 55-57.
Lal, B.K., Ruchig, M. and Upadhyay, A. (2009). Genetic variability, diversity and association of quantitative traits with grain yield in bread wheat (Triticum aestivum L.). Asian Journal of Agricultural Sciences, 1(1):4-6.
Mahalanobis, P.C. (1936). On the generalized distance on statistics, a statistical study of Chinese head measurement. Journal of the Asiatic Society of Bengal, (25): 301-307.
Meena, H.S., Kumar, D. and Prasad, S.R. (2014). Genetic variability and character association in bread wheat (Triticum aestivum). Indian Journal of Agricultural Sciences, 84 (4): 487-91.
Panse VG and PV Sukhatme (1969) Statistical Methods of Agricultural Workers. 2nd Endorsement, ICAR Publication, New Delhi, India, pp: 381.
Rao, C.R. (1952). Advance statistical methods in biometrical research. John Wiley and Sons Inc. New York, p. 383.
Sharma, I., Shoran, J., Singh, G. and Tyagi, B. S. (2011). Wheat Improvement in India. Souvenir of 50th All India Wheat and Barley Research Workers, Meet, New Delhi. p 11.
Singh Gyanendra, Kulshreshtha, N., Singh, B.N., Setter, T.L., Singh, M.K., Saharan, M.S, Tyagi, B.S., Verma Ajay and Sharma, I. (2014). Germplasm characterization, association and clustering for salinity and waterlogging tolerance in bread wheat (Triticum aestivum). Indian Journal of Agricultural Sciences, 84: 1102-10.
Singh, M.K, Sharma, P.K, Tyagi, B.S. and Singh Gyanendra. (2013). Genetic analysis for morphological traits and protein content in bread wheat (Triticum aestivum L.) under normal and heat stress environments. Indian Journal of Genetics and plant breeding, 73: 320-324.
Spark, D.N. (1973). Euclidean cluster analysis. Algorithm A. 58. Applied Statistics, (22): 126-130.
Tewari, R,, Jaiswal, J.P., Gangwar, R.P. and Singh, P.K. (2015). Genetic diversity analysis in Exotic germplasm accessions of Wheat (Triticum aestivum L.) by cluster analysis. Electronic Journal of Plant Breeding, 6: 1111-1117.
Verma, P.N., Singh, B.N., Singh, G., Singh, M.K. and Setter, T.L. (2014). Genetic diversity analysis for yield and other agronomic traits in bread wheat under water logged sodic soil condition. Journal of Wheat Research, 6: 51-58.
Vora, Z.N., Patel, J.B., Pansuriya, A.G. and Yusufzai, S.A. (2017). Genetic divergence analysis in bread wheat (Triticum aestivum L.). Research in Environments and Life Sciences, 10: 291-294.
This work is licensed under Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Â© Author (s)