Genetic variability and diversity analysis for yield and its components in wheat (Triticum aestivum L.)

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1 Indian J. Agric. Res., 51 (2) 2017 : Print ISSN: / Online ISSN: X AGRICULTURAL RESEARCH COMMUNICATION CENTRE Genetic variability and diversity analysis for yield and its components in wheat (Triticum aestivum L.) Vichitra Kumar Arya *, Jogendra Singh 1, Lokendra Kumar 1, Rajendra Kumar 1, Punit Kumar 2 and Pooran Chand 2 Sardar Vallabhbhai Patel University of Agriculture and Technology, Meerut , (UP) India. Recieved: Accepted: DOI: /ijare.v0iOF.7634 ABSTRACT Forty nine genotypes of wheat were studied for generating scientific information on nature and magnitude of genetic variability and diversity for designing breeding programme. The experiment was conducted in randomized complete block design in three replications. The data were recorded on days to 50% flowering, plant height, peduncle length, number of productive tillers per plant, days to maturity, spike length, number of spikelets per spike, number of grains per spike, grain weight, biological yield per plant, grain yield per plant, harvest index and gluten content. Analysis of variance revealed significant differences among the genotypes for all the characters under study. The highest estimates of phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV) were observed for grain yield per plant. High heritability coupled with high genetic advance was observed for grain yield per plant. Based on D 2 Statistics, 49 genotypes were grouped into eight clusters. The highest inter-cluster distance was found between cluster VII and VIII followed by III and VII. This indicates that genotypes included in these clusters possess wide genetic diversity. Grain yield per plant (31.46%) showed highest contribution towards genetic divergence; therefore, this character was major determinant of genetic diversity. On the basis of divergence and cluster mean it may be suggested that maximum heterosis and good recombinants could be obtained in crosses between genotypes of cluster VIII, VII and III in varietal improvement programme. Thus, crosses between the genetically diverse genotypes of cluster VIII with genotypes HUW 655, HP 1937, DBW 88 and HD 3058 and cluster VII with genotypes like HP 1938, HUW 656, K1006, DBW83, DBW 84, K1004, UP2822 and NW5050 are expected to exhibit high heterosis and are also likely to produce new recombinants with desired traits. Key words: Bread wheat, Diversity, Heritability, GCV, Genetic advance, PCV. INTRODUCTION Wheat (Triticum aestivum L.) is one of the most widely grown cereal crops, contributing to the global food supply and economic security. Globally, it is cultivated an area of million ha and production of million tonnes with an average yield of 3289 kg per hectare (FAOSTAT, 2014). This crop provides more nutrition to the human being in comparison to other food crops; hence, it is considered staple food for about 40% of the world s population. In India, wheat is the second most important food crop after rice occupying 30 million ha acreage with production of million tones during (DES, 2014). It is a challenging task before the breeders to enhance the present level of production as the growing population of the country will require much more food as compared to the present day requirement. It is not possible to increase the area under production. Hence, only alternative is left to *Corresponding author s aryavichitra@gmail.com 1 Indian Institute of Wheat and Barley Research, Karnal Metahelix Life Sciences Ltd. Kumhari Durg, Chhattisgarh increase the productivity by evolving superior varieties and better management of crop production to cope up with increasing demands of food. A major cause of concern to a plant breeder is the constant improvement of the best available genotypes for further enhancement in their yield potential either directly or through improvement of various factors which contribute indirectly to high yield. The breeding methodology, therefore, should be such, which in essence could incorporate the favorable changes either through selection or through hybridization of superior genotypes. In this regards, information on nature and magnitude of genetic variability is of immense value for starting any systematic breeding programme in crops. The presence of considerable genetic variability in the base material ensures better chances of evolving desired plant types (Sabharwal and Lodhi, 1995). The knowledge of genetic parameters viz., heritability and genetic advance

2 Volume 51 Issue 2 (2017) 129 among characters under selection is very useful for predicting genetic progress in breeding programme and developing efficient breeding strategies (Falconer and Mackay, 1996). Genetic diversity available in the existing germplasm determines t he success of any crop improvement programme (Harlan, 1976; Moose and Mumm, 2008). Therefore, quantitative assessment of genetic diversity present among population usually helps a plant breeder in choosing desirable parents for breeding programme. The higher genetic distance between parents, the higher heterosis in progeny can be achieved (Joshi and Dhawan, 1966). Keeping in view, an effort has been made in the present study to evaluate a set of 49 wheat genotypes with an aim, to analyze the genetic variability, heritability, and genetic advance for yield and its component traits and diversity among biological population by D 2 -statistics. MATERIALS AND METHODS The experimental materials consisted of 49 genotypes of wheat were grown in a randomized block design with three replications at Crop Research Centre of Sardar Vallabhbhai Patel University of Agriculture and Technology, Meerut (U. P.) during rabi Experiment was conducted in November 2010 in a 6 rows plot of 6 meter length. The row to row and plant to plant distance was maintained 23 cm and 10 cm, respectively. The border rows were also planted to neutralize the border effect. The recommended agronomic practices were followed for good crop of wheat and competitive crop stand. The observations were recorded on five randomly selected competitive plants from each genotype in each replication on twelve agro-morphological characters viz; days to 50% flowering, plant height (cm), peduncle length (cm), number of productive tillers per plant, days to maturity, spike length (cm), number of spikelets per spike, number of grains per spike, 1000-grain weight (g), biological yield per plant (g), grain yield per plant (g), harvest index (%) and one quality trait like gluten content (%). The treatment means for all the characters were subjected to compute the analysis of variance on the basis of model proposed by Panse and Sukhatme (1969). The phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV) were analyzed by adopting the procedure suggested by Searle (1961). Heritability in broad sense h² (b) and genetic advance as per cent of mean were estimated by the formula as suggested by Allard (1960). Mahalanobis (1936) D 2 statistic analysis was used for estimation of genetic divergence among forty nine genotypes. The clustering of D 2 values was formed by using the Tocher s method as described by Rao (1952). Table 1: Analysis of variance (ANOVA) for 13 characters in bread wheat *,** Significant at 5% and 1% level of significance respectively.

3 130 INDIAN JOURNAL OF AGRICULTURAL RESEARCH Statistical software INDOSTAT was used for analysis the data of the present study. RESULTS AND DISCUSSION Genetic Variability: The analysis of variance revealed highly significant differences among the genotypes for al l the char acter s under study (Ta bl e1), thereby suggesting the presence of sufficient variability among the genotypes and provides ample scope for further improvement. Similar results were also reported by Tanzeen et. al. (2009). The presence of large extent of variability might be due to diverse sources of breeding materials collected as well as environmental effects on phenotypes. The genetic parameters studied are presented in Table 2. The magnitude of phenotypic coefficient of variation (PCV) was greater than genotypic coefficient of variation (GCV) for all the characters studied indicating vital role of environmental interaction in the expression of the characters. The findings were in agreement with previous study in wheat (Gollen et al. 2011). A perusal coefficients of variation revealed that the highest GCV and PCV were observed for grain yield per plant (38.75%, 39.56%) followed by plant height (37.17%, 38.80%), 1000-grain weight (36.93%, 38.80%), number of spikelets per spike (35.46%, 35.94%) and number of grains per spike (35.41%, 35.76%). Similar results were also reported by Kumar et al. 2010; Kaul and Singh, 2011; Kumar et al. 2013, Yadav et al. 2014). However, harvest index and gluten content revealed high difference between GCV and PCV in comparison to other characters,suggested that environmental effect was prominent for harvest index and gluten content. The least difference between PCV and GCV was noticed for number of spikelets per spike, number of grains per spike and grain yield/plant indicating these characters are less influence by environment on the expression of the characters under study.these results match with the finding of Shankarrao et al. (2010). Moderate estimate of GCV and PCV were noticed for spike length (28.08%, 28.67%), number of productive tillers per plant (27.17%, 27.48%), Harvest index (15.29 %, 18.80%) and gluten content (15.7%, 18.96%). These results were contrary to the findings of Jagshoran et al. (1995). Days to 50% flowering (11.54%, 11.66%), peduncle length (10.04%, 11.20%) and days to maturity (10.52%, 11.27%) showed low values of GCV and PCV. This suggested low variability for such characters among genotypes. The proportion of variability inherited from parents to off spring is manifested by heritability (Lush, 1949). In this concern, high estimates of heritability were observed for grain yield per plant followed by biological yield per plant, number of grains per spike, number of spikelets per spike, days to 50% flowering, plant height, peduncle length, harvest index and 1000-grain weight whereas moderate for spike length and gluten content (Table 2). Yadav et al. (2014) also observed high heritability for days to 50% flowering, days to maturity, tillers per plant, grain per spike, test weight and grain yield per plant. This shows the presence of additive gene effect and selection may be made for the improvement of these characters. Although low heritability exhibited that the characters were highly influenced by environmental effect and genetic improvement through selection will be difficult due to effect of environment. Johanson et al. (1955) have reported that a character showing high heritability may not be inevitable impart high genetic advance. It can be find out with greater degree of accuracy when heritability coupled Table 2: Genetic parameters for 13 characters in wheat Characters Coefficient of variation (%) Heritability (%) in Genetic advance GA as percentage GCV PCV broad sense of mean Days to 50% flowering Plant height (cm) Peduncle length (cm) Number of productive tillers per plant Days to maturity Spike length (cm) Number of spikelets per spike Number of grains per spike grain weight (g) Biological yield per plant (g) Grain yield per plant (g) Harvest index (%) Gluten content (%)

4 Volume 51 Issue 2 (2017) 131 with genetic advance is studied (Dudley and Moll, 1969). Therefore, estimation of heritability along with genetic advance is more useful to understand the type of gene action involved in the expression of various polygenic characters. High heritability coupled with high genetic advance as percent mean were revealed for days to 50% flowering, plant height, number of spikelets per spike, number of grains per spike, 1000 grain weight, biological yield per plant and grain yield per plant. Similar findings were reported by Nagireddy and Jyothula, (2009) and Khokhar et al., (2011). This indicates substantial contribution of additive gene action in the expression of the characters. Hence, direct selection for such characters would be more effective. The characters peduncle length and harvest index exhibited high heritability along with low genetic advance suggested predominance of nonadditive gene action hence; direct selection for such characters would mislead the expected results. Genetic Diversity: Forty nine genotypes of wheat were grouped into eight clusters based on D 2 -statistics in such a way that genotypes within a cluster had a low D 2 values than those of in-between the characters. The composition of clusters has been depicted in (Table 3). The distribution pattern of genotypes showed that cluster VI had maximum number of genotypes (11) followed by cluster III and VII (8) genotypes each and cluster II (6) whereas, cluster I had minimum number of genotypes (3). The inter-cluster distance was observed higher than intra-cluster, suggesting wide genetic diversity among genotypes (Table 4). The inter-cluster distance varied from to The highest inter-cluster distance was noticed in the cluster VII and VIII (53.19). However, minimum distance was observed in cluster I and V (10.05), indicating close relationship between these clusters would not provide good results. The greater distance between clusters, indicating that the genotypes included in these clusters revealed broad spectrum of genetic diversity and may be used in hybridization programme for wheat improvement. The hybrids developed from the selected genotypes within the limit of compatibility of these clusters may produce desirable transgressive segregants. This would be useful in wheat breeding programme for developing the high yield potential varieties. Similar findings were registered by (Yadav et al. 2006; Chapla et al and Singh et al. 2010). The maximum intra-cluster distance was observed in cluster IV (30.06), followed by cluster VIII (23.83) and cluster VI (22.82). It was noticed that genotypes within cluster with high degree of divergence would produce more desirable breeding materials for attaining the maximum genetic advance (Dobariya et al., 2006). The minimum intra-cluster Table 3: Clustering pattern of 49 wheat genotypes on the basis of D 2 cluster analysis. No. of clusters No. of genotypes Name of genotypes I 3 WCW , HP 1935, RAJ 4247 II 6 K-9107 (C), NW 5049, RW 3715, K1005, RW 3708, NW 5054 III 8 RAJ 4245, PBW 667, WH 1121, HD 3089, HD 3038, WH 1119, RAJ 4246, HUW 654 IV 4 DBW 87, HD 2733(C), DBW 17(C), K1008 V 5 DBW 85, NW 5045, DBW 86, WH 1118, HP 1936 VI 11 HD 3086, HUW 652, PBW 666, RW 3711, K 1007, PBW 343(C), HD 3087, NW 5038, PBW 668, HUW 653, WH 1120 VII 8 HP 1938, HUW 656, K 1006, DBW 83, DBW 84, K 1004, UP 2822, NW 5050 VIII 4 HUW 655, HP 1937, DBW 88, HD 3058 Table 4: Estimates of average inter and intra- cluster distances for 8 clusters in wheat. Clusters I II III IV V VI VII VIII I II III IV V VI VII VIII Diagonal and bold values are intra cluster distances

5 132 INDIAN JOURNAL OF AGRICULTURAL RESEARCH Table 5: Cluster mean for 13 characters in wheat

6 distance was registered in cluster V (0.08) followed by cluster II (0.09) and cluster VII (0.12) indicating homogeneous nature of the genotypes with less deviation between the genotypes, hence selection will be ineffective. The cluster means analyzed for 13 characters under study are presented (Table 5) revealed that the Cluster VIII exhibited highest mean value for days to 50% flowering (88.04) and better spike length (9.48). Cluster VII showed the highest mean for plant height (96.01) with peduncle length (42.4) and grain yield per plant (21.19) and better days to maturity (138.25). However, maximum number of productive tillers per plant was observed in cluster VI. Cluster III had highest days to maturity with 1000-grain weight (44.81) and better gluten content (8.53). Cluster II showed the highest spike length (10.20) with biological yield (47.58) and better number of spikelets per spike (16.9). Cluster V revealed the highest number of spikelets per spike (17.81) with number of grains per spike (53.45) and gluten content (8.59), whereas, cluster I exhibited only highest harvest index (50.92). The results were in agreement with the findings of earlier study (Singh and Dwivedi, 2002; Dobariya et al. 2006). The results from the present study suggest that crossing among genotypes from different clusters Volume 51 Issue 2 (2017) 133 exhibiting good mean performance may help in achieving high yield. Incorporation of more divergent parents in hybridization can enhance the chances of attaining better het erosis an d provide broa d spectrum of genetic variability in segregating generation. On the basis of divergence and cluster mean it may be suggested that maximum heterosis and good recombinants could be obtained in crosses between genotypes of cluster VIII, VII and III in varietal improvement. Thus, crosses between the genetically diverse genotypes of cluster VIII with genotypes HUW 655, HP 1937, DBW 88 and HD 3058 and cluster VII with genotypes like HP1938, HUW656, K1006, DBW83, DBW84, K1004, UP2822 and NW5050 are expected to exhibit high heterosis and are also likely to produce new recombinants with desired traits. The contribution of individual trait, days to 50% flowerin g con tributed m aximum di vergence (31.46) followed by grain yield per plant (30.34), plant height (27.55), biological yield (26.43) and number of grains per spike (20.60). Thus, traits days to 50% flowering, grain yield per plant, plant hei ght, biological yield and number of grains per spike are major contributors of genetic divergence. REFERENCES Allard, R.W. (1960). Principle of Plant Breeding. John Wiley and sons, NewYork. p Bhoite, K.D., Rasal, R.N., Gadekar, D.A. (2008).Genetic variability, heritability and genetic advance in durum wheat riticurri aestivum L.). J. of Maharashtra Agri. Uni. 33: Chapla, J.N., Dobariya, K.L., Khanpara, M.D., Jivani, L.L. and Kachhadia, V.H. (2008). Genetic divergence in bread wheat (Triticum aestivum L.). J. of Plant Improvement. 10(2): Dobariya, K.L., Ribadia, K.H., Padhar, P.R. and Ponkia, H.P. (2006). Analysis of genetic divergence in some synthetic lines of bread wheat (Triticum aestivum L.). Advances in Plant Sciences. 19(1): Dwivedi, A.N. and Pawar, I.S. (2005). Evaluation of genetic diversity among bread wheat germplasm lines for yield and quality attributing traits. J. of Res. 34(1): Dudley, J.W. and Moll, R.H. (1969). Interpretation and uses of estimates of heritability and genetic advance in plant breeding. Crop Sci. (9): FAOSTAT. (2014). Food and Agriculture Organisation (FAO) of the United Nations, Rome, Italy. Available at aostat3.fao.org (accessed Jan.2014).kay Falconer, D.S. and Mackay, T.F.C. (1996) Introduction to Quantitative Genetics (4 th ed.), Longman, Essex, UK. Gollen, B., Yadav, R.K. and Kumar, Pawan (2011). Assessment of genetic parameters for spike traits and yield attributes in bread wheat genotypes following Line X Tester mating system. Environment and Ecology. 29(2): Harlan (1976). Genetic resources in wild relatives of crop. Crop Sci. 16: Johnson, H.W., Robinson, H.F. and Comstock, R.F. (1955). Estimates of genetic and environmental variability in soybean. Agron. J. 47: Joshi, A.B. and Dhawan, N.L. (1966). Genetic improvement of yield with special reference to self fertilizing crops. Indian J. Genet., 26A : Kaul, D.K. and Singh, B. (2011). Evolution for drought Tolerance in elite genotypes of Bread Wheat (Triticum aestivum L.). Advances in Plant Sciences. 24 (1): Khokhar, M.I., Hussain, M., Zulkiffal, M., Sabir, W., Mahmood, S. and JamilAnwar, M.W. (2010). Studies on genetic variability and inter-relationship among the different traits in wheat (Triticum aestivum L.) Krmiva. 52: (2) Kumar, R., Gaurav, S.S., Bhushan, B. and Pal, R. (2013). Study of genetic parameters and genetic divergence for yield and yield components of bread wheat (Triticum aestivum L.). J. of Wheat res. 5(2):

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