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Assessing Genetic Diversity in Bread Wheat (Triticum aestivum L.) using D2 Statistical Analysis

B. Abrar Yasin1,*, S. Shubhra2, Misbahu Abdu3, Garome Shiferaw3, Dula Teshome3, Gada Gudina3, Legessu Girshe3, Tesfaye Dugasa3
1School of Agriculture, Suresh Gyan Vihar University, Jaipur- 302 017, Rajasthan, India.
2G. Singh Degree College, Prayagraj-211 007, Uttar Pradesh, India.
3Mattu University-Bedele Campus, P.O.Box 318, Ethiopia.

ABSTRACT

Background: Genetic diversity is one of the potent components used to develop improved cultivars with a broad genetic base and wider adaptability. Genetic diversity between two parents of diverse origin generally displays greater heterosis and yields transgressive segregants. Therefore, knowledge of genetic diversity is the prerequisite for the genetic improvement of crop plants including wheat. D2 statistical analysis is one of the potent biometrical techniques used for the assessment of genetic diversity. 

Methods: The essence of the current experiment involved 40 providential bread wheat lines that were arranged and laid down in a randomized block design (RBD) with three replications at the experimental research farm of Suresh Gyan Vihar University, Jagatpura, Jaipur, during the rabi crop season (2020-21). The bread wheat lines of the same geographical range were assessed to determine the genetic diversity using D2 statistical analysis and heritability estimate in apropos to ten quantitative/qualitative characters.

Result: Based on D2 analysis, the 40 promising wheat strains were lined up into ten different clusters to estimate the average distance at intra and inter-cluster levels. The uttermost intra-cluster distance was recorded in cluster X, whereas, the appreciative inter-cluster distance pertained among cluster-VI and cluster-X pursued by cluster-IX and cluster-X.  Entrenched to the mean or average performance, most of the bread wheat lines were spotted to disclose appreciable distinctions concerning to their cluster means. It was perceived that the maximum percent contribution towards total divergence was exhibited by grain yield plant-1, followed by spike length, effective tiller plant-1, 1000 grain weight and plant height. Moreover, the number of effective tillers plant-1, spike length, harvest index ,1000 grain weight, number of grains spike-1 and protein content were quite substantial since these traits exhibit high heritability estimate together with high genetic advance as percent of mean.

INTRODUCTON

Genetic diversity is the sum total of genetic variation prevailing within or among the crop population or species. Genetic diversity is one of the potent components used to develop improved cultivars with a broad genetic base and wider adaptability. Moreover, the existence of genetic diversity inside and among the crop species helps to meet the current and future breeding requirements as it allows the breeders to identify worthier lines whichever promptly could be released as a new variety or used as a parent in the hybridization programmes. Genetic diversity between two parents of diverse origin generally displays greater heterosis and yields transgressive segregants than those between closely related strains. Therefore, knowledge of genetic diversity is the prerequisite for the genetic improvement of crop plants including wheat (Tejasvi et al., 2017; Girnam et al., 2018). Though, it has been asserted that many of the improved cultivars of bread wheat currently under cultivation have one or more parents in common. For example, almost all the semi-dwarf wheat varieties have Rht1, Rht2, or both these genes for reduced height; these genes have been derived from a single wheat variety viz., Noirin-10. This has led to the narrowing down of the genetic base so as the diversity as well of the wheat crop. Thus, triggered genetic vulnerability i.e. susceptibility of bread wheat to various biotic and abiotic stresses (Singh, 2015). Consequently, it is crucial to avoid genetic vulnerability by using diverse and unrelated parents in the breeding programmes. In that way, the genetic diversity of any crop population including wheat can be broadened which will encourage breeders to flourish new varieties for traits of specific interests like quality improvement and tolerance to biotic and abiotic stresses. One of the potent biometrical techniques used for the assessment of genetic diversity present within a population is the D2 statistic (Singh and Narayanam, 2015). It measures divergence in two folds viz., intracluster and intercluster. Thus, it assists the plant breeder in the choice of genetically divergent parents for manipulation in hybridization programmes. In addition, D2 statistics measure the degree of diversification and determine the relative proportion of each component character to the total divergence. The genotypes together grouped in a single cluster are less divergent than the ones, which are placed in different clusters (Santosh et al., 2019). On the other hand, heritability associated with genetic advance as per cent of mean is one of the effective selection parameters that ushers the plant breeder to perform selection and ascertain the traits showing inheritance that are purely genetical rather than environmental. On that account, a trial was accomplished with the purpose of evaluating 40 promising bread wheat lines for the study of genetic diversity using D2 statistics aiming to identify elite divergent parents and their possible efficacy for the genetic improvement of bread wheat and also to find out the magnitude of heritability and genetic advanc as percent of mean in ten quantitative/qualitative characters of bread wheat.

MATERIALS AND METHODS

The on-going field investigation was organized during the rabi crop season (November) of 2020-21 at the experimental research farm, School of Agriculture, Suresh Gyan Vihar University (SGVU), Jagatpura, Jaipur. Geographically, the study area is situated at 26°55'N latitude, 75°46'E longitude and lies on an elevation of 431 m above sea level with an average annual rainfall of 546.2 mm. The soil of the experimental farm was classified as sandy loamy with a pH level of 6.5 to 7.12. Throughout the investigation, the daily temperature ranges from 29.2°C to 31°C with average humidity extending from 40 to 47% (November to March). The farm of SGVU is turned to many field experimental studies, hence, soil management, cultural practices (irrigation and fertilizer application) and the way growing different crops form a vast diversity within the field premises.
       
The entities for the current research comprise 40 promising wheat lines that were acquainted from IARI, New Delhi and Durgapura Research Station, Jaipur, Rajasthan. All these 40 propitious lines showed differential genetical behaviour in terms of the contrasting attributes they possess. Therefore, tapping genetic diversity among or within all the 40 wheat strains and assembling the same in one place were examined and screened throughout this current study. 
       
The current trial was laid down in a randomized block design (RBD) with three replications. The plot size of 2 m width and 1.5 m length (2×1.5 m = 3 m2) was chosen with a plant geometry of 25×5 cm. Thus, each plot consisted of 8 rows and each row comprised approximately 30 plants as per 25´5 cm row-to-row and plant-to-plant distance. The distance between the adjoining blocks and plots was held at 1.5 m and 0.5 m, respectively. All the recommended cultural practices i.e. one deep ploughing followed by three harrowing’s were given at the time of field bed preparation. Pre-sowing seed treatment with thiram @ 2 g/kg seed was applied to control seed-borne diseases like loose smut. Fertilizers (NPK) were applied as per the recommended dosage i.e., 100 N: 50 P and 40 K ha-1. The crop was raised with a seed rate of 100 kg/ha. Apart from that five irrigations at different stages of crop growth were given to grow a healthy wheat crop.
       
To maintain homogeneity and avoid any sampling error, the central 4 rows of each plot were selected as representative/working samples for documentation of data sampling and observations. Plants that were raised on each side of 2 rows acts as border effects were left and not used for data collection. The observations were set down on five competitive randomly selected plants from each plot as well as from each replication for the characters under study viz., number of effective tillers plant-1, spike length, number of grains spike-1, plant height, 1000 grain weight and grain yield plant-1 except for days to 50% flowering, days to maturity,  harvest index and protein content. Both days to 50% flowering and days to maturity were recorded by counting the number of days starting from sowing up to when 50% of the plants in a plot bear flowers and when the whole plot attains maturity as well. The grain protein content was estimated by the method given by Lowry et al., (1951) whereas, harvest index is the ratio of economic yield to total biological yield and it was calculated according to Abrar et al., (2011) 
       
The genetic diversity was estimated using Mahalanobis D2 statistics to workout divergence at two levels viz., intracluster and intercluster levels (Mahalanobis, 1928). Therefore, the bread wheat lines were grouped into 10 clusters based on the D2 value and by using the Tocher’s method suggested by Rao (1952). D2 statistics were estimated by working on the following formula:
 
 
       
The per cent contribution of characters towards genetic divergence was estimated according to Nedarajan and Gunasekaran (2008) whereas heritability in a broad sense (h2b) and genetic advance as percent of the mean was estimated according to the formula suggested by Comstock and Robinson (1952) and (Johnson et al., 1955a).

RESULTS AND DISCUSSION

Cluster pattern
 
As per the assumption from D2 analysis, the 40 bread wheat lines under investigation were grouped into ten clusters (Table 1). The cluster size varies, each comprising one to nine wheat lines. Cluster III emanated with the largest enumerate of components that make up 9 bread wheat lines, specifying comprehensive genetic closeness among them. Clusters II and IV pursued it, constituting 8 lines each, whereas clusters I and X had 6 and 4 entries. The remaining clusters, i.e., V, VI, VII, VIII and IX, comprise 1 bread wheat strain each, respectively. The framework of the grouping of wheat lines in discrete clusters and vice versa suggested that there was an inclination between geographical and genetic diversity as the wheat lines used in the present study belong to the same geographical region. These results conform with the observations of Verma et al., (2014), Singh et al., (2014), Vora et al., (2017), Santosh et al., (2019). The clustering arrangement may be exploited in picking the divergent parents by determining suitable cross combinations. It will foster intense feasible variability for several attributes. Thus, the lines with high intra and inter-cluster values can be utilized in the crop improvement programmes for further selection followed by hybridization.
 

Table 1: Grouping of 40 genotypes of bread wheat in various clusters on the basis of D2 statistic.


 
Inter-cluster distances
 
The results (Table 2) specify that the maximum inter-cluster distances were perceived among VI and X clusters (66.654) followed by clusters IX and X (63.232), VI and IX (62.201), VIII and IX (61.677) and clusters VI and VIII (60.321). On the contrary, the low-lying inter-cluster distance was found between clusters II and III (14.302) followed by clusters I and VI (14.621), clusters II and V (15.321), clusters II and IV (16.111) and clusters I and VIII (16.321), propounded familiarity and the lowest level of genetic diversity among them. Cluster distance (Intra and Inter) is the foremost standard for the identification of diverse lines (parents) using the D2 statistic (Rahman et al., 2015). The genotypes which possess the greatest inter-cluster distance are genetically more divergent and hybridization among them may result in the formation of transgressive segregants with more desirable features along with ample variability (Tewari et al., 2015). Thus, the bread wheat lines fall under VI and X clusters (PBW 343 × CPAN 1796, PBW 343 × HS 420, PBW 343 × VL 892 and PBW 343 × RAJ 4393) will effectuate the possible hybridization among the parents showing broad genetic base/diversity and such crosses could hopefully ensure the blooming of desirable progenies in the F2 and subsequent segregating generations.
 

Table 2: Average inter-cluster D2 value, among 10 clusters for 40 bread wheat strains.


 
Cluster means
 
The bread wheat line with respect to their various quantitative/qualitative characters, grouped under 10 clusters, discloses appreciable distinctions concerning their cluster means (Table 3). Based on the average or mean performance, genetically diverse lines with various desirable traits were spotted in each cluster. The findings of the present study designate that cluster I recorded earlier flowering (75.21 days) followed by cluster III (75.41 days), cluster VIII (76.54 days) and cluster IV (77.64 days). The same lines discussed above also mature earlier than the rest. Cluster-I matures in 113.88 days followed by cluster III (114.58 days), cluster VIII (114.87 days) and cluster IV (115.25 days). Such lines can escape the adverse effect of drought, terminal heat stress, disease/insect infestation and at the same time, they can give a better yield than those varieties which come under the adverse effect of terminal heat/drought, etc. (Singh, 2015). Thus, these lines can be used in hybridization programmes to transfer their earlier trait into other promising lines that lack such an important trait. The inflated mean value for the number of effective tiller plant-1 was displayed by cluster VI (10.12) followed by cluster II (9.22) and cluster VIII (8.44), whereas the lowest was recorded in cluster V (6.70). The maximum average value for spike length (8.45 cm) was found in cluster VI followed by cluster I (8.41 cm) and cluster IV (8.33 cm). Cluster VI exhibited a greater cluster mean (40.71) for the number of grains spikes-1 which was at par with cluster I (40.66), cluster IV (40.23) and cluster VIII (40.09). It is perceived from the present study that maximum cluster means concerning plant height were observed by cluster VI (93.33 cm) followed by cluster II (95.51 cm). Whereas, the lowest plant height was recorded by cluster V (89.54 cm). The mean data of the harvest index indicates that among all the clusters, cluster VI recorded the highest mean value for the harvest index (45.34%) whereas the lowest was recorded by cluster V (38.42%). From the current research, it is comprehended that the highest cluster mean values for 1000 grain weight were recorded in cluster VI (42.75 g) and the lowest in cluster V (37.44 g). Cluster VI observed maximum mean values for protein content (12.22%) and a minimum was observed by cluster X (9.28). The greatest cluster means value for grain yield plant-1 was displayed by cluster VI (11.25 g) and the lowest by cluster V (9.13 g). Grain yield is not an independent character but a product of a number of the constellation of yield contributing characters such as tillers per plant-1, which contributes to raising plant population per unit area (Nazim et al., 2014); spike length, grain per spike-1 and 1000 grain weight which form the ‘sink’ (Roman et al., 2013) and the harvest index which is the ratio of economic yield to total biological yield. The harvest index is considered an effective parameter to measure yield advancement and is considered directly related to yield (Abrar et al., 2011). In cereals including wheat, plant height is one of the essential components that is directly linked with lodging resistance and plant canopy (Singh, 2015). After yield, grain quality especially protein is a very important character and is directly needed by human beings (Abrar and Ram, 2018). All the above-cited attributes that have been grouped into different clusters under the present study play a pivotal role in the identification of more desirable diverse genotypes/lines within or among the clusters. On that account, hybridization between bread wheat lines grouped in 10 distinct clusters with the highest cluster means values will result in the induction of enormous genetic variability in the F2 and subsequent generations providing an opportunity to encourage the production of desirable transgressive segregants. Thus, ensued genetic improvement in grain yield and yield contributing character as well as protein content either directly or indirectly in wheat. Similar findings were also confirmed by Kumar et al., (2013); Kumar et al., (2014a); Vora et al., (2017); Tejasvi et al., (2017); Girnam et al., (2018); Santosh et al., (2019). They all reported high cluster means for various characters in wheat.
 

Table 3: Clusters means for 10 characters in bread wheat and percent contribution of different characters towards genetic divergene.


 
Contribution of the characters toward total divergence
 
The involvement/contribution of the characters in connection with the manifestation of the entire genetic divergence (Table 4) indicated that the greatest per cent of the contribution comes from grain yield plant-1 (23.17%) followed by spike length (20.75%), effective tiller plant-1 (19.42), 1000 grain weight (14.33%), plant height (14.21%), harvest index (10.44%) and the number of grains spike-1 (8.42%).  These results conform with (Lal et al., 2009; Kolakar et al., 2014; Kumar et al., 2014a; Vora et al., 2017 and Girnam et al., 2018). The characters with a maximum percent of contribution concerning the assertion of genetic divergence shall be acquired as an indicator in the selection of diverse parents from each cluster and utilized the same in the hybridization programmes for the development of desirable segregants which ultimately may result in the selection and release of new elite cultivars from the segregating population.
 

Table 4: Per cent (%) contribution of characters towards genetic divergence.


 
Heritability
 
Heritability is one of the important biometrical techniques, a reliable and excellent indicator aids in determining how much percentage of a trait is transmitted from parent to their offspring. In the ongoing investigation apart from focalizing the genetic diversity, heritability in the broad sense (the ratio of genotypic variance to the total or phenotypic variance) was also evaluated in all the 10 attributes under study. The values of heritability as per Johnson et al., (1955a) were classified as low (less than 30%, moderate (30-60%) and high (greater than 60%). It was seen that all the 10 characters considered under the current research showed more than a 60% heritability estimate thus observing a high heritability percentage (Table 5). Amongst the 10 attributes, the highest heritability percentage in a broad sense was heeded by spike length (85.41%) ensued by 1000 grain weight (84.88%), harvest index (81.72%), number grains spike-1 (80.22%), number of effective tillers plant-1 (79.26%), plant height (74.56%), protein content (72.63%), grain yield plant-1 (69.45%), days to maturity (67.59%) and days to 50% flowering (65.41%). The findings of Yadav et al., (2011); Abrar et al., (2018); Pavan et al., (2018); Abrar et al., (2020) and Patil et al., (2023) are consistent with our results. A high heritability percentage for the aforementioned components shows that a portion of phenotypic variance has been linked to genotypic variance and hence credible selection for these traits based on phenotypic expression could be made. Characters with high heritability are wealthier for a plant breeder than those with low heritability since the traits with low heritability are more subject to environmental change and selection of such traits would result in misleading the findings. In addition to heritability, one more important biometric tool is the genetic advance which refers to the enhancement in the average genotypic value of selected genotypes over the original population (parental population) before selection. Howerver, as per Johnson et al., (1955a), heritable estimate combined with genetic advance expressed as per cent of mean is more useful than heritability alone in forecasting the eventual effect of selection and according to Johanson et al., (1955a) the range of genetic advance as per cent of mean is classified as low (less than 10%), moderate (10-20%) and high (more than 20). The number of effective tillers plant-1 (64.37%), spike length (51.88%) harvest index (45.05%),1000 grain weight (41.03%), number of grains spike-1 (39.64%) and protein content (34.55%) were all considered to be highly encouraging because of recording high genetic advance as percent of mean along with high heritability estimates. Consequently, these traits may also exhibit the least genotype x environment interaction as they are the least affected by environmental factors. Similar findings were also corroborated by (Yadav et al., 2011; Abrar et al., 2018; Pavan et al., 2018; Abrar et al., 2020 and Patil et al., 2023).
 

Table 5: Estimated heritability (bs) and genetic advance as percent of mean of 10 bread wheat attributes.

CONCLUSION

Based on the intra/inter-cluster distance, cluster means of different attributes and percent contribution of the characters towards total divergence aids in the selection of genetically divergent parents that can be exploited in the hybridization programmes for the enhancement of wheat crop in terms of yield, resistance and quality parameters. Apart from that,  number of effective tillers plant-1, spike length, harvest index,1000 grain weight, number of grains spike-1 and protein content demonstraed high heritability measures along with high genetic advance as percent of mean, indicating little or no influence of the environment on the expression of such characters. Thus, selection of such traits will be more beneficial in enhancing the yield and quality of bread wheat.

CONFLICT OF INTEREST

All authors declared that there is no conflict of interest.

REFERENCES


  1. Abrar, Y.B. and Ram, M. (2018). Improvement in protein content and leaf rust resistance in semi dwarf wheat varieties through breeding research. Annals of Plant and Soil Research. 20(2): 159-163.

  2. Abrar, Y.B. and Ram, M. (2018). Assessment of genetic variability, heritability and genetic advance of yield, yield attributes and quality traits in bread wheat (Triticum aestivum L.). Annals of Plant and Soil Research. 20(2): 182-186.

  3. Abrar, Y.B., Kumar, R. and Kumar, M. (2020). Appraýsement of varýabýlýty parameters of graýn yýeld, yýeld components and proteýn content ýn bread wheat (Triticum aestivum L.). Plant Archives. 20(2): 1436-1439.

  4. Abrar, Y.B., Ram, M., Shubhra, S., Wani, B.A. (2011). Genetic improvement in yield, yield attributes and leaf rust resistance in semi-dwarf wheat varieties developed in India from the last 40 years. International Journal of  Agricultural Research. 6(10): 747-753. DOI: 10.3923/ ijar.2011.747.753. 

  5. Comstock, R.E. and Robinson, H.F. (1952). Genetic parameters, their estimation and significance. Proceedings 6th International Grassland Conference. 1: 248-291.

  6. Girnam, S., Kumar, P., Kumar, R., Gangwar, L.K. (2018). Genetic diversity analysis for various morphological and quality traits in bread wheat (Triticum aestivum L.). Journal of Applied and Natural Science. 10(1): 24-29.

  7. Johnson, H.W., Robinson, H.R. and Comstock, R.E. (1955a). Estimation of genetic and environmental variability in soybean. Agronomy Journal. 47: 477-433.

  8. Kolakar, S.S., Hanchinal, R.R., Nadukeri, S. (2014). Analysis of genetic diversity in wheat genotypes. International Journal  of Agricultural Sciences. 10: 142-145.

  9. Kumar, R., Gaurav, S.S., Bhushan, B., Rishi, P. (2013). Study of genetic parameters and genetic divergence for yield and yield components of bread wheat (Triticum aestivum L.). Journal of  Wheat Research. 5: 39-42.

  10. Kumar, V., Payasi, D.K., Saiprasad, S.V. (2014a). Genetic divergence analysis in durum wheat (Triticum durum.). International Journal of  Current  Research. 6: 7001-7005.

  11. Lal, B.K., Ruchig, M., 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.

  12. Lowry, O.H., Rasebrough, N.J., Farr, A.L., Randoll, R.J. (1951). Protein measurement with the folin phenol reagent. Journal  of  Biochemistry. 193: 165-175.

  13. Mahalanobis, P.C. (1928). On the generalized distance on statistics, a statistical study of Chinese head measurement. Journal of the Asiatic Society of Bengal. 25: 301-307.

  14. Nadarajan, N. and Gunasekaran, M. (2008). Quantitative genetics and biometrical techniques in plant breeding. Kalyani Publishers, Ludhiana. pp-182-193

  15. Nazim, H., Muhammad,  A.Y., Nawaz, A.H. (2014). Exploring the role of row spacing in yield improvement of wheat cultivars.  Pakistan Journal of Agricultural Sciences. 51(1): 25-31.

  16. Pavan, D., Moitra, P.K., Shukla, R.S., Shukla, S.S., Suneeta, P. and Geeta, A. (2018). Analysis of variability, heritability and genetic advance of yield, its components and quality traits in wheat. International Journal of Agriculture, Environment and Biotechnology. 855-859.

  17. Patil, P., Shrivastav, S.P., Kulbhushan, P., Landge, R. and Gurjar, D. (2023). Genetic variability, heritability, genetic advance and divergence analysis in wheat (Triticum aestivum L.). Indian Journal of Agricultural Research. DOI: 10.18805/ IJARe.A-6036.

  18. Rahman, M.S., Hossain, M.S., Akbar. M.K., Islam, M.S., Ali, L. (2015). Genetic divergence in spring wheat genotypes (Triticum aestivum L.). Eco-friendly Agricultural Journal. 8(1): 1-3.

  19. Rao, C.R. (1952). Advance Statistical Methods in Biometrical Research.  John Wiley and Sons. New York, pp-383.

  20. Roman, A.S., Alzueta, I., Savin, R., Slafer, G.A. (2013). Understanding grain yield responses to source-sink ratios during grain filling in wheat and barley under contrasting environments.  Field Crops Research. 150: 42-51. https://doi.org/10.1016/ j.fcr.2013.05.016.

  21. Santosh, J.J.P., Singh, A., Gahatyari, N.C. (2019). Genetic diversity analysis in bread wheat [Triticum aestivum (L.) em.Thell.] for yield and physiological traits.  International Journal of Current Microbiology and Applied Sciences. 8(2): 3059- 3068. 

  22. Singh, G., Singh, K.N., Setter, B.N., Singh, T.L., Saharan, M.K., Tyagi, M.S., Verma, B.S., Sharma, I.A. (2014). Germplasm  characterization, association and clustering for salinity and water-logging tolerance in bread wheat (Triticum aestivum). Indian Journal of Agricultural Sciences. 84: 1102-1110.

  23. Singh, B.D. (2015). Plant Breeding, Principles and Methods. Kalyani Publishers, New Delhi, pp-130.

  24. Singh, P. and Narayanam, S.S. (2015). Biometrical Techniques in Plant Breeding. Kalyani Publishers, New Delhi. pp 38-42.

  25. Tejasvi, S., Singh, V., Singh, K.P., Verma, O.P., Mishra, S., Srivastava, A., Ahmad, R. (2017). D2 analysis in certain promising genotypes of wheat (Triticum aestivum L. em. Thell). Journal of Pharmacognosy and Phytochemistry. 6(5): 2714-2717.

  26. Tewari, R., Jaiswal, J.P., Gangwar, R.P., 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(4): 1111-1117.

  27. Verma, P.N., Singh, B.N., Singh, G., Singh, M.K., Setter, T.L. (2014). Genetic diversity analysis for yield and other agronomic traits in bread wheat under waterlogged sodic soil condition.  Journal of Wheat Research. 6: 51-58.

  28. Vora,  Z.N., Patel, J.B., Pansuriya, A.G., Yusufzai Sana, A. (2017). Genetic divergence analysis in bread wheat (Triticum aestivum L.). Research in Environment and Life Sciences.  10(3): 291-294. 

  29. Yadav, A.K., Maan, R.K., Kumar, S. and Kumar, P. (2011). Variability, heritability and genetic advance for quantitative characters in hexaploid wheat (Triticum aestivum L.). Electronic Journal of Plant Breeding. 2(3): 405-408.
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