Banner

Legume Research

  • Chief EditorJ. S. Sandhu

  • Print ISSN 0250-5371

  • Online ISSN 0976-0571

  • NAAS Rating 6.80

  • SJR 0.32, CiteScore: 0.906

  • Impact Factor 0.8 (2024)

Frequency :
Monthly (January, February, March, April, May, June, July, August, September, October, November and December)
Indexing Services :
BIOSIS Preview, ISI Citation Index, Biological Abstracts, Elsevier (Scopus and Embase), AGRICOLA, Google Scholar, CrossRef, CAB Abstracting Journals, Chemical Abstracts, Indian Science Abstracts, EBSCO Indexing Services, Index Copernicus
Submit

Research Article

Legume Research, volume 44 issue 2 (february 2021) : 123-130

Designing Selection Criteria by Using Association Studies and Estimation of Genetic Diversity in Fenugreek (Trigonella foenum-graecum L.) Germplasm

Bhawana Bhatt1,*, Manoj Raghav1, Anita Singh1, A.S. Jeena2, Sanjeev Agrawal3, S.B. Bhardwaj4
1Department of Vegetable Science, G. B. Pant University of Agriculture and Technology, Pantnagar-263 145, Uttarakhand, India.
2Department of Genetics and Plant Breeding, G. B. Pant University of Agriculture and Technology, Pantnagar-263 145, Uttarakhand, India.
3Department of Biochemistry, G. B. Pant University of Agriculture and Technology, Pantnagar-263 145, Uttarakhand, India.
4Department of Statistics, G. B. Pant University of Agriculture and Technology, Pantnagar-263 145, Uttarakhand, India.

  • Submitted09-09-2019|

  • Accepted04-01-2020|

  • First Online 18-03-2020|

  • doi 10.18805/LR-4233

Cite article:- Bhatt Bhawana, Raghav Manoj, Singh Anita, Jeena A.S., Agrawal Sanjeev, Bhardwaj S.B. (2020). Designing Selection Criteria by Using Association Studies and Estimation of Genetic Diversity in Fenugreek (Trigonella foenum-graecum L.) Germplasm . Legume Research. 44(2): 123-130. doi: 10.18805/LR-4233.

ABSTRACT

An investigation consisting of 36 fenugreek genotypes and one check (Pusa Early Bunching) was laid out in Randomized Block Design with three replications at Pantnagar Centre for Plant Genetic Resources (PCPGR) of the G.B.P.U.A & T, Pantnagar, Uttarakhand during rabi season of 2016-17 and 2017-18 and observations were recorded on sixteen different traits. The traits viz., number of pods per plant, thousand seed weight and leaf yield per plant exhibited high direct effects on seed yield per plant along with significant positive correlation indicating that these traits can be used as most important selection criteria for seed yield improvement. The diversity analysis leads to formation of nine different clusters and maximum intra-cluster distance was observed in cluster IV (21.07) while highest inter-cluster distance was recorded between cluster VII and IV (46.07). These clusters actually represent the different heterotic pools and genotypes from diverse pools can be crossed to obtain heterosis and transgressive segregants.

INTRODUCTION

Fenugreek (Trigonella foenum-graecum L.) is an important seed spice cum leafy vegetable crop belonging to the family fabaceae. It is a self-pollinated diploid species with sixteen chromosome number (Frayer, 1930). Fenugreek is a dicotyledonous crop with light green, pinnately trifoliate leaves (Srinivasan, 2006) and yellow-white papilionaceous flowers. Commercially, it has high economic value as food, fodder and medicine. Fenugreek has a wider adaptability and is grown under wide range of climatic conditions. The study on morphological parameters and their association with the sink potential is a modal tool for designing suitable selection criteria in fenugreek. Yield improvement of any crop mainly depends on the presence of genetic diversity in elite as well as wild lines. Genetic diversity can be explained as different types of alleles and genotypes present in a population that results in differences between individuals and populations at morphological, physiological and behavioural level (Frankham et al., 2002). There is an urgent need to evaluate genetic diversity of existing genotypes for development of promising and high yielding cultivars. There is need to assess and improve the existing genotypes and introduce suitable varieties of fenugreek which can be used both as leafy vegetable as well as for seed purpose. Despite of having a large global genetic diversity only some well-defined cultivars are grown in specific areas. The knowledge about the diversity of different genotypes is very important as it helps us to broaden the genetic base, reduction of genetic erosion and heterotic pooling.

MATERIALS AND METHODS

The present investigation was conducted at Pantnagar Centre for Plant Genetic Resources (PCPGR) of the GBPUA&T, Pantnagar, Uttarakhand during rabi season of 2016-17 and 2017-18. The experiment consisting of 36 fenugreek genotypes and one check (Pusa Early Bunching) was laid out in RBD with three replications and spacing of 30 cm (row to row) and 10cm (plant to plant). From each plot in each replication, five plants were randomly selected and observations were recorded in both the years for sixteen different morphological characters. The recorded data of both the years was pooled and then subjected to statistical analysis. The analysis of variance (ANOVA) was done following the method given by Fisher (1920). The genotypic and phenotypic coefficients of variation (GCV and PCV), for various traits were calculated by the formula suggested by Burton and De Vane (1953). Heritability in broad sense was also calculated by formula given by Burton and De Vane (1953). The genetic advance was calculated by formula given by Robinson et al., (1949) and genetic advance as percentage over mean was calculated by using formula of Johnson et al., (1955). Direct and indirect effects of various characters on seed yield were computed by using the formula suggested by Dewey and Lu (1959). To estimate genetic diversity Mahalanobis D2 statistics (Mahalanobis, 1928) was used and clusters were prepared by following the Tocher’s method as described by Rao (1952).

RESULTS AND DISCUSSION

The ultimate objective of any plant breeding programme is to develop promising genotypes which have higher potential in producing economic yield than the existing varieties of the crop. Presence of genetic variability determines the success of such breeding programmes and various variability parameters are used so that desirable and ideal genotypes can be selected. Hence, plant breeder need to have the knowledge of association of yield with component traits as well as among themselves.
 
Association studies by using yield and related traits (Pooled Analysis of Variance)
 
Analysis of variance (ANOVA) for various morphological characters of fenugreek for the pooled data of year 2016-17 and 2017-18 are presented in Table 1. It revealed highly significant differences among the genotypes for all the sixteen traits viz., number of primary branches per plant, number of leaves per plant at 30 days, 45 days and 60 days after sowing, plant height, days to first flowering, node of first flowering, days to 50 per cent flowering, days to seed maturity, number of pods per plant, pod length, pod width, number of seeds per pod, thousands seed weight, green leaf yield per plant and seed yield per plant. Thus wide range of genetic variability was present in the genotypes for all the traits which can be exploited for development of superior varieties. Similar results were obtained by Singh and Chandra et al., (2000), Kakani (2017), Singh and Naula (2017), Yadav et al., (2017), Naula et al., (2018) and Pant et al., (2018).
 

Table 1: Analysis of variance for different quantitative characters in fenugreek.


 
Genetic variability parameters
 
The study of pooled data revealed that in general for all the studied traits PCV was higher than the GCV (Table 2) indicating the presence of considerable amount of genetic variability in genotypes. High estimate (> 20%) of genotypic coefficient of variation was obtained for leaf yield per plant (27.78%) followed by number of pods per plant (21.38%), thousands seed weight (21.31%), number of leaves per plant at 60 DAS (20.56%). The estimates of phenotypic coefficient of variation was recorded high (>20%) for leaf yield per plant (28.99%) followed by thousands seed weight (21.99%), number of pods per plant (21.61%), number ofleaves per plant at 60 DAS (20.72%), seed yield per plant (20.68%). The characters viz., number of leaves per plant at 60 DAS (98.43%), number of pods per plant (97.86%), days to first flowering (95.83%), plant height (95.18%), thousands seed weight (93.88%), node of first flowering (93.13%), number of leaves per plant at 45 DAS (92.70%), number of leaves per plant at 30 DAS (91.99%), leaf yield per plant (91.78%), seed yield per plant (91.18%), days to 50% flowering (85.20%), primary branches per plant (80.05%), pod width (78.91%), days to seed maturity (74.04%) and number of seeds per pod (65.58%) showed high estimates (>60%) of heritability (Table 2). Highest estimate (>20%) of genetic advance as per cent of mean was obtained for leaf yield per plant (54.83 %) followed by number of pods per plant (43.58%), thousands seed weight (42.53%), number of leaves per plant at 60 DAS (42.03%), seed yield per plant (38.85%), plant height (34.16%), number of leaves per plant at 30 DAS (34.12%), number of leaves per plant at 45 DAS (33.90%), node of first flowering (24.94%) and primary branches per plant (22.06%).
 

Table 2: Estimates of genetic parameters of variation for the different morphological characters of fenugreek genotypes.


       
The traits showing high heritability along with high genetic advance were number of pods per plant, number of leaves per plant at 30, 45 and 60 DAS, thousands seed weight, node of first flowering, leaf yield per plant, primary branches per plant and seed yield per plant. The high heritability coupled with high genetic advance indicates the presence of additive gene action. Similarly, high heritability0coupled with0genetic advance as per cent of mean was reported for number of pods per plant by Prajapati et al., (2007), Plant height by Lodhi et al., (2015) thousands seed weight by Banerjee and Kole (2004) for seed yield per plant by Yadav et al., (2013), Singh and Kakani (2017) and Yadav et al., (2017).
 
Association Studies by using correlation and path coefficient
 
The study of correlation revealed that in general the magnitude of genetic correlation was higher than the phenotypic correlation (Table 3). Pooled analysis of both years’ data revealed that seed yield per plant showed significant and positive association with number of leaves per plant at 45 DAS (rp=0.157, rg=0.186*), plant height (rp=0.313**, rg=0.327**), number of pods per plant (rp=0.500**, rg=0.524**), pod length (rp=0.226*, rg=0.280**), pod width (rp=0.227*, rg=0.223*), thousands seed weight (rp=0.347**, rg=0.372**) and leaf yield per plant (rp=0.230*, rg=0.257**). The correlation of these traits with seed yield indicates that these traits could be selected directly to improve the seed yield per plant.
       
Positive association of seed yield per plant with number of pods per plant was also reported by Fufa (2013), Lodhi et al., (2015), Wojo et al., (2016) and Patil (2018) with plant height by Jain et al., (2013), Lodhi et al., (2015), Singh and Kakani (2017) and Panwar et al., (2018) with pod length was by Wojo et al., (2016) and Panwar et al., (2018) with pod width by Panwar et al., (2018) with leaf yield per plant by Lodhi et al., (2015), Gurjar et al., (2016) and Singh et al., (2016) with thousands seed weight was also obtained by Jain et al., (2013), Singh (2014), Lodhi et al., (2015), Gurjar et al., (2016), Wojo et al., (2016) and Panwar et al., (2018). High positive direct effect (0.3-0.99) on seed yield per plant were showed by number of pods per plant (0.852), thousands seed weight (0.424), green leaf yield per plant (0.376) and days to 50 per cent flowering (0.345) (Table 4).
 
 

Table 4: Path coefficient analysis showing direct and indirect effect of various characters on seed yield at phenotypic level.


       
The traits like number of pods per plant, thousand seed weight and leaf yield per plant exhibited high direct effects on seed yield per plant along with significant positivecorrelation indicating that these traits could be used as most important selection criteria for seed yield improvement in fenugreek.
 
Diversity analysis
 
For a successful crop improvement program, assessment of genetic divergence is very essential. It facilitates the selection of suitable parents for hybridization program. It is well known fact that higher the genetic diversity among the parents greater will be the possibility of getting desirable segregants or heterotic hybrids.
 
Cluster obtained
 
The analysis of pooled data results in formation of nine different clusters (Table 5), cluster I (15) contained maximum number of genotypes followed by cluster II (9) and cluster IV (6). Cluster III, cluster V, cluster VI, cluster VII, cluster VIII and cluster IX, each had only one genotype. Similarly, the fenugreek genotypes were grouped into 9 clusters by Pathak et al., (2014), 5 clusters by Kakani et al., (2015), 6 clusters by Wojo et al., (2015), 4 clusters by Tariyal et al., (2017) and 2 clusters by Panwar et al., (2018).
 

Table 5: Classification of fenugreek genotypes into different clusters based on D2 value.


       
Among eight clusters, maximum intra cluster distance (Table 6) was observed in cluster IV (21.07) followed by cluster II (17.54) and cluster I (15.64) while other clusters showed no intra cluster distances. Highest inter cluster distance was recorded between cluster VII and IV (46.07) followed by cluster VIII and IV (45.97), cluster IX and VIII (42.39), cluster VI and IV (40.22) and cluster VIII and III (37.92). The least inter cluster distance was observed in between cluster V and III (13.91). Similar findings of intra and inter cluster distances were observed by Kole and Goswami (2015), Wojo et al., (2015), Tariyal et al., (2017), Panwar et al., (2018), Sindhu et al., (2018) and Yadav et al., (2018).
 

Table 6: Inter and intra cluster distances of four clusters of fenugreek genotypes.


 
Contribution of characters to divergence
 
During the pooled analysis, among all the sixteen characters, number of pods per plant (31.68%) showed maximum contribution followed by number of leaves per plant at 60 DAS (25.68%), days to first flowering (10.96%), plant height (8.41%), seed yield per plant (7.36%), thousands seed weight (4.05%), green leaf yield per plant (3.60%), number of seeds per pod (3.30%), node of first flowering (2.40%), number of leaves per plant at 30 DAS (1.35%), number of primary branches per plant (0.30%), days to 50 per cent flowering (0.30%), days to seed maturity (0.30%), number of leaves per plant at 45 DAS (0.15%) and pod width (0.15%). The character, pod length showed negligible contribution towards the genetic divergence (Table 7). Similarly, pod number, thousands seed weight and number of primary branches per plant by Mathur et al., (1992), plant height by Jain et al., (2006) and test weight by Panwar et al., (2018) were reported as main contributing characters towards the total genetic divergence.

The above obtained results provides clue about the presence of sufficient genetic diversity among the genotypes under investigation. The grouping of genotypes in different clusters is highly useful as these groups represent the heterotic pools and genotypes from diverse groups can be crossed to obtain heterosis and transgressive segregants.

CONCLUSION

It can be concluded that there is considerable amount of genetic variability present in the experimental material. Number of pods per plant, thousand seed weight and leaf yield per plant exhibited high direct effects on seed yield per plant along with significant positive correlation indicating that these traits can be used as most important selection criteria for seed yield improvement in fenugreek. The analysis of pooled data results in formation of nine different clusters with highest inter cluster distance was recorded between cluster VII and IV (46.07). These clusters represent the heterotic pools and genotypes from diverse groups can be crossed to obtain heterosis and transgressive segregants.

REFERENCES


  1. Banerjee, A. and Kole, P.C. (2004). Genetic variability, correlation and path analysis in fenugreek (Trigonella foenum-    graecum L.). Journal of Spices and Aromatic Crops. 13: 44-48.

  2. Burton, G.W. and De Vane, E.W. (1953). Essstimating heritability in fall fescue (Festuca arundinacea) form replicated clonal material. Agronomy Journal. 45: 478-481.

  3. Chandra K., Sastry, E. V. D. and Singh, D. (2000). Genetic variation and character association of seed yield and its component character in fenugreek (Trigonella foenum-graecum L.). Agriculture Science Digest. 20: 93-95.

  4. Dewey, D.R. and Lu, K.H. (1959). A correlation and path coefficient analysis of components of created wheat grass seed production. Agronomy Journal. 51: 515-518.

  5. Fisher, R.A. (1920). A mathematical examination of the methods of determining the accuracy of an observation by the mean error and by the mean square error. Monthly Notices Roy. Astro. Soc. 80: 758–770. 

  6. Frankham, R., Ballou, J.D. and Briscoe, D.A. (2002). Introduction to conservation genetics. Cambridge University Press. 233 p.

  7. Frayer, J.K. (1930). Chromosome Atlas of Flowering Plant. Georg Allen and Unwin London. 519 p.

  8. Fufa, M. (2013). Correlation studies on yield and yield components of Fenugreek (Trigonella foenum-graecum L.) lines evaluated in South-Eastern Ethiopia. WJAR. 2: 280-282.

  9. Gurjar, M., Naruka, I.S. and Shaktawat, R.P.S. (2016). Variability and correlation analysis in fenugreek (Trigonella foenum-    graecum L.). Legume Research. 39: 459-465.

  10. Jain, A., Singh, B., Solanki, R.K., Saxena, S.N. and Kakani, R.K. (2013). Genetic variability and character association in fenugreek (Trigonella foenum- graecum L.). International Journal of Seed Spices. 3: 22-28.

  11. Jain, U.K., Singh, D., Balai, O.P. and Shiva, K.N. (2006). Genetic divergence in fenugreek (Trigonella foenum-graecum L.) germplasm. Journal of Spices and Aromatic Crops. 15: 59-62.

  12. Johnson, H.W., Robinson, H.F. and Comstock, R.E. (1955). Estimates of genetic and environmental variability in soybean. Agronomy Journal. 47: 314-318.

  13. Kakani, R.K., Singh, G. and Saxena, S.N. (2015). Genetic divergence in fenugreek (Trigonella foenum-graecum L.). International Journal of Seed Spice. 5: 37-40.

  14. Kole, P.C. and Goswami, T. (2015). Genetic divergence in fenugreek grown under sub-humid subtropical red lateritic belt of Eastern India. Inernational Journal of Bio-resource Environment and Agriculture Sciences. 1: 97-102.

  15. Lodhi, P.S., Singh, P.P., Naruka, I.S., Kushwaha, S.S. and Singh, A.K. (2015). Genetic variability, correlation and path analysis in fenugreek (Trigonella foenum-graecum L.). Indian Journal of Horticulture. 72: 429-433.

  16. Mahalanobis, P.C. (1928). A statistical study at Chinese head measurement. J. Asiatic Soc. Bengal. 25: 301- 377. 

  17. Mathur, V.L. (1992). Genetic Divergence in Fenugreek (Trigonella foenum-graecum L.). Indian Journal of Genetics and Plant Breeding. 52: 432-434.

  18. Naula, R., Singh, A. and Raghav, M. (2018). Nutritional potentials of fenugreek (Trigonella foenum-graecum L.) genotypes: Chemical characterization. International Journal of Chemical Studies. 6: 743-747.

  19. Pant, N.C., Tewari, M., Dhoundiyal, R., Pandey, C.S., Singh, J.P. and Agrawal, S. (2018). Evaluation of micronutrients in fenugreek (Trigonella foenum-graecum L.): A viable alternative for micronutrient supplementation. International Journal of Current Microbioogy and Applied Sciences 7: 2446-2464.

  20. Panwar, A., Sharma, Y.K., Meena, R.S., Solanki, R.K., Aishwath, O.P., Singh, R. and Choudhary. S. (2018). Genetic variability, association studies and genetic divergence in Indian fenugreek (Trigonella foenum- graecum L.) varieties. Legume Research. 41: 816-821.

  21. Pathak, A.R., Patel, A.I., Joshi, H.K., and Patel, D.A. (2014). Genetic divergence in fenugreek (Trigonella foenum-graecum L.) germplasm. Trends in Biosciences. 7: 295-297.

  22. Patil, J. 2018. Genetic Diversity Studies in Fenugreek (Trigonella foenum-graecum L.). Thesis, M.Sc. Dr.Y.S.R.Horticultural University – Venkataramannagudem.

  23. Prajapati, D.B., Badaya, S.N. and Prajapati, B.H. (2007). Genetic variability, character association and path analysis in fenugreek. In: National Seminar on Production, Development, Quality and Export of Seed Spices–Issues and Strategies, NRCSS, Tabiji, Ajmer. 14: 2-3.

  24. Rao, C.R. (1952). Advanced statistical methods in biometrical research. John Wiley and Sons Inc., New York. 390 p. 

  25. Robinson, H.F., Comstock, R.E., Harvey, P.H. (1949). Estimates of heritability and the degree of dominance in corn. Agronomy Journal. 41: 353-359.

  26. Sindhu, A., Tehlan, S.K. and Chaudhury, A. (2018). Effect of morphological trait variance on plant yield in different Trigonella foenum-graecum L. varieties. Australian Journal of Crop Science. 12: 1-10.

  27. Singh, A. and Naula, R. (2017). Variability parameters for growth and yield characters in fenugreek (Trigonella spp.) genotypes. International Journal of Agriculture Science. 9: 4077-4080.

  28. Singh, G. and Kakani, R.K. (2017).Variability, character association and path analysis studies in fenugreek (Trigonella foenumgraecum L.). International Journal of Pure and Applied Bioscience. 5: 945-952.

  29. Singh, G. and Kakani, R.K. (2017). Variability, character association and path analysis studies in fenugreek (Trigonella foenum-    graecum l.). Int. J. Pure App. Biosci. 5: 945-952.

  30. Singh, M.K. (2014). Genetic variability, heretibility, genetic advance and correlation coefficient analysis in fenugreek (Trigonella foenum-graecum L.). Hort Flora Research Spectrum. 3: 178-180.

  31. Singh, P.P., Gujar, M. and Naruka, I.S. (2016). Association and path analysis in fenugreek (Trigonella foenum-graecum). Indian Journal of Agricultural Sciences. 86: 951–955.

  32. Srinivasan, K. (2006). Fenugreek (Trigonella foenum-graecum L.): A review of health beneficial physiological effects. Food Reviews International. 22: 203-224.

  33. Tariyal, Y.S., Bisht, S.S., Pant, S.C. and Chauhan, R.S. (2017). Study of genetic divergence in fenugreek (Trigonella foenum-    graecum L.). Journal of Pharmacognosy and Phytochemistry. 6: 1551-1552.

  34. Wojo, A.A., Alamerew, S., Nebiyu, A. and Menamo, T.(2015). Cluster analyses based on yield and yield components in fenugreek (Trigonella foenum-graecum L.) accessions. GJSFR. 15: 41-46.

  35. Wojo, A.A., Alamerew, S., Nebiyu, A. and Menamo, T. (2016). Genotype and phenotype variability studies in fenugreek (Trigonella foenum-graecum L.) accessions in Kaffa Zone, South West Ethiopia. Journal of Spices and Aromatic Crops. 25: 159-168.

  36. Yadav, P., Tehlan, S.K. and Deswal, S. (2018). Assessment of genetic divergence in fenugreek (Trigonella foenum-graecum L.) based on biological characters. Journal of Pharmacognosy and Phytochemistry. 7: 1257-1260.

  37. Yadav, P., Tehlan, S.K. and Sheokand, R.N. (2017). Genetic variability of Indian fenugreek (Trigonella foenum-graecum L.) landraces. International Journal of Current Microbioogy and Applied Sciences. 6: 2686-2691.

  38. Yadav, Y., Yadava, P.S., Pandey, V.P. and Kumar, A. (2013). Genetic variability, correlation and path co-efficient analysis studies in fenugreek (Trigonella foenum graecum L.). TAJH. 8: 456-459.
Reviewed By

Download Article
In this Article
    Contact us
    Follow us

    Editorial Board

    View all (0)