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Full Research Article

Principal Component Analysis for Selection of Elite Lines in Faba Bean (Vicia faba L.)

A
Anuj Kumar Choudhary1,*
1Department of Plant Breeding and Genetics, Regional Research Sub Station Jalalgarh, Purnea-854 327, Bihar, India.

ABSTRACT

Background: Bakala is popularly known as broad bean, horse bean, wonder bean, English bean, field bean, tick bean, winter bean, pigeon bean and Bakla in India. It is a self-pollinating crop with significant levels of out-cross and inter-cross, ranging from 20 to 50% depending on genotype and environmental effects. Faba bean is world’s fourth most important legume crop after pea, chickpea and lentil, widely cultivated for human food, animal feed and fodder also.

Methods: Present investigation was conducted at Bhola Paswan Shastri Agricultural College, Purnea during 2019-20 without replication comprising 20 accessions with each row measuring 2 meter in length and row to row distance was kept at 45 cm while plant to plant distance was maintained at 15 cm. Experiment was conducted for evaluating the genetic variability within the existing accessions by principle component analysis.

Result: Wide range of genetic variability was observed for quantitative traits. Maximum variation contributed in first principal component (PC) for traits viz; plant height and days to flowering i.e.38.217 per cent followed by second PC component i.e.25.905 per cent traits like plant height, days to flowering and days to maturity. Maximum inter-cluster distances was observed between cluster III and VI and I (47.31) followed by cluster I and III (36.19) and minimum between cluster II and V (10.39).

INTRODUCTION

Faba bean is an annual diploid (2n = 2x = 12) and belongs to the family fabaceae and has a long history of cultivation in the human civilization. The rate of out crossing depends on the genotype, environmental factors, row space and the number of pollinating insects, especially honeybees. Faba bean is world’s fourth most important legume crop after pea, chickpea and lentil, widely cultivated for human food, animal feed and fodder also. It plays an important role in world agriculture because of its high seed protein content which ranges from 20 to 40% depending upon the genotype and the environmental conditions in which it has been grown (Kaur et al., 2014). It is an efficient nitrogen fixer and improves soil fertility through symbiotic nitrogen fixation. It can grow well on high fertility soil to N-deficient marginal lands. Faba bean can withstand salinity conditions especially chloride and sulphate salts to a greater extent than chickpea. It has a potential yield of 60-75 quintals per hectare and average yield of 40-45 quintals per hectare (Bishnoi, 2016). It is grown as a Rabi crop in India as well as Bihar and adjoining states where its raw pods are eaten as a vegetable (Kumar et al., 2017). Faba bean has been recognized as a potential grain legume and included in the All India Coordinated Research Network on Potential crops (Kumar et al., 2016). It has also been identified as one of the eight major food legumes by the CGIAR research programme for priority focus and gaining importance as a grain legume for protein security of demographically expanding and climatically changing world (Bishnoi, 2016). Unrealized yield potential and yield instability are the major constraints in faba bean cultivation. The increased yield caused by heterozygosity due to out-crossing is well documented in faba bean. Therefore, heterosis, resulting from the combined action and interaction of allelic and inter-allelic genes is effective in faba bean and improved yield can be obtained by hybrid combinations (Bishnoi et al., 2012). The heterotic effects in Vicia faba may range from significantly positive to significantly negative for different traits depending on the genetic makeup of the parents. Exploitation of heterosis in the form of hybrid varieties may contribute in the improvement of yield and its component traits (Bishnoi et al., 2015). The present study was carried out with an objective to estimate genetic variability in existing germplasm by principle componant analysis (PCA) which may be utilized further in breeding programmes.

MATERIALS AND METHODS

The present investigation was conducted at Bhola Paswan Shastri Agricultural College, Purnea during 2019-20 comprising 20 germplasm of which were collected from different district of Bihar. All these germplasm had been sown in without replication and experimental design each row measuring 2 meter in size; row to row and plant to plant distance was maintained at 45 and15cm respectively and follows standard agronomical practices. Morphological characterization were done for the selection of some elite genotypes with combining multiple traits with each other on certain limits viz;  no. branching from basal nodes (3.4-12), plant height (54.80-103.80cm),days to 50 per cent flowering (54.00-72.00), days to maturity (97.00-115.00) and no. of seeds per pod (1.60-3.20) (Table 1). Yield is dependent upon a number of component characters which is quantitatively inherited and considerably affected by the environment. Therefore, direct and indirect selection for yield may not be effective. The selection efficiency under such circumstances can be improved by considering other component characters. Smith (1936) proposed a selection model for making selection on several characters simultaneously using discriminant function of Fisher (1936). Later on, Hazel (1943) developed a simultaneous selection model. All qualitative traits were computed as per Karl Pearson, (1901), principal component analysis (PCA), cluster analysis (UPGMA method) co-efficient of variation and standard error of differences by using SPSS software 2.0.

Table 1: Qualitative and Quantitative traits of twenty genotypes Bakala.

RESULTS AND DISCUSSION

Genetic variability
 
Wide range of genetic variability were observed for quantitative traits viz; number of branching from basal nodes (3.4-12), plant height (54.80-103.80cm), days to 50 per cent flowering (54.00-72.00), days to maturity (97.00-115.00) and no. of seeds per pod (1.60-3.20) (Table 1). Similar finding had also been reported by Elshafei et al., (2019) and Ton et al., (2021). Out of 20 accessions, only ten superior lines viz, (Bak-3, Bak-4, Bak-6, Bak-8, Bak-9, Bak-12, Bak-13, Bak-14, Bak-17, Bak-20) were selected by fixing the certain limits of each quantitative traits (Table 2). These traits may be used full for further selection in a breeding programme (Choudhary et al. 2020) and (Choudhary et al. 2023).

Table 2: Selection of superior lines by combination of different traits at certain multiple traits range limit.


 
Cluster analysis
 
Cluster analysis helps in selection of genetically divergent parents for their exploitation in hybridization programme. It also measures the degree of diversification and determines the relative proportion of each component character to the total divergence. The maximum number of accessions falls under cluster II (6) followed by cluster I (4) and the minimum number in cluster III and IV (2) (Table 3). Maximum inter-cluster distances had been observed between cluster III and VI and I (47.31) followed by cluster I and III (36.19) and minimum in between cluster II and V (10.39) (Table 4) (Fig 1). The result indicated that the ample amount of genetic variability present in the population and it may be used in hybridization programme. Similar result was reported by (Kumar et al., 2016; Dewangan et al., 2022; Shferaw and Tarekegne, 2024).

Table 3: Distribution of Bakala accessions among six clusters.



Table 4: Estimates of inter-cluster distances among six clusters.



Fig 1: Euclidean distance and UPGMA clustering.


 
Principle component analysis (PCA)
 
Principal component analysis is a widely-used statistical tool to analyze genetic variation among plant genotypes and determining the most important variables contributing to variation (Price et al., 2006). It is also a well-known method of dimension reduction that can be used to reduce a large set of variables to a small set that still contains most of the information in the large set Massay (1965) and Jolliffie (1986). The principle component analysis (PCA) transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called PCA Karl Pearson, (1901). In the present investigation the PCA grouped into six main components.
       
Out of which only four components exhibited >0.5 eigen values. A Scree plot (Fig 2) explained the percentage of variance associated between eigen values and principal components with each principal component. The first principle component (PC) had accounted maximum maximum variation 38.217 per cent with eigen value 2.293 (Table 5) contributed by traits viz; plant height and days to flowering. The second PC component dominated by 25.905 per cent for traits viz, plant height, days to flowering and days to maturity. Third and fourth principle component  counted variation 17.894 and 12.56 per cent respectively for traits like Branching from basal nodes, plant height, days to Flowering and days to maturity. Similarly PC5 and PC6 dominated 4.721 and 0.700 per cent respectively for characters viz; pod length at maturity as well as no. of seeds per pod. Which indicated that traits under PC1 may be desirable for selection of lines. Similar finding also be corroborated by Jeberson et al., (2018), Girgel (2021); Beyzi et al. (2019); Mohi-Ud-Din et al. (2021) and Singh et al. (2020).

Fig 2: Scree plot based on principal components and their Eigen values.



Table 5: Principal components for six yield contributing traits of Bakala.


       
The highest loaded variables in PC1, PC2, PC3, PC4, PC5 and PC6 were for traits plant height (0.989), days to maturity (0.721), branching from basal nodes (0.953), days to flowering (0.701), pod length at maturity (0.914) and no. of seeds per pod (0.911) respectively. Therefore, these traits characters under PC1 may be useful for further breeding programmes. These results are in support from the findings of Dewangan et al. (2022). Hence these traits might be useful for effective selection of promising faba bean genotypes in segregating generations. Similar findings corroborated with (Anil et al., 2011; Tiwari and Singh, 2019; Singh et al., 2020; Dewangan et al., 2022; Shferaw and Tarekegne, 2024).

CONCLUSION

Wide range of genetic variability were observed for quantitative traits as a result ten lines were selected based on combination of all traits and fixing them a certain limits of each and every trait. Maximum genetic variation contributed by traits viz; plant height and days to flowering in PC1 (38.217 per cent). Similarly highest loaded variation was also observed in PC1 (0.989) for trait i.e. plant height. Maximum inter-cluster distances between cluster III and VI (47.31) followed by cluster I and III (36.19) and minimum in between cluster II and V (10.39). This indicated that the ample amount of genetic variability present in the population and it may be used in hybridization programme.

ACKNOWLEDGEMENT

I thank full to Bhola Paswan Shastri Agricultural College, Purnea who had given the facilities for conducting the experiments and there is no conflict between all associated authors.

CONFLICT OF INTEREST

I assure you on the behalf of all the authors there is no conflict of interest.

REFERENCES


  1. Beyzi, E., Güneş, A., Arslan, M. and Şatana, A. (2019). Effects of foliar boron treatments on yield and yield components of fenugreek (Trigonella foenum graecum L.): Detection by PCA analysis. Communications in Soil Science and Plant Analysis. 50(16): 2023-2032.

  2. Bishnoi, S.K., Hooda, J.S., Yadav, I.S. and Panchta, R. (2012). Advances on heterosis and hybrid breeding in faba bean (Vicia faba L.). Forage Res. 38(2): 65-73.

  3. Bishnoi, S.K. (2016). Genetic Diversity in Relation to Heterosis and Combining Ability in Faba Bean (Vicia faba L.). Ph.D. Thesis, College of Agriculture CCS Haryana Agricultural University (Haryana)-125004. 

  4. Choudhary, A.K., Mishra, S.B., Choudhary, V.K., Shanti, B. and Singh, A.K. (2020). Morpho-physiological diversity in Arvi [Colocasia esculenta (L.) Schott.Var. Antiquorum]. Int. J. Curr. Microbiol. App. Sci. 9(6): 3551-3560. 

  5. Choudhary, A.K., Mishra, S.B., Bhushan, S. (2023). Assessment of genetic diversity in elephant foot yam [Amorphophallus paeoniifolius(Dennst.) Nicolson var. campanulatus (Decne.) Sivad.]. Indian J. Plant Genetic Resources. 36(2): 200- 207. doi:10.5958/0976-1926.2022.00036.2.0.

  6. Dewangan, N.K., Dahiya, G.S., Janghel, D.K. and Dohare, S. (2022). Diversity analysis for seed yield and its component traits among faba bean (Vicia faba L.) germplasm lines. Legume Research. 45(6): 689-694. doi: 10.18805/LR-4301.

  7. Elshafei A.A.M., Amer, M.A., Elenany, M.A.M. and Helal A.G.A.E. (2019). Evaluation of the genetic variability of fababean (Vicia faba L.) genotypes using agronomic traits and molecular markers. Bulletin of  the National Research Centre. 43: 106. https://doi.org/10.1186/s42269-019- 0145-3.

  8. Fisher, R.A. (1936). The use of multiple measurements in taxonomic problem. Anals of Eugenics. 7: 179-188. 

  9. Girgel, U. (2021). Principle component analysis (PCA) of bean genotypes (Phaseolus vulgaris L.) concerning agronomic, morphological and biochemical characteristics. Applied Ecology and Environmental Research. 19(3): 1999-2011.

  10. Hazel, L.M. (1943). The genetic basis for constructing selection indices. Genetics. 28: 476-490.

  11. Jeberson, S.M., Shashidhar, K.S. and Singh, A.K. (2018). Genetic variability, principal component and cluster analyses in blackgram under foot-hills conditions of Manipur. Legume Research. 42(4): 454-460. doi: 10.18805/LR-3875.

  12. Jolliffie, I.T. (1986). Principal Component Analysis. Springer, New York.

  13. Kaur, S., Kimber, R.B.E. Cogan, N.O.I., Materne, M., Forster, J.W. and Paull, J.G. (2014). SNP discovery and high-density genetic mapping in faba bean (Vicia faba L.) permits identification of QTLs for ascochyta blight resistance. Plant Sciences. 217: 47-55.

  14. Kumar, P., Bishnoi, S.K. and Kaushik, P. (2017). Genetic variability, heritability and genetic advance for seed yield and other agromorphological traits in faba bean (Viciafaba L.) genotypes of different origin. Trends in Biosciences. 10(4): 1246-1248.

  15. Kumar, P., Hooda, J.S., Singh, B., Sharma, P. and Bishnoi, S.K. (2016). Genetic diversity and relationship study in faba bean (Vicia faba L.) genotypes of indian and exotic origin. The Bioscan. 11(3): 2003-2006.

  16. Massay W.F. (1965). Principal components regression in exploratory statistical research. J. Am. Stat. Assoc. 60: 234-246.

  17. Mohi-Ud-Din, M., Hossain, M.A., Rohman, M.M., Uddin, M.N., Haque, M.S., Ahmed, J.U. and Mostofa, M.G. (2021). Multivariate analysis of morpho-physiological traits reveals differential drought tolerance potential of bread wheat genotypes at the seedling stage. Plants. 10(5): 879. https://doi.org/ 10.3390/plants10050879.

  18. Karl Pearson, F.R.S. (1901). On lines and planes of closest fit to systems of points in space. Philosophical Magazine. 2(11): 559-572.

  19. Price, A.L., Patterson, N.J., Plenge, R.M., Weinblatt, M.E., Shadick, N.A and Reich, D. (2006). Principal components analysis corrects for stratification in genome-wide association studies. Nature Genetics. 38: 904-909.

  20. Shferaw, S.S. and Wossen, T. (2024).Genetic variability and cluster analysis of faba bean (Vicia faba L.) genotypes in Debre  Tabor, Northwestern Ethiopia. Advances in Applied Sciences. 9(3): 37-50.

  21. Singh, P., Jain, P.K. and Tiwari, A. (2020). Principal component analysis approach for yield attributing traits in chilli (Capsicum annum L.) genotypes. Chemical Science Review and Letters. 9(33): 87-91. 

  22. Smith, H.F. (1936). A discriminant function for plant selection. Ann. Eugen. 7: 240-250. doi:10.1111/j.1469-1809.1936.tb02 143.x

  23. Tiwari, J.K. and Singh, A.K. (2019). Principal component analysis for yield and yield traits in faba bean (Vicia faba L.). Journal of Food Legumes. 32(1): 13-15.

  24. Ton, A., Karakoy, T., Anlarsal, A.E. and Turkeri, M. (2021). Genetic diversity for agro-morphological characters and nutritional compositions of some local  fababean (Vicia  faba L.) genotypes. Turk J Agric For. 45: 301-312. doi:10. 3906/ tar-2008-74. 
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    Full Research Article

    Principal Component Analysis for Selection of Elite Lines in Faba Bean (Vicia faba L.)

    A
    Anuj Kumar Choudhary1,*
    1Department of Plant Breeding and Genetics, Regional Research Sub Station Jalalgarh, Purnea-854 327, Bihar, India.

    ABSTRACT

    Background: Bakala is popularly known as broad bean, horse bean, wonder bean, English bean, field bean, tick bean, winter bean, pigeon bean and Bakla in India. It is a self-pollinating crop with significant levels of out-cross and inter-cross, ranging from 20 to 50% depending on genotype and environmental effects. Faba bean is world’s fourth most important legume crop after pea, chickpea and lentil, widely cultivated for human food, animal feed and fodder also.

    Methods: Present investigation was conducted at Bhola Paswan Shastri Agricultural College, Purnea during 2019-20 without replication comprising 20 accessions with each row measuring 2 meter in length and row to row distance was kept at 45 cm while plant to plant distance was maintained at 15 cm. Experiment was conducted for evaluating the genetic variability within the existing accessions by principle component analysis.

    Result: Wide range of genetic variability was observed for quantitative traits. Maximum variation contributed in first principal component (PC) for traits viz; plant height and days to flowering i.e.38.217 per cent followed by second PC component i.e.25.905 per cent traits like plant height, days to flowering and days to maturity. Maximum inter-cluster distances was observed between cluster III and VI and I (47.31) followed by cluster I and III (36.19) and minimum between cluster II and V (10.39).

    INTRODUCTION

    Faba bean is an annual diploid (2n = 2x = 12) and belongs to the family fabaceae and has a long history of cultivation in the human civilization. The rate of out crossing depends on the genotype, environmental factors, row space and the number of pollinating insects, especially honeybees. Faba bean is world’s fourth most important legume crop after pea, chickpea and lentil, widely cultivated for human food, animal feed and fodder also. It plays an important role in world agriculture because of its high seed protein content which ranges from 20 to 40% depending upon the genotype and the environmental conditions in which it has been grown (Kaur et al., 2014). It is an efficient nitrogen fixer and improves soil fertility through symbiotic nitrogen fixation. It can grow well on high fertility soil to N-deficient marginal lands. Faba bean can withstand salinity conditions especially chloride and sulphate salts to a greater extent than chickpea. It has a potential yield of 60-75 quintals per hectare and average yield of 40-45 quintals per hectare (Bishnoi, 2016). It is grown as a Rabi crop in India as well as Bihar and adjoining states where its raw pods are eaten as a vegetable (Kumar et al., 2017). Faba bean has been recognized as a potential grain legume and included in the All India Coordinated Research Network on Potential crops (Kumar et al., 2016). It has also been identified as one of the eight major food legumes by the CGIAR research programme for priority focus and gaining importance as a grain legume for protein security of demographically expanding and climatically changing world (Bishnoi, 2016). Unrealized yield potential and yield instability are the major constraints in faba bean cultivation. The increased yield caused by heterozygosity due to out-crossing is well documented in faba bean. Therefore, heterosis, resulting from the combined action and interaction of allelic and inter-allelic genes is effective in faba bean and improved yield can be obtained by hybrid combinations (Bishnoi et al., 2012). The heterotic effects in Vicia faba may range from significantly positive to significantly negative for different traits depending on the genetic makeup of the parents. Exploitation of heterosis in the form of hybrid varieties may contribute in the improvement of yield and its component traits (Bishnoi et al., 2015). The present study was carried out with an objective to estimate genetic variability in existing germplasm by principle componant analysis (PCA) which may be utilized further in breeding programmes.

    MATERIALS AND METHODS

    The present investigation was conducted at Bhola Paswan Shastri Agricultural College, Purnea during 2019-20 comprising 20 germplasm of which were collected from different district of Bihar. All these germplasm had been sown in without replication and experimental design each row measuring 2 meter in size; row to row and plant to plant distance was maintained at 45 and15cm respectively and follows standard agronomical practices. Morphological characterization were done for the selection of some elite genotypes with combining multiple traits with each other on certain limits viz;  no. branching from basal nodes (3.4-12), plant height (54.80-103.80cm),days to 50 per cent flowering (54.00-72.00), days to maturity (97.00-115.00) and no. of seeds per pod (1.60-3.20) (Table 1). Yield is dependent upon a number of component characters which is quantitatively inherited and considerably affected by the environment. Therefore, direct and indirect selection for yield may not be effective. The selection efficiency under such circumstances can be improved by considering other component characters. Smith (1936) proposed a selection model for making selection on several characters simultaneously using discriminant function of Fisher (1936). Later on, Hazel (1943) developed a simultaneous selection model. All qualitative traits were computed as per Karl Pearson, (1901), principal component analysis (PCA), cluster analysis (UPGMA method) co-efficient of variation and standard error of differences by using SPSS software 2.0.

    Table 1: Qualitative and Quantitative traits of twenty genotypes Bakala.

    RESULTS AND DISCUSSION

    Genetic variability
     
    Wide range of genetic variability were observed for quantitative traits viz; number of branching from basal nodes (3.4-12), plant height (54.80-103.80cm), days to 50 per cent flowering (54.00-72.00), days to maturity (97.00-115.00) and no. of seeds per pod (1.60-3.20) (Table 1). Similar finding had also been reported by Elshafei et al., (2019) and Ton et al., (2021). Out of 20 accessions, only ten superior lines viz, (Bak-3, Bak-4, Bak-6, Bak-8, Bak-9, Bak-12, Bak-13, Bak-14, Bak-17, Bak-20) were selected by fixing the certain limits of each quantitative traits (Table 2). These traits may be used full for further selection in a breeding programme (Choudhary et al. 2020) and (Choudhary et al. 2023).

    Table 2: Selection of superior lines by combination of different traits at certain multiple traits range limit.


     
    Cluster analysis
     
    Cluster analysis helps in selection of genetically divergent parents for their exploitation in hybridization programme. It also measures the degree of diversification and determines the relative proportion of each component character to the total divergence. The maximum number of accessions falls under cluster II (6) followed by cluster I (4) and the minimum number in cluster III and IV (2) (Table 3). Maximum inter-cluster distances had been observed between cluster III and VI and I (47.31) followed by cluster I and III (36.19) and minimum in between cluster II and V (10.39) (Table 4) (Fig 1). The result indicated that the ample amount of genetic variability present in the population and it may be used in hybridization programme. Similar result was reported by (Kumar et al., 2016; Dewangan et al., 2022; Shferaw and Tarekegne, 2024).

    Table 3: Distribution of Bakala accessions among six clusters.



    Table 4: Estimates of inter-cluster distances among six clusters.



    Fig 1: Euclidean distance and UPGMA clustering.


     
    Principle component analysis (PCA)
     
    Principal component analysis is a widely-used statistical tool to analyze genetic variation among plant genotypes and determining the most important variables contributing to variation (Price et al., 2006). It is also a well-known method of dimension reduction that can be used to reduce a large set of variables to a small set that still contains most of the information in the large set Massay (1965) and Jolliffie (1986). The principle component analysis (PCA) transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called PCA Karl Pearson, (1901). In the present investigation the PCA grouped into six main components.
           
    Out of which only four components exhibited >0.5 eigen values. A Scree plot (Fig 2) explained the percentage of variance associated between eigen values and principal components with each principal component. The first principle component (PC) had accounted maximum maximum variation 38.217 per cent with eigen value 2.293 (Table 5) contributed by traits viz; plant height and days to flowering. The second PC component dominated by 25.905 per cent for traits viz, plant height, days to flowering and days to maturity. Third and fourth principle component  counted variation 17.894 and 12.56 per cent respectively for traits like Branching from basal nodes, plant height, days to Flowering and days to maturity. Similarly PC5 and PC6 dominated 4.721 and 0.700 per cent respectively for characters viz; pod length at maturity as well as no. of seeds per pod. Which indicated that traits under PC1 may be desirable for selection of lines. Similar finding also be corroborated by Jeberson et al., (2018), Girgel (2021); Beyzi et al. (2019); Mohi-Ud-Din et al. (2021) and Singh et al. (2020).

    Fig 2: Scree plot based on principal components and their Eigen values.



    Table 5: Principal components for six yield contributing traits of Bakala.


           
    The highest loaded variables in PC1, PC2, PC3, PC4, PC5 and PC6 were for traits plant height (0.989), days to maturity (0.721), branching from basal nodes (0.953), days to flowering (0.701), pod length at maturity (0.914) and no. of seeds per pod (0.911) respectively. Therefore, these traits characters under PC1 may be useful for further breeding programmes. These results are in support from the findings of Dewangan et al. (2022). Hence these traits might be useful for effective selection of promising faba bean genotypes in segregating generations. Similar findings corroborated with (Anil et al., 2011; Tiwari and Singh, 2019; Singh et al., 2020; Dewangan et al., 2022; Shferaw and Tarekegne, 2024).

    CONCLUSION

    Wide range of genetic variability were observed for quantitative traits as a result ten lines were selected based on combination of all traits and fixing them a certain limits of each and every trait. Maximum genetic variation contributed by traits viz; plant height and days to flowering in PC1 (38.217 per cent). Similarly highest loaded variation was also observed in PC1 (0.989) for trait i.e. plant height. Maximum inter-cluster distances between cluster III and VI (47.31) followed by cluster I and III (36.19) and minimum in between cluster II and V (10.39). This indicated that the ample amount of genetic variability present in the population and it may be used in hybridization programme.

    ACKNOWLEDGEMENT

    I thank full to Bhola Paswan Shastri Agricultural College, Purnea who had given the facilities for conducting the experiments and there is no conflict between all associated authors.

    CONFLICT OF INTEREST

    I assure you on the behalf of all the authors there is no conflict of interest.

    REFERENCES


    1. Beyzi, E., Güneş, A., Arslan, M. and Şatana, A. (2019). Effects of foliar boron treatments on yield and yield components of fenugreek (Trigonella foenum graecum L.): Detection by PCA analysis. Communications in Soil Science and Plant Analysis. 50(16): 2023-2032.

    2. Bishnoi, S.K., Hooda, J.S., Yadav, I.S. and Panchta, R. (2012). Advances on heterosis and hybrid breeding in faba bean (Vicia faba L.). Forage Res. 38(2): 65-73.

    3. Bishnoi, S.K. (2016). Genetic Diversity in Relation to Heterosis and Combining Ability in Faba Bean (Vicia faba L.). Ph.D. Thesis, College of Agriculture CCS Haryana Agricultural University (Haryana)-125004. 

    4. Choudhary, A.K., Mishra, S.B., Choudhary, V.K., Shanti, B. and Singh, A.K. (2020). Morpho-physiological diversity in Arvi [Colocasia esculenta (L.) Schott.Var. Antiquorum]. Int. J. Curr. Microbiol. App. Sci. 9(6): 3551-3560. 

    5. Choudhary, A.K., Mishra, S.B., Bhushan, S. (2023). Assessment of genetic diversity in elephant foot yam [Amorphophallus paeoniifolius(Dennst.) Nicolson var. campanulatus (Decne.) Sivad.]. Indian J. Plant Genetic Resources. 36(2): 200- 207. doi:10.5958/0976-1926.2022.00036.2.0.

    6. Dewangan, N.K., Dahiya, G.S., Janghel, D.K. and Dohare, S. (2022). Diversity analysis for seed yield and its component traits among faba bean (Vicia faba L.) germplasm lines. Legume Research. 45(6): 689-694. doi: 10.18805/LR-4301.

    7. Elshafei A.A.M., Amer, M.A., Elenany, M.A.M. and Helal A.G.A.E. (2019). Evaluation of the genetic variability of fababean (Vicia faba L.) genotypes using agronomic traits and molecular markers. Bulletin of  the National Research Centre. 43: 106. https://doi.org/10.1186/s42269-019- 0145-3.

    8. Fisher, R.A. (1936). The use of multiple measurements in taxonomic problem. Anals of Eugenics. 7: 179-188. 

    9. Girgel, U. (2021). Principle component analysis (PCA) of bean genotypes (Phaseolus vulgaris L.) concerning agronomic, morphological and biochemical characteristics. Applied Ecology and Environmental Research. 19(3): 1999-2011.

    10. Hazel, L.M. (1943). The genetic basis for constructing selection indices. Genetics. 28: 476-490.

    11. Jeberson, S.M., Shashidhar, K.S. and Singh, A.K. (2018). Genetic variability, principal component and cluster analyses in blackgram under foot-hills conditions of Manipur. Legume Research. 42(4): 454-460. doi: 10.18805/LR-3875.

    12. Jolliffie, I.T. (1986). Principal Component Analysis. Springer, New York.

    13. Kaur, S., Kimber, R.B.E. Cogan, N.O.I., Materne, M., Forster, J.W. and Paull, J.G. (2014). SNP discovery and high-density genetic mapping in faba bean (Vicia faba L.) permits identification of QTLs for ascochyta blight resistance. Plant Sciences. 217: 47-55.

    14. Kumar, P., Bishnoi, S.K. and Kaushik, P. (2017). Genetic variability, heritability and genetic advance for seed yield and other agromorphological traits in faba bean (Viciafaba L.) genotypes of different origin. Trends in Biosciences. 10(4): 1246-1248.

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