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Research Article

volume 53 issue 3 (june 2019) : 343-347,   Doi: 10.18805/IJARe.A-5225

Assessment of genetic diversity in germplasm of Guinea grass (Panicum maximum Jacq.) 

P
P. Ramakrishnan
C
C. Babu
K
K. Iyanar
N
N. Manivannan
1Department of Forage Crops, Centre for Plant Breeding and Genetics, Tamil Nadu Agricultural University, Coimbatore-641 003, Tamil Nadu, India.
Cite article:- Ramakrishnan P., Babu C., Iyanar K., Manivannan N. (2019). Assessment of genetic diversity in germplasm of Guinea grass (Panicum maximum Jacq.). Indian Journal of Agricultural Research. 53(3): 343-347. doi: 10.18805/IJARe.A-5225.

ABSTRACT

In the present study, sixty genotypes of Guinea grass were evaluated for assessing genetic diversity for ten different quantitative characters for exploitation in a breeding programme aimed at improving yield potential of Guinea grass by using Mahalanobis D2 statistics. The genotypes were grouped into ten clusters suggesting the presence of genetic diversity. The cluster I had maximum of 30 genotypes followed by II and III having 15 and 7 genotypes, respectively. These clusters having maximum number of genotypes, reflecting narrow genetic diversity. The inter cluster distances were greater than intra cluster distances, revealing that the selected genotypes were highly divergent. The maximum intra cluster distance was recorded for cluster III (5.63) while clusters IV, V, VI, VII and VIII and X (0.00) were solitary and showed no intra-cluster distance values. The genetically more divergent genotypes present in cluster III and IX as indicated by inter cluster distance value (21.63). Cluster VIII and Cluster V had least number of single genotype and emerged with contained highest cluster mean value for number of tillers/plant, number of leaves/plant, good leaf weight, leaf: stem ratio, green fodder yield/plant and crude protein content. Hence, GGLC 12 genotype in cluster VIII and GGLC 19 in cluster V can be successfully utilized in breeding programme for development of Guinea grass varieties with improved fodder yield and quality. 

REFERENCES

  1. AOAC, (1995). Official Methods of Analysis, 16th Ed. (Association of Official Analytical Chemists, Arlington, VA)
  2. Ganesan, K.N., Nallathambi, G., Thura, S., Senthil, N., Tamilarasi., P.M. (2010). Genetic divergence analysis in indigenous maize germplasms (Zea Mays L.). Electronic Journal of Plant Breeding and Genetics 1 (4): 1241-1243. 
  3. Langade, D. M., Ram, C. N., Vishwakarma, D.N., Sharma, A. (2013). Evaluation of genetic divergence in berseem (Trifolium alexandrinium L.) germplasms. Bioscan. 8 (3): 767-770. 
  4. Mahalanobis, P. C. (1936). The generalized distance in statistics. Proceeding of the National Ins. of Sci. of India. 12: 49-55 
  5. More, A. J., Bhiote, K.D., Pardeshi, S.R. (2006). Genetic diversity studies in forage maize (Zea mays L.). Res. Crops, 7 (3): 728-730.
  6. Murthy, B. R. and Arunachalam, V. (1966). The nature of genetic divergence in relation to breeding system in crop plants. Indian J. Genet., 26A: 188-198. 
  7. Rao, C.R. (1952). Advanced Statistical Methods in Biometrical Research. John Wiley and Sons, New York. pp. 126-152.
  8. Tiwari, K.K. and Chandra, A. (2010). Use of degenerate primers in rapid generation of microsatellite markers in Panicum maximum. J. Envir. biol., 31: 965-968.
  9. UPOV, 2007. TG/248/1 Common millet. 1-37.
  10. Verma V. S. and Mehta R. K. (1976). Genetic divergence in Lucerne. J. Maharashtra Agr. Univ., 1: 23-28. 
  11. Wouw, M.V.D., Jorge, M.A., Bierwirth, J., Hanson, J. (2008). Characterization of a collection of perennial Panicum species. Tropical Grasslands, 42: 40 - 53.
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    Research Article

    volume 53 issue 3 (june 2019) : 343-347,   Doi: 10.18805/IJARe.A-5225

    Assessment of genetic diversity in germplasm of Guinea grass (Panicum maximum Jacq.) 

    P
    P. Ramakrishnan
    C
    C. Babu
    K
    K. Iyanar
    N
    N. Manivannan
    1Department of Forage Crops, Centre for Plant Breeding and Genetics, Tamil Nadu Agricultural University, Coimbatore-641 003, Tamil Nadu, India.
    Cite article:- Ramakrishnan P., Babu C., Iyanar K., Manivannan N. (2019). Assessment of genetic diversity in germplasm of Guinea grass (Panicum maximum Jacq.). Indian Journal of Agricultural Research. 53(3): 343-347. doi: 10.18805/IJARe.A-5225.

    ABSTRACT

    In the present study, sixty genotypes of Guinea grass were evaluated for assessing genetic diversity for ten different quantitative characters for exploitation in a breeding programme aimed at improving yield potential of Guinea grass by using Mahalanobis D2 statistics. The genotypes were grouped into ten clusters suggesting the presence of genetic diversity. The cluster I had maximum of 30 genotypes followed by II and III having 15 and 7 genotypes, respectively. These clusters having maximum number of genotypes, reflecting narrow genetic diversity. The inter cluster distances were greater than intra cluster distances, revealing that the selected genotypes were highly divergent. The maximum intra cluster distance was recorded for cluster III (5.63) while clusters IV, V, VI, VII and VIII and X (0.00) were solitary and showed no intra-cluster distance values. The genetically more divergent genotypes present in cluster III and IX as indicated by inter cluster distance value (21.63). Cluster VIII and Cluster V had least number of single genotype and emerged with contained highest cluster mean value for number of tillers/plant, number of leaves/plant, good leaf weight, leaf: stem ratio, green fodder yield/plant and crude protein content. Hence, GGLC 12 genotype in cluster VIII and GGLC 19 in cluster V can be successfully utilized in breeding programme for development of Guinea grass varieties with improved fodder yield and quality. 

    REFERENCES

    1. AOAC, (1995). Official Methods of Analysis, 16th Ed. (Association of Official Analytical Chemists, Arlington, VA)
    2. Ganesan, K.N., Nallathambi, G., Thura, S., Senthil, N., Tamilarasi., P.M. (2010). Genetic divergence analysis in indigenous maize germplasms (Zea Mays L.). Electronic Journal of Plant Breeding and Genetics 1 (4): 1241-1243. 
    3. Langade, D. M., Ram, C. N., Vishwakarma, D.N., Sharma, A. (2013). Evaluation of genetic divergence in berseem (Trifolium alexandrinium L.) germplasms. Bioscan. 8 (3): 767-770. 
    4. Mahalanobis, P. C. (1936). The generalized distance in statistics. Proceeding of the National Ins. of Sci. of India. 12: 49-55 
    5. More, A. J., Bhiote, K.D., Pardeshi, S.R. (2006). Genetic diversity studies in forage maize (Zea mays L.). Res. Crops, 7 (3): 728-730.
    6. Murthy, B. R. and Arunachalam, V. (1966). The nature of genetic divergence in relation to breeding system in crop plants. Indian J. Genet., 26A: 188-198. 
    7. Rao, C.R. (1952). Advanced Statistical Methods in Biometrical Research. John Wiley and Sons, New York. pp. 126-152.
    8. Tiwari, K.K. and Chandra, A. (2010). Use of degenerate primers in rapid generation of microsatellite markers in Panicum maximum. J. Envir. biol., 31: 965-968.
    9. UPOV, 2007. TG/248/1 Common millet. 1-37.
    10. Verma V. S. and Mehta R. K. (1976). Genetic divergence in Lucerne. J. Maharashtra Agr. Univ., 1: 23-28. 
    11. Wouw, M.V.D., Jorge, M.A., Bierwirth, J., Hanson, J. (2008). Characterization of a collection of perennial Panicum species. Tropical Grasslands, 42: 40 - 53.
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