Genetic variability and divergence studies in mungbean (Vigna radiata) under rainfed conditions
232 / 219
Keywords:
Correlation, Divergence, Path analyses, Vigna radiataAbstract
Twenty three genotypes of moongbean were evaluated in two different environments for variability stability performance. The present investigation was conducted to provide information on the nature and magnitude of genetic variability and divergence among 23 genotypes of mungbean under different environments. It was measured by seed yield/plant under rainfed conditions during kharif 2016 and 2017. Seed yield/plant showed significant correlation with number of seeds/pod and negative correlation with days to maturity. These genotypes were divided into five clusters on the basis of D2 statistics. Maximum inter cluster distance was exhibited by cluster IV and intra-cluster distance by cluster I. Hence, the genotypes of cluster III & IV shall be utilized for the hybridization programme for the development of high yielding varieties (HYVs) of mungbean.Downloads
References
Falconer D S, Mackay T F C and Frankham R. 1996. Introduction to quantitative genetics. Trends in Genetics 12(7): 280. DOI: https://doi.org/10.1016/0168-9525(96)81458-2
Gupta S K, Souframanien J and Gopalakrishnan T. 2008. Construction of a genetic linkage map of blackgram based on molecular markers and comparative studies. Genome 51: 628–37. DOI: https://doi.org/10.1139/G08-050
Hadad N I, Boggo T P, and Muchibauer F J. 2004. Genetic variation of six agronomic characters in three lentil crosses. Euphytica 31: 113–20 DOI: https://doi.org/10.1007/BF00028313
Hill J, Becker H C and Tigerstedt P M. 2012. Quantitative and Ecological Aspects of Plant Breeding. Springer Science & Business Media, Berlin.
Imrie B C and Butler K L. 2005. An analysis of variability and genotype × environment interaction in mungbean (Vigna radiata) in southeastern Queensland. Australian Journal of Agriculture Research 33: 523–30. DOI: https://doi.org/10.1071/AR9820523
Mahalanobis P C. 1936. On the generalized distance in statistics. Proceedings of National Institute of Sciences 2: 49–55.
Miller D A, Williams J C I, Robinson H F and Comstock K B. 1958. Estimate of genotypic and environmental variances and covariance in upland cotton and their implication in selection. Agronomy Journal 50: 126–31. DOI: https://doi.org/10.2134/agronj1958.00021962005000030004x
Pushpa R Y, Koteswara R, Satish, Y and Sateesh B J. 2013. Estimates of genetic parameters and path analysis in blackgram (Vigna mungo (L.) Hepper). International Journal of Plant, Animal and Environmental Sciences 3: 4–5.
Rao C R. 1952. Advanced Statistical Methods in Biometrical Research, pp 236-272. John Wiley and Sons, New York.
Singh A, Singh S K, Sirohi A and Yadav R. 2009. Genetic variability and correlation studies in greengram (Vigna radiata (L.) Wilczek). Progressive Agriculture 9(1): 59–62.
Singh R K and Chaudhary B D. 1977. Biometrical Methods on Quantitative Genetic Analysis, pp. 215-218. Kalyani Publishers, New Delhi.
Sinha S and Wagh P. 2013. Genetic studies and divergence analysis for yield, physiological traits and oil content in linseed. Research Journal of Agriculture Science 4: 168-75.
Downloads
Submitted
Published
Issue
Section
License
Copyright (c) 2020 The Indian Journal of Agricultural Sciences

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The copyright of the articles published in The Indian Journal of Agricultural Sciences is vested with the Indian Council of Agricultural Research, which reserves the right to enter into any agreement with any organization in India or abroad, for reprography, photocopying, storage and dissemination of information. The Council has no objection to using the material, provided the information is not being utilized for commercial purposes and wherever the information is being used, proper credit is given to ICAR.