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“GENETIC ANALYSIS FOR YIELD, ITS COMPONENTS AND PROTEIN CONTENT IN SOME GENOTYPES OF MUNGBEAN (Vigna radiata L.) Wilzeck”

KrishiKosh

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Title “GENETIC ANALYSIS FOR YIELD, ITS COMPONENTS AND PROTEIN CONTENT IN SOME GENOTYPES OF MUNGBEAN (Vigna radiata L.) Wilzeck”
 
Creator Kumar, Sunil
 
Contributor Nair, S.K.
Nanda, H.C.
Geda, A.K.
Saxena, R.R.
 
Subject GENETIC ANALYSIS, YIELD, PROTEIN CONTENT, GENOTYPES, MUNGBEAN (Vigna radiata L.) Wilzeck
Genetics and Plant Breeding
 
Description The present investigation entitled “Genetic analysis for yield, its components and protein content in some genotypes of Mungbean (Vigna radiata L.)” was carried out at Research and Instructional farm, College of agriculture, Indira Gandhi Krishi Vishwavidyalaya, Raipur during kharif 2010. The experiment was conducted in Randomized Complete Block Design involving 33 genotypes with three replications for estimation of genetic variability, heritability, genetic advance, correlation, path analysis and genetic divergence. Observations were recorded on five randomly selected competitive plants in each plot and replication, yield on plot basis and visual observations were recorded as per the descriptor prescribed by NBPGR, New Delhi. Among visual traits days to 50% flowering and days to maturity were taken. Similarly, among metric traits, plant height, number of branches per plant, number of pod clusters per plant, number of pods per plant, pod length, number of seeds per plant, 100 seed weight, seed yield per plot, harvest-index and protein content were recorded. Analysis of variance revealed that mean sum of squares due to genotypes were highly significant for all the characters except number of primary branches per plant showing significant difference while characters plant height, number of pod clusters per plant, number of pods per plant and number of seeds per plant had non-significant difference, revealing the existence of considerable variability in the material studied. The Genotypic Coefficient of Variance (GCV) was high for seed yield per plot while the Phenotypic Coefficient of Variance (PCV) was high for number of primary branches per plant, seed yield per plot, number of seeds per plant and 100 seed weight. GCV for seed yield was high which shows maximum scope for yield improvement. Considering the available genetic variability it can be
exploited for yield improvement through characters like 100 seed weight. High heritability was found in 100 seed weight while high genetic advance was found in seed yield per plot. Low genetic advance was found in days to 50% flowering, days to maturity, plant height, number of pod clusters per plant, number of pods per plant, pod length, number of seeds per plant and protein content which indicates that these characters were governed by non-additive genes and heterosis breeding may be useful. Low heritability coupled with moderate genetic advance was observed for the traits viz. number of primary branches and harvest index which indicates the role of dominance and epistatic variance in the expression of these characters. Correlation coefficient analysis revealed that, the characters namely, number of pod clusters per plant, harvest index and plant height showed significant positive association with seed yield per plant, indicating that high seed yield can be possible by improving these characters in future. The path coefficient analysis showed that, harvest index had the highest direct effect on seed yield. Hence, this character seems to be important contributor of seed yield and must be considered in selection for high seed yield. The characters number of pod clusters per plant and plant height showed indirect effect on seed yield through various characters viz. 100 seed weight, days to 50% flowering and harvest index. Harvest index also had the highest direct effect on 100 seed weight. Hence, selection for high harvest index may give better results in improving seed yield in mungbean. Divergence analysis revealed that considerable amount of genetic divergence was present in the material under study. Based on Mahalanobis‟s D2 statistic, genotypes were grouped into four different clusters. Intercrossing of genotypes from different clusters showing superior mean performance may help in obtaining better/heterotic segregants.
 
Date 2016-12-21T14:45:25Z
2016-12-21T14:45:25Z
2011
 
Type Thesis
 
Identifier 136 p.
http://krishikosh.egranth.ac.in/handle/1/91880
 
Language en
 
Format application/pdf
 
Publisher Indira Gandhi Krishi Vishwavidyalaya, Raipur