D2 Analysis and Direct and Indirect Selection Parameter for Quantitative Character in Green Gram (Vigna Radiata (L.) Wilczek)

Abstract

The present investigation was carried out using fifty genotypes of green gram (Vignaradiata (L.)Wilczek) to examine the genetic variability, heritability, genetic advance, correlation coefficient, path coefficient analysis and genetic divergence. All the fifty genotypes of green gram were grown in randomized block design with 3 replications in 4 row plot of 4 meter length. Spacing between the row to row 30 cm and plant to plant distance was 20 cm during zaid 2017. Observations were recorded on various characters viz; days to 50% flowering, days to maturity, plant height, number of primary branches per plant, number of pods per plant, number of seeds per pod, pod length, biological yield per plant, harvest index, test weight and seed yield per plant. Analysis of variance revealed substantial amount of variability among the genotypes for all the characters, under study, indicated wide spectrum of variability among the genotypes. High genotypic and phenotypic coefficient of variation were observed for plant height, number of primary branches per plant, biological yield per plant, pod length, harvest index and seed yield per plant and moderate was observed for number of pods per plant, number of seeds per pod and test weight. High heritability coupled with high genetic advance was observed for plant height. Improvement in seed yield can be made by selecting that yield contributed trait having high heritability coupled with high genetic advance. High heritability value coupled with moderate genetic advance was recorded for biological yield per plant and harvest index indicating that these characters were less influenced by environments but governed by both additive and non-additive gene action.High heritability with low genetic advance was recorded for days to 50% flowering, days to maturity, number of primary branches per plant, number of pods per plant, number of seeds per pod, pod length, test weight and seed yield per plant, thus, indicated these traits were under the control of non-additive gene action. Seed yield per plant exhibited highly significant and positive correlation with harvest index, test weight, number of pods per plant, number of seeds per pod, number of primary branches per plant, biological yield per plant and pod length at both (genotypic and phenotypic) level. This might be due to linkage of genes determining these characters. Thus, it can be inferred that selection based on any one of these characters either alone or in combination, will help to identifying high yielding genotypes. Genotypic correlation was of higher magnitude as compare to their corresponding phenotypic correlation in most of the character combination, thereby, suggesting strong inherent association between genotypic and phenotypic level. Path coefficient analysis showed that harvest index, biological yield per plant, number of pods per plant, number of seeds per pod, test weight and pod length were the most important characters, controlling directly to seed yield. Whereas, number of pods per plant, number of primary branches per plant, number of seeds per pod, test weight, harvest index and biological yield per plant characters may improve seed yield indirectly. The results of genetic divergence analysis indicated the presences of high amount of genetic diversity among the genotypes were taken up for the study. The 50 genotypes of green gram were grouped into six clusters based on Mahalanobis D2analysis. Cluster II was largest with 13 genotypes followed by (12 genotypes), cluster V (8 genotypes), cluster I (7 genotypes), cluster VI (6 genotypes) while cluster IV (4 genotypes), This visualize that the genotypes grouped within a particular cluster are more or less genetically similar to each other and apparent wide diversity is mainly due to the remaining genotype distributed over rest of the other cluster. The highest inter cluster distance was found between cluster I and VI followed by distance between cluster I and IV indicating wide divergence among these clusters, whereas the lowest inter cluster difference was reflected between clusters I and II. The highest intra cluster difference was expressed by the cluster V and lowest by the cluster VI.