Diversity of soybean (Glycine max L.) genotypes based on agromorphological parameters (original) (raw)
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Selection of Soybean Genotypes ( Glycine max ( L . ) Merrill ) through Genetic Variability Analysis
2017
The physiomorphological divergence was assessed in twenty-seven soybean genotypes by using cluster mean analysis, principal component and principal coordinate analysis and mean difference to identify parental genotypes for future breeding program in order to develop new high yielding varieties in randomized complete block design with three replications. Plant height, days to first flowering and days to 50% flowering have the highest percent of variations anomg the traits. The genotypes under the experiment were grouped into five clusters. The highest inter cluster distance was found between cluster I and IV followed by I and V. According to principal component scores F-85-11347 and ASSET93-19-13 have the prominent influence towards varietal improvement. Selecting genotypes from distant clusters probably provide promising recombinants and better segregants for future breeding platform.
Genetics and molecular research : GMR, 2017
The genetic diversity study has paramount importance in breeding programs; hence, it allows selection and choice of the parental genetic divergence, which have the agronomic traits desired by the breeder. This study aimed to characterize the genetic divergence between 24 soybean genotypes through their agronomic traits, using multivariate clustering methods to select the potential genitors for the promising hybrid combinations. Six agronomic traits evaluated were number of days to flowering and maturity, plant height at flowering and maturity, insertion height of the first pod, and yield. The genetic divergence evaluated by multivariate analysis that esteemed first the Mahalanobis' generalized distance (D(2)), then the clustering using Tocher's optimization methods, and then the unweighted pair group method with arithmetic average (UPGMA). Tocher's optimization method and the UPGMA agreed with the groups' constitution between each other, the formation of eight distin...
Genetic Diversity Studies in Soybean [Glycine max (L.)Merrill] Genotypes
Genetic diversity among 20 soybean genotypes was worked out using Mahalanobis D 2 statistic. On the basis of genetic distance, the twenty genotypes were grouped into 5 clusters. Out of the 20 genotypes cluster II has the highest genotypes (9) followed by cluster III (4), cluster I and cluster IV (3 each) and cluster V had 1 genotype. The inter cluster divergence was observed to be highest between Cluster I and Cluster IV indicating that these two clusters were genetically diverse. Hence, the genotypes of Cluster I and Cluster IV could be utilized in hybridization program to achieve greater variability in the segregating generations. Among the different characters studied test weight followed by plant height, number of pods per plant and days to 50% flowering contributed maximum towards divergence.
Environment Conservation Journal
A study was conducted to understand genetic divergence in Randomized complete block design accommodating 30 soybean [Glycine max (L.) Merrill] genotypes randomly in three replications. These genotypes were evaluated for twenty-seven traits: five phenological, nine agro-morphological, eight physiological traits (from field-trial) and five physiological traits (from laboratory experiment) recorded and subjected to PCA (Principal Component Analysis) and cluster analysis. Among all the studied cultivars, significant diversity, as well as analysis of dispersion, was recorded for different agro-morphological characters. D2-statistic (Tocher method) framed (generalized distance-based) nine clusters: largest with eight and five were oligo-genotypic. Harvest index>seed yield per plant>germination relative index>seedling dry weight contributed maximum towards total divergence. From the most divergent clusters, 21 crosses involving cluster v genotypes (PS-1347, RKS-18, PS-1092, NRC-14...
Screening of Soybean (Glycine max L.) Genotypes through Multivariate Analysis
The physiological divergence was assessed in twenty-seven soybean genotypes by using principal component analysis, cluster mean analysis, principal coordinate analysis and canonical variate analysis to identify parental genotypes for the future breeding program in order to develop new high yielding varieties in a randomized complete block design with three replications. The genotypes under the experiment were grouped into five clusters. The highest number of genotypes found in cluster III. The highest intra-cluster distance was found in cluster II and while cluster V showed no intra-cluster distance values which revealed homogenous nature of the genotype within the cluster. The highest inter-cluster distance was found between cluster I and IV followed by I and V. Cluster II have early flowering genotypes whereas early maturity in cluster III and most of the desirable traits were found in cluster IV. Days to first flowering and pod length from cluster II, whereas pods per plant and yield per plant from cluster IV have the positive relative contribution to the entire divergence. According to principal component scores, LG-92P-1176 followed by KANH-33, AGS-79, MTD-452, GMOT-17, GC-82-332411, MTD-451 and BS-33 have the prominent influence towards varietal improvement. Selecting genotypes from distant clusters probably provide promising recombinants and better segregants for the future breeding platform.
Proceedings of the National Academy of Sciences, India Section B: Biological Sciences, 2014
In order to assess the genetic diversity and interrelationship of durum wheat line and determine the traits effective on grain yield, forty nine durum lines were evaluated based on agro-morphological characters. Correlation analysis showed the 1000-grain weight and peduncle length had highest relationship with seed yield. In regression analysis (stepwise method), 1000 grain weight, peduncle length, number of spike per 1 m 2 and number of fertile tillers remained in the final model (R 2 ≈ 0.50). 1000-grain weight and peduncle length had most positive and direct effect on grain yield (0.642 and 0.549 respectively). Number of fertile tillers and peduncle length had the most positive direct effect on grain yield. On the other hand, number of spikes per 1 m 2 had negative direct effect on grain yield. Number of spikes per 1 m 2 had the highest indirect effect via peduncle length. Thus, selecting lines having high 1000-grain weight and peduncle length improve grain yield. Based on Principal Component Analysis (PCA), the first five components explained over 73.76% of genetic variation. Cluster analysis based on squared Euclidean distance and ward's method, categorized the lines into five groups. Also, cluster analysis based on PCA using the main components produced five clusters. The information on diversity and relationships among the agro-morphological traits will be helpful to breeders in constructing their breeding populations or lines and implementing selection strategies.
Evaluation of Genetic Diversity in Different Genotypes of Soybean (Glycine max (L.) Merrill
Genotypic variations of twenty eight soybean genotypes were evaluated in a randomized complete block design during Rabi season, 2011 at Sher-e-Bangla Agricultural University, Bangladesh. The phenotypic variance was higher than the corresponding genotypic variance for most of the characters. High heritability coupled with high genetic advance was recorded for number of branches per plant, plant height, number of seeds per plant, number of pods per plant and 100-seed weight. This indicates the effectiveness of selection to improve these five characters. Plant height, pod length, number of seeds per pod, number of pods per plant, hundred seed weight, branches per plant, and number of seeds per pod showed significant positive correlation with seed yield. Based on inter genotypic distances F-85-11347, Australia, 86017-66-6, PK-327, MTD-452, Shohag, MTD-16, YESOY-4 are important for varietal improvement of soybean genotypes. Considering genetic variability, heritability and correlation analysis, emphasis should be given on traits during phenotypic selection and inter genotypic distances for genotypic selection for developing high yielding genotypes of soybean.
Genetic parameters and variability in soybean genotypes
Comunicata Scientiae, 2012
Several genetic breeding programs contributed to the development of soybean cultivars with high yield and adapted to different Brazilian edaphoclimatic conditions. However, the continuous progress of genetic breeding of this specie depends on the genetic variability and application of genetic parameters informations which helps a more efficient selection process.There are many multivariated technical approaches to study the variability among soybean groups, such as dissimilarity measures, cluster analysis, principal components and canonical variables. The heritability estimation, genetic gain and genetic correlations are important parameters which permit the breeder to choose the best improvement strategy.
Soybean is one of the most important leguminous crops grown globally for food and feed. The study of genetic diversity is invaluable for efficient utilization, conservation and management of germplasm collections. The study aims at assessing genetic diversity present among the soybean genotypes using phenotypic markers. The restriction maximum likelihood revealed highly significant differences among the genotypes for eight quantitative traits. The principal component analysis revealed three most important PCs contributing 63.19%, 25.43% and 8.88% to the total variation of 97.5%, respectively. Seed yield was highly significant and highly correlated with seed number per plant, pod weight per plant, pod number per plant, and hundred seed weight but negatively correlated with seed number per pod. The hierarchical clustering revealed three major clusters with further sub-clusters. The accessions 2015/06/12, 69 S 10, PR 154-14, R 5-4-2 M, Hawkeye (USSR), and PR 145-2 were the most diverse. There were significant differences among the accessions based on nutritional quality traits such as oil, protein and stearic acid across the locations. The protein content varied from 29.1% to 35.6%, oil content varied from 10.6% to 20.7% whereas oleic acid and ash varied between 6.8% and 30.8%, and 4.3% and 8.2%, respectively. There was vast genetic diversity among the soybean genotypes. The presence of genetic diversity will aid breeders in selections and hybridization programmes for crop improvement.
Genetic variability and correlation analysis in soybean (Glycine max (L.) Merrill) genotypes.pdf
International Journal of Chemical Studies, 2019
Abstract The present investigation was carried out at V.C.S.G. Uttarakhand University of Horticulture and Forestry, College of Forestry, Ranichauri, Tehri Garhwal, Uttarakhand, India during Kharif 2015. The experiment consisted of 20 genotypes along with two checks namely PS-1347 and PS-1092 in Randomized Block Design. The analysis of variance showed highly significant differences for all the characters which indicate that bountiful variability present among the genotypes. The values of PCV were higher than of GCV, but the difference was closer between these two estimates for all the characters means less influenced due to environmental factors. High heritability was observed for yield and contributing characters. The high magnitudes of genetic advance observed for pod length, High heritability along with high genetic advance was observed for pod length, causes additive gene action. The seed yield per plant showed highly significant and positive correlation with plant height, number of cluster per plant, number of pods per plant, number of seeds per pod and 100 seed weight. The genotypes having high variability can be used in soybean breeding and high heritability along with high genetic advance favours for selection in crop improvement programme. Positive correlation of the characters with seed yield is so meaningful for improving yield. Keywords: soybean, analysis of variance, heritability, genetic advance and correlation