Genetic Diversity and Association Studies in Greengram [Vigna Radiata (L.) Wilczek]

2019 ◽  
Author(s):  
S Mohan ◽  
A Sheeba ◽  
T Kalaimagal
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Path Analysis ◽  
Seed Yield ◽  
Morphological Traits ◽  
Principal Component ◽  
Component Analysis ◽  
Positive Direct ◽  
Number Of Seeds ◽  
Number Of Branches

The present study was conducted to evaluate 44 greengram genotypes using correlation, path analysis, principal component analysis and cluster analysis based on ten morphological traits. Basic descriptive statistics showed considerable variance for all the traits. Association analysis indicated that, number of pods per plant, number of pod clusters per plant, number of seeds per pod and number of branches per plant showed significant positive association with seed yield per plant. Path analysis specified that the highest positive direct effect on single plant yield was exerted by days to 50 % flowering, number of pods per plant and number of seeds per pod. Principal component analysis (PCA) revealed 79.12 per cent of the variability by the first five components. PC1 was associated mainly with seed yield per plant, number of pod clusters per plant, number of pods per plant and number of branches per plant. The Wards method of hierarchical cluster analysis grouped the accessions into six major clusters. The clustering of greengram genotypes based on different morphological traits would be useful to identify the promising genotypes for effective utilization in future breeding programmes..

↵Genetic Diversity and Principal Component Analysis in Mungbean [Vigna radiata (L.) Wilczek] under Rainfed Condition

2021 ◽  
Author(s):  
Deepak Gupta ◽  
Suresh Muralia ◽  
N.K. Gupta ◽  
Sunita Gupta ◽  
M.L. Jakhar ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Seed Yield ◽  
Agricultural Research ◽  
Principal Component ◽  
Component Analysis ◽  
Grain Legume ◽  
Research Station ◽  
Rainfed Condition

Background: Mungbean is a short duration grain legume widely grown in south and Southeast Asia. The extent of variability through Principal Component Analysis (PCA) and cluster analysis in promising mungbean genotypes should be known for possible yield improvement. A study was undertaken to work out the extent of variability among twenty four mungbean genotypes through cluster analysis and Principal Component Analysis (PCA). Methods: The experiment was laid out in a randomized block design with three replications during kharif 2018 and 2019 at the experimental field of Agricultural Research Station, Navgaon (Alwar) under rainfed condition. Result: Principal component analysis revealed that the first three main PCAs amounted 78.80% of the total variation among genotypes for different traits. Out of total principal components, PC1 accounts for maximum variability in the data with respect to succeeding components. Number of branches per plant (28.62%), number of clusters per plant (23.55%) and seed yield (15.58%) showed maximum per cent contribution towards total genetic divergence on pooled basis. Cluster analysis showed that genotypes fall into seven different clusters and their inter and intra cluster distance showed genetic diversity between different genotypes. The maximum number of genotypes i.e., 8 was found in cluster II followed by cluster III comprising of 6 genotypes. Genotypes RMG-1138 and IPM-02-03 representing the mono genotypic cluster signifies that it can be the most diverse variety and it would be the appropriate genotype for hybridization with ones present in other clusters to tailor the agriculturally important traits and ultimately to boost the seed yield in mungbean under rainfed conditions.

scholarly journals Multivariate Analysis and its Application for Screening Mungbean [Vigna radiata (L.) Wilczek] Landraces

2022 ◽  
Author(s):  
Berk Benlioglu ◽  
Ugur Ozkan
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Multivariate Analysis ◽  
Vigna Radiata ◽  
Morphological Traits ◽  
Principal Component ◽  
Component Analysis ◽  
Multivariate Statistical ◽  
Breeding Programs ◽  
High Level

Background: Mungbean [Vigna radiata (L.) Wilczek] is known as one of the important crop of the Vigna group. In order to determine morphological traits of mungbean, multivariate analysis will provide important advantages in the selection phase of future breeding programs. Multivariate statistical analysis was used to determine and classify these traits. Multivariate analysis, that includes principal component analysis (PCA) and cluster analysis (CA), is considered the best tool for selecting promising genotypes in the future breeding programs. Methods: Eighteen landraces and two species were used to classify morphological traits in this study. Nine different morphological traits were observed during the research period. These are; days to 50% flowering (DFT), plant height (PH), branches per plant (BPP), clusters per plant (CPP), number of pods per cluster (PPC), seed yield per plot (SYPP), biomass yield per plot (BYPP), harvest index (HI), 1000 seed weight (SW). Result: Principal component analysis (PCA) revealed a high level of variation among the genotypes. Therefore, high variability was observed in DFT (36-59 day), PH (39-76 cm), BPP (3-7), CPP (4-21), SYPP (231-824 g), BYPP (3300-10300 g), HI (6.77-11.25%) and 1000 SW (19.95-50.50 g). According to cluster analysis, landraces with the least genetic diversity distance between them in terms of morphological traits examined were determined as 2 and 3.

scholarly journals Clustering and Principal Component Analysis of Nerica Mutant Rice Lines Growing Under Rainfed Condition

2019 ◽  
Vol 7 (3) ◽  
pp. 327-334
Author(s):  
Md. Nuruzzaman ◽  
Md. Shohel Rana ◽  
Aleya Ferdausi ◽  
Md. Monjurul Huda ◽  
Lutful Hassan ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Morphological Traits ◽  
Principal Component ◽  
Component Analysis ◽  
Rice Varieties ◽  
Cluster Distance ◽  
Rice Improvement ◽  
Clear Idea ◽  
Scree Test

A field experiment was conducted at subtropical region in Bangladesh to assess the contribution of morphological traits to variability in some NERICA mutant rice lines. The experiment was conducted following RCBD with three replications. Thirty-one NERICA rice genotypes (twenty-eight mutant lines along with three parents) of advanced generations were used. Data were collected on twelve morphological traits. The results of the principal component analysis showed that the first four components account for 80% of total variation giving a clear idea of the structure underlying the variables analyzed. This result was also supported by scree test. Cluster analysis using Ward's method classified the thirty-one genotypes into five distinct groups. The maximum inter-cluster distance was observed between clusters indicating the possibility of high heterosis if individuals from these clusters are cross-bred. The results of PCA were closely in line with those of the cluster analysis. These results can now be used by breeders to develop drought tolerant high yielding rice varieties and new breeding protocols for rice improvement. Int. J. Appl. Sci. Biotechnol. Vol 7(3): 327-334  

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

Genetic Diversity Assessment in Dolichos Bean (Lablab purpureus L.) Based on Principal Component Analysis and Single Linkage Cluster Analysis

2021 ◽  
Author(s):  
S.R. Singh ◽  
S. Rajan ◽  
Dinesh Kumar ◽  
V.K. Soni
Keyword(s):  
Genetic Diversity ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Single Linkage ◽  
Pod Yield ◽  
Diversity Assessment ◽  
Single Linkage Cluster ◽  
Linkage Cluster

Background: Dolichos bean occupies a unique position among the legume vegetables of Indian origin for its high nutritive value and wider climatic adaptability. Despite its wide genetic diversity, no much effort has been undertaken towards genetic improvement of this vegetable crop. Knowledge on genetic variability is an essential pre-requisite as hybrid between two diverse parental lines generates broad spectrum of variability in segregating population. The current study aims to assess the genetic diversity in dolichos genotypes to make an effective selection for yield improvement.Methods: Twenty genotypes collected from different regions were evaluated during year 2016-17 and 2017-18. Data on twelve quantitative traits was analysed using principal component analysis and single linkage cluster analysis for estimation of genetic diversity.Result: Principal component analysis revealed that first five principal components possessed Eigen value greater than 1, cumulatively contributed greater than 82.53% of total variability. The characters positively contributing towards PC-I to PC-V may be considered for dolichos improvement programme as they are major traits involved in genetic variation of pod yield. All genotypes were grouped into three clusters showing non parallelism between geographic and genetic diversity. Cluster-I was best for earliness and number of cluster/plant. Cluster-II for vine length, per cent fruit set, pod length, pod width, pod weight and number of seed /pod, cluster III for number of pods/cluster and pod yield /plant. Selection of parent genotypes from divergent cluster and component having more than one positive trait of interest for hybridization is likely to give better progenies for development of high yielding varieties in Dolichos bean.

scholarly journals Magnetic susceptibility to identify landscape segments on a detailed scale in the region of Jaboticabal, São Paulo, Brazil

2012 ◽  
Vol 36 (4) ◽  
pp. 1073-1082 ◽  
Author(s):  
Mariana dos Reis Barrios ◽  
José Marques Junior ◽  
Alan Rodrigo Panosso ◽  
Diego Silva Siqueira ◽  
Newton La Scala Junior
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Magnetic Susceptibility ◽  
Clay Fraction ◽  
Chemical Properties ◽  
Principal Component ◽  
Component Analysis ◽  
Sao Paulo ◽  
São Paulo ◽  
Agricultural Potential

The agricultural potential is generally assessed and managed based on a one-dimensional vision of the soil profile, however, the increased appreciation of sustainable production has stimulated studies on faster and more accurate evaluation techniques and methods of the agricultural potential on detailed scales. The objective of this study was to investigate the possibility of using soil magnetic susceptibility for the identification of landscape segments on a detailed scale in the region of Jaboticabal, São Paulo State. The studied area has two slope curvatures: linear and concave, subdivided into three landscape segments: upper slope (US, concave), middle slope (MS, linear) and lower slope (LS, linear). In each of these segments, 20 points were randomly sampled from a database with 207 samples forming a regular grid installed in each landscape segment. The soil physical and chemical properties, CO2 emissions (FCO2) and magnetic susceptibility (MS) of the samples were evaluated represented by: magnetic susceptibility of air-dried fine earth (MS ADFE), magnetic susceptibility of the total sand fraction (MS TS) and magnetic susceptibility of the clay fraction (MS Cl) in the 0.00 - 0.15 m layer. The principal component analysis showed that MS is an important property that can be used to identify landscape segments, because the correlation of this property within the first principal component was high. The hierarchical cluster analysis method identified two groups based on the variables selected by principal component analysis; of the six selected variables, three were related to magnetic susceptibility. The landscape segments were differentiated similarly by the principal component analysis and by the cluster analysis using only the properties with higher discriminatory power. The cluster analysis of MS ADFE, MS TS and MS Cl allowed the formation of three groups that agree with the segment division established in the field. The grouping by cluster analysis indicated MS as a tool that could facilitate the identification of landscape segments and enable the mapping of more homogeneous areas at similar locations.

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