scholarly journals Principal component analysis for assessment of variability in phenological and morphological traits in French bean (Phaseolus vulgaris L)

2019 ◽  
Vol 10 (4) ◽  
pp. 1569
Author(s):  
Rani Shama ◽  
Nayeema Jabeen ◽  
Parvaze A. Sofi
Keyword(s):  
Principal Component Analysis ◽  
Phaseolus Vulgaris ◽  
Morphological Traits ◽  
Principal Component ◽  
Component Analysis ◽  
French Bean ◽  
Phaseolus Vulgaris L

Evaluation of the morphological diversity of tea roses (Rosa x odorata) based on phenotypic traits

2020 ◽  
Vol 18 (3) ◽  
pp. 149-158
Author(s):  
Bixuan Cheng ◽  
Chao Yu ◽  
Heling Fu ◽  
Lijun Zhou ◽  
Le Luo ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Correlation Analysis ◽  
Morphological Traits ◽  
Significant Positive Correlation ◽  
Flower Colour ◽  
Principal Component ◽  
Morphological Diversity ◽  
Component Analysis ◽  
Germplasm Resources ◽  
Trait Correlation

AbstractRosa x odorata (sect. Chinenses, Rosaceae) is an important species distributed only in Yunnan Province, China. There is an abundance of wild variation within the species. Using 22 germplasm resources collected from the wild, as well as R. chinensis var. spontanea, R. chinensis ‘Old Blush’ and R. lucidissima, this study involved morphological variation analysis, inter-trait correlation analysis, principal component analysis and clustering analysis based on 16 morphological traits. This study identified a high degree of morphological diversity in R. x odorata germplasm resources and the variation coefficients had a distribution range from 18.00 to 184.04%. The flower colour had the highest degree of variation, while leaflet length/width had the lowest degree of variation. Inter-trait correlation analysis revealed that there was an extremely significant positive correlation between leaflet length and leaflet width. There was also a significant positive correlation between the number of petals and duration of blooming, and the L* and a* values of flower colour were significantly negatively correlated. Principal component analysis screened five principal components with the highest cumulative contribution rate (81.679%) to population variance. Among the 16 morphological traits, style length, sepal width, flower diameter, flower colour, leaflet length and leaflet width were important indices that influenced the morphology of R. x odorata. This study offers guidance for the further development and utilization of R. x odorata germplasm resources.

Characterization of medicinal Senna genetic resources

2009 ◽  
Vol 7 (03) ◽  
pp. 257-259 ◽  
Author(s):  
J. B. Morris
Keyword(s):  
Pancreatic Cancer ◽  
Principal Component Analysis ◽  
Medicinal Plants ◽  
Genetic Resources ◽  
Morphological Traits ◽  
Principal Component ◽  
Component Analysis ◽  
Pharmaceutical Products ◽  
Seed Reproduction

At 50% maturity, regeneratingSennaspecies were characterized for morphological traits, seed reproduction, and evaluated for regeneration. Quality plants regenerated from all accessions produced 1018 to more than 21,215 total seeds. Principal component analysis revealed which traits contributed the greatest to variability among coffee senna accessions.Sennaspecies have potential to produce pharmaceutical products and can be grown as medicinal plants. The flavonoids quercetin and kaempferol found inSennaspecies have been clinically shown to have anti-pancreatic cancer properties.

Principal Component Analysis and Stability of Genotypes in French Bean (Phaseolus vulgaris L.)

2021 ◽  
Author(s):  
B. Rajasekhar Reddy ◽  
Maneesh Pandey ◽  
J. Singh ◽  
P.M. Singh ◽  
N. Rai
Keyword(s):  
Principal Component Analysis ◽  
Stability Analysis ◽  
Principal Components ◽  
Block Design ◽  
Principal Component ◽  
Component Analysis ◽  
French Bean ◽  
Pod Yield ◽  
Randomized Block Design ◽  
Stable Yield

Background: Principal component analysis and Finlay-Wilkinson stability analysis were carried out at research farm of ICAR-Indian Institute of Vegetable Research, Varanasi to identify diverse french bean genotypes for green pod yield and suitable genotypes for stable yield and yield related parameters.Methods: All the 24 genotypes were laid out in randomized block design with two replications during winter, 2017 and 2018. Principal component analysis and stability analysis was done to identify the diverse and stable genotypes.Result: Eight principal components were observed and the maximum variability was concentrated in the first three principal components PC1, PC2 and PC3 which contributed to 68.61% variance. Cluster analysis from principal component scores formed three clusters with a maximum of seventeen genotypes in cluster I followed by six genotypes in cluster II and one genotype in cluster III. High heritability was observed for 10 pod weight, number of pods per cluster and number of seeds per pod and moderate heritability was observed for yield per plant. Finlay-Wilkinson stability analysis identified the stable genotypes viz., FMGCV 1378, FMGCV 0958, Arka Suvidha, Valentino, Banoa and VRFBB-14-2 for green pod yield per plant, Cartagenta for pod length (cm) and Paulista, Slender Pack, Arka Suvidha, Valentino, FMGCV 0958, Banoa, FORC 6V 1136, VRFBB-14-1, VRFBB-14-2 for number of pods per plant.

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 Principal Component Analysis with Quantitative Traits in Extant Cotton Varieties (Gossypium Hirsutum L.) and Parental Lines for Diversity

2018 ◽  
Vol 6 (1) ◽  
pp. 54-64
Author(s):  
K. Rathinavel
Keyword(s):  
Principal Component Analysis ◽  
Plant Height ◽  
Morphological Traits ◽  
Quantitative Traits ◽  
Fibre Length ◽  
Principal Component ◽  
Component Analysis ◽  
Plant Growth Habit ◽  
Vigour Index ◽  
Parental Lines

The experimental material consisted of 101 extant varieties and parental lines characterized for morphological traits under Distinctiveness Uniformity and Stability (DUS) testing at CICR, Regional Station, Coimbatore, India. Twenty one quantitative traits were taken for observation. The data were utilised to estimate substantial variation and relationship within the extant varieties and to identify the best performing genotypes. Analysis of variance for quantitative traits, in diverse line, showed sizable amount of variability. The highest variation was found for vigour index, plant height, germination per cent, fibre maturity, yield per plant, plant stand, fibre uniformity and ginning per cent when mean performance genotypes were considered. Seed cotton yield showed significantly positive correlation with boll number plant-1 (0.95), boll weight-1 (0.53), lint weight (0.50), fibre length (0.27), plant growth habit (0.26), plant height (0.23) and seed index (0.21). Principal component analysis showed the extend of variation by components 1 to 8 that exhibited Eigen value >1. Cluster analysis based on various morphological traits assorted 101 extant varieties of cotton into three main clusters. Dendrogram arrived based on hierarchal clustering, grouped genotypes based on their morphological traits rather than the geography of origin.

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..

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