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  

scholarly journals Assessment of the genetic components and trait associations in diverse set of fieldpea (Pisum sativum L.) genotypes

2015 ◽  
Vol 43 (3) ◽  
pp. 323-330 ◽  
Author(s):  
AK Parihar ◽  
GP Dixit ◽  
V Pathak ◽  
D Singh
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Principal Components ◽  
Agronomic Traits ◽  
Principal Component ◽  
Component Analysis ◽  
Genetic Components ◽  
Wide Range ◽  
Cluster Distance ◽  
Biological Yield

One hundred and 40 genotypes of fieldpea were used to assess the genetic divergence for various agronomic traits. The study revealed that all the accessions were significantly different for the traits and a wide range of variability exists for most of the traits. Correlation studies exhibited that seed yield had positive significant correlation with most of the traits. Cluster analysis classified 140 genotypes into 12 distinct groups. A large number of genotypes (30) were placed in cluster IV followed by cluster III with 24 genotypes. The maximum inter-cluster distance was observed between clusters III and IV indicating the possibility of high heterotic effect if the individuals from these clusters are cross-bred. Principal component analysis yielded 12 Eigen vectors and PCA analysis revealed significant variations among traits with seven major principal components explaining about 90% of variations. The estimates of Eigen value associated with the principal components and their respective relative and accumulated variances explained 50.16% of total variation in the first two components. The characters with highest weight in component first were biological yield, pods/plant, yield/plant and branches/plant which explained 34.22% of the total variance. The results of principal component analysis were closely in line with those of the cluster analysis. The grouping of genotypes and hybridization among genetically diverse genotypes of different cluster would be helpful in broadening the genetic base of fieldpea and producing desirable recombinants for developing new fieldpea varieties. DOI: http://dx.doi.org/10.3329/bjb.v43i3.21605 Bangladesh J. Bot. 43(3): 323-330, 2014 (December)

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.

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..

scholarly journals Morphological Similarity Analysis of Elite Rice Varieties Released in 1980–2011

2018 ◽  
Vol 24 (1) ◽  
pp. 31
Author(s):  
Trias Sitaresmi ◽  
Nani Yunani ◽  
Nafisah Nafisah ◽  
Satoto Satoto ◽  
Aan A. Daradjat
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Genetic Relationship ◽  
Principal Component ◽  
Component Analysis ◽  
Yield Potential ◽  
Rice Varieties ◽  
Irrigated Rice ◽  
And Cluster Analysis ◽  
The Impact

<p>High acceptance of farmer to variety with have similar to IR64 type has led to almost all new varieties always be assessed based on their degree of similarity with IR64. Closely relations between elite upland varieties may contribute to the stagnation of yield potential and also give the impact un-durable of the resistance to pest and diseases. The aim of this study was to elucidate the morphology similarity kinship characters of elite rice varieties which were released from 1980 to 2011. The study was conducted in September–January 2012 in Indonesian Center for Rice Research field experiment. The material consisted of 46 rice varieties representing the released varieties from 1980–2011. The material was grown in 2 m × 5 m of plot size with 3 replications. Observations were conducted on qualitative and quantitative characters based on UPOV descriptors. Data were analyzed by Principal Component Analysis and Cluster Analysis. Principal component analysis revealed 40 components with 79,86% of cumulative variation that was used to determine the genetic relationship by cluster analysis. Based on the principal component analysis and cluster analysis, irrigated rice varieties released before and in 2000 and after 2008 (Inpari group) tend to be one big group and have a high phenotypic similarity. While the upland rice varieties tend to spread or were grouped in small groups. This high similarity suggested that the irrigated rice varieties have a close genetic relationship, which is derived from Ciherang or IR64.</p>

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.

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