Assessment of genetic diversity in pea (Pisum sativum L.) landraces based on physico-chemical and nutritive quality using cluster and principal component analysis

2019 ◽  
Vol 52 (2) ◽  
Author(s):  
Uzma Arif ◽  
Muhammad Jamil Ahmad ◽  
Malik Ashiq Rabbani ◽  
Ayaz Ahmed Arif
Keyword(s):  
Genetic Diversity ◽  
Principal Component Analysis ◽  
Pisum Sativum ◽  
Principal Component ◽  
Component Analysis ◽  
Pisum Sativum L ◽  
Nutritive Quality ◽  
Physico Chemical

scholarly journals Keanekaragaman genotipe-genotipe potensial dan penentuan keragaman karakter argo-morfologi ercis (Pisum sativum L.)

Jurnal Agro ◽  
10.15575/3230 ◽  
2018 ◽  
Vol 5 (2) ◽  
pp. 127-139
Author(s):  
Rawina Saragih ◽  
Darmawan Saptadi ◽  
Chindy Ulima Zanetta ◽  
Budi Waluyo
Keyword(s):  
Genetic Diversity ◽  
Principal Component Analysis ◽  
Pisum Sativum ◽  
Pearson Correlation ◽  
Principal Component ◽  
Morphological Characters ◽  
Agglomerative Hierarchical Clustering ◽  
Pair Group ◽  
Pisum Sativum L ◽  
Index Value

Ercis (Pisum sativum L.) merupakan salah satu tanaman kacang komersial yang penting di dunia termasuk di Indonesia. Ercis lokal merupakan sumber populasi untuk meningkatkan kapasitas genetik hasil panen polong dan biji melalui seleksi galur murni. Tujuan penelitian ini untuk mempelajari jarak dan keanekaragaman genetik, serta keragaman karakter 37 genotipe potensial ercis hasil seleksi galur murni varietas lokal. Penelitian dilaksanakan pada bulan Maret hingga Juni 2018 di Desa Pendem, Kecamatan Junrejo, Kota Batu. Percobaan menggunakan rancangan acak kelompok dengan 37 genotipe sebagai perlakuan dan diulang tiga kali, sehingga terdapat 111 satuan percobaan. Pengamatan dilakukan pada masing-masing tanaman yakni karakter agronomi dan morfologi. Pengelompokan genetik didasarkan pada agglomerative hierarchical clustering dengan similiritas koefisien kolerasi Pearson dan metode aglomerasi unweighted pair group method average (UPGMA). Keanekaragaman genetik didasarkan pada indeks Shannon-Wiener (H’) dan indeks Shimpson (D). Keragaman karakter agronomi dan morfologi 37 genotipe ercis menggunakan principal component analysis (PCA) dengan pendekatan tipe korelasi Pearson. Berdasarkan analisis klaster 37 genotipe ercis terbagi menjadi 6 kelompok berdasarkan 61 karakter agro-morfologi dengan koefisien kemiripan 89-99%. Diversitas genetik ercis dikategorikan sedang dengan nilai indeks Shanon-Wiener 1,5 dan nilai indeks Simpson 0,26 yang menunjukkan tidak terdapat kelompok genetik yang mendominansi. Tiga puluh tujuh genotipe ercis memiliki keragaman yang luas. Keragaman kumulatif berdasarkan 61 karakter agro-morfologi yang diamati mencapai 87,83% yang melibatkan 44 karakter pada 16 komponen utama pertama.Pea (Pisum sativum L.) is one of the important commercial legumes in the world, including in Indonesia. The aims of the research were to study  genetic distance, diversity, and characters variability of 37 genotypes of pea. The experiment was conducted on March to June 2018 in Pendem, Junrejo, Batu City. The experimental design used a randomized block design with 37 genotypes as treatments and replicated three times. Observations was made on agronomic and morphological characters. Genetic grouping according to agglomerative hierarchical clustering with Pearson correlation coefficient similarity and unweighted pair group average agglomeration method (UPGMA). Genetic diversity based on Shannon-Wiener (H') index and Shimpson (D) index. Variability of agronomic and morphological characters in 37 genotypes was analyzed by principal component analysis (PCA) with Pearson correlation approach. The results showed that cluster analysis of 37 genotypes was divided into six groups in 61 agro-morphological characters with similarity coefficients of 89-99%. Genetic diversity was medium categorized with Shanon-Wiener index value of 1.5 and Simpson index value of 0.26. It was indicated that no dominating on genotypes group. Thirty seven genotypes of pea showed high variability. Cumulative variability on 61 observed agro-morphological characters reached 87.83% which involved 44 characters in 16 first principal components.

scholarly journals GENETIC DIVERSITY IN VEGETABLE PEA (Pisum sativum L.)

2013 ◽  
Vol 26 (1) ◽  
pp. 35-40
Author(s):  
M. Rahman ◽  
M. Hasan ◽  
R. N. Chowdhury ◽  
N. A. Ivy ◽  
M. M. Hossain
Keyword(s):  
Genetic Diversity ◽  
Principal Component Analysis ◽  
Pisum Sativum ◽  
Genetic Divergence ◽  
Principal Component ◽  
Reducing Sugar ◽  
Pisum Sativum L ◽  
Close Relationship ◽  
Cluster Distance ◽  
Minimum Number

An investigation was carried out to identify the extent of genetic divergence of seventeen vegetable pea genotypes at the Bangabandhu Sheikh Mujibur Rahman Agricultural University, Bangladesh. Genetic divergence was estimated using D2 and principal component analysis. The genotypes under study fell into 4 clusters. The distribution pattern indicated that the maximum numbers of genotypes (6) were included in cluster II and cluster III and the minimum number in cluster IV (1). The inter-cluster distance was higher than the intra-cluster distance which indicated wider genetic diversity among the accessions of different groups. The highest inter-cluster distance was observed between II and IV. The lowest inter-cluster distance was observed between clusters II and III suggesting a close relationship among the genotypes of these two clusters. The highest intra-cluster distance was observed for the cluster II. The positive values of vector I and vector II for plant height, 50% flowering and non-reducing sugar indicated that these characters had the highest contribution towards the divergence among the pea genotypes. The genotypes of vegetable pea from cluster II and cluster IV maybe selected as parents in future hybridization program.DOI: http://dx.doi.org/10.3329/bjpbg.v26i1.19982

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.

↵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.

Genetic diversity based on cluster and principal component analysis in wheat and triticale genotypes

2015 ◽  
Vol 16 (4) ◽  
pp. 712
Author(s):  
Bhanu Priya ◽  
Sunil Diyali ◽  
Subhra Mukherjee ◽  
M. Srinivasarao
Keyword(s):  
Genetic Diversity ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis

scholarly journals Estimation of Genetic Diversity among Genotypes of Fodder Oat based on Principal Component Analysis

2017 ◽  
Vol 8 (6) ◽  
pp. 807-810
Author(s):  
Monica Jyoti Kujur ◽  
◽  
A. K. Mehta ◽  
S. K. Bilaiya ◽  
Prakarti Patil ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis

scholarly journals Genetic Diversity Studies in Drought Tolerant Rice (Oryza sativa L.) Genotypes

2021 ◽  
Author(s):  
A. Sheeba ◽  
S. Mohan
Keyword(s):  
Genetic Diversity ◽  
Principal Component Analysis ◽  
Association Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Large Set ◽  
Panicle Length ◽  
Rice Varieties ◽  
Drought Tolerant ◽  
Rice Genotypes

Background: Assessing the genetic diversity and relationship among breeding materials isan invaluable aid for any crop improvement programme. Principal component analysis (PCA) is a multivariate statistical technique attempt to simplify and analyze the inter relationship among a large set of variables in term of a relatively a small set of variables or components without losing any essential information of original data set. Methods: The present investigation was carried out to study the genetic diversity and relationship among the sixty five rice genotypes including popular rice varieties of Tamil Nadu, drought tolerant rice varieties, aerobic rice genotypes and land races. These genotypes were raised at Rice Research Station, Tiruvallur, during kharif, 2015 in randomized block design with three replications under aerobic condition. Data on eight yield and yield attributing traits were recorded and subjected to principal component analysis and association analysis. Result: In principal component analysis, PC1accounted for 22.91% and PC2 accounted for 19.53% of the total variation. The traits panicle length, no. of grains per panicle, plant height, days to 50% flowering, no of productive tillers per plant from the first two principal components accounted for major contribution to the total variability. Cluster analysis grouped the genotypes into six discrete clusters. The association analysis revealed that the traits viz., no. of productive tillers/plant, panicle length and hundred seed weight had positive association with higher direct effect on plot yield which could be used as selection criteria for developing high yielding rice varieties. The results of the present study have revealed the high level of genetic variation existing in the genotypes studied and explains the traits contributing for this diversity.

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