scholarly journals KARAKTERISASI EMPAT POPULASI IKAN GURAMI (Osphronemus goramy Lac.) DAN PERSILANGANNYA BERDASARKAN METODE TRUSS MORFOMETRIKS

2016 ◽  
Vol 11 (2) ◽  
pp. 125
Author(s):  
Suharyanto Suharyanto ◽  
Rita Febrianti ◽  
Sularto Sularto
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Morphological Variability ◽  
Similarity Index ◽  
Principal Component ◽  
Component Analysis ◽  
The Body ◽  
Female Population ◽  
Dorsal Fin ◽  
Pure Lines

Tahap awal yang dilakukan dalam rangka pembentukan populasi ikan gurami cepat tumbuh adalah koleksi dan karakterisasi populasi-populasi ikan gurami yang akan digunakan sebagai sumber genetik pembentukan varietas tersebut. Kegiatan ini dilakukan untuk mengevaluasi keragaman morfologi dan hubungan kekerabatan empat populasi ikan gurami, yaitu Jambi (J), Kalimantan Selatan (K), Majalengka (M), dan Tasikmalaya (T). Metode truss morfometrik digunakan untuk karakterisasi morfologi dilanjutkan dengan analisis komponen utama (principal component analysis) dan analisis pengelompokan (cluster analysis). Hasil karakterisasi menunjukkan bahwa diagram pencar populasi ikan gurami tanpa melihat jenis kelamin menunjukkan adanya pengelompokan populasi menjadi dua kelompok, yaitu kelompok pertama adalah persilangan JxM dan MxK, sedangkan kelompok kedua terdiri atas persilangan JxK, KxJ, TxJ, KxM, MxJ, KxT, galur murni KxK, JxJ, MxM, dan TxT. Hal tersebut terjadi pula pada populasi jantan. Populasi betina menunjukkan JxK dan MxK terpisah berdasarkan karakter A2 (dahi-pangkal sirip punggung) dan A3 (pangkal sirip punggung-pangkal sirip perut). Indeks kesamaan tertinggi dalam 12 populasi diperoleh pada populasi Jambi dan Majalengka berturut-turut sebesar 94,00% dan 92,00%; sedangkan indeks kesamaan terendah diperoleh pada populasi TJ sebesar 72,00%. Ikan gurami ukuran konsumsi terdapat empat kelompok besar berdasarkan bentuk badannya. Dua kelompok pada galur murni menunjukkan populasi galur murni Kalimantan, Majalengka, dan Tasikmalaya kekerabatannya dekat, akan tetapi dengan Jambi memiliki kekerabatan yang jauh. Dua kelompok lainnya pada populasi persilangan, yaitu: persilangan JxM dan MxK dan kelompok lainnya adalah persilangan KxJ, KxM, JxK, TxJ, MxJ, dan KxT. Populasi galur murni dan persilangan memiliki jarak genetik yang jauh, sehingga populasi galur murni dan persilangan itu berbeda.The first step in breeding program towards generating fast-growing strain of giant gourami is the collection and characterization of giant gourami populations have been used as a genetic source. Giant gourami had been collected from South Kalimantan, Jambi, Majalengka, and Tasikmalaya. The aim of this experiment was to determine the morphological diversity among these collected populations using truss morphometric method. Principal component analysis followed by cluster analysis were used to identify the pattern of morphological variability among populations and varieties. The results showed that dendrogram populations of giant gourami regardless of gender showed a grouping of some of the population into two groups: the first group was J×M and M×K crosses, while the second population consisted of: Jambi Kalimantan (J×K), Kalimantan Jambi (K×J), Tasikmalaya Jambi (T×J), Kalimantan Majalengka (K×M), Majalengka Jambi (M×J), Kalimantan Tasikmalaya (K×T), purebred Kalimantan (K×K), Jambi (J×J), Majalengka (M×M), and Tasikmalaya (T×T). This was true for the male population. Female population showed J×K and M×K apart, the difference lies in the character of the forehead-base of the dorsal fin (A2) and the base of the dorsal fin-fin base stomach (A3). The highest similarity index was found Jambi (94.00%) Majalengka (92.00%) populations, while the lowest similarity index was T×J (72.00%). At market size of the consumption of giant gourami there are four major groups, based on the shape of the body. Two groups on pure strains showed a population of pure lines Kalimantan, Majalengka, and Tasikmalaya close kinship, but Jambi had a distant kinship. Two other groups in the population crosses, namely: cross J×M and M×K and the other group was a cross K×J, M×K, J×K, T×J, M×J, and K×T. The population of pure lines and crosses had a genetic distance away, so that the population of pure lines and crosses were different.

scholarly journals Morphological variability of evergreen oaks (Quercus) in Turkey

2017 ◽  
Vol 24 (1) ◽  
pp. 39-47 ◽  
Author(s):  
Yilmaz Aykut ◽  
Uslu Emel ◽  
Babaç M. Tekin
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Morphological Variability ◽  
Principal Component ◽  
Component Analysis ◽  
The South ◽  
Plant Taxon ◽  
Evergreen Oaks

The genus Quercus L. has a problematic taxonomy because of widespreadhybridization among them. Evergreen Quercus contain three species in section Ilex Loudon namely, Q. ilex L., Q. coccifera L. and Q. aucheri Jaub. et Spach in Turkey. Here, two species, Q. coccifera and Q. aucheri are usually confused with each other. However, Q. coccifera and Q. calliprinos are accepted as different species but this subject is still controversial. Morphometric leaf and fruit variations of Q. ilex, Q. coccifera and Q. aucheri in 26 populations were measured for 25 characters. Variations within and among populations of species were detected by cluster analysis and principal component analysis. This study shows that populations of Q. coccifera from the south region of Turkey form a second group within Q. coccifera. Secondly, Q. coccifera show more similarity to Q. aucheri than Q. ilex, and finally there are two groups within Q. coccifera, which may be evaluated as Q. coccifera and Q. calliprinos.Keywords: Ilex; Morphometric, UPGMA; Turkey.Bangladesh J. Plant Taxon. 24(1): 39–47, 2017 (June)

scholarly journals Short Communication: Morphology and taxonomic relationships of shallot (Allium cepa L. group aggregatum) cultivars from Indonesia

2019 ◽  
Vol 20 (10) ◽  
Author(s):  
Nita Fitriana ◽  
Ratna Susandarini
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Allium Cepa ◽  
Short Communication ◽  
Agronomic Traits ◽  
Morphological Variability ◽  
Principal Component ◽  
Component Analysis ◽  
Taxonomic Relationships ◽  
Allium Cepa L

Abstract. Fitriana N, Susandarini R. 2019. Short Communication: Morphology and taxonomic relationships of shallot (Allium cepa L. group aggregatum) cultivars from Indonesia. Biodiversitas 20: 2809-2814. Shallot is an important vegetable crop commodity in Indonesia, and its ranks third in terms of cultivation area after chili (Capsicum annuum L.) and cabbage (Brassica oleracea L.). There are a number of superior cultivars of this species that have been studied in terms of their morpho-agronomic traits, but their morphological variability and its implication to taxonomic relationships have not been studied in depth. This study aimed at documenting morphological variability and taxonomic relationships of 12 superior shallot cultivars using numerical taxonomic approaches. Sixteen morphological characters from leaves and bulbs were used in cluster analysis and principal component analysis. The results revealed that twelve shallot cultivars from Indonesia showed significant variability in leaf and bulb morphology, particularly in quantitative characteristics as indicated from ANOVA. The grouping of cultivars and their taxonomic relationships generated from cluster analysis and principal component analysis revealed the role of bulb morphology as determining characteristics in the grouping of cultivars. This study suggested the importance of bulb morphology in the characterization of shallot cultivars.

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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