scholarly journals Morphological Variability and Taxonomic Relationship of Sorghum bicolor (L.) Moench Accessions Based on Qualitative Characters

2020 ◽  
pp. 40-52
Author(s):  
Ika Nugraheni Ari Martiwi ◽  
Laurentius Hartanto Nugroho ◽  
Budi Setiadi Daryono ◽  
Ratna Susandarini
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Sorghum Bicolor ◽  
Morphological Variability ◽  
Principal Component ◽  
Staple Food ◽  
Grain Color ◽  
Qualitative Characters ◽  
Relationship Of ◽  
Sorghum Bicolor L

Sorghum bicolor (L.) Moench is a member of Poaceae family with three agronomic types corresponds to their specific uses namely grain sorghum, sweet sorghum, and forage sorghum. Although sorghum in Indonesia is considered as under-utilized crop, but it has the potential as an alternative resource for staple food in diversification program to support food security. The potential of sorghum as alternative staple food is due to its high nutritional value, and the ability to grow well in marginal lands with maintaining good productivity. The objectives of this study were to assess the extent of morphological variability and to resolve taxonomic relationship of sorghum accessions from Java Island based on morphological characters. Twenty nine sorghum samples were collected from three provinces in Java, namely Central Java, Special Province of Yogyakarta, and East Java. Morphological data observed refers to Descriptors for Sorghum, generating 19 qualitative characters used in cluster analysis and principal component analysis. Cluster analysis resulted in the grouping of 29 accessions into three groups corresponding to the racial classification of sorghum. Results of principal component analysis showed that the main distinguishing characters between groups were inflorescence compactness and shape, glume coverage, presence or absence of awn, presence or absence of grain sub-coat, grain shape, and grain color. Analysis of correlation showed that there were very strong relationship between glume coverage and the presence of awn, and between the presence of grain sub-coat and grain color. Results of this study revealed that sorghum accessions cultivated on Java Island consisted of four races namely Bicolor, Guinea, Caudatum, and Kafir.

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 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 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.

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