scholarly journals Agronomic evaluation of cowpea cultivars developed for the West African Savannas

2017 ◽  
Author(s):  
A. Y. Kamara ◽  
S. Ewansiha ◽  
H. Ajeigbe ◽  
L. Omoigui ◽  
A. I. Tofa ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Selection Index ◽  
Principal Component ◽  
Cowpea Cultivars ◽  
Breeding Populations ◽  
Fodder Yield ◽  
Superior Genotypes ◽  
And Cluster Analysis ◽  
On Farm

The goal of this research was to evaluate diverse cowpea genotypes developed over the past 4 decades in the Nigerian Sudan Savannas for their agronomic performance and to identify groups of cultivars with similar quantitative characters. Characterization would facilitate the efficient synthesis of breeding populations for further improvement of cowpea. Also superior genotypes with desirable characteristics could be identified and disseminated in the dry savannas of West Africa. Significant variations were observed in the agronomic characteristics of the cultivars in this study. Principal component analysis (PCA) and cluster analysis were performed on these genotypes and found that there were significant correlations among the variables measured. Modern cultivars outperformed the older ones and from the results of PCA, it was found that the most important variables for the classification of cowpea cultivars are high canopy, high seed weight, high total dry matter, high HI, and high grain and fodder yield. This suggests that these traits could be used in selection index for genetic improvement of cowpea. Cluster analysis resulted in 5 groups mostly corresponding to era of release except cluster I which contained cowpea cultivars from all eras. Two distinct groups in clusters IV and V were identified. Cultivars in cluster IV which were released in the 1990-2000 eras, had high grain and fodder yield. These cultivars could be evaluated on-farm for eventual release to farmers. They could also be used in breeding program for improvements in grain and fodder yield of cowpea. Cluster V contained two cultivars that distinctly had the highest fodder yield suggesting that they could be used to improve fodder yield of cowpea.

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.

scholarly journals Structured Laser Illumination Planar Imaging Based Classification of Ground Coffee Using Multivariate Chemometric Analysis

2016 ◽  
Vol 8 (3) ◽  
pp. 32 ◽  
Author(s):  
Olivier K. Bagui ◽  
Kenneth A. Kaduki ◽  
Edouard Berrocal ◽  
Jeremie T. Zoueu
Keyword(s):  
Cluster Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Planar Imaging ◽  
Reliable Measurement ◽  
Laser Illumination ◽  
Coffee Beans ◽  
Chemometric Techniques ◽  
Ground Coffee

<p class="1Body">Most commercially available ground coffees are processed from Robusta or Arabica coffee beans. In this work, we report on the potential of Structured Laser Illumination Planar Imaging (SLIPI) technique for the classification of five types of Robusta and Arabica commercial ground coffee samples (Familial, Belier, Brazil, Colombia and Malaga). This classification is made, here, from the measurement of the extinction coefficient µ<sub>e</sub> and of the optical depth OD by means of SLIPI. The proposed technique offers the advantage of eliminating the light intensity from photons which have been multiply scattered in the coffee solution, leading to an accurate and reliable measurement of µ<sub>e</sub>. Data analysis uses the chemometric techniques of Principal Component Anaysis (PCA) for variable selection and Hierarchical Cluster Analysis (HCA) for classification. The chemometric model demonstrates the potential of this approach for practical assessment of coffee grades by correctly classifying the coffee samples according to their species.</p>

scholarly journals Use of Highly Discriminating Morphological Characters and Isozymes in the Study of Spanish Chestnut Cultivars

2006 ◽  
Vol 131 (6) ◽  
pp. 770-779 ◽  
Author(s):  
Santiago Pereira-Lorenzo ◽  
María Belén Díaz-Hernández ◽  
Ana María Ramos-Cabrer
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Multivariate Analysis ◽  
Castanea Sativa ◽  
Principal Component ◽  
Market Value ◽  
Morphological Characters ◽  
Minor Importance ◽  
Breeding Programs ◽  
And Cluster Analysis

Morphological characters (six traits) and isozymes (four systems, five loci) were used to discriminate between Spanish chestnut cultivars (Castanea sativa Mill.) from the Iberian Peninsula. A total of 701 accessions (representing 168 local cultivars) were analyzed from collections made between 1989 and 2003 in the main chestnut growing areas: 31 were from Andalucía (12 cultivars), 293 from Asturias (65 cultivars), 25 from Castilla-León (nine cultivars), four from Extremadura (two cultivars) and 348 from Galicia (80 cultivars). Data were synthesized using multivariate analysis, principal component analysis, and cluster analysis. A total of 152 Spanish cultivars were verified: 58 cultivars of major importance and 94 of minor importance, of which 18 had high intracultivar variation. Thirty-seven cultivars were clustered into 14 synonymous groups. Six of these were from Galicia, one from Castilla-León (El Bierzo), four from Asturias, one from Asturias and Castilla-León (El Bierzo), and two from Asturias, Castilla-León (El Bierzo), and Galicia. The chestnut cultivars from Galicia and Asturias were undifferentiated in genetic terms, indicating that they are not genetically isolated. Overall, chestnut cultivars from southern Spain showed the least variation. Many (58%) of Spanish cultivars produced more than 100 nuts/kg; removing this low market-value character will be a high priority. The data obtained will be of use in chestnut breeding programs in Spain and elsewhere.

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