scholarly journals Bayesian principal component analysis of Nigerian indigenous normal feather chickens' body linear measurements

2020 ◽  
Vol 44 (1) ◽  
pp. 10-20
Author(s):  
A. E. Sonubi ◽  
A. S. Adenaike ◽  
A. A. Dauda ◽  
T. P. Alao ◽  
B. O. Shonubi ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Body Weight ◽  
Principal Components ◽  
Body Shape ◽  
Principal Component ◽  
Component Analysis ◽  
Body Measurements ◽  
Indigenous Chicken ◽  
Body Dimension ◽  
Linear Measurements

The indigenous chicken is a store house of unique genes that could be used in other parts of the world for improving other breeds. This study was carried out using bayesian principal component analysis and aimed objectively at determining the effect of sex on Nigerian indigenous normal feather chickens' body dimension, describing their body shape, and predicting their body weights from body measurements using orthogonal conformation traits derived from the principal components score. The parameters measured at 16 weeks of age were body weight, body length, breast girth, thigh length, shank length, shank diameter, keel length, wing length, wing span, and tail length on 233 randomly selected adult chickens. Sexual dimorphism was observed in all the traits with higher values recorded for males. Bayesian correlations among body weight and biometric traits were positive (r = 0.09 to 0.651 and 0.017 to 0.579 in male and female chickens respectively). The descriptive statistics showed that the mean body weight was 1.8085 ± 0.263 kg for males and 1.403 ± 0.226 kg for females. The first two principal components (PCs) were extracted for the males, both PCs components account for 72.21%. For the females, three PCs were extracted and they account for 77%. The first PC in each case accounted for the greatest percentage of the total variation. The use of orthogonal body shape characteristics derived from components' scores was more appropriate than the use of original traits in body weight prediction as multi-collinearity problems were eliminated. This led to simultaneous analysis of these body measurements rather than on individual basis. These components could be used as selection criteria for improving body weight of indigenous Nigerian chickens.

Study of Body Conformation of Carpet Wool Type Chitarangi Sheep of India using Principal Component Analysis

2021 ◽  
Author(s):  
A.K. Mishra ◽  
Anand Jain ◽  
S. Singh ◽  
R.K. Pundir
Keyword(s):  
Principal Component Analysis ◽  
Body Weight ◽  
Total Variation ◽  
Principal Components ◽  
Principal Component ◽  
Component Analysis ◽  
Sheep Population ◽  
Body Conformation ◽  
Face Length ◽  
Minimum Number

Background: The principal component analysis is applied to identify minimum number of combined variables that account for maximum portion of the variance existing in all variables studied. Chitarangi is a lesser known carpet type wool sheep distributed in Fazilka and Muktsar districts of Punjab, Sri Ganganagar district of Rajasthan and the adjoining areas. The information on body biometry is a prerequisite to characterize the lesser known sheep population available in the country. Hence, it is important to describe the body conformation by recording minimum number of biometric traits. Methods: Body biometry traits of Chitarangi sheep, a lesser known carpet quality wool producing sheep population were studied using Principal Component Analysis. The traits studied were body length (BL), height at wither (HW), chest girth (CG), paunch girth (PG), ear length (EL), face length (FL), face width (FW), tail length (TL) and adult body weight (BW). The data were collected on 297 ewes in the breeding tract of Chitarangi sheep. The descriptive statistics were determined for all the traits. The phenotypic correlations between different body biometric traits were estimated using partial correlations. Principal components were estimated using correlation matrix. Principal component analysis (PCA), a multivariate approach, is used when the recorded traits are highly correlated. Rotation of principal components was through the transformation of the components to approximate a simple structure. Factor analysis using oblique (promax) rotation was used. All the analysis was carried out using the SPSS statistical package. Result: The averages for body weight and biometry traits confirmed large size of Chitarangi animals. Most of the phenotypic correlations amongst the studied traits were positive and significant (p less than 0.01). The three components extracted from nine principal components accounted for 69.06% of the total variance. The first component, which described body size of ewes, accounted for 43.68% of the total variation with high loading for BW, CG, PG, HW, BL and FL. The components two and three explained 13.54 and 11.83% of total variance, respectively. The communalities ranged from 0.490 (FL) to 0.888 (PG). The lower communalities for face length indicated lower contribution of the trait to explain the total variation than others. The study indicates that principal components provided a means of reduction in number of biometric traits to explain body confirmation of adult female Chitarangi sheep.

scholarly journals Variability in size and shape in muscovy duck with age: Principal component analysis

2014 ◽  
Vol 30 (1) ◽  
pp. 125-136 ◽  
Author(s):  
D.M. Ogah ◽  
M. Kabir
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Principal Component ◽  
Component Analysis ◽  
The Body ◽  
Dependence Structure ◽  
Muscovy Duck ◽  
Body Measurements ◽  
Male And Female ◽  
Size And Shape

Body weight and six linear body measurements, body length (BL), breast circumference (BCC), thigh length (TL), shank length (SL), total leg length (TLL) and wing length were recorded on 150 male and female muscovy ducklings and evaluated at 3, 5, 10, 15 and 20 weeks of age. Principal component analysis was used to study the dependence structure among the body measurements and to quantify sex differences in morphometric size and shape variations during growth. The first principal components at each of the five ages in both sexes accounted between 71.54 to 92.95% of the variation in the seven measurements and provided a linear function of size with nearly equal emphasis on all traits. The second principal components in all cases also accounted for between 6.7 to 16.17% of the variations in the dependence structure of the system in the variables as shape, the coefficient for the PCs at various ages were sex dependent with males showing higher variability because of spontaneous increase in size and shape than females. Contribution of the general size factor to the total variance increase with age in both male and female ducklings, while shape factor tend to be stable in males and inconsistent in females.

scholarly journals Variability in size and shape in muscovy duck with age: Principal component analysis

2013 ◽  
Vol 29 (3) ◽  
pp. 493-504
Author(s):  
D.M. Ogah ◽  
M. Kabir
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Principal Component ◽  
Component Analysis ◽  
The Body ◽  
Dependence Structure ◽  
Muscovy Duck ◽  
Body Measurements ◽  
Male And Female ◽  
Size And Shape

Body weight and six linear body measurements, body length (BL), breast circumference (BCC), thigh length (TL), shank length (SL), total leg length (TLL) and wing length were recorded on 150 male and female muscovy ducklings and evaluated at 3, 5, 10, 15 and 20 weeks of age. Principal component analysis was used to study the dependence structure among the body measurements and to quantify sex differences in morphometric size and shape variations during growth. The first principal components at each of the five ages in both sexes accounted between 71.54 to 92.95% of the variation in the seven measurements and provided a linear function of size with nearly equal emphasis on all traits. The second principal components in all cases also accounted for between 6.7 to 16.17% of the variations in the dependence structure of the system in the variables as shape, the coefficient for the PCS at various ages were sex dependent with males showing higher variability because of spontaneous increase in size and shape than females. Contribution of the general size factor to the total variance increase with age in both male and female ducklings, while shape factor tend to be stable in males and inconsistent in females.

Stormwater inflow prediction using radar rainfall data compressed by principal component analysis

2006 ◽  
Vol 1 (1) ◽  
Author(s):  
K. Katayama ◽  
K. Kimijima ◽  
O. Yamanaka ◽  
A. Nagaiwa ◽  
Y. Ono
Keyword(s):  
Principal Component Analysis ◽  
Prediction Model ◽  
Principal Components ◽  
Prediction Method ◽  
Principal Component ◽  
Component Analysis ◽  
Rainfall Data ◽  
Radar Rainfall ◽  
Input Variables ◽  
Inflow Prediction

This paper proposes a method of stormwater inflow prediction using radar rainfall data as the input of the prediction model constructed by system identification. The aim of the proposal is to construct a compact system by reducing the dimension of the input data. In this paper, Principal Component Analysis (PCA), which is widely used as a statistical method for data analysis and compression, is applied to pre-processing radar rainfall data. Then we evaluate the proposed method using the radar rainfall data and the inflow data acquired in a certain combined sewer system. This study reveals that a few principal components of radar rainfall data can be appropriate as the input variables to storm water inflow prediction model. Consequently, we have established a procedure for the stormwater prediction method using a few principal components of radar rainfall data.

The Design of Index about Corporate Governance Based on PCA Method

2014 ◽  
Vol 926-930 ◽  
pp. 4085-4088
Author(s):  
Chuan Jun Li
Keyword(s):  
Principal Component Analysis ◽  
Corporate Governance ◽  
Principal Components ◽  
Principal Component ◽  
Component Analysis ◽  
Contribution Rate ◽  
Variance Contribution ◽  
Pca Method

This article uses the PCA method (Principal component analysis) to evaluate the level of corporate governance. PCA is used to analyze the correlation among 10 original indicators, and extract some principal components so that most of the information of the original indicators is extracted. The formulation of the index of corporate governance can be got by calculating the weight based on the variance contribution rate of the principal component, which can comprehensively evaluate corporate governance.

Identification of Liquor Brands Based on near Infrared Spectroscopy

2013 ◽  
Vol 834-836 ◽  
pp. 935-938
Author(s):  
Lian Shun Zhang ◽  
Chao Guo ◽  
Bao Quan Wang
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Principal Components ◽  
Near Infrared ◽  
Scatter Correction ◽  
Principal Component ◽  
Component Analysis ◽  
Correction Method ◽  
Variance Contribution

In this paper, the liquor brands were identified based on the near infrared spectroscopy method and the principal component analysis. 60 samples of 6 different brands liquor were measured by the spectrometer of USB4000. Then, in order to eliminate the noise caused by the external factors, the smoothing method and the multiplicative scatter correction method were used. After the preprocessing, we got the revised spectra of the 60 samples. The difference of the spectrum shape of different brands is not much enough to classify them. So the principal component analysis was applied for further analysis. The results showed that the first two principal components variance contribution rate had reached 99.06%, which can effectively represent the information of the spectrums after preprocessing. From the scatter plot of the two principal components, the 6 different brands of liquor were identified more accurate and easier than the spectra curves.

scholarly journals Grouping sunflower genotypes for yield, oil content, and reaction to Alternaria leaf spot using GGE biplot

2015 ◽  
Vol 50 (8) ◽  
pp. 649-657 ◽  
Author(s):  
Regina Maria Villas Bôas de Campos Leite ◽  
Maria Cristina Neves de Oliveira
Keyword(s):  
Principal Component Analysis ◽  
Disease Severity ◽  
Principal Components ◽  
Leaf Spot ◽  
Oil Content ◽  
Principal Component ◽  
Component Analysis ◽  
High Yield ◽  
Gge Biplot ◽  
Alternaria Leaf Spot

Abstract:The objective of this work was to evaluate the suitability of the multivariate method of principal component analysis (PCA) using the GGE biplot software for grouping sunflower genotypes for their reaction to Alternaria leaf spot disease (Alternariaster helianthi), and for their yield and oil content. Sixty-nine genotypes were evaluated for disease severity in the field, at the R3 growth stage, in seven growing seasons, in Londrina, in the state of Paraná, Brazil, using a diagrammatic scale developed for this disease. Yield and oil content were also evaluated. Data were standardized using the software Statistica, and GGE biplot was used for PCA and graphical display of data. The first two principal components explained 77.9% of the total variation. According to the polygonal biplot using the first two principal components and three response variables, the genotypes were divided into seven sectors. Genotypes located on sectors 1 and 2 showed high yield and high oil content, respectively, and those located on sector 7 showed tolerance to the disease and high yield, despite the high disease severity. The principal component analysis using GGE biplot is an efficient method for grouping sunflower genotypes based on the studied variables.

scholarly journals Principal component analysis for evaluating a ranking method used in the performance testing in sheep of Morada Nova breed

2015 ◽  
Vol 36 (6) ◽  
pp. 3909
Author(s):  
Michelle Santos da Silva ◽  
Luciana Shiotsuki ◽  
Raimundo Nonato Braga Lôbo ◽  
Olivardo Facó
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Correlation Coefficients ◽  
Performance Testing ◽  
Principal Component ◽  
Component Analysis ◽  
Ranking Method ◽  
Daily Weight Gain ◽  
Meat Production ◽  
Body Depth

A multivariate approach was adopted to evaluate the relationship among traits measured in the performance testing of Morada Nova sheep, verify the efficiency of a ranking method used in these tests and identify the most significant traits for use in future analyses. Data from 150 young rams participating in five versions of the performance tests for the Morada Nova breed were used. Twenty traits were measured in each animal: initial weight (IW), final weight (FW), average daily weight gain (ADG), loin eye area (LEA), scrotal circumference (SC), fat thickness (FT), conformation (C), precocity (Pc), muscularity (M), breed features (BF), legs (L), withers height (WH), chest width (CW), rump height (RH), rump width (RW), rump length (RL), body length (BL), body depth (BD), heart girth (HG) and body condition scoring (BCS). The Pearson’s correlation coefficients ranged from –0.10 to 0.93, with the highest correlations were between body weight variables and morphometric measurements. The three first principal components explained 72.28% of the total variability among all traits. The variables related to animal size defined the first principal component, whereas those related to visual appraisal and suitability for meat production defined the second and third principal components, respectively. The combination of traits from the principal component analysis showed that the ranking method currently used in the performance testing of Morada Nova sheep is efficient for selecting larger rams with better breed features and higher degrees of specialization for meat production.

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