Using Principal Component Analysis to Identify Components Predictive of Shape Index in Chicken, Quail and Guinea Fowl

The total number of (189) three genetic quail chicks (White = 54, brown = 64, and gray = 71) were used to identify the components effecting body weight Using the principal component analysis. Means, standard errors, and standard deviation of body weight and the cuts parts were calculated using the descriptive statistic of SPSS/PASW statistics for Windows version 19. One-way analysis of variance was used to test the effect of genetic line on the traits. The results of the statistical analysis indicated that the highest Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy for three lines of Japanese quail recorded to the brown quail, and also brown quail given higher and significant superiority of most parts of carcass and internal viscera which included weights of chest (g), thigh (g), back (g), neck (g), and wing (g) compared with white and grey quails. The first principal component (PC1), second (PC2) and three (PC3) were higher from white quail with Eigen values compared with brown and grey quails. Higher percentage of carcass component for white quail recorded to wings while for brown and grey quail recorded to chest part. We concluded from this study that the three principal components that extracted from White, Brown and Grey can be used for genetic improvement body weight, and also to predicting the best carcass parts weight while we can be used brown Japanese quail as a broiler due to its high carcass parts weight compared white and gray Japanese quail.

Section: Introductionmentioning

confidence: 99%

Using the Principal Component Analysis of Body Weight in Three Genetic Groups of Japanese Quail

Al-Hadeedy,

Ameen,

Shaker

et al. 2023

“…It's extremely versatile with applications in many disciplines. PCA was used by [1] to the egg traits between two genetic lines of the Kurdish local chicken, and it was used to study the components effect the egg shape index in three species (chickens, quail, and guinea fowl) by [2]. The egg internal components effects the egg weight in Kurdish local chicken were also studied faithfully by [3], and in the waterfowl by [4].…”

Section: Introductionmentioning

confidence: 99%

Principal Component Analysis for the Egg Traits in Three Genetic Lines of Japanese Quail

Al-Tamimy,

Shaker

2023

The current study was carried out at the poultry farms of animal production department, college of agriculture, University of Kirkuk from May 2022 to June 2023. A total of (300) eggs were collecting from three genetic groups of the Japanese quail (brown, grey, and white) to evaluate the external egg traits concerning (Egg weight, Egg length, Egg breadth, Shell thickness). The Kaiser–Meyer–Olkin (KMO) for the three genetic lines (brown, grey, and white) of the Japanese quail were (0.651, 0.728, and 0.667) respectively. The communalities represent estimates of variance in each variable accounted, for the brown genetic group of the three traits length, breadth, and eggshell thickness was (0.990, 0.817, and 0.872) respectively. Three Eigen values were extract for the three genetic lines. We conclude that the principal component analysis of the external egg traits that effected the egg weight in the three genetic lines of the Japanese quail differ among the lines due to the genetic makeup of the lines.

“…External egg traits (Length, and breadth) play important roles for embryo growing [2], and chick survival. It's high correlated with species [3][4][5], genotype [6][7][8][9][10], age [7][8][9][10][11], oviposition time [12], and hen health. Also it defer between the birds in same species [13].…”

Section: Introductionmentioning

confidence: 99%

The Regression of Chukar Partridge (Alectoris chukar) Egg External Traits After One Generation of Inbreeding

Ameen,

Mohammed,

Al-Jobouri

et al. 2023

Self Cite

The current experiment was done form (20/12/2018) until (28/7/2019); One hundred eggs were supplied from local farm to hatch the base family. When the flock was reaching the sexual maturity, the eggs were collected to measure the external traits (egg length, egg breadth and egg shape index). The data were analysed by using Statistical Product and Service Solutions (SPSS) software, to calculate the descriptive values concerning mean, standard deviation, and then multiple linear regression analyses were performed to determine the equation of estimating the two geometric elements. The egg shape index was decrease because of the egg length; it increases from 41.01 to 42.92 in generation 1, and 2 respectively. The correlation between the egg shape index and egg length is negative with high significant. Furthermore the correlation was decreased in the generation two comparing with the generation one. The correlation between the egg shape index and the egg breadth approximate stable in generation one and two (0.713, 0.699) respectively. As it was described above the egg length affected by environment. The result showed that both the linear multiple regression model adequate for select the models in the two generations (p<0.001). The value of R2 for both generations was (0.999, and 0.990) respectively. The regression equation for the egg shape index is changed according to the environments, these changes happened according the egg length value unlike the egg breadth, which is genetically controlled.