Principal component analysis of linear udder type traits and their relationship with milk yield and composition in indigenous Sahiwal cattle

Abstract

Objective: The present study was aimed at (i) reduction in dimensionality using principal component analysis of 17 linear udder type traits to define those components which best represent udder and teat conformation of Indian Sahiwal cattle and (ii) to identify those components having strongest relationship with milk production traits in Sahiwal cattle. Methods: The Principal Component Analysis (PCA) included 17 linear udder type traits using the correlation matrix between the traits to ensure that all traits are standardized in the analysis. Bartlett's sphericity test was used to determine the degree of interrelations between variables and adequacy for use in factor analysis. The relationship of these principal components with milk production traits was analysed using the general linear model. Results: The means of linear udder type trait measurements varied between-2.21 for udder balance and 136.35 for udder circumference. Out of 17 principal components, six were extracted using Kaiser Rule criterion. The identified six components could explain 75.777% of the cumulative variance between the linear udder type traits. The communality ranged from 0.476 (rear udder width) to 0.967 (fore teat length) for all these 17 different udder type traits. A relation between first principal component and 305-day milk production was significant with a positive and non-significant effect of 305-day milk-fat yield (305 DF) and 305-day milk-protein yield (305 DP). Conclusion: Results of PCA suggest that the use of orthogonal synthetic variables principal component one (PC1), two (PC2) and three (PC3) provided a means of reduction in the number of linear udder type traits to be recorded in Sahiwal cattle which could be used in A c c e p t e d A r t i c l e explaining the whole udder and teat conformation. The PC1 can be used in selection as a means to explain the mammary system of better milk producing animals to be used in breeding programmes.

Genetic Evaluation and Principal Components Analysis for Milk ‎traits in Holstein Friesian Cattle

“…Table 5, shows the extent of the contribution of principal component analysis (PC 1 or PC 2 ) in the variance of the traits under this study; indicating that PC 1 contributes a greater amount of the variance of CI traits reached on 0.371. While it does not contribute badly to the contrast of LP and NSPC traits, In addition to PC 1 contributes a moderate amount to the total milk yield = 0.273, also., the PC2 for TMY=0.213, while DO = -0.004 for PC 1 and PC 2 = 0.949, this study agrees by Eyduran et al, (2013), Rebeka et al (2020) and Sinha et al (2021). 4.…”

“…Estimates breeding values(EBV) of TMY, LP, CI, NSPC, and DO in Holstein cattle cows and the general mean value of Kaiser-Meyer Olkin (KMO) measures of sampling adequacy was obtained as 0.674, Chi-Square was 590.086, this indicated that the suitability of the data for PCA. The same model was reported by several authors Eyduran et al (2013) and Sinha et al (2021) with different cattle and observed that KMO measure of sampling adequacy were 0.867; 0.692 and 0.669, respectively.…”

Prediction of Milk Production of Holstein Cattle Using Principal Component Analysis

“…While, the second component weights varied from -0.072 to 0.892 for DO and CI, respectively. The same trend found that by Campos et al, (2015) and Sinha et al, (2020). Prediction of TMY using production and reproduction traits Multiple regression analysis VIF values greater than 3, VIF were 47.6, 57.6, 48.9 and 1.2 for LP, CI, and DP and DO; respectively.…”

Deep learning based udder classification for cattle traits analysis

“…They found that the single trait, single record model and the simple repeatability model were not appropriate in predicting breeding values at mature ages for rear udder width and rear udder height. More recently, Sinha et al [20] used principal component analysis to investigate 17 linear udder type traits representing udder and teat conformation and to identify those components having strongest relationship with milk production traits. Correspondingly, Soeharsono et al [21] predicted daily milk production based on linear body and udder morphometry of Holstein Friesian (HF) dairy cows.…”