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Vol. 12, Special Issue 12 (2023)

Analysing the growth curve in cross-bred cattle using random regression models

Author(s):
K Sakunthala Devi, V Anjali, K Sudhakar and Ch. Venkatseshaiah
Abstract:
The data was recorded on around 30,000 number of body weight records at repeated intervals pertaining to 7300 Jersey x Sahiwal crossbred cattle, belonging to 108 sires and 7363 dams from birth to 54 months of age i.e. BW0, BW6, BW12, BW18, BW24, BW30, BW36, BW42, BW48 and BW54 and were subjected to random regression analysis for additive genetic, individual permanent environmental, maternal genetic and maternal permanent environmental effects, by using different Legendre polynomials like quadratic, cubic and quartic etc. Preliminary analysis revealed that all fixed effects included in the study were having significant (p<0.01) influence on the body weight at repeated intervals and were included in the estimation of random regression coefficients. Models using heterogeneous error variance were found to be significantly (p<0.01) superior over homogenous error variance. Comparison among 24 models revealed that smaller values of AIC & BIC observed at model 4 (3333B) with minimum number of (33) parameters. The trajectories for 1st and 2nd Eigen functions accounting for >98% and 1.26% of total genetic variation, whereas 3rd Eigen function was found to be zero. In general there were strong positive correlations between intercept (L0), linear (L1) and quadratic (L2) coefficients among all random effects, indicating linear relationship between body weight and age. The correlations between the intercept (L0) and linear (L1) coefficients of G and P were higher (0.98) than those of M and C (0.96).
Pages: 479-484  |  75 Views  43 Downloads
How to cite this article:
K Sakunthala Devi, V Anjali, K Sudhakar and Ch. Venkatseshaiah. Analysing the growth curve in cross-bred cattle using random regression models. The Pharma Innovation Journal. 2023; 12(12S): 479-484.
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