Genetic Parameters of Milk Fat, Protein and Solid-Not-Fat Percentage Using Univariate Animal Model

Abstract

Aims: To estimate genetic parameters of milk fat percentage, protein percentage, and solids-not-fat (SNF) percentage in Holstein Friesian crossbred cattle using Univariate Animal Model. Study Design:  A total of 1,59,950 first lactation test day milk yield records of 17135 HF crossbred cows sired by 259 sires spread over a period of 23 years (1997-2019) belonging to four districts of Gujarat state were collected from the Information Network for Animal Productivity and Health Management Information System (INAPH-MIS) database maintained by National Dairy Development Board (NDDB), Anand. First lactation test-day records between 5 and 325 days in milk were analyzed after applying standard data editing procedures. Place and Duration of Study: The study was conducted at the Department of Animal Genetics and Breeding, College of Veterinary Science and Animal Husbandry, Anand in collaboration with the NDDB, Anand, for a duration of six months. Methodology: Heritability, variance and covariance components for milk percentage traits were estimated using Average Information Restricted Maximum Likelihood (AIREML) algorithm of Legendre polynomial (LP) function of univariate animal model (for fat%, SNF% and protein%). Estimated variance and covariance components were used for estimation of breeding value using univariate best linear unbiased prediction (BLUP) method. Results: Results revealed that additive genetic and permanent environmental variances varied across lactation stages, with higher genetic variance observed during mid-lactation. Heritability estimates ranged from 0.18 to 0.42 for fat percentage, 0.20 to 0.38 for protein percentage, and 0.15 to 0.35 for SNF percentage, indicating moderate scope for genetic improvement. Genetic correlations among the three traits were positive and high, suggesting that selection for one trait would lead to favorable responses in others. Conclusion: The study demonstrates that Univariate Animal Model provides more accurate and dynamic estimates of genetic parameters than conventional lactation models, thereby offering a reliable basis for improving milk composition traits in dairy cattle breeding programs.