Random regression models using different functions to model milk flow in dairy cows.

Area of Science:

• Animal Genetics
• Dairy Science
• Quantitative Genetics

Background:

• Milk flow is a key indicator of dairy cow productivity and udder health.
• Understanding the genetic and environmental factors influencing milk flow is essential for improving dairy cattle breeding programs.

Purpose of the Study:

• To analyze milk flow records from Holstein cows to estimate genetic parameters.
• To identify the most parsimonious and adequate statistical model for describing milk flow variation.

Main Methods:

• Analysis of 75,555 test-day milk flow records from 2175 primiparous Holstein cows.
• Application of single-trait Random Regression Models incorporating genetic and environmental effects.
• Utilized orthogonal Legendre polynomials and B-spline functions to model milk flow trends and covariances.

Main Results:

• A model with third-order Legendre polynomials for additive genetic effects and sixth-order for permanent environmental effects, including 7 residual classes, was most adequate and parsimonious.
• Estimated moderate to high heritability for milk flow.
• Identified significant additive genetic and permanent environmental influences on milk flow variation.

Conclusions:

• The chosen model effectively describes variations in milk flow in Holstein cows.
• The findings provide valuable insights for genetic selection strategies aimed at improving milk flow traits in dairy cattle.