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

M M M Laureano1, A B Bignardi2, L El Faro2

  • 1Departamento de Zootecnia, Faculdade de Ciências Agrárias e Veterinárias, Universidade Estadual Paulista, Jaboticabal, SP, Brasil monyka.laureano@gmail.com.

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Summary
This summary is machine-generated.

This study analyzed Holstein cow milk flow using random regression models. The most parsimonious model identified moderate to high heritability for milk flow, crucial for genetic selection.

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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.