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Related Experiment Videos

Random regression models for male and female fertility evaluation using longitudinal binary data.

T Averill1, R Rekaya, K Weigel

  • 1National Agriculture Statistics Service, USDA, Concord, NH 03302-1444, USA.

Journal of Dairy Science
|August 11, 2006
PubMed
Summary

This study analyzed Holstein cow insemination success using Bayesian models. Model 1, with 3 parameters, was more plausible, showing heritability estimates for fertility traits.

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Area of Science:

  • Animal Breeding and Genetics
  • Reproductive Biology
  • Statistical Genetics

Background:

  • Optimizing dairy cattle fertility is crucial for herd productivity and profitability.
  • Accurate genetic evaluation of fertility traits requires robust statistical models.
  • Previous models may not fully capture the complex genetic and environmental factors influencing insemination success.

Purpose of the Study:

  • To compare two random regression models for analyzing longitudinal insemination outcomes in Holstein cows.
  • To estimate heritability and genetic correlations for insemination success over time.
  • To jointly evaluate male and female fertility using all available breeding information.

Main Methods:

  • Longitudinal Bayesian threshold analysis of 369,353 insemination records from first-parity Holstein cows.

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  • Two random regression models (3-parameter and 5-parameter) were employed to estimate genetic values on the liability scale.
  • Systematic effects (herd-year, month, technician, age, milk yield) and random effects (cow, service sire) were included.

    • Main Results:

    • Model 1 (3 parameters) was deemed more plausible than Model 2 (5 parameters) based on the Bayes factor.
    • Heritability estimates for insemination success varied with days in milk (DIM), decreasing initially then increasing.
    • Genetic correlations for insemination success decreased as the interval between inseminations increased, indicating time-dependent genetic effects.

    Conclusions:

    • The 3-parameter random regression model provides a plausible framework for evaluating fertility in dairy cows.
    • Genetic selection for improved insemination success is feasible, with time-dependent genetic correlations influencing strategy.
    • Joint evaluation of male and female fertility is essential for comprehensive genetic improvement programs.