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Related Experiment Videos

Contrasting models for lactation curve analysis.

F Jaffrezic1, I M S White, R Thompson

  • 1Institute of Cell, Animal and Population Biology, University of Edinburgh, UK. jaffrezic@dga2.jouy.inra.fr

Journal of Dairy Science
|May 23, 2002
PubMed
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Character process models offer improved genetic analysis of dairy cattle lactation curves compared to traditional random regression models. Combining models enhances performance and parameter efficiency for accurate genetic evaluations.

Area of Science:

  • Animal Genetics
  • Dairy Cattle Breeding
  • Statistical Modeling

Background:

  • Genetic evaluation of dairy cattle production traits relies on statistical models using test-day records.
  • Key approaches include random regression, orthogonal polynomials, and character process models.

Purpose of the Study:

  • To elucidate the relationships between different statistical models for genetic analysis.
  • To compare the performance of these models in analyzing dairy cattle lactation curves.
  • To assess the equivalence of sire and animal models in repeated measures analyses.

Main Methods:

  • Comparison of random regression, orthogonal polynomials, and character process models.
  • Application of sire and animal models for genetic analysis.

Related Experiment Videos

  • Evaluation using likelihood criteria and genetic variance estimation.

    • Main Results:

    • An 11-parameter character process model outperformed a 31-parameter quartic random regression model based on the likelihood criterion.
    • Significant differences in genetic variance were observed between the character process and unstructured models, highlighting model selection challenges.
    • A hybrid model combining quadratic random regression for environmental effects and a character process model for residuals showed superior performance and parameter efficiency.

    Conclusions:

    • Character process models provide a more effective framework for analyzing lactation curves in dairy cattle.
    • Hybrid modeling approaches can enhance accuracy and reduce parameter complexity in genetic evaluations.
    • Careful consideration of model selection criteria is crucial, especially when interpreting genetic variance estimates from novel models.