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This study compared nonlinear quantile regression models for Iranian Holstein dairy cows, finding the Wilmink, Dijkstra, and Ali & Schaeffer models best for milk yield, fat, and protein percentages, respectively. The 0.50 quantile best fit production traits, while the 0.25 quantile best fit somatic cell score.

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Area of Science:

  • Animal Science
  • Quantitative Genetics
  • Statistical Modeling

Background:

  • Accurate modeling of milk production traits and somatic cell score (SCS) is crucial for dairy cattle breeding programs.
  • Nonlinear quantile regression offers a flexible approach to analyze trait distributions beyond the mean.

Purpose of the Study:

  • To compare the performance of various nonlinear quantile regression models for milk production traits (yield, fat, protein percentages) and SCS in Iranian Holstein dairy cows.
  • To identify the optimal quantile and model for describing these traits.

Main Methods:

  • Utilized data from 13,977 Iranian Holstein cows (1991-2011) with 101,051 monthly records.
  • Implemented four nonlinear functions (Wood, Wilmink, Dijkstra, Ali & Schaeffer) within a quantile regression framework.
  • Evaluated model performance using residual mean square, Akaike information criterion, and log-likelihood at quantiles 0.25, 0.50, and 0.75.

Main Results:

  • The Wilmink model was optimal for milk yield, Dijkstra for fat percentage, and Ali & Schaeffer for protein percentage at specific quantiles.
  • Quantile 0.50 provided the best overall fit for milk yield, fat, and protein percentages across models.
  • For SCS, quantile 0.25 showed the best fit, with Dijkstra and Ali & Schaeffer models performing best at different quantiles. The Wood function performed poorly.

Conclusions:

  • Nonlinear quantile regression models effectively describe variations in milk production traits and SCS in dairy cows.
  • Model and quantile selection depends on the specific trait and its distribution characteristics.
  • Quantile regression is particularly suitable for analyzing SCS due to its complex, multimodal distribution.