Modeling lactation curves and estimation of genetic parameters in Holstein cows using multiple-trait random...

Area of Science:

• Animal Science
• Dairy Science
• Quantitative Genetics

Background:

• Accurate modeling of lactation curves is crucial for dairy cattle breeding programs.
• Understanding genetic and environmental factors influencing milk production traits is essential for improving herd efficiency.
• Previous studies have utilized various mathematical functions to describe lactation dynamics, but optimal models may vary by population.

Purpose of the Study:

• To compare the goodness of fit of three mathematical functions for describing the lactation curve in primiparous Iranian Holstein cows.
• To evaluate additive genetic (AG) and permanent environmental (PE) variance components using multiple-trait random regression models (MT-RRM).
• To determine the most adequate model for fitting lactation data based on established statistical criteria.

Main Methods:

• Employed multiple-trait random regression models (MT-RRM) to analyze lactation data from primiparous Iranian Holstein cows.
• Compared the fitting performance of Legendre polynomials, Lidauer-Mäntysaari function, and Wilmink function.
• Utilized Akaike's Information Criterion, Schwarz's Bayesian Information Criterion, and -2*logarithm of the likelihood function to assess model adequacy.

Main Results:

• Lactational submodels indicated largest daily AG variance at lactation's end and PE variance at its onset.
• Heritability estimates for milk, fat, and protein yields were highest from 245 to 305 days of lactation.
• Genetic correlations were generally lowest between milk and fat yields, intermediate between fat and protein, and highest between milk and protein.
• A Legendre polynomial model with 2 coefficients for AG effects and 5 for PE effects demonstrated the best fit for the data.

Conclusions:

• The Legendre polynomial model is the most suitable for describing lactation curves in primiparous Iranian Holstein cows.
• The findings provide valuable insights into the genetic architecture and environmental influences on milk production traits in this specific population.
• Optimal management and breeding strategies should consider the dynamic changes in genetic and environmental variances throughout the lactation period.