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Area of Science:

  • Animal Genetics
  • Dairy Science
  • Quantitative Genetics

Background:

  • Feed efficiency traits, including residual feed intake (RFI) and feed saved (FS), are crucial for sustainable genetic improvement programs in dairy cattle.
  • Current genetic evaluations face challenges with sparse dry matter intake (DMI) data, particularly from commercial settings, limiting sustainability.
  • Multiple-trait random regression (MTRR) modeling offers a solution for more flexible genetic evaluations, especially when incorporating DMI data across different lactation stages.

Purpose of the Study:

  • To develop and apply a multiple-trait random regression (MTRR) model for estimating genetic parameters of feed efficiency traits (RFI and FS) in dairy cows.
  • To investigate the impact of days in milk (DIM) and metabolic body weight (MBW) changes on DMI, RFI, and FS.
  • To assess the feasibility of using MTRR for genetic evaluations with varied data recording scenarios.

Main Methods:

  • Utilized daily data on DMI, MBW, and milk energy (MilkE) from 2,532 primiparous and 2,379 multiparous US Holstein cows (50-200 DIM).
  • Applied MTRR to estimate daily heritabilities, genetic correlations, and genetic partial regressions of DMI on MBW, MilkE, and change in MBW.
  • Derived genetic RFI and FS using estimated MTRR variance components.

Main Results:

  • Daily heritabilities for RFI and FS varied across lactation for both primiparous and multiparous cows.
  • Genetic correlations for RFI across DIM showed wide variation, while FS correlations were more stable.
  • Heritability estimates based on average lactation measures were higher than daily estimates; partial genetic regression coefficients also varied across lactation.

Conclusions:

  • MTRR modeling provides a flexible strategy for genetic evaluations of feed efficiency traits, accommodating diverse data recording scenarios.
  • The model's ability to provide DIM-specific inferences enhances the understanding of feed efficiency dynamics throughout lactation.
  • Despite computational challenges, MTRR holds significant potential for improving the sustainability and accuracy of genetic selection for feed efficiency in dairy.