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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Animal Genomics and Improvement Laboratory » Research » Publications at this Location » Publication #392067

Research Project: Improving Dairy Animals by Increasing Accuracy of Genomic Prediction, Evaluating New Traits, and Redefining Selection Goals

Location: Animal Genomics and Improvement Laboratory

Title: Phenotypic and genotypic impact of milk components and bodyweight composite on dry matter intake

item Toghiani, Sajjad
item Vanraden, Paul
item PARKER GADDIS, KRISTEN - Council On Dairy Cattle Breeding
item VANDEHAAR, MICHAEL - Michigan State University
item TEMPELMAN, ROBERT - Michigan State University

Submitted to: Journal of Dairy Science
Publication Type: Abstract Only
Publication Acceptance Date: 3/10/2022
Publication Date: 6/19/2022
Citation: Toghiani, S., Van Raden, P.M., Parker Gaddis, K.L., Vandehaar, M.J., Tempelman, R.J. 2022. Phenotypic and genotypic impact of milk components and bodyweight composite on dry matter intake [abstract]. Journal of Dairy Science. 105(Suppl. 1):200(abstr. 1499V).

Interpretive Summary:

Technical Abstract: Large datasets allow estimating feed required for individual milk components or cow maintenance. Phenotypic regressions are useful for nutrition management, but genetic regressions are more useful in breeding programs. Dry matter intake (DMI) records from 6,338 lactations of 5,094 Holstein cows were predicted from phenotypes or genomic evaluations for milk components and body size traits. The mixed models also included days in milk, age-parity subclass, trial date, management group, and bodyweight change during 28- and 42-days feeding trials in mid-lactation. Phenotypic regressions of DMI on milk (0.007+-0.008), fat (2.82+-0.13), and protein (5.32+-0.31) were much less than corresponding genomic regressions (0.076+-0.029, 10.82+-0.60, and 7.88+-1.34) or sire genomic regressions multiplied by 2 (0.043+-0.054, 6.43+-1.14, and 6.66+-2.35). For standardized 100 kg of milk with 3.5% fat and 3.0% protein, estimated marginal feed costs totaled 18% of milk revenue by phenotypic regression, 46% by genomic regression, and 31% by sire genomic regression multiplied by 2. The energy corrected milk formula assumes that 69% more DMI is required for fat than protein production with regressions of 0.122 for milk, 4.82 for fat, and 2.85 for protein whereas the new net merit formula (NM$ 2021) assumes that 20% more DMI is needed for protein than fat production with regressions of 0.12 for milk, 5.0 for fat, and 6.0 for protein, and a marginal feed cost of 32% of the milk price ($36.38/100 kg). Estimates of annual maintenance in kg DMI/ kg bodyweight/lactation were similar from phenotypic regression (5.8+-0.2), genomic regression (6.0+-0.4), and sire genomic regression multiplied by 2 (5.7+-0.7) and were revised upward to 4.5 in NM$ 2021. Multiple regressions on genomic evaluations for the traits in bodyweight composite (BWC) showed that strength was most associated with both bodyweight and DMI, agreeing with the current BWC formula, whereas other traits were less significant predictors, especially for DMI. Breeding programs should select smaller cows with negative RFI and produce more milk, fat, and protein to improve profit.