Consistency assessment of milk fat and protein percentages across 3 daily milkings in Holstein and Jersey dairy herds

Authors: WU, XIAO-LIN - Council On Dairy Cattle Breeding; CAPUTO, MALIA - Council On Dairy Cattle Breeding; Miles, Asha; Baldwin, Ransom; SIEVERT, STEVEN - Collaborator; MATTISON, JAY - Collaborator; COLE, JOHN - Council On Dairy Cattle Breeding; BURCHARD, JAVIER - Council On Dairy Cattle Breeding; DURR, JOAO - Council On Dairy Cattle Breeding

Submitted to: Journal of Dairy Science Communications
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/21/2025
Publication Date: 7/1/2025
Citation: Wu, X., Caputo, M.J., Miles, A.M., Baldwin, R.L., Sievert, S., Mattison, J., Cole, J., Burchard, J., Durr, J. 2025. Consistency assessment of milk fat and protein percentages across 3 daily milkings in Holstein and Jersey dairy herds. Journal of Dairy Science Communications. 6(4):532-537. https://doi.org/10.3168/jdsc.2025-0748.
DOI: https://doi.org/10.3168/jdsc.2025-0748

Interpretive Summary: Dairy producers who want to measure milk, fat, and protein yields participate in milk testing where a partial sample from one milking is collected and the relevant parameters are measured. However, milk composition is known to vary throughout the day and between different milkings which confounds these estimates. This paper introduces intraclass correlation coefficients as a better method of assessing consistency across multiple milkings for an individual cow. This analysis demonstrates minimal systematic bias in protein percentages but high variation in fat percentages throughout the day. These results demonstrate the need for adjustments to milk fat percentage phenotypes in genetic evaluations to remove sampling bias. Dairy producers will benefit directly from this research due to the increased accuracy of milk and fat estimates thanks to the adoption of intraclass correlation coefficient adjustments.

Technical Abstract: Dairy cattle milking test plans in the United States and globally have evolved significantly toward cost-effective sampling methods since the 1960s. Test-day recording frequencies vary, adapting to the specific management needs of different herds. Typically, a cow is milked two or more times daily. Still, milk fat and protein percentages are commonly assessed from a single milking sample, assuming that these percentages remain stable throughout the day, though some milk recording programs apply adjustment factors to account for differences in milk composition among milkings. In reality, a reliability measure for assessing the quality of milk components data is needed to capture variations in milk compositional percentages. In this paper, we introduced intraclass correlation coefficients to assess consistency across multiple milkings within a cow. This metric extends beyond simple pairwise correlations, enabling robust comparisons across multiple milkings. Additionally, we address its relevance to accuracy when using single-milking fat and protein percentages as proxies for daily milk fat and protein percentages. The results show that while protein percentages exhibit minimal systematic bias, fat percentages show notable variability, suggesting a potential need to calibrate milk fat percentages in genetic evaluations. Furthermore, adjusting for the effects of the lactation stage and days in milk on fat and protein percentages may enhance accuracy, especially when these factors are unevenly represented in the data. Intraclass correlations also align closely with accuracy measures, assuming that true milk fat and protein percentages remain stable throughout the test day. Overall, the results demonstrate the utility of intraclass correlation as a reliability measure, providing a framework for SOPs (Standard Operating Procedures) in assessing the data quality of single-milking sample fat and protein percentages for daily breeding and management decisions.