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

## TL;DR

This study assesses the consistency of milk fat and protein percentages across multiple daily milkings in Holstein and Jersey cows using intraclass correlation coefficients.

## Contribution

The paper introduces intraclass correlation coefficients as a novel method to evaluate the consistency of milk components across multiple milkings.

## Key findings

- Milk protein percentages showed high consistency across milkings.
- Milk fat percentages varied significantly, requiring adjustments based on milking intervals and other factors.
- Intraclass correlation proved effective for assessing data quality in dairy management.

## Abstract

Summary: 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 2 or more times daily. Still, milk fat and protein percentages are commonly assessed from 1 milking sample, assuming the percentages are stable throughout the day. However, milk compositional percentages vary across milkings, and a reliability measure is needed to assess milk component data quality. We used intraclass correlation coefficients to assess consistency across multiple milkings within a cow and applied them to explore data quality of milking components in 4 dairy farms.

Summary: 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 2 or more times daily. Still, milk fat and protein percentages are commonly assessed from 1 milking sample, assuming the percentages are stable throughout the day. However, milk compositional percentages vary across milkings, and a reliability measure is needed to assess milk component data quality. We used intraclass correlation coefficients to assess consistency across multiple milkings within a cow and applied them to explore data quality of milking components in 4 dairy farms.

•Intraclass correlation enables robust comparisons across multiple milkings.•Milk protein percentages exhibited high consistency across milkings.•Milk fat percentages varied notably, needing adjustments or rotative sampling.•Intraclass correlation proved to be a valuable tool for assessing milking data quality.

Intraclass correlation enables robust comparisons across multiple milkings.

Milk protein percentages exhibited high consistency across milkings.

Milk fat percentages varied notably, needing adjustments or rotative sampling.

Intraclass correlation proved to be a valuable tool for assessing milking data quality.

Dairy cattle milking test plans in the United States and globally have evolved substantially since the 1960s toward cost-effective sampling methods. Test-day recording frequencies vary, adapting to the specific management needs of different herds. Typically, a cow is milked twice or more daily; however, milk fat and protein percentages are commonly assessed from single-milking samples. In this paper, we introduced intraclass correlation coefficients to determine the consistency of intraday milk fat and protein percentages across multiple milkings within the same cow. This metric extends beyond simple pairwise correlations, enabling robust comparisons across multiple milkings. Various forms of intraclass correlations are also demonstrated. Our results show that although protein percentages exhibit high consistency, fat percentages display notable variability throughout the test day. Hence, adjustment factors for milk fat percentage should differ according to individual milkings and consider the effects of the milking interval, DIM, and parity. Overall, the results demonstrate the utility of intraclass correlation as a consistency measure, providing a valuable tool for assessing the data quality of milk components for dairy breeding and management decisions.