Performance of Online Somatic Cell Count Estimation in Automatic Milking Systems.

Area of Science:

• Dairy Science
• Animal Health
• Diagnostic Technologies

Background:

• Somatic cell count (SCC) is a key indicator for mastitis in dairy cows.
• Automatic milking systems (AMS) increasingly utilize online SCC measurements for early detection.
• Online California Mastitis Test (O-CMT) offers high-frequency screening potential, but its performance relative to laboratory SCC (L-SCC) requires further evaluation.

Purpose of the Study:

• To assess the agreement between O-CMT measurements (averaged over various time windows) and L-SCC.
• To identify the optimal time window for averaging O-CMT to best correlate with L-SCC.
• To explore the utility of O-CMT time-series data for individual cow udder health monitoring.

Main Methods:

• Data collected from 50 farms across 6 countries using AMS with O-CMT and regular L-SCC testing.
• Concordance correlation coefficient (CCC) used to compare O-CMT and L-SCC.
• O-CMT measurements were averaged over different time windows (e.g., 24 hours) for analysis.

Main Results:

• Overall CCC between O-CMT and L-SCC was 0.53, with significant farm-to-farm variation.
• Averaging O-CMT measurements over a 24-hour window improved the CCC with L-SCC.
• Time-series analysis of daily O-CMT data proved effective for screening high SCC episodes.

Conclusions:

• O-CMT shows moderate agreement with L-SCC for mastitis diagnostics.
• Averaging O-CMT data over 24 hours optimizes its correlation with L-SCC.
• The high frequency of O-CMT measurements makes it a promising on-farm tool for monitoring udder health in dairy herds.