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What information counts when detecting mastitis in automatic milking systems? A mixed methods approach from a Swedish
L Ekman1, D Anglart2, I Gillsjö3
1Department of Clinical Sciences, Swedish University of Agricultural Sciences, 750 07 Uppsala, Sweden; Växa Sverige, 112 51 Stockholm, Sweden.
Journal of Dairy Science
|July 20, 2025
Summary
Automatic milking systems (AMS) are changing mastitis detection in dairy farms. Farmers often watch cow behavior instead of using direct AMS data, highlighting a need for better integration and training for improved udder health management.
Area of Science:
- Animal Science
- Agricultural Technology
- Veterinary Medicine
Background:
- The widespread adoption of automatic milking systems (AMS) is transforming dairy farm management.
- Mastitis detection is shifting from direct human observation to data-driven processes within AMS.
Purpose of the Study:
- To investigate how Swedish dairy farmers utilize automatic milking system (AMS) data for mastitis detection.
- To explore the influence of different AMS brands and features on farmers' udder health management practices.
- To identify challenges and opportunities for optimizing mastitis detection and herd health strategies in AMS-managed herds.
Main Methods:
- A mixed-methods approach was employed, combining a quantitative survey of 246 Swedish dairy farmers with qualitative in-depth interviews of 9 farmers.
- The survey assessed the use of AMS data for mastitis detection across various herd sizes, AMS brands, and technological configurations.
- Qualitative interviews provided detailed insights into farmers' decision-making, practices, and perceptions of udder health management.
Main Results:
- Different AMS brands and their associated tools create unique working environments that shape farmers' mastitis detection behaviors.
- Farmers frequently relied on indirect behavioral indicators, such as cows being late for milking, over direct AMS data like somatic cell count (SCC) or electrical conductivity.
- Somatic cell count (SCC) remains the primary indicator of udder health for farmers, despite AMS data availability.
- The integration of AMS data into comprehensive herd health strategies and veterinary collaboration is currently underutilized.
Conclusions:
- Current practices in AMS-managed herds often prioritize behavioral observation over direct data analysis for mastitis detection.
- There is a significant opportunity to improve udder health management by enhancing farmers' training in AMS customization and data integration.
- Optimizing the use of AMS data requires closer collaboration with advisory systems and veterinarians to refine mastitis detection and overall herd health strategies.
