Making Good Decisions on Dry Cow Management to Improve Udder Health - Synthesising Evidence in a Bayesian Framework

Area of Science:

• Veterinary Medicine
• Animal Science
• Dairy Herd Health

Background:

• The dry period is critical for controlling mastitis in dairy cattle.
• Intramammary infections (IMI) during the dry period often lead to clinical mastitis or elevated somatic cell counts (SCC) in early lactation.
• Variability exists in dry period IMI patterns, with limited understanding of farm-specific risk determinants beyond dry cow treatments.

Purpose of the Study:

• To investigate cow characteristics, farm facilities, and herd management strategies influencing dry period IMI risk.
• To analyze the relationship between dry period factors and clinical mastitis or raised SCC in early lactation.
• To present a Bayesian framework for integrating existing knowledge and new data for optimized dry cow management.

Main Methods:

• Review of new research on cow characteristics, farm facilities, and herd management during the dry period.
• Analysis of factors related to clinical mastitis and raised SCC in early lactation.
• Description of a Bayesian approach for decision-making in dry cow management.

Main Results:

• Identified key cow characteristics, farm facilities, and management strategies impacting dry period IMI risk.
• Established links between dry period factors and subsequent mastitis or SCC levels.
• Demonstrated a framework for synthesizing data to support farm-specific dry cow management.

Conclusions:

• Dry period management significantly impacts dairy cattle udder health in early lactation.
• Understanding farm-specific determinants of IMI is essential for effective mastitis control.
• A data-driven, Bayesian approach can enhance decision-making for individual farm dry cow management strategies.