Estimating US dairy clinical disease costs with a stochastic simulation model

Area of Science:

• Veterinary Economics
• Dairy Cattle Health Management
• Stochastic Modeling

Background:

• Clinical diseases significantly impact dairy farm profitability.
• Accurate cost estimation is crucial for effective disease management strategies.
• Previous economic analyses often lack comprehensive stochastic modeling.

Purpose of the Study:

• To estimate the economic costs of seven common clinical diseases in US dairy farms.
• To identify the primary cost drivers for each disease and parity.
• To analyze the influence of market prices and herd performance on disease costs.

Main Methods:

• Development of a farm-level stochastic model incorporating Monte Carlo simulation (5,000 iterations).
• Inclusion of seven common diseases (mastitis, lameness, metritis, retained placenta, left-displaced abomasum, ketosis, hypocalcemia).
• Modeling of stochastic market prices (milk, feed, slaughter, replacement cow) and herd performance factors.

Main Results:

• Left-displaced abomasum incurred the highest estimated total costs ($432.48 primiparous, $639.51 multiparous cows).
• Milk production loss and treatment costs were the largest contributing categories across diseases.
• Disease costs were generally higher in multiparous cows compared to primiparous cows.

Conclusions:

• Economic impact of dairy cattle diseases is substantial, with left-displaced abomasum being particularly costly.
• Understanding cost breakdowns is essential for targeted interventions and improved herd management.
• Market price fluctuations significantly influence overall disease-related economic losses.