Exploring the relationship between mastitis risk management, milk yield and global warming potential in dairy farms

Mastitis represents a significant challenge for the dairy industry, causing reduced milk yield, economic losses, and increased environmental impact. This study presents a predictive model to assess the risk of mastitis according to farm structures and management, and its impact on milk production and Global Warming Potential (GWP). The model incorporates factors such as bedding materials, hygiene practices, milking systems, and herd health monitoring. It has been tailored to an Italian production environment, considering Holstein cows reared in free stall housing. The impact of mastitis on the GWP of milk production has been assessed using the Life Cycle Assessment method with a ‘cradle to farm gate’ approach. A simulation study has been performed considering 27,456 scenarios with a milking parlor (MP) and 1,152 scenarios with the automatic milking system (AMS). In MP scenarios, the average milk production was 29.99 ± 1.96 kg, reflecting a 13% decrease compared to the baseline (optimal situation without any mastitis). Factors such as overcrowding, health surveillance, cleanliness of resting areas, and post-dipping practices were identified as key influences on production. Overcrowding led to an average 16.26% reduction in milk yield, while continuous health surveillance reduced milk loss by 10%. Bedding material also played a relevant role, with sand and straw related to smaller reductions in production. GWP in MP scenarios ranged from 1.37 to 1.78 kg CO2eq / kg FPCM. The optimal performance in MP scenarios occurred with effective health management, continuous surveillance, sand bedding, and proper pre- and post-dipping routines, achieving 34.16 kg FPCM and 1.07 kg CO2eq / kg FPCM in deep litter systems. In AMS scenarios, the average FPCM production was 34.75 ± 4.26 kg, with an average GWP of 1.43 ± 0.26 kg CO2eq / kg FPCM. Continuous health monitoring and cleanliness of resting areas had a significant impact on milk yield, as well as the AMS type influenced performance. The optimal performance in AMS scenarios was observed with no overcrowding, sand bedding, cleanliness, and health group separation, yielding 43.12 kg FPCM and 0.93 kg CO2eq / kg FPCM in deep litter systems. The model provides a framework for optimising dairy farm practices, highlighting the critical role of health monitoring, hygiene, and bedding selection in achieving sustainable milk production.