Advancing Herd Health Management: Real‐Time Biomarkers Analysis for Early Detection of Metabolic Disorders in Dairy Cows

Introduction: This study evaluates the application of innovative technologies to improve herd health management in dairy farms. The primary focus is on the use of automated monitoring systems for assessing cow health through non­ invasive measurements of milk biomarkers, specifically the milk fat­ to­ protein ratio (F/P). We hypothesized that changes in milk F/P could indicate metabolic health, aiding in the early detection of subclinical ketosis (SCK) and acidosis (SCA).Materials and Methods The study, conducted between June 1 and September 1, 2023, included 320 lactating dairy cows within 5 to 30 days postpartum. Milk F/P ratios were continuously recorded using in­ line analyzers, and cows were categorized based on clinical examinations into groups: SCK, SCA, and healthy controls. Blood samples were analyzed for non­ esterified fatty acids (NEFA), glucose, and liver enzymes to correlate with milk data. Results and Discussion: Cows with SCK exhibited significantly higher milk F/P ratios (1.66 ± 0.29) and NEFA levels compared to healthy cows (1.22 and 0.31 ± 0.25 mmol/L, respectively). In contrast, cows with SCA had lower F/P ratios (0.93 ± 0.10) and elevated AST and GGT activity. A strong positive correlation was identified between milk F/P and NEFA concentrations (r = 0.499, p < 0.01).This study confirms the utility of milk F/P ratios as a reliable, non­ invasive biomarker for identifying cows at risk of SCK and SCA. The integration of real­ time monitoring with digital health records offers actionable insights for improving herd health, reducing antibiotic use, and enhancing farm productivity. Conclusions: Real­ time milk biomarker analysis provides a valuable tool for early detection of metabolic disorders in dairy cows. This approach complements traditional health management practices, ensuring improved animal welfare and farm efficiency. Future studies should explore its applications under varying environmental and management conditions.