Development and evaluation of a milk yield-based indexing framework for mastitis monitoring in dairy cows

Objective: Mastitis is a major disease affecting dairy cows that requires timely detection to enable effective management and reduce substantial economic losses.In this study, we developed a practical mastitis monitoring framework based exclusively on daily milk yield, a routinely collected and noninvasive variable readily available from automated milk-recording systems.Methods: Daily milk yield records (n = 121,692) from 555 Holstein cows on four Korean dairy farms were analyzed.Lactations were reconstructed, and mastitis datasets were matched 1:1 with non-mastitis controls by farm, date, and days in milk.After completeness screening, 63 A c c e p t e d A r t i c l ematched pairs were divided into development (44 pairs) and test datasets (19 pairs).Expected yield was estimated using exponential smoothing and a modified Wilmink equation.The index combined milk yield reduction, a day-specific 90% lower prediction threshold, and consecutive negative deviations.Index changes were compared across 7-, 9-, and 11-day intervals, and alert rules based on index level, relative decline, or both were optimized in the development dataset and evaluated in the test dataset.Results: In mastitis datasets, mean index scores progressively decreased from the prediagnosis to post-diagnosis intervals across all window sizes, with significant differences among all three intervals (p<0.05).No significant interval-dependent differences were observed in matched non-mastitis datasets.In the development dataset, the highest F1-score was 0.800 at day +7 using a 3-day evaluation window and a 5% relative-decline rule.In the test dataset, the highest and most balanced performance was observed from days +4 to +6, with accuracy, sensitivity, specificity, and F1-score all reaching 0.790.Conclusion: Sustained deviations from expected daily milk production patterns can be converted into an interpretable alert signal for mastitis-associated changes.The proposed framework may support practical and noninvasive identification of cows requiring closer observation or confirmatory testing without additional sensors or invasive sampling.