Prediction of quarter level subclinical mastitis by combining in-line and on-animal sensor data

Abstract

We investigated the potential for automatic detection of subclinical mastitis (SCM) in pasture-based automatic milking systems. The objective of the study was to determine the ability of electrical conductivity (EC), together with relative changes in daily activity (activity) and daily rumination (rumination) recorded using heat and rumination–long-distance tags, to predict quarter-level SCM. Activity (arbitrary unit/day) and rumination (min/day) data were determined across 21 days using heat and rumination–long-distance tags for 170 cows. Cows were allocated into the following three groups: SCM (n = 32, EC ≥ 7.5 millisiemens/cm (mS/cm) in one or more quarters and a positive bacteriological culture in the corresponding quarter(s)); true-negative (TN, n = 9, EC ≥ 7.5 mS/cm and a negative culture in all four quarters); and apparently healthy (n = 129, no culture test and EC < 7.5 mS/cm). Group mean differences in activity and rumination were compared using Welch’s t-tests. Logistic mixed models were used to predict SCM by EC, activity and rumination changes before mastitis detection, including parity information between SCM and TN groups. Cow- and quarter-specific information were included as random effects, followed by model assessment by producing receiver operating-characteristic curve and area under the curve (AUC) value. In total, 287 quarters were used in the prediction model, including 143 quarters with a positive culture (Gram-positive; n = 131, Gram-negative; n = 6, mixed; n = 6) and 144 quarters with a negative culture. On average, SCM group had 4.65% greater (P < 0.01) activity and 9.89% greater (P < 0.001) rumination than did the TN group and 11.70% greater (P < 0.001) activity than did the apparently healthy group. A combined model with terms for EC, activity changes, rumination changes prior to detect SCM and parity had a better SCM prediction (AUC = 0.92) ability than did any of them separately (all AUC < 0.8). Hence, we conclude that EC in combination with activity and rumination information can improve the accuracy of prediction of quarter-level SCM.