In Theriogenology ; h5-index 37.0
To reduce losses of dams and calves due to unfortunate events, such as dystocia and freezing to death, identifying the onset of calving and providing necessary assistance are crucial. Prepartum increase in blood glucose concentration is a known indicator to detect labor in pregnant cows. However, some issues, including the need for frequent blood sampling and stress on cows, must be resolved before establishing a method for anticipating calving using changes in blood glucose concentrations. Herein, instead of measuring the blood glucose concentrations, subcutaneous tissue glucose concentration (tGLU) was measured in peripartum primiparous (n = 6) and multiparous (n = 8) cows at 15 min intervals using a wearable sensor. A transient increase in tGLU was observed in the peripartum period, with peak individual concentrations occurring between 2.8 h before and 3.5 h after calving. tGLU in primiparous cows was significantly higher than that in multiparous cows. To account for individual variations in basal tGLU, the maximum relative increase in the 3-h moving average of tGLU (Max MA) was used to predict calving. Cutoff points for Max MA were established by parity, with receiver operating characteristic analysis predicting calving within 24, 18, 12, and 6 h. Except for one multiparous cow that showed an increase in tGLU just before calving, all cows reached at least two cutoff points and calving was predicted successfully. The time interval between reaching the tGLU cutoff points that predicted calving within 12 h and actual calving was 12.3 ± 5.6 h. In conclusion, this study demonstrated the potential role of tGLU as a predictive indicator of calving in cows. Advancements in machine learning-based prediction algorithms and bovine-optimized sensors will help in increasing the accuracy of calving prediction using tGLU.
Wakatsuki Takuji, Nakamura Tsukasa, Ishii Ayumi, Konishi Kanta, Okubo Michiko, Souma Kousaku, Hirayama Hiroki
2023-Mar-13
Calving, Calving prediction systems, Cows, Dystocia, Subcutaneous tissue glucose concentration, Wearable sensors
