Forty-four Holstein calves (19 male and 25 female) were used in this study of the relationships among age at first colostrum feeding, IgG intake, and apparent efficiency of IgG absorption. Time of birth was recorded for each calf and the calves were fed colostrum ad libitum after birth at either 0930 or 1630 h. Blood samples were collected immediately before and 24h after colostrum feeding. Data from calves were then categorized into 4 groups representing time from birth to colostrum feeding: A=fed within 1h (n=5); B=fed from 1 to 6h (n=10); C=fed from 6 to 12 h (n=21); and D=fed from 12 to 18 h (n=8) after birth. Average total intake of colostrum was 3.6 ± 0.1L. Over 80% of the calves consumed ≥3 L of colostrum. Apparent efficiency of IgG absorption declined remarkably 12 h after birth. Mean apparent efficiency of absorption of IgG in group D (15.8 ± 3.0%) was lower than that in groups A (30.5 ± 3.9%) and B (27.4 ± 2.8%). Serum IgG concentration in calves was positively correlated with IgG intake in all groups. The relationship between mass of IgG consumed and calf serum IgG at 24 h was different for each time of colostrum feeding, with only limited differences observed between groups A and B. We concluded that failure of transfer of passive immunity in newborn calves may be avoided if calves consume ≥3 L of colostrum with IgG concentration >40 mg/mL within 6 h after birth. These findings help define the opportunity to minimize failure of transfer of passive immunity to newborn calves under management programs similar to those used on commercial dairy farms.

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ABSTRACT. Serum leptin concentrations were measured in antenatal and postnatal cows housed at two different locations. The mean serum leptin concentration was 9.2 ± 0.6 ng/ml (n=22) in one group, and was slightly lower in the other (7.4 ± 0.4 ng/ml, n=54), probably because of the different nutritional conditions between the two groups. There was no consistent variation in relation to the menstrual cycle and the periparturient period in both groups. Moreover, serum leptin concentrations during the periparturient period were independent of the number of delivery and the incidence of mastitis and milk fever. These results are quite different from those in rodents and human, suggesting the different regulatory mechanism of circulating leptin concentration in cows.

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We previously reported the possibility of using the electrocardiogram variable to estimate blood calcium (Ca) concentration in dairy cows based on the strong positive correlation between the blood Ca concentration and the inverse of the corrected ST peak interval (STc −1 ).To improve the accuracy of the estimation of blood Ca concentration, we investigated the relationship between blood Ca concentration and STc −1 for each postpartum day and available variables other than STc −1 . We measured multiple variables (milk yield, calving number, age, body temperature, etc.), including serum total Ca concentration (tCa), blood ionized Ca concentration (iCa) and STc −1 in 462 Holstein cows on days 0, 1, 2, 3, 5, and 7 postpartum. A very high correlation was observed between iCa and tCa. The association between tCa and STc −1 for each postpartum day had a high coefficient of determination of 0.61-0.79 postpartum 0-2 days but decreased after the third day. In the investigation using the data from postpartum days 0-2, STc −1 , heart rate interval, calving number, and age were highly correlated with tCa.In addition, a multiple regression equation was obtained with tCa as the objective variable and STc −1 and calving number as explanatory variables. The estimation accuracy was improved as compared with the simple regression equation using only STc −1 as the explanatory variable. This multiple regression equation was used for 11 cows suspected of having hypocalcemia, and it was able to correctly detect cows requiring early treatment, except for one cow.

In this study, we developed calving prediction models for 24-h and 6-h periods before calving using data on physiological (tail skin temperature) and behavioral (activity intensity, lying time, posture change, and tail raising) parameters obtained using a multimodal tail-attached device (tail sensor). The efficiencies of the models were validated under tethering (tie-stall) and untethering (free-stall and individual pen) conditions. Data were collected from 33 and 30 pregnant cattle under tethering and untethering conditions, respectively, from approximately 15 days before the expected calving date. Based on pre-calving changes, 40 features (8 physiological and 32 behavioral) were extracted from the sensor data, and one non-sensor-based feature (days to the expected calving date) was added to develop models using a support vector machine. Cross-validation showed that calving within the next 24 h under tethering and untethering conditions was predicted with a sensitivity of 97% and 93% and precision of 80% and 76%, respectively, while calving within the next 6 h was predicted with a sensitivity of 91% and 90% and precision of 88% and 90%, respectively. Calving prediction models based on the tail sensor data with supervised machine learning have the potential to achieve effective calving prediction, irrespective of the cattle housing conditions.