Prepartum change in ventral tail base surface temperature in beef cattle: comparison with vaginal temperature and behavior indices, and effect of ambient temperature

Monitoring ventral tail base surface temperature for fever detection in calves

In this study, we investigated whether monitoring the ventral tail base surface temperature (ST) using a wearable wireless sensor could be effective for fever detection in calves with experimentally induced pneumonia after inoculation with Histophilus somni strain 2336. We found a significant difference in the changes in ST values between the control and H. somni-inoculated groups after 24 h of inoculation and detected fever; however, the rectal temperature showed a significant difference between the groups after 12 h of inoculation. When a significant difference in the ST between the two groups was observed, serum haptoglobin concentration and exacerbation of clinical score increased in the H. somni-inoculated group compared with those in the control group. Pneumonia was observed in the H. somni-inoculated group at necropsy, indicating that the changes in ST may reflect fever with inflammation caused by H. somni infection. Our results demonstrated that monitoring ST using a sensor attached to the ventral tail base can detect fever in calves and may be a useful and labor-saving tool for the health management of calves.

Detection of foaling using a tail-attached device with a thermistor and tri-axial accelerometer in pregnant mares

It is desirable to attend to the mare at the time of foaling in order to assist fetal delivery and prevent complications. The early detection of the onset of labor is an important issue for the equine industry. The purpose of this study was to examine the applicability of a sensor for foaling detection using the data of surface temperature (ST), roll angle (rotation about the y-axis) and y-axis (long axis of the tail) acceleration which were collected from a multimodal device attached to the ventral tail base of the mare. The data were collected every 3 minutes in 17 pregnant mares. Roll angle differences from the reference values and the mare's posture (standing or recumbent) confirmed by video were compared and associated. Cohen's kappa coefficient was 0.99 when the threshold was set as ± 0.3 radian in roll angle differences. This result clearly showed that the sensor data can accurately distinguish between standing and recumbent postures. The hourly sensor data with a lower ST (LST < 35.5°C), a recumbent posture determined by the roll angle, and tail-raising (TR, decline of 200 mg or more from the reference value in y-axis acceleration) was significantly higher during the last hour prepartum than 2-120 hours before parturition (P < 0.01). The accuracy of foaling detection within one hour was verified using the following three indicators: LST; lying down (LD, change from standing to recumbent posture); and TR. When LST, LD and TR were individually examined, even though all indicators showed that sensitivity was 100%, the precision was 13.1%, 8.1% and 2.8%, respectively. When the data were combined as LST+LD, LST+TR, LD+TR and LST+LD+TR, detection of foaling improved, with precisions of 100%, 32.1%, 56.7% and 100%, respectively. In conclusion, the tail-attached multimodal device examined in this present study is useful for detecting foaling.

Assessment of Ventral Tail Base Surface Temperature for the Early Detection of Japanese Black Calves with Fever

The objective in the present study was to assess the ventral tail base surface temperature (ST) for the early detection of Japanese Black calves with fever. This study collected data from a backgrounding operation in Miyazaki, Japan, that included 153 calves aged 3-4 months. A wearable wireless ST sensor was attached to the surface of the ventral tail base of each calf at its introduction to the farm. The ventral tail base ST was measured every 10 min for one month. The present study conducted an experiment to detect calves with fever using the estimated residual ST (rST), calculated as the estimated rST minus the mean estimated rST for the same time on the previous 3 days, which was obtained using machine learning algorithms. Fever was defined as an increase of ≥1.0 °C for the estimated rST of a calf for 4 consecutive hours. The machine learning algorithm that applied was a random forest, and 15 features were included. The variable importance scores that represented the most important predictors for the detection of calves with fever were the minimum and maximum values during the last 3 h and the difference between the current value and 24- and 48-h minimum. For this prediction model, accuracy, precision, and sensitivity were 98.8%, 72.1%, and 88.1%, respectively. The present study indicated that the early detection of calves with fever can be predicted by monitoring the ventral tail base ST using a wearable wireless sensor.

Prediction of 24-h and 6-h Periods before Calving Using a Multimodal Tail-Attached Device Equipped with a Thermistor and 3-Axis Accelerometer through Supervised Machine Learning

Simple Summary

Routine visual observation of signs of imminent calving, such as softening of ligaments around the tailhead and udder distension, is time-consuming, and the resulting calving predictions are relatively unreliable. To address this issue, we used a multimodal tail-attached device (tail sensor) and developed calving prediction models through supervised machine learning. The tail sensor is equipped with a thermistor and 3-axis accelerometer, and can monitor tail skin temperature, activity intensity, lying time, posture changes (standing to lying or vice versa), and tail raising behavior. Using the sensor data with a non-sensor-based data (days to the expected calving date), we developed calving prediction models for 24-h and 6-h periods before calving and evaluated their predictive ability under two distinct housing conditions, tethering (tie-stall) and untethering (free-stall and individual pen). Our results demonstrated that calving prediction models based on tail sensor data with supervised machine learning have the potential to achieve effective calving prediction, irrespective of the cattle housing conditions.

Abstract

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.

Accuracy to Predict the Onset of Calving in Dairy Farms by Using Different Precision Livestock Farming Devices

Simple Summary

If the onset of calving can be accurately detected as well as appropriate calving assistance can be performed on a dairy farm, at that time, the prevalence of dystocia, stillbirth, vaginal laceration, retained fetal membranes, and consequent clinical metritis/endometritis can be decreased significantly. Therefore, in order to reduce these losses, our primary task must be to predict the onset of calving accurately and provide timely and professional calving assistance. This review focuses on the diagnostic possibilities and limitations of detecting the onset calving in the field.

Abstract

Besides traditional methods such as evaluation of the external preparatory and behavioral signs, which even presently are widely used also in large dairy farms, there are several new possibilities such as measuring body (intravaginal, ventral tail-base surface, ear surface, or reticulo-ruminal) temperature, detecting behavioral signs (rumination, eating, activity, tail raising) or detecting the expulsion of the device inserted into the vagina or fixed to the skin of the vulva when allantochorion appears in the vulva to predict the onset of the second stage of calving. Presently none of the single sensors or a combination of sensors can predict the onset of calving with acceptable accuracy. At the same time, with the exception of the iVET^® birth monitoring system, not only the imminent onset of calving could be predicted with high accuracy, but a significantly lower prevalence rate of dystocia, stillbirth, retained fetal membranes, uterine diseases/clinical metritis could be reached while calving-to-conception interval was significantly shorter compared with the control groups. These results may confirm the use of these devices in dairy farms by allowing appropriate intervention during calving when needed. In this way, we can reduce the negative effect of dystocia on calves and their dams and improve their welfare.

How to Predict Parturition in Cattle? A Literature Review of Automatic Devices and Technologies for Remote Monitoring and Calving Prediction

Cattle farming is facing an increase in number of animals that farmers must care for, together with decreasing time for observation of the single animal. Remote monitoring systems are needed in order to optimize workload and animal welfare. Where the presence of personnel is constant, for example in dairy farms with great number of lactating cows or with three milking/day, calving monitoring systems which send alerts during the prodromal stage of labor (stage I) could be beneficial. On the contrary, where the presence of farm personnel is not guaranteed, for example in smaller farms, systems which alert at the beginning of labor (stage II) could be preferred. In this case, time spent observing periparturient animals is reduced. The reliability of each calving alarm should also be considered: automatic sensors for body temperature and activity are characterized by a time interval of 6-12 h between the alarm and calving. Promising results have been shown by devices which could be placed within the vaginal canal, thus identifying the beginning of fetal expulsion and optimizing the timing of calving assistance. However, some cases of non-optimal local tolerability and cow welfare issues are reported. Future research should be aimed to improve Sensitivity (Se), Specificity (Sp) and Positive Predictive Value (PPV) of calving alert devices in order to decrease the number of false positive alarms and focusing on easy-to-apply, re-usable and well tolerated products.

Changes in immunoglobulin A and immunoglobulin G concentrations in the saliva of Japanese Black beef cows during calving: A pilot study

Immunoglobulin A (IgA) in saliva, mostly consisting of secretory IgA, plays an important role in the mucosal immune mechanism. This study evaluated changes in salivary IgA and Immunoglobulin G (IgG) concentrations in Japanese Black cows (n = 16) during calving. Individual saliva samples were collected -2, 0, and 2 weeks postpartum. Immunoglobulin concentrations differed significantly among weeks (P < 0.05), but the effect of parity and week × parity was insignificant. Salivary IgA concentrations decreased drastically (P < 0.05) after calving compared with those at -2 weeks postpartum and remained low until 2 weeks postpartum. The salivary IgG concentrations decreased gradually during peripartum and differed at -2 and 2 weeks postpartum (P < 0.05). Considering the immunoglobulin concentrations at -2 weeks postpartum as the reference standard for 100%, the rates of decrease in IgA concentrations (36.7 ± 6.9%) were significantly lower (P < 0.05) than those of IgG (70.3 ± 10.1%) at calving day. To our knowledge, this is the first report indicating that salivary IgA concentrations decreased drastically after calving in Japanese Black cows. Further studies monitoring the secretory functions of IgA in the salivary gland are essential for understanding maternal immunity in cattle.

An attempt at estrus detection in cattle by continuous measurements of ventral tail base surface temperature with supervised machine learning

We aimed to determine the effectiveness of estrus detection based on continuous measurements of the ventral tail base surface temperature (ST) with supervised machine learning in cattle. ST data were obtained through 51 estrus cycles on 11 female cattle (six Holsteins and five Japanese Blacks) using the tail-attached sensor. Three estrus detection models were constructed with the training data (n = 17) using machine learning techniques (random forest, artificial neural network, and support vector machine) based on 13 features extracted from sensing data (indicative of estrus-associated ST changes). Estrus detection abilities of the three models on test data (n = 34) were not statistically different among models in terms of sensitivity and precision (range 50.0% to 58.8% and 60.6% to 73.1%, respectively). The relatively poor performance of the models might indicate the difficulty of separating estrus-associated ST changes from estrus-independent fluctuations in ST.

Evaluation of a commercial intravaginal thermometer to predict calving in a Hungarian Holstein-Friesian dairy farm

In this study, the utility of a commercial intravaginal thermometer was evaluated as an automated method for the prediction of calving in a total of 257 healthy pregnant Holstein-Friesian female cattle. The accuracy and the sensitivity of predicting calving within 48 hr before calving were also evaluated. The intravaginal temperature changes from 72 hr before and up to calving were significantly (p ≤ .001) affected by parity, season (summer vs. autumn), the time of day (8 a.m. or 8 p.m.) and the 6-hr time intervals (38.19°C: first interval 0 to 6 hr before calving vs. 38.78°C: twelfth interval 66 to 72 hr before calving), while the gender (p = .943), and the weight of the calf (p = .610), twinning (p = .300), gestation length (p = .186), foetal presentation (p = .123), dystocia (p = .197) and retention of foetal membranes (p = .253) did not affect it significantly. The sensitivity of the SMS of expecting calving within 48 hr and the positive predictive value were 62.4% and 75%, respectively, while the sensitivity and the positive predictive value for the SMS of expulsion reached 100%. It can be concluded that the investigated thermometer is not able to predict calving within 48 hr accurately; however, imminent calving can be accurately alerted.