Technical note: Calving prediction in dairy cattle based on continuous measurements of ventral tail base skin temperature using supervised machine learning

Abstract

In this study, we developed a calving prediction model based on continuous measurements of ventral tail base skin temperature (ST) with supervised machine learning and evaluated the predictive ability of the model in 2 dairy farms with distinct cattle management practices. The ST data were collected at 2or 10-min intervals from 105 and 33 pregnant cattle (mean ± standard deviation: 2.2 ± 1.8 parities) reared in farms A (freestall barn, in a temperate climate) and B (tiestall barn, in a subarctic climate), respectively. After extracting maximum hourly ST, the change in values was expressed as residual ST (rST = actual hourly ST − mean ST for the same hour on the previous 3 d) and analyzed. In both farms, rST decreased in a biphasic manner before calving. Briefly, an ambient temperature-independent gradual decrease occurred from around 36 to 16 h before calving, and an ambient temperature-dependent sharp decrease occurred from around 6 h before until calving. To make a universal calving prediction model, training data were prepared from pregnant cattle under different ambient temperatures (10 data sets were randomly selected from each of the 3 ambient temperature groups: <15°C, ≥15°C to <25°C, and ≥25°C in farm A). An hourly calving prediction model was then constructed with the training data by support vector machine based on 15 features extracted from sensing data (indicative of pre-calving rST changes) and 1 feature from non-sensor-based data (days to expected calving date). When the prediction model was applied to the data that were not part of the training process, calving within the next 24 h was predicted with sensitivities and precisions of 85.3% and 71.9% in farm A (n = 75), and 81.8% and 67.5% in farm B (n = 33), respectively. No differences were observed in means and variances of intervals from the calving alerts to actual calving between farms (12.7 ± 5.8 and 13.0 ± 5.6 h in farms A and B, respectively). Above all, a calving prediction model based on continuous measurement of ST with supervised machine learning has the potential to achieve effective calving prediction, irrespective of the rearing condition in dairy cattle.

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

“…They reported a decrease in rumen temperature approximately 20 h before calving (0.48 ± 0.05 • C) in cows with normal delivery, while in animals with dystocia, temperature dropped 32 h before parturition (0.23 ± 0.02 • C). More recently, a thermistor (Prototype: 103JT-025, SEMITEC Corporation, Tokyo, Japan) for the continuous measurement of the tail skin temperature has been evaluated in adult cows for prediction of calving [46,47], and successively implemented through machine-learning algorithms [48]. Calving within the next 24 h was successfully predicted by the algorithm, but at the best of authors knowledge, this sensor is not commercially available.…”

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

“…The ventral tail base surface temperature (TBST) can be measured using a tail-attached wireless sensor ( Table 2 ) as described previously [ 58 , 59 , 65 ]. TBST is classified as peripheral temperature, which depends on the core temperature, environmental conditions, and peripheral blood system regulation.…”

“…It is important to mention that the accuracy of predicting calving tended to be lower at lower ambient temperatures. At the same time, the rearing condition (free-stall barn vs. tie-stall barn) in dairy cattle did not influence the accuracy of predicting calving [ 59 ].…”

Activity-Integrated Hidden Markov Model to Predict Calving Time

“…In order to predict when calving begins, different researchers have proposed various methods and technologies, including monitoring through the use of wearable sensors [9][10][11][12], observing changes in body temperature [13], and a variety of techniques using biosensors for monitoring tail elevations [14,15]. Mee and English discuss methods and devices developed for calving prediction, presenting a good summary of the current status of methodologies and technologies in this area of research [5].…”