Using animal-mounted sensor technology and machine learning to predict time-to-calving in beef and dairy cows. (13th June 2020)

• Record Type: Journal Article
• Authors: Miller, G. A.; Mitchell, M.; Barker, Z. E.; Giebel, K.; Codling, E. A.; Amory, J. R.; Michie, C.; Davison, C.; Tachtatzis, C.; Andonovic, I.; Duthie, C.-A.
• Is Part Of: Animal. Volume 14:Number 6(2020)
• Page Start: 1304
• Page End: 1312
• Publication Date: 2020-06-13
• Subjects: precision livestock farming, -- parturition, -- bovine, -- random forest, -- animal-mounted sensors; Animal breeding -- Periodicals; Animal genetics -- Periodicals; Animal nutrition -- Periodicals; Animal physiology -- Periodicals; Environmental sciences -- Periodicals; 636.005
• DOI: 10.1017/S1751731119003380
• Languages: English
• ISSNs: 1751-7311

Abstract

Abstract : Worldwide, there is a trend towards increased herd sizes, and the animal-to-stockman ratio is increasing within the beef and dairy sectors; thus, the time available to monitoring individual animals is reducing. The behaviour of cows is known to change in the hours prior to parturition, for example, less time ruminating and eating and increased activity level and tail-raise events. These behaviours can be monitored non-invasively using animal-mounted sensors. Thus, behavioural traits are ideal variables for the prediction of calving. This study explored the potential of two sensor technologies for their capabilities in predicting when calf expulsion should be expected. Two trials were conducted at separate locations: (i) beef cows ( n = 144) and (ii) dairy cows ( n = 110). Two sensors were deployed on each cow: (1) Afimilk Silent Herdsman (SHM ) collars monitoring time spent ruminating (RUM ), eating (EAT ) and the relative activity level (ACT ) of the cow, and (2) tail-mounted Axivity accelerometers to detect tail-raise events (TAIL ). The exact time the calf was expelled from the cow was determined by viewing closed-circuit television camera footage. Machine learning random forest algorithms were developed to predict when calf expulsion should be expected using single-sensor variables and by integrating multiple-sensor data-streams. The performance of the models was tested using the Matthew's correlation coefficient (MCC ), the area under the curve, and the sensitivity and specificity of predictions. The TAIL model was slightly better at predicting calving within a 5-h window for beef cows (MCC = 0.31) than for dairy cows (MCC = 0.29). The TAIL + RUM + EAT models were equally as good at predicting calving within a 5-h window for beef and dairy cows (MCC = 0.32 for both models). Combining data-streams from SHM and tail sensors did not substantially improve model performance over tail sensors alone; therefore, hour-by-hour algorithms for the prediction of time of calf expulsion were developed using tail sensor data. Optimal classification occurred at 2 h prior to calving for both beef (MCC = 0.29) and dairy cows (MCC = 0.25). This study showed that tail sensors alone are adequate for the prediction of parturition and that the optimal time for prediction is 2 h before expulsion of the calf.