Reproductive events such as oestrus, ovulation, mating and pregnancy status in extensively managed animals are notoriously difficult to monitor and detect. Next generation on-animal sensors that utilise accelerometer, Bluetooth proximity and geo-location data stand to revolutionise our ability to detect such events for use in precision reproductive management of livestock.
The aim of this study was to establish an acceleration ‘signature’ of ram mating activity using tri-axial accelerometers and to determine the optimal attachment point of the sensor. Accelerometers (Digibale Pty Ltd, Sydney, Australia) were fitted to the necks and ears of Merino rams (n=14) prior to introduction to a ewe (replicated twice). Using decision tree analysis (SAS; v. 9.4., 2003; SAS Institute Inc. Cary. NC. USA), accelerometers attached to the necks of rams successfully predicted mating events with a positive predictive value (PPV) of 68% and a sensitivity of 80%. The PPV and sensitivity of ear-attached accelerometers were considerably poorer at 59% and 43%, respectively, likely due to the mobility of the ear compared with the neck. The specificity for both accelerometer types was high (96% and 91% for neck-attached and ear-attached, respectively) as was the accuracy (94% for the neck-attached accelerometers and 89% for the ear-attached accelerometers). Mating events were characterised by extreme peaks and troughs in acceleration across all three axes for both accelerometer positions. As a result, the standard deviation of the z- and x- axes were the variables of most importance to the model for the neck and ear accelerometer, respectively. Future studies will further refine this mating signature before its application to paddock mating systems, where its use as an investigative tool will unlock nuanced information on the differences in fertility, libido and hormonal fluctuations of individual sheep.