The use of accelerometers for the remote detection of mounting in rams and testosterone-treated wethers

Abstract

It has been estimated that up to a third of all rams have little to no sexual interest in ewes. Despite the prevalence of this issue, it is challenging to monitor the reproductive behaviour of individual sheep in an extensive farming environment. In this study, we developed a single-feature algorithm to detect in-paddock mounting activity from the accelerometer records of rams and androgenised wethers. Tri-axial accelerometers were first deployed on the necks and withers of the rams (n = 15) in a controlled pen test to determine the optimal attachment point for detecting mounting events. A Moving Average Convergence Divergence (MACD) algorithm was applied to accelerometer data with a threshold of 0.3 g. Validation and performance evaluation of the algorithm was based on observed video footage. The MACD was more sensitive and precise when applied to data collected from the withers than the neck (100% vs 89%; 98% vs 86%; respectively). Following the pen test, the MACD was applied to data collected in-paddock from accelerometers attached to the withers of rams (n = 6) and androgenised wethers (n = 8). A MACD + was simultaneously evaluated, which included additional conditions that must be satisfied for the detected peak to be deemed a mounting event. For both algorithms, a series of thresholds were tested. The threshold that returned the highest F1 score (the harmonic mean of precision and sensitivity) for the respective algorithm and male type was selected as the optimal threshold. At their respective optimal thresholds, the MACD + was marginally more precise than the MACD in-paddock for both wethers (91% vs 84%, respectively) and rams (94% vs 84%, respectively). The sensitivity of the MACD in detecting in-paddock mounting behaviour was 91% in rams and 86% in wethers. The MACD + was similarly sensitive at 91% for both rams and wethers. This research demonstrates the success of this algorithm, particularly the MACD+, in detecting mounting activity. Following integration into a commercial sensor and further validation, this algorithm would allow producers to identify rams with low libido and improve reproduction and productivity.