Evaluation of the tri-axial accelerometer to identify and predict parturition-related activities of Debouillet ewes in an intensive setting

Abstract

Identifying and monitoring parturition of individual animals may help producers increase attentiveness, enabling early detection of dystocia during parturition. Parturition events are marked by subtle behavioral changes often difficult to detect by observation alone. The aim of this study was to determine the ability of tri-axial accelerometer data to accurately identify and predict parturition-related behavior of mature ewes in a pen setting. Tri-axial accelerometers recording at 12.5 Hz were placed on ear tags and attached to 13 Debouillet mature ewes before parturition. Activity was monitored 7 days prior to lambing (d −7); on the day of lambing (d 0); and 7 days post lambing (d +7). Using random forest machine learning, accelerometer data and visual observations were used to predict (i) seven mutually-exclusive behaviors; and (ii) activity (active and inactive behavior) based on five metrics calculated using variation of movements recorded by the accelerometer. The accuracy of seven predicted behaviors from an independent validation set was 66.7 %, and the accuracy for activity was 87.2 %. In addition to predicted behavior and activity, metrics calculated from accelerometer data and used for random forest predictions were evaluated 7 d before and after lambing and 12 h before and after lambing on six of the 13 ewes where the actual time of lambing was observed. No differences were detected in the seven predicted behaviors either before or after lambing. Four of five accelerometer metrics (P ≤ 0.002) were higher during the 7 d after lambing than the 7 d before lambing. Values for three of the five metrics were highest (P < 0.01) on the day of lambing. All five accelerometer metrics varied during the 12 h pre- and 12 h post parturition (P ≤ 0.004). All accelerometer metrics increased (P ≤ 0.008) 1–2 h before parturition compared to 3–4 h before parturition. In this current study, calculated direct sensor metrics served as a better indicator of lambing than predicted behaviors, processed through complex machine learning algorithms. Commercial use of accelerometers by producers may allow for detection of prolonged labor indicating potential dystocia during parturition, which may reduce lamb mortality and increase production efficiency. These results suggest that real time accelerometers could remotely monitor ewes and potentially provide managers an indication that the dam may lamb soon.