LAUSR

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