Precalving behavior in dairy cattle with different calving times.

Predicting the calving time in dairy cattle can help in avoiding calving accidents and reducing burdens on animal caretakers. In this study, we analyzed the behavior of pregnant dairy cattle for 7 days prior to calving, to assess the feasibility of predicting the calving time. Eleven Holstein cows were divided into two groups based on their calving times, that is, in the morning (the Morning Parturition Group) or the evening (the Evening Parturition Group). Their behavior was recorded on video. An analysis was conducted of the daily occurrences of each type of behavior and the number of switches of behavior during the day and at night. A statistical analysis was conducted, using a two-way factorial analysis. The behavioral sequence was analyzed using an adjacency matrix. Hierarchical structure charts were created, using Interpretive Structural Modeling. The results suggest that feeding and exploratory behaviors are associated with the calving time period and thus can be useful when predicting that period. The hierarchical structure charts suggest that the Morning Parturition Group had no definite behavioral sequence pattern, unlike the Evening Parturition Group. The detection of an unstable behavioral sequence pattern might predict the calving time period.

Dairy cattle behavior classifications based on decision tree learning using 3-axis neck-mounted accelerometers.

Demand has been increasing recently for an automated monitoring system of animal behavior as a tool for the management of livestock animals. This study investigated the association between the behavior of dairy cattle and the acceleration data collected using three-axis neck-mounted accelerometers, as well as the feasibility of improving the precision of behavior classifications through machine learning. In total 38 Holstein dairy cows were used, and kept in four different farms. A logger was mounted to each collar to obtain acceleration data for calculating the activity level and variations. At the same time the behavior of the cattle was observed visually. Characteristic acceleration waves were recorded for eating, rumination, and lying, respectively; and the activity level and variations were significantly different among these behaviors (p < 0.01). Decision tree learning was performed on the data set from Farm A and validated its precision; which proved to be 99.2% in cross-validation, and 100% in test data sets from Farms B to D. This study showed that highly precise classifications for eating, rumination, and lying is possible by using decision tree learning to calculate the activity level and variations of cattle based on the data obtained by three-axis accelerometers mounted to a collar.