Prediction of calving time based on sensor measurements of behaviour in cattle

Although pregnant cows can be assigned an expected calving date, such forecasts remain imprecise due to individual physical and hormonal changes affecting cows' behaviour. Proper management during calving is crucial for the health of cows and calves. This study aimed to predict calving time based on behavioural symptoms in cows of two breeds: 38 Polish Holstein-Friesian (PHF) and 14 Brown Swiss cows from a single farm. Using CowManager sensors, the behaviour of the dairy cows was monitored 24 h a day, with data from 3D accelerometers classified into specific activities (eating, ruminating, inactive, active, highly active). As the study was conducted on a single farm, the generalisability of the results to other herd management systems or environmental conditions may be limited. A preliminary graphical analysis identified changes in behaviour in the last hours before calving. Statistical analysis included the bootstrap method, logistic regression, and analysis of change points in the time series (separately for each cow and trait, for moving averages covering 6 h). One-day periods were considered in the analysis, starting from 168 h before calving. The daily period was shifted by 1 h until 6 h before calving. The applied methodology showed satisfactory effectiveness (recall of 81.03% and precision of 66.75% for PHF cows). Differences in precalving behaviour between breeds were observed. These findings indicate that sensor-based monitoring of behaviour can provide timely predictions of calving and highlight breed-specific behavioural differences, supporting farm management and animal welfare.