Calving is a critical point in both a cow and calf’s life, when both become more susceptible to disease and risk of death. Ideally, this period is carefully monitored. In extensive grazing systems, however, it is often not economically or physically possible for producers to continuously monitor animals, and thus, calving frequently goes undetected. The development of sensor systems, particularly in these environments, could provide significant benefits to the industry by increasing the quantity and quality of individual animal monitoring. In the time surrounding calving, cows undergo a series of behavioral and physiological changes, which can potentially be detected using sensing technologies. Before developing a sensor-based approach, it is worthwhile considering these behavioral and physiological changes, such that the appropriate technologies can be designed and developed. A systematic literature review was conducted to identify changes in the dam’s behavioral and physiological states in response to a calving event. Articles (n = 104) consisting of 111 independent experiments were assessed following an intensive search of electronic databases. Commonly reported indicators of parturition (n = 38) were identified, and temporal trend graphs were generated for 13 of these changes. The results compare trends in behavioral and physiological changes across a variety of animal-related factors and identifies several reliable indicators of parturition for detection with sensors, namely calf grooming behavior, changes in rumination duration, and lying bouts. This synthesis of literature suggests that variability exists between individuals and thus, combining several calving indicators may result in a more broadly applicable and accurate detection of parturition.

The present study aimed to develop predictive models of calf birth weight (CBW) from liveweight (LW) data collected remotely and individually using an automated in-paddock walk-over-weighing scale (WOW). Twenty-eight multiparous Charolais cows were mated with two Brahman bulls. The WOW was installed at the only watering point to capture LW over five months. Calf birth date and weight were manually recorded, and the liveweight change experienced by a dam at calving (ΔLWC) was calculated as pre-LW minus post-LW calving. Cow non-foetal weight loss at calving (NFW) was calculated as ΔLWC minus CBW. Pearson’s correlational analysis and simple linear regressions were used to identify associations between all variables measured. No correlations were found between ΔLWC and pre-LW (p = 0.52), or post-LW (p = 0.14). However, positive associations were observed between ΔLWC and CBW (p < 0.001, R2 = 0.56) and NFW (p < 0.001, R2 = 0.90). Thus, the results suggest that 56% of the variation in ΔLWC is attributed to the calf weight, and consequently could be used as an indicator of CBW. Remote, in-paddock weighing systems have the potential to provide timely and accurate LW data of breeding cows to improve calving management and productivity.

Context: Neonate management remains a key issue in extensive beef production systems where producers are faced with substantial environmental and management challenges that limit their ability to monitor and manage livestock in a timely manner. Parturition is a critical event and can affect the calf health and survival, particularly in the perinatal period (up to 48 h after birth). As such, monitoring parturition using precision livestock technologies may provide producers with additional tools to manage their cattle and mitigate the impacts of neonatal mortality in extensive beef systems. Aims: The purpose of this study was to determine whether data from a global navigation satellite system (GNSS) collar could be used to detect parturition events in extensively grazed beef cattle. Methods: Forty-eight Bos taurus cows (583.5 kg body weight ± 9.25 s.e.m.) were allocated to a 28 ha paddock between 8 January 2021 and 6 March 2021 during the calving season. Thirty of the animals were fitted with GNSS-equipped collars (Smart Paddock, Vic., Australia) that captured data at 10 min intervals. Parturition events were recorded daily by visual observation. Collected data were used to calculate key predictive features related to calving behaviour. Derived features were compared and assessed for changes in the period surrounding parturition. Key results: Increases were observed in distance to nearest neighbour and to herd aggregate features, and decreases were observed in paddock utilisation and distance travelled features in the lead-up to calving (P < 0.05). Furthermore, the number of animals within a 20 m radius decreased significantly (P < 0.05) in the lead up to parturition, supporting known isolation behaviours. Conclusions: With further development of predictive algorithms, on-animal sensors may be valuable in the prediction of calving events in extensive beef production systems. Implications: Remote management and monitoring with on-animal sensor technologies, such as GNSS collars and tags, will provide producers with an additional means of monitoring their animals, while overcoming many of the management challenges associated with extensive grazing operations.

Calf mortality is a key issue for the north Australian beef industry; however, the sector faces several challenges that preclude the detection and reduction of calf mortality rates. A systematic literature review methodology was employed to explore scientific literature describing the scope of calf loss in northern Australia. Online databases were used to locate articles reporting on beef calf mortality rates within the north Australian region. Articles (n = 43) consisting of 668 beef cattle cohorts were retrieved that reported calf mortality between 1936 and 2014. Of these, 13 different observation periods were identified. Most cohorts (n = 201) examined mortality between the pregnancy to weaning period, whereas only 20 cohorts in four studies were located that investigated calf mortality in the perinatal and postnatal periods. A broad seasonal and regional influence was identified, however, the dilution of the datasets due to the high number of timeline variations, prevented robust statistical analysis and the further examination of influential factors, such as breed. The results of the systematic literature review indicate that the resolution of the data available does not allow for producers or researchers to accurately target the occurrence or cause of calf mortality. Experimental protocols for future research pertaining to reproductive efficiency and calf loss in northern Australia should be standardised. Consistency in reporting factors and periods must first occur for robust statistical analyses to be achieved.

Timely calving detection is important but difficult in grazing systems because cows may calve in remote areas. This study developed and evaluated a near real-time calving alert system based exclusively on behavioral anomalies calculated from Global Navigation Satellite System (GNSS) collar data. Data were collected from 149 cows across three grazing farms in Spain and Australia, including 76 calving events. Individual, social, and herd-relative behavioral indicators were calculated every 30 min using 24 h rolling windows. Anomalies were defined as directional deviations from recent behavior using delta-self and Z-score transformations and were evaluated either at each 30 min timestamp or accumulated over rolling 3 h windows. Detection performance was assessed within the 20 days before calving, using a 48 h calving window as the reference period. Increasing the number of anomalies required to trigger a calving alert improved precision but reduced recall, while 3 h anomaly accumulation improved the stability of the alert system. Intermediate anomaly thresholds maximized F1-score, reaching 0.41 with individual behavioral indicators and 0.58 with the complete indicator set, irrespective of whether anomalies were evaluated at 30 min intervals or accumulated over 3 h. Alerts and indicator-specific anomalies were concentrated during the final two days before calving, mainly reflecting increased spatial separation and reduced movement relative to the group. These results support GNSS-based anomaly accumulation as a transparent and flexible approach for calving detection under grazing conditions.