GNSS collar data may help flag calving in grazing cows
Bottom line
A new study in Animals reports a near real-time calving alert system that uses only GNSS collar data to flag behavioral anomalies in grazing cows, aiming to solve a long-standing problem in extensive systems where cows often calve far from direct observation. According to the study summary provided, the dataset included 149 cows across three grazing farms in Spain and Australia, with 76 calving events, and behavioral indicators were recalculated every 30 minutes. The broader research program from the same University of Córdoba group shows why that matters: earlier work found that herd-level GNSS indicators, especially isolation from the herd, were more informative than individual movement measures alone, and a 2025 conference abstract described a seven-step anomaly-detection workflow that traded fewer false alarms for lower sensitivity as alert thresholds increased. (sciencedirect.com)
Why it matters: For veterinary professionals working with beef and grazing herds, the appeal is practical rather than flashy. Earlier detection could make it easier to identify cows needing assistance for dystocia, check calf viability sooner, and reduce labor-intensive pasture surveillance in large or remote paddocks. But the same body of research also points to the main limitation: these systems can improve specificity by tightening alert thresholds, yet that may sharply reduce recall, meaning some calving events could still be missed. In other words, this looks more like a decision-support tool than a replacement for clinical judgment or herd checks. (sciencedirect.com)
What to watch: Watch for the full Animals paper’s performance metrics, external validation across more herds and landscapes, and whether developers can improve sensitivity without creating too many false alerts. (academic.oup.com)
Key facts
- Study type
- Near real-time calving alert system study
- Journal
- Animals
- Data source
- GNSS collar data only
- Sample size
- 149 cows
- Study sites
- Three grazing farms in Spain and Australia
- Calving events
- 76
- Update interval
- Every 30 minutes
- Main limitation
- Tighter alert thresholds improved specificity but reduced recall
A new Animals study highlights a near real-time approach to calving detection in grazing cows using GNSS-derived behavioral anomalies, an area of growing interest for extensive beef systems where direct observation is difficult and delays can mean missed dystocia, calf loss, or late postpartum checks. Based on the source summary, the study analyzed 149 cows across three farms in Spain and Australia, including 76 calving events, and generated behavioral indicators every 30 minutes from collar data alone. That makes it notable because many remote calving systems have relied on additional sensors, intravaginal devices, or more complex hardware. (mdpi.com)
This paper appears to build on a multi-year line of work from researchers at the University of Córdoba. In a 2023 paper in Animal, the group showed that commercial GNSS collars could capture behavioral changes associated with calving on rangelands, especially social separation from the herd in the 24 hours before calving. That study found herd-based indicators, such as distance to the herd centroid or nearest peer, outperformed single-cow indicators, suggesting that relative behavior within the group may be more informative than movement changes alone. (sciencedirect.com)
A 2025 Journal of Animal Science abstract from overlapping authors then described a near real-time workflow for automated calving detection using GNSS data transmitted via SigFox, GSM, or LoRa networks. In that system, data were resampled at 30-minute intervals, transformed into individual and social indicators, standardized with z-scores, and screened for anomalies using a ±2.5 threshold. The researchers reported that raising the number of anomalies required for an alert improved accuracy from 0.73 to 0.95 and specificity from 0.74 to 0.99, but recall fell from 0.60 to nearly zero, underscoring the central challenge in this field: reducing false alarms without missing too many true calving events. (academic.oup.com)
That trade-off is consistent with the wider precision livestock farming literature. Reviews of pasture-based systems note that most PLF tools were originally developed for indoor or intensive production, where power, connectivity, and direct access to animals are easier to manage. In extensive systems, GNSS-based monitoring has been attractive because collars are already used commercially for tracking, but converting location data into reliable health or reproductive alerts remains harder than simple geofencing. A recent MDPI study in rangeland cattle using GNSS plus accelerometer and temperature data reported that combining anomaly detection with supervised classification detected and classified up to 80% of calving events, though the authors also emphasized that performance may vary by pasture, herd structure, and environment. (sciencedirect.com)
I didn’t find a press release or independent veterinarian quote tied specifically to this Animals paper. What I did find is broader expert consensus in the literature that timely calving detection matters because peripartum losses affect both welfare and profitability, and that remote monitoring is especially valuable where cows may calve in isolated terrain. Prior work in northern Australia using intravaginal devices linked to GNSS collars framed the same problem in similar terms: producers need the dam’s identity, time of calving, and location quickly enough to intervene before the cow-calf pair moves away. (sciencedirect.com)
Why it matters: For veterinarians and herd-health advisers, the clinical value is straightforward. A GNSS-only system could be easier to deploy at scale than multi-sensor or intravaginal setups, particularly in beef herds managed over large grazing areas. If reliable enough, alerts could support earlier dystocia response, faster neonatal assessment, and more targeted postpartum checks, while also helping producers prioritize labor when routine visual monitoring is impractical. At the same time, the available evidence suggests caution: these models may perform best as triage tools, not standalone calving confirmation systems, and practices should pay close attention to false-negative risk, local terrain, herd density, and network coverage before integrating alerts into care protocols. (sciencedirect.com)
There are also implementation questions that matter on the ground. The strongest signals in prior studies came from social and herd-relative behavior, which means performance may depend on how many animals are collared, whether cows are managed in stable groups, and how often position fixes are transmitted. Systems that work in one ranching environment may not generalize cleanly to another. That’s especially relevant for veterinary teams advising clients across mixed terrains, breeds, and management systems. (sciencedirect.com)
What to watch: Next, watch for the full Animals paper to clarify sensitivity, specificity, alert timing relative to calving, and whether the model was prospectively validated across all three farms. The most important next step for the field is external validation in commercial herds, along with evidence that GNSS-only alerts can deliver actionable lead time without overwhelming staff with false positives. (academic.oup.com)