Influence of Virtual Fencing Technology in Cattle Management and Animal Welfare

1. Introduction

Grazing livestock has been a fundamental agricultural practice for centuries, shaping landscapes and providing essential food resources [1]. Historically, controlling the movement of grazing animals relied primarily on herding and physical barriers such as stone walls, hedges, and wire fences [2]. While these traditional methods were effective, they required significant labor and infrastructure investment. The past century has seen substantial changes in temperate grazing dairy systems, particularly due to the increasing demand for animal-derived protein, which is projected to nearly double by 2050 [3]. This growing demand has intensified pressure on grazing lands, necessitating more efficient and flexible management strategies [4].

In response, technological advancements have led to the development of Precision Livestock Farming (PLF), which aims to enhance animal management through automated monitoring [5,6]. PLF technologies, including electronic identification systems, on-animal sensors (e.g., accelerometers and GPS), and stationary management systems, are increasingly being adopted in both intensive and extensive farming operations [7,8]. Within this technological progression, virtual fencing (VF) has emerged as an innovative solution to manage animal movement without physical barriers [2,6]. VF systems typically involve wearable collars that deliver sensory cues, such as audio warnings followed by mild electrical pulses, to contain animals within predefined virtual boundaries [9]. This technology represents a significant advancement in flexible, data-driven grazing management, offering potential benefits for pasture utilization and environmental conservation [10].

Virtual fencing is defined as an enclosure or boundary system without physical structures, relying instead on wearable devices such as collars [2,4,6]. When an animal approaches a virtual boundary, it first receives an audio cue, followed by a low-intensity electrical pulse if it continues moving forward [9]. This operant conditioning approach trains animals to retreat upon hearing the initial warning, minimizing the need for electrical corrections over time [7]. Research has demonstrated that cattle can successfully adapt to these systems, with prior exposure to physical electric fences accelerating learning [11,12]. Companies such as Halter, Vence, Nofence, eShepherd/Gallaghers, and Boviguard have commercialized VF technology, leveraging GPS tracking and wireless communication to dynamically adjust grazing areas [13].

A key advantage of VF is its ability to facilitate rotational and strip-grazing systems, which traditionally require labor-intensive fence adjustments [10]. By enabling farmers to modify grazing zones remotely via smartphones or computers, VF enhances pasture utilization efficiency while reducing labor costs [14]. Furthermore, VF can protect environmentally sensitive areas, such as riparian zones, by temporarily excluding livestock to prevent soil erosion and water contamination [9,15]. Studies have shown that VF can effectively restrict cattle from vulnerable ecosystems while maintaining pasture productivity [16].

A critical aspect of VF is the animal’s ability to learn and adapt to virtual boundaries through associative learning [7]. Research on beef heifers in rotational grazing systems has shown a decline in electric-to-acoustic signal ratios over time, indicating successful behavioral adaptation [12]. Similarly, nursing cows undergoing VF training exhibited reduced electrical stimuli as they learned to respond to audio cues alone [14]. However, concerns remain regarding animal welfare, particularly the potential stress induced by electrical pulses [17]. Studies assessing fecal cortisol metabolites (FCM), a stress indicator, have yielded mixed results. Some research found lower FCM levels in virtually fenced cattle compared to those in physical fences, suggesting reduced stress [17], while others reported no significant differences in welfare indicators such as lying time and weight gain [13]. These findings highlight the need for further investigation into the long-term welfare implications of VF across different livestock species.

This paper examines the potential of VF as an innovative tool for modern grazing management. It explores the technological foundations of VF systems, including their reliance on wearable collars with GPS and auditory-electrical stimuli to control livestock movement without physical barriers. The paper reviews studies on animal learning and behavioral adaptation, assessing how cattle respond to virtual boundaries over time. Additionally, it evaluates animal welfare implications, particularly the stress responses associated with electrical stimuli, by analyzing physiological indicators such as fecal cortisol metabolites (FCM) and behavioral metrics.

Beyond animal behavior, the paper discusses the practical applications of VF in grazing systems, including its role in rotational grazing, pasture optimization, and environmental protection (e.g., preventing overgrazing near riparian zones; [9,15]). It also situates VF within the broader framework of PLF, highlighting how sensor-based monitoring enhances livestock management efficiency. Finally, the paper considers barriers to adoption, such as cost and farmer acceptance, while emphasizing the technology’s potential to support sustainable livestock production in the face of growing global food demand and environmental challenges.

2. Materials and Methods

2.1. Literature Search

To comprehensively assess the potential of virtual fencing (VF) for cattle management, a systematic literature search was conducted across multiple academic databases, including Google Scholar, ScienceDirect, Scopus, PubMed, and Web of Science. The goal was to gather peer-reviewed studies evaluating the efficacy, animal welfare implications, and practical applications of VF in grazing systems. Both controlled experiments and field trials were included to ensure a balanced understanding of real-world applicability. While peer-reviewed journal articles formed the core of the review, relevant conference papers and technical reports were also considered to capture emerging trends in the field.

2.2. Searching Criteria

The search strategy employed targeted keywords such as "virtual fencing cattle behavior," "precision livestock farming grazing," "animal welfare virtual fencing," and "GPS collars grazing management." Boolean operators (AND/OR) were used to refine results, focusing primarily on studies involving cattle. Additional filters were applied to prioritize studies examining animal learning, stress responses (e.g., cortisol levels), and pasture utilization efficiency. No restrictions were placed on publication years to ensure the inclusion of both foundational and recent research. To maintain scientific rigor, non-peer-reviewed sources, commercial advertisements, and non-English publications without verified translations were excluded. Reference lists of key articles were manually screened to identify further relevant studies.

3. The Concept and Mechanism of Virtual Fencing for Cattle

Virtual fencing represents an innovative advancement in precision livestock farming, offering a boundary control system that eliminates the need for physical barriers [18]. This technology has gained attention for its potential to revolutionize grazing management through targeted grazing approaches, labor reduction, and application in environmentally sensitive areas where traditional fencing proves impractical [19]. Unlike conventional fencing methods, VF systems utilize wearable technology, typically GPS-enabled collars, to establish and maintain virtual boundaries (Figure 1).

The operational mechanism of virtual fencing relies on a sophisticated integration of positioning technology and behavioral conditioning [20]. As [21] explain, these systems continuously monitor an animal's position through GPS tracking, comparing it against predefined virtual boundaries established by farmers using digital mapping tools. When cattle approach these boundaries, the system initiates a two-stage deterrent process: first emitting an audible warning signal, followed by a mild electrical stimulus if the animal continues moving toward the boundary [22]. This design is rooted in operant conditioning principles, where animals learn to associate the initial auditory cue with the subsequent aversive stimulus [7].

Research demonstrates that cattle can effectively learn this association, with studies showing a significant reduction in electrical stimuli required over time as animals learn to respond to audio cues alone [9]. [23] reported that experienced cattle showed containment rates comparable to physical fencing, with electrical stimuli decreasing by up to 80% after the initial learning period. This learning capacity is crucial for both the effectiveness and ethical justification of virtual fencing systems [24]. Current commercial systems like eShepherd and NoFence have demonstrated impressive containment capabilities, with some studies reporting ≥99% effectiveness in keeping cattle within designated areas [25]. However, as [4] note, these systems may not completely eliminate boundary breaches to the same degree as physical barriers. Despite this limitation, the technology offers unique advantages, including the ability to create exclusion zones within pastures and precisely track animal movements [10]. These features enable more dynamic grazing management strategies, such as rotational grazing systems that can be adjusted remotely in response to pasture conditions [16].

Several virtual fencing systems and manufacturers have been evaluated in research (Table 1). For instance, the Halter® system, developed for intensive pastoral dairy farming, utilizes sound and vibration as primary cues and a low-energy electrical pulse as a secondary aversive cue [26]. Research has shown that cows can learn to respond to sound cues within a day and can be remotely herded to the milking parlor [27]. Another widely studied system, Nofence®, has demonstrated success in keeping cattle within virtual enclosures, with animals learning to respond to auditory warnings over time [28,29]. Similarly, eShepherd® by Gallagher has been tested on pasture-raised Angus and Jersey cows, showing effective boundary compliance through proximity-based beep and pulse responses [30]. Early research by [31] and [32] laid the groundwork by defining virtual fencing and reviewing its evolution, highlighting its potential in rangeland settings.

A wide range of parameters have been measured to assess the efficacy, animal welfare, and behavioral responses to virtual fencing (Table 1). Technology efficacy, often measured by the percentage of time animals remain within virtual boundaries, has generally shown high containment rates (≥99%) for various cattle types, including heifers, steers, dry cows, and lactating dairy cows [33]. [34] studied lactating dairy cows in intensive grazing systems and found that while virtual front-fences did not entirely eliminate entry into exclusion zones, containment within inclusion zones was high. These findings align with earlier studies on beef and dry dairy cattle [35]. However, [36] reported occasional containment failures, suggesting that effectiveness may vary depending on environmental conditions and herd dynamics. The number and type of cues delivered (audio vs. electrical pulses) are frequently recorded to assess animal learning. A common finding is that electrical pulses decrease over time as cattle learn to associate auditory warnings with boundary limits [37].

Animal welfare is a critical aspect investigated in virtual fencing studies. Researchers have measured physiological stress indicators such as cortisol levels in milk, hair, and feces [29,38]. While [34] noted transient increases in milk cortisol concentrations during the initial week of virtual fencing, [39] found no significant changes in manure cortisol levels. A systematic review by [40] concluded that most welfare indicators, including fecal cortisol metabolites (FCMs), showed no significant differences between virtually and physically fenced cattle, with some studies even reporting lower stress levels in VF-managed herds. Behavioral responses, such as time spent near virtual fences, lying duration, and activity levels, have also been analyzed. Studies indicate that cattle may initially avoid fence boundaries but adapt over time [41]. Individual differences in behavior suggest that temperament influences adaptation, with more exploratory animals requiring more corrective stimuli [42].

3.1. Learning Behavior and Social Adaptation

Learning behavior is a key focus, with studies examining how quickly cattle respond to audio cues and avoid electrical pulses. Naïve heifers have been shown to comply with virtual fence boundaries within 3-7 days, with learning retention observed over extended periods [43]. The decreasing ratio of electrical to acoustic signals over time indicates successful associative learning [44]. Social learning also plays a role, as cattle react to herd mates receiving stimuli, suggesting that group training may enhance compliance [45]. Training protocols, whether individual or group-based, have been explored to optimize learning efficiency, with some studies recommending phased training to minimize stress [46].

3.2. Impact on Livestock Performance and Pasture Management

The impact of virtual fencing on livestock performance, including weight gain and milk production, has been evaluated. [38] found no negative effects on beef heifer weight gain compared to traditional electric fencing. Similarly, [36] reported no adverse effects on dairy cow milk yield or live weight. Pasture utilization studies present mixed findings: some suggest lower grazing efficiency with VF, while others highlight its potential to prevent overgrazing and protect sensitive ecosystems [44]. Virtual fencing is applied in rotational grazing, strip-grazing, and conservation grazing, offering labor and cost savings over physical fences [28]. It also enables dynamic pasture management, such as creating firebreaks or protecting riparian zones [47]. However, limitations remain, including the need for further research on large-scale deployments, suckler cows with calves, and long-term welfare impacts [48]. Future studies should explore individual animal variability, optimal training methods, and integration with precision livestock farming technologies [44]. While challenges remain, virtual fencing represents a significant advancement in precision livestock farming, providing a flexible, welfare-compatible tool for pasture-based management. Ongoing innovations and research will continue to enhance its applicability, making it a promising solution for sustainable cattle production.

4. Commercial Cattle Monitoring Systems in Pasture-Based Systems

Diverse commercial Precision Livestock Farming (PLF) technologies are designed for pasture-based livestock systems (Table 2). These systems vary in sensor types (e.g., accelerometers, GPS, thermometers), target species, and geographic origins, reflecting the adaptability of PLF solutions to different agricultural contexts [58]. Devices such as smaXtec (Austria/Germany) and Allflex SenseHub (Israel) utilize accelerometers and thermometers to monitor rumination, body temperature, and calving events, aligning with research indicating that accelerometers effectively detect behavioral changes linked to health and reproduction, while thermometers enable early disease detection [58]. In contrast, Ceres Tag (Australia) and digitanimal (Spain) integrate GPS with accelerometers to track activity and geofencing, addressing challenges like theft prevention and pasture utilization, particularly valuable in large-scale grazing systems where manual monitoring is impractical [58].

Virtual fencing technologies like eShepherd (Australia), Halter (New Zealand), and Vence (USA) rely on GPS and accelerometers to guide livestock via auditory cues and electric stimuli, reducing labor costs and enabling dynamic pasture allocation, though their success depends on consistent animal training and ethical welfare considerations [58]. Reproductive efficiency is a key focus for devices like Datamuster (Australia) and Moocall (Ireland), which use walk-over-weigh systems and accelerometers to predict calving and maternal parentage, supporting automated weight monitoring for genetic selection and reduced postpartum losses [58]. Similarly, IceTag (UK) and Moomonitor+ (Ireland) prioritize lameness and heat detection (Table 2), addressing welfare concerns in pasture-based systems.

Despite these advancements, challenges such as cost, data transmission reliability, and farmer adoption persist. GPS collars and multi-sensor systems face limitations in battery life, connectivity, and affordability for small-scale farmers [58]. This highlights the need for user-friendly, energy-efficient designs to enhance scalability. Emphasizing multi-sensor integration while acknowledging economic and operational barriers is crucial for broader implementation.

5. Impact of Virtual Fencing Devices on Animal Behaviour and Welfare

Virtual fencing systems have been increasingly studied for their effectiveness in livestock containment and their implications for animal behaviour and welfare [59]. The findings from multiple studies suggest that while these devices generally achieve successful containment, their impact on animal welfare varies depending on factors such as device design, electric pulse intensity, acoustic signals, and individual animal responses. Most collars used in VF systems weigh between 1.4-1.45 kg, with Nofence® and eShepherd® being the most commonly tested [60,61,62]. The lightweight design appears to have minimal physical impact, though some studies reported occasional jaw abrasions, particularly with prolonged use [34,36]. This suggests that while collar weight itself may not be a major welfare concern, improper fit or extended wear could lead to minor physical discomfort in some animals. The electric pulses delivered by these devices typically range from 0.2-3 kV, often preceded by an 82 dB acoustic warning lasting 5-20 seconds [39,60,63]. The use of an auditory cue before the electric pulse is critical in promoting associative learning, allowing animals to avoid the stimulus over time. Studies found that cattle generally adapt within 5-7 days, with a decreasing ratio of electric to acoustic stimuli [9,64]. However, individual variability exists, with some animals requiring more stimuli than others [46,57].

5.1. Impact of Device Weight and Electric Pulses on Animal Welfare

The design and weight of virtual fencing (VF) collars play a significant role in animal welfare, with variations in materials and construction influencing comfort and long-term usability. [65] reported that device weight and materials directly affect animal welfare, noting that the eShepherd collar (2.1 kg; Figure 2) incorporates a nylon strap and counterweight, while lighter alternatives such as the Halter system (1.420 kg) and Nofence cattle collar (1.446 kg) use materials like polyester, foam, and rubber to reduce strain. In contrast, the Vence collar (1.13 kg) employs stainless steel and plastic chain links, which may require careful fitting to avoid pressure sores [65]. While cattle generally tolerate heavier collars well, smaller livestock like sheep may experience fatigue, highlighting the need for species-specific designs [65]. These findings align with studies on cattle, where collars weighing 1.4-1.45 kg (e.g., Nofence® and eShepherd®) showed minimal adverse behavioural effects [60,61,62]. However, [36] and [34] reported jaw abrasions in dairy cows, suggesting that even within cattle, prolonged wear or improper fit can lead to physical discomfort. This contrasts with [65]'s observation that cattle adapt well to heavier collars, indicating that while weight alone may not be a major issue, material choice and ergonomic design remain critical to prevent localized injuries.

Electric pulses, typically ranging from 0.2 J at 3 kV to low-voltage 800 V stimuli, are often preceded by acoustic warnings to facilitate associative learning. [64] found that cattle progressively reduced their reliance on electrical stimuli, with heifers decreasing their electrical-to-audio (E:A) response ratio from 17.9% during training to 5.2% during grazing. Similarly, [63] reported that Limousin cows learned to avoid restricted zones with minimal stress, as indicated by stable cortisol levels. However, [57] observed that some individuals received repeated pulses, raising concerns about potential aversion and welfare costs in less responsive animals. This variability suggests that while most cattle adapt well, individual differences in temperament and learning speed must be considered to minimize unnecessary stress. The implications of these findings highlight a balance between effective containment and animal welfare. While current VF systems are generally well-tolerated by cattle, improvements in collar materials, such as softer straps or adjustable fits, could mitigate abrasion risks [36,65]. Additionally, optimizing pulse intensity and ensuring consistent acoustic warnings may further reduce reliance on electrical stimuli, particularly for slower-learning individuals [57,64].

5.2. Role of Acoustic Signals in Animal Training

Acoustic cues play a crucial role in conditioning cattle to virtual boundaries. Most studies reported using tones between 82 dB and 2700 Hz, with rising pitch signals proving effective in eliciting avoidance behaviour [39,60,66]. [67] noted that dairy cows achieved 90% containment within four days, with decreasing reliance on pulses as they learned to respond to auditory warnings. However, individual variability exists, as [64] found that some heifers required more time to adapt, while [63] observed that shorter audio tones improved responsiveness. These findings suggest that while acoustic signals are generally effective, their duration and frequency should be optimized to minimize stress and enhance learning efficiency.

5.3. Cortisol Data and Stress Implications

Cortisol measurements across studies indicate that VF systems do not generally induce significant stress compared to traditional electric fencing (EF). [60] and [62] found no significant differences in faecal cortisol metabolites (FCM) between VF and EF groups, while [39] reported stable manure cortisol levels throughout their study. [63] further supported this, showing no significant changes in hair cortisol concentrations (HCC) before and after VF exposure. However, [36] recorded elevated milk cortisol levels in dairy cows during early VF exposure, though these stabilized over time. These discrepancies may reflect differences in study duration, animal type (beef vs. dairy), or individual stress susceptibility. Overall, the cortisol data suggest that once animals adapt, VF does not impose chronic stress, but initial exposure may require careful management to mitigate short-term welfare impacts.

5.4. Behavioural Adaptations and Welfare Outcomes

Behavioural observations indicate that cattle generally adapt well to VF, with most studies reporting high containment rates (>90%) and reduced escape attempts over time (Table 3; [9,46]). [61] found that cattle learned to avoid virtual boundaries with minimal welfare concerns, even in winter conditions, while [68] reported successful herd conditioning in dairy cows, albeit with slightly more vocalizations and displacements compared to EF groups. However, some studies highlight potential welfare trade-offs, such as reduced lying time [9] and altered grazing distribution [52]. Additionally, [34] noted that pasture depletion reduced VF efficacy, requiring more auditory cues, which could increase stress if not managed properly.

Beef cattle appear to adapt more readily to VF than dairy cattle, possibly due to differences in temperament and prior handling [60 vs. 36]. Additionally, younger animals and previously trained individuals (e.g., first-calf cows in [64]) show faster learning, suggesting that prior experience enhances VF efficacy. The presence of uncollared companions (e.g., calves) did not significantly disrupt containment [64], supporting VF use in mixed groups (Table 3). However, device-related injuries [36] and individual variability in stimulus response [57] highlight the need for ergonomic collar designs and tailored training protocols.

Virtual fencing technology has demonstrated significant potential in livestock management, with studies reporting high containment rates and rapid animal learning (Table 4). Grinnell et al. (2025) found that VF achieved a success ratio of 94.6% to 97.9%, comparable to electric fencing, with no adverse effects on welfare or weight gain. Similarly, Harland et al. [49] observed that cattle learned to associate audio cues with electrical pulses within 5–7 days, reducing the EP-to-AC ratio from 17.9% during training to just 5.2% during grazing. These findings suggest that VF is an effective containment method, though individual variability in learning rates was noted [49,57].

Despite these successes, several limitations were identified across studies (Table 4). Short trial durations (e.g., 8 weeks in Grinnell et al. [43]; 28 days in Verdon et al. [67] and small sample sizes (e.g., n=20 in Confessore et al. [29]; n=13 studies in Wilms et al. [13] restrict the generalizability of findings. Additionally, reliance on controlled environments, such as double-fence setups [43] or artificial paddocks [69], may not reflect real-world conditions. Technical challenges, including GPS inaccuracies [66] and collar connectivity issues [37], further complicate implementation. Ethical concerns regarding EPs were also raised, though no direct welfare harms were reported [49,70].

Future research should prioritize longer-term studies to assess habituation and welfare impacts, as suggested by multiple authors [9,67]. Improved collar designs, better GPS accuracy, and alternative stimuli (e.g., vibration or bidirectional sound) could enhance reliability [4,67]. Testing in diverse environments, such as arid or tropical systems [49], and integrating visual cues [27] may further optimize VF efficacy. Economic feasibility remains a barrier, with high costs limiting small-scale adoption [71]. The implications of these findings are twofold: VF offers a promising tool for precision grazing and ecological management [48,72], but scalability depends on addressing technical, ethical, and economic challenges. Future studies should adopt standardized protocols, larger sample sizes, and multi-breed comparisons to ensure broader applicability [13,54]. Refining technology and validating long-term impacts could revolutionize sustainable livestock management through virtual fencing.

6. Conclusions

Virtual fencing technology has emerged as a transformative tool in precision livestock farming, offering a flexible and labor-efficient alternative to traditional fencing. Studies demonstrate that cattle rapidly learn to associate auditory cues with mild electrical stimuli, achieving high containment rates (≥90%) with minimal long-term welfare impacts, as evidenced by stable cortisol levels and normal behavioral patterns. However, short-term behavioral disruptions and occasional collar-related abrasions highlight the need for optimized device design and training protocols. The integrated findings suggest that VF can significantly enhance pasture management by enabling dynamic rotational grazing, protecting sensitive ecosystems, and reducing labor costs. However, challenges such as high initial costs, connectivity limitations, and individual animal variability must be addressed for broader adoption. Regulatory standards should ensure welfare-compliant designs, while farmer education programs can facilitate smoother transitions to VF systems. The novelty of VF lies in its integration of GPS tracking and behavioral conditioning, providing a scalable solution for sustainable livestock production. Future research should focus on long-term welfare assessments, cost-effective scalability, and the development of non-aversive stimuli. As the technology evolves, VF has the potential to redefine cattle management, aligning productivity with environmental and animal welfare goals.