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28 April 2026

17 Pages

Applicability of Rumen Bolus Sensors in Sustainable Precision Livestock Farming

Alba Regia Faculty, Obuda University, 8000 Székesfehérvár, Hungary
AgriEngineering2026, 8(5), 167;https://doi.org/10.3390/agriengineering8050167
This article belongs to the Special Issue Smart Technologies for Sustainable Livestock Production and Animal Farming
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Abstract

Rumen bolus sensor technology has been applied for roughly a decade in the precision livestock farming of ruminants. Despite its substantial potential, widespread adoption remains limited, largely due to the incomplete functionality of current sensors, their relatively high cost, and the still insufficient scientific understanding of the physiological and husbandry-related parameters they are intended to measure. This article reviews the capabilities of rumen bolus sensors, their technical and informatic foundations, their role within precision livestock farming, and the results reported to date. It further examines how the functions and applications of bolus sensors align with the Sustainable Development Goals (SDGs 2, 3, 8, 9, 12, and 13), as well as the sustainability challenges associated with their use. Finally, the paper identifies potential technological development pathways that could enable rumen bolus sensors to become highly effective and widely adopted tools in the future.

1. Introduction

In January 2015, the United Nations General Assembly launched the negotiation process for the post-2015 development strategic plan. This process led to the acceptance of the 2030 Sustainable Development Agenda—including the Sustainable Development Goals (SDGs) in September 2015 [1,2]. These goals are continuously monitored and evaluated to determine annual progress or regression [2]. This study focuses on precision animal husbandry, specifically dairy cattle production, and examines how modern technologies—primarily rumen bolus systems—support the realization of sustainability objectives (SDGs 2, 3, 8, 9, 12, 13) and what sustainability challenges emerge from their use [1].
Contemporary livestock farming is marked by new trends and increasing challenges. Dairy farming remains highly economically sensitive, requiring milk production at minimal cost, a concern highlighted by recent crises in the dairy sector [3]. Achieving economic production necessitates maximizing automation to optimize labor use. Although cattle can convert plant nutrients unusable to humans into valuable food (SDG 3 [4], which can significantly contribute to alleviating hunger (SDG 2), animal husbandry, particularly ruminant production, has been widely criticized in sustainability debates for its resource use and environmental impacts [5].
Two central concerns are often raised. First, only about 10% of biomass is transferred between trophic levels, implying that producing 1 kg of meat requires at least 10 kg of plant organic matter [6]. Based on such material-balance reasoning, some argue that plant-based nutrition may more efficiently meet global food needs [1]. Second, ruminant farming is criticized for its substantial ecological footprint due to high water usage and significant greenhouse gas (GHG) emissions. In addition to the greenhouse gases (CO2 and CH4), N2O and NH3 are also released from animal manure. Conversely, proponents note that animal-derived foods contain essential nutrients (methionine, lysine, tryptophan, histidine, vitamin B12) that are difficult to replace with plant-based alternatives [7].
Enteric methane (CH4) produced by ruminants accounts for approximately 28% of global anthropogenic CH4 emissions, making accurate CH4 monitoring crucial for mitigation strategies [7].
Agriculture, particularly in developed regions, is continuously affected by labor shortages; automation not only contributes to mitigating this lack of workforce but can also ensure decent working conditions for people employed in the sector (SDG 8: Decent Work and Economic Growth) [2]. Within such an automated Agriculture 5.0 system, it becomes possible to implement mass production tailored to the individual even in agricultural practice. In this context, a high level of automation is combined with a high degree of efficiency, while fully considering the individual needs of the animal. In such a system, the animal and its environment provide the input for the sensor network, and following data evaluation, the machine-based adjustment of housing conditions, milking, and animal care—including early detection and treatment—are realized through the output [8,9,10,11]. The more comprehensive the sensory data collection, the greater the likelihood that the defined principles can be implemented in practice. In the future economic indicators will also be linked to compliance with sustainability objectives in the dairy cattle sector, as only economic incentives can enable significant progress toward these goals. However, the calculation and continuous monitoring of these indicators, and potentially their targeted management, will require the collection of large volumes of high-temporal resolution, individually recorded multimodal data. This, in turn, will necessitate complex sensor systems in everyday livestock management practices, both in barns and on pastures. At the same time, the boluses themselves and, more generally, the growing number of electronic devices applied to animals raise sustainability concerns as their use becomes more widespread, which should be taken into consideration and appropriately addressed.
In the future of precision cattle farming, the importance of rumen boluses will increase, as these devices are capable of providing the most comprehensive data collection through the application of intelligent data analysis algorithms [12,13]. In the following, the aim is to substantiate this assertion from several perspectives. The research questions include the following: What is the current state of rumen bolus sensor technology? What types of measurements can be performed using rumen bolus sensors? How can rumen bolus sensors be applied in animal welfare-oriented PLF systems? Which sustainability goals can be associated with precision livestock farming? The present publication represents a summary prepared following the collection and review of publications in the relevant research field. The literature review conducted for this publication was highly extensive and multidisciplinary. Its core was based on searches performed in the ScienceDirect and Web of Science databases, with the aim of identifying publications on rumen bolus sensors relevant to the dairy cattle sector over the period from 2000 to 2026. Because boluses are also used for veterinary therapeutic purposes, the term sensor had to be included in the search query in order to improve the relevance of the results. Nevertheless, even after this refinement, publications focusing on boluses used as carriers of active substances still had to be excluded manually on the basis of their titles. In addition, publications with zero citations were also excluded. As a result, 149 publications remained from the Scopus database and 50 from the Web of Science database. The exact search string is provided in the Appendix A.
The core of the manuscript was developed through full-text analysis of the publications retrieved by this search process. However, further information was also sought through Google Scholar and the Google search engine, particularly with regard to sensor-based data acquisition technologies, sustainability objectives, and the official documentation of commercially available sensor systems.
In summarizing these publications, we also considered the practical research experience gained with rumen boluses.

2. Developments and Capabilities of Rumen Bolus Technologies

The first use of rumen boluses involved the administration of active substances. The earliest sensor-based rumen measurement was described in 1968, although the author did not refer to the device as a bolus sensor [14]. The earliest measurements focused on the assessment of rumen temperature and pH. These measurements were initially carried out on fistulated cows. The first publication on electronic boluses that provided a detailed description of the concept of the “rumen bolus” as a long-term, intraruminal electronic identification device was published in 1999 [15]. This publication also reported the first baseline measurements, based on which it could be concluded that the bolus remains stably within the animal and, according to the investigations, does not cause harm [15]. Repeated observations indicated that the bolus sensor is located either in the rumen or in the reticulum; therefore, it is sometimes referred to as a ruminal bolus (Table 1) sensor and in other cases as a reticulorumen bolus sensor [15].
Table 1. The most commonly used sensors and the detectable characteristics based on official documentations. ++, optimal solution; +, acceptable solution; -, not feasible; ?, uncertain whether it is feasible.
Subsequently, bolus functionality expanded beyond RFID identification, and in 2011, the first reports on radiotelemetric communication appeared [22].
Rumen boluses in that period were primarily used for research, and numerous publications have reported early and significant detection of various diseases [23,24,25]. For example, pH measurement enables the early diagnosis of acidosis [26,27,28]. Notably, Mottram’s 2010 publication was the first to propose the technical concept of lifelong boluses [29], establishing the foundation for commercial bolus sensors suitable for everyday livestock management [20,30,31,32,33,34].
Although the earliest studies primarily addressed electronic animal identification and later focused on ruminal pH measurement [26,27,28], the development of a long-lifetime pH bolus has not yet been achieved (Table 1). As a result, pH boluses are still largely restricted to the monitoring of specific critical periods, are used mainly for research purposes, and have seen only limited integration into everyday livestock farming practice.
During the past decade, rumen bolus devices have progressively entered the commercial market. While a proportion of these products remain research-oriented tools with operational lifetimes of only a few months, long-lifetime boluses that are well suited for routine on-farm use have also become available [20,30,31,32,35]. An overview of the commercially available rumen boluses and their most important technical characteristics is provided in Table 2.
Rumen boluses typically incorporate small, low-cost sensors characterized by low power consumption and reliable long-term operation without the need for recalibration. Virtually all such devices are equipped with a temperature sensor and an accelerometer and/or gyroscope, which together form the core of data acquisition. Rumen temperature is a relatively stable parameter; therefore, sampling frequencies higher than 1 Hz are generally unnecessary. In contrast, acceleration data require higher sampling rates, typically at least 12 Hz or 25 Hz, to capture relevant dynamic information. This basic sensor configuration may be extended with pH sensors and various gas sensors, most notably methane sensors, for which sampling frequencies comparable to those used for temperature measurements are usually sufficient.
Early generations of rumen boluses attempted to transmit the collected data in compressed form, essentially as raw and unprocessed signals. It soon became evident, however, that this approach was suboptimal. A more effective strategy is to employ microcontrollers with sufficient computational capacity to perform at least primary data processing directly within the bolus, and to transmit derived physiological, or physiologically related, parameters using appropriate communication technologies.
Relatively little information is available regarding the data storage and data processing strategies implemented in commercial rumen boluses. Communication is most commonly based on Bluetooth, Wi-Fi, LoRaWAN, or NB-IoT technologies, although some systems rely on proprietary solutions. Antenna design and integration constitute a separate and nontrivial research challenge. Communication remains one of the weakest components of the overall system, as reliable data transmission typically requires a relatively dense network of nearby gateways. This requirement largely confines the practical application of rumen bolus sensors to housed dairy farming systems, rather than extensive pasture-based operations.
Table 2. Commercially available bolus sensors and their characteristic features based on their respective documentation [20,30,31,32,33,34,36].
The SmaxTec bolus, for example, has a data-storage strategy enabling retrospective data retrieval in case of network failure.
The availability of commercial boluses has enabled extensive research and increased acceptance of technology [36,37,38,39,40,41]. It has become clear that the rumen bolus is not merely a simple measuring and data-transfer device but a complex multimodal sensing system that must also perform data processing. Recently, machine learning- [39,42] and artificial intelligence-based algorithms [43,44] have appeared in these systems.
Table 1 summarizes the parameters considered measurable using rumen bolus sensors, in comparison with several other commonly applied complex sensor types. The characteristics of the sensors were compiled based on the official documentation of widely used sensor types. Within the rumen, certain variables can be measured directly using relatively simple sensing techniques and can be derived using straightforward algorithms.
In principle, the simplest measurement is ruminal pH, as the data obtained from an appropriate sensor can be used directly without further transformation. pH measurement is of high importance, as this parameter can essentially only be assessed in this manner. However, at the current level of technological development, a lifetime pH-measuring rumen bolus has not yet been realized [28,45,46,47].
Temperature measurement is also technically straightforward; however, the scientific interpretation of temperature data is considerably more complex. Relating rumen temperature to core body temperature—which is a key indicator of animal health—requires sophisticated algorithms. This complexity arises because ruminal fermentation generates heat, which can increase rumen temperature by 1–2 °C, while feed intake causes a moderate decrease and water intake leads to a pronounced and rapid reduction in the measured rumen temperature [48]. Nevertheless, drinking frequency can be reliably determined from temperature data using relatively simple algorithms [41]. Similarly, general activity levels can be estimated with comparatively simple computational methods based on accelerometer and gyroscope data.
A wide range of additional parameters can be detected using accelerometer- and gyroscope-equipped rumen boluses; however, their reliable determination typically requires more complex algorithms [36,42,49]. These include, for example, heart rate estimation and the characterization of rumination patterns. Heart rate detection is feasible using an accelerometer, as the pressure wave generated by the heartbeat is superimposed on the acceleration signals measured within the rumen and can be separated from them using a suitably complex signal-processing algorithm. Ref. [49] Estrus detection in some systems can be achieved through activity monitoring alone, while in the case of rumen boluses this can be further enhanced by incorporating information derived from rumen temperature data.
The determination of certain parameters (detailed characteristics of activity, standing, lying time, lameness) using rumen boluses remains uncertain, such as the reliable detection of the onset of calving. The onset of calving could, in principle, also be inferred from changes in body temperature [20], locomotor activity, and drinking patterns [43,50,51]. Although this is generally considered feasible in principle, no published studies have yet described a solution that can be regarded as robust and guaranteed. The detection of respiratory rate would also be of considerable interest; however, to the author’s knowledge, no publications have addressed this topic to date.
At the same time, significant results have been reported using experimental rumen boluses that are still under development and have not yet reached the commercial market or were originally designed to support specific research objectives. These studies have demonstrated promising approaches for the detection of methane concentration, rumination behavior, and heart rate [43,50].

3. Limitations and Perspective of the Widespread Adoption of Rumen Bolus Sensors

Exact information on the adoption of rumen bolus sensors has not been provided by manufacturers. It can be stated, however, that boluses have not been widely adopted, and that the technology has been applied far less frequently in the beef sector than in dairy herds. Although the technology was developed to enable individual-animal management within a PLF system, an application has been proposed by smaXtec in which boluses are administered to approximately 10% of the herd, thereby allowing herd-level monitoring to be implemented in a more cost-effective manner [20]. For the widespread adoption of rumen bolus technology, it is essential to develop reliable bolus devices capable of meeting a substantial proportion of the sensor-related requirements of precision livestock farming. What are these requirements? Core parameters include the monitoring of feed and water intake quantities and dynamics, the assessment of animal health status—specifically core body temperature, ruminal pH, and the duration and dynamics of rumination—as well as the evaluation of animal activity (reduced or increased activity, lameness), the detection of estrus, and the indication of calving. These parameters can be regarded as fundamental requirements [3,52].
Table 1 illustrates, based on published research, parameters that can theoretically be determined by the sensor types most applied in precision livestock farming practice. The table has been divided—admittedly in a somewhat subjective manner—into two sections. The first section includes primarily detectable parameters that can be measured directly by sensors or derived with minimal computational effort, whereas the determination of calculated parameters generally requires more advanced data processing.
The estimation of certain parameters additionally necessitates the use of machine learning or other artificial intelligence-based algorithms. As shown in the table, ear tags, neck transponders, and leg-mounted sensors are typically capable of detecting only a limited number of parameters; however, these measurements are usually obtained with high accuracy and reliability. In contrast, rumen boluses and camera-based monitoring systems can be considered more general-purpose, multimodal sensing solutions, at least in terms of their theoretically achievable functionality. The sensitivity and specificity of rumen bolus-based sensor systems have not been examined widely, largely because the required experimental studies are highly labor-intensive. In an evaluation of the smaXtec Classic bolus, a sensitivity of 5% was reported for the detection of locomotor disorders, and sensitivities ranging from 24% to 64% were reported for the detection of metritis, mastitis, and retained placenta, based on the analysis of recorded temperature and acceleration data. Importantly, these conditions were not indicated as distinct diagnostic categories; instead, a general health alert was generated from patterns in temperature, drinking frequency, and locomotor activity. A positive predictive value of 22% was reported [53]. Despite these seemingly modest performance metrics, the implementation of such systems was nevertheless classified as a cost-effective investment in the connected economic assessment [53].
Without proposing a specific sensor system configuration, two contrasting strategic approaches can be identified. One approach involves the deployment of multiple, relatively low-cost sensors, while the other relies on the use of fewer but more expensive multimodal sensors. Such multimodal solutions may include either rumen bolus sensors or camera-based monitoring systems. Camera systems offer the advantage of being widely adopted in security and surveillance applications, whereas rumen boluses have the advantage of substantially lower maintenance requirements compared with camera-based systems.
It is evident that, except for pH, all parameters considered can be determined using multiple types of sensing devices. The successful development of a long-lifetime pH-measuring rumen bolus would therefore represent a clear breakthrough in this field. Several studies have examined the use of rumen boluses for pH measurement. In general, SARA detection has been reported to operate with sensitivity and specificity of approximately 90%. However, evaluations of individual sensors have indicated that systematic measurement error may still occur in these systems, and that practical usability is limited to roughly 150 days [54]. To promote wider adoption of rumen boluses, the information provided must be useful to farmers [3,55].
The initial investment required to implement a bolus system can be substantial, as it includes the development of the necessary infrastructure, namely a telecommunications solution (typically an internet connection) linking the gateway to the server, as well as the deployment of a gateway network for the primary acquisition of sensor data. The magnitude of the first two cost components varies considerably, depending on the existing infrastructure and, proportionally, on the size of the area to be covered. In addition, the procurement cost of the sensors must be taken into account, amounting between EUR 25 and 200 per animal and the cost of the software [33]. The devices must also be economically viable, requiring the development of business models that facilitate cost-effective implementation. In this respect, SmaxTech offers a good example [20]. System deployment is often offered at a discounted upfront price, which is subsequently offset by the recurring subscription fees. These costs are commonly billed on a monthly or annual basis, that cover cloud-based data processing, herd-level analytics, alerting functionalities, software updates, and technical support. Pricing is frequently structured on a per-animal or per-activated-bolus basis, allowing operating costs to scale proportionally with the number of devices in active use. The model may also include warranty replacement provisions and additional expert or decision-support services aimed at interpreting bolus-derived data and translating it into actionable herd management decisions [20].
All devices used in precision agriculture must integrate into a unified software system enabling efficient information flow and integration with related systems, such as dairy-farm operational software, sensor-system software, and automatic milking systems [11]. Although agricultural professionals recognize this need, integration is challenging due to limited cooperation among manufacturers. A similar process occurred in industry, where major advancements were achieved through layered system architectures and standardized communication protocols. Precision agriculture may adopt industrial standards or develop its own; however, given the convergence of agriculture and industry, the former seems more practical. Efforts toward developing integrated software, such as AI-based early detection health disorders systems, are already emerging [44].
The number of rumen bolus devices is steadily increasing, and their application within precision livestock farming (PLF) is gradually becoming more widespread. However, sustainability considerations also require attention to the environmental footprint of the bolus devices themselves. This includes ensuring the retrieval and proper disposal of boluses at slaughterhouses. It would be advisable for boluses recovered at the slaughterhouse to be returned to the manufacturer via the livestock farms through an organized take-back pathway, and for farms to be incentivized to participate through discounted replacement options, thereby encouraging manufacturers to adopt reuse and recycling practices, as well as conducting environmental footprint analyses during the design phase of bolus development (SDG 12: Responsible Consumption and Production) [2].
It may therefore be advisable to incorporate retrieval, recycling, and safe disposal mechanisms into the commercial business models of rumen bolus systems, thereby ensuring that electronic components do not pose a risk of environmental contamination. At present, rumen boluses predominantly rely on lithium-based batteries; in the future, emerging sodium-based battery technologies may provide a more sustainable alternative.
Ensuring reliable power supply represents an important area for further technological development. Contactless charging has already been successfully implemented in mobile devices, with an effective range of approximately 1 m. In principle, this would allow for the recharging of rumen sensors during routine farm operations, for example while animals are present in the milking parlor. An even more promising and potentially realistic approach may involve harvesting energy from chemical processes occurring within the rumen or from the animal’s kinetic energy, thereby contributing to self-sustaining power solutions for long-term bolus operation.

4. Animal Welfare Considerations

Beyond maintaining healthy livestock, modern agriculture increasingly incorporates animal welfare considerations [8,10,52,55]. According to contemporary definitions, “animal welfare refers to the provision of living conditions that allow animals to express all natural behaviors [1,7,37]. The perceived importance of animal welfare varies substantially across countries and social groups. In general, beyond geographic location, it is shaped by educational attainment, religious affiliation, economic circumstances, and prevailing cultural currents [7].
Moreover, animal welfare concerns not only production farms but also scientists and the general public [7].
At first glance, animal welfare is often perceived as difficult to reconcile with cost-efficient production and the sustainability of large-scale, quantity-oriented output [7,37]. However, in many cases, animal welfare aligns with economic interests, as stress responses often reduce fertility and milk-production efficiency [7,9].
The stress response is initiated when the central nervous system detects a change that may compromise the organism’s homeostasis [10]. Factors such as trauma, noise, heat, humidity, environmental conditions, feed restriction, and water limitation induce stress [10]. Measuring or quantifying stress and anxiety in animals under variable conditions is difficult; however, PLF offers solutions by integrating sensory data, automated systems, data analytics, and AI models with farm-management software.
According to Berckmans (2017) [9], “the primary objective of PLF is the management of individual animals through continuous, real-time monitoring of health, welfare, production/reproduction, and environmental impact.” This concept is essential because it requires viewing the animal as a “complex, individually varying, time-dependent, and dynamic (CITD) system [8]. Each animal is unique and may exhibit responses to external stimuli that differ in both timing and intensity, depending on innate characteristics and adaptive mechanisms. Excessively intense stimuli can elicit a stress response [56].
The real-time detection and interpretation of these stress signals is in the focus of next-generation PLF systems. Ciliberti (2025) [7] outlines a four-pillar approach for implementing this principle:
(i)
Continuous measurement of physiological and stress parameters;
(ii)
Continuous measurement of environmental parameters;
(iii)
Continuous detection of stress-related behaviors;
(iv)
Continuous detection of milk characteristics.
Here, “continuous” implies 24/7 measurement and analysis of physiological and stress parameters, environmental parameters, behavior, and milk characteristics. These constitute PLF system inputs, while appropriate actuators serve as outputs.
Environmental parameters can be measured with sensor systems installed in barns or on pastures. Milk quantity and quality measurements increasingly enter daily practice through automatic milking systems [11,57]. Physiological and stress-related characteristics include body temperature, drinking and feeding behaviors, heart rate, respiration, and activity-related features (standing, lying, etc.). A key review on animal welfare-related bolus applications is Caja et al. (2016) [52]. In their DairyCare Action summary, Caja et al. were among the first to outline the concept of a comprehensive technical system for implementing precision livestock farming (PLF), designed with a primary focus on animal welfare while also accounting for economic considerations. The publication presents an integrated, multidisciplinary approach in which expertise ranging from ethology and agricultural sciences to electrical engineering, mechatronics, and software development is leveraged to achieve the stated objectives. It already emphasized the importance of rumen bolus technologies, an importance that has only grown since.
Heat stress requires special attention due to climate change. Several studies have demonstrated the link between heart rate and stress (Kovács et al., 2014) [56] emphasizes Rumen temperature is the foundation for many welfare-related bolus measurements, such as drinking-frequency detection [38,41] and disease identification [25,58]. Rumen temperature has also been studied in relation to heat stress [59,60,61].
The relationship between heart rate and stress has been demonstrated in several studies [62]. Heart rate variability (HRV), in particular, is regarded as an excellent indicator of stress, enabling continuous stress monitoring without the need for invasive procedures or costly and relatively slow chemical analyses. Heart-rate detection via rumen bolus has become technically possible, although detailed assessments of practical applicability are still lacking [43]. Knight (2020) [3] provides a particularly important overview of rumen boluses. In his review, the sensors applicable to precision livestock farming (PLF) in dairy cattle and the key characteristics targeted for measurement are surveyed. Particular emphasis is placed on rumen bolus technology, and PLF is discussed with a strong focus on animal welfare considerations, while also considering prevailing economic and informatics trends and associated challenges.

5. Climate Considerations

Approximately 15% of human-induced GHG emissions are attributed to the livestock sector [63]. These emissions are predominantly caused by methane (CH4), largely produced through enteric fermentation in cattle. During the ruminal processing of high-fiber feed, hydrogen (H2) accumulates as a fermentation by-product and is toxic to most fermentative microorganisms. Consequently, during methanogenesis the excess H2 is removed through reductive pathways, most notably via the reduction of carbon dioxide (CO2). By contrast, the fermentation of high concentrate diet proceeds via alternative metabolic routes and typically results in lower methane (CH4) emissions. Other important pollutants include nitrous oxide (N2O) and ammonia, originating from manure and slurry application on pastures; these can be measured with environmental sensors [64].
Methane is a by-product of fermentation conducted by methanogenic archaea in the rumen and reduces fermentation efficiency by 2–12% [64]. It is therefore worthwhile—and feasible—to develop management strategies that both reduce methane emissions and increase milk yield. However, achieving this requires extensive methane-emission datasets and, ideally, a device capable of monitoring the emissions of each individual animal—not only in research but also in daily practice [64].
Despite methane’s importance, relatively limited data exists because traditional detection is difficult, imprecise, and often incompatible with routine farm management. Consequently, results are often questioned [64]. Methane emissions are frequently estimated indirectly using measurements of feed dry-matter intake and/or rumination activity—measurements that rumen boluses can significantly support [64].
Traditional measurement methods include respiration chambers, which use sealed chambers to determine heat balance and gas exchange [64]. While effective, these chambers alter animal behavior, leading to unrealistic emission estimates under farm conditions.
A more practical approach involves sampling exhaled air, usually supported by a tracer gas (SF6 or deuterated CH4) to determine detection ratios. Spot-sampling (“sniffer”) techniques measure CH4 concentrations in exhaled air over short periods. A more advanced version is the GreenFeed® system (C-Lock Inc., Rapid City, USA SD), which combines this technique with automated feeding and also measures CO2, H2, O2, and H2S [4,50,64].
Several innovations have emerged, such as Zelp (London, UK), which developed a wearable device recording CH4 emissions directly from nasal exhalation. Devices using infrared absorption spectroscopy and portable lasers placed 1–3 mm from the nostrils have also been developed [4]. Other technology like open-path lasers offer a new and innovative method for assessing herd-level CH4 concentrations [5].
Portable accumulation chambers (PACs) represent another important new technology. Although substantial innovation is directed toward CH4 detection, these systems remain expensive, often imprecise, and not yet ready for agricultural practice [4].
Low-cost, stable CH4 sensors suitable for rumen boluses already exist on the market (approx. 20 mm × 20 mm). A bolus with methane detection capability is reportedly available [33], although no scientific description and evaluation has yet been found. Methane detection inside the rumen poses additional sampling challenges, as methane accumulates at the top of the rumen due to its low density. A Korean patent [65] has addressed this issue.
If methane-measuring rumen boluses become feasible in daily practice, carbon-footprint and methane-emission monitoring will become practical, enabling the optimization of livestock conditions, reducing GHG emissions, and enabling quota-based management similar to industrial applications, potentially generating new revenue for agriculture and promoting the fusion of precision farming and industry.

6. Usage Scenarios for Rumen Boluses

In current agricultural practice, rumen bolus sensors are most commonly used in housed systems, where gateway placement and communication infrastructure can be ensured. Moreover, PLF systems can generate real-time interventions (estrus detection → artificial insemination; calving difficulty → intervention; heat stress → cooling). With the ongoing development of PLF, interest may also grow in monitoring grazing animals. Several experimental publications already exist on this topic [66].
However, their use on pastures faces challenges, as gateways cannot always be positioned to ensure continuous communication. Even when communication relies on service providers, data transmission issues may occur. Current solutions rely on concentrated data-collection points—typically gateways installed at watering sites, where animals visit at least three times daily. This results in several hours of delay between measurement and alert. A limiting factor is the availability of such locations. Experiments in New Mexico applied such a setup [45].
Another conceptual scenario—proposed here for consideration—operates on principles like Udvardy’s 2023 data-collection method [67]. This approach uses a single gateway mounted on a UAV (drone) that regularly flies over the area to collect sensor data. In Udvardy’s method, communication energy losses are minimized through precise time synchronization between the sensors, gateway, and drone, with data collection occurring in predefined time windows. Within such a system, the use of a Low Power Wide Area Network (LPWAN) technology is recommended, most notably LoRaWAN or NB-IoT. Because the former is independent of the availability and coverage of cellular networks, it is preferred. The adoption of LoRaWAN is further supported by its star topology [68]. Although the volume of transmitted data and the associated transmission conditions and synchronization requirements should also be examined, such an analysis would likely necessitate a dedicated, separate study. Although this theoretical concept is technically demanding, this solution is feasible and could offer a universal method for collecting data from widely dispersed animals at user-defined intervals. An additional advantage is the possibility of integration with security systems and detection of immobilized animals.

7. Implementing Digital Twin Concept with Bolus Technology

In the industrial sector, the objectives of Industry 4.0 are frequently realized through the digital twin concept, which enables the complete automation and remote supervision of production lines. Operational data are stored in databases from which they can be reloaded into the digital twin, allowing specific processes to be replayed or modelled retrospectively.
In agricultural systems, an analogous function is provided by data collected from sensors placed on the animals and within their environment, complemented by continuous tracking of animal location. Such a system enabled detailed monitoring of pasture use within a PLF framework through the collection of localization, feeding-related, and activity data [67]. Although such systems currently appear predominantly in scientific experimental settings, the demand for these capabilities following the industry is increasing. As sensor capabilities continue to expand—particularly in the areas of localization, physiological monitoring, and behavioral analysis—rumen boluses may facilitate the development of farm-level digital twin models capable of continuously monitoring data at the level of individual animals.
These systems conceptually would reflect the real-time physiological and behavioral state of each animal, thereby supporting predictive diagnostics, optimized management strategies, and enhanced decision-making for farmers and veterinarians.
The implementation of localization is feasible indoors through RFID or camera-based systems, and outdoors through GPS or similar camera-based methods.

8. Conclusions

Rumen bolus technology—conceptually at least 30 years old—refers to complex sensors administered into a cow’s rumen. Over the past 10–15 years, the technology has advanced to the point where it is practically applicable in PLF systems. Essential prerequisites for this were extended sensor lifespan, stable communication with a central system, and the ability to perform high-quality measurements, which have been validated in the scientific literature.
Rumen bolus technologies support multiple SDGs:
SDG 2 and SDG 3: improving food security and nutritional value through optimized dairy production.
SDG 8: enabling automation and decent working conditions.
SDG 9: contributing to innovation in agricultural infrastructure and communication technologies.
SDG 12: requiring responsible manufacturing and end-of-life management.
SDG 13: enabling quantification and mitigation of greenhouse-gas emissions.
Recently, rumen boluses have evolved into well-functioning multimodal sensors capable of reliably detecting body temperature, general activity, drinking frequency, and pH. In the future, further functional expansion is expected in the precise and long-time measurement of pH, rumination, heart rate, methane concentration, and behavioral traits. If achieved, boluses may become one of the best sensor options for PLF implementations. Their use currently dominates in housed systems, but grazing-based applications are also conceivable. With sensors enabling accurate localization, digital-twin technology for individual-animal tracking may also become feasible, supporting the work of caretakers and veterinarians.
A critical challenge in the development of rumen boluses is the provision of a reliable power supply, as this directly determines sensor lifetime and enables higher-energy data transmission, which is often a crucial factor in bolus communication performance. Increased power availability would also allow the integration of additional sensing modalities within the bolus, thereby enhancing its functionality and potentially making it a particularly valuable tool for continuous monitoring. However, the current bolus literature appears to offer little to no discussion of alternative solutions to this problem. This is noteworthy, given that in the field of human wearable sensors, several advanced approaches have already been explored, including recharging based on kinetic energy harvesting, chemical energy conversion, and remote wireless charging via radio-frequency waves.
Another key development area is the expansion of the sensor suite. A major breakthrough would be achieved through the development of durable, long-term pH sensors, as well as through the safe and reliable measurement of methane and other chemical constituents. Given the relatively large size of the rumen and its connection to the reticulum, these compartments may exhibit highly heterogeneous chemical and physical gradients.
In this context, the concept of a miniaturized device has already been proposed, capable of actively or passively changing its position within the rumen–reticulum system. Such a device could map spatial variations in physical and chemical processes, enabling highly complex analyses through the application of artificial intelligence-based algorithms. Achieving this vision would require not only the accurate localization of the animal itself but also precise intra-body localization of the bolus within the rumen–reticulum environment.

Funding

This research was funded by NKFIH Hungary grant number 2020-1.1.2-PIACI-KFI-2020-00109.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Acknowledgments

During the preparation of this work, the author used generative AI to improve language and readability. After using this tool/service, the author reviewed and edited the content as needed and takes full responsibility for the content of the published article.

Conflicts of Interest

The author declares no conflicts of interest. The funders had no role in the design of the study; the collection, analyses, or interpretation of data; the writing of the manuscript; or the decision to publish the results.

Abbreviations

The following abbreviations are used in this manuscript:
AIArtificial Intelligence
CITDComplex, Individually Time-varying and Dynamic (system)
GHGGreenhouse Gas
IoTInternet of Things
NB-IoTNarrowband Internet of Things
PACPortable Accumulation Chamber
PLFPrecision Livestock Farming
SDGSustainable Development Goal
UAVUnmanned Aerial Vehicle

Appendix A

Scopus database query string:
TITLE-ABS-KEY (“bolus”) AND TITLE-ABS-KEY (“cattle” OR “cow” OR “bovine”) AND TITLE-ABS-KEY (“dairy”) AND PUBYEAR > 2000
AND (LIMIT-TO (SUBJAREA, “AGRI”) OR EXCLUDE (SUBJAREA, “t LIMIT-TO SUBJAREA”) OR LIMIT-TO (SUBJAREA, “COMP”)) AND
(LIMIT-TO (DOCTYPE, “ar”) OR LIMIT-TO (DOCTYPE, “cp”) OR LIMIT-TO (DOCTYPE, “re”) OR LIMIT-TO (DOCTYPE, “no”))
AND (LIMIT-TO (LANGUAGE, “English”))
Web of Science database query string:
bolus (Title) and cow (All Fields) and cattle (All Fields) and bovine (All Fields) or bolus (Abstract) and 2000–2026 (Year Published) and sensor (Abstract)
Refined By: Web of Science Categories: Agriculture Dairy Animal Science or Engineering Electrical Electronic or Veterinary Science.

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