Advancements in Real-Time Monitoring of Enteric Methane Emissions from Ruminants

Abstract

The agricultural sector is responsible for a significant proportion of global anthropogenic methane (CH₄) emissions, with enteric CH₄ produced from ruminant livestock representing approximately 28% of the total. The development of effective mitigation strategies necessitates the accurate and actionable monitoring of CH₄ emissions. However, a considerable research gap remains concerning real-time monitoring techniques capable of supporting on-farm enteric CH₄ mitigation strategies. To bridge this research gap, this study explores the current status of real-time enteric CH₄ emission monitoring techniques and technologies for ruminants. The study achieves this by reviewing key biomarkers and proxies for ruminant emissions, examining established animal-based measurement techniques, exploring emerging technologies, and critically assessing technological limitations and opportunities. By shedding light on this research area, this study aims to assist stakeholders in developing a viable pathway for on-farm emission monitoring, with the hope of facilitating a meaningful reduction in GHG emissions from the livestock sector.

1. Introduction

The growing global population has put increasing pressure on the agricultural sector to increase food production. Simultaneously, the pressing issues of climate change and resource scarcity requires the urgent development of sustainable and efficient farming technologies and practices. For instance, the agricultural sector has consistently accounted for approximately 12% of total global greenhouse gas (GHG) emissions and 11% within the European Union (EU) [1,2]. These emissions have been predominantly driven by the release of methane (CH₄), largely attributable to bovine and ovine enteric fermentation, and nitrous oxide (N₂O), stemming from the application of manure and animal excreta onto pasture [3]. Notably, livestock enteric fermentation alone represented 43% of total EU agricultural emissions in 2020 [1]. Having the majority of sector emissions originating from biogenic sources presents a unique challenge, as emissions derived from agriculture sources do not easily allow for technological solutions as would exist or evolve within other sectors [4]. Consequently, there is an urgent need for the development and adoption of technologies and strategies to mitigate these GHG emissions, while ensuring maximum sustainable agriculture productivity.

Precision livestock farming (PLF) has emerged as a promising mechanism to assist with the challenges facing the agriculture sector, where it utilises a range of technologies, including sensors, data analysis, and automation, to enhance farm management practices and foster data-driven decision making [5]. One particularly promising innovation emerging from the PLF field is the real-time monitoring of enteric CH₄ emissions from ruminants, where it can offer researchers and farmers actionable insights into animal environmental footprints, health, and welfare under various conditions [6]. By precisely quantifying farm emissions, stakeholders can make data-driven decisions that simultaneously reduce their carbon footprint while optimising herd care, due to the association between enteric emissions and key health factors such as feed intake, dietary chemical composition, digestive health, and animal management [7]. Moreover, the real-time data generated hold significant promise in accelerating the development and refinement of targeted emission mitigation strategies. Such strategies include selective breeding for low-emission animals and evaluating the effectiveness of precision feeding, dietary changes, and farming practices interventions. Furthermore, the implementation of such mitigation strategies, combined with verifiable emission measurements, could underpin the creation of traceable carbon credit markets within the livestock sector, potentially rewarding farmers financially for their GHG mitigation efforts. Such mitigation efforts could have a meaningful and lasting impact on the livestock industry, as underscored by recent research showing a variation in emissions by over 30% among similar animals under similar conditions, suggesting significant potential for reduction [8].

Despite the potential of real-time monitoring technologies to accurately quantify individual ruminant emissions, a significant research gap remains in the development of practical, on-farm techniques that can effectively inform and support CH₄ mitigation strategies within the livestock sector. Therefore, this study contributes to the current literature by reviewing the existing and emerging real-time monitoring techniques for enteric CH₄ emissions in ruminants. Through a critical analysis and discussion of these approaches, this study aims to assist stakeholders in establishing a viable pathway for on-farm emission monitoring, ultimately contributing to a reduction in GHG emissions from the livestock sector. To achieve this goal, the following objectives were put forward: (i) to review and critically assess the key biomarkers and proxies indicative of CH₄ emissions from ruminants; (ii) to comprehensively examine and evaluate established animal-based techniques for measuring enteric CH₄ emissions; (iii) to explore emerging technologies and techniques for real-time enteric emission monitoring; and (iv) to critically evaluate the major technological limitations and future opportunities for accurate on-farm CH₄ monitoring.

Enteric CH₄ Emissions

On a worldwide basis, enteric CH₄ produced from ruminant livestock is a substantial source of anthropogenic CH₄ emissions, representing approximately 28% of the global total [9]. Enteric CH₄, a colourless and odourless gas, is produced as a by-product of the microbial fermentation process within the digestive systems of ruminant animals (e.g., cattle, swine, and sheep). During this fermentation process, carbohydrates are broken down by microbes into simple molecules, facilitating absorption into the bloodstream. A major by-product is CH₄, formed when methanogenic archaea combine carbon dioxide (CO₂) and hydrogen (H₂) [10]. The vast majority of this CH₄ (97%) is released through the mouth and nostrils during breathing and eructation, with a significantly smaller amount (3%) emitted from the hindgut as flatus (see Figure 1) [11,12].

Understanding and measuring enteric CH₄ production can play an essential role in the evaluation of ruminant livestock productivity, as methanogenesis plays a key role in their energy metabolism. Methanogens play an important role in the rumen by reducing CO₂ to CH₄ and water (H₂O), utilising excess H₂ that would otherwise negatively impact fermentation rates and microbial growth [13]. Therefore, reducing enteric methanogenesis holds the potential of simultaneously improving livestock production efficiencies and reducing environmental impact. This is evidenced by the significant energy loss ruminants experience through enteric CH₄ production, ranging from 2% to 12% of gross energy intake, depending on diet [14]. Thus, the accurate measurement of ruminant enteric CH₄ emissions could be essential to facilitating improvements in livestock productivity, improved health, and the development of CH₄ mitigation strategies.

Figure 1. Processes of methane production in ruminants. Source [15].

Figure 1. Processes of methane production in ruminants. Source [15].

2. Monitoring of Biomarkers and Proxies for Ruminant Health and CH₄ Emissions

The use of biomarkers and proxies to assess ruminant health and CH₄ emissions is a rapidly developing field with the potential to transform livestock management. These indicators offer alternatives to direct CH₄ measurement, providing valuable insights into the complex mechanisms of ruminant digestion. This growing interest has largely been driven by recent advancements in animal-based monitoring and sensors capable of real-time health and emission measurement. Specifically, these real-time data can be critical in reducing the occurrence of animal health issues, thereby decreasing incidents of reduced productivity, morbidity, and mortality in livestock [16]. Subsequently, such advancements contribute to the reduction of animal emission levels by directly improving livestock health and productivity. This section examines the specific behavioural and physiological parameters used for the real-time monitoring of ruminants, particularly focusing on biomarkers and proxies for ruminant health and emission levels.

2.1. Feed Intake and Feeding Behaviour

Feed intake is a complex trait influenced by a multitude of factors including animal genetics, rumen function, feed availability and quality, and overall health. Given that enteric CH₄ is a by-product of microbial fermentation originating from the rumen and hindgut, both feed intake and diet quality have long been recognised as key parameters in enteric CH₄ production [17]. Consequently, associated characteristics such as dry matter intake, metabolisable energy intake, and feed efficiency are often used as essential inputs in various emission modelling algorithms and techniques [7,17]. However, these emission estimates can vary significantly (+35%) for a particular diet, reflecting the complexity of factors influencing enteric CH₄ production [18]. The influence of feed intake is supported in the experimental literature, with Ellis et al. (2007) demonstrating that dry matter intake predicted CH₄ production in dairy cattle with an R² of 0.64 and metabolizable energy intake with an R² of 0.53 [17,19]. These results are consistent with earlier findings by Mills et al. (2003), who demonstrated similar predictions of CH₄ production with dry matter intake (R² of 0.60) and metabolisable energy intake (R² of 0.55) [17]. Expanding on these findings, Moraes et al. (2014) reported gross energy intake as a major explanatory variable for predicting CH₄ emissions, achieving a root mean squared prediction error of 3.01 MJ/day [17]. Feed efficiency is another influential factor affecting enteric CH₄ production, where it represents the difference between a cow’s energy expenditure (milk production, maintenance, growth, and reproduction) and the energy it gains from consumed nutrients. Recent research has revealed significant variations in the ability of individual animals to digest various feedstuffs, challenging the previous assumption that digestibility was relatively similar across cows [20]. This greater understanding highlights the potential of targeted breeding programmes, where the selection of animals with low residual feed intake (high feed efficiency) could simultaneously lower CH₄ output while improving feed efficiency [21]. To facilitate such CH₄-mitigating programmes, a suite of technological advancements has emerged, enabling the precise monitoring of livestock feed intake and feeding behaviour. These real-time data empower stakeholders to make informed adjustments to feeding strategies, tailor diets to meet individual nutritional needs, reduce waste, and closely monitor animal feeding patterns. Such technologies have been widely reported in the literature, with many commercialised, including electronic rumen boluses, smart feeders with radio frequency identification (RFID)-embedded ear tags, acoustic monitoring, AI-embedded cameras, and behaviour-sensing neck collars. Notably, noseband pressure sensors have also been utilised to estimate the duration of eating and ruminating in cattle and to determine the number and frequency of chews and bites [22,23]. They typically work by using pressure sensors to measure the size and rate of bites by analysing the frequency and amplitude of ‘peaks and troughs’ in the data collected during eating [23]. Braun et al. (2013) assessed rumination behaviour using a noseband pressure sensor and found a significant correlation between the results obtained from the sensor and those from visual observations [24]. Similarly, Leiber et al. (2016) attempted to measure the feed intake of dairy cows fed a high-forage total mixed ration using noseband sensors, based on daily eating time and rumination [25]. However, this was unsuccessful due to significant differences in feeding behaviours among animals. The RumiWatch System (Itin+Hoch GmbH, Liestal, Switzerland), integrated into a cow halter, combines data from a pressure sensor with information from a triaxial accelerometer to identify various behavioural characteristics in dairy cows [26]. Various studies have utilised this system to study a range of behavioural characteristics in cows, including bolus counts, chews per bolus, number of rumination chews, prehension bites, and number of eating chews along with derived durations such as rumination time, eating time, and prehension time [26,27].

Acoustic monitoring techniques have also been used to a limited degree in the measuring of livestock feeding behaviour. Galli et al. (2011) successfully estimated dry matter intake during short feeding sessions in sheep with a relatively high accuracy (R² = 0.92) using acoustic monitoring; they did so by calculating both the chewing energy per bite and the total energy expended in chewing [23,28]. Similarly, a Hi-Tag system (SCR Engineers Ltd., Netanya, Israel) has been successfully shown to provide accurate measurements of rumination time, the intervals between regurgitation of boluses, and the chewing rate in cattle [29,30]. The Hi-Tag logger, positioned on a neck collar, records and processes the distinctive sounds produced by regurgitation and rumination using a microphone. The software then defines the start of a rumination event when the system detects the sound associated with regurgitation [29]. Schirmann (2009) validated the system’s accuracy, reporting a high correlation between its results and those obtained from direct observation (r = 0.93, R² = 0.87, n = 51) [30]. Overall, it is evident that such feeding behaviour-monitoring technologies have significant potential to optimise sustainable farming practices while empowering CH₄-mitigating strategies and breeding programmes.

Another feeding behaviour-monitoring technique garnering increasing attention in both academic and industrial circles is electronic rumen boluses, due to their proficiency in accurately and continuously monitoring the internal rumen environment of ruminants, particularly cattle [31]. This established technology, offered by commercial providers such as Moonsyst (Cork, Ireland), extends beyond the monitoring of ruminant drinking behaviour to encompass a wide range of vital parameters, including animal health, heat detection and optimisation, disease detection, calving prediction and detection, and overall animal activity [32]. Administered orally using a bolus applicator, these devices settle in the reticulum due to gravity and remain there throughout the animal’s life. These devices consist of a cylindrical ceramic capsule containing an internal battery, a data transmission transmitter, and specialised sensors such as temperature, pH, and accelerometer sensors, tailored to the specific monitoring needs [29,33]. They operate by gathering real-time data and transmitting them wirelessly to a central processing unit. These data are then uploaded to cloud-based storage, enabling users to access and analyse the information conveniently via a smartphone or computer [34]. Due to its stable placement, the rumen bolus offers lifelong animal monitoring, with a battery lasting for months to years and the ability to wirelessly transmit data at adjustable intervals.

2.2. Rumination Time

Ruminating time, characterised by cattle regurgitating and chewing previously ingested food, plays an important role in aiding nutrient absorption and digestion. It also serves as an indicator of a cow’s health, as a decrease in rumination time is often observed in animals experiencing illness, pain, hunger, or maternal anxiety [35]. Typically, cattle ruminate for about 30 min to 1 h across 10–17 daily periods, producing 30–60 boluses per period, with each rumination cycle involving 30–60 chews and lasting around 40 s [35,36]. However, a variety of factors can alter these rumination times and patterns, such as changes in diet composition, feeding management practices, health issues, the onset of calving, and environmental conditions.

Rumination time can indirectly influence methane emissions in dairy cows, largely due to its role in stabilising the pH of rumen fluid. Stable ruminal pH is critical because it directly impacts the activity of methanogenic microorganisms within the rumen, which may affect methane synthesis [34,37]. Mikuła (2022) successfully demonstrated a correlation between longer rumination time and reduced methane emissions per unit of milk in high-yielding dairy cows [34]. This study included 365 high-yielding Polish Holstein–Friesian multiparous dairy cows, covering a lactation period ranging from 24 to 304 days. The data collected from the cows were segmented into three categories according to their daily rumination durations: those in the low-rumination group ruminated for up to 412 min per day; the medium-rumination group included cows ruminating between 412 and 527 min per day; and the high-rumination group comprised cows ruminating for more than 527 min per day. The results showed that cows in the high-rumination group emitted 2.9% less methane per unit of milk than those in the medium-rumination group, and 4.6% less compared to the low-rumination group.

2.3. Rumen Status

The rumen ecosystem within ruminants offers significant research opportunities, particularly regarding mitigating its considerable impact on the environment, improving animal health, and contributing to feeding a growing global population. Specifically, the process of fibre fermentation in the rumen serves as a crucial mechanism, enabling ruminants to transform low-grade, human-inedible materials into high-quality protein suitable for human consumption [38]. Given its crucial role, this section explores the literature examining the rumen’s role in real-time health and emission monitoring.

2.3.1. Reticulo-Ruminal pH

The real-time monitoring of the rumen pH is a promising approach for tackling animal health disorders, specifically in determining and preventing acute acidosis or sub-acute ruminal acidosis (SARA). This technology has attracted significant attention for its capacity to produce sensory pH data that highly correlate with readings from calibrated laboratory pH probes [29,39]. Moreover, the continuous or intermittent monitoring of rumen pH allows for the early detection of suboptimal rumen conditions, thus enabling the implementation of timely intervention strategies including adjustments to diet formulation, feeding management, and the monitoring of feeding behaviour [23,40]. Due to the rumen’s non-homogenous nature, significant fluctuations in ruminal pH may occur throughout the day, depending on the quantity and composition of the diet as well as changes in feeding behaviour [41]. As previously mentioned, the pH value has been shown to be a reliable indicator of the onset of SARA. The reported pH thresholds for SARA vary as they are influenced by several influencing factors such as the timing of sample collection, the frequency of measurements, and the location of sampling. In a recent study, a cumulative period exceeding 3 h per day with ruminal pH values below 5.6 was found to be sufficient for diagnosis [42]. Another study has proposed that a single occurrence of rumen pH dropping below a threshold of 5.5 is a fitting measure [42,43].

Recent advancements have led to the development and commercial availability of various rumen pH sensors, such as the reticulo-ruminal pH boluses [31,32]. These devices have revolutionised the data collection process by continuously transmitting pH data wirelessly to a central processing unit, thus greatly simplifying data collection and analysis [33]. For data interpretation, animal ruminal acidosis and feeding behaviours can be evaluated using various factors, including the average ruminal pH, the pattern of ruminal pH over time, the variation in the pattern of ruminal pH, and the duration of suboptimal ruminal pH [44]. It is essential to accurately interpret these data to account for the variations in pH observed at different locations within the rumen and reticular. A notable limitation of pH boluses is their susceptibility to significant drift over time, coupled with a relatively short lifespan and the impossibility of easily recovering the device [45]. Despite this, the potential to incorporate pH sensing into broader, wireless multi-parameter rumen monitoring systems presents a promising avenue for enhancing the comprehensiveness and accuracy of animal health assessments.

2.3.2. Reticulo-Ruminal Temperature

The real-time monitoring of body temperature changes in ruminants offers an effective method for the early detection of inflammatory conditions, oestrus, and diseases such as mastitis [31,46,47,48]. Core temperature monitoring has traditionally relied on the use of rectum and vaginal thermometers [49]. These methods are often considered low in functionality due to their labour-intensive nature and the time-consuming requirement of manually checking each animal individually [38]. Given these constraints, there is a growing trend of adopting automated monitoring technologies [49]. Among these, reticulo-ruminal temperature sensors have received significant attention, due particularly to the use of electronic boluses that facilitate the continuous monitoring of ruminal temperature variations throughout an animal’s lifespan [38]. This technology has been particularly effective in detecting the onset of oestrus and as a useful predictor of calving in cows [50,51,52].

In the context of calving, constant monitoring is especially beneficial, as such vigilance can significantly decrease the mortality rate of newborns by up to 50% and mitigate losses caused by dystocia [49]. Precision technologies, such as temperature sensors, can serve as reliable predictors of calving. Several studies have observed decreases of up to 1 °C in body temperature during the period preceding calving, predominantly attributed to the thermogenic effect of progesterone [53,54,55]. Research has consistently shown variations in reticulo-rumen temperature (Trr) in cows before calving. Kim et al. (2021) report that in Hanwoo (Bos taurus coreanae) cows, Trr can decrease by 0.5 °C from 24 h to 3 h before calving, in comparison to 48 h prior to parturition [56]. Cooper-Prado et al. (2011) [55] observed similar trends in Angus cows, where they documented a reduction of up to 0.33 °C in Trr two days before calving using temperature-sensing boluses placed in the rumen. Costa Jr. et al. (2016) [49] also employed this technique in Holstein cows and recorded Trr decreases of 0.32 °C in primiparous and 0.36 °C in multiparous cows 24 h before calving. Furthermore, Kovács et al. (2017) investigated crossbred Holstein–Japanese Black cows and noted that the extent of Trr reduction varied with the calving condition, ranging from 0.23 °C in distocic cows to 0.48 °C in eutocic cows [57]. The variance in these results is likely due to differences in the methods applied, including factors such as the biological characteristics of the animal, the site of body temperature measurement, and environmental variables [58].

As previously mentioned, monitoring changes in ruminal temperature and the cow’s overall activity can facilitate the early detection of oestrus. The accurate identification of oestrus is key as failing to inseminate at the appropriate time can result in failure to conceive or pregnancy loss, thus impacting revenue [59]. Researchers have established a positive correlation between rumen temperature and the onset of oestrus [38]. This relationship is primarily attributed to hormonal fluctuations during the oestrus cycle, which can lead to fluctuations in the cow’s body temperature. Notably, one study highlighted that body temperature increases by approximately 0.4 °C during oestrus in cows [60]. Moreover, the overall activity level of a cow can be a reliable indicator of the onset of oestrus, characterised by a notable rise in movement and restlessness [38,59]. Such behavioural changes are predominantly influenced by a surge in oestrogen levels during oestrus, which triggers the cows’ natural mating instincts and results in increased overall activity. Recognising this, several manufacturers have brought to market ruminal boluses that incorporate accelerometers and temperature sensors for oestrus detection [42]. These devices enable more timely breeding interventions, thus facilitating better management of the reproductive cycle and enhancing overall production efficiency.

While reticulo-ruminal temperature monitoring can offer valuable insights into animal health and behaviour, it is important to acknowledge the challenge of determining accurate readings. Factors such as ambient temperature, water temperature and consumption, rumen fermentation, and fibre-related diseases can significantly influence rumen temperature measurements [29,61]. For instance, Bewley et al. (2008) observed that drinking water could lower rumen temperature by up to 8.5 °C, with a recovery to core body temperature levels taking up to two hours [38,62]. Despite these challenges, such interference can also present opportunities, where reticulo-ruminal boluses can be used to monitor drinking events and investigate factors influencing cattle drinking behaviour [29]. These temperature fluctuations can provide insights into the daily distribution of water drinking events, and while they may not precisely measure the quantity of water consumed, they can indicate changes in drinking patterns [38]. Such changes can be vital for identifying potential health issues or stress, enabling farm managers to take timely action to maintain herd health and wellbeing.

2.3.3. Reticulo-Rumen Motility

Monitoring the frequency and amplitude of ruminal contractions, known as motility, plays a crucial role in diagnosing various metabolic diseases in cattle [30,38]. Conditions such as ruminal acidosis and hypocalcemia, along with other ailments that induce stress or impact productivity, directly influence these contractions [63,64]. Consequently, measuring changes in the rate and strength of these contractions can be a key indicator of animal health, welfare, and overall production performance [38].

Bovines have a distinctive digestive system comprising four chambers specifically adapted for efficient cellulose digestion. This process, known as alloenzymatic digestion, utilises microbial enzymes for the effective digestion of cellulose-rich complex carbohydrates, rather than relying on enzymes produced by the host animal. The first chamber provides an anaerobic environment essential for microbial fermentation of ingesta, a key step in feed breakdown. The reticulum, as the initial compartment, separates particulate matter from liquid and acts as a sump to trap heavy foreign bodies, protecting the digestive system [38]. Following this is the rumen, the largest chamber, which is primarily responsible for the fermentation and digestion of organic matter [38]. These chambers, jointly known as the reticulo-rumen, undergo periodic contractions, facilitating fluid and material movement and enabling regurgitation for rumination, a vital part of digestion. These contractions occur in two distinct phases and are central to the fermentation process and the efficiency of nutrient breakdown and assimilation (see Figure 2) [65]. The primary contraction cycle, known as the A-wave, begins with a biphasic contraction of the reticulum, which may become triphasic during rumination [66,67]. This cycle starts in the reticulum and moves through the rumen in a cranio-caudal direction [66,67]. These contractions are crucial for several digestive functions, including the mixing of digesta, its passage through the reticulo-omasal orifice, and regurgitation [66]. The secondary contraction cycle, known as the B-wave, is crucial in facilitating eructation, a process vital for managing the gas produced during fermentation [68]. Starting in the rumen’s caudal sac after the A-wave cycle, it progresses forward independently of reticular contractions [66]. In healthy cattle, the vagus nerve regulates ruminal contraction at a rate of three to four times per two-minute interval [65,69]. Several studies have indicated possible correlations between bovine contraction cycles and various aspects of animal behaviour, including feeding, stress response, and periods of rest [30,70].

Historically, the assessment of rumen motility has relied on physiological evaluations using strain gauge force transducers or ultrasonography techniques [71,72]. However, these methods are limited by significant drawbacks, including being labour-intensive, costly, and impractical for regular use on most farms [30]. For instance, the force transducer method requires a surgical procedure in which the device is sutured onto the serosa of the dorsal sac of the rumen [38]. The ultrasonography technique is constrained for long-term use as it requires the device to be consistently positioned against the animal’s body wall [38]. Recent developments in wireless forestomach motility sensors have been recognised as a reliable, practical, and long-lasting solution for monitoring rumen health [73,74]. These sensors can effectively detect rumen atony and, through advanced data processing, can continuously track ruminal motility patterns such as rumination. Moreover, the latest advancements in technology have facilitated the enhancement of bolus wireless sensors, which can streamline rumen motility monitoring, allowing for long-term observation periods. Several research groups have utilised boluses equipped with accelerometers to continuously monitor reticulo-rumen motility [42,50,75]. Boluses with integrated accelerometers are capable of detecting the motion of the reticulo-rumen, thus capturing essential data on the contractions and relaxations that occur during the digestive process. A study by Arai (2019) demonstrated a significant positive correlation between the ruminal contractions measured by force transducers and the acceleration detected by the bolus sensor (p < 0.01) [76]. Furthermore, these accelerometers can identify movement patterns associated with rumination, enabling the measurement of both its frequency and duration. On the other hand, pressure sensors are effective in measuring the internal pressure changes within the reticulo-rumen, which are closely linked to muscular contractions that facilitate digestion. Notably, significant variations in pressure patterns can serve as an indicator of digestive issues, such as bloat in cattle [33]. Considerable opportunity exists in the integration of such motility data with other complementary sets of information, such as pH, temperature, volatile fatty acids, or other data, which could together provide a more comprehensive understanding of reticulo-rumen motility and yield practical insights for on-farm application.

3. Animal-Based Techniques for Measuring Enteric CH₄

Within the livestock sector, the development and deployment of reliable methods and devices to quantify enteric CH₄ emissions are becoming increasingly critical for improving animal productivity and environmental impact. Such monitoring is often facilitated by data generated via wireless wearable sensors, which offer continuous, automated analysis, providing stakeholders with precise information to inform and enhance farm management decisions. With a rapidly growing market of CH₄-monitoring techniques, each with its own advantages and limitations, stakeholders now have a wider range of options to choose from, as illustrated in Figure 3. To assist with this, this section provides a comprehensive review and critical discussion of the most prevalent techniques reported in the literature for the real-time measurement of enteric CH₄ emissions.

3.1. Respiration Chambers

Respiration chambers are among the most widely utilised techniques in the measurement of CH₄ emissions released from ruminants, often regarded as the “gold standard” due to their integral use in the development of predictive models and equations that inform national inventories [77,78,79]. While the design of chambers may differ, the fundamental principle remains the same: a sealed and environmentally controlled chamber is used to determine an individual test animal’s heat balance and gaseous exchange [7,77]. There are two main categories of respiration chambers: open-circuit chambers and closed-circuit chambers. The more commonly used open-circuit chambers feature an air inlet and exhaust, creating a one-way stream of air through which the animal breathes [80]. The sampling of incoming and outgoing air streams is conducted using either gas analysers, infrared photoacoustic monitors, or gas chromatography systems [81,82,83]. In contrast, closed-circuit chambers, now seldom used, analyse the composition of air that accumulates over time. By employing these methodologies, it is possible to estimate the whole-body metabolic rate of the animal through the measurement of CH₄, gaseous exchanges (including oxygen (O₂) consumption and carbon dioxide (CO₂) production), and evaluation of additional trace gases [77].

A significant constraint of using respiration chambers is that the approach may alter the natural behaviours of free-ranging animals due to its artificial environment, thus resulting in an inaccurate estimation of actual emissions compared to farm conditions [84]. To mitigate potential behavioural changes, research trials typically employ acclimatisation periods prior to measurement, along with the use of transparent chambers to enable the animal to maintain visual contact [85,86]. Moreover, this method can require significant financial and labour resources and is constrained by its capacity to monitor only one animal at any one time, making it impractical for large-scale assessments [14,21,87,88]. Currently, respiration chambers are primarily utilised by governmental agricultural agencies within a research setting rather than by individual farmers; this is due to the substantial capital investment associated with their use. Efforts to address this have focused on incorporating more accessible measurement techniques as the primary testing mechanism, with respiration chambers used to validate the results obtained [89]. Despite these limitations, respiration chambers continue to be considered the most accurate and precise method available for gathering air composition data over an extended period of time.

3.2. Sulphur Hexafluoride (SF₆) Tracer Method

Tracer techniques have attracted considerable attention for their use in determining CH₄ emissions in ruminants, particularly for free-range cattle, as they allow monitoring without constraining the animals’ behaviour [77]. This method involves the insertion of a permeation tube or bolus in the reticulo-rumen, where a tracer (such as sulphur hexafluoride (SF₆) or deuterated CH₄ (¹³CH₃D)) is released at a known rate [90,91]. After the tracer is administered, the cow can resume its normal grazing activities. During this period, the animal’s breath is continuously sampled using a tube placed near the animal’s nostril, typically attached to a halter, and directed into an evacuated cylinder for collection [10,13,80]. When SF₆ is used as the tracer gas, the calculation of enteric CH₄ emission rates is conducted by multiplying the established SF₆ release rate with the ratio of CH₄ to SF₆ concentrations found in the collected samples [92,93]. The concentration of SF₆ and CH₄ in the collected samples is quantified using gas chromatography [94]. SF₆ has been shown to be an ideal tracer gas as it is easily detectable, traceable at low concentrations, and synthetic in origin [77]. However, the SF₆ tracer gas utilised is a highly potent greenhouse gas with a global warming potential of 22,800 [92,95]. While this technology offers key benefits, such as being non-invasive and enabling simultaneous monitoring of multiple animals, its accuracy can be affected by several factors. These include equipment malfunctions, background concentrations of emission sources in ambient air, the high-level expertise required, and the variability in the tracer gas’s release rate [13,96]. Such limitations in accuracy and applicability can be mitigated by integrating the method with other established enteric emission measurement technologies. Furthermore, integrating the SF₆ tracer method with additional PLF technologies, such as electronic rumen boluses, behaviour-monitoring neck collars, RFID-embedded ear tags, and smart feeders, presents opportunities for enhancing the accuracy and comprehensiveness of the generated results. This integration opens up significant research opportunities in applying artificial intelligence (AI) to effectively mange and identify patterns from such complex datasets resulting from diverse sensors, ultimately providing more unified and accurate understanding of an animal’s enteric emissions over its lifespan [97].

3.3. Spot Sampling

Spot sampling, also known as the “sniffer” technique, involves the direct measurement of the CH₄ concentration in an individual animal’s breath over a short duration [13,98]. The CH₄ concentrations in the breath samples are measured and used to calculate the animal’s CH₄ emission rate. Typically, these sampling visits last for 3 to 10 min, with the CH₄ concentrations being expressed as either the overall mean or the mean of eructation peaks [10]. Sufficient data collection allows for the derivation of a repeatable estimate of the CH₄ emission rate and the extrapolation of short-term emission rates to whole-day CH₄ emissions [92,94]. This approach is generally non-intrusive, automated, and non-invasive, thus enabling the efficient measurement of emissions from a large number of animals [92]. However, studies using the sniffer method have reported it to be significantly less precise than respiration chambers, owing to considerable variation in CH₄ concentrations within and between animals’ breath [13,99,100]. Additionally, other potential sources of error, such as cow head position, number of measurements, and proximity to the sampling point, need to be considered for accurate emission estimates.

A device that has attracted significant attention in the literature is the patented GreenFeed^® system (C-Lock Inc., Rapid City, USA), which operates similarly to the sniffer technique, combining an automatic feeding system with advanced gas-flux quantification capabilities [7,92,99]. The system attracts animals using a small amount of pelleted concentrate as “bait” and measures emissions (including CH₄, CO₂, H₂, O₂, and H₂S) over periods of 3–6 min repeatedly throughout the day [7]. A key advantage of GreenFeed lies in its ability to estimate an animal’s daily average methane emission by aggregating multiple short-term measurements taken during each visit to the unit [79]. However, the method is limited by the need for multiple short-term measurements and the reliance on an “bait” to draw the animal to the device, which alters the measured results [23,92,98].

Interestingly, several start-up companies are gaining traction in the field, aiming to commercialise more affordable spot-sampling devices. For instance, Agri Data Analytics (Offaly, Ireland)has developed a portable, solar-powered “sniffer” device that attaches to feed troughs, allowing for automated, continuous monitoring of CH₄ emissions [101]. The device utilises laser spectroscopy to measure CH₄ concentrations in real time and transmit the data wirelessly to a cloud-based platform for analysis. Meanwhile, Zelp (London, UK), has taken an alternative approach by creating a wearable device for cattle that captures CH₄ emissions directly from the animal’s nostrils [102]. This halter-like device utilises a catalyst to oxidize methane into carbon dioxide and water, thereby mitigating emissions while simultaneously quantifying them. Both technologies, while still in their early stages of development and commercialisation, represent promising advancements in the ongoing effort to monitor and mitigate methane emissions from livestock.

3.4. Laser CH₄ Detectors

Lasers, using infrared absorption spectroscopy, have long been used for CH₄ gas detection across various industries, including environmental, air quality monitoring, healthcare, security, and agriculture [92,103]. In the context of agricultural research, these handheld devices have been used to measure CH₄ concentrations in the exhaled air of individual animals [11,104,105]. These CH₄ concentrations are manually measured using a portable device positioned 1–3 m from the cow’s nostrils for 2–4 min, ensuring minimal disturbance to the animal’s behaviour [13,79,105,106]. The technique works by emitting infrared light, which is selectively absorbed by the CH₄ molecules in the cow’s breath. It then measures the intensity of light absorption, allowing it to accurately calculate the concentration of methane present. From the data acquired, it is possible to segregate the CH₄ concentrations based on the physiological activity being performed by the animal, such as ruminating, sleeping, and feeding [13]. Typically, higher CH₄ concentrations are observed during eructation, in contrast to lower concentrations noted during respiration. While this method relies on spot sampling of an animal’s breath, these initial measurements can then be extrapolated to represent daily CH₄ production [7,13]. However, such scaling typically requires an impressive number of assumptions [3]. This method offers some advantages over traditional enteric CH₄ measurement techniques by providing a rapid response, allowing for real-time measurements and offering a non-invasive process without requiring physical contact [11,92]. The results of this technique can be affected by external factors, such as temperature, wind velocity, humidity, atmospheric pressure, and the proximity of other animals [92,104,107,108]. While some studies, such as Chagunda and Yan (2011), have found strong agreement between measurements from laser CH₄ detectors and those in respiration chambers (r = 0.8), others like Ricci et al. (2014) have reported inconsistencies, highlighting the method’s variability under different conditions [11,13,105,109]. Chagunda et al. (2012) visually illustrates this variability between individual CH₄ concentration measurements in Figure 4 [11]. Although the absolute values between the laser CH₄ detector and the respiration chamber differ, the trends over time showed good agreement.

3.5. Open-Path Laser

Open-path lasers are a novel method developed for quantifying CH₄ concentrations from herds of animals using a system of lasers and detectors. These systems are already utilised for both research and commercial applications in whole-farm ruminant enteric methane measurements across various countries, including the United States [110,111,112], Australia [113,114], New Zealand [115], China [116], and Canada [117,118]. Central to this system is the tuneable infrared diode laser, part of a programmable unit that can direct the laser beam across various paths [18]. This technique operates by sending tuneable infrared diode lasers to a retro reflector along a direct path, which then reflects the beam back to a detector [7]. The intensity of the reflected light received by the detector reveals the CH₄ concentration along the path [7,92]. Depending on the configuration, the herd can either act as a collective surface source or, with GPS-collared individual animals, as separate point sources [92]. Most recently, the technique has been enhanced through the integration of various analysers and atmospheric parameters into drones and aircraft, leading to consistent and encouraging results [119]. Moreover, this technology has been proven capable of detecting ammonia (NH₃) and CH₄ from distances up to 7 km and 25 km, respectively, when originating from a high-strength source [13,119]. While the technology remains promising, it still has limitations, such as sensitivity to environmental conditions, complex setup and operation, and high capital costs [7,92,120].

4. Emerging Technologies and Techniques

4.1. Portable Accumulation Chamber

An emerging technology for measuring emissions from individual animals is portable accumulation chambers (PACs). These systems generally consist of an airtight polycarbonate chamber without airflow, which operates by trapping all exhaled gases during a 1 to 2 h sampling period [92]. During this time, the O₂ content inside the chamber is gradually depleted, and a portable air sampler is used to retrieve a single measurement of CH₄ taken at the end of the sampling period [92]. These systems have garnered significant commercial and academic interest due to their automated operation, portability, ability to screen a large throughput of animals, and relative affordability, making them a useful technology in identifying high-emitting animals for targeted mitigation strategies [7]. Specifically, PACs have primarily been utilised in research settings by governmental agricultural agencies across various countries to identify ruminants with high and low CH₄ emissions [121,122]. In Ireland, the agricultural research agency (Teagasc) has employed PACs as the primary ruminant CH₄ measurement technique, with results validated by the SF₆ tracer method and respiration chambers [89]. By combining various measurement techniques, researchers can leverage the key advantages of PACs, such as their portability and ability to assess multiple animals efficiently, while minimising the technologies shortcomings including limited accuracy, limited chamber size, timing of measurements relative to animal feeding, and the influence of external environmental factors [7,92,123].

4.2. CO₂ Tracer Method

Most recently, CO₂ tracers have become an emerging technique to quantify CH₄ emissions from livestock [13,80]. This approach differs from traditional methods such as using externally added SF₆ instead of relying on the naturally emitted CO₂ from cattle [124]. Central to this approach is understanding the relationship between CO₂ production and several other factors such as heat produced by the animals, the energy content of their feed, and the ration consumption [93]. By continuously monitoring and sampling the exhaled air for its CO₂ and CH₄ content, usually using Fourier transform infrared detection, it is then possible to determine the concentration of both gases [78]. The estimation of total enteric CH₄ production is then derived from the measured ratio of CO₂ to CH₄ in the exhaled air [125,126]. In contrast to the traditional respiration chambers method, the CO₂ tracer technique exhibits a comparatively higher level of variability, with the coefficient of determination between the two methods being 0.4 [80,126,127]. Consequently, CO₂ tracers are not considered suitable for precisely quantifying CH₄ emission changes in cattle, limiting their application to scenarios where an approximation is appropriate [92]. Despite these challenges, the method’s ability to be easily applied to many animals makes it possible to reduce the standard error in large-scale experiments [10,78,128]. Moreover, it utilises a naturally occurring tracer (CO₂), thereby simplifying the data collection process and reducing the stress on the animals. Looking forward, there is significant opportunity in integrating AI algorithms with CO₂ tracer methods

4.3. Optical Gas Imaging (OGI)

Optical gas imaging (OGI) cameras offer a promising, non-invasive, real-time approach to quantifying enteric CH₄ emissions from livestock. While traditionally used in the oil and gas industry for detecting leaks, air pollutants and fugitive emissions [129], their capabilities translate well to the agricultural context. OGI uses specialised thermal infrared cameras to visualise gases, including CH₄ and various other organic compounds, based on their unique spectral absorption properties [130,131]. Highlighting this potential, Huang (2023) explored the OGI’s ability to quantify CH₄ emissions, employing a physical respiratory simulator to replicate cattle breaths under controlled laboratory conditions [129]. The study established a strong positive correlation between detected CH₄ intensity and the actual CH₄ concentration within the breath (1000–4000 ppm), demonstrating the potential of OGI for providing quantitative insights into enteric emissions. However, as this technology is still in its early stages of development, considerable challenges still remain, including establishing its suitability with live animals, reducing costs for wider adoption, and validating its accuracy in real-word conditions. While the technology is still emerging, significant opportunities exist for enhancing its capabilities, including integrating it with established techniques such as SF₆ tracers and expanding its already established integration with drone technology to measure herd emissions at a larger scale [132,133].

5. Discussion and Future Outlook

CH₄ emissions from enteric fermentation in ruminant livestock are a major contributor to the agricultural sector’s global environmental footprint [134]. In light of such environmental concerns, substantial efforts have been made in the development of reliable methods and devices to quantify enteric CH₄ emissions, which are essential in the creation of national inventories, genetic selection, and the assessment of mitigation strategies [135].

As illustrated in

Table 1. Comparison of different methods to monitor and estimate enteric CH₄ emissions.

Technique	Cost	Application	Suitability	Advantages	Disadvantages
Respiration chamber	Generally high	Research	Requires controlled conditions; measurements are limited to a single animal at a time.	Highly accurate data collection using a controlled environment; individual animal monitoring; precise measurement of dry matter intake.	Expensive to construct and maintain; requires an animal acclimation period; may disrupt animal normal behaviour.
Sulphur hexafluoride (SF6) tracer method	Moderate	Research	Applicable for grazing animals; suitable for a large number of individual animals.	Provides accurate data; suitable for enclosed and free-range animals; few interferences by other gases; far less intrusive than respiration chambers.	Relies on a highly potent GHG; high risk of equipment failure; depends on spot concentration measurements; may disrupt animal normal behaviour.
Spot sampling	Moderate	Research and Commercial	Applicable for grazing animals; suitable for multiple animals at once.	More affordable and simpler than SF6 tracer and respiration chamber methods; non-invasive technique.	Restricted measurement periods; the GreenFeed method requires an animal bait.
Laser CH4 detector	Moderate	Research and Commercial	Requires semi-controlled conditions; suitable for a large number of individual animals.	Immediate results; reduced labour requirement; minimal stress on the animal.	Sensitive to environmental factors; animal needs to stay relatively still.
Open-path laser	High	Research	Suitable for grazing animals; able of collecting measurements from a large herd of animals; emissions cannot be attributed to a single animal.	Non-intrusive; large-scale monitoring; suitable for enclosed and free-range animals.	Requires expensive equipment; utilises sensitive instrumentation; sensitive to environmental factors; requires animal cooperation.
Portable accumulation chamber	Moderate	Research	Requires controlled conditions; measurements are limited to a single animal at a time.	Relatively simple and portable; short-term measurement.	Restricted measurement periods; may disrupt animal normal behaviour.
CO2 tracer method	Moderate	Research	Applicable for a large number of individual animals.	Suitable for enclosed and free-range animals; far less intrusive than respiration chambers.	Less accurate for short-term variations, sensitive to background CO2, requires careful calibration.
Optical gas imaging (OGI)	High	Research	Requires controlled conditions; applicable for a large number of individual animals.	Non-intrusive; minimal animal discomfort, suitable for enclosed and free-range animals.	Requires expensive equipment; technology is still under development; sensitive to environmental factors.

, a diverse range of techniques and methodologies are now available, with new options continuously emerging. It is evident that each approach encompasses a range of application scopes, advantages, and limitations; thus, no single technique can be deemed universally superior [13]. While some methods are more suited to small-scale and others to large-scale applications, this distinction does not guarantee their universal applicability. Consequently, the technique selected should be chosen based on the intended purpose and the required precision, as its incorrect use may result in the overestimation or underestimation of CH₄ emissions [13,80]. Among these current methods, many demonstrate high accuracy compared to respiration chambers, the traditional gold standard. Notably, hand-laser CH4 detectors and open-path lasers have emerged as promising tools when effectively accounting for atmospheric variations such as air humidity, wind speed, and atmospheric pressure. Despite these advancements, it is important to acknowledge that all measurement techniques inherently possess uncertainties due to random factors such as fluctuations in animal diets, management practices, and environmental conditions [97]. Thus, the development of robust and accurate estimation methods remains crucial for effectively addressing the environmental impact of livestock emissions.

The multifaceted nature of ruminant enteric CH₄ production was highlighted in Section 2, identifying animal physiology, behaviour, feed intake, and management as key contributors. Although the enteric CH₄-monitoring techniques presented in Section 3 and Section 4 can offer valuable insights, their accuracy and applicability can often be constrained by sensor types, algorithm selection, and short-term monitoring periods. To address these limitations, the authors propose sensor fusion as an effective approach to providing a complete understanding of ruminant’s health and environmental footprint over their entire lifespan. This approach would involve the merging of data from a diverse range of sensors and sources, creating a unified and more accurate depiction of an animal’s enteric CH₄ emissions, productivity, and overall health. Initial progress can be achieved by integrating CH₄-monitoring systems with market-ready PLF technologies, such as electronic rumen boluses, behaviour-monitoring neck collars, RFID-embedded ear tags, and smart feeders. However, the successful implementation of such enteric emission-monitoring and mitigation strategies necessitates a collaborative approach among diverse stakeholders, including farmers, researchers, policy makers, and technology providers. This collaborative effort is essential to ensure that collected data are effectively translated into tailored mitigation measures that are both practical and sustainable for implementation at the farm level. Over time, the authors anticipate that the continued evolution of Internet of Things (IoT) devices combined with the integration of machine learning will greatly enhance and accelerate the adoption of such data fusion techniques.

6. Conclusions

Given the livestock sector’s key role in global GHG production, this review underscores the importance of real-time quantification of enteric CH₄ emissions from ruminant livestock. The accurate and actionable insights gained from such monitoring are essential for the rapid development and implementation of effective GHG mitigation strategies, especially considering the inherent complexities of livestock farming. Based on the literature, this study first examines the biological basis of current and potential biomarkers and proxies relevant to ruminant emissions. Next, it critically evaluates a diverse range of existing and emerging methods for monitoring ruminant enteric CH₄ emissions. While there are many techniques available, it is clear that they all have limitations, with their appropriateness heavily dependent on the intended purpose and the level of precision required. This analysis compares their strengths and limitations, revealing that significant progress has been made towards achieving an accurate, cost-effective, and rapid mechanism for enteric CH₄ monitoring. Looking forward, it is hoped that the insights generated will assist in accelerating the widespread adoption of real-time enteric CH₄ monitoring, enabling the development of data-driven CH₄ mitigation strategies and selective breeding programmes.