Evaluation of the application opportunities of precision livestock farming (PLF) for water buffalo (Bubalus bubalis) breeding: SWOT analysis

The use of technology in agriculture is increasing daily with the development of technology in all areas. With the help of PLF (precision livestock farming) technologies and efficient use of inputs, economic, environmentally friendly, and better-quality products are obtained. Significantly its use in dairy cattle is increasing daily, contributing to sustainable milk production in both economic and ecological terms. As the demand increased in the world for water buffalo meat, milk, and dairy products, different breeding systems have been applied for more and higher-quality production purposes. This way the number of water buffalo farms breeding in intensive conditions is increasing. It is necessary to investigate the possibilities of using PLF technologies, which are still widespread in dairy cattle, in water buffalo breeding, and to benefit from the advanced technology in this regard. This study aims to discuss the applicability of PLF technologies by surveying buffalo breeders. With the data obtained from the survey results made with the water buffalo breeders, the strengths, opportunities, threats, and effects of the weaknesses were discussed with the SWOT analysis.

Predicting dry matter intake in beef cattle

Technology that facilitates estimations of individual animal dry matter intake (DMI) rates in group-housed settings will improve production and management efficiencies. Estimating DMI in pasture settings or facilities where feed intake cannot be monitored may benefit from predictive algorithms that use other variables as proxies. This study examined the relationships between DMI, animal performance, and environmental variables. Here we determined whether a machine learning approach can predict DMI from measured water intake variables, age, sex, full body weight, and average daily gain (ADG). Two hundred and five animals were studied in a drylot setting (152 bulls for 88 d and 53 steers for 50 d). Collected data included daily DMI, water intake, daily predicted full body weights, and ADG using In-Pen-Weighing Positions and Feed Intake Nodes. After exclusion of 26 bulls of low-frequency breeds and one severe (>3 standard deviations) outlier, the final number of animals used for modeling was 178 (125 bulls, 53 steers). Climate data were recorded at 30-min intervals throughout the study period. Random Forest Regression (RFR) and Repeated Measures Random Forest (RMRF) were used as machine learning approaches to develop a predictive algorithm. Repeated Measures ANOVA (RMANOVA) was used as the traditional approach. Using the RMRF method, an algorithm was constructed that predicts an animal's DMI within 0.75 kg. Evaluation and refining of algorithms used to predict DMI in drylot by adding more representative data will allow for future extrapolation to controlled small plot grazing and, ultimately, more extensive group field settings.

Review: An overview of beef production from pasture and feedlot globally, as demand for beef and the need for sustainable practices increase

Beef is a high-quality source of protein that also can provide highly desirable eating experiences, and demand is increasing globally. Sustainability of beef industries requires high on-farm efficiency and productivity, and efficient value-chains that reward achievement of target-market specifications. These factors also contribute to reduced environmental and animal welfare impacts necessary for provenance and social licence to operate. This review provides an overview of beef industries, beef production, and beef production systems globally, including more productive and efficient industries, systems and practices. Extensive beef production systems typically include pasture-based cow-calf and stocker-backgrounding or grow-out systems, and pasture or feedlot finishing. Cattle in pasture-based systems are subject to high levels of environmental variation to which specific genotypes are better suited. Strategic nutritional supplementation can be provided within these systems to overcome deficiencies in the amount and quality of pasture- or forage-based feed for the breeding herd and for younger offspring prior to a finishing period. More intensive systems can maintain more control over nutrition and the environment and are more typically used for beef and veal from dairy breeds, crosses between beef and dairy breeds, and during finishing of beef cattle to assure product quality and specifications. Cull cows and heifers from beef seedstock and cow-calf operations and dairy enterprises that are mostly sent directly to abattoirs are also important in beef production. Beef production systems that use beef breeds should target appropriate genotypes and high productivity relative to maintenance for the breeding herd and for growing and finishing cattle. This maximizes income and limits input costs particularly feed costs which may be 60% or more of production costs. Digital and other technologies that enable rapid capture and use of environmental and cattle performance data, even within extensive systems, should enhance beef industry productivity, efficiency, animal welfare and sustainability.

What, how, and how much do herbivores eat? The Continuous Bite Monitoring method for assessing forage intake of grazing animals

Determining herbage intake is pivotal for studies on grazing ecology. Direct observation of animals allows describing the interactions of animals with the pastoral environment along the complex grazing process. The objectives of the study were to evaluate the reliability of the continuous bite monitoring (CBM) method in determining herbage intake in grazing sheep compared to the standard double-weighing technique method during 45-min feeding bouts; evaluate the degree of agreement between the two techniques; and to test the effect of different potential sources of variation on the reliability of the CBM. The CBM method has been used to describe the intake behavior of grazing herbivores. In this study, we evaluated a new approach to this method, that is, whether it is a good proxy for determining the intake of grazing animals. Three experiments with grazing sheep were carried out in which we tested for different sources of variations, such as the number of observers, level of detail of bite coding grid, forage species, forage allowance, sward surface height heterogeneity, experiment site, and animal weight, to determine the short-term intake rate (45 min). Observer (Pexp1  = 0.018, Pexp2  = 0.078, and Pexp3  = 0.006), sward surface height (Pexp2  < 0.001), total number of bites observed per grazing session (Pexp2  < 0.001 and Pexp3  < 0.001), and sward depletion (Pexp3  < 0.001) were found to affect the absolute error of intake estimation. The results showed a high correlation and agreement between the two methods in the three experiments, although intake was overestimation by CBM on experiments 2 and 3 (181.38 and 214.24 units, respectively). This outcome indicates the potential of CBM to determining forage intake with the benefit of a greater level of detail on foraging patterns and components of the diet. Furthermore, direct observation is not invasive nor disrupts natural animal behavior.

Understanding intake on pastures: how, why, and a way forward

An assessment of dietary intake is a critical component of animal nutrition. Consumption of feed resources is the basis upon which feeding strategies and grazing management are based. Yet, as far back as 1948, researchers have lauded the trials and tribulations of estimation of the phenomenon, especially when focused on grazing animals and pasture resources. The grazing environment presents a unique situation in which the feed resource is not provided to the animal but, rather, the animal operates as the mechanism of harvest. Therefore, tools for estimation must be developed, validated, and applied to the scenario. There are a plethora of methods currently in use for the estimation of intake, ranging from manual measurement of herbage disappearance to digital technologies and sensors, each of which come with its share of advantages and disadvantages. In order to more firmly grasp these concepts and provide a discussion on the future of this estimation, the Forages and Pastures Symposium at the 2020 ASAS-CSAS-WSASAS Annual Meeting was dedicated to this topic. This review summarizes the presentations in that symposium and offers further insight into where we have come from and where we are going in the estimation of intake for grazing livestock.

Advancements in sensor technology and decision support intelligent tools to assist smart livestock farming

Remote monitoring, modern data collection through sensors, rapid data transfer, and vast data storage through the Internet of Things (IoT) have advanced precision livestock farming (PLF) in the last 20 yr. PLF is relevant to many fields of livestock production, including aerial- and satellite-based measurement of pasture’s forage quantity and quality; body weight and composition and physiological assessments; on-animal devices to monitor location, activity, and behaviors in grazing and foraging environments; early detection of lameness and other diseases; milk yield and composition; reproductive measurements and calving diseases; and feed intake and greenhouse gas emissions, to name just a few. There are many possibilities to improve animal production through PLF, but the combination of PLF and computer modeling is necessary to facilitate on-farm applicability. Concept- or knowledge-driven (mechanistic) models are established on scientific knowledge, and they are based on the conceptualization of hypotheses about variable interrelationships. Artificial intelligence (AI), on the other hand, is a data-driven approach that can manipulate and represent the big data accumulated by sensors and IoT. Still, it cannot explicitly explain the underlying assumptions of the intrinsic relationships in the data core because it lacks the wisdom that confers understanding and principles. The lack of wisdom in AI is because everything revolves around numbers. The associations among the numbers are obtained through the “automatized” learning process of mathematical correlations and covariances, not through “human causation” and abstract conceptualization of physiological or production principles. AI starts with comparative analogies to establish concepts and provides memory for future comparisons. Then, the learning process evolves from seeking wisdom through the systematic use of reasoning. AI is a relatively novel concept in many science fields. It may well be “the missing link” to expedite the transition of the traditional maximizing output mentality to a more mindful purpose of optimizing production efficiency while alleviating resource allocation for production. The integration between concept- and data-driven modeling through parallel hybridization of mechanistic and AI models will yield a hybrid intelligent mechanistic model that, along with data collection through PLF, is paramount to transcend the current status of livestock production in achieving sustainability.

Performance of Angus weaner heifers varying in residual feed intake-feedlot estimated breeding values grazing severely drought-affected pasture

Context Beef industry productivity and profitability would be enhanced by improved efficiency at pasture. Our research is evaluating performance at pasture of Angus heifers divergent in estimated breeding values for residual feed intake determined from feedlot data (RFI-f-EBV) under a range of grazing conditions. Aims To determine whether Low- and High-RFI-f-EBV cattle differ in their growth response when pasture quality and availability become limiting to performance. Methods Eight-month-old heifers (n = 40) weaned at 6 months of age grazed within two replicates of 20, each with 10 low feedlot-efficiency (High-RFI-f-EBV) and 10 high feedlot-efficiency (Low-RFI-f-EBV) heifers. Each replicate grazed each of eight 1.25-ha paddocks comprising severely drought-affected, low-quality (mean dry-matter (DM) digestibility 44.1%, crude protein 7.3% DM, and 6.1 MJ metabolisable energy/kg DM) mixed perennial and annual native temperate grasses at 7-day intervals during repeated 28-day cycles, with Phase 1 with 2834 kg DM/ha and Phase 2 with 1890 kg DM/ha mean starting biomass. Heifers were yard-weighed weekly on nine occasions during the 8-week study. Key results During Phase 1 of grazing, the heifers gained 6.2 kg liveweight (LW) and during Phase 2 of grazing they lost 10 kg LW on average. Differences in LW between the RFI-f-EBV groups were not evident at the start or end of the study. However, over the 56 days of study, average daily change in LW calculated from the difference between starting and final LW was higher for Low-RFI-f-EBV heifers than for High-RFI-f-EBV heifers (–33 vs –127 g/day, s.e.m. = 41 g/day, P = 0.026). A similar result was evident when average daily LW change was determined from regression of LW on the day of study (–6 vs –96 g/day, s.e.m. = 41 g/day, P = 0.033). No significant interactions between grazing Phase and RFI-EBV group were evident for the growth responses. Conclusions Higher feedlot-efficiency (Low-RFI-f-EBV) weaner heifers maintained LW somewhat better than lower feedlot-efficiency (High-RFI-f-EBV) heifers, as the nutritional availability at pasture became more limiting. Implications Low-RFI-f-EBV weaner heifers may be more nutritionally resilient than are High-RFI-f-EBV heifers under drought conditions and, hence, may require less supplementary feed to maintain growth performance.

Towards sensor-based calving detection in the rangelands: a systematic review of credible behavioral and physiological indicators

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

Short communication: Evaluation of an eating time sensor for use in pasture-based dairy systems

The assessment of grazing behavior is important for research and practice in pasture-grazed dairy farm systems. However, few devices are available that enable assessment of cow grazing behavior at an individual animal level. This study investigated whether commercially available Smarttag "eating time" sensors (Nedap Livestock Management, Groenlo, the Netherlands) were suitable for recording the grazing time of cows. Smarttag sensors were mounted on the neck collars of multiparous Holstein-Friesian cows in a herd in Taranaki, New Zealand. Cows were randomly selected each observation day from the milking herd for 8 separate days across a 1-mo period. Trained observers conducted 90-min observation periods to evaluate the relationship between the sensor eating time measure and grazing time. A set of 5 defined cow behaviors (2 "head up" and 3 "head down" behaviors) were assessed. In total, observations of 37 cows were recorded in 14 sessions over 8 d in the study period, providing 55.5 total hours of observations. Observation data were aligned with sensor data according to the sensor time stamps and grouped into matching 15-min intervals. Interobserver reliability was assessed both before and after the main trial period, and the mean percentage eating time per observer had a coefficient of variation of 0.46% [mean 93.2, standard deviation (SD) 0.425] before and 0.07% (mean 96.3, SD 0.074) after. In the main trial, the relationship between observed (mean 70.8%) and sensor-derived (mean 69.3%) percentage eating time over the observation period gave a Pearson correlation coefficient of 0.971, concordance correlation coefficient 0.968, mean difference 1.50% points, and SD 5.8% points. Therefore, sensor-identified percentage "eating time" and observed percentage active grazing time were shown to be both very well correlated and concordant (in agreement, with high correlation and little bias). Therefore, the relationship between observed and sensor-derived data had a high degree of agreement for identifying cow grazing activity. In conclusion, Smarttag sensors are a valid and useful tool for estimating grazing activity at time periods of 1 h or more.

Review: Precision nutrition of ruminants: approaches, challenges and potential gains

A plethora of sensors and information technologies with applications to the precision nutrition of herbivores have been developed and continue to be developed. The nutritional processes start outside of the animal body with the available feed (quantity and quality) and continue inside it once the feed is consumed, degraded in the gastrointestinal tract and metabolised by organs and tissues. Finally, some nutrients are wasted via urination, defecation and gaseous emissions through breathing and belching whereas remaining nutrients ensure maintenance and production. Nowadays, several processes can be monitored in real-time using new technologies, but although these provide valuable data 'as is', further gains could be obtained using this information as inputs to nutrition simulation models to predict unmeasurable variables in real-time and to forecast outcomes of interest. Data provided by sensors can create synergies with simulation models and this approach has the potential to expand current applications. In addition, data provided by sensors could be used with advanced analytical techniques such as data fusion, optimisation techniques and machine learning to improve their value for applications in precision animal nutrition. The present paper reviews technologies that can monitor different nutritional processes relevant to animal production, profitability, environmental management and welfare. We discussed the model-data fusion approach in which data provided by sensor technologies can be used as input of nutrition simulation models in near-real time to produce more accurate, certain and timely predictions. We also discuss some examples that have taken this model-data fusion approach to complement the capabilities of both models and sensor data, and provided examples such as predicting feed intake and methane emissions. Challenges with automatising the nutritional management of individual animals include monitoring and predicting of the flow of nutrients including nutrient intake, quantity and composition of body growth and milk production, gestation, maintenance and physical activities at the individual animal level. We concluded that the livestock industries are already seeing benefits from the development of sensor and information technologies, and this benefit is expected to grow exponentially soon with the integration of nutrition simulation models and techniques for big data analysis. However, this approach may need re-evaluating or performing new empirical research in both fields of animal nutrition and simulation modelling to accommodate a new type of data provided by the sensor technologies.

Genetic Improvement in South African Livestock: Can Genomics Bridge the Gap Between the Developed and Developing Sectors?

South Africa (SA) holds a unique position on the African continent with a rich diversity in terms of available livestock resources, vegetation, climatic regions and cultures. The livestock sector has been characterized by a dual system of a highly developed commercial sector using modern technology vs. a developing sector including emerging and smallholder farmers. Emerging farmers typically aim to join the commercial sector, but lag behind with regard to the use of modern genetic technologies, while smallholder farmers use traditional practices aimed at subsistence. Several factors influence potential application of genomics by the livestock industries, which include available research funding, socio-economic constraints and extension services. State funded Beef and Dairy genomic programs have been established with the aim of building reference populations for genomic selection with most of the potential beneficiaries in the well-developed commercial sector. The structure of the beef, dairy and small stock industries is fragmented and the outcomes of selection strategies are not perceived as an advantage by the processing industry or the consumer. The indigenous and local composites represent approximately 40% of the total beef and sheep populations and present valuable genetic resources. Genomic research has mostly provided insight on genetic biodiversity of these resources, with limited attention to novel phenotypes associated with adaptation or disease tolerance. Genetic improvement of livestock through genomic technology needs to address the role of adapted breeds in challenging environments, increasing reproductive and growth efficiency. National animal recording schemes contributed significantly to progress in the developed sector with regard to genetic evaluations and estimated breeding values (EBV) as a selection tool over the past three decades. The challenge remains on moving the focus to novel traits for increasing efficiency and addressing welfare and environmental issues. Genetic research programs are required that will be directed to bridge the gap between the elite breeders and the developing livestock sector. The aim of this review was to provide a perspective on the dichotomy in the South African livestock sector arguing that a realistic approach to the use of genomics in beef, dairy and small stock is required to ensure sustainable long term genetic progress.

Predicting Lameness in Sheep Activity Using Tri-Axial Acceleration Signals

Simple Summary

Monitoring livestock farmed under extensive conditions is challenging and this is particularly difficult when observing animal behaviour at an individual level. Lameness is a disease symptom that has traditionally relied on visual inspection to detect those animals with an abnormal walking pattern. More recently, accelerometer sensors have been used in other livestock industries to detect lame animals. These devices are able to record changes in activity intensity, allowing us to differentiate between a grazing, walking, and resting animal. Using these on-animal sensors, grazing, standing, walking, and lame walking were accurately detected from an ear attached sensor. With further development, this classification algorithm could be linked with an automatic livestock monitoring system to provide real time information on individual health status, something that is practically not possible under current extensive livestock production systems.

Abstract

Lameness is a clinical symptom associated with a number of sheep diseases around the world, having adverse effects on weight gain, fertility, and lamb birth weight, and increasing the risk of secondary diseases. Current methods to identify lame animals rely on labour intensive visual inspection. The aim of this current study was to determine the ability of a collar, leg, and ear attached tri-axial accelerometer to discriminate between sound and lame gait movement in sheep. Data were separated into 10 s mutually exclusive behaviour epochs and subjected to Quadratic Discriminant Analysis (QDA). Initial analysis showed the high misclassification of lame grazing events with sound grazing and standing from all deployment modes. The final classification model, which included lame walking and all sound activity classes, yielded a prediction accuracy for lame locomotion of 82%, 35%, and 87% for the ear, collar, and leg deployments, respectively. Misclassification of sound walking with lame walking within the leg accelerometer dataset highlights the superiority of an ear mode of attachment for the classification of lame gait characteristics based on time series accelerometer data.

One-hour portable chamber methane measurements are repeatable and provide useful information on feed intake and efficiency

Feed intake (FI), live weight (LW), and ADG were recorded over 31 d in ninety-six 12-month-old ewes (progeny of 4 sires) given ad libitum access to chaffed lucerne/cereal hay. Methane (CH) and CO emissions of each ewe were measured for 40 to 60 min in portable accumulation chambers (PAC) and in respiration chambers (RC) over 22 h. Testing in RC increased the variability of FI on the test day and depressed the amount eaten from an average of 1,384 to 1,062 g/d; FI depression increased by 0.63 ± 0.24 percentage points for every kilogram of additional LW. Repeatabilities of PAC measurements were 0.76 (CH) and 0.81 (CO). After adjusting for LW and ADG, repeatabilities were 0.47 (PAC CH) and 0.43 (PAC CO). Daily FI measurements had similar repeatability (0.76 before and 0.42 after adjustment for LW and ADG). The PAC measurements were highly correlated with mean 31-d FI ( = 0.81 for both CH and CO). After adjustment for LW and ADG, PAC measurements were moderately correlated with residual feed intake (RFI; = 0.37 for CH, 0.31 for CO). The CH:CO ratio was also significantly correlated with mean 31-d FI ( = 0.52). After most of the ewes had given birth and raised lambs, repeat PAC measurements were available for 91 of the ewes at 2 years of age (with ad libitum access to the same feed). Correlations with the 2012 PAC measurements were 0.64 (CH) and 0.75 (CO). After adjusting 2014 PAC measurements for LW, correlations with RFI in 2012 were 0.34 (CH) and 0.33 (CO), with a clear relationship between sire means for RFI in 2012 and PAC CH adjusted for LW in 2014. These results suggest that PAC tests under similar feeding conditions are repeatable over an extended time period and can provide useful information on FI and feed efficiency as well as methane emissions. Analyses of RC measurements might need to consider FI depression.

Comparison of repeated measurements of methane production in sheep over 5 years and a range of measurement protocols

Emissions of 710 ewes at pasture were measured for 1 h (between 09:00-16:30 h) in batches of 15 sheep in portable accumulation chambers (PAC) after an overnight fast continuing until 2 h before measurement, when the sheep had access to baled hay for 1 h. The test was used to identify a group of 104 low emitters (I-Low) and a group of 103 high emitters (I-Hi) for methane emissions adjusted for liveweight (CHawt). The 207 ewes selected at the initial study were remeasured in 5 repeat tests from 2009 through 2014 at another location. The first repeat used the original measurement protocol. Two modified protocols, each used in 2 yr, drafted unfasted sheep on the morning of the test into a yard or holding paddock until measurement. Emissions of the I-Hi sheep were higher (102-112%) than I-Low sheep in all subsequent PAC tests, with statistical significance ( < 0.05) in 3 tests. Tests without overnight fasting were simpler to conduct and had repeatabilities of 51 to 60% compared with 31 and 43% for the initial and first repeat tests, respectively. After habituation to a diet fed at 20 g/kg liveweight, 160 of the 207 sheep were measured in respiration chambers (RC); 10 high (Hi-10) and 10 low (Low-10) sheep were chosen, representing extremes (top and bottom 6.25%) for methane yield (MY; g CH/kg DMI). The Hi-10 group emitted 14% more methane (adjusted for feed intake) in a follow-up RC test, but Low-10 and Hi-10 sheep differed in only 1 of the 5 PAC tests, when Hi-10 sheep emitted less CHawt than Low-10 sheep ( = 0.002) and tended to eat less in the feeding opportunity ( = 0.085). Compared with their weight on good pasture, Low-10 sheep were proportionately lighter than Hi-10 sheep in the relatively poor pasture conditions of the initial test. Sheep identified as low emitters by PAC tests using the initial protocol did not produce less CH (mg/min) when fed a fixed level of intake in RC. Correlations between estimates of an animal's CHawt measured in PAC and CH adjusted for feed intake in RC were quite low ( = 0-19%) and significant ( < 0.05) in only 1 test of unfasted sheep. With moderate repeatability over the 5 yr, PAC tests of CHawt could be a viable way to select for reduced emissions of grazing sheep. As well as exploiting any variation in MY, selecting for reduced CHawt in PAC could result in lower feed intake than expected for the animals' liveweight and might affect the diurnal feeding pattern. Further work is required on these issues.

Development and application of a livestock phenomics platform to enhance productivity and efficiency at pasture

Our capacity to measure performance- and efficiency-related phenotypes in grazing livestock in a timely manner, ideally in real-time without human interference, has been severely limited. Future demands and constraints on grazing livestock production will require a step change beyond our current approaches to obtaining phenotypic data. Animal phenomics is a relatively new term that describes the next generation of animal trait measurement, including methodologies and equipment used to acquire data on traits, and computational approaches required to turn data into phenotypic information. Phenomics offers a range of emerging opportunities to define new traits specific to grazing livestock, including intake and efficiency at pasture, and to measure many traits simultaneously or at a level of detail previously unachievable in the grazing environment. Application of this approach to phenotyping can improve the precision with which nutritional and other management strategies are applied, enable development of predictive biological traits, and accelerate the rate at which genetic gain is achieved for existing and new traits. In the present paper, we briefly outline the potential for livestock phenomics and describe (1) on-animal sensory-based approaches to develop traits diagnostic of productivity and efficiency, as well as resilience, health and welfare and (2) on-farm methods for data collection that drive management solutions to reduce input costs and accelerate genetic gain. The technological and analytical challenges associated with these objectives are also briefly considered, along with a brief overview of a promising field of work in which phenomics will affect animal agriculture, namely efficiency at pasture.

Intra-ruminal gas-sensing in real time: a proof-of-concept

An intra-rumen (IR) gas-sensing system incorporating commercially available gas sensors [methane (CH4), carbon dioxide (CO2) and hydrogen (H2)] and a wireless sensor network was developed to measure rumen gas concentrations of grazing animals in real-time. The IR gas-sensing devices also measure temperature and pressure near the sensors and the design isolates the electronics and battery from exposure to gases. Membranes were developed that allow the desired gases to diffuse through to the sensors while excluding corrosive hydrogen sulfide (H2S). Performance of the prototype IR devices was tested in cattle and sheep fed once a day as a proof-of-concept. Concentrations of expired gases from respiration chambers were compared with the concentrations obtained by the IR gas-sensing device within the rumen digesta. Direct measurements of rumen gas cap samples demonstrate a similar gas profile to that observed with the IR gas-sensing device with the ratio of CO2 : CH4 peaking shortly after feeding and CO2 levels nearly 2.5 times greater than those of CH4. The gas ratio then declines over time to a point when at 23 h post-feeding the concentration of CH4 exceeds that of CO2. The H2 gas concentration in the rumen varied throughout the day reaching maximum levels of 2500 ppm after feeding and declining to 250 ppm over the day. Although the IR device was able to detect H2 in the rumen throughout the entire day, expired H2 was often below the limits of detection in the respiration chamber. Current work is focussed on extending the longevity of the devices in the rumen so that replicated trials can be performed on the accuracy and precision of the measurements.