Introduction
Livestock production, especially cattle, is the leading contributor to global methane (CH₄) emissions. Methane is a potent greenhouse gas with a global warming potential 85 times higher than that of carbon dioxide (CO₂) over 20 years. A large number of these emissions belong to the enteric fermentation occurring in ruminants' rumens. Tackling these emissions would significantly help combat climate change, advance sustainability, and address food security.
Precision livestock farming (PLF) is a new technology-oriented solution that can help to transform methane mitigation strategies in cattle. Using wearable sensors and complex data analytics, farmers could track, measure and manage methane emissions. In this article, we discuss the opportunity of field wearable sensor technology and data-based decision improvement to reduce methane emissions in cattle farming and maintain the quality of animal productivity.
Wearable Sensors: Revolutionizing Methane Monitoring
New breakthroughs in wearable sensor technology are now allowing methane emissions from individual animals to be continuously monitored. The sensors provide farmers with important data regarding methane output, enabling them to make targeted interventions that optimize diet, feeding strategies, and overall management. Below are a few examples of wearable sensors for methane reduction.
Snout Sensors: Sniffing out methane emissions from livestock
These collar devices (e.g., Zero Emission Livestock Project, ZELP) can capture exhaled methane in breath and convert it into carbon dioxide and water vapor. These devices monitor cow breath and provide real-time data on cattle respiration and activity with up to 50% meat hen-reducing ability.
Dairy farmers in the United Kingdom have started using ZELP collars on high-producing cows. Initial findings have indicated a substantial reduction in methane emissions with associated improvements in overall animal health, welfare, and performance. The real-time data gathered from these sensors allows farmers to immediately make required changes in diets and management strategies.
Rumen Boluses
These are ingestible sensors (they stay in the rumen) that continuously measure methane production without any external tracking. The sensors offer data to evaluate the potential impacts of different dietary treatments on methane emissions over time.
Research on cattle at Australian feedlots using rumen boluses showed that methane-reducing feed additives could be optimized and implemented properly at the individual-animal level using real-time feedback from sensors. The study later found that the methane emissions were reduced by 30–50% in cattle feed wands tailored to their metabolic profiles based on the sensor data.
Accelerometers and Motion Sensors
These devices monitor feeding behavior, rumination patterns, and activity levels and indicate inefficient feeding practices. This information can help interpret methane production trends. The devices can also help monitor activity patterns that lead to excess methane emissions.
AgResearch, New Zealand, is now getting data from wearable sensors trained on AI models to predict methane output in grazing dairy cows. Climate data from national climate models combined with the
AI used pasture quality and cattle behavior to generate precision feeding plans that reduce methane output without having any negative effects on milk production.
Smart Halters + GPS Trackers
These devices track grazing behavior, movement patterns, and environmental data to provide evidence of the relationship between pasture quality and the amount of methanogenesis affected by grazing time budgets.
Data Analytics: Improving Methane Abatement Decisions
Combined with data analytics, wearable sensors really become powerful. The individual animal data, when mined through artificial intelligence (AI) and machine learning (ML) algorithms, provide actionable insights. Data analytics has the following applications in methane mitigation.
Diet Optimization
AI-driven formulation can help adjust rations to add seaweed (Asparagopsis), tannins, essential oils, and feed supplements that can potentially reduce methanogenesis. The data collected using rumen boluses enables the assessment of feed supplements' efficacy in reducing methane emissions while maintaining animal productivity and welfare.
Predictive Modelling
Predictive models can estimate emission trends based on historical data and, therefore, suggest preventive management strategies for future methane emissions. Including diet composition, weather patterns, and animal health parameters in the models can further help refine emission mitigation approaches.
Selective Breeding Programs
This is the first time we have an opportunity to breed cattle with genetically low methane output by using methane emission data collected through wearable sensors and technologies such as the GreenFeed system. AI allows for identifying cows that naturally produce less methane without compromising milk or meat productivity.
Real-Time Monitoring and notifications/alerts
When methane emissions cross optimal thresholds, farmers receive automated alerts to facilitate quick corrective measures as and when required. The data dashboard's visualization of emission trends makes it easier to identify areas for improvement.
Challenges and Future Directions
The potential of wearable technology combined with data analytics to reduce methane emissions is pretty vast. However, several challenges need to be addressed for its widespread application.
- Cost and Availability: The high cost of wearable sensors and the data infrastructure exceeds the smallholder farmers' usual economic status and could be a potential barrier.
- Data Integration and Management: Large data volumes require an advanced analytics platform and skills for interpreting and extracting insights that can make an impact.
- Farmer Adoption and Training: This will require training farmers on how to effectively implement the sensor data to mitigate methane production.
- Legal and Ethical Implications: AI and sensor-based monitoring are raising questions related to data privacy and ethical considerations for animal monitoring.
Conclusion
Precision livestock farming using wearable sensors and data analytics offers a holistic approach to mitigating methane emissions in cattle. These technologies provide real-time monitoring, help make data-driven decisions, and allow farmers to adopt approaches that enhance environmental sustainability practices while increasing the productivity of their animals. As sensor technology continues to improve and data analytics become better and more precise, the livestock industry is about to reach a revolution, a revolution in which methane is not only tracked but actively managed and reduced. This may also be in line with future global climate goals and ultimately ensures the long-term sustainability of livestock production systems. Concluding remarks, however, include developing policies to incentivize methane mitigation sensor technologies, along with further improvement of artificial intelligence analytics for methane prediction.
References
- Hristov, A. N., Oh, J., Lee, C., Meinen, R., Montes, F., Ott, T., & Firkins, J. L. (2021). Mitigation of methane and nitrous oxide emissions from animal operations: I. A review of enteric methane mitigation options. Journal of Animal Science, 99(1), skaa364.
- Roque, B. M., Venegas, M., Kinley, R. D., de Nys, R., Duarte, T. L., Yang, X., & Kebreab, E. (2021). Red seaweed (Asparagopsis spp.) supplementation reduces enteric methane by over 80 percent in beef steers. PLOS ONE, 16(3), e0247820.
- Garnsworthy, P. C., Difford, G. F., Bell, M. J., Bayat, A. R., Huhtanen, P., Kuhla, B., & Lassen, J. (2019). Comparison of methods to measure methane for use in genetic evaluation of dairy cattle. Animals, 9(11), 837.
