AI tools developed for greenhouse emissions reduction in livestock
Credit: Loughborough University
Loughborough University computer scientists in the UK have developed AI tools that offer insights into how greenhouse gas emissions associated with British livestock farming and land use can be reduced.
The tools – which are hosted on an online digital platform and created as part of research funded by UK Research and Innovation (UKRI) and the Engineering and Physical Sciences Research Council (EPSRC) – aim to provide farmers, farming organisations, and government bodies with valuable data on how changes in livestock practices and land use can help the UK achieve its 2050 net zero goal.
Developed by a team led by Professor Baihua Li and Professor Qinggang Meng, key features of the platform include machine learning models designed to estimate methane emissions from livestock farming, predict milk productivity and ammonia emissions from dairy farms, and analyse how land use and environmental factors influence methane emissions across the UK.
“Our mission is to bridge the gap between innovation and practicality, offering a platform that supports data-driven decisions to combat climate change, advance sustainable farming, and achieve global net-zero emissions goals,” said Professor Li.
“By harnessing AI, our platform can offer data-driven insights that can help forecast future emissions based on a diverse range of data, giving stakeholders actionable intelligence to make cost-effective proactive decisions.”
The AI tools developed for livestock farms allow farmers to input details about their specific animals and practices to estimate their current annual greenhouse gas emissions. Farmers can easily explore potential changes to their practices – simply by selecting options from drop-down menus or entering variable values. These adjustments provide immediate insights into their potential impact on both emissions and farm productivity.
One tool is designed specifically for dairy farmers, helping them estimate how their current practices affect individual cow milk yield and ammonia levels in waste. Monitoring ammonia is crucial, as it interacts with soil microbes to produce nitrous oxide and may also indicate dietary imbalances. This development was made possible through the support of the National Bovine Data Centre and the Cattle Information Service.
Another tool, developed for beef farmers, predicts methane emissions for individual cows based on farm-specific data. It also helps farmers understand emissions in context by offering relatable comparisons—such as the number of trees needed to offset a cow’s annual emissions, the equivalent emissions from flights between London and New York, or the months of energy use in an average UK household.
The team has also developed a livestock emissions calculator based on Intergovernmental Panel on Climate Change (IPCC) guidelines, the global standard for climate reporting. Suitable for farmers worldwide, it simplifies complex government formulas and presents them in a user-friendly format, helping farmers compare their emissions to official baselines.
Digital twin
Beyond farm-level tools, the research team has harnessed artificial intelligence to develop a user-friendly, web-based platform – referred to as a ‘digital twin’ – to provide detailed insights into how different types of land use affect methane emissions across the UK.
The digital twin features heatmaps of ruminant livestock distribution, land cover types (such as agriculture, urban areas, and woodland), and methane emission concentrations across the UK. It integrates real-time satellite methane observations from Sentinel-5P TROPOMI, AI models, datasets, and various intuitive visualisation tools.
It is hoped the tool will be used by policymakers, government bodies, and farming organisations to deepen understanding of how environmental factors influence emissions and enable data-backed decisions to be made to reduce emissions.
Call for collaboration
Before the digital platform hosting the tools can be deployed to users, the Loughborough researchers need to further refine and test their AI models, which requires additional data.
The team is calling for the following collaborators:
- Individual farmers that can share data on their specific practices, for example, livestock details and feeding strategies
- Farming cooperatives and organisations that have data from multiple farms
- Governmental and regulatory bodies that manage compliance data, and can provide historical and geographical data
- Agricultural research institutions
- Retailers and supply chain stakeholders with data relevant to the agricultural industry.
Professor Meng said: “We hope key stakeholders recognise the value of this platform, support efforts to achieve net zero emissions, and contribute essential data to help bring the technology to life, ultimately transforming our practises and ensuring a sustainable future for all.”
