4 minute read

Richard Round, Innovation Associate, Agricultural Sustainability, UK Agri-Tech Centre

Data that works for farmers

Agriculture accounts for around 10% of the UK’s total greenhouse gas emissions, driven mainly by methane from livestock and nitrous oxide from fertilisers. As regulators and retailers push to cut emissions, farmers face growing pressure to monitor and reduce their footprint, while staying profitable under increasingly challenging climate conditions. The challenge then is twofold: how do we measure emissions accurately, and how do we ensure farmers benefit from the process?

These two issues are naturally linked. Accurately monitoring emissions within complex living systems like soil and livestock is difficult and requires active farmer participation. Yet without a clear return on investment, why would farmers commit time and resources to data collection?

This is where AI can play a transformative role. When implemented effectively, AI tools can turn fragmented agricultural data into actionable insights, helping farmers improve efficiency and cut emissions. At the same time, these platforms can provide regulators and organisations with robust datasets to shape decarbonisation strategies and develop new incentive schemes to reward low-emission farming practices.

The challenge of measuring agricultural emissions

Accurate emissions data in agriculture is notoriously difficult to achieve. Farm emissions come from living systems where biological variability plays a huge role. Emissions are not only influenced by management practices, but by weather, soil microbiology, and other factors that can cause emissions to fluctuate daily.

Traditional sampling methods are expensive and time-consuming, and only provide snapshots of the whole picture. Meanwhile, other valuable data sources such as machinery logs, weather stations, soil sensors, and third-party services are often fragmented across multiple platforms and subscriptions. These sources generate vast amounts of valuable information, but they’re often siloed, hard to access, and even harder to integrate. Add on top of this concerns over who owns and benefits from the data gathered from their farm, and it’s not difficult to see why farmers hesitate to invest in systems that don’t deliver clear, tangible value to their business.

Turning data into action

AI however has the potential to integrate various data streams and uncover patterns, predict outcomes and provide suggestions that can help farmers achieve their productivity and sustainability goals.

We are already seeing examples of projects and agri-tech business piloting solutions to tackle agricultural emissions and improve efficiency. Advanced machine learning models are being used to predict yields, track carbon sequestration, model emissions, and simulate how changes in feed or management practices could influence both emissions and yield.

Meanwhile AI-enabled nutrient management tools are being developed to help growers maximise fertiliser use efficiency by integrating sensor data with weather forecasts and crop models to provide optimal fertiliser timing and rates.

By turning complex datasets into practical suggestions, these services enable farmers to improve efficiency, cut emissions, and maintain or even improve profitability. Crucially, they transform emissions data collection from a compliance burden into a strategy to drive productivity and resilience.

AI and carbon markets

While AI is starting to become available to help farmers improve efficiency and reduce emissions, it has the potential to go further and underpin carbon reward schemes. Initiatives, like the UK Farm Soil Carbon Code that is still under development, aim to lay the groundwork by developing clear protocols for tracking changes in soil carbon and greenhouse gas emissions, whilst funding mechanisms such as the Exchange Market Fund and retailer-led carbon insetting projects are exploring how to directly reward farmers for reducing emissions or sequestering carbon. Likewise, the proposed relaunch of the Sustainable Farming Incentive could again open a pathway for financial support to farmers for adopting low-emission practices.

AI has the potential to make these schemes more accessible and reliable by providing farm-specific reports and recommendations, not only alleviating the administrative burden but helping farmers make informed decisions to best access these incentives.

What AI platforms must deliver

Trust is difficult to gain and easy to lose, especially when people are tying their livelihoods to new technologies. To achieve widespread trust and adoption platforms must be designed with several key features that prioritise transparency and reliability:

  • Validated methodologies and transparent algorithms, with traceable data sources aligned to recognised standards
  • Detailed outputs formatted to comply with national and international carbon accounting standards, ensuring compatibility with third-party monitoring, reporting, and verification requirements
  • Full auditability, with every data point is linked to its original source and every recommendation linked to its relevant data points
  • Safeguards against manipulation or false reporting
  • Exportable and interoperable data compatible with systems used by verifiers, regulators, and supply chain partners
  • Continuous or high-frequency data collection to capture variability over time
  • High-resolution data and recommendations that reflects real management practices and enables site-specific interventions
  • Farmer control over how their data is used and which recommendations to follow
  • Affordability and accessibility for farms of all sizes

From data to decarbonisation

Emerging AI platforms can enable farmers to reduce emissions while maintaining or improving profitability. But with thoughtful development and integration, AI platforms could become the foundation for how the agricultural sector monitors, reports, and acts on its environmental impact. By connecting fragmented data sources, automating complex measurements, and providing practical insights, these systems could support both farm-level decision-making and supply chain-wide decarbonisation. To realise this potential, stakeholders across industry must invest and collaborate to develop and demonstrate trustworthy, scalable AI solutions that empower farmers and drive meaningful climate action.

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