Beef farmers in the UK are struggling to balance profitability, animal welfare, and environmental impact, and a new project aims to build an AI-powered "digital twin" of the entire beef supply chain to help them do it. Current approaches to sustainable beef farming typically focus on only one part of the system—feed, on-farm sensors, or processing—and often rely on solutions designed for dairy cows, which are easier to track than free-grazing beef cattle. The UK’s beef sector is dominated by small, distributed farms with diverse breeds, making centralised monitoring and methane-reduction methods impractical. This project brings together environmental, biological, computer, and management scientists to tackle the full picture: from feed conversion to farming practice to beef quality. If successful, the digital twin will recommend alternative feed and farming practices that balance environmental, social, and economic outcomes. This could help small beef farms become more profitable while reducing greenhouse gas emissions per kilogram of protein produced—a shift that would quietly reshape the UK’s food supply chain without requiring farmers to adopt one-size-fits-all technology.
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This project is proposed to radically transform how beef is farmed in the UK, through: reduced GHG emissions per kg protein produced; increased beef farming productivity; higher beef quality and improved animal welfare. Thus, the proposal establishes an exemplary approach and activates the transition for the UK to reduced beef production. Current approaches to "sustainable" beef farming face challenges in practice to balance the potential Triple Bottom Line (Economic, Environmental,Social) trade offs. Previous studies have mostly focused on just one stage of a supply chain. For example, upstream (Feed) farm-level biology-centred innovation projects measure beef cattle biometrics and feed conversion; midstream (Farm) computer and data scientists utilise sensors and on-farm devices to capture data and improve elements of farming production; downstream (Food) business and engineering researchers evaluate resource configurations to reduce emissions in food processing. These developments are also often limited to dairy farming where cows can return to the milking station on a regular basis for tracking as opposed to beef cattle who graze out in the open rendering existing centralised systems ineffective. Whilst dairy cattle are predominantly Holstein, beef cattle could be any of the over 200 breeds recognised by the UK Government alone or indeed hybrid or composite breeds. Solutions working on one breed will need further validation to determine their efficacy for others. In the UK, beef farms are usually SMEs with small indoor-outdoor reared herds. Highly distributed farms and field systems make scaling-up farm/animal-centric solutions, including known methane treatment methods, impossible for grazed animals. The logistics of live beef cattle transport also poses further challenges due to the potential for animal stress, weight loss, and emissions in transportation. Small-scaled distributed farming practice strangled by beef price domination from consumer-facing organisations, left beef farms unprofitable. Beef farming requires a fundamental transformation across Feed (conversion), Farming practice and Food (beef) quality to meet the requirements of the TBL (3F-TBL). This unique complexity and scope of the problem case presented, makes interdisciplinary research approaches essential. Therefore, the project consists of multifaceted expertise from a set of "minimum viable" disciplines. As a result, we identified environmental sciences (RHUL), biological sciences (UoN), computer sciences (UoS) and management sciences (NTU) as core disciplines to be brought together, to conduct the underpinning scientific research. UoL brings Agri-food Systems thinking and abundant pathways to real-life impact from its unique flagship position in precision farming. The output of this project will be an AI powered Digital Twin for Beef Farming. The Digital Twin will empower suggestions on feed and farming practice alternative solutions, that can balance the impact among Environmental, Social and Economic factors.
David Lowry (Co-Investigator)Jungong Han (Co-Investigator)Louise Manning (Co-Investigator)Rebecca Fisher (Co-Investigator)Ruth Nisbet (Co-Investigator)Xiao Ma (Principal Investigator)
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