Trustworthy Federated Learning enabled Predictive Analytics for Sustainable Nutrient and Carbon Management in Intensive Livestock Systems

About the project

About the Project Intensive livestock farming is a major contributor to environmental degradation, including nutrient runoff, water pollution, and greenhouse gas emissions. Existing regulatory frameworks often rely on fixed calendar-based rules and retrospective measurements, which fail to reflect the dynamic nature of farm systems. This PhD project addresses the urgent need for data-driven, site-specific approaches to nutrient and carbon management that support both environmental sustainability and agricultural productivity. The research will involve the development of predictive analytics using federated learning and trustworthy AI techniques. It will build on the X10AI AGRISMART digital twin platform, which integrates real-world data from anaerobic digestion, ammonia recovery, pyrolysis, and precision agriculture across UK farms. The project will focus on fusing hyperspectral drone imagery with structured (e.g., yield, weather) and unstructured (e.g., farm logs, regulatory reports) datasets through multimodal data harmonisation and summarisation using large language models. A federated learning framework will be designed to enable collaborative model training across farms while preserving data privacy. The models will incorporate both physics-informed and data-driven components and will be validated through two case studies: (1) predicting grass growth to support phosphorus “geo-mining” and sustainable manure export, and (2) forecasting slurry spreading windows based on local soil and weather conditions. Training will include advanced skills in machine learning, remote sensing, environmental modelling, and explainable AI. The PhD offers opportunities to work with academic experts and industry partners (x10AI, ABP, Sainsbury’s, NFU), access real-world datasets, and contribute to research with direct environmental protection policy and industry relevance. The PhD project is ideal for students with strong programming and mathematical skills, and a passion for AI and sustainability. Supervisors: Prof. Sean McLoone ( s.mcloone@qub.ac.uk ) Dr. Iain Gould ( igould@lincoln.ac.uk ) Dr. Shaun Coutts ( scoutts@lincoln.ac.uk ) Mr Thomas Cromie, X10AI Applications: Our fully-funded studentship package includes: All PhD tuition fees paid. A tax-free stipend at UKRI rates to cover living costs. A Research Training Support Grant (RTSG) of £3,000 each year to support travel, training and consumables costs (up to £12,000 in total). Additional funding to support outreach and dissemination, attendance at summer schools, research events, and development projects." Interested applicants should visit ' https://www.sustain-cdt.ai/how-to-apply ' for full instructions on how to submit their application. Requirements: Candidates must have an honours degree (minimum 2:1) in Computer Science, Engineering or related disciplines, a strong mathematical background and good programming skills. It is essential that the PhD student is self-driven, curious, interested in working across disciplines and exploring new areas, as well as eager to work as part of an interdisciplinary team. The student will be expected to engage with their peers and other academic staff, get involved in departmental events and seminars, and show enthusiasm for public/policy engagement activities. Desirable: a Master’s degree in AI, Machine Learning, Data Science, or Control Systems Experience of working with AI for practical applications Prior experience of engaging with industry on R&D projects Enquiries: SUSTAIN@Lincoln.ac.uk Application Deadline: 12:00 Midday on Friday, 16 th October 2026 (UK time)

Research areas