Precision and Sustainability in Livestock Farming: Integrating Data from Automated Systems and Predictive Modelling (PhD Studentship)

This PhD studentship at the University of Edinburgh offers an exciting opportunity to address the sustainability challenges facing global livestock farming. Livestock systems are significant contributors to greenhouse gas (GHG) emissions, particularly enteric methane from ruminants. The project aims to develop innovative solutions by integrating precision livestock farming (PLF) technologies and decision support systems (DSS) to optimize farm management, reduce emissions, and enhance productivity. Mitigation strategies such as improved diets, feedlot finishing, on-pasture supplementation, methane-inhibiting additives, and soil organic carbon (SOC) sequestration are available, but their implementation at the farm level is complex. Farmers must balance nutritional needs, pasture growth, economic viability, and the costs of adopting new technologies. DSS and PLF provide powerful tools to manage this complexity by integrating data on feed composition, growth rates, and market conditions, and by generating high-resolution, automated on-farm data through electronic scales, sensors, and satellite technologies. This project will develop a comprehensive DSS that unifies PLF technologies with predictive modelling, data assimilation, and diet optimization. The research will address key gaps in integrating on-farm data with predictive models and reducing implementation costs, aiming to translate research innovations into practical solutions for livestock production systems. The four-year training plan includes: Year 1: Literature review, data analysis, modelling of animal, crop, and SOC systems, model calibration and validation. Year 2: Application of data assimilation methods (e.g., Kalman Filters) using EMBRAPA farm datasets to combine real-world data with predictive models, improving estimates of weight gain, nutritional needs, and methane emissions. Year 3: Optimization techniques (e.g., linear programming) for diet formulation and farm-level decision models to identify profitable and low-emission strategies. Year 4: Development of the core DSS using AIMMS or Python, and thesis writing. The studentship is open to UK and international students for on-campus study. Successful applicants will receive a monthly stipend matched to the UKRI minimum level for 42 months, along with a research budget for research costs, training, and conference attendance. Applicants should have a strong background in agricultural sciences, environmental science, biology, or a related discipline, with experience or interest in data analysis, modelling, and programming. The project is supervised by Dr R De Oliveira Silva and Prof D Moran, experts in the field. Applications must be submitted by noon (GMT) on 13th February 2026. For further details and to download the application form, visit the project website. Completed forms should be emailed to [email protected], including the reference code “2026-27-011” in the subject line. References: [1] PCC, 2022: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report (AR6 WGIII) [2] Tedeschi, O. L., et al. Advancements in sensor technology and decision support intelligent tools to assist smart livestock farming, Journal of Animal Science, Volume 99, Issue 2, February 2021, skab038, https://doi.org/10.1093/jas/skab038