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Towards Sustainable Net-Zero Farming Using Machine Learning

Dr Vladimir Stankovic, University of Strathclyde; Dr Marta Dondini, University of Aberdeen; Dr Lina Stankovic, University of Strathclyde; RHD (Scotland) Ltd

The agri-sector, especially dairy farming, is a major contributor to greenhouse gas (GHG) emissions with 18% of annual worldwide GHG emissions attributed to animal farming. Understanding the variation in GHG emissions among different farming practices helps to identify sustainable approaches that reduce pollution.

 

GHG emissions from the agri-sector result from complex process and diverse sources, that are often hard to measure and estimate. Besides methane that comes mainly from livestock digestion, and nitrous oxide, that is emitted as a result of agricultural fertilizers, CO2 emissions from energy consumption to drive machinery, using a combination of diesel and other fuels and electricity, cannot be neglected. Hence, with clear GHG emission reduction targets, the agri-sector is under pressure to quickly adapt its well-establish practices, introducing green alternatives. Reducing the carbon footprint would not only improve environment, but would also have strong positive impact on farming business models (green environmentally-friendly farming label) and reduce the cost.

The aim of the PhD research is to develop an AI-driven methodology for holistic quantification of GHG emissions in dairy farming, taking into account all sources of GHG emissions and provide recommendations for GHG reduction.

 

The research question to be answered is: How to optimise infrastructure and activities in dairy farms to minimise GHG emissions towards net- zero and sustainable farming?

 

To answer this research question, the following objectives are set: 1) Improve capabilities of dairy farms to participate in energy demand flexibility services and community storage; 2) devise an AI-driven approach to calculate CO2 emissions considering multiple factors including energy prosumption, storage, energy import from the grid considering the local fuel mix ; 3) estimate other sources of GHG emissions using a range of available modelling tools and assess how GHG emissions can be reduced by optimising the underlying processes and feeding practices; 4) develop an overall GHG emission model by combining estimated emissions from various farming processes ; 5) evaluate scalability of models and measures across different types of dairy farms.

 

The student will interact with farmers to collect quantitative and qualitative data, understand flexibilities in current established farming practices to develop solutions towards net-zero farming. The student will be embedded into multi-disciplinary teams at Strathclyde and Aberdeen that have established records of developing successful researchers capable of effectively working across disciplines and sectors. Besides hands-on mentored research, the student will develop broader research skills related to AI and mathematical modelling, and transferrable skills related to professional development, through a range of specialised courses. The student will also receive advanced interdisciplinary and intersectoral research and innovation training through a range of activities organised by the supervisors and research groups. The student will have an opportunity to form their network of contacts in research and industry by attending relevant conferences, workshops, outreach events, doctoral schools, and interacting with regular academic and industrial visitors.

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