About the Project
Carbon farming has significant potential to support climate change mitigation while improving the resilience and livelihoods of smallholder farmers. However, reliable measurement, reporting and verification (MRV) of carbon outcomes across fragmented and heterogeneous farms remains costly and challenging to scale. Current approaches often depend on extensive field measurements, creating a need for more efficient methods that maintain confidence and credibility of the carbon estimates while…
reducing monitoring costs.
This project will investigate how artificial intelligence and integrated data sources can optimise carbon MRV across smallholder farming systems. The research will assess existing MRV practices and investigate how ground observations can be combined with drone, satellite, environmental and farm-level data. AI-based models will be developed to estimate carbon outcomes and quantify uncertainty. An adaptive sampling and optimisation framework will then determine where, when and how much additional ground monitoring is required to achieve specified levels of accuracy at minimum cost.
The research will provide training in machine learning, remote sensing, spatial and environmental data analysis, economic modelling, uncertainty assessment, adaptive sampling and optimisation, alongside economic evaluation of monitoring strategies. The student will gain experience working with an industry partner and applying AI to a real-world sustainability challenge. The resulting framework aims to support more cost-effective, reliable and scalable carbon MRV, helping enable the expansion of carbon farming among smallholder producers.
Supervisors:
- Pete Smith (pete.smith@abdn.ac.uk)
- Paul Williams (p.williams@qub.ac.uk)
- Milan Markovic (milan.markovic@abdn.ac.uk)
- Olusola Omole (aglaneltd@gmail.com)
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.
Please note that this project is not open to researchers from Afghanistan, Cameroon, Myanmar, Sudan or Iran.
Requirements:
Honours degree (minimum 2:1) in agriculture, agricultural economics, computer science or environmental science or engineering with strong expertise in agricultural extension, education or farmer engagement.
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, Agriculture, Agricultural Economics, or Environmental Sciences, is highly desirable.
- Quantitative or qualitative research focused BSc/MSc Dissertation/Thesis.
Enquiries:
Prof Pete Smith (pete.smith@abdn.ac.uk)
Application Deadline:
12:00 noon (UK time) Friday, 16 October 2026
References
- Jiang, Z.W., Yang, S.H., Smith, P. & Pang, Q.Q. 2023. Ensemble machine learning for modeling greenhouse gas emissions at different time scales from irrigated paddy fields. Field Crops Research 292, 108821. doi: 10.1016/j.fcr.2023.108821
- Li Y., Jia W., Abia W.A., Haughey S.A., Carey M., McCreanor C., Mooney M., Croffie M., Daly K., Maestroni B.M., Meharg C., Meharg A.A., Elliott C.T., Williams P.N. 2026. Advancing environmental and food system monitoring with machine-learning-enhanced X-ray fluorescence analytics. Environment International, 212. doi: 10.1016/j.envint.2026.110294
- Smith, P., Soussana, J.-F., Angers, D., Schipper, L., Chenu, C., Rasse, D.P., Batjes, N.H., van Egmond, F., McNeill, S., Kuhnert, M., Arias-Navarro, C., Olesen, J.E., Chirinda, N., Fornara, D., Wollenberg, E., Álvaro-Fuentes, J., Sanz-Cobena, A. & Klumpp, K. 2020. How to measure, report and verify soil carbon change to realize the potential of soil carbon sequestration for atmospheric greenhouse gas removal. Global Change Biology, 26, 219-241. doi: 10.1111/gcb.14815.