Kevin Donkers



Enrichment Student

Cohort year


Partner Institution


Kevin Donkers began his doctoral studies with the Environmental Intelligence CDT at the University of Exeter in September 2020. He is also an applied data scientist at the Met Office Informatics Lab where he has worked on data science, machine learning and technology innovation for weather and climate science since January 2019. His background is in physics and chemistry, having studied for an MSci in Chemical Physics at the University of Glasgow, but has worked on data science for weather and climate since joining the UK Met Office in 2017. His doctoral research focusses the capacity of land use choices to achieve Net Zero targets, using agroforestry as a test case to understand trade-offs between carbon sequestration and other important factors such as food security.

Research interests

Kevin's current research focusses on understanding the trade-offs in UK land use between carbon sequestration for Net Zero and food security. Given that the current government plans to plant at least 30,000 ha of trees per year (in England) by 2024 and 70% of UK land is used for agriculture, he is particularly interested in understanding to what extent trees planted in agroforestry systems can be used in conjunction with traditional forestry to achieve Net Zero targets. To do this he is using biophysical models with UK climate projection data to simulate different systems of tree planting under future climates. A key aspect to this research is to understand how uncertainty in climate projections and within the biophysical models themselves affect both the resilience of and risks associated with land use choices. Uncertainty quantification and probabilistic programming techniques look promising for this. A physical understanding of what combination of land use types leads to optimal natural capital doesn’t mean that it is desirable or even possible to implement. Therefore an assessment of policy options is also needed. Kevin plans to explore techniques such as agent based modelling and decision theory to simulate how different incentives affect the uptake of tree planting in a way that achieves carbon sequestration targets but also maximises food security.