Drivers of global land abandonment available for bioenergy crops
Lead supervisors: Dr Naomi Vaughan
Location: School of Environmental Sciences, Tyndall Centre, University of East Anglia
Duration: 8 weeks
Suitable undergraduate degrees: Environmental Science, Geography, any science or quantitative subject.
Achieving a net zero greenhouse gas emission future requires the decarbonisation of the global energy system and some greenhouse gas removal. This is likely to include a large scale expansion of modern bioenergy (up to 10%-30% of global energy supply by 2100 (Fuss et al,. 2014)). New land area will be required to grow these bioenergy crops (Vaughan et al., 2018). However, future land availability is highly uncertain and driven by changes in agricultural systems (e.g. yields), food production (e.g. diets), other ecosystem service needs (e.g. biodiversity) and land abandonment. Trends in, and drivers of, land abandonment differ in different regions and agricultural systems.
This research project will underpin the next stage in development of the C-LLAMA model – an open-source country level model of land availability for crops and pasture based on UN food production data (Ball et al, submitted to GMDD). The work fits within a NERC funded research project, FAB-GGR, that investigates the Feasibility of large-scale Afforestation and Biomass energy with carbon capture and storage for Greenhouse Gas Removal. These methods of GGR increase the pressures on land availability.
The project will (i) investigate the trends in, and drivers of, land abandonment in two case study countries spanning industrial and subsistence agricultural systems and (ii) analyse country-level data for relationships that can be used to approximate the complex and diverse drivers of global land abandonment. This will contribute to the development and application of an open source modelling tool to address these critical trade-offs around bioenergy use in future pathways to net zero.
Reading and discussions on drivers of global land abandonment and wider context of the project.
Support: (i) directed reading, (ii) paper discussion meeting with supervisory team (iii) feedback on oral presentation and (iv) optional training on how to run the C-LLAMA model (Python) – no previous experience required and tailored to fit interests and skills.
Output: Oral presentation on main drivers of global land abandonment.
Selection of two case study countries that sit at either end of the spectrum of economic development and level of industrialisation of agricultural systems.
Support: (i) academic literature, (ii) available data sets, (iii) weekly discussions with supervisory team and (iv) written feedback on justification text.
Output: Written justification of country selection.
Explore country-level data (e.g. land use, food production, economic drivers and agricultural system metrics) from sources such as the UN and World Bank. Analysis can be conducted in Excel, MATLAB, Python or R based on student preference.
Support: (i) weekly meetings with supervisory team, (ii) written feedback on presentation and (iii) ad-hoc online support with analysis and presentation of results.
Output: Presentation of initial data analysis.
Complete analysis and write up main findings including suggestion for metrics to represent land abandonment in C-LLAMA model.
Support: (i) weekly meeting with supervisory team (ii) ad-hoc online support as needed and (iii) reflection and discussion of previous feedback and its application to final task.
Output: Write up of main findings.