Mathematical modelling will be used to understand the dynamics of land use change and agricultural intensification and deforestation related to beef cattle and soybean production in Brazil.
Land use change (LUC) for agricultural continues to be a significant driver of forest loss globally (Fig.1). There is a need to reconcile agricultural production with the protection of globally significant ecosystems. In Brazil, cattle ranching is the main driver of deforestation, and pastures occupy around 80% of all recently deforested area in the Amazon. However, the relationship between beef production and deforestation is not straightforward. It is still unclear to what extent beef production acts as strong economic driver or as an opportunistic channel for land occupation - for gaining property rights, and for mere speculation. To understand the dynamics of deforestation we need to understand the heterogeneity of the processes underlying beef cattle and soybean production and pasture intensification/degradation in Brazil. We also need to understand the role of different policy measures and incentives and spatially dependent factors, e.g., proximity to slaughterhouses and roads.
1. How do we define sustainable intensification in livestock production and what economic and environmental trade-offs are implied by alternative definitions?
2. How do spatial-and temporal patterns of beef cattle intensification relate to agricultural expansion in Brazil?
3. How much of the direct and indirect LUC and deforestation are explained by pasture degradation?
4. What are spatially dependent drivers of sustainable intensification?
5. How can future intensification through pasture restoration combined with pasture to crop conversion (P2C) influence deforestation and GHG emissions in the Amazon and Cerrado biomes?
The first phase of this PhD will draw on several spatially explicit datasets from the TerraME programming environment (www.terrame.org/doku.php), Terraclass initiative (TerraClass, 2014), Mapbiomas, Agricultural Census,Rural Environmental Registry (CAR), FAOSTAT, EMBRAPA PECUS and the new TNC pasture degradation data (https://agroideal.org/
), and will use polygons and spatial analysis tools to identify spatial-temporal agricultural intensification/pasture degradation patterns. A second phase will model the degradation/restoration patterns using behavioural models to couple phase 1 results with a deeper understanding/modelling of socio economic and other indirect drivers of intensification and deforestation. The PhD will involve close collaboration/training with INPE (Brazilian Institute for Space Research ), EMBRAPA and TNC data scientists.
Year 1 Chapter planning, literature review, presentation and research skills courses. Attendance of appropriate masters degree modules – e.g., programming, Agent Based Modelling, ecological economics and GIS/statistics model training. Conference attendance, training at INPE, Field visit (Brazil)
Year 2 Data processing and analysis using TerraME, field work (EMBRAPA) in Brazil, Conference attendance
Year 3 Behavioural modelling – write up and publication planning
A comprehensive training programme will be provided comprising both specialist scientific training and generic transferable and professional skills.
A student seeking to expand their expertise in the field of global environmental change with big data analysis and spatially explicit modelling. Applicants with a quantitative background (maths, engineering, physics, economics and agricultural sciences) aiming to develop cross-disciplinary skills.
Rafael De Oliveira Silva GAAFS [email protected]
Dominic Moran GAAFS [email protected]
Luis Gustavo Barioni EMBRAPA [email protected]
Peter Alexander School of GeoSciences [email protected]
De Oliveira Silva, et al (2016) Decoupling livestock production from deforestation in Brazil: how increasing beef consumption can lower greenhouse gas emissions, Nature Climate Change . DOI: 10.1038/NCLIMATE2916.
Curtis, P.G., Slay, C.M., Harris, N.L., Tyukavina, A. and Hansen, M.C., 2018. Classifying drivers of global forest loss. Science, 361(6407), pp.1108-1111.
Pretty et al (2018) Global assessment of agricultural system redesign for sustainable intensification. Nature Sustainability, 1. 441–446. doi: 10.1038/s41893-018-0114-0
Balmford al (2018) The environmental costs and benefits of high-yield farming.Nature Sustainability, 1. 477–485.
TERRACLASS, P. Levantamento de informações de uso e cobertura da terra na Amazônia. (2014) https:// ainfo.cnptia.embrapa.br/digital/bitstream/item/152807/1/ TerraClass.pdf. Accessed in 23 August 2018.