Modelling the agriculture-deforestation interface in the Brazilian Amazon

Project Summary

Mathematical and socio-ecological 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.

Project background

Land use change (LUC) for agricultural production continues to be a significant driver of forest loss globally. Curtis et al. (2018) suggest that for 2001-15 7% of global forest loss can be attributed to deforestation through permanent land use change for commodity production. There is a need to reconcile agricultural production with forest conservation and the protection of globally significant ecosystem goods and services. In Brazil, cattle ranching is the main driver of deforestation, and pastures occupy around 60% - 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 and property rights, and for mere speculation.
There are also intervening links emerging in terms of the potential to restore degraded pastures, which in turn may reconcile increased production and the reduction of GHG emissions. Depending on market conditions, ranchers might also sell degraded pastures (that then transfer into other commodities e.g. soybeans), yet subsequently expand pastures into areas of natural vegetation or abandon land for secondary vegetation growth.
To understand the dynamics of LUC and intensification and deforestation we need to understand the heterogeneity of the processes underlying beef cattle and soybean production and pasture degradation in Brazil. We also need to understand the role of different policy measures and incentives, (including law enforcement) and spatially dependent factors, e.g., proximity to slaughterhouses and roads. Focussing on production in the Amazon and Cerrado biomes, this PhD fits into ongoing and high-profile debates on agricultural sustainability (and intensification) and deforestation causes and effects. It will have privileged access to data from project partners (EMBRAPA and The Nature Conservancy) with opportunities to collaborate in country. The work is likely to lead to possible extensions in Colombia and Paraguay.

Key research questions

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 beet 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?

Methodology
The first phase of this PhD will draw on several spatially explicit datasets from the Terraclass initiative (TerraClass, 2014), Mapbiomas, Agricultural Census, 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 with EMBRAPA and TNC data scientists.

Requirements

This award is likely suit a student seeking to expand their expertise in the field of global environmental change with specific emphasis on big data analysis and farm to global systems modelling. Applicants from a quantitative disciplinary background (masters equivalent in maths, engineering, physics, economics and agricultural sciences) aiming to develop cross disciplinary skills are particularly welcome. This student is also relevant to students from the current UoE Masters degrees in Geosciences (e.g. Ecological Economics, Ecosystem Services Environmental Sustainability, Environment and Development, Integrated Resource Management, GIS).