Understanding English agri-environment schemes: uptake, engagement and outcomes
Lead Research Organisation:
University of Exeter
Department Name: Geography
Abstract
Agriculture is on the frontline of changes following from Brexit in 2020. This proposed PhD project aims to document the current practices and ambitions of famers and policy-makers who are now able to contribute to the development of a British Agricultural Policy (BAP) to replace the EU's Common Agricultural Policy (CAP). The project will explore possible farming futures in Cornwall, focusing on farming inputs, outputs, labour supplies and the impact on landscape with the following key aims.
1) Identifying the range of policy supports and their implications for farm inputs, outputs and landscape management after Brexit. A sample of farm representatives reflecting a range of farm types, sizes and land occupancy arrangements will be developed in partnership with the National Farmers Union and other contacts. Once recruited, farmers will be interviewed and asked to provide data about current farming inputs, outputs and their ambitions for landscape management in future. The data collected will be used to populate a database and GIS maps showing the location of firms providing inputs and the geographical destination of outputs on these farms that can be revisited at subsequent intervals after Brexit. This data will also feed the proposed modelling work (see point 3 below) while facilitating a significant contribution to evaluation of policy development regarding the creation of the BAP by national government.
2) Identifying the implications of Brexit for labour supply, retention and training. The sample of farm representatives will also be asked about their current workforce, their place of birth, time of employment, in-house and off-site training, and anticipated requirements for the future. This data will feed into policy development concerning the implications of Brexit for agricultural labour supply, while also providing a benchmark for subsequent data collection after Brexit when the balance of local and EU labour supplies is expected to shift. This data can also be mapped using GIS to highlight the levels of current dependence upon labour from the EU, and the potential implications of this for future substitution with local labour supplies and/or immigration policy to sustain agricultural outputs in the region.
3) Modelling the effects of Brexit on the geography of farm suppliers, outputs, labour and landscape. Models of farm management, economics and ecosystem service provision that are currently available (including Decision-Support tools) will be collated (e.g. SIP Dynamic Landscape Tool; ALMaSS by Topping et al; Bateman et al 2016; Berentssen 1995), with the aim of combining them into one or more model that can examine the trade-offs between economic and environmental goals, as driven by policy changes. This approach of using individual models as component parts of a more complex holistic systems model has been used successfully in BBSRC projects of bee colonies ("economies") in the farmed landscape. For individual farm units, no such over-arching model yet exists that is both temporally and spatially explicit, although models of the individual components are available. Different policy scenarios can be simulated using the data and the GIS maps. This research will then be used to contribute to ongoing policy development and practice at local and national scales.
1) Identifying the range of policy supports and their implications for farm inputs, outputs and landscape management after Brexit. A sample of farm representatives reflecting a range of farm types, sizes and land occupancy arrangements will be developed in partnership with the National Farmers Union and other contacts. Once recruited, farmers will be interviewed and asked to provide data about current farming inputs, outputs and their ambitions for landscape management in future. The data collected will be used to populate a database and GIS maps showing the location of firms providing inputs and the geographical destination of outputs on these farms that can be revisited at subsequent intervals after Brexit. This data will also feed the proposed modelling work (see point 3 below) while facilitating a significant contribution to evaluation of policy development regarding the creation of the BAP by national government.
2) Identifying the implications of Brexit for labour supply, retention and training. The sample of farm representatives will also be asked about their current workforce, their place of birth, time of employment, in-house and off-site training, and anticipated requirements for the future. This data will feed into policy development concerning the implications of Brexit for agricultural labour supply, while also providing a benchmark for subsequent data collection after Brexit when the balance of local and EU labour supplies is expected to shift. This data can also be mapped using GIS to highlight the levels of current dependence upon labour from the EU, and the potential implications of this for future substitution with local labour supplies and/or immigration policy to sustain agricultural outputs in the region.
3) Modelling the effects of Brexit on the geography of farm suppliers, outputs, labour and landscape. Models of farm management, economics and ecosystem service provision that are currently available (including Decision-Support tools) will be collated (e.g. SIP Dynamic Landscape Tool; ALMaSS by Topping et al; Bateman et al 2016; Berentssen 1995), with the aim of combining them into one or more model that can examine the trade-offs between economic and environmental goals, as driven by policy changes. This approach of using individual models as component parts of a more complex holistic systems model has been used successfully in BBSRC projects of bee colonies ("economies") in the farmed landscape. For individual farm units, no such over-arching model yet exists that is both temporally and spatially explicit, although models of the individual components are available. Different policy scenarios can be simulated using the data and the GIS maps. This research will then be used to contribute to ongoing policy development and practice at local and national scales.
