Nearly 50% of European Union (EU) land is used for agriculture. These landscapes have been shaped by centuries of large-scale human impacts through traditional land-use systems (i.e. practices that are not part of modern, intensive agriculture; (Bignal et al., 1995) intended to fulfil societal demands for agro-ecological products and services (Antrop, 2005); (Fisher et al., 2009). Today’s agricultural landscapes, ‘representing the combined work of nature and man’ (as defined by UNESCO’s World Heritage Committee) are valued for their ecological, social and historic functions (Plieninger et al., 2013), (Hartel et al., 2014). However, the ecosystem services (ESS) provided by these agro-ecosystems and their related natural resources – including food, bioenergy, water, carbon storage and biodiversity – are threatened by unsustainable land-use intensification, abandonment, and climate change. To maintain economic growth, as well as nature’s benefits to people and the livelihoods of 10 million EU farmers, policy instruments and harmful subsidies must be revised, assisted by new indicators that incorporate well-being, environmental quality, employment and equity, biodiversity conservation and nature’s ability to contribute to people (IPBES, 2018). However, understanding the environmental impacts of farmers’ actions, in particular their adoption of various agri-environmental schemes (AES) such as planting hedgerows or setting aside buffer strips, is still limited for the majority of AES and ESS.
This project will build on the large volume of data available in the UK on adoption of AES (e.g. for the Environmental Stewardship (2006-2014) we have >1 million records of AES contracts!) and on the status and trends of a range of ESS and biodiversity. The main aim of the project is to improve the knowledge base on the impacts of AES on the natural environment (biodiversity), wider environment (e.g. water quality) and climate system (greenhouse gases and ammonia emissions, soil carbon etc.). The project will involve one or more of the following:
1. Meta-analysis of published experimental studies, including DEFRA funded research and grey literature. This will build upon and further develop the outcomes of the Public Goods projects funded by iCASP (see https://icasp.org.uk/resources/public-goods/
2. Statistical analysis of AES data from Natural England and other data from Rural Payments Agency, DEFRA, CEH etc.
3. Modelling / meta-modelling using simple models (e.g. using the InVEST suite developed by the Natural Capital Project)
The PhD student will have the opportunity to join the Horizon 2020 BESTMAP (bestmap.eu) project activities. BESTMAP is a 4 year programme dedicated to improving policy impact assessment models for post-2020 Common Agricultural Policy (CAP).
The student will work under the supervision of Dr. Guy Ziv within the Ecology and Global Change research cluster in the School of Geography and Prof. Pippa Chapman in the River Basins cluster in the School of Geography. The project provides a high-level of training in (i) meta-analysis and data analysis; (ii) modelling of ecosystem services. The student will be supported throughout the studentship by a comprehensive PGR skills training programme that follows the VITAE Research Development Framework and focuses on knowledge and intellectual abilities; personal effectiveness; research governance and organisation; and engagement, influence and impact. Training needs will be assessed at the beginning of the project and at key stages throughout the project and the student will be encouraged to participate in the numerous training and development course that are run within the university to support PGR students, including statistics training (e.g. R, SPSS), academic writing skills, grant writing etc (http://www.emeskillstraining.leeds.ac.uk/
). Supervision will involve regular meetings between all supervisors and further support of a research support group.
The student should have a keen interest in agriculture and its environmental impacts with a strong background in a physical geography, Earth sciences, plant sciences, environmental sciences or related discipline. Strong analytical/statistical/modelling skills are desirable but not essential, as full training will be provided during the PhD.