Sustainable Uplands: Management for Multiple Benefits, 2004-2005
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<p>This is a mixed methods study. The study is part of the Rural Economy and Land Use (RELU) programme.<br> <br> The project developed a learning process designed to help people better anticipate, monitor and respond to rural change in UK uplands. It identified data sources, gaps and methods that could be used to further develop, test and streamline the process in the next phase of the research. Working in the Peak District National Park, the research focussed on managed burning, and contributed to the Department for Environment, Food and Rural Affairs (DEFRA) consultation on their review of the Heather and Grass Burning Code.<br> <br> Semi-structured interviews were conducted with a broad cross-section of upland management stakeholders from the Peak District National Park. The research found deep-seated conflict over burning and little regular contact between many groups. However, most of the individuals that were interviewed from each group recognised considerable overlap between their views on upland management and the views of those they knew from opposing groups.<br> <br> The study pioneered the use of Social Network Analysis in environmental management, to ensure all relevant groups were involved in the research. The analysis identified a few key individuals who were both well known and with whom many people felt they shared views. By getting these people involved in negotiations about the way land managers can respond to future change, it may be possible to reach agreements more easily and gain wider acceptance.<br> <br> Further information for this study may be found through the <a href="https://www.researchcatalogue.esrc.ac.uk/grants/RES-224-25-0088/read" target="_blank">ESRC Research Catalogue webpage: Sustainable Upland Management for Multiple Benefits.</a><a href="http://relu.data-archive.ac.uk/explore-data/search-browse/project/?ID=RES-224-25-0088"></a></p>
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Publication Details
Subfield
Forestry
Field
Agricultural and Biological Sciences
Domain
Life Sciences
Confidence Score
39%
Source
Scholar Data Model