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A study of the cultural ecosystem services (CES) arising from peoples' interactions with the rural environment is conducted within the context of a landscape scale, 'nature improvement' initiative in the United Kingdom. Taking a mixed methodological approach, the research applies, and demonstrates empirically, a framework for CES developed under the UK National Ecosystem Assessment (Fish et al., 2016). Applications of the framework involve the study of the 'environmental spaces' and 'cultural practices' that contribute to the realisation of benefits to well-being. In this paper empirical work is undertaken to inform the CES evidence base informing management priorities of the Northern Devon Nature Improvement Area (NDNIA) in south west England. Findings from a questionnaire survey, qualitative mapping, group discussion and a participatory arts-based research process are presented to document the many and diverse ways this study area matters to local communities. The paper analyses the qualities that research participants attribute to the environmental space of the NDNIA, the cultural practices conducted and enabled within it, and their associated benefits. The implications of the study for applying this framework through mixed methodological research are discussed, alongside an account of the impact of this approach within the NDNIA itself. ; UK Department of the Environment, Food and Rural Affairs (Defra), the Welsh Government, the UK Natural Environment Research Council (NERC), Economic and Social Research Council (ESRC), and Arts and Humanities Research Council (AHRC). ; © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) This research was funded through the UK National Ecosystem Assessment Follow-On (Work Package 5: Cultural ecosystem services and indicators) funded by the UK Department of the Environment, Food and Rural Affairs (Defra), the Welsh Government, the UK Natural Environment Research Council (NERC), Economic and Social Research Council (ESRC), and Arts and Humanities Research Council (AHRC).

A detailed understanding of herd types is needed for animal disease control and surveillance activities, to inform epidemiological study design and interpretation, and to guide effective policy decision-making. In this paper, we present a new approach to classify herd types in livestock systems by combining expert knowledge and a machine-learning algorithm called self-organising-maps (SOMs). This approach is applied to the cattle sector in Ireland, where a detailed understanding of herd types can assist with on-going discussions on control and surveillance for endemic cattle diseases. To our knowledge, this is the first time that the SOM algorithm has been used to differentiate livestock systems. In compliance with European Union (EU) requirements, relevant data in the Irish livestock register includes the birth, movements and disposal of each individual bovine, and also the sex and breed of each bovine and its dam. In total, 17 herd types were identified in Ireland using 9 variables. We provide a data-driven classification tree using decisions derived from the Irish livestock registration data. Because of the visual capabilities of the SOM algorithm, the interpretation of results is relatively straightforward and we believe our approach, with adaptation, can be used to classify herd type in any other livestock system. ; Department of Agriculture, Food and the Marine ; Projekt DEAL