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[This dataset is embargoed until August 31, 2024]. This dataset contains information about surface and sub-surface hydraulic and hydrological soil properties across the Thames (UK) catchment. Soil dry bulk density, estimated soil porosity, soil moisture and soil moisture retention (to 100 cm suction) were determined through laboratory analysis of soil samples collected at five depths between the surface and 100 cm below ground level (where possible). Surface soil infiltration rates were measured, and soil saturated hydraulic conductivity was calculated at 25 cm and 45 cm depths (where possible). Field scale point data were collected at seven sites in the Thames Catchment, with three sub-groups of sites under different land use and management practices. The first land management group included three arable fields in the Cotswolds, Gloucestershire, on shallow soils over Limestone with no grass in rotation, herbal leys in rotation or rye and clover in rotation. The second group included two arable fields in near Wantage, Oxfordshire, on free draining loamy soils over chalk with conventional management or controlled traffic. The final group included a permanent grassland and broadleaf woodland on slowly permeable soil over mudstone near Oxford, Oxfordshire. Data were collected in representative infield areas; trafficked areas (e.g. tramlines or animal tracks), and untrafficked margins. Samples and measurements were taken between April 2021 and October 2021, with repeats taken before and after harvest. Soil samples were collected using Eijkelkamp 07.53.SC sample ring kit with closed ring holder and the Edelman auger and Stony auger when required. Infiltration measurements were taken using Mini Disk Infiltrometers. Soil saturated hydraulic conductivity was measured using Guelph permeameters. Soil bulk density and porosity were calculated using oven drying methods. Soil moisture retention was calculated using an Eijelkamp Sandbox. This dataset was collected by UKCEH as part of the 'Land management in lowland catchments for integrated flood risk reduction' (LANDWISE) project. LANDWISE seeks to examine how land use and management can be used to reduce the risk of flooding for communities. LANDWISE is one of three projects comprising the Natural Environment Research Council Natural Flood Management Research Programme. The work was supported by the Natural Environment Research Council Grant NE/R004668/1. Full details about this dataset can be found at https://doi.org/10.5285/a32f775b-34dd-4f31-aafa-f88450eb7a90
This data set consists of the tabulated results of bird surveys on Peak District farms and moorlands. Bird abundance and distribution on Peak District farms and moorlands, 2007-2008 The study is part of the NERC Rural Economy and Land Use (RELU) programme. The project used the Peak District National Park as a case study to examine the impact of hill farming practices on upland biodiversity (using birds as an indicator group); how hill farms were responding to ongoing and future changes to policies and prices; what this would in turn imply for upland biodiversity; what the public wanted from upland ecosystems and how policies could be designed better to deliver public goods from hill farms. To answer these questions, the project team conducted ecological and economic surveys on hill farms; used survey results to parameterise ecological and economic models of this farming system; developed new ways to integrate these into coupled ecological and economic models and paid particular attention to interactions across farm boundaries; used the models to evaluate the performance of existing policies and to test designs that could lead to more effective policies; and conducted a range of choice experiments with different cross-sections of the general public to evaluate their preferences for upland landscapes. Choice experiment, socio-economic survey and model data from this study are available at the UK Data Archive under study number 6363 (see online resources). Further documentation for this study may be found through the RELU Knowledge Portal and the project's ESRC funding award web page (see online resources).
This dataset consists of a survey of the vegetational impacts of deer in 20 forests as part of the NERC Rural Economy and Land Use (RELU) programme. It is widely accepted, at least in principle, that most kinds of natural resources are best handled collaboratively. Collaborative management avoids conflict and enhances the efficiency with which the resource is managed. However, simply knowing that collaboration is a good idea does not guarantee that collaboration can be achieved. In this project, the researchers have addressed issues of conflict and collaboration in ecological resource management using the example of wild deer in Britain. Deer are an excellent example since they highlight problems around ownership and because they offer both societal benefits and drawbacks. Wild deer are not owned, though the land they occupy is. As deer move around, they usually cross ownership boundaries and thus provoke potential conflicts between neighbouring owners who have differing management goals. Deer themselves are valued and a key component of the natural environment, but their feeding commonly limits or prevents woodland regeneration and can thus be harmful to ecological quality. Deer provide jobs but they also provoke traffic accidents. This study used a variety of methods from across the natural and social sciences, including choice experiments, semi-structured interviews with individuals and focus groups. It also incorporated the use of participatory GIS to map deer distributions and habitat preferences in conjunction with stakeholders. The study confirmed conventional wisdom about the importance of collaboration. However, it also showed that there were many barriers to achieving effective collaboration in practice, such as contrasting objectives, complex governance arrangements, power imbalances and personal relationships. Mechanisms for enhancing collaboration, such as incentives and incorporating deer within broader landscape management objectives, were examined. Though these proposals were worked out for the case of deer, they are likely to be applicable much more widely and should be considered in other cases of disputed or rapidly changing ecological resource management. This dataset consists of a survey of the vegetational impacts of deer in 20 forests. The interview and focus group transcripts, and the choice experiment datasets from this study are available at the UK Data Archive under study number 6545 (see online resources). Further documentation for this study may be found through the RELU Knowledge Portal and the project's ESRC funding award web page (see online resources).