This dataset contains urine frequency and volume data measured from tri-axial accelerometers on Welsh mountain ewes free-grazing two contrasting upland field sites (semi-improved and unimproved pasture) in North Wales, across two seasons each (spring and autumn). The data, were collected using tri-axial accelerometers glued to the hind of Welsh Mountain ewes to study the urination behaviour of free-grazing sheep. Using a Boolean algorithm, the characteristic squatting position that ewes exhibit upon urination was detected in the accelerometer data. Initially the performance of the accelerometers with sheep in urine collection pens was assessed. Data were collected on the volume of each urination event and recorded the time of each observed urination event. This initial data was used to assess whether the accelerometers and Boolean algorithm were successful in identifying urination events, but also to ascertain whether the time spent in the squatting position would correlate with the volume of urine produced (thus allowing the technique to be able to estimate urine volume from squatting time only in subsequent field deployments). Information on when, where and how often livestock urinate are key data to be able to assess the scale and nature of nitrogen pollution arising from grazed agroecosystems. Urine patches deposited by grazing livestock are large sources of emissions of the greenhouse gas, nitrous oxide, due to high concentrations of nitrogen deposited over relatively small areas. These data were collected in the NERC funded Uplands-N2O project (grant award: NE/M015351/1). Full details about this dataset can be found at https://doi.org/10.5285/127afd24-d2cd-457f-b837-2dd5d328f101

This dataset contains the results of 211 household surveys conducted in Mambwe District, Zambia, as part of a wider study looking at human and animal trypanosomiasis and changing settlement patterns in the area. The interviews were conducted from June 2013 to August 2013. The objective of the survey was to set the health of people and their animals in the context of overall household wellbeing, assets and access to resources. The topics covered included household demographics, human and animal health, access to and use of medical and veterinary services, livestock and dog demographics, livestock production, human and animal contacts with wildlife, crop and especially cotton production, migration, access to water and fuel use, household assets and poverty, resilience and values. The dataset has been anonymised by removing names of respondents, Global Positioning Satellite (GPS) location of their homes and names of interviewers. Household numbers were retained. Written consent was obtained prior to commencing all interviews. This research was part of a wider research project, the Dynamic Drivers of Disease in Africa Consortium (DDDAC), and these data contributed to the research carried out by the consortium. The research was funded by NERC with support from the Ecosystem Services for Poverty Alleviation Programme (ESPA). Full details about this dataset can be found at https://doi.org/10.5285/b1647138-49f5-4777-a39d-e7359bf7b98d

This dataset contains total green biomass, palatable green biomass, sheep stocking rate, Pinus contorta tree density, P. contorta basal area and percentage canopy cover in sites across northwest Patagonia, during the summer of 2020. We measured total green biomass in the peak production (kg /hectare/year), as a metric for aboveground annual productivity, in five different sites and for a wide range of P. contorta abundance. We also measured palatable green biomass (kg/hectare/year), considering only those plant species foraged by sheep. We calculated the sheep stocking rate that can sustainably support the grasslands of our study based on the feeding requirement of an Ovine Livestock Unit (OLU). The OLU represents a Merino wether (castrated male sheep) with an average live weight of 40 kg that consume 365 kg of dry forage in a year in Patagonia grasslands. We counted the number of P. contorta individuals to calculate density (trees/ha) and recorded their diameter at ground level to calculate basal area (m2/ha) (an alternative measure of P. contorta abundance). Additionally, on each subplot we took a hemispheric picture to estimate the canopy cover (%) of P. contorta (a third measure of P. contorta abundance). Full details about this dataset can be found at https://doi.org/10.5285/066b0d36-d28a-422e-b29a-298c98b8a536

The dataset provides qualitative data from anonymised in-depth interviews conducted in 2017 with domestic poultry owners, commercial poultry farm workers and market sellers of live poultry in Bangladesh. The dataset comprises interview transcripts in Bangla. Household and farm interviews were carried out in rural areas of Mirzapur sub-district, Tangail. Interviews with market sellers of poultry were carried out in Dhaka city. An interview guide was used to explore themes and topics relating to poultry-raising practices, hygiene and waste disposal practices relating to poultry and use of antibiotics in poultry. The objective was to understand human behaviours and practices that may contribute to environmental contamination with antibiotic-resistant bacteria from poultry and potential pathways of transmission of antibiotic-resistant bacteria from poultry to humans. The research was part of a wider research project, Spatial and Temporal Dynamics of Antimicrobial Resistance (AMR) Transmission from the Outdoor Environment to Humans in Urban and Rural Bangladesh. The research was funded by NERC/BBSRC/MRC on behalf of the Antimicrobial Resistance Cross-Council Initiative, award NE/N019555/1. Full details about this dataset can be found at https://doi.org/10.5285/630759ac-b0ca-4561-8eec-414b47e14829

This data set contains Global maps of five ecosystem services using 6 different among-model ensemble approaches: the provisioning services of water supply, biomass for fuelwood and forage production, the regulating service Carbon Storage for CO2 retention and the cultural non-material service Recreation. For water, the data comes as one shapefile with polygons per watershed, each polygon containing seven ensemble estimates. The other services – recreation, carbon storage, biomass for fuelwood and forage production – come as seven tiff- maps at a 1-km2 resolution with associated world files for each tiff-map contains 43,200 x 18,600 pixels for one ensemble approach, with LZW compressed file sizes between 400MB and 950MB. For all maps, 600dpi jpg depictions are added to the supporting information with uniform colour scaling set for the median ensemble per service. Ensemble output maps were calculated with different approaches following the supporting documentation and associated publication. Uncertainty estimates for these services are included as variation among contributing model outputs and among the employed ensemble approaches. The work was completed under the ‘EnsemblES - Using ensemble techniques to capture the accuracy and sensitivity of ecosystem service models’ project (NE/T00391X/1) funded by the UKRI Landscape Decisions programme, with additional funding from ES/R009279/1 (MobilES) & ES/T007877/1 (RUST). Full details about this dataset can be found at https://doi.org/10.5285/bd940dad-9bf4-40d9-891b-161f3dfe8e86

This dataset holds survey data of individual farming households in the Terai region in Nepal relating to their nitrogen use. The survey was conducted in 2022 and the questions covered two seasons (summer and winter) asked at a single visit in the summer season. The questions on the winter season were based on recall. The data cover the following topics: household characteristics, general farm characteristics, plot characteristics, crop production and harvest, synthetic and organic fertilizer use and compost production, labour, irrigation, pesticides, livestock, information sources, drivers of and barriers to adoption of sustainable practices, attitude, behaviour, perception and opinion, household expenditure and income, household asset and wealth, subsidies. The data were collected primarily to assess differences in nitrogen use efficiency (NUE) and sustainable nitrogen practices between households. The data also aim to enhance understanding of farmers’ attitudes, opinion and decision making affecting NUE in crop production and farm related factors which enable adoption of sustainable practices. The data are part of a wider SANH (South Asian Nitrogen Hub) harmonised household survey covering Bangladesh, India, Maldives, Pakistan and Sri Lanka. Full details about this dataset can be found at https://doi.org/10.5285/3b7a3e0b-48e5-4395-b4c6-79bb43ae31e3

This dataset holds survey data of individual farming households in the Eastern region in Bhutan relating to their nitrogen use. The survey was conducted in 2022 and the questions covered two seasons (2022 and 2021 farming seasons) asked at a single visit in the 2022 season. The questions on the winter season were based on recall. The data cover the following topics: household characteristics, general farm characteristics, plot characteristics, crop production and harvest, synthetic and organic fertiliser use and compost production, labour, irrigation, pesticides, livestock, information sources, drivers of and barriers to adoption of sustainable practices, attitude, behaviour, perception and opinion, household expenditure and income, household asset and wealth, subsidies. The data were collected primarily to assess differences in nitrogen use efficiency (NUE) and sustainable nitrogen practices between households. The data also aim to enhance understanding of farmers’ attitudes, opinion and decision making affecting NUE in crop production and farm related factors which enable adoption of sustainable practices. The data are part of a wider SANH (South Asian Nitrogen Hub) harmonised household survey covering Bangladesh, India, Maldives, Nepal, Pakistan and Sri Lanka. Full details about this dataset can be found at https://doi.org/10.5285/cd35ca67-8121-4a0d-81c9-c4a7fae25117

The data resource contains daily time-series of simulated streamflow, ground water levels and estimated demands, from humans, livestock and irrigation across the Narmada Basin, India. The data were generated using the Global Water Availability Assessment (GWAVA) Model 5. For the Upper Narmada, a baseline of 1970-2013 is presented along with a future time slice of 2028- 2060. For the whole Narmada, a baseline of 1981-2013 and future period of 2021-2099 is included. The data were produced to help predict how climate and land use change in the region would impact on future water security. The research was funded by NERC research grant NE/R000131/1 Full details about this dataset can be found at https://doi.org/10.5285/9fc7ab01-c622-46f1-a904-0bcd54073da3

This dataset contains information about soil near-surface physical and hydrological properties, vegetation observations and land use & management information across the Thames catchment (UK). It was collected during the ‘Landwise' project's ‘Broad-scale field survey' which sampled 1836 location points across a total of 164 fields/land parcels. The aim of the survey was to quantify the impact of innovative land use and management on soil properties, with implications for natural flood management. The surveyed fields were selected to represent four broad land use and management classes (arable with and without grass in rotation, permanent grassland and broadleaf woodland) and five generalised soil/geology classes. Approximately eight fields were sampled for each of the twenty combinations of land use and soil/geology class. The sampled fields cover a range of traditional and innovative agricultural practices. Within each field/parcel, representative sampling locations were selected to cover the anticipated range of soil variability, including typical infield, untrafficked margins and trafficked headlands/tramlines etc. Sampling was undertaken once during the period 2018-2021. Samples were measured and analysed using a range of field and laboratory techniques (see Data Lineage). Point data include: 1. Survey point location (British National Grid coordinates) 2. Soil quantitative measurements (near-surface: 0 – 50 mm below ground level): dry bulk density, volumetric water content, organic matter, derived porosity, derived porosity accounting for variable organic matter, particle size distribution and texture classification 3. Vegetation quantitative measurements: maximum and minimum height 4. Soil qualitative measurements: hand texture classification, aggregate stability test slaking and dispersion results, hydrochloric acid test for calcareous soil, and for a subset of locations Visual Evaluation of Soil Structure (VESS) score 5. Observations (also classified into groups): soil surface condition (e.g. slaked/unslaked/capped/poached etc.), vegetation type Field contextual data include: 1. Land owner/manager responses to a land use and management questionnaire (primary data) including information on: crop types/rotation, cover crops, herbal leys, organic or conventional, organic amendments, lime additions, tillage, last ploughed, tramlines, buffer strips, field drainage, grass species, livestock, last grazed, stocking density, grazing weeks per year, stock out-wintering, mob or paddock grazing, woodland management, tree species, woodland age, path management, land use history, flooding history, waterlogging, water or sediment runoff 2. Classification of selected questionnaire free text responses into categories (derived secondary data) 3. General field observations (primary data) including: slope gradient and shape, surface form, surface water, surface condition (slaking, capped, ruts, wheelings, poaching etc.), soil erosion or deposition features As agreed with the survey participants, this dataset has been anonymised by removing location specific information, such as farm and field names, along with any other personally identifiable information. As also agreed, point data location coordinates have been degraded to the nearest 1 km grid point. The dataset was co-produced by the UK Centre for Ecology and Hydrology and Landwise Partners as part of the Landwise Natural Flood Management project, supported by the Natural Environment Research Council (Grant NE/R004668/1). The participation and assistance of the land owners and managers is gratefully acknowledged. Full details about this dataset can be found at https://doi.org/10.5285/9ab5285f-e9c4-4588-ba21-476e79e87668