Agricultural and Aquaculture Facilities

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  • This web map service shows bee nectar plant richness across Great Britain . The source data uses counts of bee nectar plants in Countryside Survey area vegetation plots in 2007 and extrapolates to 1km squares across GB using a generalised additive mixed model. Co-variables used in the model are Broad Habitat (the dominant broad habitat of the 1km square), air temperature, nitrogen deposition, precipitation and altitude. The map has the following layers: plantCount = a modelled estimate of the count of all bee nectar plants within a 1km by 1km square, SEM = a measure of the variance of the plantCount attribute Understanding the distribution of bee nectar plants does provide valuable information on the potential distribution of pollinators and hence pollination.

  • The data presented are quantitative polymerase chain reaction (qPCR) read outs from antimicrobial resistance gene (AMRG) assays and associated metadata from this project. In this dataset, the mean gene copy numbers per microlitre of DNA extract are shown. The data were collected from faecal and environmental samples which were obtained from a single British commercial pig unit. The former were collected from the sow housing barn, pig growing houses and slurry tanks within the farm unit and the latter were obtained through random stratified sampling of the farm and the surrounding land. These samples were taken from what will be referred to as the 'main study'. A further study was carried out to obtain samples after a partial depopulation which took place on this farm. Faecal samples were obtained from the sow housing barn, pig growing houses and slurry tanks and will be referred to as the 'depopulation (depop) study'. For the main study, the samples were collected between October 19th 2016 and April 5th 2017. For the depop study, the samples were collected between 19th June 2017 and 13th November 2017. The data associated with all samples were generated between August 1st 2017 and May 1st 2018. Full details about this dataset can be found at

  • This dataset contains over 4000 faecally-contaminated environmental samples collected over 2 years across 53 dairy farms in England. The samples were analysed for E. coli resistance to amoxicillin, streptomycin, cefalexin, tetracycline and ciprofloxacin and detection of resistant strains is presented in the dataset as a binary result, along with mechanisms of resistance to third generation cephalosporins where relevant. In addition there is comprehensive farm management data including antibiotic usage data. Full details about this dataset can be found at

  • This dataset consists of landscape and agricultural management archetypes (1 km resolution) at three levels, defined by different opportunities for adaptation. Tier 1 archetypes quantify broad differences in soil, land cover and population across Great Britain, which cannot be readily influenced by the actions of land managers; Tier 2 archetypes capture more nuanced variations within farmland-dominated landscapes of Great Britain, over which land managers may have some degree of influence. Tier 3 archetypes are built at national levels for England and Wales and focus on socioeconomic and agro-ecological characteristics within farmland-dominated landscapes, characterising differences in farm management. The unavailability of several input variables for agricultural management prevented the generation of Tier 3 archetypes for Scotland. The archetypes were derived by data-driven machine learning. The three tiers of archetypes were analysed separately and not as a nested structure (i.e. a single Tier 3 archetype can occur in more than one Tier 2 archetype), predominantly to ensure that archetype definitions were easily interpreted across tiers. Full details about this dataset can be found at

  • The data comprises physiological and yield measurements from an ozone (O3) exposure experiment, during which three varieties of sweet potato (Ipomoea batatas) were exposed to Low, Medium and High O3 treatments using heated dome shaped glasshouses (solardomes). The Erato orange variety was exposed to the three treatments from June to October 2019 and the Murasaki variety from June to October 2021. The Beauregard variety was grown on two occasions, with treatments from August to October 2020, and June to October 2021. Measurements were taken of leaf stomatal conductance, leaf chlorophyll content index as well as the harvest (fresh) weight of tubers. All measurements were made by the corresponding author. The experiments were carried out in the UKCEH Bangor Air Pollution Facility. This work was carried out as part of the UK Centre for Ecology & Hydrology Long-Term Science Official Development Assistance ‘SUNRISE’ project, NEC06476. Stomatal conductance was found to be significantly reduced in the elevated ozone treatments. Yield for the Erato orange and Murasaki varieties was reduced by ~40% and ~50% (Medium and High, respectively, vs Low) whereas Beauregard yield (2021) was reduced by 58% in both (the tubers for the Beauregard plants grown in 2020 were not fully formed). Sweet potato is a staple food crop grown in locations deemed to be at risk from O3 pollution (e.g. Sub-Saharan Africa), and this dataset adds much needed stomatal conductance and yield data of sweet potato grown under different O3 exposure conditions. This can be used to improve model predictions of O3 impacts on sweet potato, along with associated risk assessments. Full details about this dataset can be found at

  • This dataset represents a cohort of heifers followed from birth to 18 months or first pregnancy on 37 farms in the South West of England. Faecally-contaminated environmental samples were collected over 2 years and the samples analysed for E. coli resistance to amoxicillin, cefalexin and tetracycline with detection of resistant strains presented in the dataset as a binary result. Farm-level antibiotic usage data is also given. Full details about this dataset can be found at

  • This dataset consists of faecally-contaminated samples taken from the environment around pre-weaned calves on 51 farms in South-West England during 2017/2018 and is a subset of a larger dataset investigating antibiotic resistance in E. coli across 53 farms. The samples were analysed for presence of E. coli resistant to amoxicillin, streptomycin, cephalexin, tetracycline and/or ciprofloxacin. Management factors deemed related to pre-weaned calves are included, including antibiotic usage data at farm level. Full details about this dataset can be found at

  • This data is the fruit set and marketable fruit set (percentage and success: failure) of commercial raspberry plants under 4 different pollination treatments. The data also includes fruit measurements (weight in grams and length and width in mms) of these fruit and the number of seeds per fruit for a subset of the collected fruits. Full details about this dataset can be found at

  • The data pertains to a single time point ‘snapshot’ spatial sampling of site characteristics, soil parameters and soil greenhouse gas emissions for two sites (Extensive and Intensive). The extensively managed site (‘Extensive’; 240-340 m above sea level; a.s.l.) consisted of an 11.5 ha semi-improved, sheep-grazed pasture at Bangor University’s Henfaes Research Station, Abergwyngregyn, North Wales (53°13’13’’N, 4°0’34’’W). The intensively managed site (‘Intensive’; on average 160 m a.s.l.) was a 1.78 ha sheep-grazed pasture located in south-west England, at the North Wyke Farm Platform (NWFP), Rothamsted Research, Okehampton, Devon (50°46’10’’N, 30°54’05’’W). At the Extensive site soil and gas sampling was conducted on 30th November 2016. At the Intensive site soil and gas sampling was conducted on 1st August 2016. The data contains: site characteristics including elevation, slope, compound topographic index, vegetation type or manure application, and sample point grid references; soil parameters including soil bulk density, soil percentage water-filled pore space, soil moisture, soil organic matter contents, soil pH, soil nitrate nitrogen concentration, soil ammonium nitrogen concentration, soil percentage total carbon contents, soil percentage total nitrogen contents, and carbon to nitrogen content ratio; and soil greenhouse gas flux data for nitrous oxide, carbon dioxide and methane. The study was conducted as a wider part of the NERC funded Uplands-N2O project and BBSRC-supported Rothamsted Research, North Wyke Farm Platform (Grant Nos: NE/M015351/1, NE/M013847/1, NE/M013154/1, BBS/E/C/000J0100, BBS/E/C/000I0320, BBS/E/C/000I0330). Quantifying the spatial and variability of the drivers of greenhouse gas emissions and their interactions in grazing systems is critical to improve our understanding of nitrous oxide, carbon dioxide and methane fluxes, enabling better estimates of aggregated greenhouse gas emissions and associated uncertainties at the landscape scale. Full details about this dataset can be found at

  • [This dataset is embargoed until April 30, 2023]. This dataset details information collected from smallholder oil palm farms in Sabah, Malaysian Borneo. Including: management practices, oil palm fruit yield, understorey vegetation, and soil chemical properties (SOC, total N, total P and available P). We collected data between August to November 2019 from 40 smallholdings (defined as farms < 50 ha) across six governance areas in Sabah. We used responses from face-to-face questionnaires to collect information about their management practices, including Best Management Practices (BMPs), and reported Fresh Fruit Bunch (FFB) yields. We also carried out field surveys on these farms to quantify vegetation cover and soil chemical properties. All smallholder farms had mature fruiting trees i.e. > 8 years since planting. The project received ethical approval from the Biology Ethics Committee, University of York (Ref. SGA201906), and permission from the Sabah Biodiversity Council (Ref. JKM/MBS.1000-2/2 JLD.8), Danum Valley Management Committee (Ref. YS/DVMC/2019/27), and South East Asia Rainforest Research Partnership (project number 18033) for permission to conduct our research in Sabah, Malaysia. This work was funded by the NERC iCASE studentship (NE/R007624/1) and Proforest. Full details about this dataset can be found at