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  • 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

  • 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 consists of butterfly and bumblebee counts, winter bird counts, number of flowering units, and seed mass data, along with categories of soil type and quality, and temperature data. Data were collected from arable farms under the English Entry Level agri-environment Scheme (ELS) for two options: Nectar Flower Mixture option (NFM) and Wild Bird Seed Mixture (WBM). Surveys were carried out in 2007 and repeated in 2008. All data were collected using standardised protocols: butterfly and bumblebee counts were collected from transects in the NFM options during summer; flowering units were counted within quadrats along the same transects in summer; bird counts were made in winter within the whole WBM areas; seed resource was calculated for the WBM areas from seeds collected in quadrats along transects. The dataset also contains results from farmer interviews. The interviews were designed to explore farmer attitudes towards, and history of, environmental management and their perceptions and understanding of the management requirements. Three measures of farmer attitude were then calculcated from their responses: experience (4-point scale), concerns (5-point scale) and motivation (3-point scale). All data were collected as part of the FarmCAT project, the principal aim of which was to develop a holistic understanding of the social and ecological factors which lead to the successful delivery of agri-environmental schemes. This project was funded as part of the ESRC Rural Economy and Land Use (RELU) programme. 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 contains yield data for wheat, oilseed rape and field beans grown in fields under different agri-environment practices. The fields were located at the Hillesden Estate in Buckinghamshire, UK, where a randomised block experiment had been implemented to examine the effects of converting differing proportions of arable land to wildlife habitat. The fields were planted with wheat (Triticum aestivum L.) followed by break crops of either oilseed rape (Brassica napus L.) or field beans (Vicia faba L.). Three treatments were applied at random: a control ("business as usual"), Entry Level Stewardship (ELS) treatment and ELS Extra treatment. The ELS treatment involved removing 1% of land to create wildlife habitats. The ELS Extra had a greater proportion of land removed (6%) and additional wildlife habitats included. The total yield of each crop was measured at the time of harvesting using a yield meter attached to the combine harvester. From these values, yield per hectare and the ratio of crop yield to regional average yield were calculated. Full details about this dataset can be found at

  • This dataset includes values of 15 traits (total dry mass; root length to shoot length ratio; leaf mass fraction; root mass fraction; shoot mass fraction; leaf thickness; leaf force to punch; leaf area to shoot area ratio; leaf concentrations of N, P, K, Ca and Mg; leaf N: P concentration ratio; specific maximum root length) measured in February 2020 on 394 seedlings of 15 woody plant species growing in logged in the Ulu Segama Forest Reserve or unlogged forest in the Danum Valley Conservation Area, Malaysia. The purpose of this data collection was to determine whether the expression of plant functional traits differed between tree seedlings recruited into logged and unlogged forests. This information is important for understanding the drivers of variation in seedling growth and survival in response to logging disturbance, and to uncover the mechanisms giving rise to differentiation in tree seedling composition in response to logging. These data were collected as part of NERC project “Seeing the fruit for the trees in Borneo: responding to an unpredictable community-level fruiting event” (NE/T006560/1). 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 four 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

  • This data were created as part of the NIMFRU project and consists of 21 flood matrices. These have been completed by community members from the project target communities of Anyangabella, Agule and Kaikamosing which are all found in the Katakwi district. Five of the matrices were completed by local district officers. The data were collected in December 2020. These data were collected to understand how communities resilience had changed as a result of the NIMFRU project. Full details about this dataset can be found at

  • The data consist of nitrogen gene data, soil biodiversity indices and microbial community composition for three soil depths (0-15, 15-30 and 30-60 cm) from a winter wheat field experiment located in the United Kingdom and collected between April 2017 and August 2017. The sites were Rothamsted Research at North Wyke in Devon and Bangor University at Henfaes Research Station in North Wales. At each site measurements were taken from 15 plots, organised within a randomised complete block design where 5 plots did not receive fertilizers (controls), 5 plots received food-based digestate, and 5 plots received acidified food based digestate a nitrification inhibitor. Soil samples were taken within two weeks of digestate application and shortly before winter wheat harvest. Soil chemical parameters were: soil nitrate, ammonium, dissolved organic carbon and nitrogen, amino acids and peptides, soil organic matter content as loss-on-ignition, pH, sodium, potassium, calcium, magnesium, permanganate oxdisable carbon citric acid extractable phosphorous, Olsen-P and total carbon, nitrogen and phosphorus. Soil biological measure were: microbial biomass carbon and nitrogen. Soil samples were taken by members of staff from Centre of Ecology & Hydrology (Bangor), Bangor University, School of Environment, Natural Resources & Geography Sustainable Agricultural Sciences, and Rothamsted Research North Wyke. Measurements were carried out Rothamsted Research Harpenden and the Centre of Ecology & Hydrology (Wallingford). Soil physico-chemical parameters were measured on the same soil samples and are presented in a related dataset. Data was collected for the Newton Fund project “UK-China Virtual Joint Centre for Improved Nitrogen Agronomy”. Funded by Biotechnology and Biological Sciences Research Council (BBSRC) and NERC - Ref BB/N013468/1 Full details about this dataset can be found at

  • This dataset comprises 259 smallholder agricultural field surveys collected from twenty-six villages across three Districts in Mozambique, Africa. Surveys were conducted in ten fields in each of six villages in Mabalane District, Gaza Province, ten villages in Marrupa District, Niassa Province, and ten villages in Gurue District, Zambezia Province. Data were collected in Mabalane between May-Sep 2014, Marrupa between May-Aug 2015, and Gurue between Sep-Dec 2015. Fields were selected based on their age, location, and status as an active field at the time of the survey (i.e. no fallow fields were sampled). Structured interviews using questionnaires were conducted with each farmer to obtain information about current management practices (e.g. use of inputs, tilling, fire and residue management), age of the field, crops planted, crop yields, fallow cycles, floods, erosion and other problems such as crop pests and wild animals. The survey also includes qualitative observations about the fields at the time of the interview, including standing live trees and cropping systems. This dataset was collected as part of the Ecosystem Services for Poverty Alleviation (ESPA) funded ACES project , which aims to understand how changing land use impacts on ecosystem services and human wellbeing of the rural poor in Mozambique. Full details about this dataset can be found at