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  • [This dataset is embargoed until September 1, 2023]. 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 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

  • 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

  • Data are presented from an ozone exposure experiment performed on four African crops. The crops (Beans, Cowpeas, Amaranth and Sorghum) were exposed to three different levels of ozone and two heat treatments in the UK CEH Bangor solardomes. The experiment ran from May 2018 to September 2018. The crop plants were grown from seed, in pots in solardomes. The aim of the experiment was to investigate the impact of ozone exposure on the crop yield and plant health. The dataset comprises of manually collected data on plant physiology, biomass and yield. In addition the automatically logged data of ozone concentration and meteorological variables in the solardomes are presented. Plant physiology data is stomatal conductance of individual leaves, measured on an ad-hoc basis. The dataset includes the associated data measured by the equipment (relative humidity, leaf temperature, photosynthetically active radiation – a small number of photosynthetically active radiation measurements are missing due to faulty readings). Soil moisture of the pots was always measured at the same time, and chlorophyll content of the measured leaf was usually, but not always, determined at the same time. Yield of beans and cowpeas was determined for each plant. For Amaranth, only the seed head weight was determined. Sorghum did not reach yield, therefore, total biomass at harvest is given as an alternative. Total biomass was not determined for those plants of other crop types that did reach yield. The ozone and meteorological dataset is complete, but with some gap-filling for short periods when the computer was not logging data The work was carried out as part of the NERC funded SUNRISE project (NE/R000131/1). 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 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

  • Data from 38 experimental sites across the UK and Ireland were collated resulting in 623 separate mineral fertiliser N2O emission factors (EF) estimates derived from field measurements. Data were either i) extracted from published studies in which one aim of the experimentation was to explicitly measure N2O and report EFs after a mineral fertiliser application, or ii) raw data were used from the Agricultural and Environmental Data Archive (AEDA). To find the published data, a survey of literature was conducted using Google Scholar for articles considered ‘recent’ (20 years or fewer), i.e. published after January 1998 and submitted before April 2019. The following search terms and their variations were used: N2O, nitrous oxide, emission factor, mineral fertiliser, ammonium nitrate, urea, nitrification inhibitor, nitrogen use efficiency, agriculture, greenhouse gas, grassland and arable. This search based on keywords was complemented with a search through the literature cited in the articles found and known previous research. Full details about this dataset can be found at

  • The data consist of soil physicochemical and biological data for three soil depths (0-15, 15-30 and 30-60 cm) from a three-cut silage plot trial located at three grassland sites within the UK collected between April 2016 and October 2016. The sites were Rothamsted Research at North Wyke in Devon, Bangor University at Henfaes Research Station in North Wales, and Easter Bush in Scotland. At each site measurements were taken from sixteen plots, organised within a randomised complete block design: four (control) plots did not receive fertilizer, four plots received urea only, four plots received urea and urea-inhibitors, and four plots received ammonium-nitrate (Nitram). Fertiliser was applied three times and three cuts were performed. All parameters were measured following fertiliser application. Samples were taken before fertilizer additions at peak growth and before the last silage cut. Soil physical parameters were: aggregate size distribution, aggregate stability, texture (sand/silt/clay) and soil moisture. Soil chemical parameters were: soil nitrate and 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 measures were: microbial biomass, carbon and nitrogen. Microbial community composition and nitrogen genes were measured on the same soil samples and are presented in a separate dataset ( Measurements were undertaken by members of staff from the Centre of Ecology & Hydrology (Bangor, Edinburgh, Lancaster, Wallingford), Bangor University, School of Environment, Natural Resources & Geography and Rothamsted Research, Sustainable Agricultural Sciences, North Wyke. 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

  • The dataset contains greenhouse gas fluxes (N2O, CO2 and CH4) following artificial and real sheep urine applied to organic soils within the Carneddau mountain range (556 m a.s.l.) in Snowdonia National Park, North Wales, UK. The study was conducted across two contrasting seasons (summer and autumn). Soil greenhouse gas emission data was collected using a combination of automated chambers and manually sampled chambers, with gas samples analysed via gas chromatography. Supporting data include characterisation of the soil properties at each site, meteorological data, soil moisture and soil chemistry on a time-series following treatment application. The data were used to calculate sheep urine patch N2O-N emission factors, to improve estimates of greenhouse gas emissions from sheep urine deposited to extensively grazed montane agroecosystems. 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