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 https://doi.org/10.5285/463b2bcc-731a-42af-ba69-1662aa21f1bf

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 https://doi.org/10.5285/c9bc537a-d1c5-43a0-b146-42c25d4e8160

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 https://doi.org/10.5285/de5b4f33-f679-4798-8daf-51a314e78204

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 https://doi.org/10.5285/f7da626c-f39c-474f-b2e7-8638ab26d166

[THIS DATASET HAS BEEN WITHDRAWN]. Modelled average percentage yield loss due to ground-level ozone pollution (per 1 degree by 1 degree grid cell) are presented for the crops maize (Zea mays), rice (Oryza sativa), soybean (Glycine max) and wheat (Triticum aestivum) for the period 2010-2012. Data are on a global scale, based on the distribution of production for each crop, according to the Food and Agriculture Organisation’s (FAO) Global Agro-Ecological Zones (GAEZ) crop production data for the year 2000. Modelled ozone data (2010-2012) needed for yield loss calculations were derived from the EMEP MSC-W (European Monitoring and Evaluation Programme, Meteorological Synthesising Centre-West) chemical transport model (version 4.16). Mapping the global crop yield losses due to ozone highlights the impact of ozone on crops and allows areas at high risk of ozone damage to be identified, which is one of the first steps towards mitigation of the problem. The yield loss calculations were done as part of the NERC funded SUNRISE project (NEC06476). Full details about this dataset can be found at https://doi.org/10.5285/181a7dd5-0fd4-482a-afce-0fa6875b5fb3

[This dataset is embargoed until August 1, 2024]. This dataset includes results from biodiversity, social and environmental surveys of 46 oil palm smallholders and farms in Riau, Indonesia. Biodiversity data includes: pitfall trap data on arthropod abundance and higher-level order identification, sticky trap data on flying invertebrate abundance (identified to higher-level order), transect data on assassin bugs, Nephila spp. spiders and butterflies (identified to species), counts of insects visiting oil palm inflorescences if any open (identified to Elaeidobius kamerunicus and higher-level orders for other groups) and data on meal worm removal from each plot. Environmental data includes: soil temperature readings recorded over 24 hours, information on size of plot, crop type and cover, GPS location, vegetation cover, vegetation height, canopy density, epiphyte cover, soil pH, soil moisture, leaf litter depth, horizon depths, palm herbivory and palm health. Social data includes information (all anonymised) on: plot area, number of palms, sociodemographic data, plantation management practices applied, knowledge and value assigned to wildlife, and yield. Data were collected from November 2021 to June 2022. Full details about this dataset can be found at https://doi.org/10.5285/b61a12a2-d091-41af-b451-a14de4f4a3c3

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 https://doi.org/10.5285/78c5dcee-61c1-44be-9c47-8e9e2d03cb63

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 https://doi.org/10.5285/7c3ad803-fbd4-45c3-826b-fa04c902ded8

This dataset contains information on soil physico-chemical characteristics and palm nutrient concentrations collected in 2019 across twenty-five smallholder oil palm farms in Perak, Malaysia. Leaf and rachis were sampled from 3 palms within each plot. Soils were sampled to 30cm depth in the palm circle of the same 3 palms and the adjacent inter-row area. These data were collected to assess the soil condition and nutritional status of oil palms across smallholder farms. This information was used to advise on best agronomic practice. The work was supported by the Natural Environment Research Council (Grant No. 355 NE/R000131/1). Full details about this dataset can be found at https://doi.org/10.5285/4d3813b6-714b-403a-aeeb-e2fa518a1520

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 https://doi.org/10.5285/66e73c38-5b85-44a1-818a-52189bdcffda