[This dataset is embargoed until June 1, 2027]. The dataset contains single nucleotide polymorphism (SNP) genotyping data for 323 diverse wheat accessions from Pakistan, Mexico, and Australia. DNA was extracted from 21-day-old seedlings grown in a glasshouse at Rothamsted Research, UK, and genotyped using the commercially available TaNG v1.1 Affymetrix Axiom array at the Bristol Genomics Facility, University of Bristol. The genotypic data are provided in HapMap double-bit format. This work aimed to improve nitrogen use efficiency (NUE) in wheat as part of efforts toward sustainable yield enhancement and global food security. Genetic dissection of key agronomic traits presents a viable strategy for developing high-yielding, nitrogen-efficient wheat cultivars. The data support a genome-wide association study (GWAS) that related NUE traits to potential genetic markers. The research was funded by the Natural Environment Research Council (Grant NE/S009019/1). Full details about this dataset can be found at https://doi.org/10.5285/291ca8d7-6178-4e6f-98a5-f59225d884cd

This dataset is a product of the raw HEA (household economy approach) data that were collected in sixteen communities in the Katakwi district, and the raw IHM (individual household method) data that was collected with 42 households in the community of Anyangabella, and 51 households in the community of Kaikamosing. These data were collected in December 2020 and shows the crop calendars of the Katakwi district. These data consist of quantitative information relating to crop and fishing production timelines throughout a typical agricultural year. The data were collected to support the analysis of vulnerability levels of different to further support livelihood impact modelling, and the development of targeted policies to support resilience at household and community level. The data collection team comprised of local, Ugandan partners. All data were collected in the local language and translated into English. Full details about this dataset can be found at https://doi.org/10.5285/d91bd655-ad51-42c1-a8d0-91923246244b

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 https://doi.org/10.5285/e54069b6-71a9-4b36-837f-a5e3ee65b4de

The data comprises records of phenotypic traits of outbred Gryllus bimaculatus that were bred in a laboratory. Inbreeding was minimized by avoiding crossing family lines. Traits that were measured include adult body mass, development time, and lifespan. The data were collected in 2023 by researchers from the University of Glasgow to investigate the effects of parental age and temperature conditions on offspring life-history. We chose this species because we have complementary field projects on field crickets and they are easily maintained in a lab. Full details about this nonGeographicDataset can be found at https://doi.org/10.5285/085ec3ff-110d-4dbf-96da-7418ab9a7c5a

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. https://catalogue.ceh.ac.uk/id/90df9dfa-a0c8-4ead-a13d-0a0a13cda7ab 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 https://doi.org/10.5285/391c0294-07f1-4856-b592-428bd44055ca

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 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 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 https://doi.org/10.5285/3b44375a-cbe6-468c-9395-41471054d0f3

This dataset contains responses from an online choice experiment with associated socio-economic covariates on the topic of environmental land management schemes. Sample: 348 farmers based in the north of England in 2022. Full details about this dataset can be found at https://doi.org/10.5285/1409404f-564f-43c5-81dd-00339a674dc8

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