[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

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

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 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 https://doi.org/10.5285/e738e8af-554a-4940-bb56-267c7377d74d

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

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 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 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

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 data contains the emissions of ammonia (NH3) from agricultural sources in the South Asia region, for the year 2015. Agriculture is represented by five sub-sectors: crop residue burning (CRB), crop residues left in fields (CRR), livestock management (MNM), livestock grazing and manure applications (GRM), application of synthetic fertilisers (SFA). Data are bottom-up calculations using activity data and emission factors, using methods outlined in the EDGARv6.1 methodology, the IPCC 2006 Guidelines, the IPCC 2019 Guidelines Refinement and in the EMEP/EEA air pollutant emission inventory guidebooks 2019 and 2023. Full details about this dataset can be found at https://doi.org/10.5285/e0114a4f-32c2-41d9-9c2a-c46f365d4c30