Keyword

Farming

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  • Samples were collected from the slurry tank of a 200-cow dairy farm in the East Midlands once per month between June and October 2017 (n=5). Triplicate extractions were performed on each sample using two extraction kits: PowerFecal Kit (Qiagen) and Isolate II Fecal DNA kit (BioLine) (30 extractions in total). DNA was quantified using a Qubit fluorometer (Invitrogen) while quality was assessed via Nanodrop (ThermoFisher). Extracted DNA was stored at 4˚ C pending sequencing. Metagenomic shotgun sequencing of extracted slurry DNA was performed and demultiplexed by Edinburgh Genomics using the Illumina NovaSeq platform (150bp paired end library). Viral metagenomes were prepared firstly by homogenising cattle slurry in PBS. The homogenate was ultracentrifuged to pellet unwanted solids and bacteria. To further remove bacteria, the supernatant was passed sequentially through 0.45um and 0.22um filters. The filtrate was concentrated on an Amicon column and DNA was extracted using a standard phenol-chloroform extraction. Sequencing was conducted using Illumina Novaseq with a 2x150bp library. This data is NERC-funded but not held by the EIDC. This data is archived in the European Nucletotide Archive.

  • Genotype-by-sequencing and chloroplast genome sequencing were carried out for 192 accessions of wild and landrace wheat accessions. This produced 10 nuclear DNA sequences, each 10-20 kb in length, and one 80 kb chloroplast DNA sequence, from each of 192 accessions of wild or domesticated emmer wheat. NB The data are stored in the European Nucleotide Archive (ENA) with accession number Study PRJEB42105 ena-STUDY-UOM-23-08-2017-14:32:05:787-517 and can be accessed at https://www.ebi.ac.uk/ena/browser/view/PRJEB42105

  • This data is NERC-funded but not held by the EIDC. This data is archived in the European Nucleotide Archive (ENA). This dataset contains sequences of the genomic DNA of gut microbiota of calves in response to preventive antibiotic therapy florfenicol obtained by DNA-seq. Importantly, the dataset also contains sequences of genes resistant to different antibiotics. The dataset was created from faecal samples (n=3) of the antibiotic treated animals over seven days and samples (n=3) of animals which have not been subjected to the antibiotic over the same time.

  • [This dataset is embargoed until March 12, 2022]. 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 data is NERC-funded but not held by the EIDC. This data is archived in the European Nucleotide Archive (ENA). This dataset contains sequences of 16S rRNA genes of gut microbiota of calves in response to preventive antibiotic therapy florfenicol by DNA-seq. The dataset was created from faecal samples (n=3) of the antibiotic treated animals over seven days and samples (n=3) of animals which have not been subjected to the antibiotic over the same time.

  • Modelled predictions of annual pollutant loads in rivers from agricultural source areas for Scotland, reported at Water Framework Directive (WFD) catchment scale. The modelled pollutants include total phosphorous, nitrate (NO3-N), faecal indicator organisms (FIOs), suspended solids, methane (CH4) and nitrous oxide (N2O) gas emissions. The agricultural source areas include arable land, improved grassland, rough grazing land and others (e.g. steadings, tracks and other non-field losses). Modelled predictions account for current (c. 2012) implementation of General Binding Rules, Nitrate Vulnerable Zone Action Programme and a number of SRDP options. The values specify pollutant losses in 10^6 colony forming units (cfu) per year for FIOs and kilograms per year for the other pollutants. Full details about this dataset can be found at https://doi.org/10.5285/d4d5a10e-1612-4bb5-97b2-2b850cccdcb2

  • Estimates of annual volumes of manure produced by six broad farm livestock types for England and Wales at 10 km resolution, modelled with MANURES-GIS [1]. The farm livestock classes are: dairy cattle; beef cattle; pigs; sheep and other livestock; laying hens; broilers and other poultry. The quantities produced by each type are subsequently apportioned into managed and field-deposited manure. The managed manure sources are categorised as beef farmyard manure, beef slurry, dairy farmyard manure, dairy slurry, broiler litter, layer manure, pig farmyard manure, pig slurry and sheep farmyard manure. The destinations are recorded as grass, winter arable, spring arable and direct excreta when grazing. For each 10 km square, the quantity of manure going from each source to each destination is estimated. The values specify amount of excreta, in kilograms for solid manure and in litres for liquid manure. [1] ADAS (2008) The National Inventory and Map of Livestock Manure Loadings to Agricultural Land: MANURES-GIS. Final Report for Defra Project WQ0103 Full details about this dataset can be found at https://doi.org/10.5285/517717f7-d044-42cf-a332-a257e0e80b5c

  • [This dataset is embargoed until December 1, 2022]. 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 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 2018, and consist of multiple aspects of household and individual income sources and expenditure in the Katakwi District. The data were collected to support the analysis of vulnerability levels 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/e736e22c-f409-49ee-930d-a415ade89e79

  • Data on resilience of wheat yields in England, derived from the annual Defra Cereals and Oilseeds production survey of commercial farms. The data presented here are summarised over a ten-year time-series (2008-2017) at 10km x10km grid cell (hectad) resolution. The data give the mean yield, relative yield, yield stability and resistance to an extreme event (the poor weather of 2012), for all hectads with at least one sampled farm holding in each year of the time-series (i.e. the minimum data required to calculate the resilience metrics). These metrics were calculated to explore the impact of landscape structure on yield resilience. The data also give the number of samples per year per hectad, so that sampling biases can be explored and filtering applied. No hectads are included that contain data from <9 holdings across the time series (the minimum level required by Defra to maintain anonymity is <5). The data were created under the ASSIST (Achieving Sustainable Agricultural Systems) project by staff at the UK Centre for Ecology & Hydrology to enable exploration of the impacts of agriculture on the environment and vice versa, enabling farmers and policymakers to implement better, more sustainable agricultural practices. Full details about this dataset can be found at https://doi.org/10.5285/7dbcee0c-00ca-4fb2-93cf-90f2a5ca37ea