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Farming

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  • 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 consists of vegetation abundance data from four experiments investigating the management of arable field options for rare plants. These experiments consisted of a margin management experiment, a herbicide screening experiment, a cereal headland experiment and a crop rotation experiment. All experiments were conducted between 2011 and 2014. The margin management experiment investigated the effects of different cultivation timing and methods and herbicide treatments on the vegetation species composition and abundance within arable field margins. The herbicide screening experiment investigated the effects of different herbicides and their timing of application on the condition of 15 species of rare arable plants. The cereal headland experiment investigated the effects of standard cereal sowing density versus reduced cereal sowing density, and of standard application of N fertilizer vs no application, on sown rare arable species and on the spontaneous weed flora of cereal stands. The crop rotation experiment was designed to provide baseline data for modelling population dynamics of rare arable species in relation to crop rotation scenarios. The data comes from a project funded by Defra (BD5204: Improving the management and success of arable plant options in ELS and HLS). Full details about this dataset can be found at https://doi.org/10.5285/4592780d-734f-4f62-9780-87afe27555d2

  • 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 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

  • 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

  • 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

  • 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

  • [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

  • The data presented are quantitative polymerase chain reaction (qPCR) read outs from antimicrobial resistance gene (AMRG) assays and associated metadata from this project. In this dataset, the mean gene copy numbers per microlitre of DNA extract are shown. The data were collected from faecal and environmental samples which were obtained from a single British commercial pig unit. The former were collected from the sow housing barn, pig growing houses and slurry tanks within the farm unit and the latter were obtained through random stratified sampling of the farm and the surrounding land. These samples were taken from what will be referred to as the 'main study'. A further study was carried out to obtain samples after a partial depopulation which took place on this farm. Faecal samples were obtained from the sow housing barn, pig growing houses and slurry tanks and will be referred to as the 'depopulation (depop) study'. For the main study, the samples were collected between October 19th 2016 and April 5th 2017. For the depop study, the samples were collected between 19th June 2017 and 13th November 2017. The data associated with all samples were generated between August 1st 2017 and May 1st 2018. Full details about this dataset can be found at https://doi.org/10.5285/e548dc5d-49e3-467d-9435-c199da40e7be

  • 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