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 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 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

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 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

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

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

This data set includes over 500 individual flux measurements of nitrous oxide (N2O) from a mixed livestock farm, carried out in 2012 and 2013 using a high-precision flux chamber method. Measurements of soil properties are also reported for each individual flux measurement. Soil pH, temperature moisture content, bulk density and ammonia and nitrate concentrations (extracted via the KCl method) are reported in the data. The data represents arable and grazed fields (cattle and sheep) as well as other sources of N2O from agricultural lands such as barns, manure heaps and silage storage. This data was used to assess farms scale emissions of N2O from a variety of sources over four seasons. Full details about this dataset can be found at https://doi.org/10.5285/54edbdcf-086e-40a7-b2cc-c1e4fcbfbbbc