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 fifteen 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

Projections of global changes in water scarcity with the current extent of croplands were combined to identify the potential country level vulnerabilities of cropland land to water scarcity in 2050. The data relate to an analysis of the impact changes in water availability will have on cropland availability in 2050. Full details about this dataset can be found at https://doi.org/10.5285/1011037f-4f41-41db-ac7a-0d8e9b8bc933

This dataset contains carbon and nitrogen stock data from soils collected from Salisbury Plain, UK. The sites were selected to reflect the four main grassland management types on Salisbury Plain ranging from arable cropland to species rich grassland, with six representative grassland plots for each type (24 sites in total). Each site had two replicates for each variable measured. The data collected was intended to illustrate a gradient of ecosystem functioning and vegetation change as the grassland becomes more extensively managed. The field sampling was conducted by the University of Manchester and the Centre for Ecology & Hydrology at Wallingford. Soil C and N were analysed by the University of Manchester. The data includes carbon and nitrogen budgets to depth at all sites. Full details about this dataset can be found at https://doi.org/10.5285/58709d9b-2b52-4f5d-8f3b-49354e664aea

This dataset contains ecosystem function and vegetation survey data from soils collected from Salisbury Plain, UK. The sites were selected to reflect the four main grassland management types on Salisbury Plain ranging from arable cropland to species rich grassland, with six representative grassland plots for each type (24 sites in total). Each site had four replicates for each variable measured. The data collected was intended to illustrate a gradient of ecosystem functioning and vegetation change as the grassland becomes more extensively managed. The field sampling was conducted by the University of Manchester and the Centre for Ecology & Hydrology at Wallingford. Soils were extracted for nutrients and analysed by the University of Manchester. The data includes vegetation surveys, where the cover of each plant species present was assessed, and diversity indices were calculated. Furthermore, greenhouse gas fluxes were measured in situ, and soil nutrients and microbial biomass were also determined. Full details about this dataset can be found at https://doi.org/10.5285/0e0a89e1-21f9-413e-8d7e-764b0b714dd6

The dataset contains model output from the land surface model JULES and the econometric agricultural land use model ECO-AG, at kilometre scale resolution over Great Britain for 8 different scenarios using unmitigated climate change. Modelled arable area, net primary productivity, runoff and irrigation demand are provided for scenarios combining and isolating the effects of climate, CO2 and irrigation. The driving climate data used to drive the models is from Regional Climate Model runs performed for the period 1998-2008 and for an eleven year period at 2100 for CO2 levels corresponding to the unmitigated Regional Concentration Pathway RCP8.5. Full details about this dataset can be found at https://doi.org/10.5285/2efac82b-2438-4806-999d-374663210c34

Estimates of in-river concentrations (mg/l) and loads (kg/day) of nutrients to rivers in England and Wales from multiple sector sources, modelled with SAGIS (Source Apportionment GIS). The nutrients include nitrate (mg/l N) and ortho-phosphate (mg/l P); the estimate loads are expressed as kilograms per day (kg/day) and the in-river concentrations as milligrams per litre (mg/l). Sources are both diffuse and point. Diffuse sources include livestock farming, arable farming, highways, urban runoff, background (from soils), onsite wastewater treatment systems and atmospheric deposition. Point sources include treated wastewater effluent, combined sewer overflows and storm tanks, industrial discharges and mine water discharges. Concentrations and loads are modelled using the Environment Agency's catchment river model, SIMCAT, at the locations of model features or every 1 km along each river, taking into account all upstream sources and user defined river losses. SAGIS is a modelling framework was developed through the UK Water Industry Research Programme (UKWIR) project 'Chemical Source Apportionment under the WFD' [1], with support from the Environment Agency and SEPA. The model is also described in [2] [1] UKWIR (2012) Chemical Source Apportionment under the WFD (12/WW/02/3). Final report for UK Water Industry Research, 1 Queen Annes Gate, London, ISBN: 1 84057 637 5. [2] Comber, S.D.; Smith, R.; Daldorph, P.; Gardner, M.J.; Constantino, C.; Ellor, B. (2013) Development of a Chemical Source Apportionment Decision Support Framework for Catchment Management. Environ. Sci. Technol. 47, 9824-9832 Full details about this dataset can be found at https://doi.org/10.5285/8c5d9e38-0244-4a39-8600-a85513a6fecf

Data from 38 experimental sites across the UK and Ireland were collated resulting in 623 separate mineral fertiliser N2O emission factors (EF) estimates derived from field measurements. Data were either i) extracted from published studies in which one aim of the experimentation was to explicitly measure N2O and report EFs after a mineral fertiliser application, or ii) raw data were used from the Agricultural and Environmental Data Archive (AEDA). To find the published data, a survey of literature was conducted using Google Scholar for articles considered ‘recent’ (20 years or fewer), i.e. published after January 1998 and submitted before April 2019. The following search terms and their variations were used: N2O, nitrous oxide, emission factor, mineral fertiliser, ammonium nitrate, urea, nitrification inhibitor, nitrogen use efficiency, agriculture, greenhouse gas, grassland and arable. This search based on keywords was complemented with a search through the literature cited in the articles found and known previous research. Full details about this dataset can be found at https://doi.org/10.5285/9948d1b9-caa1-4894-93e6-cc0f4326fced

This data collection results from abundance surveys of seven species of weeds in ca. 500 lowland arable fields in 49 farms over three years. Each field was divided into large grids of 20x20 metre cells, and the density of seven species was estimated three times a year. The study is part of the NERC Rural Economy and Land Use (RELU) programme. In the context of changing external and internal pressures on UK agriculture, particularly those associated with the ongoing reform of the EU Common Agricultural Policy, it is imperative to determine whether all of the various dimensions of sustainability - including the relevant economic and environmental objectives as well as social and cultural values - can be integrated successfully at the farm and landscape levels. Although the ways in which economic, technological, and regulatory changes are likely to affect the profitability and management of farms of varying size are reasonably well understood, there is not the knowledge or understanding to predict the resulting effects on biodiversity. For example, the effect of changes in arable farming practices on field weeds and, in turn, on habitats and food supply required to sustain farm birds is a case in point. This knowledge is critical, however, if we are to understand the ecological consequences of changes in agricultural policy. Furthermore, it is also important if we are to design and justify changes in farming methods that can not only enhance nature conservation, but do this is ways that are practical and appealing from a farmer's point of view. This understanding is essential if we are to achieve an agriculture that is sustainable in both economic and environmental terms and is widely perceived to have social and cultural value. A consistent theme in all components of this research project is to understand the behaviour (of farmers, weeds or birds) and then use this information to produce predictive models. Whilst there have been a number of models of economic behaviour, weed populations and bird populations - including many by the research team here - the really novel component of this research is to integrate these within one framework. Farmer interviews on economic attitudes and preferences associated with and importance of different land-use objectives to lowland arable farmers are available at the UK Data Archive under study number 6728 (see online resources). Further documentation for this study may be found through the RELU Knowledge Portal and the project's ESRC funding award web page (see online resources).

This dataset consists of farm management data which includes crop drilling dates and herbicide application dates. The data relate to arable fields in which a range of ecological measurements were collected, including species counts in the following areas: weed seedbank, vegetation in the crop, field edge vegetation, invertebrates. Each field was sown with a combination of genetically modified and conventional crops, either Beet, Maize, Spring-sown Oilseed Rape or Winter-sown oilseed Rape. The data were collected as part of the Farm Scale Evaluations (FSEs), a four-year programme of research by independent researchers aimed at studying the effect that the management practices associated with Genetically Modified Herbicide Tolerant (GMHT) crops might have on farmland wildlife, when compared with weed control used with non-GM crops. Data were collected by a consortium of: the Institute of Terrestrial Ecology, ITE (now the Centre for Ecology & Hydrology, CEH), the Institute of Arable Crop Research (now Rothamstead Research, IACR) and the Scottish Crop Research Institute, SCRI (now the James Hutton Institute, JHI). Data were collected for four crops overall (Beet, Maize, Spring-sown Oilseed Rape and Winter-sown oilseed Rape).

This dataset consists of a range of ecological measurements collected from a set of arable fields, each sown with a combination of genetically modified and conventional beet crops. Measurements include species counts in the following areas: weed seedbank, vegetation in the crop, field edge vegetation, invertebrates. The data were collected as part of the Farm Scale Evaluations (FSEs), a four-year programme of research by independent researchers aimed at studying the effect that the management practices associated with Genetically Modified Herbicide Tolerant (GMHT) crops might have on farmland wildlife, when compared with weed control used with non-GM crops. Data were collected by a consortium of: the Institute of Terrestrial Ecology (now the Centre for Ecology & Hydrology), the Institute of Arable Crops Research (now Rothamsted Research) and the Scottish Crop Research Institute, SCRI (now the James Hutton Institute). Data were collected for four crops overall (Beet, Maize, Spring-sown Oilseed Rape and Winter-sown oilseed Rape). Full details about this dataset can be found at https://doi.org/10.5285/86cd1a60-64f1-4087-a9f1-a3d8a9f8f535