This dataset is the 2012 revised Corine Land Cover (CLC) map, consisting of 44 classes in the hierarchical three level Corine nomenclature, produced during the CLC2018 production to improve the CLC2012 inventory. CLC 2018, CLC change 2012-2018 and CLC 2012 revised are three of the datasets produced within the frame of the Copernicus programme on land monitoring. Corine Land Cover (CLC) provides consistent information on land cover and land cover changes across Europe; these two maps are the UK component of Europe. This inventory was initiated in 1985 (reference year 1990) and established a time series of land cover information with updates in 2000, 2006 and 2012 being the last iteration. CLC products are based on photointerpretation of satellite images by national teams of participating countries – the EEA member and cooperating countries – following a standard methodology and nomenclature with the following base parameters: 44 classes in the hierarchical three level Corine nomenclature; minimum mapping unit (MMU) of status layers is 25 hectares; minimum width of linear elements is 100 metres; minimum mapping unit (MMU) for Land Cover Changes (LCC) for the change layers is 5 hectares. The resulting national land cover inventories are further integrated into a seamless land cover map of Europe. Land cover and land use (LCLU) information is important not only for land change research, but also more broadly for the monitoring of environmental change, policy support, the creation of environmental indicators and reporting. CLC datasets provide important datasets supporting the implementation of key priority areas of the Environment Action Programmes of the European Union as protecting ecosystems, halting the loss of biological diversity, tracking the impacts of climate change, assessing developments in agriculture and implementing the EU Water Framework Directive, among others. More information about the Corine Land Cover (CLC) and Copernicus land monitoring data in general can be found at http://land.copernicus.eu/. Full details about this dataset can be found at https://doi.org/10.5285/9bb7caab-764d-407b-9a81-0d758722d900

The dataset contains annual soil greenhouse gas emissions following sheep urine (real and artificial) applications to a semi-improved upland grassland in North Wales, UK, across two seasons (spring and autumn) within the year 2016-2017. Soil greenhouse gas data were collected using a combination of automated chambers and manually sampled chambers, both analysed via gas chromatography. Supporting data include meteorological data, soil chemistry and above ground biomass data collected on a time-series throughout the study, following urine application. The data were used to calculate sheep urine patch nitrous oxide emission factors from an upland environment, to improve estimates of greenhouse gas emissions from extensively grazed agroecosystems. Full details about this dataset can be found at https://doi.org/10.5285/0434c74c-4a8e-45b8-a187-13e422c0ed0f

This dataset is the 2018 Corine Land Cover map, consisting of 44 classes in the hierarchical three level Corine nomenclature. Corine Land Cover (CLC) 2018, CLC change 2012-2018 and CLC 2012 revised are three of the datasets produced within the frame of the Copernicus programme on land monitoring. Corine Land Cover (CLC) provides consistent information on land cover and land cover changes across Europe; these two maps are the UK component of Europe. This inventory was initiated in 1985 (reference year 1990) and established a time series of land cover information with updates in 2000, 2006 and 2012 being the last iteration. CLC products are based on photointerpretation of satellite images by national teams of participating countries – the EEA member and cooperating countries – following a standard methodology and nomenclature with the following base parameters: 44 classes in the hierarchical three level Corine nomenclature; minimum mapping unit (MMU) of status layers is 25 hectares; minimum width of linear elements is 100 metres; minimum mapping unit (MMU) for Land Cover Changes (LCC) for the change layers is 5 hectares. The resulting national land cover inventories are further integrated into a seamless land cover map of Europe. Land cover and land use (LCLU) information is important not only for land change research, but also more broadly for the monitoring of environmental change, policy support, the creation of environmental indicators and reporting. CLC datasets provide important datasets supporting the implementation of key priority areas of the Environment Action Programmes of the European Union as protecting ecosystems, halting the loss of biological diversity, tracking the impacts of climate change, assessing developments in agriculture and implementing the EU Water Framework Directive, among others. More information about the Corine Land Cover (CLC) and Copernicus land monitoring data in general can be found at http://land.copernicus.eu/. Full details about this dataset can be found at https://doi.org/10.5285/084e0bc6-e67f-4dad-9de6-0c698f60e34d

This data set consists of measurements of areas of landscape features and associated attributes from sites across Wales, collected between 2013 and 2016. Data are presented as areas of Broad (or Priority) Habitats, with associated landscape attributes (such as plant species and land use), within a set of 300 x 1km squares across Wales, collected as part of the Glastir Monitoring and Evaluation Programme (GMEP). The monitoring programme was set up by the Welsh Government in 2013 to monitor the effects of the Glastir agri-environment scheme on the environment and ran from 2013 to 2016. The field survey element was based on a stratified random sampling design of 300 x 1km square sites across Wales, and was managed by the UK Centre for Ecology & Hydrology. Full details about this dataset can be found at https://doi.org/10.5285/82c63533-529e-47b9-8e78-51b27028cc7f

A Yield Constraint Score (YCS; scale of 1-5) was developed for the effect of five key crop stresses (ozone, pests and diseases, soil nutrients, heat stress and aridity) on the production of the crops maize (Zea mays), rice (Oryza sativa), soybean (Glycine max) and wheat (Triticum aestivum). Data are on a global scale at 1 deg by 1deg resolution, based on the distribution of production for each crop, according to the Food and Agriculture Organisation’s (FAO) Global Agro-Ecological Zones (GAEZ) crop production data for the year 2000. To derive the YCS for each crop stress, spatial data on a global scale were gathered. Modelled ozone data (2010-2012) were derived from the EMEP MSC-W (European Monitoring and Evaluation Programme, Meteorological Synthesising Centre-West) chemical transport model (version 4.16). Pests and diseases data (2002-2004) were downloaded from a Centre for Agriculture and Biosciences International (CABI) database providing estimates for pre-harvest crop losses due to weeds, animal, pathogens and viruses, compiled from the literature. Soil nutrient classifications (for 2009, derived using soil attributes from the Harmonized World Soil Database (HWSD)) were downloaded from the GAEZ data portal. A heat stress index was calculated using daily temperature data (1990-2014) to determine whether the temperature within a 30-day thermal-sensitive period exceeded crop tolerance thresholds. Global Aridity Index data (1950-2000) were downloaded from the Consultative Group for International Agricultural Research’s Consortium for Spatial Information (CGIAR-CSI). The Yield Constraint Score provides an indication of where each stress is predicted to be affecting crop yield globally and the magnitude of the effect. The YCS data were developed as part of the NERC funded SUNRISE project and the National Capability Project NC-Air quality impacts on food security, ecosystems and health. Full details about this dataset can be found at https://doi.org/10.5285/d347ed22-2b57-4dce-88e3-31a4d00d4358

This dataset is the Corine Land Cover (CLC) change map between 2012 and 2018, consisting of 44 classes in the hierarchical three level Corine nomenclature. The Corine land cover changes between 2012 and 2018 for the UK, Isle of Man, Jersey and Guernsey dataset forms part of the Corine Land Cover Maps collection and is produced within the frame of the Copernicus programme on land monitoring. Corine Land Cover (CLC) provides consistent information on land cover and land cover changes across Europe; this change map is the UK component of the European coverage. This inventory was initiated in 1985 (reference year 1990) and established a time series of land cover information with updates in 2000, 2006 and 2012 being the last iteration. CLC products are based on photointerpretation of satellite images by national teams of participating countries – the EEA member and cooperating countries – following a standard methodology and nomenclature with the following base parameters: 44 classes in the hierarchical three level Corine nomenclature; minimum mapping unit (MMU) of status layers is 25 hectares; minimum width of linear elements is 100 metres; minimum mapping unit (MMU) for Land Cover Changes (LCC) for the change layers is 5 hectares. The resulting national land cover inventories are further integrated into a seamless land cover map of Europe. Land cover and land use (LCLU) information is important not only for land change research, but also more broadly for the monitoring of environmental change, policy support, the creation of environmental indicators and reporting. CLC datasets provide important datasets supporting the implementation of key priority areas of the Environment Action Programmes of the European Union as protecting ecosystems, halting the loss of biological diversity, tracking the impacts of climate change, assessing developments in agriculture and implementing the EU Water Framework Directive, among others. More information about the Corine Land Cover (CLC) and Copernicus land monitoring data in general can be found at http://land.copernicus.eu/. Full details about this dataset can be found at https://doi.org/10.5285/027b6432-f44d-41ab-b349-89fae673d5f5

[THIS DATASET HAS BEEN WITHDRAWN]. Modelled average percentage yield loss due to ground-level ozone pollution (per 1 degree by 1 degree grid cell) are presented for the crops maize (Zea mays), rice (Oryza sativa), soybean (Glycine max) and wheat (Triticum aestivum) for the period 2010-2012. Data are on a global scale, based on the distribution of production for each crop, according to the Food and Agriculture Organisation’s (FAO) Global Agro-Ecological Zones (GAEZ) crop production data for the year 2000. Modelled ozone data (2010-2012) needed for yield loss calculations were derived from the EMEP MSC-W (European Monitoring and Evaluation Programme, Meteorological Synthesising Centre-West) chemical transport model (version 4.16). Mapping the global crop yield losses due to ozone highlights the impact of ozone on crops and allows areas at high risk of ozone damage to be identified, which is one of the first steps towards mitigation of the problem. The yield loss calculations were done as part of the NERC funded SUNRISE project (NEC06476). Full details about this dataset can be found at https://doi.org/10.5285/181a7dd5-0fd4-482a-afce-0fa6875b5fb3

This dataset contains the percentage of the total pasture area in each country classified as vulnerable to water scarcity (annual run-off is declining and the water shed is defined as water scarce in 2050). Projections of global changes in water scarcity with the current extent of pasture land were combined to identify the potential country level vulnerabilities of pasture land to water scarcity in 2050. The data relate to an analysis of the impact changes in water availability will have on pasture availability in 2050. Full details about this dataset can be found at https://doi.org/10.5285/ec5cc84e-a8da-4ff8-80d4-26fca1a31e1f

Modelled annual average production loss (thousand tonnes per 1 degree by 1 degree grid cell) due to ground-level ozone pollution is presented for the crops maize (Zea mays), rice (Oryza sativa), soybean (Glycine max) and wheat (Triticum aestivum), for the period 2010-2012. Data are on a global scale, based on the distribution of production for each crop, according to the Food and Agriculture Organisation’s (FAO) Global Agro-Ecological Zones (GAEZ) crop production data for the year 2000. Modelled ozone data (2010-2012) needed for production loss calculations were derived from the EMEP MSC-W (European Monitoring and Evaluation Programme, Meteorological Synthesising Centre-West) chemical transport model (version 4.16). Mapping the global crop production losses due to ozone highlights the impact of ozone on crops and allows areas at high risk of ozone damage to be identified, which is a step towards mitigation of the problem. The production loss calculations were done as part of the NERC funded SUNRISE project (NEC06476) and National Capability Project NC-Air quality impacts on food security, ecosystems and health (NEC05574). Full details about this dataset can be found at https://doi.org/10.5285/0aa7911a-ab5f-4b08-a225-28b1e8344d01

Modelled average percentage yield loss due to ground-level ozone pollution (per 1 degree by 1 degree grid cell) are presented for the crops maize (Zea mays), rice (Oryza sativa), soybean (Glycine max) and wheat (Triticum aestivum) for the period 2010-2012. Data are on a global scale, based on the distribution of production for each crop, according to the Food and Agriculture Organisation’s (FAO) Global Agro-Ecological Zones (GAEZ) crop production data for the year 2000. Modelled ozone data (2010-2012) needed for yield loss calculations were derived from the EMEP MSC-W (European Monitoring and Evaluation Programme, Meteorological Synthesising Centre-West) chemical transport model (version 4.16). Mapping the global crop yield losses due to ozone highlights the impact of ozone on crops and allows areas at high risk of ozone damage to be identified, which is one of the first steps towards mitigation of the problem. The yield loss calculations were done as part of the NERC funded SUNRISE project and National Capability Project NC-Air quality impacts on food security, ecosystems and health. Full details about this dataset can be found at https://doi.org/10.5285/2a932995-f040-4724-ad21-3e92ae8a2540