Dynamic access to data on agricultural areas in Europe is derived from the Corine Land Cover 2006 inventory. Data are available as 100 meter pixel raster images at small scales up to 1:800.000 and at higher scales as vectors.CORINE Land Cover (CLC) is a geographic land cover/land use database encompassing most of the countries of Europe. In 1985 the Corine programme was initiated in the European Union. Corine means 'coordination of information on the environment' and it was a prototype project working on many different environmental issues. The Corine databases and several of itsprogramme have been taken over by the EEA. One of these is an inventory of land cover in 44 classes organised hierarchically in three levels, and presented as a cartographic product, at a scale of 1:100 000. The first level (5 classes) corresponds to the main categories of the land cover/land use (artificial areas, agricultural land, forests and semi-natural areas, wetlands, water surfaces). The second level (15 classes) covers physical and physiognomic entities at a higher level of detail (urban zones, forests, lakes, etc), finally level 3 is composed of 44 classes. CLC was elaborated based on the visual interpretation of satellite images (SPOT, LANDSAT TM and MSS). Ancillary data (aerial photographs, topographic or vegetation maps, statistics, local knowledge) were used to refine interpretation and the assignment of the territory into the categories of the CORINE Land Cover nomenclature.The smallest surfaces mapped (minimum mapping units) correspond to 25 hectares. Linear features less than 100 m in width are not considered. The scale of the output product was fixed at 1:100.000. Thus, the location precision of the CLC database is 100 m.This database is operationally available for most areas of Europe. Original inventories, based on and interpreted from satellite imagery as well as ancillary information sources, are stored within national institutions.One of the major tasks undertaken in the framework of the Corine programme has been the establishment of a computerised inventory on the land cover. Data on land cover is necessary for the environment policy as well as for other policies such as regional development and agriculture. At the same time it provides one of the basic inputs for the production of more complex information on other themes (soil erosion, pollutant emission into the air by the vegetation, etc.). The objectives of the land cover project are: - to provide those responsible for and interested in the European policy on the environment with quantitative data on land cover, consistent and comparable across Europe.Geographic coverage: Albania, Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Hungary, Iceland, Ireland, Italy, Kosovo under UNSCR 1244/99, Latvia, Liechtenstein, Lithuania, Luxembourg, Macedonia the former Yugoslavian Republic of, Malta, Montenegro, Netherlands, Norway, Poland, Portugal, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom Corine Land Cover 2006 seamless vector data - version 16 (04/2012) can be accessed here:http://www.eea.europa.eu/data-and-maps/data/clc-2006-vector-data-version-2

This map service provides dynamic access to data from the Corine Land Cover 2006 inventory. Data are available as 100 meter pixel raster images at small scales up to 1:800.000 and at higher scales as vectors. CORINE Land Cover (CLC) is a geographic land cover/land use database encompassing most of the countries of Europe. In 1985 the Corine programme was initiated in the European Union. Corine means 'coordination of information on the environment' and it was a prototype project working on many different environmental issues. The Corine databases and several of its programme have been taken over by the EEA. One of these is an inventory of land cover in 44 classes organised hierarchically in three levels, and presented as a cartographic product, at a scale of 1:100 000. The first level (5 classes) corresponds to the main categories of the land cover/land use (artificial areas, agricultural land, forests and semi-natural areas, wetlands, water surfaces). The second level (15 classes) covers physical and physiognomic entities at a higher level of detail (urban zones, forests, lakes, etc), finally level 3 is composed of 44 classes. CLC was elaborated based on the visual interpretation of satellite images (SPOT, LANDSAT TM and MSS). Ancillary data (aerial photographs, topographic or vegetation maps, statistics, local knowledge) were used to refine interpretation and the assignment of the territory into the categories of the CORINE Land Cover nomenclature. The smallest surfaces mapped (minimum mapping units) correspond to 25 hectares. Linear features less than 100 m in width are not considered. The scale of the output product was fixed at 1:100.000. Thus, the location precision of the CLC database is 100 m. This database is operationally available for most areas of Europe. Original inventories, based on and interpreted from satellite imagery as well as ancillary information sources, are stored within national institutions. One of the major tasks undertaken in the framework of the Corine programme has been the establishment of a computerised inventory on the land cover. Data on land cover is necessary for the environment policy as well as for other policies such as regional development and agriculture. At the same time it provides one of the basic inputs for the production of more complex information on other themes (soil erosion, pollutant emission into the air by the vegetation, etc.). The objectives of the land cover project are: - to provide those responsible for and interested in the European policy on the environment with quantitative data on land cover, consistent and comparable across Europe. Geographic coverage: Albania, Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Hungary, Iceland, Ireland, Italy, Kosovo under UNSCR 1244/99, Latvia, Liechtenstein, Lithuania, Luxembourg, Macedonia the former Yugoslavian Republic of, Malta, Montenegro, Netherlands, Norway, Poland, Portugal, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom Corine Land Cover 2006 seamless vector data - version 16 (04/2012) can be accessed here: http://www.eea.europa.eu/data-and-maps/data/clc-2006-vector-data-version-2

Success.ai’s Agricultural Data provides unparalleled access to verified profiles of agriculture and farming leaders worldwide. Sourced from over 700 million LinkedIn profiles, this dataset includes actionable insights and contact details for professionals shaping the global agricultural landscape. Whether your objective is to market agricultural products, establish partnerships, or analyze industry trends, Success.ai ensures your outreach is powered by accurate, enriched, and continuously updated data.

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Real-time updates reflect role changes, organizational shifts, and emerging trends in agriculture and farming. Tailored for Agricultural Insights

Enriched profiles include professional histories, areas of specialization, and industry affiliations for deeper audience understanding. Data Highlights: 700M+ Verified LinkedIn Profiles: Gain access to a global network of agricultural and farming professionals. 100M+ Work Emails: Communicate directly with decision-makers in agribusiness and farming. Enriched Professional Histories: Understand career trajectories, expertise, and organizational affiliations. Industry-Specific Segmentation: Target professionals in crop farming, agtech, and sustainable agriculture with precision filters. Key Features of the Dataset: Agriculture and Farming Professional Profiles

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This dataset provides values for GDP FROM AGRICULTURE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This map service provides dynamic access to data from the Corine LandCover 2000 inventory. Data are available as 100 meter pixel raster images at small scales up to 1:800.000 and at higher scales as vectors. CORINE Land Cover (CLC) is a geographic land cover/land use database encompassing most of the countries of Europe. In 1985 the Corine programme was initiated in the European Union. Corine means 'coordination of information on the environment' and it was a prototype project working on many different environmental issues. The Corine databases and several of its programme have been taken over by the EEA. One of these is an inventory of land cover in 44 classes organised hierarchically in three levels, and presented as a cartographic product, at a scale of 1:100 000. The first level (5 classes) corresponds to the main categories of the land cover/land use (artificial areas, agricultural land, forests and semi-natural areas, wetlands, water surfaces). The second level (15 classes) covers physical and physiognomic entities at a higher level of detail (urban zones, forests, lakes, etc), finally level 3 is composed of 44 classes. CLC was elaborated based on the visual interpretation of satellite images (SPOT, LANDSAT TM and MSS). Ancillary data (aerial photographs, topographic or vegetation maps, statistics, local knowledge) were used to refine interpretation and the assignment of the territory into the categories of the CORINE Land Cover nomenclature. The smallest surfaces mapped (minimum mapping units) correspond to 25 hectares. Linear features less than 100 m in width are not considered. The scale of the output product was fixed at 1:100.000. Thus, the location precision of the CLC database is 100 m. This database is operationally available for most areas of Europe. Original inventories, based on and interpreted from satellite imagery as well as ancillary information sources, are stored within national institutions. One of the major tasks undertaken in the framework of the Corine programme has been the establishment of a computerised inventory on the land cover. Data on land cover is necessary for the environment policy as well as for other policies such as regional development and agriculture. At the same time it provides one of the basic inputs for the production of more complex information on other themes (soil erosion, pollutant emission into the air by the vegetation, etc.). The objectives of the land cover project are: - to provide those responsible for and interested in the European policy on the environment with quantitative data on land cover, consistent and comparable across Europe. Geographic coverage: Albania, Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Kosovo under UNSCR 1244/99, Latvia, Liechtenstein, Lithuania, Luxembourg, Macedonia the former Yugoslavian Republic of, Malta, Montenegro, Netherlands, Norway, Poland, Portugal, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom Corine Land Cover 2000 seamless vector data - version 16 (04/2012) can be accessed here: http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2000-clc2000-seamless-vector-database-4

This version contains errors; please use the data from version 1.1 or later.

Inventory description

The Harmonized IACS inventory of Europe-LAND is a harmonized collection of data from the Geospatial Aid (GSA) system of the Integrated Control and Administration System (IACS), which manages and controls agricultural subsidies in the European Union (EU). The GSA data are a unique data source with field-levels of land use information that are annually generated. The data carry information on crops grown per field, a unique identifier of the subsidy applicants that allows to aggregate fields to farms, information on organic cultivation and animal numbers per farm.

Due to General Data Protection Regulations (GDPR), we are not allowed to share all data that we collected and harmonized. Therefore, there are two versions of the inventory, a public version and an internal version. The internal version contains more information and covers more countries and years.

The public version contains all data that can be shared following the GDPR of the data providers. It covers 18 countries with time series up to 17 years. For most countries, only the crop information can be shared. However, for 6 countries also the applicant identifier and for two of them also the organic management information can be shared. If you use the data, please also cite the original sources of the data. You can find the references in the provided documentation that is in the "_Documentation.zip".

The crop information were harmonized using the Hierarchical Crop and Agriculture Taxonomy (HCAT) of the EuroCrops project (Schneider et al., 2023). To allow for interoperability with EuroCrops, the harmonized Europe-LAND data come with the same column names that relate to the crop information. All crop mapping tables can be found in our GitHub repository.

Column names:

• field_id (mandatory): Unique identifier for each field per country, state or region
• farm_id (optional): Unique identifier for each applicant per country, state, or region
• crop_code (mandatory): The original, country-specific crop code
• crop_name (mandatory): The original, country-specific crop name
• EC_trans_n (mandatory): The original crop name translated into English
• EC_hcat_n (mandatory): The machine-readable HCAT name of the crop
• EC_hcat_c (mandatory): The 10-digit HCAT code indicating the hierarchy of the crop
• organic (optional): Indicates whether a field was organically cultivated or not. Values: [0,1]
• field_size (mandatory): Size of field in hectares
• crop_area (optional): Area in hectares of crop reported in crop column. This occurs only if multiple crops are reported per parcel and refers to the area of the main crop.

More detailed information for all countries in our harmonized inventory (including those that are not publicly available) can also be found in the documentation.

The inventory will be updated at least annually. In future versions, we will add a new crop classification, harmonized animal data, and harmonized agri-environmental measures/eco-schemes.

Information on data provision

All files come as .geoparquets to stay within the space limitations of Zenodo. Geoparquets can simply be opened in QGIS via drag and drop. Additionally, various libraries from different porgramming languages are able to handle geoparquets, e.g. geoarrow and sgarrwo in R, GDAL/OGR in C++, GeoParquet.jl in Julia or Fiona in Python.

We bundled multiple years of each country to stay below the file number limitation of Zenodo. Each zip file name indicates the country, region, or federal state and the years covered. The meaning of the abbreviations of the countries, regions, and federal states can be found in the "country_region_codes.xlsx" in the "_Documentation.zip".

The Spanish data are also bundled across regions, as they are separated into more than 50 regions. See the country_regions_codes.xlsx tables for the meaning of the abbreviations:

• ES_Bundle1 (Northeast): BAL, BAR, CAS, GIR, HEC, LLE, NAV, TAR, TER, ZAR
• ES_Bundel2 (Northwest): ACO, ALA, AST, BUR, CAN, GUI, LEO, LRI, LUG, OUR, PAL, PON, VIZ, VLD, ZAM
• ES_Bundle3 (West): ALB, AVI, CAC, CIU, CUE, GUA, MAD, SAL, SEG, SOR, TOL
• ES_Bundle4 (Southwest): ALI, ALM, BAD, CAD, CDB, GRA, HEV, JAE, LAP, MAL, MUR, SAN, SEV, VLC

Here we present a data set of crop performance in France, one of Europe’s major crop producers. The data set comprises ten crops (barley, maize, oats, potatoes, rapeseed, sugarbeet, sunflower, durum wheat, soft wheat and wine) and covers the years 1900 to 2018. It contains harvested area, production and yield data for all 96 French départements (i.e. counties or NUTS3 level) with a total number of 375,264 data points. Entries until 1988 have been digitized manually from statistical yearbooks.

Most of the countries of Europe are encompassed in this land cover map, which provides dynamic access to data from the three layers of Corine Land Cover (1990, 2000 and 2006). Choose a layer for each of the two maps from the map legend pull-down menu (open and close from the triangular icons). Zoom into your area of interest and use the slider bar to see land cover changes.Data are available as 100 meter pixel raster images at small scales up to 1:800.000 and at higher scales as vectors. A seperate dataset and map showing only Corine Land Cover 2000-2006 changes is also available. CORINE Land Cover (CLC) is a geographic land cover/land use database encompassing most of the countries of Europe. In 1985 the Corine programme was initiated in the European Union. Corine means 'coordination of information on the environment' and it was a prototype project working on many different environmental issues. The Corine databases and several of its programmes have been taken over by the EEA. One of these is an inventory of land cover in 44 classes organised hierarchically in three levels, and presented as a cartographic product, at a scale of 1:100 000. The first level (5 classes) corresponds to the main categories of the land cover/land use (artificial areas, agricultural land, forests and semi-natural areas, wetlands, water surfaces). The second level (15 classes) covers physical and physiognomic entities at a higher level of detail (urban zones, forests, lakes, etc), finally level 3 is composed of 44 classes. CLC was elaborated based on the visual interpretation of satellite images (SPOT, LANDSAT TM and MSS). Ancillary data (aerial photographs, topographic or vegetation maps, statistics, local knowledge) were used to refine interpretation and the assignment of the territory into the categories of the CORINE Land Cover nomenclature. The smallest surfaces mapped (minimum mapping units) correspond to 25 hectares. Linear features less than 100 m in width are not considered. The scale of the output product was fixed at 1:100.000. Thus, the location precision of the CLC database is 100 m. This database is operationally available for most areas of Europe. Original inventories, based on and interpreted from satellite imagery as well as ancillary information sources, are stored within national institutions. One of the major tasks undertaken in the framework of the Corine programme has been the establishment of a computerised inventory on the land cover. Data on land cover is necessary for the environment policy as well as for other policies such as regional development and agriculture. At the same time it provides one of the basic inputs for the production of more complex information on other themes (soil erosion, pollutant emission into the air by the vegetation, etc.). The objectives of the land cover project are: - to provide those responsible for and interested in the European policy on the environment with quantitative data on land cover, consistent and comparable across Europe. Geographical coverage: Varies depending on layer:Corine Land Cover 1990 raster data - version 16 (04/2012)Corine Land Cover 2000 seamless vector data - version 16 (04/2012)Corine Land Cover 2006 seamless vector data - version 16 (04/2012)

Database created for identifying mixed agriculture landscape in the framework of workpackage 3, task 3.3 Mapping mixed landscapes in Europe using existing data (top-down approach) of the MIXED project (Multi-actor and transdisciplinary development of efficient and resilient MIXED farming and agroforestry-systems)

This database is presented and used in deliverable 3.3 Identifying the potential for expansion of mixed farming in European regions.

This data record contains

• the final database used for delivererable 3.3 (databaseD3.3.csv)
• a description of the data in the database (datadescription.xlsx)
• the shapfiles to create maps from the database (map.xxx)

Note that the data description files mentions the source of the raw data. Please consult the deliverable to understand how the data was processed and how the map was created.

Keywords; Search terms: historical time series; historical statistics; histat / HISTAT . Abstract: The author`s analysis explains to what extent the Central European agriculture and food industry has managed to satisfy the demand of the population in the centuries since the Middle Ages. For this purpose, the author collects and analyses prices, wages, rents, agricultural products, and population movements, as well as the costs of living of broad levels of the population. The price data at hand (prices of wheat and rye in Germany, Europe and America) provide a substantial basis for his analysis. On the basis of the long-term fluctuation of corn prices in England, France, Northern Italy, Germany and Austria, three waves of development can be identified: 1. An upswing in the 13th and partly also at the beginning of the 14th century is followed by a downswing in the late Middle Ages. 2. Another upswing in the 16th century was interrupted in the 17th century; 3. a third upswing in the 18th century dissolved in the 19th century into shorter and partly opposed movements that merge again only in the late 19th and 20th century. What do these waves mean? There are two approaches which could explain these developments: 1. Such price fluctuations are the consequence of a fluctuating supply of money in the Central European economy. 2. The rise in prices is caused by the growing demand of a rapidly growing population. On the one hand, the author verifies the ´laws of development´ by MALTHUS and RICARDO on the basis of the historical facts. On the other hand, the historical series of developments are interpreted by way of an appropriate scheme of terms and relations regarding their meaning. Topics: Tables in the ZA-Online-Database HISTAT: - prices of rye in Germany (1341-1940) - prices of wheat and rye in Europe and America (1991-1830) - prices of wheat and rye in Central Europe (1201-1960)

The Land Parcel Identification System (LPIS) is a reference database of the agriculture parcels used as a basis for area related payments to farmers in relation to the Common Agricultural Policy (CAP). These payments are (co)financed by the European Agricultural Guarantee Fund (‘EAGF’) and the European Agricultural Fund for Rural Development (‘EAFRD’). To ensure that payments are regular, the CAP relies on the Integrated Administration and Control System (IACS), a set of comprehensive administrative and on the spot checks on subsidy applications, which is managed by the Member States. The Land Parcel Identification System (LPIS) is a key component of the IACS. It is an IT system based on ortho imagery (aerial or satellite photographs) which records all agricultural parcels in the Member States. It serves two main purposes: to clearly locate all eligible agricultural land contained within reference parcels and to calculate their maximum eligible area (MEA). The LPIS is used for cross checking during the administrative control procedures and as a basis for on the spot checks by the paying agency.

Description copied from catalog.inspire.geoportail.lu.

The database with a specific focus on conservation agriculture consists of 11 field experiments located in 11 sites with mostly loamy soil texture across 8 European countries, thus covering different European climate zones, from northern over western to southern Europe. Among the 11 studies, a total of 3 were in Spain, 2 in Denmark, and 1 Belgium, Italy, Germany, Finland, Switzerland and the UK, respectively. Particularly, among the 11 studies, in 8 no-tillage was applied, while in 2 and in 1 conservation tillage minimum tillage was adopted, respectively. The duration of soil N2O emission monitoring ranges from 79 to 365 days.The database with a specific focus on organic agriculture consists of 11 field experiments, from 8 field studies located in sites with mostly silt loamy soil texture across 7 European countries, thus covering different European climate zones, from northern over western to southern Europe. With durations of experiments ranging from 1 to 36 years, a total of 3 studies were conducted in Denmark, 2 in France and Switzerland, and 1 in Austria, Finland and Italy.

Abstract copyright UK Data Service and data collection copyright owner.This study sought to reflect the diversity of rural contexts and farm structure across Western Europe. It consists of two surveys - the baseline survey and the final survey. The baseline survey, conducted in 1987, covered basic conditions, work and income patterns of farm households in different socio-economic situations in Europe with a view to further investigation over the next three years of the reasons, extent and effects of change experienced by some of them at farm, local, regional and national levels with special reference to multiple job-holding. The final survey was carried out in 1991. The baseline survey data are held as separate study numbers for each country (see list of constituent studies). The dataset containing the merged data of the baseline and final surveys for all countries is held as SN:2973. Main Topics: This dataset comprises the collation of the individual datasets of participating institutes with the addition of computed derived variables. Baseline Survey: Farm size and tenure; agricultural production and livestock; ownership and control; farming background; farm buildings and machinery; contract work; finance and income; retrospective events; residence; household members and farm workforces; personal data and activit ies for each person; use of policies; future expectations. Final Survey: Change of farm operator since 1987; farm size and land use; agricultural production; buildings equipment and land improvement; agricultural machinery; contract work and relief labour services; famr management; finance capital credit and income; use of policies; household amenities/facilitieis; household residence and farm workforce personal data and activities for each person; future expectations; commitment/attachment to farming; indicators of attitudes. See documentation that accompanies this dataset for details of sampling procedures used for the individual countries. Face-to-face interview 1987 1991 ACCOUNTS ADVICE AGE AGRICULTURAL BUILDINGS AGRICULTURAL COOPER... AGRICULTURAL ECONOMICS AGRICULTURAL ENTERP... AGRICULTURAL EQUIPMENT AGRICULTURAL EQUIPM... AGRICULTURAL LAND AGRICULTURAL MARKETING AGRICULTURAL POLICY AGRICULTURAL PRODUC... AGRICULTURAL PRODUCTS AGRICULTURAL SUBSIDIES AGRICULTURAL TRAINING AGRICULTURAL WORKERS AGRICULTURE AGRICULTURE EDUCATION ANCILLARY FARM ENTE... ANIMAL HUSBANDRY ANIMAL PRODUCTS APICULTURE ARABLE LAND ATTITUDES Agriculture and rur... Austria CATTLE CEREALS CHILD BEHAVIOUR CHILD CARE CHILDREN COLOUR TELEVISION R... COMMON LAND COMMON RIGHTS COMMUTING COMPUTERS CONTRACT FARMING CROP YIELDS CROPPING SYSTEMS CROPS CULTIVATION Community DAIRY PRODUCTS DECISION MAKING DECORATIVE PLANTS DISTANCE MEASUREMENT DOMESTIC APPLIANCES DOMESTIC RESPONSIBI... ECONOMIC ACTIVITY EDIBLE FUNGI EDUCATION EDUCATIONAL BACKGROUND EMPLOYERS EMPLOYMENT ENVIRONMENTAL CONSE... ENVIRONMENTAL PLANN... EUROPEAN ECONOMIC C... EVALUATION EXHIBITIONS FAMILIES FAMILY MEMBERS FAMILY ROLES FARMERS FARMERS ASSOCIATIONS FARMING SYSTEMS FARMS FERTILIZERS FINANCE FINANCIAL EXPECTATIONS FINANCING FORAGE FORESTS FRINGE BENEFITS FRUIT FULL TIME EMPLOYMENT FURTHER TRAINING France GENDER GLASSHOUSE CULTIVATION GOATS GRAIN CROPS GRANTS GRASSES GRAZING LAND Great Britain Greece HABITATS HOLIDAYS HOME BUYING HOME OWNERSHIP HORSES HORTICULTURE HOURS OF WORK HOUSEHOLD BUDGETS HOUSEHOLDS HOUSEWORK HOUSING HOUSING AGE INCOME INDUSTRIAL CROPS INDUSTRIES INFORMATION SOURCES INHERITANCE INTEREST FINANCE INVESTMENT INVESTMENT RETURN IRRIGATION Ireland Italy JOB DESCRIPTION LAND AMELIORATION LAND OWNERSHIP LAND TENURE LAND TRANSFERS LAND USE LAVATORIES LIVESTOCK LOANS MANAGEMENT MARITAL STATUS MORAL CONCEPTS MORTGAGES MOTOR VEHICLES Multi nation Netherlands OCCUPATIONAL STATUS OCCUPATIONAL TRAINING OCCUPATIONS ORCHARDS ORGANIC FARMING PARENT CHILD RELATI... PARENTAL ROLE PARENTS PART TIME EMPLOYMENT PART TIME FARMING PARTICIPATION PERIODICALS PIGS PLACE OF RESIDENCE PLANTATIONS POULTRY PRESCHOOL CHILDREN PRIVATE GARDENS Portugal QUALITY OF LIFE RABBITS RECREATIONAL FACILI... RENTED ACCOMMODATION RENTS RESPONSIBILITY ROADS ROOMS ROOT CROPS RURAL ENVIRONMENT SATISFACTION SAVINGS SEASONAL EMPLOYMENT SEEDS SET ASIDE LAND SHEEP SHOPPING SILAGE SIZE SOCIAL SECURITY BEN... SOFT FRUIT SPOUSES SUBSIDIARY EMPLOYMENT SUBSIDIES Spain Sweden Switzerland TELEPHONES TELEVISION RECEIVERS TEMPORARY EMPLOYMENT TOWNS TRAINING TRANSPORT TRAVELLING TIME UNWAGED WORKERS URBAN ENVIRONMENT VEGETABLES VIDEO RECORDERS VILLAGES VINEYARDS VOCATIONAL EDUCATION WAGES WASHING FACILITIES WILDLIFE WOMEN S EMPLOYMENT WOODLANDS WORKING MOTHERS West Germany Octobe... urban and rural life

This dataset provides global maps describing the land surface into 22 classes, which have been defined using the United Nations Food and Agriculture Organization’s (UN FAO) Land Cover Classification System (LCCS). In addition to the land cover (LC) maps, four quality flags are produced to document the reliability of the classification and change detection. In order to ensure continuity, these land cover maps are consistent with the series of global annual LC maps from the 1990s to 2015 produced by the European Space Agency (ESA) Climate Change Initiative (CCI), which are also available on the ESA CCI LC viewer. To produce this dataset, the entire Medium Resolution Imaging Spectrometer (MERIS) Full and Reduced Resolution archive from 2003 to 2012 was first classified into a unique 10-year baseline LC map. This is then back- and up-dated using change detected from (i) Advanced Very-High-Resolution Radiometer (AVHRR) time series from 1992 to 1999, (ii) SPOT-Vegetation (SPOT-VGT) time series from 1998 to 2012 and (iii) PROBA-Vegetation (PROBA-V), Sentinel-3 OLCI (S3 OLCI) and Sentinel-3 SLSTR (S3 SLSTR) time series from 2013. Beyond the climate-modelling communities, this dataset’s long-term consistency, yearly updates, and high thematic detail on a global scale have made it attractive for a multitude of applications such as land accounting, forest monitoring and desertification, in addition to scientific research.

This dataset is part of the database compiled as an outcome of Work Area 1 in project OrganicYieldsUP. Variable definitions can be found here: https://doi.org/10.5281/zenodo.15276082

Work Package 3 (WP3) of the OrganicYieldsUP project focused on compiling off-site data from peer-reviewed scientific literature to complement the on-site experimental data gathered in WP2. The goal was to identify, extract, and structure data on yield-enhancing strategies under organic management across Europe and comparable climate zones. This process was essential for broadening the project’s evidence base and informing subsequent analysis and modelling activities in WP4 and WP5. WP3 followed a systematic approach aligned with PRISMA methodology to ensure transparent and consistent literature screening. A total of 751 publications were initially identified based on defined search criteria. After applying inclusion and exclusion filters, 170 studies passed the first screening phase. From these, data were successfully extracted from 60 scientific publications and entered into the standard WP2/WP3 data template developed in WP1.

The screening of published scientific papers focused on papers published between 2009 and 2024. This time frame was chosen to ensure the use of the most current and relevant studies reflecting recent developments in organic farming methods, data quality standards, and policy frameworks. The screening prioritized English-language publications to maintain consistency in terminology and ensure broad understanding across project partners. Only original peer-reviewed research articles were considered, including case study reports where applicable. The search excluded reviews, editorials, and opinion papers due to the risk of duplicating data already included in WP2. Studies needed to focus explicitly on the impact of organic crop management strategies on yields. Only field trials, long-term experiments, and case studies were included, while pot experiments and single-year studies were excluded to avoid misleading conclusions caused by seasonal anomalies or short-term effects. All included studies had to come from Europe or similar climate zones such as North America or North Africa. During initial screening, titles, abstracts, and keywords had to contain terms related to "yield" and "organic" to be considered relevant. Full-text screening followed, using specific keywords aligned with the WP1 database. Only studies containing the obligatory data fields identified in WP1 were accepted. Publications that had already been included in the WP2 analysis were not considered again in WP3.

Wood biomass for energy is largely produced in Europe from forest land resulting from silvicultural and management practices or from agricultural land in the form of fast growing plantations. The present paper reviews and compares the estimated current potentials for wood biomass production in 25 countries in Europe. The potentials are divided attending to these sources to identify the most suitable method of wood biomass production on a country level, based on its current forest and agriculture levels of production. 2. 3. Europe. 4. Data has been collected and compiled from previous models and estimations.

This dataset provides daily surface meteorological data for the period from 1979 to present as input for agriculture and agro-ecological studies. This dataset is based on the hourly ECMWF ERA5 data at surface level and is referred to as AgERA5. Acquisition and pre-processing of the original ERA5 data is a complex and specialized job. By providing the AgERA5 dataset, users are freed from this work and can directly start with meaningful input for their analyses and modelling. To this end, the variables provided in this dataset match the input needs of most agriculture and agro-ecological models. Data were aggregated to daily time steps at the local time zone and corrected towards a finer topography at a 0.1° spatial resolution. The correction to the 0.1° grid was realized by applying grid and variable-specific regression equations to the ERA5 dataset interpolated at 0.1° grid. The equations were trained on ECMWF's operational high-resolution atmospheric model (HRES) at a 0.1° resolution. This way the data is tuned to the finer topography, finer land use pattern and finer land-sea delineation of the ECMWF HRES model. The data was produced on behalf of the Copernicus Climate Change Service.

The European Commission - Joint Research Centre (JRC) provides access to results from a new series of studies on wind erosion at Pan-European scale: Soil erosion by wind in European agricultural soils: A GIS version of the Revised Wind Erosion Equation (RWEQ) was developed in JRC to model at large scale wind erosion. The model is designed to predict the daily soil loss potential by wind erosion at 1km spatial resolution. Land susceptibility to wind erosion: An Index of Land Susceptibility to Wind Erosion (ILSWE) was created by combining spatiotemporal variations of the most influential wind erosion factors (i.e. climatic erosivity, soil erodibility, vegetation cover and landscape roughness). Wind erosion susceptibility of soils: The wind-erodible fraction of soil (EF) is one of the key parameters for estimating the susceptibility of soil to wind erosion. Former studies: Agriculture Field Parameters on NUTS-3 regions Metadata for the 4 datasets: Title: Soil loss by wind erosion in European agricultural soils (Quantitative assessment)Description: GIS-RWEQ is a simplified GIS-based application of the RWEQ model (ARS-USDA). It follows a spatially distributed approach based on a grid structure, running in R and Python scripts. The model scheme is designed to describe the daily soil loss potential at regional or larger scale. A complete description of the methodology and the application in Europe is described in the paper: Borrelli, P., Lugato, E., Montanarella, L., & Panagos, P. (2017). A New Assessment of Soil Loss Due to Wind Erosion in European Agricultural Soils Using a Quantitative Spatially Distributed Modelling Approach. Land Degradation & Development, 28: 335–344, DOI: 10.1002/ldr.2588 Spatial coverage: 28 Member States of the European Union Pixel size: c.a 1Km Projection: ETRS89 Lambert Azimuthal Equal Area Temporal coverage: from January 2001 to December 2010 Title: Land susceptibility to wind erosion Description: Wind erosion is a complex geomorphic process governed by a large number of variables. Field-scale models such as the Wind Erosion Prediction System (WEPS—Wagner, 1996) employ up to some tens of parameters to predict soil loss. A preliminary pan-European assessment of land susceptibility to wind erosion calls for a simplified and more practical approach. Therefore, a limited number of key parameters which can express the complex interactions between the variables controlling wind erosion should be considered. The ILSWE is based on the combination of the most influential parameters, i.e. climate (wind, rainfall and evaporation), soil characteristics (sand, silt, clay, CaCO3, organic matter, water-retention capacity and soil moisture) and land use (land use, percent of vegetation cover and landscape roughness). The spatial and temporal variability of factors are appropriately defined through Geographic Information System (GIS) analyses. Harmonised dataset and a unified methodology were employed to suit the pan- European scale and avoid generating misleading findings that could result from heterogeneous input data. The selected soil erosion parameters were conceptually divided into three groups, namely (i) Climate Erosivity, (ii) Soil Erodibility and (iii) Vegetation Cover and Landscape Roughness. Sensitivity to the contributing group of factors was calculated using the fuzzy logic technique, which allows the sensitivity range of each factor in Europe to be unambiguously defined. A complete description of the methodology and the application in Europe is described in the paper: Borrelli, P., Panagos, P., Ballabio, C., Lugato, E., Weynants, M. Montanarella, L (2014). Towards a pan-European assessment of land susceptibility to wind erosion. Land Degradation & Development, In Press. DOI: 10.1002/ldr.2318 Spatial coverage: 28 Member States of the European Union and 8 other European States (three European Union candidate countries (Montenegro, Serbia, the Former Yugoslav Republic of Macedonia), three potential European Union candidate countries (i.e. Albania, Bosnia and Herzegovina, and Kosovo), Norway and Switzerland). Pixel size: 500m Projection: ETRS89 Lambert Azimuthal Equal Area Temporal coverage:1981-2010 Title: Wind erosion susceptibility of European soilsDescription: The wind-erodible fraction of soil (EF) is one of the key parameters for estimating the susceptibility of soil to wind erosion.The predication of the spatial distribution of the EF and a soil surface crust index drew on a series of related but independent covariates, using a digital soil mapping approach. A complete description of the methodology and the application in Europe is described in the paper: Borrelli, P., Ballabio, C., Panagos, P., Montanarella, L. (2014). Wind erosion susceptibility of European soils. Geoderma, 232, 471-478.Spatial coverage: 25 Member States of the European Union where data available (All EU member states except Bulgaria, Romania and Croatia). Pixel size: 500m Projection: ETRS89 Lambert Azimuthal Equal Area Temporal coverage:2014 Agriculture Field Parameters data: The dataset contains averaged Field Size, Field Orientation, Field Length, Average Number of Images, Percentage of Large Fields and Length to Width Ratios for the EU 27 Member states and Switzerland, aggregated to NUTS region. The analysis is based on approximately 400 satellite images from the IMAGE2000 archive. Each image was segmented using a fractal net evolution approach, which is a region merging technique (Baatz and Schape, 2000). - Field Size Area (ha) : The average field size for the reporting unit for wind erosion fields - Field Direction (in degrees) : The average direction of the field – by assuming that usually the longer side of the field is the main working direction-Length to Witdh Ratio: The average Length to Width Ratio on the agricultural fields- Field Length (Km): The average Length on the agricultural fields- Average Number of Images: The average number of images which have been averaged for this reporting unit - Percentage Agricultural Wind Erosion Susceptible fields/ non susceptible field : The percentage of agricultural area which could be clearly indentified with the applied method References - Documentation: Borrelli, P., Lugato, E., Montanarella, L., & Panagos, P. (2017). A New Assessment of Soil Loss Due to Wind Erosion in European Agricultural Soils Using a Quantitative Spatially Distributed Modelling Approach. Land Degradation & Development, 28: 335–344, DOI: 10.1002/ldr.2588, DOI: 10.1002/ldr.2588 Borrelli, P., Panagos, P., Ballabio, C., Lugato, E., Weynants, M. Montanarella, L. 2016. Towards a pan-European assessment of land susceptibility to wind erosion. Land Degradation & Development, 27(4): 1093-1105, DOI: 10.1002/ldr.2318. Borrelli, P., Panagos, P., Montanarella, L. 2015. New Insights into the Geography and Modelling of Wind Erosion in the European Agricultural Land. Application of a Spatially Explicit Indicator of Land Susceptibility to Wind Erosion. Sustainability 2015, 7(7), 8823-8836; doi:10.3390/su7078823 Borrelli, P., Ballabio, C., Panagos, P., Montanarella, L. 2014. Wind erosion susceptibility of European soils. Geoderma, 232, 471-478

Abstract copyright UK Data Service and data collection copyright owner.This study sought to reflect the diversity of rural contexts and farm structure across Western Europe. It consists of two surveys - the baseline survey and the final survey. The baseline survey, conducted in 1987, covered basic conditions, work and income patterns of farm households in different socio-economic situations in Europe with a view to further investigation over the next three years of the reasons, extent and effects of change experienced by some of them at farm, local, regional and national levels with special reference to multiple job-holding. The final survey was carried out in 1991. The baseline survey data are held as separate study numbers for each country (see list of constituent studies). The dataset containing the merged data of the baseline and final surveys for all countries is held as SN:2973. Main Topics: Farm size and tenure; agricultural production and livestock; farm buildings and machinery; finance and income. Residence. Household members and farm work forces; agricultural and farm-based activities; off-farm activities. Face-to-face interview 1987 ACCOUNTS AGE AGRICULTURAL BUILDINGS AGRICULTURAL ECONOMICS AGRICULTURAL ENTERP... AGRICULTURAL EQUIPMENT AGRICULTURAL EQUIPM... AGRICULTURAL EXTENSION AGRICULTURAL LAND AGRICULTURAL MARKETING AGRICULTURAL POLICY AGRICULTURAL PRODUC... AGRICULTURAL PRODUCTS AGRICULTURAL SUBSIDIES AGRICULTURAL TRAINING AGRICULTURAL WORKERS AGRICULTURE AGRICULTURE EDUCATION ALTITUDE ANCILLARY FARM ENTE... ANIMAL PRODUCTS APICULTURE ARABLE LAND ATTITUDES Agriculture and rur... CATTLE CEREALS CHILDREN COLOUR TELEVISION R... COMMON LAND COMMON RIGHTS COMMUTING COMPUTERS CONTRACT FARMING CROP YIELDS CROPS CULTIVATION Community DAIRY PRODUCTS DECISION MAKING DECORATIVE PLANTS DISTANCE MEASUREMENT DOMESTIC APPLIANCES ECONOMIC ACTIVITY EDIBLE FUNGI EDUCATIONAL BACKGROUND EMPLOYERS EMPLOYMENT FAMILIES FAMILY MEMBERS FARMERS FARMING SYSTEMS FARMS FINANCE FINANCIAL EXPECTATIONS FORAGE FORESTS FRINGE BENEFITS FRUIT FULL TIME EMPLOYMENT GENDER GLASSHOUSE CULTIVATION GOATS GRAIN CROPS GRANTS GRASSES GRAZING LAND HOME BUYING HOME OWNERSHIP HORSES HORTICULTURAL LAND HORTICULTURE HOUSEHOLDS HOUSEWORK HOUSING HOUSING AGE INCOME INDUSTRIAL CROPS INDUSTRIES INHERITANCE INTEREST FINANCE INVESTMENT RETURN IRRIGATION JOB DESCRIPTION LAND OWNERSHIP LAND TENURE LAND TRANSFERS LAND USE LAVATORIES LIVESTOCK LOANS MANAGEMENT MARITAL STATUS MORTGAGES MOTOR VEHICLES OCCUPATIONAL STATUS OCCUPATIONAL TRAINING OCCUPATIONS ORCHARDS PARENTS PART TIME EMPLOYMENT PART TIME FARMING PIGS PLACE OF RESIDENCE PLANTATIONS POULTRY PRIVATE GARDENS RABBITS RENTED ACCOMMODATION RENTS RESPONSIBILITY ROADS ROOMS ROOT CROPS RURAL ENVIRONMENT SAVINGS SEASONAL EMPLOYMENT SEEDS SHEEP SILAGE SIZE SOCIAL SECURITY BEN... SOFT FRUIT SPOUSES SUBSIDIARY EMPLOYMENT SUBSIDIES Switzerland TELECOMMUNICATIONS TELEPHONES TELEVISION RECEIVERS TEMPORARY EMPLOYMENT TOWNS TRANSPORT TRANSPORT INFRASTRU... TRAVELLING TIME UNWAGED WORKERS URBAN ENVIRONMENT VEGETABLES VIDEO RECORDERS VILLAGES VINEYARDS VOCATIONAL EDUCATION WAGES WASHING FACILITIES WOODLANDS urban and rural life

Important Note: This item is in mature support as of April 2024 and will be retired in December 2026. Please use the following layers at replacements: World Soils 250m Percent Sand, World Soils 250m Percent Silt, World Soils 250m Percent Clay. Esri recommends updating your maps and apps to use the new version. Soil is a key natural resource that provides the foundation of basic ecosystem services. Soil determines the types of farms and forests that can grow on a landscape. Soil filters water. Soil helps regulate the Earth's climate by storing large amounts of carbon. Activities that degrade soils reduce the value of the ecosystem services that soil provides. For example, since 1850 35% of human caused green house gas emissions are linked to land use change. The Soil Science Society of America is a good source of of additional information.Soil texture is an important factor determining which kinds of plants can be grown in a particular location. Texture determines a soil's susceptibility to erosion or compaction and how well a soil holds nutrients and water. For example sandy soils tend to be well drained and dry quickly often holding few nutrients while clay soils may hold much more water and many more plant nutrients.Dataset SummaryThis layer provides access to a 30 arc-second (roughly 1 km) cell-sized raster with attributes related to soil texture derived from the Harmonized World Soil Database v 1.2. The values in this layer are for the dominant soil in each mapping unit (sequence field = 1).Fields for topsoil (0-30 cm) and subsoil (30-100 cm) are available for each of these attributes related to soil texture:USDA Texture ClassGravel - % volumeSand - % weightSilt - % weightClay - % weightThe layer is symbolized with the topsoil texture class.The document Harmonized World Soil Database Version 1.2 provides more detail on the soil texture attributes contained in this layer.Other attributes contained in this layer include:Soil Mapping Unit Name - the name of the spatially dominant major soil groupSoil Mapping Unit Symbol - a two letter code for labeling the spatially dominant major soil group in thematic mapsData Source - the HWSD is an aggregation of datasets. The data sources are the European Soil Database (ESDB), the 1:1 million soil map of China (CHINA), the Soil and Terrain Database Program (SOTWIS), and the Digital Soil Map of the World (DSMW).Percentage of Mapping Unit covered by dominant componentMore information on the Harmonized World Soil Database is available here.Other layers created from the Harmonized World Soil Database are available on ArcGIS Online:World Soils Harmonized World Soil Database - Bulk DensityWorld Soils Harmonized World Soil Database – ChemistryWorld Soils Harmonized World Soil Database - Exchange CapacityWorld Soils Harmonized World Soil Database – GeneralWorld Soils Harmonized World Soil Database – HydricThe authors of this data set request that projects using these data include the following citation:FAO/IIASA/ISRIC/ISSCAS/JRC, 2012. Harmonized World Soil Database (version 1.2). FAO, Rome, Italy and IIASA, Laxenburg, Austria.What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop.This layer has query, identify, and export image services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometers on a side or an area approximately the size of Europe. The source data for this layer are available here.This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.