{"type": "FeatureCollection", "features": [{"id": "10.15454/2zqkir", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:21:01Z", "type": "Dataset", "title": "Science for policy 6: Urban planning: sealing the future of soil functions - datasets?", "description": "Open AccessThis dataset is part of Deliverable and 5.3 and produced by the WP4 team of the Landmark H2020 project. It contains the following shapefiles: PO6_BAU_NoZoning_50.shp PO6_BAU_NoZoning_100.shp PO6_BAU_Zoning_50.shp PO6_Sprawl_Zoning_50.shp PO6_BAU_NoZoning_50.shp PO6_Compact_Zoning_50.shp PO6_Compact_NoZoning_50.shp The metronamica Model was applied on six scenarios with combinations of business as usual, suburban sprawl or compact city development which build on the socio-economic projections and density assumptions of the ESPON-ET2050 project, and use the land use allocation parameters from the RECARE and SoilCare Integrated Assessment Models. Spatial development (zoning) was for some scenarios restricted in high productive fields. The model results give probabilities (0 \u2013 1) of urban development within the 1 km\u00b2 cells. Based on these probability percentages the different soil functions are reduced (100% of the probability and 50% of the probability) compared to the current soil functioning and, for the 50% scenarios, partly replaced by low productive grasslands as gardens and other public greenery. Z-scores are calculated from the spatial SF maps for each of the environmental zones. These environmental zones are derived from the Metzger et al. (2013). The z-scores give the signed fractional number of standard deviations by which SF means for an environmental zone are above or below the mean value and allow us indicate which areas have a higher or lower soil function performance compared to the mean value. Z-scores from the current SF maps and scenario maps were then compared to each other to calculate the change in z-scores. This change in z-scores is given in the shapefiles and describes the relative change in soil function performance. Positive values indicate an improvement in soil functioning compared to the current situation, negative values a decrease. More information regarding calculation and interpretation of both this dataset and the soil function maps used to calculate the z-scores can be found in: Vrebos D., F. Bampa, R. Creamer, A. Jones, E. Lugato, L. O\u2019Sullivan, P. Meire, R.P.O. Schulte, J. Schr\u00f6der and J. Staes (2018). Scenarios maps: visualizing optimized scenarios where supply of soil functions matches demands. LANDMARK Report 4.3. and Jones A. et al. (2019). An options document to propose future policy tools for functional soil management. LANDMARK 5.3. All available from www.landmark2020.eu.", "keywords": ["Water resources", "Food Safety", "Food Safety and Toxicology", "Nutritional Sciences", "Social Sciences", "7. Clean energy", "Pathology and Forensic Medicine", "Health and Life Sciences", "Farming Systems and Practices", "11. Sustainability", "13. Climate action", "Agriculture", " Forestry", " Horticulture", "Human Health and Pathology", "Soils and soil sciences", "Agricultural Sciences", "Life Sciences", "Hydrology and Hydrogeology", "15. Life on land", "Rural and Agricultural Sociology", "Human Nutrition and food security", "Farming Systems", "Medicine", " Health and Life Sciences", "Earth and Environmental Sciences", "Soil Sciences", "Medicine", "Geosciences"], "contacts": [{"organization": "Vrebos, Dirk, Bampa, Francesca, Schulte, Rogier, Creamer, Rachel, Jones, Arwyn, Staes, Jan, Zwetsloot Marie, Debernardini, Mariana, O\u2019Sullivan, Lilian,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.15454/2zqkir"}, {"rel": "self", "type": "application/geo+json", "title": "10.15454/2zqkir", "name": "item", "description": "10.15454/2zqkir", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.15454/2zqkir"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-01-01T00:00:00Z"}}, {"id": "10.15454/iw9cwa", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:21:02Z", "type": "Dataset", "title": "Science for policy 5: Strategic Plans: opportunities to maximise the supply of soil functions but beware trade-offs! \u2013 datasets.", "description": "Open AccessThis dataset is part of Deliverable 4.2, 4.3 and 5.3 and was produced by the WP4 team of the Landmark H2020 project. It contains the following shapefiles: PO5_Current_SFs_PrimaryProductivity.tiff PO5_Current_SFs_ClimateRegulation.tiff PO5_Current_SFs_WaterRegulation_Drought.tiff PO5_Current_SFs_WaterRegulation_WaterLoggging.tiff PO5_Current_SFs_WaterPurification.tiff PO5_Current_SFs_NutrientCycling.tiff PO5_Current_SFs_Biodiversity.tiff PO5_Current_SFs_EnvZone.shp PO5_Current_SFs_NUTS1.shp PO5_Maximization_ClimateRegulation.shp PO5_Maximization_Drought.shp PO5_Maximization_NCycling.shp PO5_Maximization_PrimaryProductivity.shp PO5_Maximization_Waterlogging.shp PO5_Maximization_Waterpurification.shp PO5_Maximization_Waterpurification.shp The tiff-files give the spatial variation in soil function performance for 6 soil functions in in agricultural soils across the EU. The soil functions were mapped by applying a number of crop specific Bayesian networks on a combination of spatial maps which describe soil properties, climate, land use and land management on agricultural soils throughout the European Union. PO5_Current_SFs_EnvZone.shp and PO5_Current_SFs_NUTS1.shp give the z-scores for both grasslands and cropland in 12 environmental zones for the six soil functions. The z-scores give the signed fractional number of standard deviations by which SF means for an environmental zone are above or below the mean value and allow us indicate which areas have a higher or lower soil function performance compared to the mean value. These values were extracted from the tiff-files provided in this dataset. The PO5_Maximization shapefiles give an estimation of the change in soil function performance across the EU when one soil function is maximized through changes in management. This spatial variation is represented in change in z-scores compared to the current SF supply. To develop the scenario, for each of the locations, the soil function was maximized in the underlying Bayesian networks, by allowing it to change different types of management (irrigation, fertilizer, etc.) for each location taking soil, climate and crop type into account. These changes also impact the performance of the other soil functions. For each of the soil functions a separate spatial map was created. Which was then used to calculate z-scores for each of the environmental zones. Z-scores from the current SF maps and scenario maps were then compared to each other to calculate the change in z-scores. This change in z-scores is given in the shapefiles and describes the relative change in soil function performance. Positive values indicate an improvement in soil functioning compared to the current situation, negative values a decrease. More information regarding calculation and interpretation of both this dataset and the soil function maps used to calculate the z-scores can be found in: Vrebos D., J. Staes, R. Schulte, L. O\u2019Sullivan, E. Lugato, A. Jones, A. Georgoulas and P. Meire (2018). Soil function supply maps. LANDMARK Report 4.2. Vrebos D., F. Bampa, R. Creamer, A. Jones, E. Lugato, L. O\u2019Sullivan, P. Meire, R.P.O. Schulte, J. Schr\u00f6der and J. Staes (2018). Scenarios maps: visualizing optimized scenarios where supply of soil functions matches demands. LANDMARK Report 4.3. and Jones A. et al. (2019). An options document to propose future policy tools for functional soil management. LANDMARK 5.3. All available from www.landmark2020.eu.", "keywords": ["2. Zero hunger", "Earth and Environmental Science", "Water resources", "Soils and soil sciences", "Ecology", "Agricultural Sciences", "Climate", "Hydrology and Hydrogeology", "15. Life on land", "Farming Systems", "Biodiversity and Ecology", "Farming Systems and Practices", "13. Climate action", "Earth and Environmental Sciences", "Soil Sciences", "Agriculture", " Forestry", " Horticulture", "Geosciences"], "contacts": [{"organization": "Vrebos, Dirk, Bampa, Francesca, Schulte, Rogier, Creamer, Rachel, Jones, Arwyn, Staes, Jan, Zwetsloot Marie, Debernardini, Mariana, O\u2019Sullivan, Lilian,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.15454/iw9cwa"}, {"rel": "self", "type": "application/geo+json", "title": "10.15454/iw9cwa", "name": "item", "description": "10.15454/iw9cwa", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.15454/iw9cwa"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-01-01T00:00:00Z"}}, {"id": "10.15454/srhcuh", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:21:03Z", "type": "Dataset", "title": "Science for policy 1: FaST hidden benefits: needs based targeting of cleaner water through better use of nutrients - datasets", "description": "Open AccessThis dataset is part of both Deliverable 4.3 and 5.3 and was produced by the WP4 team of the Landmark H2020 project. It contains the following shapefile: PO1_GAEC5.shp The shapefile gives an estimation of the change in soil function performance across the EU in agricultural soils after implementation of the GAEC5 under the proposed CAP. This spatial variation is represented in change in z-scores compared to the current supply on a NUTS1 level. To implement the scenario, for each crop within each environmental zone the 20% area with the lowest values of the N Cycling indicator are selected from the current SF supply map and this indicator is increased to the lowest values in the other 80% of the same crop \u2013 environmental zone combination. In a second step, for each crop within each environmental zone the 20% area with the lowest values of the water purification indicator from the current SF supply map are selected and this indicator is increased to the lowest values in the other 80% of the crop \u2013 environmental zone combination, while maintaining the N Cycling improvements. This simulates potential improvements in both N Cycling and water purification due to the implementation of the Farm Sustainability Tool for Nutrients (GAEC 5) Z-scores are calculated from the spatial SF maps for each of the NUTS1 zones. The z-scores give the signed fractional number of standard deviations by which SF means for a NUTS1 zone are above or below the mean value and allow us indicate which areas have a higher or lower soil function performance compared to the mean value. Z-scores from the current SF maps and scenario maps were then compared to each other to calculate the change in z-scores. This change in z-scores is given in the shapefiles and describes the relative change in soil function performance. Positive values indicate an improvement in soil functioning compared to the current situation, negative values a decrease. More information regarding calculation and interpretation of both this dataset and the soil function maps used to calculate the z-scores can be found in: Vrebos D., F. Bampa, R. Creamer, A. Jones, E. Lugato, L. O\u2019Sullivan, P. Meire, R.P.O. Schulte, J. Schr\u00f6der and J. Staes (2018). Scenarios maps: visualizing optimized scenarios where supply of soil functions matches demands. LANDMARK Report 4.3. and Jones A. et al. (2019). An options document to propose future policy tools for functional soil management. LANDMARK 5.3. All available from www.landmark2020.eu.", "keywords": ["2. Zero hunger", "Water resources", "Soils and soil sciences", "Agricultural Sciences", "6. Clean water", "Hydrology and Hydrogeology", "15. Life on land", "Farming Systems", "12. Responsible consumption", "Farming Systems and Practices", "13. Climate action", "Earth and Environmental Sciences", "Soil Sciences", "Agriculture", " Forestry", " Horticulture", "Geosciences"], "contacts": [{"organization": "Vrebos, Dirk, O\u2019Sullivan, Lilian, Bampa, Francesca, Schulte, Rogier, Creamer, Rachel, Jones, Arwyn, Staes, Jan, Zwetsloot, Marie, Debernardini, Mariana, Wall, David,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.15454/srhcuh"}, {"rel": "self", "type": "application/geo+json", "title": "10.15454/srhcuh", "name": "item", "description": "10.15454/srhcuh", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.15454/srhcuh"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-01-01T00:00:00Z"}}, {"id": "3114970092", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:28:35Z", "type": "Dataset", "title": "Science for policy 6: Urban planning: sealing the future of soil functions - datasets?", "description": "Open AccessThis dataset is part of Deliverable and 5.3 and produced by the WP4 team of the Landmark H2020 project. It contains the following shapefiles: PO6_BAU_NoZoning_50.shp PO6_BAU_NoZoning_100.shp PO6_BAU_Zoning_50.shp PO6_Sprawl_Zoning_50.shp PO6_BAU_NoZoning_50.shp PO6_Compact_Zoning_50.shp PO6_Compact_NoZoning_50.shp The metronamica Model was applied on six scenarios with combinations of business as usual, suburban sprawl or compact city development which build on the socio-economic projections and density assumptions of the ESPON-ET2050 project, and use the land use allocation parameters from the RECARE and SoilCare Integrated Assessment Models. Spatial development (zoning) was for some scenarios restricted in high productive fields. The model results give probabilities (0 \u2013 1) of urban development within the 1 km\u00b2 cells. Based on these probability percentages the different soil functions are reduced (100% of the probability and 50% of the probability) compared to the current soil functioning and, for the 50% scenarios, partly replaced by low productive grasslands as gardens and other public greenery. Z-scores are calculated from the spatial SF maps for each of the environmental zones. These environmental zones are derived from the Metzger et al. (2013). The z-scores give the signed fractional number of standard deviations by which SF means for an environmental zone are above or below the mean value and allow us indicate which areas have a higher or lower soil function performance compared to the mean value. Z-scores from the current SF maps and scenario maps were then compared to each other to calculate the change in z-scores. This change in z-scores is given in the shapefiles and describes the relative change in soil function performance. Positive values indicate an improvement in soil functioning compared to the current situation, negative values a decrease. More information regarding calculation and interpretation of both this dataset and the soil function maps used to calculate the z-scores can be found in: Vrebos D., F. Bampa, R. Creamer, A. Jones, E. Lugato, L. O\u2019Sullivan, P. Meire, R.P.O. Schulte, J. Schr\u00f6der and J. Staes (2018). Scenarios maps: visualizing optimized scenarios where supply of soil functions matches demands. LANDMARK Report 4.3. and Jones A. et al. (2019). An options document to propose future policy tools for functional soil management. LANDMARK 5.3. All available from www.landmark2020.eu.", "keywords": ["Water resources", "Food Safety", "Food Safety and Toxicology", "Nutritional Sciences", "Social Sciences", "7. Clean energy", "Pathology and Forensic Medicine", "Health and Life Sciences", "Farming Systems and Practices", "11. Sustainability", "13. Climate action", "Agriculture", " Forestry", " Horticulture", "Human Health and Pathology", "Soils and soil sciences", "Agricultural Sciences", "Life Sciences", "Hydrology and Hydrogeology", "15. Life on land", "Rural and Agricultural Sociology", "Human Nutrition and food security", "Farming Systems", "Medicine", " Health and Life Sciences", "Earth and Environmental Sciences", "Soil Sciences", "Medicine", "Geosciences"], "contacts": [{"organization": "Vrebos, Dirk, Bampa, Francesca, Schulte, Rogier, Creamer, Rachel, Jones, Arwyn, Staes, Jan, Zwetsloot Marie, Debernardini, Mariana, O\u2019Sullivan, Lilian,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/3114970092"}, {"rel": "self", "type": "application/geo+json", "title": "3114970092", "name": "item", "description": "3114970092", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3114970092"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-01-01T00:00:00Z"}}, {"id": "389c003f24135035b4136d7c1658b237", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:29:01Z", "type": "Dataset", "title": "Science for policy 4: Manure: spreading the load? \u2013 datasets.", "description": "Open AccessThis dataset is part of Deliverable 5.3 and was produced by the WP4 team of the Landmark H2020 project. It contains the following tiffs and shapefiles: PO4_35km_Borders.shp PO4_35km_NoBorders.shp PO4_100km_Borders.shp PO4_100km_NoBorders.shp PO4_Max_Borders.shp PO4_Max_NoBorders.shp These shapefiles give an estimation of the change in soil function performance across the EU in agricultural soils after nitrogen surplus redistribution within a 35km, 100km and no limited range, with and without limitation in cross-border transport. The spatial variation is represented in change in z-scores compared to the current SF supply. To develop the scenario, current nutrient applications in areas where concentrations in ground water are above 50 mg nitrates per liter (mg/L), were decreased in order to reach the 50 mg /L level. The excess organic nutrient surplus was then redistributed on other fields within a range of 35km, 100km or no distance limitation, which can receive additional nutrients without exceeding the 50 mg N /L threshold for each location. Excess nutrient surpluses were allowed or not to cross borders and no increases in N application were allowed in Natura 2000 sites. Z-scores are calculated from the spatial SF maps. Environmental zones are derived from the Metzger et al. (2013). The z-scores give the signed fractional number of standard deviations by which SF means for an environmental zone are above or below the mean value and allow us indicate which areas have a higher or lower soil function performance compared to the mean value. Z-scores from the current SF maps and scenario maps were then compared to each other to calculate the change in z-scores. This change in z-scores is given in the shapefiles and describes the relative change in soil function performance. Positive values indicate an improvement in soil functioning compared to the current situation, negative values a decrease. More information regarding calculation and interpretation of both this dataset and the soil function maps used to calculate the z-scores can be found in: Vrebos D., F. Bampa, R. Creamer, A. Jones, E. Lugato, L. O\u2019Sullivan, P. Meire, R.P.O. Schulte, J. Schr\u00f6der and J. Staes (2018). Scenarios maps: visualizing optimized scenarios where supply of soil functions matches demands. LANDMARK Report 4.3. and Jones A. et al. (2019). An options document to propose future policy tools for functional soil management. LANDMARK 5.3. All available from www.landmark2020.eu.", "keywords": ["Water resources", "Soils and soil sciences", "Agricultural Sciences", "Social Sciences", "Hydrology and Hydrogeology", "Rural and Agricultural Sociology", "Farming Systems", "Farming Systems and Practices", "2. Zero hunger", "Earth and Environmental Sciences", "Soil Sciences", "13. Climate action", "15. Life on land", "Agriculture", " Forestry", " Horticulture", "Geosciences"], "contacts": [{"organization": "Vrebos, Dirk, Bampa, Francesca, Schulte, Rogier, Creamer, Rachel, Jones, Arwyn, Staes, Jan, Zwetsloot, Marie, Debernardini, Mariana, O\u2019Sullivan, Lilian,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/389c003f24135035b4136d7c1658b237"}, {"rel": "self", "type": "application/geo+json", "title": "389c003f24135035b4136d7c1658b237", "name": "item", "description": "389c003f24135035b4136d7c1658b237", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/389c003f24135035b4136d7c1658b237"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-01-01T00:00:00Z"}}, {"id": "4417a3789a8f521f04e520915c7d3bfb", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:29:10Z", "type": "Dataset", "title": "Science for policy 3: Climate change: no winners when it comes to soil functions \u2013 datasets.", "description": "Open Access<p>This dataset is part of both Deliverable 5.3 and was produced by the WP4 team of the Landmark H2020 project. It contains the following shapefiles:</p> <p>&nbsp;</p> <ul> <li>PO3_RCP26_NoIrrigation.shp</li> <li>PO3_RCP45_Irrigation.shp</li> <li>PO3_RCP45_NoIrrigation.shp</li> <li>PO3_RCP85_Irrigation.shp</li> <li>PO3_RCP85_NoIrrigation.shp</li> </ul> <p>&nbsp;These shapefiles give estimations of the change in soil function performance across the EU in agricultural soils by 2050 under an RCP2.6", "keywords": ["Earth and Environmental Science", "Water resources", "Soils and soil sciences", "Agricultural Sciences", "Climate", "6. Clean water", "Hydrology and Hydrogeology", "Farming Systems", "Farming Systems and Practices", "2. Zero hunger", "Earth and Environmental Sciences", "Soil Sciences", "13. Climate action", "11. Sustainability", "15. Life on land", "Agriculture", " Forestry", " Horticulture", "Geosciences"], "contacts": [{"organization": "Vrebos, Dirk, Bampa, Francesca, Schulte, Rogier, Creamer, Rachel, Jones, Arwyn, Staes, Jan, Zwetsloot, Marie, Debernardini, Mariana, O\u2019Sullivan, Lilian,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/4417a3789a8f521f04e520915c7d3bfb"}, {"rel": "self", "type": "application/geo+json", "title": "4417a3789a8f521f04e520915c7d3bfb", "name": "item", "description": "4417a3789a8f521f04e520915c7d3bfb", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/4417a3789a8f521f04e520915c7d3bfb"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-01-01T00:00:00Z"}}, {"id": "50|userclaim___::389c003f24135035b4136d7c1658b237", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:29:26Z", "type": "Dataset", "title": "Science for policy 4: Manure: spreading the load? \u2013 datasets.", "description": "Open AccessThis dataset is part of Deliverable 5.3 and was produced by the WP4 team of the Landmark H2020 project. It contains the following tiffs and shapefiles: PO4_35km_Borders.shp PO4_35km_NoBorders.shp PO4_100km_Borders.shp PO4_100km_NoBorders.shp PO4_Max_Borders.shp PO4_Max_NoBorders.shp These shapefiles give an estimation of the change in soil function performance across the EU in agricultural soils after nitrogen surplus redistribution within a 35km, 100km and no limited range, with and without limitation in cross-border transport. The spatial variation is represented in change in z-scores compared to the current SF supply. To develop the scenario, current nutrient applications in areas where concentrations in ground water are above 50 mg nitrates per liter (mg/L), were decreased in order to reach the 50 mg /L level. The excess organic nutrient surplus was then redistributed on other fields within a range of 35km, 100km or no distance limitation, which can receive additional nutrients without exceeding the 50 mg N /L threshold for each location. Excess nutrient surpluses were allowed or not to cross borders and no increases in N application were allowed in Natura 2000 sites. Z-scores are calculated from the spatial SF maps. Environmental zones are derived from the Metzger et al. (2013). The z-scores give the signed fractional number of standard deviations by which SF means for an environmental zone are above or below the mean value and allow us indicate which areas have a higher or lower soil function performance compared to the mean value. Z-scores from the current SF maps and scenario maps were then compared to each other to calculate the change in z-scores. This change in z-scores is given in the shapefiles and describes the relative change in soil function performance. Positive values indicate an improvement in soil functioning compared to the current situation, negative values a decrease. More information regarding calculation and interpretation of both this dataset and the soil function maps used to calculate the z-scores can be found in: Vrebos D., F. Bampa, R. Creamer, A. Jones, E. Lugato, L. O\u2019Sullivan, P. Meire, R.P.O. Schulte, J. Schr\u00f6der and J. Staes (2018). Scenarios maps: visualizing optimized scenarios where supply of soil functions matches demands. LANDMARK Report 4.3. and Jones A. et al. (2019). An options document to propose future policy tools for functional soil management. LANDMARK 5.3. All available from www.landmark2020.eu.", "keywords": ["Water resources", "Soils and soil sciences", "Agricultural Sciences", "Social Sciences", "Hydrology and Hydrogeology", "Rural and Agricultural Sociology", "Farming Systems", "Farming Systems and Practices", "2. Zero hunger", "Earth and Environmental Sciences", "Soil Sciences", "13. Climate action", "15. Life on land", "Agriculture", " Forestry", " Horticulture", "Geosciences"], "contacts": [{"organization": "Vrebos, Dirk, Bampa, Francesca, Schulte, Rogier, Creamer, Rachel, Jones, Arwyn, Staes, Jan, Zwetsloot, Marie, Debernardini, Mariana, O\u2019Sullivan, Lilian,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/50|userclaim___::389c003f24135035b4136d7c1658b237"}, {"rel": "self", "type": "application/geo+json", "title": "50|userclaim___::389c003f24135035b4136d7c1658b237", "name": "item", "description": "50|userclaim___::389c003f24135035b4136d7c1658b237", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/50|userclaim___::389c003f24135035b4136d7c1658b237"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-01-01T00:00:00Z"}}, {"id": "50|userclaim___::4417a3789a8f521f04e520915c7d3bfb", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:29:26Z", "type": "Dataset", "title": "Science for policy 3: Climate change: no winners when it comes to soil functions \u2013 datasets.", "description": "Open Access<p>This dataset is part of both Deliverable 5.3 and was produced by the WP4 team of the Landmark H2020 project. It contains the following shapefiles:</p> <p>&nbsp;</p> <ul> <li>PO3_RCP26_NoIrrigation.shp</li> <li>PO3_RCP45_Irrigation.shp</li> <li>PO3_RCP45_NoIrrigation.shp</li> <li>PO3_RCP85_Irrigation.shp</li> <li>PO3_RCP85_NoIrrigation.shp</li> </ul> <p>&nbsp;These shapefiles give estimations of the change in soil function performance across the EU in agricultural soils by 2050 under an RCP2.6", "keywords": ["Earth and Environmental Science", "Water resources", "Soils and soil sciences", "Agricultural Sciences", "Climate", "6. Clean water", "Hydrology and Hydrogeology", "Farming Systems", "Farming Systems and Practices", "2. Zero hunger", "Earth and Environmental Sciences", "Soil Sciences", "13. Climate action", "11. Sustainability", "15. 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