Science for policy 5: Strategic Plans: opportunities to maximise the supply of soil functions but beware trade-offs! – datasets.

[Lien vers la fiche source] This 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’Sullivan, 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’Sullivan, P. Meire, R.P.O. Schulte, J. Schröder 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].