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Climate data from the nearest climate grid point was used for each 16 km forest simulation grid cell.\n\nThese datasets contain simulations results for selected response variables of PREBAS forest model for 3 climate change scenarios (RCP 2.6, 4.5, 8.5) and 5 climate model inputs.\nhttps://www.avoindata.fi/data/fi/dataset/monilahteisen-valtakunnan-metsien-inventoinnin-mvmi-kartta-aineisto-2011\n\nSimulations for the period 1981-2100 were split to four 30 year periods. The periods were simulated independently of each other. At the beginning of each period, forest state variables (i.e. forest data) were initialized with the forest inventory data of 2011.\n\nDuring the simulations, PREBAS harvested the simulated forests following the Tapio rules. This means that harvests were somewhat higher than recorded in harvest statistics for the reference period (1981-2010), but the reported growth values are likely little affected. Conservations areas were not managed at all.\nhttps://www.climateguide.fi/articles/ecosystem-modelling\n\nThe PREBAS model and various tests has have been described in detail elsewhere (Peltoniemi et al., 2015a, Minunno et al., 2016, Valentini and Mäkelä 2008, Minunno et al., submitted). The modelled GPP has been compared to GPP of JSBACH model earlier (Peltoniemi et al., 2015b). Source code and the model parameters of the GPP and water balance submodule are available at https://github.com/MikkoPeltoniemi/Rpreles/releases (release 1.0). The GPP and water components of the model originate from re-calibration of the model to eddy-covariance at 10 sites in Scandinavia (Minunno et al. 2016).\n\nResources\n\nMaps of the climate change indicators and descriptive statistics for Finland or selected regions can be viewed in climateguide.fi under the section ‘Impacts of climate change’. The following indicators are available from PREBAS simulations: gross primary production (GPP), total ecosystem respiration (TER), net ecosystem exchange (NEE), forest growth and start, end and length of vegetation active period. The user can select maps showing the mean values for four time periods, a baseline (1981–2010) and for three scenario periods (2011–2040, 2041–2070, 2071–2100). Additionally, changes from the baseline can be viewed for a given scenario period.\n\nMaps of the climate change indicators and descriptive statistics for Finland or selected regions can be viewed in climateguide.fi under the section ‘Impacts of climate change’. The following indicators are available from PREBAS simulations: gross primary production (GPP), total ecosystem respiration (TER), net ecosystem exchange (NEE), forest growth and start, end and length of vegetation active period. The user can select maps showing the mean values for four time periods, a baseline (1981–2010) and for three scenario periods (2011–2040, 2041–2070, 2071–2100). Additionally, changes from the baseline can be viewed for a given scenario period.\n\nhttps://www.climateguide.fi/articles/impact-scenarios-of-climate-change\n\n\nData access\n\nThe data can be accessed via a Web Feature Service (WFS) supporting versions 1.0.0 and 1.1.0. WFS services can be opened in various GIS applications, for example ArcGIS for Desktop and open source desktop application QGIS.\n\nTypically, the GIS application loads data from a WFS service in a GML (Geography Markup Language) format. 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"en": "Climate change causes effects on crop growth conditions in Finland. With agroclimatic indicators we can see how agroclimatic suitability for cultivating crop- and grassland is shifting and what areas are most prone to climatic risks. This dataset consists of agroclimatic indicators calculated on a regular 10 km x 10 km over Finland under current and future climate. These help to identify the areas where crop yield formation is currently most prone to climate-induced stresses and how the severity of these stresses are likely to develop in the future.\n\nProcessing\n\n30-year average are shown on a regular 10 km x 10 km over Finland for the baseline period 1971-2000 and three future periods, 2011–2040, 2041–2070 and 2071–2100, under three future scenarios that were selected from the CMIP3 ensemble: low emissions (SRES B1) simulated with the CSIRO-Mk3.5 Global Climate Model (GCM), medium emissions (SRES A1B)/MIROC3.2 (medres) GCM, and high emissions (SRES A2)/IPSL-CM4 GCM.\n\nIndicators are calculated using N-AgriCLIM, a tool that was developed for the automatic generation of indicators describing basic agroclimatic conditions and it was applied over the whole of Finland. The input data used in the tool are variables related to crop, soil and current and projected future weather. The crop data applied was based on characteristics of the popular barley cultivar Scarlett. The tool was applied to assess shifts in agroclimatic suitability for cultivating crop- and grassland, and identify areas most prone to climatic risks under present-day and future climate (Rötter et al. 2013). The following indicators are included:\nsowing date (unit: days deviating from 1 May)\nnumber of effective growing days (days)\ndate of the last frost (day-of-the-year)\ntotal precipitation 3–7 weeks after sowing (mm)\nnumber of days with maximum temperature of 28 °C or higher around flowering (days)\n\nResources\n\nMaps of the climate change indicators can be viewed in climateguide.fi under the section ‘Impact scenarios of climate change’.\nhttps://www.climateguide.fi/articles/impact-scenarios-of-climate-change\n\nThe data can be accessed via a Web Feature Service (WFS) supporting versions 1.0.0 and 1.1.0. WFS services can be opened in various GIS applications, for example ArcGIS for Desktop and open source desktop application QGIS. Typically, the GIS applications loads data from a WFS service in a GML (Geography Markup Language) format. Additionally, the data can be loaded as zip compressed ESRI Shape files, a CSV file or a JSON file. The service includes 5 feature types representing N-AgriClim simulations.\nagro_delayedsowing - sowing date (days deviating from 1 May), agro_effectivegrowingdays - number of effective growing days (days),\nagro_lastfrostdate - date of the last frost (day-of-the-year),\nagro_rainaftersowing - total precipitation 3–7 weeks after sowing (mm), agro_veryhightemperaturestress - number of days with maximum temperature of 28 °C or higher around flowering (days).\n\nThe attributes of each feature type are labelled as [climateModel]_[emissionScenario]_[timePeriod]_0_[valueType]. There are three climate models, CSIRO-Mk3.5, MIROC3.2 (medres) and IPSL-CM4; and three emission scenarios b1, a1b and a2. The timePeriod includes the start year and the end year of the period. There is one option for valueType, absolute value av. For example, the attribute labeled as csiromk35_b1_20712100_0_ac describes the climate model CSIRO-Mk3.5, the emission scenario SRES B1, the time period from 2071 to 2100 and absolute value.\n\nhttps://paikkatiedot.ymparisto.fi/geoserver/ilmo-climateguide/wms?request=getcapabilities"
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Pixels with a value of 1 identify the top suppliers, whereas pixels with a value of 0 identify other forested land parcels (i.e. bottom 80%).\n\nPollination\nBasic information: the data shows the 20% of forests showing the strongest pollination potential on a per unit area basis. Pollination potential is a qualitative indicator measuring the capacity of ecosystems, including forest edges, to support pollinator insects. Pixels with a value of 1 identify the top suppliers, whereas pixels with a value of 0 identify other forested land parcels (i.e. bottom 80%).\n\nHabitat provision\nBasic information: the data shows the 20% of forests with the strongest relative potential as a habitat for bird species. Habitat provision is expressed as the ratio (%) between local bird species richness and the average richness in the regional context (250-km radius). Pixels with a value of 1 identify the top suppliers, whereas pixels with a value of 0 identify other forested land parcels (i.e. bottom 80%).\n\nSoil formation\nBasic information: the data shows the 20% of forest soils storing the largest amount of organic carbon on a per unit area basis. Soil organic carbon is intended as the content of organic carbon in both the topsoil (0-30 cm) and the subsoil (30-100 cm), and is measured in tons/ha. Pixels with a value of 1 identify the top suppliers, whereas pixels with a value of 0 identify other forested land parcels (i.e. bottom 80%).\n\nClimate regulation\nBasic information: the data shows the 20% of forests storing the largest amount of above- and below-ground carbon on a per unit area basis. The data, which is expressed in tons/ha, accounts for forest type (coniferous or broadleaved) and ecological zone. Pixels with a value of 1 identify the top suppliers, whereas pixels with a value of 0 identify other forested land parcels (i.e. bottom 80%).\n\nRecreation\nBasic information: the data shows the 20% of forests guaranteeing the highest recreation potential on a per unit area basis. The recreation potential is a qualitative indicator accounting for recreational quality and accessibility. Pixels with a value of 1 identify the top suppliers, whereas pixels with a value of 0 identify other forested land parcels (i.e. bottom 80%).\n\nData processing steps: processing\nDescription: Wood\n\nData processing step: the data was extracted from the relevant dataset produced in the GlobBiomass project (Santoro et al., 2018). Top 20% supplying pixels were given a value of 1, whereas all other pixels were given a value of 0.\n\nWater supply\nData processing step: freshwater supply was estimated as the amount of water running off each land parcel in the landscape owing to the combined effect of precipitation and evapotranspiration, using the Annual Water Yield model of InVEST 3.3 (Integrated Valuation of Ecosystem Services and Tradeoffs). Inouts for the modeling were obtained as explained below. Data about average annual precipitation at each location were extracted from the WorldClim global climate database (http://www.worldclim.org) The average annual reference evapotranspiration was obtained from the Global Reference Evapotranspiration Version 2 dataset (Trabucco and Zomer, 2019). The root restricting layer depth was obtained from the JRC European Soil Data Centre’s (ESDAC) European Soil Database (Panagos et al., 2012; Hiederer, 2013). The plant available water content was derived from the relevant map (Available Water Capacity) in the “Topsoil physical properties for Europe” database by ESDAC (Panagos et al., 2012; Ballabio et al., 2016). Land use/land cover information was directly imported from the Corine land cover, while watersheds were delineated from the European Digital Elevation Model (EU-DEM) using GIS-based hydrological tools. The maximum root depth per vegetated land use class was extracted from the Forest and Agriculture Organization (FAO) guidelines (Allen et al., 1998) and Schenk and Jackson (2002). The vapotranspiration coefficient (Kc) was extracted from Allen et al. (1998) and Nistor et al. (2017) for vegetated areas, and from InVEST guidelines for urban areas and water bodies. Top 20% supplying pixels were given a value of 1, whereas all other pixels were given a value of 0.\n\nErosion control\nData processing step: the data was extracted from the relevant dataset produced by the Joint Research Centre (JRC) (Maes et al., 2015; Guerra et al., 2016). Top 20% supplying pixels were given a value of 1, whereas all other pixels were given a value of 0.\n\nPollination\nData processing step: the data was extracted from the Relative Pollination Potential (RPP) map produced by the Joint Research Centre (JRC) (Zulian et al., 2013). Top 20% supplying pixels were given a value of 1, whereas all other pixels were given a value of 0.\n\nHabitat provision\nData processing step: the data was extracted from the JRC’s Habitat Quality dataset (Vallecillo et al., 2016). A resampling was performed to bring the JRC dataset from a 10-km resolution to a 1-km one. Top 20% supplying pixels were given a value of 1, whereas all other pixels were given a value of 0.\n\nSoil formation\nData processing step: the data was extracted from the amended Harmonised World Soil Database (Hiederer and Köchy, 2012). Top 20% supplying pixels were given a value of 1, whereas all other pixels were given a value of 0.\n\nClimate regulation\nData processing step: the data was extracted from the Pan-European dataset of forest above- and below-ground carbon produced by the JRC (de Rigo et al., 2013). Top 20% supplying pixels were given a value of 1, whereas all other pixels were given a value of 0.\n\nRecreation\nData processing step: the data was obtained through a reclassification of the Recreation Opportunity Spectrum (ROS) map produced by the JRC at a 100-m resolution (Paracchini et al., 2014). The reclassification was based on the assumption that the supply of recreation opportunities is proportional to the product of the two ROS variables (i.e. quality and accessibility). The reclassified map was made continuous by running a neighborhood operation assigning each pixel the mean of pixels within a radius of 1000 m. A nearest neighbor resampling was finally performed to bring the resolution to 1 km. Top 20% supplying pixels were given a value of 1, whereas all other pixels were given a value of 0.\n\nData processing steps\tprocessing\nDescription\tWood\nData processing step: the data was extracted from the relevant dataset produced in the GlobBiomass project (Santoro et al., 2018). Top 20% supplying pixels were given a value of 1, whereas all other pixels were given a value of 0.\n\nWater supply\nData processing step: freshwater supply was estimated as the amount of water running off each land parcel in the landscape owing to the combined effect of precipitation and evapotranspiration, using the Annual Water Yield model of InVEST 3.3 (Integrated Valuation of Ecosystem Services and Tradeoffs). Inouts for the modeling were obtained as explained below. Data about average annual precipitation at each location were extracted from the WorldClim global climate database (http://www.worldclim.org) The average annual reference evapotranspiration was obtained from the Global Reference Evapotranspiration Version 2 dataset (Trabucco and Zomer, 2019). The root restricting layer depth was obtained from the JRC European Soil Data Centre’s (ESDAC) European Soil Database (Panagos et al., 2012; Hiederer, 2013). 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Top 20% supplying pixels were given a value of 1, whereas all other pixels were given a value of 0.\n\nErosion control\nData processing step: the data was extracted from the relevant dataset produced by the Joint Research Centre (JRC) (Maes et al., 2015; Guerra et al., 2016). Top 20% supplying pixels were given a value of 1, whereas all other pixels were given a value of 0.\n\nPollination\nData processing step: the data was extracted from the Relative Pollination Potential (RPP) map produced by the Joint Research Centre (JRC) (Zulian et al., 2013). Top 20% supplying pixels were given a value of 1, whereas all other pixels were given a value of 0.\n\nHabitat provision\nData processing step: the data was extracted from the JRC’s Habitat Quality dataset (Vallecillo et al., 2016). A resampling was performed to bring the JRC dataset from a 10-km resolution to a 1-km one. Top 20% supplying pixels were given a value of 1, whereas all other pixels were given a value of 0.\n\nSoil formation\nData processing step: the data was extracted from the amended Harmonised World Soil Database (Hiederer and Köchy, 2012). Top 20% supplying pixels were given a value of 1, whereas all other pixels were given a value of 0.\n\nClimate regulation\nData processing step: the data was extracted from the Pan-European dataset of forest above- and below-ground carbon produced by the JRC (de Rigo et al., 2013). Top 20% supplying pixels were given a value of 1, whereas all other pixels were given a value of 0.\n\nRecreation\nData processing step: the data was obtained through a reclassification of the Recreation Opportunity Spectrum (ROS) map produced by the JRC at a 100-m resolution (Paracchini et al., 2014). 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"en": "In agriculture, nutrient streams eddy from field blocks to receiving waterbodies as part of the hydrologic cycle. In the waterways, the nutrients circulate through the sediments, organisms and vegetation, and partly end up into the water again and flow into the next waterbody and finally into the sea. Agricultural nutrient fluxes in different scales have been studied and monitored since 1980s in i) experimental fields (e.g. Puustinen et al. 2005, Turtola et al. 2007 and Uusi-Kämppä 2005), ii) the small study catchments (Vuorenmaa et al. 2002, Tattari et al., 2017) and iii) large river basins (e.g. Ekholm et al. 2015).\n\nExamination of such long-term data compiles phenomena at different scales and their total variations in simultaneous viewing, which is necessary to understand the nature and variation of nutrient flows. On the other hand, this is also necessary for the development of models and for the interlinking of models operating in different scales. Because the data is typically scattered in databases of different institutions or in the possession of individual scientists, it is worthwhile to pool the data together so that their value in the future is much greater than in the present situation. The pooling of data is part of a comprehensive examination of the nutrient cycles in drainage areas, catchments and river basins. Without perception of the importance and effects of these scales, understanding of the connections between agriculture, circular economy and water ecology remain incomplete.\n\nHere, data from 6 experimental fields, 12 small study catchments and 32 large river basins in Finland have been compiled separately per each site on yearly and monthly basis including runoff (mm), arithmetic and flow-proportional mean concentrations (mg/l and µg/l), sediment and nutrient loads (kg/ha), cultivation practices (in experimental fields) and weather data (precipitation in mm and temperature in °C).\n\nProcessing\n\nThe most common water sampling and runoff measurement method in the experimental fields was tipping bucket system with composite sampling, which was used in Aurajoki, Kotkanoja and Lintupaju experimental fields (see Puustinen et al. 2005, Turtola et al. 2007 and Uusi-Kämppä 2005).\n\nIn small study catchments both grab and composite water sampling was used. Runoff was most often determined according to water-height measurements with drawing limnigraphs at v-notch weirs and respective stage-discharge relationships (see Vuorenmaa et al. 2002 and Tattari et al. 2017).\n\nIn large river basins the data for flux calculations was based on grab sampling and discharge measurements made by environmental authorities (see Ekholm et al. 2015)."
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"en": "<jats:p>Conservation tillage practices were tested against autumn mouldboard ploughing for differences in physical properties of soil, surface runoff, subsurface drainflow and soil erosion. The study (1991 -2001) was performed on a gently (2%) sloping clayey soil of southern Finland, with two replicates of the tillage treatments on 0.5 ha plots. The annual shares of surface runoff of the total flow (surface runoff + subsurface drainflow) were 842% for ploughing (depth 2023 cm), 3666% for shallow autumn stubble cultivation (depth 58 cm) and 3682% for soil left untilled over winter. Surface runoff increased with decrease in the tillage intensity, and in line with the values of depressional water storage, macroporosity and saturated hydraulic conductivity. Erodibility of this gently sloping soil was at highest after autumn and spring tillage operations and decreased with time. Shallow autumn tillage produced erosion as high as mouldboard ploughing (4071700 kg ha1yr1), but 48% and 12% lower erosion levels were measured from plots left untilled in autumn, covered by grass or barley residues, respectively. Eroded soil particles moved relatively freely to the subsurface drains, which carried 3794% of the annual soil losses from the field. The study shows that even on the relatively flat clayey soils typical for southern Finland, tillage has a great influence on soil losses. The frequency of tillage needs to be reduced rather than the depth of tillage on clayey soils with poor water conductivity and structural stability if soil loss is to be diminished by conservation tillage.;</jats:p>",
"fi": "<jats:p>Conservation tillage practices were tested against autumn mouldboard ploughing for differences in physical properties of soil, surface runoff, subsurface drainflow and soil erosion. The study (1991 -2001) was performed on a gently (2%) sloping clayey soil of southern Finland, with two replicates of the tillage treatments on 0.5 ha plots. The annual shares of surface runoff of the total flow (surface runoff + subsurface drainflow) were 842% for ploughing (depth 2023 cm), 3666% for shallow autumn stubble cultivation (depth 58 cm) and 3682% for soil left untilled over winter. Surface runoff increased with decrease in the tillage intensity, and in line with the values of depressional water storage, macroporosity and saturated hydraulic conductivity. Erodibility of this gently sloping soil was at highest after autumn and spring tillage operations and decreased with time. Shallow autumn tillage produced erosion as high as mouldboard ploughing (4071700 kg ha1yr1), but 48% and 12% lower erosion levels were measured from plots left untilled in autumn, covered by grass or barley residues, respectively. Eroded soil particles moved relatively freely to the subsurface drains, which carried 3794% of the annual soil losses from the field. The study shows that even on the relatively flat clayey soils typical for southern Finland, tillage has a great influence on soil losses. The frequency of tillage needs to be reduced rather than the depth of tillage on clayey soils with poor water conductivity and structural stability if soil loss is to be diminished by conservation tillage.;</jats:p>",
"und": "<jats:p>Conservation tillage practices were tested against autumn mouldboard ploughing for differences in physical properties of soil, surface runoff, subsurface drainflow and soil erosion. The study (1991 -2001) was performed on a gently (2%) sloping clayey soil of southern Finland, with two replicates of the tillage treatments on 0.5 ha plots. The annual shares of surface runoff of the total flow (surface runoff + subsurface drainflow) were 842% for ploughing (depth 2023 cm), 3666% for shallow autumn stubble cultivation (depth 58 cm) and 3682% for soil left untilled over winter. Surface runoff increased with decrease in the tillage intensity, and in line with the values of depressional water storage, macroporosity and saturated hydraulic conductivity. Erodibility of this gently sloping soil was at highest after autumn and spring tillage operations and decreased with time. Shallow autumn tillage produced erosion as high as mouldboard ploughing (4071700 kg ha1yr1), but 48% and 12% lower erosion levels were measured from plots left untilled in autumn, covered by grass or barley residues, respectively. Eroded soil particles moved relatively freely to the subsurface drains, which carried 3794% of the annual soil losses from the field. The study shows that even on the relatively flat clayey soils typical for southern Finland, tillage has a great influence on soil losses. The frequency of tillage needs to be reduced rather than the depth of tillage on clayey soils with poor water conductivity and structural stability if soil loss is to be diminished by conservation tillage.;</jats:p>"
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