{"type": "FeatureCollection", "features": [{"id": "10.5281/zenodo.6566752", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-03T16:25:26Z", "type": "Dataset", "title": "Soil carbon stock, litter decomposition, and weather data from Ethiopian forests", "description": "Open AccessIntroduction 100 sampling units (SU) were selected from the total of 631 SUs of the Forest Reference Level submission 2017 (FRL 2017). The sampling was designed unbiased for total growing stock per SU, altitude,and mean litter depth per SU. The actual field sampling succeeded on 98 of the pre-selected SUs due to accessibility restrictions. Soil profile sampling Soil sampling was performed from November 2017 till mid-January 2018. Samples were taken from undisturbed soil from depths of 0-10 cm, 10-20 cm, and 20-30 cm below the organic layer. Volumetric samples of 107.5 cm3 were taken vertically, using a 10 cm long conically shaped corer with a cutting lower edge diameter of 37 mm and upper diameter of 40 mm. Composite samples were formed by combining the volumetric samples taken from different depths of two parallel soil profiles. The samples were transported to EEFRI Soil Laboratory in Addis Ababa after 1-4 weeks of sampling at distant locations. Soil physical characteristics The soil samples were air-dried, homogenized, and subjected to oven-drying at 105\u00b0C until constant mass. Total bulk density was determined using the total dry mass and volume of the composite samples. Organic carbon content (C % by wet oxidation method), and soil physical characteristics: moisture content, bulk density of the total sample, and bulk density of fine fraction (particles passing the 2 mm sieve). The mass of the coarse fraction was weighed. The soil fine fraction was also subjected to laser diffraction for more accurate particle size analysis for proportions of clay, silt, and sand. In addition to this 28 samples were also analyzed for C content in the laboratory of Natural Resources Institute Finland to determine C content by LECO CHN analyzer. This was done to calibrate the bulk of wet digestion-based estimates (Fig. 1). Before analysis, the soils were tested for the presence of inorganic C. For Figure 1. See Soil_C_Ethiopia.pdf Figure 1. Comparison of results from wet oxidation (Walkley-Black) and dry oxidation (CHN analyzer). The dotted line shows the theoretical 1:1 match between the axis, the solid line shows linear regression (intercept = 0) between the methods. The estimated slope value of 1.165 was used in adjusting the wet digestion results to match those obtained by dry oxidation: OCadj = 1.165 * OCwet. Based on a linear regression between the wet and dry oxidation analysis results, a correction factor of 1.165 was applied to adjust the organic C% obtained by wet digestion. The adjusted data are shown in the file \u201cSOC_Ethiopia_2017-2018.csv\u201d. SOC stocks were calculated by multiplying the proportion of organic C with BD of fine earth, after which the result was corrected for stoniness, a visually estimated proportion of large stones (S, value from 0 to 1) in the soil profile that could not be included in the volumetric soil samples (FAO VS-FAST). (SOCstock = C_{org} * BD_{fe} * (1-S) ) Soil organic carbon stock data Files: \u201cSOC_Ethiopia_2017-2018.csv\u201d and \u201cSOC_Ethiopia_2017-2018.xlsx\u201d The file includes soil characteristics from layers of 0-10 cm, 10-20 cm, and 20-30 cm below the loose organic layer on top of the soil. The data are used for SOC stock estimation in the respective layers as described above. In the .csv file individual columns are for LAT is the latitude of the sampling site corresponding to FieldCode and SU_nr LON is the longitude of the sampling site corresponding to FieldCode and SU_nr The coordinates are expressed as decimal degrees of the WGS84 system FieldCode refers to the Region and Sampling Unit number of the Ethiopian NFI (see below) SU_nr is the Sampling Unit number of the Ethiopian NFI Region is the name of the administrative region where the sample was taken Biome is the name of the forest biome type where the sample was collected BiomeSimplified is the name of a biome with some close types combined DepthRange is the upper and lower limit of the soil sample in the field, cm StoninessVFAST is a percentage of stones (VS-FAST by FAO) in the ca. 40 cm deep soil profile exposed during the sampling FreshMassInField is the mass of the total composite soil sample of the given layer, g, primarily indicative of checking the correct number of subsamples in composite NrComposites is the number of subsamples included in the composite for each soil layer CorerVolume is a constant of 107.5 cm3 because only one type of corer was used for undisturbed, volumetric sampling CompositeVolume is the volume of the composite sample for each soil depth layer CoarseFractionMass is the dry mass, g of soil particles > 2mm that did not pass the sieve, but were included in the sample volume FE_DryMass is oven-dry mass, g of the fine fraction that passed the 2 mm sieve. BDtot is total bulk density, g m-3, calculated for the composite sample BDfe is the bulk density of the fine earth fraction, g m-3 OC_adj is organic carbon (OC) content (%) in the composite sample, adjusted according to the comparison between dry and wet oxidation methods (Fig. 1) SOCfe is SOC stock calculated for soil fine earth fraction, t ha-1 in the 10 cm deep soil layer SOCfe_stoniness is SOC stock of the fine earth fraction, t ha-1 in the 10 cm deep soil layer, adjusted for stoniness. The correction assumes that the volume occupied by larger stones would be void of OC. Litter stock data File: \u201cLitter_Ethiopia_2017-2018.csv\u201d The file includes measurements of litter layer on Ethiopian NFI Sampling Unit (SU) sites where sampling for SOC stock determination was done. The depth of the litter layer was measured in the SU\u2019s of the NFI, and this data contains in addition to depth also a volumetric sample of the litter layer. The dry bulk density was used to calculate the carbon stocks in the litter pool. The depth of the litter layer was measured in the field. Litter from the respective spot was sampled quantitatively from a frame of 0.01m2 of area for litter dry mass estimate. The organic C stock in a litter (L) was calculated as, (L = {M over z} * {C_{om} over A}, ) where M = Dry mass of the litter sample, g z = Depth of the litter layer in the field, m Com = Conversion factor from dry organic matter to carbon (C), 0.5 A = area of quantitative collection of litter (0.01 m2) In the .csv file individual columns are for LAT, LON is the GPS coordinates (decimal degrees of WGS84) for the Sampling Units (SU_ID) SU_ID is the Sampling Unit identification number of the Ethiopian NFI FieldCode refers to the Region and Sampling Unit number of the Ethiopian NFI (see below) Region is the name of the administrative region where the sample was taken Litter_dry is the dry mass, g of the litter sample Area_m2 is the area, m2 of litter sampling MeanLitterDepth is the mean depth of the litter layer at the sampling area CDensityLitter is the dry bulk density of the litter, g m-2 multiplied by the assumed organic C proportion of the oven-dry litter materials (0.50) LitterCStock_tha is the litter stock, t ha-1 calculated from the C density of the litter layer Litter bag data (decomposition and quality) The leaves and twigs were sampled from 2 species (Juniperus and Podocarpus) and 3 locations of the elevation gradient in the Chilimo forest (Table 1). The forest was considered an old-growth with Juniperus procera and Podocarpus falcatusbeing the main species forming the tree canopy. The sites form an elevation gradient (Table 1). Table 1. Geographical locations of the study sites in the Chilimo forest. id Latitude (deg.) Longitude (deg.) Elevation (m a.s.l) 1 9.0672 38.1443 2500 2 9.0712 38.1556 2670 3 9.0869 38.1684 2800 The dying and dead leaves were sampled directly from the trees later referred to as \u201cfresh\u201d and from the branches found on the ground, referred to as \u201cold\u201d. The old leaves were assumed to be dead for around 3 months. The diameter of the branches/twigs was less than 1 cm in diameter. The samples were first sorted and air-dried in an elevated temperature of the greenhouse and thereafter oven-dried in the oven overnight at 45 \u00b0C. The samples were analyzed for acid, water, ethanol dissolved,and undissolved fractions (AWEN) (Table 2) and for the decomposition rates of the litter installed into the litter bags corresponding to each of the Chilimo sites. Table 2. Acid, water, ethanol (A, W, E, respectively) dissolved and undissolved fractions (N) from the litter components of the dominant tree species in the Chilimo forest. Litter type Species A W E N leaves fresh Juniperus 0.45 0.13 0.1 0.33 leaves fresh Podocarpus 0.42 0.28 0.05 0.25 leaves old Juniperus 0.44 0.07 0.08 0.41 leaves old Podocarpus 0.44 0.09 0.05 0.42 twigs Juniperus 0.61 0.04 0.02 0.32 twigs Podocarpus 0.56 0.15 0.02 0.27 A sufficient amount of litter was placed into the litter bags (polyurethane mesh 1 mm) and the mesh bags were installed on top of the soil surface under the forest canopy (later referred to as \u201ccanopy\u201d) and in the forest gap caused by harvesting (later referred as \u201copen\u201d). The installation of the litter bags (for each species 3 replicates of each litter type for each site and canopy type for the 3 periods, in total 12 litter bags for leaves and 6 bags for twigs) was done on 22.9.2017. The mesh bags were left on the ground, protected from grazing by the fence, and retrieved subsequently on 12.10.2017, 31.10.2017, and 12.12.2017. Despite the efforts took few samples were lost. The retrieved samples were oven-dried and initial mass and mass loss data for each period and litter type with a detailed description of the variables can be found in the file \u201clitter.chilimo_07.02.22.xlsx\u201d. Soil temperature data During the period from 22.9.2017 to 12.12.2017, we monitored the soil temperature at 5 cm depth under the canopy and in the open canopy on all Chilimo sites continuously every 4 hours intervals with the Maxim iButton temperature loggers. However, some sensors were lost. Daily means and their standard deviation of the continuous temperatures can be found in the file \u201csoil.temp.chilimo_07.02.22.xlsx\u201d. Processed weather data The air temperature and precipitation data for 98 sampling units corresponding to soil carbon data originated from 73 weather stations located across Ethiopia and were obtained from Ethiopian Meteorological Agency (http://www.ethiomet.gov.et/). Sampling units were joined with weather data by the closest proximity to their corresponding weather stations. Precipitation was unaltered. The air temperature required correction by elevation is described in more detail in Lehtonen et al. (2020). The monthly values of air temperature and precipitation with an accompanied readme description of the variables can be found for 98 sampling units in the file \u201csampling.units98_meteo_07.02.22.xlsx\u201d and the Chilimo study sites in the file \u201cmonthly.weather.chilimo_07.02.22.xlsx\u201d. The monthly values in the file 'sampling.units98_meteo_07.02.22.xlsx' correspond to long-term average over the period from 1986 to 2017. References: Lehtonen, A., \u0164upek, B., Nieminen, T.M., Bal\u00e1zs, A., Anjulo, A., Teshome, M., Tiruneh, Y. and Alm, J., 2020. Soil carbon stocks in Ethiopian forests and estimations of their future development under different forest use scenarios. Land Degradation & Development, 31(18), pp.2763-2774. FRL 2017. https://redd.unfccc.int/files/ethiopia_frel_3.2_final_modified_submission.pdf", "keywords": ["2. Zero hunger", "REDD", " soil carbon stock", " litter bag studies", " Ethiopia", "15. Life on land", "6. Clean water"], "contacts": [{"organization": "Alm, Jukka, \u0164upek, Boris, Anjulo, Agena, Teshome, Mindaye, Tiruneh, Yibeltal, Abay, Abebe, Alebachew, Mehari, Tervahauta, Arja, Lehtonen, Aleksi,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6566752"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6566752", "name": "item", "description": "10.5281/zenodo.6566752", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6566752"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-05-20T00:00:00Z"}}, {"id": "10.5281/zenodo.6622619", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-03T16:25:27Z", "type": "Report", "title": "STUDIES IN CHALKONES. PART I. CHALKONES DERIVED FROM RESACETOPHENONE AND ITS DIMETHYL ETHER.", "description": "Resacetophenone dimethyl ether has been condensed with o-vanillin, isovanillin, \u00adand 6-methoxylsalicylaldehyde, and resacetophenone with o-vanillin to yield correspond\u00ading hydroxymethoxychalkones. Attempt has been made to determine the optimum condition for these condensations.", "keywords": ["solution", "sand-bath", "3. Good health"], "contacts": [{"organization": "JAGRAJ BEHARI LAL", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6622619"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6622619", "name": "item", "description": "10.5281/zenodo.6622619", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6622619"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "1939-12-31T00:00:00Z"}}, {"id": "10.5281/zenodo.6821261", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-03T16:25:28Z", "type": "Journal Article", "created": "2022-07-11", "title": "Behavioural drivers and barriers for adopting microbial applications in arable farms: Evidence from the Netherlands and Germany", "description": "Open AccessMicrobial applications contribute to more sustainable agriculture by stimulating plant growth, increasing resistance to pests and diseases and relieving stresses from climate change. To stimulate the adoption of microbial applications, it is important to understand the underlying reasons for farmers' adoption decision. In this article, we investigate the behavioural drivers and barriers associated with the likelihood to adopt microbial applications. We employ the Behaviour Change Wheel and its capability, opportunity, motivation-behaviour (COM-B) model. Data were collected via an online survey among 196 Dutch and German arable farmers. We find that trust in microbial applications is an important driver and that lack of knowledge and professional support are barriers for the adoption of microbial applications. On this basis, we recommend three interventions: i) norm creation and enablement, ii) education and learning, and iii) trust building by providing incentives. The acceptance and success of a behavioural intervention depends on the choice of the interventionist. For instance, the role of governmental institutions in enforcing the adoption of microbial applications is perceived as problematic by farmers. Instead, farmers expect advisers and farmer organisations to become active in knowledge transmission and field studies.", "keywords": ["2. Zero hunger", "0301 basic medicine", "03 medical and health sciences", "Technology uptake", "13. Climate action", "Microbial applications", "Technology uptakeMicrobial applicationsBehaviour change wheel", "15. Life on land", "Behaviour change wheel", "01 natural sciences", "12. Responsible consumption", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6821261"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Technological%20Forecasting%20and%20Social%20Change", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6821261", "name": "item", "description": "10.5281/zenodo.6821261", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6821261"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-09-01T00:00:00Z"}}, {"id": "10.5281/zenodo.6866444", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-03T16:25:28Z", "type": "Report", "title": "ThermoMechanoChemical (TMC) fractionation of aquaculture by products by twin screw extrusion for the production of biobased fertilisers", "description": "The SEA2LAND project is a 4-year collaborative Innovation Action (IA) funded by the EU in the frame of the Horizon 2020 programme. Based on the circular economy model, SEA2LAND promotes the production of fertilisers in the EU from own raw materials. This solution is expected to reduce the soil nutrient imbalance in Europe. The basis of the project is the regional production of bio-based fertilizers (BBF) by developing dedicated demonstration pilots that can be replicated across Europe, boosting local growth.
For the Atlantic area, the Sea2Land project aims at producing BBF\u2019s from the by-products of the aquaculture domain using ThermoMechanoChemical (TMC) fractionation by twin-screw extrusion. TMC process is a continuous process, working at low liquid/solid ratios and able to provide a solid and a liquid fraction. Until now, the processes concerning extrusion/fish/fertilizer had been limited to: a) the mixing of fish with vegetal raw materials by twin-screw extrusion for the production of pellets for the feed industry, b) the pretreatment of lignocellulosic raw materials with enzymes by twin-screw extrusion to initiate the enzymatic hydrolysis, c) the transformation of fish bones and heads by extrusion for the production of gelatins films, and d) the production of organic fertilizers by urea and derivatives. The use of TMC process for fertilizers production is an innovative approach that makes it possible to recover not only products with an agronomic value but also other components such as lipids to reach a ZERO-waste process. TMC process had never been used in the presence of enzymes for the transformation of aquaculture by-products into BBF\u2019s.
Retained by-products are heads, fishbones and viscera from Steelhead truit (Oncorhynchus mykiss). The pilot implemented in the Atlantic Area includes the following technological units: (i) Grinding technologies, (ii) TMC fractionation by twin-screw extrusion, (iii) Enzymatic Hydrolysis, (iv) Separation by centrifugation technologies and (v) Concentration technologies. The objectives are the production of 20 kg BBF/week and of 200 kg BBF/week at 2 different pilot scales in optimized operating conditions.", "keywords": ["2. Zero hunger", "Bio-based fertilisers", "13. Climate action", "By-products", "BBF", "14. Life underwater", "Aquaculture", "7. Clean energy", "6. Clean water", "12. Responsible consumption"], "contacts": [{"organization": "Laure Candy, V. Vandenbossche", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6866444"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6866444", "name": "item", "description": "10.5281/zenodo.6866444", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6866444"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.6821262", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-03T16:25:28Z", "type": "Journal Article", "created": "2022-07-11", "title": "Behavioural drivers and barriers for adopting microbial applications in arable farms: Evidence from the Netherlands and Germany", "description": "Open AccessMicrobial applications contribute to more sustainable agriculture by stimulating plant growth, increasing resistance to pests and diseases and relieving stresses from climate change. To stimulate the adoption of microbial applications, it is important to understand the underlying reasons for farmers' adoption decision. In this article, we investigate the behavioural drivers and barriers associated with the likelihood to adopt microbial applications. We employ the Behaviour Change Wheel and its capability, opportunity, motivation-behaviour (COM-B) model. Data were collected via an online survey among 196 Dutch and German arable farmers. We find that trust in microbial applications is an important driver and that lack of knowledge and professional support are barriers for the adoption of microbial applications. On this basis, we recommend three interventions: i) norm creation and enablement, ii) education and learning, and iii) trust building by providing incentives. The acceptance and success of a behavioural intervention depends on the choice of the interventionist. For instance, the role of governmental institutions in enforcing the adoption of microbial applications is perceived as problematic by farmers. Instead, farmers expect advisers and farmer organisations to become active in knowledge transmission and field studies.", "keywords": ["2. Zero hunger", "0301 basic medicine", "03 medical and health sciences", "Technology uptake", "13. Climate action", "Microbial applications", "Technology uptakeMicrobial applicationsBehaviour change wheel", "15. Life on land", "Behaviour change wheel", "01 natural sciences", "12. 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Climate action", "soil biodiversity", "15. Life on land", "global change"], "contacts": [{"organization": "Phillips, Helen RP, Cameron, Erin K, Eisenhauer, N, Burton, Victoria J, Ferlian, Olga, Kanabar, Sahana, Malladi, Sandhya, Murphy, Rowan E, Peter, Anne, Petrocelli, Isis, Ristok, Christian, Tyndall, Katharine, van der Putten, Wim, Beaumelle, L\u00e9a,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6903151"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6903151", "name": "item", "description": "10.5281/zenodo.6903151", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6903151"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.6907278", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:28Z", "type": "Other", "title": "Bacterias Promotoras del Crecimiento Vegetal", "description": "Adopci\u00f3n de nuevas t\u00e9cnicas de gesti\u00f3n para aumentar la producci\u00f3n y la calidad de los cultivos (Bacterias Promotoras del Crecimiento Vegetal) This work was funded by the European Commission Horizon 2020 project SoildiverAgro [grant agreement 817819].", "keywords": ["Bacterias promotoras del crecimiento de las plantas", "suelo", "PGPB", "microbioano", "15. Life on land", "Bacterias promotoras del crecimiento de las plantas"], "contacts": [{"organization": "Zornoza, Ra\u00fal", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6907278"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6907278", "name": "item", "description": "10.5281/zenodo.6907278", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6907278"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-06-29T00:00:00Z"}}, {"id": "10.5281/zenodo.6907265", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-03T16:25:28Z", "type": "Other", "title": "Plant Growth Promoting Bacteria", "description": "Adoption of new management practices to increase crop production and quality (Plant Growth Promoting Bacteria) This work was funded by the European Commission Horizon 2020 project SoildiverAgro [grant agreement 817819].", "keywords": ["2. Zero hunger", "microbial", "PGPB", "Plant growth promoting bacteria", "15. Life on land", "6. Clean water", "soil"], "contacts": [{"organization": "Zornoza, Ra\u00fal", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6907265"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6907265", "name": "item", "description": "10.5281/zenodo.6907265", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6907265"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-06-29T00:00:00Z"}}, {"id": "10.5281/zenodo.6907266", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-03T16:25:28Z", "type": "Report", "title": "Plant Growth Promoting Bacteria", "description": "Adoption of new management practices to increase crop production and quality (Plant Growth Promoting Bacteria) This work was funded by the European Commission Horizon 2020 project SoildiverAgro [grant agreement 817819].", "keywords": ["2. Zero hunger", "microbial", "PGPB", "Plant growth promoting bacteria", "15. Life on land", "6. Clean water", "soil"], "contacts": [{"organization": "Zornoza, Ra\u00fal", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6907266"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6907266", "name": "item", "description": "10.5281/zenodo.6907266", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6907266"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-06-29T00:00:00Z"}}, {"id": "10.5281/zenodo.6992753", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:31Z", "type": "Dataset", "title": "Dataset to Manuscript: Schiedung et al. (2023; SBB) Enhanced loss but limited mobility of pyrogenic and organic matter in continuous permafrost-affected forest soils.", "description": "Dataset to Schiedung et al. (2023; SBB) Enhanced loss but limited mobility of pyrogenic and organic matter in continuous permafrost-affected forest soils. All published data is provided in the files 'dd_'. This includes: dd_cores: All data of soil cores and with depth dd_fractions: All data obtained from fractionation of the 0-3cm core layers dd_teabag: All data and mass losses of incubated teabags dd_temperature: All data and recorded soil temperatures All parameters and names are described in the corresponding file starting with 'Var_names_'. Details on methods and calculations are given in the manuscript and supporting information. NanoSIMS data is provided in the folder 'dd_NanoSIMS.zip'. This contains a file with descriptions of the provided tif-files 'dd_NanoSIMS'. Descriptions of the variables and parameters as well as further instructions are given in the file 'Var_names_description_dd_NanoSIMS'. Images and additional data can be requested by the corresponding author (marcusschiedung@gmail.com).", "keywords": ["Pyrogenic carbon", "Isotopes", "Soil organic carbon", "Permafrost", "in-situ incubation", "Teabag", "15. Life on land", "13C lebelling"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6992753"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6992753", "name": "item", "description": "10.5281/zenodo.6992753", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6992753"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-08-15T00:00:00Z"}}, {"id": "10.5281/zenodo.7024773", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:31Z", "type": "Dataset", "title": "Dataset of the paper \"Vermicomposting as a sustainable option for the management of the biomass of the invasive tree Acacia dealbata Link.\"", "description": "Data generated during an experiment of vermicomposting of Acacia dealbata fresh biomass. Four files are included: 'vermicompost_and_earthworm_data.csv' and 'readme.csv' are the raw data of different parameters measured in vermicompost samples during the vermicomposting of Acacia dealbata by the earthworm Eisenia andrei and an explanation of each parameter and the unit in which the parameter is expressed.\u00a0 'germination test.csv' and 'radicle_length.csv' are the results of an ecotoxicological test on the effect of A. dealbata biomass and vermicompost on the germination and radicle elongation in Lepidium sativum.", "keywords": ["Acacia dealbata", "ecotoxicological test", "vermicompost", "Eisenia andrei", "15. Life on land", "earthworm", "Lepidium sativum"], "contacts": [{"organization": "Quintela-Sabar\u00eds, Celestino, Mendes, Lu\u00eds A., Dom\u00ednguez, Jorge,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7024773"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7024773", "name": "item", "description": "10.5281/zenodo.7024773", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7024773"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-08-26T00:00:00Z"}}, {"id": "10.5281/zenodo.7024774", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:31Z", "type": "Dataset", "title": "Dataset of the paper \"Vermicomposting as a sustainable option for the management of the biomass of the invasive tree Acacia dealbata Link.\"", "description": "Data generated during an experiment of vermicomposting of Acacia dealbata fresh biomass. Four files are included: 'vermicompost_and_earthworm_data.csv' and 'readme.csv' are the raw data of different parameters measured in vermicompost samples during the vermicomposting of Acacia dealbata by the earthworm Eisenia andrei and an explanation of each parameter and the unit in which the parameter is expressed.\u00a0 'germination test.csv' and 'radicle_length.csv' are the results of an ecotoxicological test on the effect of A. dealbata biomass and vermicompost on the germination and radicle elongation in Lepidium sativum.", "keywords": ["Acacia dealbata", "ecotoxicological test", "vermicompost", "Eisenia andrei", "15. Life on land", "earthworm", "Lepidium sativum"], "contacts": [{"organization": "Quintela-Sabar\u00eds, Celestino, Mendes, Lu\u00eds A., Dom\u00ednguez, Jorge,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7024774"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7024774", "name": "item", "description": "10.5281/zenodo.7024774", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7024774"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-08-26T00:00:00Z"}}, {"id": "10.5281/zenodo.7261592", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-03T16:25:35Z", "type": "Report", "title": "D4.2. Plan for exploitation and dissemination of the project results", "description": "This document is a deliverable of the Co-UDlabs project, funded under the European Union\u2019s Horizon 2020 research and innovation programme under grant agreement No 101008626. The aim of this document is to provide the first version of the Plan for Dissemination and Exploitation of Results (PEDR), produced at M6 as part of the Work Package 4 on communication, dissemination and exploitation of results. The aim of the PEDR is to provide the Co-UDlabs partners with guidelines on the different communication and dissemination activities that are planned and their schedule, who are the partners responsible for each activity and what tools and channels are available for dissemination. A section on exploitation will define the actions planned to achieve the exploitation of the results and impact of the project. More specifically, in terms of dissemination and communication the PEDR will: \u00a0Propose a communication and dissemination policy, and define the objectives of the actions; \u00a0Identify the target audience for each objective or main result; \u00a0List the communication and dissemination channels to be used for project promotion; \u00a0Present a schedule of the communication and dissemination actions throughout the project duration; \u00a0Define and monitor a series of Key Performance Indicators (KPIs) to assess the success of the implementation (e.g. number of publications, size of the audience reached, number of visits on the website, feedback received from audiences at conferences, etc.) and update the plan according to the evolution of the project. In terms of the exploitation of the results, the PEDR will contain the following information, if applicable and when relevant, especially within the final exploitation plan to be submitted at the end of the project: The identification of exploitable main outputs of the project; The identification of the factors influencing exploitation and wide deployment of the project\u2019s results The identification of new and existing measures for the project sustainability. The document is drafted by Euronovia, which is leader of this Work Package, with inputs from all partners. While Euronovia is the leading partner in charge of WP4, all partners have the responsibility to participate in the communication activities and dissemination of the results of the project. According to the grant agreement and unless it goes against their legitimate interests, each beneficiary must, as soon as possible, disseminate its results by disclosing them to the public by appropriate means (other than those resulting from protecting or exploiting the results), including in scientific publications. The PEDR is an evolving document which will be updated at the end of each reporting period (October 2022, April 2024 and April 2025).", "keywords": ["Research Infrastructure", "Co-UDlabs", "Urban Drainage Systems", "12. Responsible consumption"], "contacts": [{"organization": "De Nale, Laura, Guilloteau, Lucie, Anta, Jose,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7261592"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7261592", "name": "item", "description": "10.5281/zenodo.7261592", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7261592"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-04-26T00:00:00Z"}}, {"id": "2164/10968", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:27:56Z", "type": "Journal Article", "created": "2018-08-08", "title": "Simulation of Soil Organic Carbon Effects on Long-Term Winter Wheat (Triticum aestivum) Production Under Varying Fertilizer Inputs", "description": "Soil organic carbon (SOC) has a vital role to enhance agricultural productivity and for mitigation of climate change. To quantify SOC effects on productivity, process models serve as a robust tool to keep track of multiple plant and soil factors and their interactions affecting SOC dynamics. We used soil-plant-atmospheric model viz. DAISY, to assess effects of SOC on nitrogen (N) supply and plant available water (PAW) under varying N fertilizer rates in winter wheat (Triticum aestivum) in Denmark. The study objective was assessment of SOC effects on winter wheat grain and aboveground biomass accumulation at three SOC levels (low: 0.7% SOC; reference: 1.3% SOC; and high: 2% SOC) with five nitrogen rates (0-200 kg N ha-1) and PAW at low, reference, and high SOC levels. The three SOC levels had significant effects on grain yields and aboveground biomass accumulation at only 0-100 kg N ha-1 and the SOC effects decreased with increasing N rates until no effects at 150-200 kg N ha-1. PAW had significant positive correlation with SOC content, with high SOC retaining higher PAW compared to low and reference SOC. The mean PAW and SOC correlation was given by PAW% = 1.0073 \u00d7 SOC% + 15.641. For the 0.7-2% SOC range, the PAW increase was small with no significant effects on grain yields and aboveground biomass accumulation. The higher winter wheat grain and aboveground biomass was attributed to higher N supply in N deficient wheat production system. Our study suggested that building SOC enhances agronomic productivity at only 0-100 kg N ha-1. Maintenance of SOC stock will require regular replenishment of SOC, to compensate for the mineralization process degrading SOC over time. Hence, management can maximize realization of SOC benefits by building up SOC and maintaining N rates in the range 0-100 kg N ha-1, to reduce the off-farm N losses depending on the environmental zones, land use and the production system.", "keywords": ["0301 basic medicine", "Crop productivity; DAISY model; Grain yield; Long-term experiment; Nitrogen; Pedotransfer functions; Plant available water;", "Nitrogen", "QH301 Biology", "DAISY model", "pedotransfer functions", "Plant Science", "nitrogen", "SB1-1110", "QH301", "03 medical and health sciences", "Long-term experiment", "SDG 13 - Climate Action", "Grain yield", "SDG 2 - Zero Hunger", "European Commission", "289694", "crop productivity", "SDG 15 - Life on Land", "2. Zero hunger", "020", "Pedotransfer functions", "0303 health sciences", "grain yield", "Plant culture", "15. Life on land", "plant available water", "13. Climate action", "Crop productivity", "Plant available water", "SMARTSOIL", "long-term experiment"]}, "links": [{"href": "https://flore.unifi.it/bitstream/2158/1138671/1/Ghaley%20et%20al%202018_Frontiers%20in%20Plant%20Science.pdf"}, {"href": "https://doi.org/2164/10968"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Plant%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2164/10968", "name": "item", "description": "2164/10968", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2164/10968"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-08-08T00:00:00Z"}}, {"id": "10.5281/zenodo.7267318", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-03T16:25:35Z", "type": "Journal Article", "title": "Useful Gut Model for Plastic Particles Assessing", "description": "Emerging evidence suggest that human exposure to micro-plastics is rising and it most happened via ingestion and/or inhalation (Wright SL et al. 2017). In fact, plastics particles were detected in various product for human consumption such as seafood, honey, sugar, and drinking water (von Moos et al. 2012; Liebezeit et al. 2013; Koelmans et al. 2019). Once ingested, they can reach the intestinal epithelium. Their biocompatibility or toxic effects on gastro-intestinal barrier is matter of debate, also because of the lack of suitable models of physiological digestion and gastro-intestinal barrier. The simplest model developed is represented by Caco-2 cells cultured on semi-permeable filter supports for 21 days, a time point that make them differentiating into enterocyte-like cells, with transport and permeability features like human intestinal tissue (Lundquist et al 2016). A more complex model is composed of the co-culture of Caco-2 and HT29-MTX cells that to methotrexate (MTX) develop the ability to produce mucus (Gamboa et al. 2013): this model includes the mucus-secreting goblet cells important for the presence of mucus layer in the intestine. To better mimic the intestinal physiology, another cell type (Raji B) can be added to the co-culture to allow Caco-2 differentiation into gut-associated lymphoid tissue microfold cells (M-cells) that can transport micro- and nanoparticles through transcytosis process (Gullberg et al. 2000).", "keywords": ["microplastics", "toxicity", "cytotoxicity", "Caco-2", "gastro-intestinal barrier", "HT29-MTX", "nanoplastics", "plastic particles"], "contacts": [{"organization": "Giulia, Antonello, Esposito, Marianna, Barbero, Francesco, Macaraig Mansilungan, Camille, Fenoglio, Ivana, Riganti, Chiara, Bergamaschi, Enrico,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7267318"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/CUSP%20Meeting", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7267318", "name": "item", "description": "10.5281/zenodo.7267318", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7267318"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.7219753", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:34Z", "type": "Dataset", "title": "Dataset to: Deforestation for agriculture leads to soil warming and enhanced litter decomposition in subarctic soils", "description": "Deforestation for agriculture leads to soil warming and enhanced litter decomposition in subarctic soils
T. Peplau, C. Poeplau, E. Gregorich, J. Schroeder This repository contains a dataset of soil temperature, soil parameters, farm management and additional site informations. Soil_temperature_data_Yukon.zip: Temperature data from different farms across the Yukon.
Each .xlsx file contains data from one temperature logger that logged soil temperature every 2 hours. The individual sheets are named in the following scheme:
Farm_landuse_depth.xlsx
Farm contains two letters corresponding to the identifier in the soil data set
landuse contains either F ('Forest'), CM ('Cropland / Market Garden') or G ('Grassland')
Depth is either 10 cm or 50 cm teabags.csv contains raw data about the initial weight of the teabags buried, their location and their weight after two years in the soil tea_decomposition contains the mean decomposition (n=3) of the tabags from each plot and corresponding temperature statistics, based on the logger data Soil_I_IV.csv contains soil parameters from soil samples at 0-10 cm and 40-60 cm site_data_R.csv contains geographical information and soil data that has only been measured once per site", "keywords": ["2. Zero hunger", "land-use change", "soil temperature", "Tea bags", "carbon", "soil organic matter", "15. Life on land"], "contacts": [{"organization": "Peplau, Tino, Poeplau, Christopher, Gregorich, Edward, Schroeder, Julia,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7219753"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7219753", "name": "item", "description": "10.5281/zenodo.7219753", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7219753"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-10-18T00:00:00Z"}}, {"id": "10.5281/zenodo.7261593", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-03T16:25:35Z", "type": "Report", "title": "D4.2. Plan for exploitation and dissemination of the project results", "description": "This document is a deliverable of the Co-UDlabs project, funded under the European Union\u2019s Horizon 2020 research and innovation programme under grant agreement No 101008626. The aim of this document is to provide the first version of the Plan for Dissemination and Exploitation of Results (PEDR), produced at M6 as part of the Work Package 4 on communication, dissemination and exploitation of results. The aim of the PEDR is to provide the Co-UDlabs partners with guidelines on the different communication and dissemination activities that are planned and their schedule, who are the partners responsible for each activity and what tools and channels are available for dissemination. A section on exploitation will define the actions planned to achieve the exploitation of the results and impact of the project. More specifically, in terms of dissemination and communication the PEDR will: Propose a communication and dissemination policy, and define the objectives of the actions; Identify the target audience for each objective or main result; List the communication and dissemination channels to be used for project promotion; Present a schedule of the communication and dissemination actions throughout the project duration; Define and monitor a series of Key Performance Indicators (KPIs) to assess the success of the implementation (e.g. number of publications, size of the audience reached, number of visits on the website, feedback received from audiences at conferences, etc.) and update the plan according to the evolution of the project. In terms of the exploitation of the results, the PEDR will contain the following information, if applicable and when relevant, especially within the final exploitation plan to be submitted at the end of the project: The identification of exploitable main outputs of the project; The identification of the factors influencing exploitation and wide deployment of the project\u2019s results The identification of new and existing measures for the project sustainability. The document is drafted by Euronovia, which is leader of this Work Package, with inputs from all partners. While Euronovia is the leading partner in charge of WP4, all partners have the responsibility to participate in the communication activities and dissemination of the results of the project. According to the grant agreement and unless it goes against their legitimate interests, each beneficiary must, as soon as possible, disseminate its results by disclosing them to the public by appropriate means (other than those resulting from protecting or exploiting the results), including in scientific publications. The PEDR is an evolving document which will be updated at the end of each reporting period (October 2022, April 2024 and April 2025).", "keywords": ["Research Infrastructure", "Co-UDlabs", "Urban Drainage Systems", "12. Responsible consumption"], "contacts": [{"organization": "de Nale, Laura", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7261593"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7261593", "name": "item", "description": "10.5281/zenodo.7261593", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7261593"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-10-26T00:00:00Z"}}, {"id": "10.5281/zenodo.7307449", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:35Z", "type": "Dataset", "title": "Components of the complete budget for SAFE intensive carbon plots", "description": "Description: Measured components of total carbon budget at SAFE project, values, with standard errors, for each 1-ha carbon plots for 11 plots investigated across a logging gradient from unlogged old-growth to heavily logged.

These data are also published in below-ground carbon cycle in Riutta et al 2021 GBC and allocation of net primary productivity from Riutta et al 2019 GCB. This worksheet include two addititional carbon plots from Lambir Hills National Park (see Kho et al. 2013 JGR), which are not part of the SAFE Project. Below-ground carbon cycle data can be found at DOI 10.5281/zenodo.3266770 and leaf respiration at DOI 10.5281/zenodo.3247630.

SAFE Intensive Carbon Plots, part of the Global Ecosystem Monitoring (GEM) network, see http://gem.tropicalforests.ox.ac.uk/. All the methods and installation is described in detail in the GEM Intensive Carbon Plots manual, available at http://gem.tropicalforests.ox.ac.uk/files/rainfor-gemmanual.v3.0.pdf. Project: This dataset was collected as part of the following SAFE research project: Changing carbon dioxide and water budgets from deforestation and habitat modification Funding: These data were collected as part of research funded by: Sime Darby Foundation (Grant, SAFE Core data) European Research Council Advanced Investigator Grant, GEM-TRAIT (Grant, Grant number 321131) NERC Human-Modified Tropical Forests Programme: Biodiversity And Land-use Impacts on tropical ecosystem function (BALI) Project (Grant, NE/K016369/1) NERC standard grant: The multi-year impacts of the 2015/2016 El Ni\u00f1o on the carbon cycle of tropical forests worldwide (Grant, NE/P001092/1) HSBC Malaysia (Grant) The University of Zurich (Grant) This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs. Permits: These data were collected under permit from the following authorities: Sabah Biodiversity Council (Research licence JKM/MBs.1000-2/2 JLD.6 (76)) XML metadata: GEMINI compliant metadata for this dataset is available here Files: This consists of 1 file: SAFE_CarbonBalanceComponents.xlsx SAFE_CarbonBalanceComponents.xlsx This file contains dataset metadata and 1 data tables: Carbon balance components data (described in worksheet Data) Description: Carbon balance components and carbon budget of intensive carbon plots at SAFE project Number of fields: 64 Number of data rows: 11 Fields: ForestType: Old-growth or Logged (Field type: categorical) SAFEPlotName: SAFE plot name, as in the SAFE Gazetteer (Field type: location) PlotName: Plot name (used in field work) (Field type: id) ForestPlotsCode: Plot code, as in the ForestPlots database (this should be used in publications, instead of plot name) (Field type: id) WoodyNPP_Stem: Woody stem productivity (subcomponent of woody net primary productivity) (Field type: numeric) WoodyNPP_CoarseRoot: Coarse root productivity (subcomponent of woody net primary productivity) (Field type: numeric) WoodyNPP_BranchTurnover: Branch turnover productivity (subcomponent of woody net primary productivity) (Field type: numeric) WoodyNPP_Total: Total woody net primary producivity (Field type: numeric) CanopyNPP_Leaf: Leaf productivity (subcomponent of canopy net primary productivity) (Field type: numeric) CanopyNPP_Twig: Twig productivity (subcomponent of canopy net primary productivity) (Field type: numeric) CanopyNPP_Reproductive: Reproductive productivity, i.e. fruit, seed and flowers (subcomponent of canopy net primary productivity) (Field type: numeric) CanopyNPP_Miscellaneous: Unidentified canopy debris (subcomponent of canopy net primary productivity) (Field type: numeric) CanopyNPP_Herbivory: Leaf productivity lost to herbivory (subcomponent of canopy net primary productivity) (Field type: numeric) CanopyNPP_Total: Total canopy net primary producivty (Field type: numeric) FineRootNPP: Fine root productivity (Field type: numeric) TotalNPP_WithoutMycorrhiza: Total net primary productivity without mycorrhiza (Field type: numeric) TotalNPP_WithMycorrhiza: Total net primary productivity including mycorrhiza (Field type: numeric) GPP_WithoutMycorrhiza: Gross primary productivity without mycorrhiza (Field type: numeric) GPP_WithMycorrhiza: Gross primary productivity including mycorrhiza (Field type: numeric) R_Stem: Respiration from woody stems (Field type: numeric) R_Leaf: Leaf Respiration (Field type: numeric) R_FineRoots: Respiration from fine roots (Field type: numeric) R_CoarseRoots: Respiration from coarse roots (Field type: numeric) R_SOM: Respiration from soil organic matter (Field type: numeric) R_Mycorrhiza: Respiration from mycorrhiza (Field type: numeric) R_Litter: Respiration from litter layer (Field type: numeric) R_Deadwood: Deadwood respiration (Field type: numeric) R_auto: Total autotrophic respiration (Field type: numeric) R_het: Total heterotrophic respiration (Field type: numeric) R_eco: Total ecosystem respiration (Field type: numeric) NEP_WithoutMycorrhiza: Total net ecosystem productivity (also known as net ecosystem exchange) without including mycorrhiza, whereby positive values indicate a net source of carbon to the atmosphere (Field type: numeric) NEP_WithMycorrhiza: Total net ecosystem productivity (also known as net ecosystem exchange) including mycorrhiza, whereby positive values indicate a net source of carbon to the atmosphere (Field type: numeric) AbovegroundBiomassCarbonStock: Plot above-ground biomass carbon stock (Field type: numeric) CoarseRootBiomassCarbonStock: Biomass carbon stock of coarse roots (Field type: numeric) SE_WoodyNPP_Stem: Standard error of woody stem productivity (Field type: numeric) SE_WoodyNPP_CoarseRoot: Standard error of coarse root productivity (Field type: numeric) SE_WoodyNPP_BranchTurnover: Standard error of branch turnover productivity (Field type: numeric) SE_WoodyNPP_Total: Standard error of total woody net primary producivity (Field type: numeric) SE_CanopyNPP_Leaf: Standard error of leaf productivity (Field type: numeric) SE_CanopyNPP_Twig: Standard error of twig productivity (Field type: numeric) SE_CanopyNPP_Reproductive: Standard error of reproductive productivity, i.e. fruit, seed and flowers (Field type: numeric) SE_CanopyNPP_Miscellaneous: Standard error of unidentified canopy debris (Field type: numeric) SE_CanopyNPP_Herbivory: Standard error of leaf productivity lost to herbivory (Field type: numeric) SE_CanopyNPP_Total: Standard error of total canopy net primary producivty (Field type: numeric) SE_FineRootNPP: Standard error of fine root productivity (Field type: numeric) SE_TotalNPP_WithoutMycorrhiza: Standard error of total net primary productivity without mycorrhiza (Field type: numeric) SE_TotalNPP_WithMycorrhiza: Standard error of total net primary productivity including mycorrhiza (Field type: numeric) SE_GPP_WithoutMycorrhiza: Standard error of gross primary productivity without mycorrhiza (Field type: numeric) SE_GPP_WithMycorrhiza: Standard error of gross primary productivity including mycorrhiza (Field type: numeric) SE_R_Stem: Standard error of respiration from woody stems (Field type: numeric) SE_R_Leaf: Standard error of leaf Respiration (Field type: numeric) SE_R_FineRoots: Standard error of respiration from fine roots (Field type: numeric) SE_R_CoarseRoots: Standard error of respiration from coarse roots (Field type: numeric) SE_R_SOM: Standard error of respiration from soil organic matter (Field type: numeric) SE_R_Mycorrhiza: Standard error of respiration from mycorrhiza (Field type: numeric) SE_R_Litter: Standard error of litter layer respiration (Field type: numeric) SE_R_Deadwood: Standard error of deadwood respiration (Field type: numeric) SE_R_auto: Standard error of total autotrophic respiration (Field type: numeric) SE_R_het: Standard error of total heterotrophic respiration (Field type: numeric) SE_R_eco: Standard error of total ecosystem respiration (Field type: numeric) SE_NEP_WithoutMycorrhiza: Standard error of total net ecosystem productivity (Field type: numeric) SE_NEP_WithMycorrhiza: Standard error of total net ecosystem productivity (Field type: numeric) SE_AbovegroundBiomassCarbonStock: Standard error of plot above-ground biomass carbon stock (Field type: numeric) SE_CoarseRootBiomassCarbonStock: Standard error of biomass carbon stock of coarse roots (Field type: numeric) Date range: 2011-08-25 to 2018-07-17 Latitudinal extent: 4.1830 to 5.0700 Longitudinal extent: 114.0190 to 117.8200", "keywords": ["2. Zero hunger", "Soil carbon cycle", "Soil organic matter", "Flux", "Respiration", "15. Life on land", "Carbon balance", "Autotrophic respiration", "6. Clean water", "SAFE core data", "13. Climate action", "SAFE project", "Heterotropchic respiration", "Litter", "Carbon plot", "Carbon flux", "Productivity"], "contacts": [{"organization": "Riutta, Terhi, Ewers, Robert M, Malhi, Yadvinder, Majalap, Noreen, Khoon, Kho Lip, Mills, Maria,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7307449"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7307449", "name": "item", "description": "10.5281/zenodo.7307449", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7307449"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-11-09T00:00:00Z"}}, {"id": "10.5281/zenodo.7297158", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-03T16:25:35Z", "type": "Dataset", "title": "Measured magnetic susceptibility data for different magnetite tracer stacking scenarios", "description": "Open AccessPeer reviewed", "keywords": ["Magnetic susceptibility", "Soil erosion", "Bartington MS2 system", "15. Life on land"], "contacts": [{"organization": "Zumr, David, Li, Tailin, G\u00f3mez, Jos\u00e9, Guzm\u00e1n, Gema,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7297158"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7297158", "name": "item", "description": "10.5281/zenodo.7297158", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7297158"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-11-07T00:00:00Z"}}, {"id": "10.5281/zenodo.7307470", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:35Z", "type": "Dataset", "title": "Soil biological, chemical and physical parameters and herbage yield in a field experiment with organic and inorganic fertilizers on peat grassland in the Netherlands", "description": "Open AccessTo evaluate the performance of organic and inorganic fertilizers for regeneration of ecosystem services in peat grasslands with biodiversity goals, we carried out a field experiment in the western peat district in the Netherlands. The fertilizers tested represent the current practice and potential alternatives for regenerative grassland management on drained peat. Experimental setup The field experiment (2013 \u2013 2015) was conducted on a permanent grassland on peat soil (Terric Histosol; SOM 56 g 100 g\u22121 and pHKCl of 4.5 in 0-10 cm) at the experimental dairy farm at Zegveld (the Netherlands). In March 2013, a randomized block experiment (six blocks) was laid out with six fertilizer treatments and a control treatment (no fertilizer: \u201cContr\u201d). The fertilizer used were: conventional dairy cattle slurry manure (\u201cSlurry\u201d), mature compost of kitchen and garden waste (\u201cComp\u201d), dairy cattle farmyard manure (\u201cFYM\u201d), solid fraction of the cattle slurry manure (\u201cSFrac\u201d, obtained by pressurized filtration), inorganic N fertilizer (\u201cIF\u201d; calcium ammonium nitrate, 27% N) and a combination of inorganic N fertilizer and sawdust (\u201cIF+SD\u201d). Plot size was 4 \u00d7 10 m; for the Slurry treatment plots were 5.2 \u00d7 10 m. Slurry was applied by slit injection, the other fertilizers were applied by hand. Target application rate was 120 kg total N ha\u22121 yr\u22121, divided in two applications per year (February/March and May). This is relatively low for conventional grasslands but usual for grasslands with biodiversity goals (Kleijn et al., 2004). The amount of Ctotal applied in Comp was taken for the rate of sawdust to be applied. All plots were fertilized with 200 kg K2O ha\u22121 yr\u22121 (applications in March and May) (Commissie Bemesting Grasland en Voedergewassen, 2019). Fertilizer application quantities and organic matter and nutrient inputs are provided in Fertilizer_intput.csv (dataset). The grassland had an history of conventional management with mainly cutting, winter grazing with sheep and a normal fertilization regime with both slurry manure and inorganic fertilizer. The normal cutting and grazing regime was continued in the first two years of the experiment; during 2015, the monitoring year, the plots were not grazed and only cut for herbage measurements. Measurements From April to October 2015, soil and aboveground measurements were carried out. Most soil parameters were measured in October. Earthworms and insect larvae are an important food source for meadow birds during the pre-breeding period in spring (Galbraith, 1989) and were therefore sampled in April. Soil moisture and penetration resistance were measured both in April and October. Soil biological parameters Earthworms and insect larvae were sampled in the top soil layer in two soil cubes (20 \u00d7 20 \u00d7 20 cm) per plot. Earthworms were hand-sorted, counted, weighed and fixed in alcohol prior to identification. Both adults and juveniles were identified to species (Sims and Gerard, 1985; St\u00f6p-Bowitz, 1969) and classified into functional groups (Bouch\u00e9, 1977). Crane flies (Tipulidae; leatherjackets) or click beetles (Elateridae; wireworms) larvae were counted. Phospholipid fatty acids (PLFA) were measured in October. PLFA were extracted from 4 g of fresh soil (Paloj\u00e4rvi, 2006), and analyzed by gas chromatography (Hewlett-Packard, USA). PLFA i15:0, a15:0, 15:0, i16:0, 16:1\u03c99, i17:0, a17:0, cy17:0, 18:1\u03c97 and cy19:0 were chosen to represent bacteria and PLFA 18:2\u03c96 was used as a marker of saprotrophic fungi (Hedlund, 2002). The neutral lipid fatty acid (NLFA) 16:1\u03c95 occurs in storage lipids of arbuscular mycorrhizal fungi (AMF) and was used as marker of AMF (Vestberg et al., 2012). PLFA i15:0, a15:0, i16:0, i17:0 and a17:0 were used as a measure of Gram-positive bacteria, and cy17:0 and cy19:0 for Gram-negative bacteria. PLFA 10Me16:0, 10Me17:0 and 10Me18:0 represented actinomycetes. Soil chemical parameters A soil sample from the 0\u221210 cm layer (c. 50 randomly taken soil cores) per experimental plot was collected in October (auger diameter 2.3 cm; Eijkelkamp grass plot sampler, Giesbeek, the Netherlands), was sieved (1 cm mesh size) and homogenized. One sub-sample was taken for analysis of hot water extractable carbon (HWC) according to Ghani et al. (2003) and one for chemical analysis. Prior to analysis of soil acidity (pHKCl), soil organic matter (SOM), total carbon (Ctotal), total nitrogen (Ntotal), total phosphorus (Ptotal) and ammonium-lactate extractable P (PAL) by Eurofins Agro (Wageningen, the Netherlands), the sub sample was dried at 40\u00b0C. Soil pHKCl was measured according to NEN-ISO 10390 2005. SOM was determined by loss-on-ignition (NEN 5754 2005). Ctotal was measured by incineration at 1150\u00b0C, and determination of the CO2 produced by an infrared detector (LECO Corporation, St. Joseph, Mich., USA). For Ntotal, evolved gasses after incineration were reduced to N2 and measured with a thermal-conductivity detector (LECO Corporation, St. Joseph, Mich., USA). Ptotal was analysed with Fleishmann acid (Houba et al., 1997). PAL is used to assess the P supply capacity of grassland soils (Reijneveld et al., 2014) and was determined according to Egn\u00e9r et al. (1960) (NEN 5793). Soil physical parameters Soil moisture was determined in April and October in a homogenized 0\u221210 cm soil sample after drying at 105\u00b0C for 24 hrs. Moisture content was expressed as percentage of fresh soil weight. Penetration resistance was measured (April and October) with a penetrologger (Eijkelkamp, Giesbeek, the Netherlands; cone of 2.0 cm2 penetration surface and 60\u00b0 apex angle. Penetration resistance was expressed as an average of 7 penetrations per plot and per soil layer of 0\u221210, 10\u221220, and 20\u221230 cm. Soil structure and rooting density were assessed in October in the 0\u221210 cm and 10\u221225 cm layers. The percentage of crumbs, sub-angular blocky elements and angular blocky elements was estimated by one experienced person as described by Peerlkamp (1959) and Shepherd (2000), Root density was estimated by scoring visible roots (score 1\u201310; 1 for no roots and 10 for above average). Water infiltration rate was measured in October at three spots per experimental plot in 5 of the 6 blocks (35 plots). A PVC pipe (15 cm high, 15 cm diameter) was pushed into the soil to a depth of 10 cm. 500 ml water was poured into each pipe and the infiltration time was recorded. If the infiltration time exceeded 15 min, the remaining water volume was estimated to calculate the infiltration rate (mm min\u22121). Grass yield and botanical composition Grass dry matter (DM) and N yield were determined during 2015 with a Haldrup plot harvester (J. Haldrup a/s, L\u00f8gst\u00f8r, Denmark). The four harvest dates were May 15, June 29, August 19 and September 30. Fresh biomass, DM content (70\u00b0C for 24 hrs) and total N content (Kjeldahl) were determined for each harvest. Herbage DM yield (Mg DM ha\u22121) and herbage N yield (kg N ha\u22121) were calculated. Apparent N recovery (ANR; kg N.kg N\u22121) was calculated as (N yield(fertilized) \u2013 N yield(non-fertilized))/(N fertilization rate) (Vellinga and Andr\u00e9, 1999). In June 2015, botanical composition was measured by visually estimating the relative soil cover of the sward and the proportion of each species therein (Sikkema, 1997). Data files Data_soil_grass.csv Content: Dataset with soil biological (earthworms, microbial PLFA), soil chemical, soil physical parameters, herbage dry matter and N yields, and botanical parameters. Column names and units: plot: Experimental plot number (1-42) treatment: Treatment code (see text) block: Block number (1-6) EW_species_number: Earthworm - number of species EW_totalnumber: Earthworm - total number per m2 EW_epigeic: Earthworm - number of epigeic adults and juveniles per m2 EW_endogeic: Earthworm - number of endogeic adults and juveniles per m2 EW_adults: Earthworm - number of adults per m2 EW_juveniles: Earthworm - number of juveniles per m2 EW_adult_epigeic: Earthworm - number of epigeic adults per m2 EW_adult_endogeic: Earthworm - number of endogeic adults per m2 EW_juven_epigeic: Earthworm - number of epigeic juveniles per m2 EW_juven_endogeic: Earthworm - number of endogeic juveniles per m2 EW_L_rubellus: Earthworm - number of L. rubellus adults and juveniles per m2 EW_A_chlorotica: Earthworm - number of A. chlorotica adults and juveniles per m2 EW_A_caliginosa: Earthworm - number of A. caliginosa adults and juveniles per m2 EW_O_lacteum: Earthworm - number of O. lacteum adults and juveniles per m2 EW_A_rosea: Earthworm - number of A. rosea adults and juveniles per m2 EW_O_cyaenum: Earthworm - number of O. cyaneum adults and juveniles per m2 EW_L_castaneus: Earthworm - number of L. castaneus adults and juveniles per m2 EW_D_rubida: Earthworm - number of D. rubida adults and juveniles per m2 EW_adult_L_rubellus: Earthworm - number of L. rubellus adults per m2 EW_adult_A_chlorotica: Earthworm - number of A. chlorotica adults per m2 EW_adult_A_caliginosa: Earthworm - number of A. caliginosa adults per m2 EW_adult_O_lacteum: Earthworm - number of O. lacteum adults per m2 EW_adult_A_rosea: Earthworm - number of A. rosea adults per m2 EW_adult_O_cyaenum: Earthworm - number of O. cyaneum adults per m2 EW_adult_L_castaneus: Earthworm - number of L. castaneus adults per m2 EW_adult_D_rubida: Earthworm - number of D. rubida adults per m2 EW_juven_L_rubellus: Earthworm - number of L. rubellus juveniles per m2 EW_juven_A_chlorotica: Earthworm - number of A. chlorotica juveniles per m2 EW_juven_A_caliginosa: Earthworm - number of A. caliginosa juveniles per m2 EW_non_determined: Earthworm - number of non determined individuals per m2 EW_total_biomass: Earthworm - total fresh biomass per m2 Leatherjackets: number of leatherjackets per m2 Wireworms: number of wireworms per m2 TOTmicrPLFA: total microbial PLFA in nmol.g-1 dry soil bactPLFA: bacterial PLFA in nmol.g-1 dry soil saprofungPLFA: saprotrophic fungal PLFA in nmol.g-1 dry soil Fung_bactPLAF_ratio: ratio of fungal to bacterial PLFA GramPLUSplfa: gram positive PLFA in nmol.g-1 dry soil GramMINplfa: gram negative PLFA in nmol.g-1 dry soil ratioGram_PLUS_MIN: ratio of gram positive to gram negative PLFA AMFsporNLFA: AMF spores NLFA in nmol.g-1 dry soil ActinomPLFA: Actinomycetes PLFA in nmol.g-1 dry soil ShannonPLFA: PLFA shannon diversity index SOM: soil organic matter in g.100 g-1 dry soil Ctotal: total C in g.100 g-1 dry soil HWC: hot water extractable C in \u03bcg.100 g-1 dry soil Ntotal: total N in g.100 g-1 dry soil Ptotal: total P2O5 in mg.100 g-1 dry soil P_AL: total P-AL in mg.100 g-1 dry soil pH_KCl: pH-KCl CN_ratio: C:N ratio C_SOM: C:SOM ratio Soilmoisture_April: soil moisture content in April in g.100g-1 fresh soil Penetrationresistance_April_cm010: penetration resistance in April in 10-20 cm in Newton Penetrationresistance_April_cm1020: penetration resistance in April in 20-30 cm in Newton Penetrationresistance_April_cm2030: penetration resistance in April in 0-10 cm in Newton Soilmoisture_October: soil moisture content in October in g.100g-1 fresh soil Penetrationresistance_October_cm010: penetration resistance in October in 10-20 cm in Newton Penetrationresistance_October_cm1020: penetration resistance in October in 20-30 cm in Newton Penetrationresistance_October_cm2030: penetration resistance in October in 0-10 cm in Newton crumb_struct_cm010: percentage of crumb elements in 0-10 cm round_struct_cm011: percentage of sub-angular elements in 0-10 cm rootdensity_cm010: score (1-10) of root density in 0-10 cm crumb_struct_cm1025: percentage of crumb elements in 10-25 cm round_struct_cm1025: percentage of sub-angular elements in 10-25 cm sharp_struct_cm1025: percentage of angular elements in 10-25 cm rootdensity_cm1025: score (1-10) of root density in 10-25 cm water_infiltration: water infiltration rate in mm per minute DM_yield_year: total herbage dry matter yield in kg.ha-1 per year DM_yield_H1: herbage dry matter yield of harvest 1 in kg.ha-1 DM_yield_H2: herbage dry matter yield of harvest 2 in kg.ha-1 DM_yield_H3: herbage dry matter yield of harvest 3 in kg.ha-1 DM_yield_H4: herbage dry matter yield of harvest 4 in kg.ha-1 N_yield_year: total herbage N yield in kg.ha-1 per year N_yield_H1: herbage N yield of harvest 1 in kg.ha-1 N_yield_H2: herbage N yield of harvest 2 in kg.ha-1 N_yield_H3: herbage N yield of harvest 3 in kg.ha-1 N_yield_H4: herbage N yield of harvest 4 in kg.ha-1 DMperc_yield_year: herbage dry matter content (per year; weighed average over the 4 harvests) in g.100g-1 fresh weight DMperc_yield_H1: herbage dry matter content of harvest 1 in g.100g-1 fresh weight DMperc_yield_H2: herbage dry matter content of harvest 2 in g.100g-1 fresh weight DMperc_yield_H3: herbage dry matter content of harvest 3 in g.100g-1 fresh weight DMperc_yield_H4: herbage dry matter content of harvest 4 in g.100g-1 fresh weight Ncontent_yield_year: herbage N content (per year; weighed average over the 4 harvests) in g.kg-1 dry matter Ncontent_yield_H1: herbage N content of harvest 1 in g.kg-1 dry matter Ncontent_yield_H2: herbage N content of harvest 2 in g.kg-1 dry matter Ncontent_yield_H3: herbage N content of harvest 3 in g.kg-1 dry matter Ncontent_yield_H4: herbage N content of harvest 4 in g.kg-1 dry matter fresh_yield_H1: herbvage fresh yield of harvest 1 in Mg.ha-1 ANR: apparent N recovery in kg N.kg N-1 productive_grasses: cover percentage of L. perenne and P trivialis monocotyledons: cover percentage of monocotyledons dicotyledons: cover percentage of dicotyledons plant_species: number of plant species monocot_species: number of monocotyledon species dicot_species: number of dicotyledon species Lolium_perenne: plant cover % Poa_trivialis: plant cover % Phleum_pratense: plant cover % Elytrigia_repens: plant cover % Poa_annua: plant cover % Agrostis_stolonifera: plant cover % Holcus_lanatus: plant cover % Alopecurus_pratensis: plant cover % Alopecurus_geniculatus: plant cover % Trifolium_repens: plant cover % Taraxacum_officinale: plant cover % Ranunculus_arvensis: plant cover % Rumex_obtusifolius: plant cover % Rumex_crispus: plant cover % Ranunculus_acris: plant cover % Stellaria_media: plant cover % Cardamine_pratensis: plant cover % Bellis_perennis: plant cover % Rumex_acetosa: plant cover % Ranunculus_sceleratus: plant cover % Polygonum_aviculare: plant cover % Capsella_bursa-pastoris: plant cover % Glechoma_hederacea: plant cover % Geranium_molle: plant cover % Fertilizer_input.csv Content: Application quantities of fertilizers and ash, organic matter, C and mineral inputs, and fertilizer C:N ratio. Total N input is the sum of mineral N (Nmin) and organic N (Norg). Average values per hectare and per year over the years 2013\u22122015. Column names and units: Treatment: Treatment code (see text) Fertilizer_fresh: Applied fertilizer in Mg.ha-1 per year (fresh weight) Fertilizer_DM: Applied fertilizer in Mg.ha-1 per year (dry matter weight); for IF+SD this is the sum of 2.72 Mg sawdust + 0.45 Mg N fertilizer Ash: Mineral fraction in kg.ha-1 per year OM: Organic matter in kg.ha-1 per year C: Total C in kg.ha-1 per year Nmin: Mineral N in kg.ha-1 per year Norg: Organic N in kg.ha-1 per year P2O5: kg.ha-1 per year C_N_ratio: C:N ratio", "keywords": ["2. Zero hunger", "herbage production", "manure", "PLFA", "Life Science", "earthworms", "soil quality", "15. Life on land", "regenerative farming", "6. Clean water"], "contacts": [{"organization": "Deru, Joachim, Bloem, Jaap, De Goede, Ron, Brussaard, Lijbert, Van Eekeren, Nick,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7307470"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7307470", "name": "item", "description": "10.5281/zenodo.7307470", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7307470"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.7415163", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:36Z", "type": "Dataset", "title": "SoilCare WP5 database with experimental monitoring data", "description": "Here the database with the experimental data collected in all SoilCare study sites is included. The SoilCare database: schema (empty database) and Report 34 (D5.1) which\u00a0\u00a0reports and explains the database, which the SoilCare project developed and used for storing the monitoring results from the tested cropping systems and/or field agricultural experiments in the 16 Study sites can be accessed in\u00a0 \u00a010.5281/zenodo.5541295.", "keywords": ["2. Zero hunger", "SoilCare", " database", " monitoring", " soil improving cropping systems", " agricultural experiments", "15. Life on land"], "contacts": [{"organization": "Ioanna Panagea, Rudi Hessel, Alaoui, Abdallah, Bachmann, Felicitas, Baer, Roger, Fleskens, Luuk, Tits, Mia, Elsen, Annemie, B\u00f8e, Frederik, Skaalsveen, Kamilla, Stolte, Jannes, Seehusen, Till, Toth, Zoltan, Dunai, Attila, De Notaris, Chiara, Rubaek, Gitte Holton, Dalgaard, Tommy, Bussell, Jenny, Stoate, Chris, Mayer-Gruner, Paula, Hallama, Moritz, Pilz, Stefan, Pekrun, Carola, Kandeler, Ellen, Calciu, Irina, Vizitu, Olga, Sartori, Felice, Piccoli, Ilaria, Berti, Antonio, Fr\u0105c, Magdalena, Lipiec, Jerzy, Usowicz, Boguslaw, Boulet, Anne-Karine, Ferreira, Antonio, Tsanis, Ioannis, Vozinaki, Irini, Alexakis, Dimitris, Sarchani, Sofia, Koutroulis, Aristeidis, B\u00f6rjesson, Gunnar, Bolinder, Martin, K\u00e4tterer, Thomas, Kirchmann, Holger, Kus\u00e1, Helena, Cuevas, Juli\u00e1n, Pinillos, Virginia, Chiamolera, Fernando, del Moral, Fernando, Cant\u00f3n, Yolanda, Aznar, Jose \u00c1ngel, Galdeano, Emilio, Guilhou, Robin, Le Campion, Antonin, Mar\u00e9chal, Goulven, Guido Wyseure,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7415163"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7415163", "name": "item", "description": "10.5281/zenodo.7415163", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7415163"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-12-08T00:00:00Z"}}, {"id": "10.5281/zenodo.7415164", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:36Z", "type": "Dataset", "title": "SoilCare WP5 database with experimental monitoring data", "description": "Here the database with the experimental data collected in all SoilCare study sites is included. The SoilCare database: schema (empty database) and Report 34 (D5.1) which\u00a0\u00a0reports and explains the database, which the SoilCare project developed and used for storing the monitoring results from the tested cropping systems and/or field agricultural experiments in the 16 Study sites can be accessed in\u00a0 \u00a010.5281/zenodo.5541295.", "keywords": ["2. Zero hunger", "SoilCare", " database", " monitoring", " soil improving cropping systems", " agricultural experiments", "15. Life on land"], "contacts": [{"organization": "Ioanna Panagea, Rudi Hessel, Alaoui, Abdallah, Bachmann, Felicitas, Baer, Roger, Fleskens, Luuk, Tits, Mia, Elsen, Annemie, B\u00f8e, Frederik, Skaalsveen, Kamilla, Stolte, Jannes, Seehusen, Till, Toth, Zoltan, Dunai, Attila, De Notaris, Chiara, Rubaek, Gitte Holton, Dalgaard, Tommy, Bussell, Jenny, Stoate, Chris, Mayer-Gruner, Paula, Hallama, Moritz, Pilz, Stefan, Pekrun, Carola, Kandeler, Ellen, Calciu, Irina, Vizitu, Olga, Sartori, Felice, Piccoli, Ilaria, Berti, Antonio, Fr\u0105c, Magdalena, Lipiec, Jerzy, Usowicz, Boguslaw, Boulet, Anne-Karine, Ferreira, Antonio, Tsanis, Ioannis, Vozinaki, Irini, Alexakis, Dimitris, Sarchani, Sofia, Koutroulis, Aristeidis, B\u00f6rjesson, Gunnar, Bolinder, Martin, K\u00e4tterer, Thomas, Kirchmann, Holger, Kus\u00e1, Helena, Cuevas, Juli\u00e1n, Pinillos, Virginia, Chiamolera, Fernando, del Moral, Fernando, Cant\u00f3n, Yolanda, Aznar, Jose \u00c1ngel, Galdeano, Emilio, Guilhou, Robin, Le Campion, Antonin, Mar\u00e9chal, Goulven, Guido Wyseure,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7415164"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7415164", "name": "item", "description": "10.5281/zenodo.7415164", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7415164"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.7457162", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-03T16:25:36Z", "type": "Report", "title": "D4.2. Plan for exploitation and dissemination of the project results", "description": "This document is a deliverable of the Co-UDlabs project, funded under the European Union\u2019s Horizon 2020 research and innovation programme under grant agreement No 101008626. The aim of this document is to provide the first version of the Plan for Dissemination and Exploitation of Results (PEDR), produced at M6 as part of the Work Package 4 on communication, dissemination and exploitation of results. The aim of the PEDR is to provide the Co-UDlabs partners with guidelines on the different communication and dissemination activities that are planned and their schedule, who are the partners responsible for each activity and what tools and channels are available for dissemination. A section on exploitation will define the actions planned to achieve the exploitation of the results and impact of the project. More specifically, in terms of dissemination and communication the PEDR will: Propose a communication and dissemination policy, and define the objectives of the actions; Identify the target audience for each objective or main result; List the communication and dissemination channels to be used for project promotion; Present a schedule of the communication and dissemination actions throughout the project duration; Define and monitor a series of Key Performance Indicators (KPIs) to assess the success of the implementation (e.g. number of publications, size of the audience reached, number of visits on the website, feedback received from audiences at conferences, etc.) and update the plan according to the evolution of the project. In terms of the exploitation of the results, the PEDR will contain the following information, if applicable and when relevant, especially within the final exploitation plan to be submitted at the end of the project: The identification of exploitable main outputs of the project; The identification of the factors influencing exploitation and wide deployment of the project\u2019s results The identification of new and existing measures for the project sustainability. The document is drafted by Euronovia, which is leader of this Work Package, with inputs from all partners. While Euronovia is the leading partner in charge of WP4, all partners have the responsibility to participate in the communication activities and dissemination of the results of the project. According to the grant agreement and unless it goes against their legitimate interests, each beneficiary must, as soon as possible, disseminate its results by disclosing them to the public by appropriate means (other than those resulting from protecting or exploiting the results), including in scientific publications. The PEDR is an evolving document which will be updated at the end of each reporting period (October 2022, April 2024 and April 2025).", "keywords": ["Research Infrastructure", "Co-UDlabs", "Urban Drainage Systems", "12. Responsible consumption"], "contacts": [{"organization": "de Nale, Laura", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7457162"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7457162", "name": "item", "description": "10.5281/zenodo.7457162", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7457162"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-10-26T00:00:00Z"}}, {"id": "10.5281/zenodo.7464243", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-03T16:25:36Z", "type": "Report", "title": "Extracting VV and VH polarization from Sentinel 1 at Soil Moisture measurement points", "description": "The presentation shows the key steps for extracting Sentinel 1 backscattering coefficient in VV (vertical transmit/vertical receive) and VH (vertical transmit/horizontal receive) polarization, in corrispondence with the Soil Moisture ground measurements. The script run in the Google Earth Engine (GEE) platform which offer the oppurtunity to process a huge amount of calibrated, ortho-corrected Sentinel 1 product.", "keywords": ["Sentinel 1", " backscattering coefficient", " VV co-polarization", " VH cross-polarization", "1"], "contacts": [{"organization": "Nino, Pasquale", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7464243"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7464243", "name": "item", "description": "10.5281/zenodo.7464243", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7464243"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.7464242", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-03T16:25:36Z", "type": "Report", "title": "Extracting VV and VH polarization from Sentinel 1 at Soil Moisture measurement points", "description": "The presentation shows the key steps for extracting Sentinel 1 backscattering coefficient in VV (vertical transmit/vertical receive) and VH (vertical transmit/horizontal receive) polarization, in corrispondence with the Soil Moisture ground measurements. The script run in the Google Earth Engine (GEE) platform which offer the oppurtunity to process a huge amount of calibrated, ortho-corrected Sentinel 1 product.", "keywords": ["Sentinel 1", " backscattering coefficient", " VV co-polarization", " VH cross-polarization", "1"], "contacts": [{"organization": "Nino, Pasquale", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7464242"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7464242", "name": "item", "description": "10.5281/zenodo.7464242", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7464242"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.7464300", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-03T16:25:36Z", "type": "Software", "title": "Extract_S1_VV_VV", "description": "Source script to run in Google Earth Engine platform.", "keywords": ["Sentinel 1", " backscattering coefficient", " VV co-polarization", " VH cross-polarization", " Soil Moisture"], "contacts": [{"organization": "Pasquale Nino", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7464300"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7464300", "name": "item", "description": "10.5281/zenodo.7464300", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7464300"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-12-20T00:00:00Z"}}, {"id": "10.5281/zenodo.7464481", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-03T16:25:36Z", "type": "Software", "title": "Extract_S1_VV_VH_GEE", "description": "Correct name of the GEE script", "keywords": ["Sentinel 1", " backscattering coefficient", " VV co-polarization", " VH cross-polarization", " Soil Moisture"], "contacts": [{"organization": "Pasquale Nino", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7464481"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7464481", "name": "item", "description": "10.5281/zenodo.7464481", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7464481"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-12-20T00:00:00Z"}}, {"id": "10.5281/zenodo.7625435", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:37Z", "type": "Dataset", "title": "Rates of greenhouse gas (carbon dioxide, methane and nitrous oxide) fluxes, denitrification-derived N2O and N2 fluxes and nitrification-derived N2O fluxes from salt marsh soils in Quebec, Canada and Louisiana, U.S. under ambient and elevated temperature and nutrient loading.", "description": "Dataset used in\u00a0Elevated temperature and nutrients lead to increased N2O emissions from salt marsh soils from cold and warm climates. The dataset contains fluxes calculated from headspace gas samples taken over a 24 hour period from intact soil cores, as well as corresponding environmental data. Intact soil cores (0-15 cm depth, 2.5 cm diameter) were taken at five sampling locations along a 20 m transect using a soil auger or piston corer. Samples were collected along a transect in four marsh sites in Quebec, Canada (La Pocati\u00e8re: 47\u00b022'24.7'N 70\u00b003'26.3'W) and Louisiana, U.S. (Barataria Basin: 29\u00b033'47.3'N 90\u00b004'22.8'W and 29\u00b029'52.2'N 89\u00b055'00.2'W) from two vegetation types (Sporobolus alterniflorus formerly known as Spartina alterniflora and Sporobolus pumilus formerly known as Spartina patens). In Quebec, the two vegetation zones were in the same marsh whereas in Louisiana two separate marshes, dominated by the relevant vegetation, were chosen. Soil samples were collected on the 20-21st July 2021 from Louisiana and the 9-10th August 2021 from Quebec. Environmental data was collected including in-situ soil temperature and salinity, and gravimetric soil moisture, extractable soil dissolved organic carbon (DOC), extractable soil total dissolved nitrogen (TDN), extractable soil nitrate, extractable soil ammonium, extractable soil soluble reactive phosphate, soil total carbon, soil total nitrogen, soil carbon to nitrogen ratio, soil d13C and soil d15N determined from additional 0-15 cm core samples. This project has received funding from the European Union\u2019s Horizon 2020 Research and Innovation Programme under Grant Agreement no. 838296, a NSERC Discovery Grant and a Natural Environment Research Council grant number (NE/T012323/1). Stable 15N tracers were added to the intact soil cores so that at each location, at each treatment level (ambient and elevated, described below), there was one core receiving no tracer for greenhouse gas fluxes, one core receiving 15N-NO3\u2011 for denitrification rates and one core receiving 15N-NH4+ for nitrification rates. The cores were incubated at ambient temperature (16 \u2103 and 28.1 \u2103 for Quebec and Louisiana, respectively) and nutrient concentrations (3.2 NO3-, 2.0 NH4+; 2.9 NO3-, 2.5 NH4+; 0.5 NO3-, 7.3 NH4+ and 5.7 NO3-, 2.8 NH4+ mg g wet soil-1 for Quebec S. alterniflorus, Quebec S. pumilus, Louisiana S. alterniflorus and Louisiana S. pumilus, respectively), and elevated temperature (ambient temperature +5 \u2103) and nutrient concentration (double ambient concentration). Gas samples were collected from the headspace of 0-15 cm intact cores in a 20 cm high PVC pipe, capped at the top and bottom to create a 5 cm headspace. Gas samples were analysed for greenhouse gases (GHGs: N2O, CH4, CO2) and 15N in denitrification-derived N2O, denitrification-derived N2 and nitrification-derived N\u00ad2O. Soil temperature (YSI 30, Baton Rouge, USA or DeltaTrak 11050, Pleasanton, USA) and porewater salinity (YSI 30, Baton Rouge, USA or portable ATC refractometer) were measured in-situ or in the laboratory using the portable refactometer.\u00a0Additional soil samples were used for multiple analyses; one subsample was extracted with ultrapure water (18.2 M\u03a9) for DOC and TDN analysis, one subsample was extracted with 2M KCl for NO3- and NH4+, one subsample was extracted with Olsen-P solution (0.5 M NaHCO3, pH 8.5), for soluble reactive phosphate analysis and one subsample was weighed and dried for soil moisture and then finely ground and analysed for total carbon, total nitrogen, d13C and d15N. N2O, CH4 and CO2 concentrations were measured in the gas samples using a gas chromatograph interfaced with a PAL3 autosampler\u00a0(Agilent 7890A, Agilent Technologies Ltd, USA) fitted with a flame ionisation detector (FID) for CH4 analysis and a micro electron capture detector (mECD) for N2O analysis. CO2 was methanised to CH4 before analysis on the FID. The instrument precision as the relative standard deviation was < 5 % for all of the gases, while the minimum detectable concentration difference (MDCD) was 9 ppb N2O, 72 ppb CH4 and 31 ppm CO2. Potential GHG fluxes were calculated from the linear portion or where the highest production was observed in the concentration-time series ( https://doi.org/10.2134/jeq2003.2436). If fluxes were below the MDCD they were set to zero see\u00a0(https://doi.org/10.1002/2017JG003783). The 15N content of the N2 and N2O was determined using a continuous flow isotope ratio mass spectrometer (Elementar Isoprime PrecisION; Elementar Analysensysteme GmbH, Hanau, Germany) coupled with a trace-gas pre-concentrator inlet with autosampler (isoFLOW GHG; Elementar Analysensysteme GmbH, Hanau, Germany), with a standard deviation of d15N < 0.05 %. Extractable dissolved organic carbon and total dissolved nitrogen were analysed in soil extractant (ultrapure water 18.2 M\u03a9, 7:1 of extractant to soil) on a TOC/TDN analyser (TOC VCSn +\u00a0TMN-1, Shimadzu, Kyoto, Japan), with 50 mg C l-1 and 10 mg l-1 standards resulting in accuracy and precision of 0.3 and \u00b10.3 mg C l-1, and 0.5 and \u00b10.3 mg N l-1, respectively. Extractable nitrate+nitrite (assumed to be nitrate) and ammonium were analysed in soil extractant (2M KCl, 5:1 of extractant to soil) using a microplate reader and methods in Sims et al., 1995 (https://doi.org/10.1080/00103629509369298) with a limit of detection of 0.1 ppm and accuracy of \u00b15 %. Extractable phosphate was analysed in soil extractant (Olsen-P solution 0.5M NaHCO\u00ad3, pH 8.5, 10:1 of extractant to dry soil) using a microplate reader and methods in Jeannotte et al., 2004 (https://doi.org/10.1007/s00374-004-0760-4) with a limit of detection of 1 mg P l-1 and accuracy of \u00b16 %. Soil total carbon, total nitrogen, d13C and d15N analysis was performed using a continuous flow isotope ratio mass spectrometer (Elementar Isoprime PrecisION; Elementar Analysensysteme GmbH, Hanau, Germany) coupled with an elemental analyser (EA) inlet (vario PYRO cube; Elementar Analysensysteme GmbH, Hanau, Germany). The precision was < 5 % for both C and N and the precision as a standard deviation was < 0.06 % for both d13C and d15N. Results from the experiments were entered into an Excel spreadsheet for ingestion into the Zenodo data repository.", "keywords": ["2. Zero hunger", "Salt marsh", "Canada", "Saltmarsh", "Nitrous oxide", "Spartina patens", "Temperature", "Sporobolus pumilus", "Nutrient loading", "Sporobolus alterniflorus", "15. Life on land", "Greenhouse gas", "Nitrification", "6. Clean water", "United States", "12. Responsible consumption", "Carbon dioxide", "13. Climate action", "Denitrification", "Spartina alterniflora", "Methane", "Global change", "Nitrogen loading"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7625435"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7625435", "name": "item", "description": "10.5281/zenodo.7625435", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7625435"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-02-09T00:00:00Z"}}, {"id": "10.5281/zenodo.7657746", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:37Z", "type": "Journal Article", "created": "2023-02-16", "title": "Does population density influence fluctuating asymmetry of Sitophilus oryzae laboratory populations?", "description": "RestrictedThe rice weevil, Sitophilus oryzae, is one of the most pernicious pests of stored grain. It is a primary pest and causes a reduction in weight, quality, seed viability and commercial value of various cereals. For this study, we reared S. oryzae on wheat grains under two different adult densities, low and high, with an aim to assess the influence of population density on fluctuating asymmetry of the adult\u2019s ventral body. Fluctuating asymmetry represents slight and random deviations from bilateral symmetry normally distributed around a 0 mean, and its level is usually higher under a disturbed developmental process. Accordingly, we expected that environmental stress caused by higher density would increase its level. Opposite to our hypothesis, the study showed that population density did not influence fluctuating asymmetry of S. oryzae adults. Both experimental populations exhibited a similar, non-significant level of fluctuating asymmetry.", "keywords": ["2. Zero hunger", "0106 biological sciences", "0301 basic medicine", "abundance", "rice weevil", "03 medical and health sciences", "wheat", "fluctuating asymmetry", "Fluctuating asymmetry", "Abundance", " Rice weevil", " Wheat", "01 natural sciences"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7657746"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Stored%20Products%20Research", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7657746", "name": "item", "description": "10.5281/zenodo.7657746", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7657746"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-03-01T00:00:00Z"}}, {"id": "10.5281/zenodo.7907114", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:40Z", "type": "Report", "title": "SWAP Field-scale modelling protocol", "description": "Open AccessThe H2020 OPTAIN project involves both, catchment-, and field-scale modelling of the transport of water and nutrients. The catchment-scale modelling is performed at fourteen case study catchments across Europe using the SWAT+ model. At seven OPTAIN case studies, field-scale modelling is applied using the SWAP model. The aim of the SWAP modelling is to provide data on soil water balance elements using a more detailed (at field-scale) soil hydrological model and to cross-validate this data with the relevant fields in SWAT+. As the official manual from the SWAP model developers is rather detailed and complex, the OPTAIN SWAP modelling protocol focuses on practical issues, without overwhelming the modellers with information unnecessary for their case-studies. It also describes new tools, such as rswap, developed within the OPTAIN project for reference data quality check, model calibration and visualisation of the model results.", "keywords": ["13. Climate action", "rswap: https://moritzshore.github.io/rswap/", "soil hydrology", " SWAP model", " water balance", "15. Life on land"], "contacts": [{"organization": "Csilla Farkas, Moritz Shore, G\u00f6khan C\u00fcceloglu, Levente Czelnai, Attila Nemes, Brigitta Szab\u00f3, Natalja \u010cerkasova, Rasa Idzelyt\u00e9, Sinja Weiland,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7907114"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7907114", "name": "item", "description": "10.5281/zenodo.7907114", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7907114"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-05-08T00:00:00Z"}}, {"id": "10.5281/zenodo.7907115", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:40Z", "type": "Report", "title": "SWAP Field-scale modelling protocol", "description": "Open AccessThe H2020 OPTAIN project involves both, catchment-, and field-scale modelling of the transport of water and nutrients. The catchment-scale modelling is performed at fourteen case study catchments across Europe using the SWAT+ model. At seven OPTAIN case studies, field-scale modelling is applied using the SWAP model. The aim of the SWAP modelling is to provide data on soil water balance elements using a more detailed (at field-scale) soil hydrological model and to cross-validate this data with the relevant fields in SWAT+. As the official manual from the SWAP model developers is rather detailed and complex, the OPTAIN SWAP modelling protocol focuses on practical issues, without overwhelming the modellers with information unnecessary for their case-studies. It also describes new tools, such as rswap, developed within the OPTAIN project for reference data quality check, model calibration and visualisation of the model results.", "keywords": ["13. Climate action", "rswap: https://moritzshore.github.io/rswap/", "soil hydrology", " SWAP model", " water balance", "15. Life on land"], "contacts": [{"organization": "Farkas, Csilla, Shore, Moritz, C\u00fcceloglu, G\u00f6khan, Czelnai, Levente, Nemes, Attila, Szab\u00f3, Brigitta, \u010cerkasova, Natalja, Idzelyt\u00e9, Rasa, Weiland, Sinja,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7907115"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7907115", "name": "item", "description": "10.5281/zenodo.7907115", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7907115"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-05-08T00:00:00Z"}}, {"id": "10.5281/zenodo.7956363", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-03T16:25:40Z", "type": "Other", "title": "EJP SOIL project, WP6 - Questionnaire for supporting harmonised soil information and reporting", "description": "Open AccessThis stocktaking activity aims at collecting metadata information on the georeferenced soil data available in the EJP-SOIL countries. This stocktaking concerns not just the soil data itself, but also auxiliary information needed for the soil mapping activity, and the mapping experience hold in our institutions. Available doesn\u2019t mean that this information is freely available, but just that it exists, with a specific data owner (which can also be different from your institution) and a specific sharing policy. The first sheet, named \u201cdescription of data sources\u201d, is to insert the list of your data sources. We have put some Italian examples to help you understanding the kind of information to be inserted in this sheet (you can delete them). Basically, it is a list of data sources available, either for basic soil data (point data or mapped data), and for auxiliary data. Among auxiliary data we are also looking for mapped data on soil management. Once the first sheet is compiled, the listed data sources will constitute a drop-down list to be used in the compilation of the following sheets. The second sheet, named \u201csoil property_data (SP)\u201d, is for the compilation of the soil property data available in your basic soil data sources. It is most probable that more the one data source exists in your country, storing soil data properties. Each one of these soil data sources should have been described in the first sheet. Then, the soil properties store in each soil data source should be inserted in the second sheet. For each soil property it is requested to indicate the unit of measure used and the analytical method(s) used (can be more then one). In order to help you in the compilation, we have listed, in the third 'methods' sheet, the most commonly used analytical methods, but you can add more methods if you adopt different ones. If the data source list is a soil map already published, we are asking you to compile the method used for mapping. In the fourth sheet, named \u201csoil management (MG)\u201d, you can list the kind of soil management practices which are available in you data sources. We must stress here, that the data sources for soil management that we are looking for, are georeferenced data sources. The last 2 sheets are the drop-down lists used in the questionnaire and a description of the terms used.", "keywords": ["2. Zero hunger", "15. Life on land", "soil property dataset", " metadatabase"], "contacts": [{"organization": "Fantappie, Maria, Bispo, Antonio, Wetterlind, Johanna, Smreczak, Bozena, van Egmond, Fenny, Bakacsi, Zs\u00f3fia, Farkas-Iv\u00e1nyi, Kinga, Moln\u00e1r, S\u00e1ndor,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7956363"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7956363", "name": "item", "description": "10.5281/zenodo.7956363", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7956363"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-05-22T00:00:00Z"}}, {"id": "10.5281/zenodo.7956364", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-03T16:25:40Z", "type": "Other", "title": "EJP SOIL project, WP6 - Questionnaire for supporting harmonised soil information and reporting", "description": "Open AccessThis stocktaking activity aims at collecting metadata information on the georeferenced soil data available in the EJP-SOIL countries. This stocktaking concerns not just the soil data itself, but also auxiliary information needed for the soil mapping activity, and the mapping experience hold in our institutions. Available doesn\u2019t mean that this information is freely available, but just that it exists, with a specific data owner (which can also be different from your institution) and a specific sharing policy. The first sheet, named \u201cdescription of data sources\u201d, is to insert the list of your data sources. We have put some Italian examples to help you understanding the kind of information to be inserted in this sheet (you can delete them). Basically, it is a list of data sources available, either for basic soil data (point data or mapped data), and for auxiliary data. Among auxiliary data we are also looking for mapped data on soil management. Once the first sheet is compiled, the listed data sources will constitute a drop-down list to be used in the compilation of the following sheets. The second sheet, named \u201csoil property_data (SP)\u201d, is for the compilation of the soil property data available in your basic soil data sources. It is most probable that more the one data source exists in your country, storing soil data properties. Each one of these soil data sources should have been described in the first sheet. Then, the soil properties store in each soil data source should be inserted in the second sheet. For each soil property it is requested to indicate the unit of measure used and the analytical method(s) used (can be more then one). In order to help you in the compilation, we have listed, in the third 'methods' sheet, the most commonly used analytical methods, but you can add more methods if you adopt different ones. If the data source list is a soil map already published, we are asking you to compile the method used for mapping. In the fourth sheet, named \u201csoil management (MG)\u201d, you can list the kind of soil management practices which are available in you data sources. We must stress here, that the data sources for soil management that we are looking for, are georeferenced data sources. The last 2 sheets are the drop-down lists used in the questionnaire and a description of the terms used.", "keywords": ["2. Zero hunger", "15. Life on land", "soil property dataset", " metadatabase"], "contacts": [{"organization": "Fantappie, Maria, Bispo, Antonio, Wetterlind, Johanna, Smreczak, Bozena, van Egmond, Fenny, Bakacsi, Zs\u00f3fia, Farkas-Iv\u00e1nyi, Kinga, Moln\u00e1r, S\u00e1ndor,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7956364"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7956364", "name": "item", "description": "10.5281/zenodo.7956364", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7956364"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-05-22T00:00:00Z"}}, {"id": "10.5281/zenodo.8057232", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:41Z", "type": "Dataset", "title": "Upscaling soil organic carbon measurements at the continental scale using multivariate clustering analysis and machine learning", "description": "Data Description: To improve SOC estimation in the United States, we upscaled site-based SOC measurements to the continental scale using multivariate geographic clustering (MGC) approach coupled with machine learning models. First, we used the MGC approach to segment the United States at 30 arc second resolution based on principal component information from environmental covariates (gNATSGO soil properties, WorldClim bioclimatic variables, MODIS biological variables, and physiographic variables) to 20 SOC regions. We then trained separate random forest model ensembles for each of the SOC regions identified using environmental covariates and soil profile measurements from the International Soil Carbon Network (ISCN) and an Alaska soil profile data. We estimated United States SOC for 0-30 cm and 0-100 cm depths were 52.6 + 3.2 and 108.3 + 8.2 Pg C, respectively. Files in collection (32): Collection contains 22 soil properties geospatial rasters, 4 soil SOC geospatial rasters, 2 ISCN site SOC observations csv files, and 4 R scripts gNATSGO TIF files: \u251c\u2500\u2500 available_water_storage_30arc_30cm_us.tif [30 cm depth soil available water storage]
\u251c\u2500\u2500 available_water_storage_30arc_100cm_us.tif [100 cm depth soil available water storage]
\u251c\u2500\u2500 caco3_30arc_30cm_us.tif [30 cm depth soil CaCO3 content]
\u251c\u2500\u2500 caco3_30arc_100cm_us.tif [100 cm depth soil CaCO3 content]
\u251c\u2500\u2500 cec_30arc_30cm_us.tif [30 cm depth soil cation exchange capacity]
\u251c\u2500\u2500 cec_30arc_100cm_us.tif [100 cm depth soil cation exchange capacity]
\u251c\u2500\u2500 clay_30arc_30cm_us.tif [30 cm depth soil clay content]
\u251c\u2500\u2500 clay_30arc_100cm_us.tif [100 cm depth soil clay content]
\u251c\u2500\u2500 depthWT_30arc_us.tif [depth to water table]
\u251c\u2500\u2500 kfactor_30arc_30cm_us.tif [30 cm depth soil erosion factor]
\u251c\u2500\u2500 kfactor_30arc_100cm_us.tif [100 cm depth soil erosion factor]
\u251c\u2500\u2500 ph_30arc_100cm_us.tif [100 cm depth soil pH]
\u251c\u2500\u2500 ph_30arc_100cm_us.tif [30 cm depth soil pH]
\u251c\u2500\u2500 pondingFre_30arc_us.tif [ponding frequency]
\u251c\u2500\u2500 sand_30arc_30cm_us.tif [30 cm depth soil sand content]
\u251c\u2500\u2500 sand_30arc_100cm_us.tif [100 cm depth soil sand content]
\u251c\u2500\u2500 silt_30arc_30cm_us.tif [30 cm depth soil silt content]
\u251c\u2500\u2500 silt_30arc_100cm_us.tif [100 cm depth soil silt content]
\u251c\u2500\u2500 water_content_30arc_30cm_us.tif [30 cm depth soil water content]
\u2514\u2500\u2500 water_content_30arc_100cm_us.tif [100 cm depth soil water content] SOC TIF files: \u251c\u2500\u250030cm SOC mean.tif [30 cm depth soil SOC]
\u251c\u2500\u2500100cm SOC mean.tif [100 cm depth soil SOC]
\u251c\u2500\u250030cm SOC CV.tif [30 cm depth soil SOC coefficient of variation]
\u2514\u2500\u2500100cm SOC CV.tif [100 cm depth soil SOC coefficient of variation] site observations csv files: ISCN_rmNRCS_addNCSS_30cm.csv 30cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data ISCN_rmNRCS_addNCSS_100cm.csv 100cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data
Data format: Geospatial files are provided in Geotiff format in Lat/Lon WGS84 EPSG: 4326 projection at 30 arc second resolution. Geospatial projection: 
GEOGCS['GCS_WGS_1984', DATUM['D_WGS_1984', SPHEROID['WGS_1984',6378137,298.257223563]], PRIMEM['Greenwich',0], UNIT['Degree',0.017453292519943295]] (base) [jbk@theseus ltar_regionalization]$ g.proj -w GEOGCS['wgs84', DATUM['WGS_1984', SPHEROID['WGS_1984',6378137,298.257223563]], PRIMEM['Greenwich',0], UNIT['degree',0.0174532925199433]]
", "keywords": ["gNATSGO", "the United States SOC", "US soil properties", "15. Life on land", "Gridded National Soil Survey Geographic Database", "International Soil Carbon Network (ISCN)"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8057232"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8057232", "name": "item", "description": "10.5281/zenodo.8057232", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8057232"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-01-25T00:00:00Z"}}, {"id": "10.5281/zenodo.8084805", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-03T16:25:41Z", "type": "Journal Article", "created": "2021-09-06", "title": "Shelterbelts Planted on Cultivated Fields Are Not Solutions for the Recovery of Former Forest-Related Herbaceous Vegetation", "description": "

Establishing shelterbelts for field protection is one of the rediscovered agroforestry practices in Europe and Hungary. Several studies have focused on the effects of these plantations on agricultural production. Prior scholarship reveals that shelterbelts enhance the diversity of bird and insect communities but generally fail to consider herbaceous cover. Our study aimed to describe the herbaceous vegetation in shelterbelts of different origins, tree species composition, and land management. We investigated surveys in four agricultural landscapes of North West Hungary, where the intensity of the landscape transformation is different. The diversity and species composition of the herbaceous vegetation were analyzed, including plant sociology and forest affinity. Our results highlight the importance of landscape history in herbaceous flora. Shelterbelts planted on cultivated without an immediate connection to former woody vegetation soil are not appropriate for the appearance of forest-related herbaceous species, regardless of tree species composition or the extent of the shelterbelt. On the contrary, the remnants of former woody vegetation are refuges for those herbaceous species that are very slow at colonizing new plantations. These findings expose that protecting existing woody areas is an essential task of agricultural land management.

", "keywords": ["0106 biological sciences", "2. Zero hunger", "S", "shelterbelt; herbaceous flora; diversity; plant sociology", "herbaceous flora", "Agriculture", "plant sociology", "15. Life on land", "01 natural sciences", "shelterbelt", "diversity"]}, "links": [{"href": "http://www.mdpi.com/2073-445X/10/9/930/pdf"}, {"href": "https://www.mdpi.com/2073-445X/10/9/930/pdf"}, {"href": "https://doi.org/10.5281/zenodo.8084805"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Land", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8084805", "name": "item", "description": "10.5281/zenodo.8084805", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8084805"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-09-03T00:00:00Z"}}, {"id": "10.5281/zenodo.8091218", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:43Z", "type": "Journal Article", "created": "2021-04-15", "title": "Wheat-root associated prokaryotic community: interplay between plant selection and location", "description": "Background Root-associated microbiomes are important for plant nutrient uptake, disease suppression and plant growth. It is important to reveal wheat-root associated microbial community assembly and dominant drivers determining their variability. Methods Using 16S rRNA gene profiling, we investigated the effects of sample type, location, growth stage and variety on prokaryotic communities in the root endosphere and rhizosphere of wheat and bulk soil based on the field samples including 5 varieties from 4 locations along similar latitude with the distance about 157 to 800 km apart between any two locations. Results Prokaryotic communities were more diverse in the bulk soil and rhizosphere than in root endosphere. Wheat-root associated prokaryotic community assembly was shaped predominantly by sample type, while within each sample type, location had stronger effects on the variation in prokaryotic community than growth stage or variety. Wheat variety effects varied substantially among different locations and growth stages in root endosphere and rhizosphere samples, and the variety effects were location-specific and growth stage-specific. Root endosphere specially enriched Pseudomonas, relative to other two sample types, while rhizosphere mainly enriched Bacillus. Conclusions This study characterized prokaryotic communities of wheat-root endosphere and rhizosphere and their relationships, and demonstrated significant interactive effects between wheat variety, location and growth stage on prokaryotic community assembly in field condition.", "keywords": ["2. Zero hunger", "Triticum aestivum L", "0301 basic medicine", "0303 health sciences", "03 medical and health sciences", "Key drivers", "Prokaryotic community", "Rhizosphere", "Endosphere", "15. Life on land"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8091218"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Plant%20and%20Soil", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8091218", "name": "item", "description": "10.5281/zenodo.8091218", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8091218"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-04-15T00:00:00Z"}}, {"id": "10.5281/zenodo.8090708", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:42Z", "type": "Journal Article", "created": "2021-04-13", "title": "In situ determination of guard cell ion flux underpins the mechanism of ABA-mediated stomatal closure in barley plants exposed to PEG-induced drought stress", "description": "ABA regulates stomatal movement by affecting ion transport in guard cells; yet in situ measurement of ABAmediated dynamics of guard cell ion transport and the involvement of other phytohormones in regulating stomatal aperture under drought stress are still lacking. In this study, hydroponically grown plants of wild type barley Steptoe (WT) and its correspondent ABA-deficient barley mutant Az34 were treated with 10% polyethylene glycol (PEG) 6000 for 0, 2, 4, and 24 h or 9 d to mimic short- and long-term drought stress. The K+, H+ and Ca2+ fluxes in the guard cell were monitored in situ by noninvasive micro-test technology. Upon 10% PEG treatment, leaf ABA concentration ([ABA]leaf) of both barley genotypes increased dramatically after 2 h and reached the highest level after the 24 h. Compared to the control, a significant increase in Ca2+ influx in both genotypes was observed after 2 h exposure to PEG, and reached the largest value after 4 h in WT. The increase of [ABA]leaf coincided with the increase of K+ efflux and Ca2+ influx and the decrease of stomatal conductance in WT under short-term drought stress, though the concentrations of IAA, GA3 and ZR in WT were all increased at 4 h. K+ efflux of guard cells was significantly greater in WT than in Az34 at 24 h after PEG treatment. The results elucidate the role of ABA in mediating ion transports in guard cells, hereby regulating the stomatal movement in barley exposed to drought stress.", "keywords": ["2. Zero hunger", "Ion fluxes", "0106 biological sciences", "0301 basic medicine", "Drought stress", "03 medical and health sciences", "Mesophyll cell", "Phytohormone", "Noninvasive micro-test technology", "Guard cell", "15. Life on land", "01 natural sciences", "6. Clean water"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8090708"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20and%20Experimental%20Botany", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8090708", "name": "item", "description": "10.5281/zenodo.8090708", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8090708"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-07-01T00:00:00Z"}}, {"id": "10.5281/zenodo.8091249", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:43Z", "type": "Journal Article", "created": "2021-07-13", "title": "Aboveground Biomass Mapping of Crops Supported by Improved CASA Model and Sentinel-2 Multispectral Imagery", "description": "

The net primary productivity (NPP) and aboveground biomass mapping of crops based on remote sensing technology are not only conducive to understanding the growth and development of crops but can also be used to monitor timely agricultural information, thereby providing effective decision making for agricultural production management. To solve the saturation problem of the NDVI in the aboveground biomass mapping of crops, the original CASA model was improved using narrow-band red-edge information, which is sensitive to vegetation chlorophyll variation, and the fraction of photosynthetically active radiation (FPAR), NPP, and aboveground biomass of winter wheat and maize were mapped in the main growing seasons. Moreover, in this study, we deeply analyzed the seasonal change trends of crops\u2019 biophysical parameters in terms of the NDVI, FPAR, actual light use efficiency (LUE), and their influence on aboveground biomass. Finally, to analyze the uncertainty of the aboveground biomass mapping of crops, we further discussed the inversion differences of FPAR with different vegetation indices. The results demonstrated that the inversion accuracies of the FPAR of the red-edge normalized vegetation index (NDVIred-edge) and red-edge simple ratio vegetation index (SRred-edge) were higher than those of the original CASA model. Compared with the reference data, the accuracy of aboveground biomass estimated by the improved CASA model was 0.73 and 0.70, respectively, which was 0.21 and 0.13 higher than that of the original CASA model. In addition, the analysis of the FPAR inversions of different vegetation indices showed that the inversion accuracies of the red-edge vegetation indices NDVIred-edge and SRred-edge were higher than those of the other vegetation indices, which confirmed that the vegetation indices involving red-edge information can more effectively retrieve FPAR and aboveground biomass of crops.

", "keywords": ["2. Zero hunger", "NPP", "seasonal variation", "improved CASA", "biomass", "Science", "Q", "0401 agriculture", " forestry", " and fisheries", "improved CASA; red-edge band; NPP; biomass; seasonal variation", "04 agricultural and veterinary sciences", "15. Life on land", "red-edge band", "12. Responsible consumption"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/14/2755/pdf"}, {"href": "https://doi.org/10.5281/zenodo.8091249"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8091249", "name": "item", "description": "10.5281/zenodo.8091249", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8091249"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-07-13T00:00:00Z"}}, {"id": "10.5281/zenodo.8091934", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:43Z", "type": "Journal Article", "created": "2022-08-18", "title": "Data mining of urban soil spectral library for estimating organic carbon", "description": "Accurate quantification of urban soil organic carbon (SOC) is essential for understanding anthropogenic changes and further guiding effective city managements. Visible and near infrared (vis\u2013NIR) spectroscopy can monitor the SOC content in a time- and cost-effective manner. However, processes and mechanisms dominating the relationships between SOC and spectral data in urban soils remain unknown. The main objective of this paper was to evaluate whether multiple stratification strategies (i.e., based on land-use/land-cover [LULC], pH, and spectral clustering) resulted in better predicted performance for SOC compared to the non-stratified (global) models. Results showed that regarding the non-stratified models, the convolutional neural network (CNN) model exhibited the best performance (validation R2 = 0.73), followed by Cubist (validation R2 = 0.66) and memorybased learning (validation R2 = 0.65). After LULC stratification, Cubist model achieved the best prediction (validation R2 = 0.76), improving the value of ratio of performance to interquartile distance by 0.11 compared to the global CNN model. Areas with high SOC values were mainly located in the city center. Stratification by LULC class is a promising strategy for addressing the impact of the soil-landscape diversity and complexity on vis\u2013NIR spectral estimation of SOC in urban soil spectral library.", "keywords": ["Urban soil", "Stratified modeling", "13. Climate action", "Soil organic carbon", "11. Sustainability", "0401 agriculture", " forestry", " and fisheries", "Deep learning", "04 agricultural and veterinary sciences", "15. Life on land", "Soil spectral library"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8091934"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoderma", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8091934", "name": "item", "description": "10.5281/zenodo.8091934", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8091934"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-11-01T00:00:00Z"}}, {"id": "10.5281/zenodo.8092653", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:43Z", "type": "Journal Article", "created": "2021-11-26", "title": "Drought priming alleviated salinity stress and improved water use efficiency of wheat plants", "description": "Global warming and salinization are inducing adverse efects on crop yield. Drought priming has been proved to improve drought tolerance of plants at later growth stages, however, whether and how drought priming at early growth stage alleviating salinity stress at later growth stage and improving water use efciency (WUE) of plants remains unknown. Therefore, two wheat cultivars were subjected to drought priming at the 4th and 6th leaf stage and subsequent moderate salinity stress at 100 mmol NaCl applied at the later jointing growth stage. The growth, physiological responses, ABA signaling and WUE were investigated to unravel the regulating mechanisms of drought priming on subsequent salinity stress. The results showed that drought priming imposed at the early growth stage improved the leaf and root water potential while attenuated the ABA concentration in the leaves ([ABA]leaf) for the primed plants, which increased the stomatal conductance (gs) and photosynthesis (Pn). Consequently, the biomass under the salinity stress was signifcantly increased due to earlier drought priming. Moreover, drought priming improved the specifc leaf N content due to the facilitated root growth and morphology, and this could beneft high leaf photosynthetic capacity during the salinity stress period, improving the Pn and water uptake for the primed plants. Drought priming signifcantly improved plant level WUE (WUEp) due to considerably enhanced dry biomass compared with non-primed plants under subsequent salinity stress. The signifcantly increased leaf \u03b413C under drought priming further demonstrated that the improved leaf \u03b413C and WUEp was mainly ascribed to the improvement of Pn. Drought primed plants signifcantly improved K+ concentration and maintained the K+/Na+ ratio compared with non-primed plants under subsequent salinity stress, which could mitigate the adverse efects of excess Na+ and minimize salt-induced ionic toxicity by improving salt tolerance for primed plants. Therefore, drought priming at early growth stage could be considered as a promising strategy for salt-prone areas to optimize agricultural sustainability and food security under changing climatic conditions.", "keywords": ["Triticum aestivum L", "2. Zero hunger", "0106 biological sciences", "0301 basic medicine", "Water stress", "15. Life on land", "01 natural sciences", "Salinity tolerance", "Hormones", "6. Clean water", "03 medical and health sciences", "ABA", "13. Climate action", "\u03b413C"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8092653"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Plant%20Growth%20Regulation", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8092653", "name": "item", "description": "10.5281/zenodo.8092653", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8092653"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-11-26T00:00:00Z"}}, {"id": "10.5281/zenodo.8146228", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:44Z", "type": "Dataset", "title": "Dataset of the manuscript \"Assessing the influence of Eisenia andrei on the decomposition of Casuarina equisetifolia litter in vermicompost.\"", "description": "Data generated during an experiment of decomposition of Casuarina equisetifolia litter by the application of vermicompost (VC) or the combination vermicompost + the earthworm Eisenia andrei (E). Six files are included: 'readme.csv' is a file where we explain the meaning of each column (and in which units is expressed) in each of the other five files. 'earthworm_N_biomass.csv' is a table with the number of Eisenia andrei individuals and the total earthworm fresh weight in each of the experimental units we sampled 'FTIR_spectra.csv' is a file with the raw spectral data we obtained from the litter by Fourier Transform Infrared spectroscopy combined with Attenuated Total Reflectance (FTIR-ATR). First column indicate the wavenumber (cm-1) and the other columns indicate the absorbance values of each litter sample for each wavenumber. 'litter_chemical_composition.csv' is a file with the raw data of the concentrations of different chemical elements measured in C. equisetifolia litter collected at different decomposition times. 'litter_mass_loss.csv' contains the dry weight data of the litter at time 0 and after each collection time, as well as the percentage of litter mass loss with time. . 'mesofaunal_com.csv' are the numbers of individuals of several groups of mesofaunal organisms (collembolans, mites, and others) we recovered in each of our experimental units.", "keywords": ["Fourier Transform Infrared spectroscopy", "decomposition", "Eisenia andrei", "litter", "litterbag experiment", "Casuarina equisetifolia", "microcosm"], "contacts": [{"organization": "Quintela-Sabar\u00eds, Celestino, Mendes, Luis Andr\u00e9, Dom\u00ednguez, Jorge,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8146228"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8146228", "name": "item", "description": "10.5281/zenodo.8146228", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8146228"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-07-14T00:00:00Z"}}, {"id": "10.5281/zenodo.8146229", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:44Z", "type": "Dataset", "title": "Dataset of the manuscript \"Assessing the influence of Eisenia andrei on the decomposition of Casuarina equisetifolia litter in vermicompost.\"", "description": "Data generated during an experiment of decomposition of Casuarina equisetifolia litter by the application of vermicompost (VC) or the combination vermicompost + the earthworm Eisenia andrei (E). Six files are included: 'readme.csv' is a file where we explain the meaning of each column (and in which units is expressed) in each of the other five files. 'earthworm_N_biomass.csv' is a table with the number of Eisenia andrei individuals and the total earthworm fresh weight in each of the experimental units we sampled 'FTIR_spectra.csv' is a file with the raw spectral data we obtained from the litter by Fourier Transform Infrared spectroscopy combined with Attenuated Total Reflectance (FTIR-ATR). First column indicate the wavenumber (cm-1) and the other columns indicate the absorbance values of each litter sample for each wavenumber. 'litter_chemical_composition.csv' is a file with the raw data of the concentrations of different chemical elements measured in C. equisetifolia litter collected at different decomposition times. 'litter_mass_loss.csv' contains the dry weight data of the litter at time 0 and after each collection time, as well as the percentage of litter mass loss with time. . 'mesofaunal_com.csv' are the numbers of individuals of several groups of mesofaunal organisms (collembolans, mites, and others) we recovered in each of our experimental units.", "keywords": ["Fourier Transform Infrared spectroscopy", "decomposition", "Eisenia andrei", "litter", "litterbag experiment", "Casuarina equisetifolia", "microcosm"], "contacts": [{"organization": "Quintela-Sabar\u00eds, Celestino, Mendes, Luis Andr\u00e9, Dom\u00ednguez, Jorge,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8146229"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8146229", "name": "item", "description": "10.5281/zenodo.8146229", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8146229"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-07-14T00:00:00Z"}}, {"id": "2117/364526", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:27:54Z", "type": "Journal Article", "created": "2022-03-17", "title": "Quantification of the dust optical depth across spatiotemporal scales with the MIDAS global dataset (2003\u20132017)", "description": "

Abstract. Quantifying the dust optical depth (DOD) and its uncertainty across spatiotemporal scales is key to understanding and constraining the dust cycle and its interactions with the Earth System. This study quantifies the DOD along with its monthly and year-to-year variability between 2003 and 2017 at global and regional levels based on the MIDAS (ModIs Dust AeroSol) dataset, which combines Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua retrievals and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), reanalysis products. We also describe the annual and seasonal geographical distributions of DOD across the main dust source regions and transport pathways. MIDAS provides columnar mid-visible (550\u2009nm) DOD at fine spatial resolution (0.1\u2218\u00d70.1\u2218), expanding the current observational capabilities for monitoring the highly variable spatiotemporal features of the dust burden. We obtain a global DOD of 0.032\u00b10.003 \u2013 approximately a quarter (23.4\u2009%\u00b12.4\u2009%) of the global aerosol optical depth (AOD) \u2013 with about 1\u00a0order of magnitude more DOD in the Northern Hemisphere (0.056\u00b10.004; 31.8\u2009%\u00b12.7\u2009%) than in the Southern Hemisphere (0.008\u00b10.001; 8.2\u2009%\u00b11.1\u2009%) and about 3.5 times more DOD over land (0.070\u00b10.005) than over ocean (0.019\u00b10.002). The Northern Hemisphere monthly DOD is highly correlated with the corresponding monthly AOD (R2=0.94) and contributes 20\u2009% to 48\u2009% of it, both indicating a dominant dust contribution. In contrast, the contribution of dust to the monthly AOD does not exceed 17\u2009% in the Southern Hemisphere, although the uncertainty in this region is larger. Among the major dust sources of the planet, the maximum DODs (\u223c1.2) are recorded in the Bod\u00e9l\u00e9 Depression of the northern Lake Chad Basin, whereas moderate-to-high intensities are encountered in the Western Sahara (boreal summer), along the eastern parts of the Middle East (boreal summer) and in the Taklamakan Desert (spring). Over oceans, major long-range dust transport is observed primarily along the tropical Atlantic (intensified during boreal summer) and secondarily in the North Pacific (intensified during boreal spring). Our calculated global and regional averages and associated uncertainties are consistent with some but not all recent observation-based studies. Our work provides a simple yet flexible method to estimate consistent uncertainties across spatiotemporal scales, which will enhance the use of the MIDAS dataset in a variety of future studies.

", "keywords": ["Mineral dusts", "3702 Climate change science (for-2020)", "QC1-999", "0201 Astronomical and Space Sciences (for)", "0401 Atmospheric Sciences (for)", "3701 Atmospheric Sciences (for-2020)", "01 natural sciences", "Meteorology & Atmospheric Sciences (science-metrix)", "Atmospheric Sciences", "\u00c0rees tem\u00e0tiques de la UPC::Enginyeria agroaliment\u00e0ria::Ci\u00e8ncies de la terra i de la vida::Climatologia i meteorologia", "Simulaci\u00f3 per ordinador", "Pols", "Meteorology & Atmospheric Sciences", "Datasets", "Dust optical depth (DOD)", "Earth System", "QD1-999", "0105 earth and related environmental sciences", ":Enginyeria agroaliment\u00e0ria::Ci\u00e8ncies de la terra i de la vida::Climatologia i meteorologia [\u00c0rees tem\u00e0tiques de la UPC]", "3701 Atmospheric sciences (for-2020)", "Physics", "MIDAS global dataset", "16. Peace & justice", "Climate Action", "Chemistry", "37 Earth Sciences (for-2020)", "13. Climate action", "Mineral dust particles", "13 Climate Action (sdg)", "Astronomical and Space Sciences"]}, "links": [{"href": "https://acp.copernicus.org/articles/22/3553/2022/acp-22-3553-2022.pdf"}, {"href": "https://escholarship.org/content/qt9v38c6qs/qt9v38c6qs.pdf"}, {"href": "https://doi.org/2117/364526"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Atmospheric%20Chemistry%20and%20Physics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2117/364526", "name": "item", "description": "2117/364526", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2117/364526"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-07-19T00:00:00Z"}}, {"id": "10.5281/zenodo.8354397", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:46Z", "type": "Dataset", "title": "Data and R-scripts for estimating carbon dioxide emissions from drained peatland forest soils for the greenhouse gas inventory of Finland", "description": "Open Access Introduction A new method for estimating carbon dioxide emissions from rained peatland forest soils was developed for the Greenhouse Gas Inventory of Finland (GHG inventory). The method is based on a set of models (Ojanen et al. 2014, Tuomi et al., 2009) that dynamically compile all relevant carbon inputs and outputs into a time series of soil CO2 emission. A complete description of the method is described in Alm et al. (2023). Here we present the input data and R-scripts (R Core Team, 2020) for computing the time series from year 1990 to 2022 of CO2 emission from soil in forest land on drained organic soil, like it was reported by the Finnish GHG inventory (Statistics Finland, 2023). Time series data The source of forest and area data is the Finnish National Forest Inventory (NFI) as a part of Luke Statutory Services. The NFI standing forest data in the data files includes annual country-wide estimates of mean basal area and standing biomass of Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) H. Karst) and all the broadleaved forest trees combined. The data concerns forest land on drained organic soil only (class FRA 1 according to the FAO forest land definition). The NFI data for each year has been averaged by different drained peatland forest site types (FTYPE) and by inventory regions of southern and northern Finland. The areas and proportions of FTYPEs of all drained peatland \u201cforests remaining forests\u201d (i.e., forests that have not undergone another change in land use in the past 20 years) in southern and northern Finland (Alm et al., 2023), derived from NFI12 (2014\u20132018). Annual litter input from harvest residues was estimated using statistics of harvested stem volumes by species, collected and published by Luke (Luke statistics). The stem volumes were converted to whole trees and further to litter fractions and further to The share of residues remaining in forest is estimated by subtracting the amount of the logging residues collected for energy use, the data obtained from Luke statistics/energy. The biomass of live trees, annual litterfall from live trees aboveground and root litter belowground are derived from the National Forest Inventory of Finland (inventory rounds NFI8 to NFI13). The R-code also includes calculation of annual litter production from the harvesting residues. The regression-based transfer models, implemented in the R-code, also need meteorological time series inputs: The soil organic matter decomposition model (Ojanen et al. 2014) uses May-October mean temperature. Decomposition model yasso07 (Tuomi et al., 2009), applied for estimating the CO2 release by decomposition of harvesting residues and above ground litter from natural mortality, is constrained by annual temperature, annual temperature amplitude and annual precipitation. Starting from the original country-wide grid produced by the Finnish Meteorological Institute (FMI) the weather time series were spatially averaged so that the FMI weather grid values were collected from those locations where peatlands representing each FTYPE in southern and northern Finland were observed by the NFI, respectively. The pre-prepared input data are given in files, see Table 1 for descriptions. Table 1. Description of input data files. File Description of data basal.areas.csv Time series of years 1990-2022 for annual average basal area (m2 ha-1) by year, by peatland forest site type (peat_type) and by tree species or group (tree_type). Values of peat_type correspond to FTYPE: 1 Herb-rich type 2 Vaccinium myrtillus type 4 Vaccinium vitis-idaea type 6 Dwarf shrub type 7 Cladina type Values of tree species or group correspond to: 1 Scots pine 2 Norway spruce 3 Broadleaved species biomass.csv Time series of years 1990-2022 for annual biomass (biomass, t ha-1 of dry mass) by year, by biomass component, by tree species and by peatland forest site type (tkg). Values of peat_type correspond to FTYPE: 1 Herb-rich type 2 Vaccinium myrtillus type 4 Vaccinium vitis-idaea type 6 Dwarf shrub type 7 Cladina type dead_litter.csv Time series of years 1990-2022 of annual aboveground litter from dead wood: Harvesting residues and natural mortality combined (C, t ha-1 of dry mass; lognat_litter). Values of region correspond to GHG inventory region: south South Finland north North Finland ghgi_litter.csv Time series of years 1990-2022 for litter AWEN-fractions (A=acid soluble, W=water soluble, E=ethanol soluble, N=non-soluble; C, t ha-1) by different litter types: Above-ground coarse woody litter (coarse_woody_litter), fine woody litter (fine_woody_litter), non-woody litter (non_woody_litter) by litter source and deposition type by region. \u201corg\u201d denotes organic soil. Values of region correspond to GHG inventory region: south South Finland north North Finland Values of ground correspond to litter deposition environment: above Above-ground litter below Below-ground litter lognat_decomp.csv Time series of years 1990-2022 for C, t ha-1 of dry mass, decomposed from logging residues and natural mortality by region. Values of variable \u201cregion\u201d correspond to GHG inventory region: south South Finland north North Finland logyasso_weather_data.csv Time series of years 1990-2022 for regional (region) precipitation sum (mm, sum_P), average annual temperature (\u00b0C, mean_T) and amplitude of the annual temperature (\u00b0C , ampli_T). Values of region correspond to GHG inventory region: south South Finland north North Finland total_area.csv Areas (ha) of drained peatland forests remaining forest land by region and peat_type. Values of variable \u201cregion\u201d correspond to GHG inventory region: south South Finland north North Finland Values of peat_type correspond to FTYPE: 1 Herb-rich type 2 Vaccinium myrtillus type 4 Vaccinium vitis-idaea type 6 Dwarf shrub type 7 Cladina type weather_data.csv Time series of years 1990-2022 for 30-year rolling mean temperature for the May-October period (roll_T) used by the soil decomposition models. The values are calculated for each FTYPE (peat_type) using their spatial distributions (see details in Alm et al., 2023). Values of variable \u201cregion\u201d correspond to GHG inventory region: south South Finland north North Finland Values of peat_type correspond to FTYPE: 1 Herb-rich type 2 Vaccinium myrtillus type 4 Vaccinium vitis-idaea type 6 Dwarf shrub type 7 Cladina type The R-scripts The scripts are an excerpt from the Finnish greenhouse gas inventory code set, applying the necessary pre-processed input data and producing the soil CO2 emissions for each FTYPE separately. The necessary R-packages (R Core Team, 2020) are managed in the script LIBRARIES.R. Guidance for running the R-scripts is given in the README.txt. References Alm, J., Wall, A., Myllykangas, J-P., Ojanen, P., Heikkinen, J., Henttonen, H. M., Laiho, R., Minkkinen, K., Tuomainen, T. and Mikola, J. A new method for estimating carbon dioxide emissions from drained peatland forest soils for the greenhouse gas inventory of Finland. Biogeosciences https://doi.org/10.5194/bg-20-1-2023, 2023. LUKE Statistics https://www.luke.fi/en/statistics/total-roundwood-removals-and-drain, last access 8.12.2022. https://www.luke.fi/en/statistics/commercial-fellings/commercial-fellings-72023. last access 8.12.2022. Statistics Finland 2023. URL: https://unfccc.int/documents/627718 (last access 13.9.2023). Ojanen, P., Lehtonen, A., Heikkinen, J., Penttil\u00e4, T., and Minkkinen, K.: Soil CO2 balance and its uncertainty in forestry drained peatlands in Finland, Forest Ecol. Manage., 325, 60\u201373, 2014. R Core Team: R: A language and environment for statistical computing. R Foundation forStatistical Computing, Vienna, Austria, URL https://www.R-project.org, 2020. Tuomi, M., Thum, T., J\u00e4rvinen, H., Fronzek, S., Berg, B., Harmon, M., Trofymow, J.A., Sevanto, S. and Liski, J.: Leaf litter decomposition - Estimates of global variability based on Yasso07 model, Ecol. Modell. 220 (23):3362-3371, 2009.", "keywords": ["13. Climate action", "greenhouse gas inventory", "11. Sustainability", "method", "peatland", "15. Life on land", "time series", "soil carbon dioxide balance", "Finland", "12. Responsible consumption"], "contacts": [{"organization": "Alm, Jukka, Wall, Antti, Myllykangas, Jukka-Pekka, Ojanen, Paavo, Heikkinen, Juha, Henttonen, Helena M., Laiho, Raija, Minkkinen, Kari, Tuomainen, Tarja, Mikola, Juha,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8354397"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8354397", "name": "item", "description": "10.5281/zenodo.8354397", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8354397"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-09-18T00:00:00Z"}}, {"id": "10.5281/zenodo.8171861", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:45Z", "type": "Dataset", "title": "Pan-EU Landmask: 10m Resolution Geospatial Land Coverage with Administrative Boundary details on country and regional level", "description": "Pan-EU Land Mask Summary Considering the land mask for pan-EU, we will closely match the data coverage of https://land.copernicus.eu/pan-european i.e. the official selection of countries listed here: https://lanEEA39d.copernicus.eu/portal_vocabularies/geotags/eea39. There are a total of three landmask files available, each of which is aligned with the standard spatial/temporal resolution and sizes of AI4SoilHealth Data Cube specifications, which is: Xmin = 900,000, Ymin = 899,000, Xmax = 7,401,000, Ymax = 5,501,000, with Coordinate reference system of epsg:3035. Additionally, these files include a corresponding look-up table that provides explanations for the values present in the raster data. The scripts used to generate these masks can be found here. The masks are: Landmask ISO-code country mask NUTS3 mask Name convention To ensure consistency and ease of use across and within the projects, the files here are named according to the standard OpenLandMap file-naming convention. The OpenLandMap file-naming convention works with 10 fields that basically define the most important properties of the data, this way users can search files, prepare data analysis etc, without even needing to access or open files. The 10 fields include: Generic variable name: country.code Variable procedure combination i.e. method standard (standard abbreviation): iso.3166 Position in the probability distribution / variable type: c Spatial support (usually horizontal block) in m or km: 30m Depth reference or depth interval e.g. below ('b'), above ('a') ground or at surface ('s'): s Time reference begin time (YYYYMMDD): 20210101 Time reference end time: 20211231 Bounding box (2 letters max): eu EPSG code: epsg.3035 Version code i.e. creation date: v20230722 An example of a file-name based on the description above: country.code_iso.3166_c_100m_s_20210101_20211231_eu_epsg.3035_v20230722 Landmask The basic principle to create the land mask is to include as much as land as possible, to avoid missing any land pixels and ensure precise differentiation between land, ocean and inland water bodies. Two reference datasets are used, WorldCover, 10 m resolution. EuroGlobalMap, with shapefiles of administrative boundaries, inland water bodies, ocean and landmask. When generating the land mask, the two reference datasets in a way that: If either of the two reference datasets identifies a pixel as land, it is considered a land pixel in our mask. Regarding ocean and inland water bodies, a pixel is classified as a water pixel only when both reference datasets confirm its identification as water. The landmask consists of 4 values: 10: not in the pan-EU area, i.e. out of mapping scope 1: land 2: inland water 3: ocean This landmask is available in 10m, 30m, 100m, 250m, and 1km resolution formats respectively. The coarse resolution landmasks (>10 m) are generated by resampling from the 10m resolution base map using resampling method \u201cmin\u201d in GDAL. This \u201cmin\u201d method allows taking the minimum values from the contributing pixels, to keep as much land as possible. ISO-3166 country code mask This ISO-3166 country code mask is created from EuroGlobalMap country shapefile. This mask is available in 10m, 30m and 100m resolution. In this raster file, each country is assigned a unique value, which allows for the interpretation and analysis of data associated with a specific country. The values are assigned to each country according to iso-3166 country code, which can be found in the corresponding look-up table. The coarse resolution masks (>10 m) are generated by resampling from the 10m resolution base map using resampling method \u201cmode\u201d in GDAL. NUTS-3 mask The nuts-3 code mask is created from the European NUTS3 shapefile. In this raster file, each unique NUT3 level area is assigned a unique value, which allows for the interpretation and analysis of data associated with specific NUTS3 regions. The values of pixels and its associated meanings can be found in the corresponding look-up table. This nut-3 code mask is available in 10m, 30m and 100m resolution formats. The coarse resolution masks (>10 m) are generated by resampling from the 10m resolution base map using resampling method \u201cmode\u201d in GDAL. It should be noted that the ISO-code country mask covers a more extensive area compared to the NUTS3 mask. This broader coverage includes countries like Ukraine and others beyond the NUTS3 mask, while NUTS mask shows more details about regional administrative boundaries.", "keywords": ["remote sensing", "EuroGlobalMap", "soil health", "WorldCover", "land mask", "pan Europe", "nuts3", "iso-3166", "15. Life on land", "earth obeservation"], "contacts": [{"organization": "Tian, Xuemeng, Ho, Yu-Feng, Witjes, Martijn, Parente, Leandro, Hengl, Tom, Minarik, Robert,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8171861"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8171861", "name": "item", "description": "10.5281/zenodo.8171861", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8171861"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-07-27T00:00:00Z"}}, {"id": "10.5281/zenodo.8171860", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:45Z", "type": "Dataset", "title": "Pan-EU Landmask: 10m Resolution Geospatial Land Coverage with Administrative Boundary details on country and regional level", "description": "Pan-EU Land Mask Summary Considering the land mask for pan-EU, we will closely match the data coverage of https://land.copernicus.eu/pan-european i.e. the official selection of countries listed here: https://lanEEA39d.copernicus.eu/portal_vocabularies/geotags/eea39. There are a total of three landmask files available, each of which is aligned with the standard spatial/temporal resolution and sizes of AI4SoilHealth Data Cube specifications, which is: Xmin = 900,000, Ymin = 899,000, Xmax = 7,401,000, Ymax = 5,501,000, with Coordinate reference system of epsg:3035. Additionally, these files include a corresponding look-up table that provides explanations for the values present in the raster data. The scripts used to generate these masks can be found here. The masks are: Landmask ISO-code country mask NUTS3 mask Name convention To ensure consistency and ease of use across and within the projects, the files here are named according to the standard OpenLandMap file-naming convention. The OpenLandMap file-naming convention works with 10 fields that basically define the most important properties of the data, this way users can search files, prepare data analysis etc, without even needing to access or open files. The 10 fields include: Generic variable name: country.code Variable procedure combination i.e. method standard (standard abbreviation): iso.3166 Position in the probability distribution / variable type: c Spatial support (usually horizontal block) in m or km: 30m Depth reference or depth interval e.g. below ('b'), above ('a') ground or at surface ('s'): s Time reference begin time (YYYYMMDD): 20210101 Time reference end time: 20211231 Bounding box (2 letters max): eu EPSG code: epsg.3035 Version code i.e. creation date: v20230722 An example of a file-name based on the description above: country.code_iso.3166_c_100m_s_20210101_20211231_eu_epsg.3035_v20230722 Landmask The basic principle to create the land mask is to include as much as land as possible, to avoid missing any land pixels and ensure precise differentiation between land, ocean and inland water bodies. Two reference datasets are used, WorldCover, 10 m resolution. EuroGlobalMap, with shapefiles of administrative boundaries, inland water bodies, ocean and landmask. When generating the land mask, the two reference datasets in a way that: If either of the two reference datasets identifies a pixel as land, it is considered a land pixel in our mask. Regarding ocean and inland water bodies, a pixel is classified as a water pixel only when both reference datasets confirm its identification as water. The landmask consists of 4 values: 10: not in the pan-EU area, i.e. out of mapping scope 1: land 2: inland water 3: ocean This landmask is available in 10m, 30m, 100m, 250m, and 1km resolution formats respectively. The coarse resolution landmasks (>10 m) are generated by resampling from the 10m resolution base map using resampling method \u201cmin\u201d in GDAL. This \u201cmin\u201d method allows taking the minimum values from the contributing pixels, to keep as much land as possible. ISO-3166 country code mask This ISO-3166 country code mask is created from EuroGlobalMap country shapefile. This mask is available in 10m, 30m and 100m resolution. In this raster file, each country is assigned a unique value, which allows for the interpretation and analysis of data associated with a specific country. The values are assigned to each country according to iso-3166 country code, which can be found in the corresponding look-up table. The coarse resolution masks (>10 m) are generated by resampling from the 10m resolution base map using resampling method \u201cmode\u201d in GDAL. NUTS-3 mask The nuts-3 code mask is created from the European NUTS3 shapefile. In this raster file, each unique NUT3 level area is assigned a unique value, which allows for the interpretation and analysis of data associated with specific NUTS3 regions. The values of pixels and its associated meanings can be found in the corresponding look-up table. This nut-3 code mask is available in 10m, 30m and 100m resolution formats. The coarse resolution masks (>10 m) are generated by resampling from the 10m resolution base map using resampling method \u201cmode\u201d in GDAL. It should be noted that the ISO-code country mask covers a more extensive area compared to the NUTS3 mask. This broader coverage includes countries like Ukraine and others beyond the NUTS3 mask, while NUTS mask shows more details about regional administrative boundaries.", "keywords": ["remote sensing", "EuroGlobalMap", "soil health", "WorldCover", "land mask", "pan Europe", "nuts3", "iso-3166", "15. Life on land", "earth obeservation"], "contacts": [{"organization": "Tian, Xuemeng, Ho, Yu-Feng, Witjes, Martijn, Parente, Leandro, Hengl, Tom, Minarik, Robert,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8171860"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8171860", "name": "item", "description": "10.5281/zenodo.8171860", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8171860"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-07-27T00:00:00Z"}}, {"id": "10.5281/zenodo.8194045", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:45Z", "type": "Dataset", "title": "Supplementary material/Organic carbon dynamics in clay soils: impact of management practices on microorganism structure and abundance under semi-arid conditions", "description": "Proper management of soil organic matter in arid and semi-arid regions improves organic carbon storage in the soil, helps in compact soil degradation, and mitigates climate change impacts, and preserves ecosystem functionality and sustainability food security. This study aims to provide a better insight into the biogeochemical processes that drive the organic carbon dynamics of saline clay soil in a semi-arid climate. The study is not intended to be exhaustive but contributes to analyzing the relationship between bacterial microflora, physicochemical properties, and organic carbon dynamics as a function of different soil management modes. The monitoring was carried out on three different plots located at the National Institute of Agronomic Research of Algeria. A physicochemical characterization of the soils was performed. A metagenomic study was also conducted to identify bacterial biodiversity using PCR-amplified DNA sequencing. The study results show that the control plot has the highest average organic carbon stock value at 47 Mg ha-1. This was followed by the amended plot and the conventional plot, respectively, with 43 Mg ha-1 and 38 Mg ha-1. In the context of this study, organic carbon dynamics would appear to depend on the interaction of several biotic and abiotic factors. Soil management methods would impact the density and diversity of bacterial microflora. This, in turn, affects the soil's physicochemical properties and, more specifically, organic carbon dynamics and storage.", "keywords": ["2. Zero hunger", "13. Climate action", "Biogeochemical processes", " organic carbon dynamics", " clay soil", " semi-arid area", " bacterial microflora", " physicochemical properties", " soil management methods.", "15. Life on land", "6. Clean water"], "contacts": [{"organization": "Fatiha, Faraoun", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8194045"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8194045", "name": "item", "description": "10.5281/zenodo.8194045", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8194045"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-07-28T00:00:00Z"}}, {"id": "10.5281/zenodo.8194083", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:45Z", "type": "Dataset", "title": "Organic carbon dynamics in clay soils: impact of management practices on microorganism structure and abundance under semi-arid conditions", "description": "Proper management of soil organic matter in arid and semi-arid regions improves organic carbon storage in the soil, helps in compact soil degradation, mitigates climate change impacts, and preserves ecosystem functionality and sustainability food security. This study aims to provide a better insight into the biogeochemical processes that drive the organic carbon dynamics of saline clay soil in a semi-arid area. The study is not intended to be exhaustive but contributes to analyzing the relationship between bacterial microflora, physicochemical properties, and organic carbon dynamics as a function of different soil management modes. The monitoring was carried out on three different plots located at the National Institute of Agronomic Research of Algeria. A physicochemical characterization of the soils was performed. A metagenomic study was also conducted to identify bacterial biodiversity using PCR-amplified DNA sequencing. The study results show that the control plot has the highest average organic carbon stock value at 47 Mg ha-1. This was followed by the amended plot and the conventional plot, respectively, with 43 Mg ha-1 and 38 Mg ha-1. In the context of this study, organic carbon dynamics would appear to depend on the interaction of several biotic and abiotic factors. Soil management methods would impact the density and diversity of bacterial microflora. This, in turn, affects the soil's physicochemical properties and, more specifically, organic carbon dynamics and storage.", "keywords": ["2. Zero hunger", "13. Climate action", "Biogeochemical processes", " organic carbon dynamics", " clay soil", " semi-arid area", " bacterial microflora", " physicochemical properties", "soil management methods.", "15. Life on land", "6. Clean water"], "contacts": [{"organization": "Bekhit, Nadia, Faraoun, Fatiha, Bennabi, Faiza, Abbassia Ayache, Toumi, Fawzia, Mlih, Rawan,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8194083"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8194083", "name": "item", "description": "10.5281/zenodo.8194083", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8194083"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-07-28T00:00:00Z"}}, {"id": "10.5424/sjar/2013113-3747", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:49Z", "type": "Journal Article", "created": "2013-07-31", "title": "Simulating Improved Combinations Tillage-Rotation Under Dryland Conditions", "description": "

Crop simulation models allow analyzing various tillage-rotation combinations and exploring management scenarios. This study was conducted to test the DSSAT (Decision Support System for Agrotechnology Transfer) modelling system in rainfed semiarid central Spain. The focus is on the combined effect of tillage system and winter cereal-based rotations (cereal/legume/fallow) on the crop yield and soil quality. The observed data come from a 16-year field experiment. The CERES and CROPGRO models, included in DSSAT v4.5, were used to simulate crop growth and yield, and DSSAT-CENTURY was used in the soil organic carbon (SOC) and soil nitrogen (SN) simulations. Genetic coefficients were calibrated using part of the observed data. Field observations showed that barley grain yield was lower for continuous cereal (BB) than for vetch (VB) and fallow (FB) rotations for both tillage systems. The CERES-Barley model also reflected this trend. The model predicted higher yield in the conventional tillage (CT) than in the no tillage (NT) probably due to the higher nitrogen availability in the CT, shown in the simulations. The SOC and SN in the top layer only, were higher in NT than in CT, and decreased with depth in both simulated and observed values. These results suggest that CT-VB and CT-FB were the best combinations for the dry land conditions studied. However, CT presented lower SN and SOC content than NT. This study shows how models can be a useful tool for assessing and predicting crop growth and yield, under different management systems and under specific edapho-climatic conditions.

", "keywords": ["2. Zero hunger", "Sequential simulation", "S", "sequential simulation", "Soil organic carbon", "Agricultura", "Agriculture", "04 agricultural and veterinary sciences", "15. Life on land", "soil organic carbon", "CENTURY model", "Crop simulation models", "CERES-Barley", "0401 agriculture", " forestry", " and fisheries", "agriculture; plant production", "CENTURY model; CERES-Barley; Crop simulation models; DSSAT; sequential simulation; soil organic carbon", "DSSAT"]}, "links": [{"href": "https://doi.org/10.5424/sjar/2013113-3747"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Spanish%20Journal%20of%20Agricultural%20Research", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5424/sjar/2013113-3747", "name": "item", "description": "10.5424/sjar/2013113-3747", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5424/sjar/2013113-3747"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2013-07-18T00:00:00Z"}}, {"id": "10.5424/sjar/2020181-13807", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:49Z", "type": "Journal Article", "created": "2020-03-13", "title": "The cost of mitigating greenhouse gas emissions in farms in Central Andes of Ecuador", "description": "

Aim of study: Reduction of the greenhouse gas (GHG) emissions derived from food production is imperative to meet climate change mitigation targets. Sustainable mitigation strategies also combine improvements in soil fertility and structure, nutrient recycling, and the use more efficient use of water. Many of these strategies are based on agricultural know-how, with proven benefits for farmers and the environment. This paper considers measures that could contribute to emissions reduction in subsistence farming systems and evaluation of management alternatives in the Central Andes of Ecuador. We focused on potato and milk production because they represent two primary employment and income sources in the region\u2019s rural areas and are staple foods in Latin America.Area of study: Central Andes of Ecuador: Carchi, Chimborazo, Ca\u00f1ar provincesMaterial and methods: Our approach to explore the cost and the effectiveness of mitigation measures combines optimisation models with participatory methods.Main results: Results show the difference of mitigation costs between regions which should be taken into account when designing of any potential support given to farmers. They also show that there is a big mitigation potential from applying the studied measures which also lead to increased soil fertility and soil structure improvements due to the increased soil organic carbon.Research highlights: This study shows that marginal abatement cost curves derived for different agro-climatic regions are helpful tools for the development of realistic regional mitigation options for the agricultural sector.

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