{"type": "FeatureCollection", "features": [{"id": "10.5281/zenodo.7079708", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:03Z", "type": "Report", "title": "Sentinel-2 and Landsat-8 for High-Resolution Land Cover Mapping in Sustainable Agriculture", "description": "Land cover mapping has become an increasingly important source of information in agriculture. Farmers use it for on-field decision-making, retailers and stock traders for planning and governments for making agricultural strategies and setting subsidy levels. Besides agricultural stakeholders, there are biologists and environmental scientists who use this kind of information for monitoring the quality of habitats.There are a number of optical EO satellites which offer images free of charge, with Sentinel-2, a part of ESA\u2019s Copernicus programme, and Landsat-8, launched by NASA and USGS, being the most popular ones. This work focused on their application in land cover mapping in northern Serbia. Using joint information from these satellites we improved the system in many aspects. Data fusion allowed us to have images from more dates available. In this way we decreased the risk of misclassification due to missing data caused by high cloud coverage. Also, it allowed us to train a more accurate classifier compared to those trained on individual satellites. Finally, the spatial resolution of resulting maps was higher than the resolution of input images. This is extremely important for the observed region which is mostly constituted of small fields, 400 x 60 m in average. The database included more than 3 billion pixels with around 200 features each, i.e. all image channels from key dates between January and September. Classifiers were trained to distinguish between following crops: maize, wheat, sunflower, sugar beet and soybean, as well as forest and water bodies. Random forest proved to be the best classification algorithm, in terms of accuracy, speed and ability to deal with missing data. In classification of forest and water bodies, accuracy of up to 97 - 98% was achieved even without data fusion. However, since crop classification is a more difficult problem, performances of Sentinel and Landsat based classifiers could not match the performance of the joint classifier. Data fusion increased the overall system accuracy through the increase of average accuracy over all classes, as well as through more equal distribution of accuracy values over categories, in addition to higher spatial resolution of final decisions. The most significant improvement was observed in soybean classification, where Sentinel, Landsat and joint classifiers achieved accuracies of 84%, 87%, 89%, respectively. Other crops, such as sugar beet and wheat, which could be accurately classified with Sentinel and Landsat, were not improved further. This work is a step towards next year\u2019s case, when besides these two satellites, Sentinel-2b will be available. It will cut the revisit time of Sentinels to only 6 days meaning that there will be even more data available and even better classification performance can be expected. The system developed in this research is intended to be a part of a broader geo service. This service would offer solutions customised for a vast variety of users, utilising the full potential of land cover mapping.", "keywords": ["2. Zero hunger", "Land cover mapping", " Sentinel-2", "13. Climate action", "15. Life on land"], "contacts": [{"organization": "Predrag Lugonja, Oskar Marko, Marko Pani\u0107, Branko Brklja\u010d, Sanja Brdar, Vladimir Crnojevi\u0107,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7079708"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7079708", "name": "item", "description": "10.5281/zenodo.7079708", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7079708"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-03-16T00:00:00Z"}}, {"id": "10.5281/zenodo.7079583", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:03Z", "type": "Report", "title": "Satellite based estimation of the arable topsoil texture at regional scale using Sentinel-2 data", "description": "Although satellite imaging has been present as a source of valuable spatial data for a long time, it was not until very recently that high quality satellite imagery products produced by high resolution multispectral instruments became affordable and broadly available. On the other hand, information contained in such measurements proved to have significant impact on the overall improvement of the best practices in agricultural production and environmental monitoring. One of the applications that could benefit from the large scale satellite based measurements is characterization of topsoil properties of arable land. More exactly, bare soil spectra acquired by multispectral instruments can directly provide information about soil texture, represented by the content of clay, sand, or silt, over the observed vegetation free area. There have been a few attempts to investigate such possibilities in the context of the current and forthcoming multispectral and hyperspectral imagers. In a recently published study, a comprehensive evaluation of the capabilities of several imagers in the task of soil texture estimation was performed. However, those findings were based only on the simulated and resampled spectral responses derived from the soil spectral signature libraries acquired under controlled laboratory conditions using high precision hyperspectral instruments. Among the simulated imagers was also Sentinel-2 MSI. In line with these efforts, aim of this paper is to further investigate applicability of this instrument in the real working environment, characterized by the challenging factors introduced by the atmosphere, tillage and plant remains, missing data due to cloud coverage, variable soil moisture as a consequence of climate and volatile weather conditions, as well as natural soil spatial variability, due to the large spatial extent of the performed analysis.", "keywords": ["2. Zero hunger", "13. Climate action", "Sentinel-2", " Satellite imaging", " Topsoil texture", " Estimation", "15. Life on land"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7079583"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7079583", "name": "item", "description": "10.5281/zenodo.7079583", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7079583"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-09-28T00:00:00Z"}}, {"id": "10.5281/zenodo.7079709", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:03Z", "type": "Report", "title": "Sentinel-2 and Landsat-8 for High-Resolution Land Cover Mapping in Sustainable Agriculture", "description": "Land cover mapping has become an increasingly important source of information in agriculture. Farmers use it for on-field decision-making, retailers and stock traders for planning and governments for making agricultural strategies and setting subsidy levels. Besides agricultural stakeholders, there are biologists and environmental scientists who use this kind of information for monitoring the quality of habitats.There are a number of optical EO satellites which offer images free of charge, with Sentinel-2, a part of ESA\u2019s Copernicus programme, and Landsat-8, launched by NASA and USGS, being the most popular ones. This work focused on their application in land cover mapping in northern Serbia. Using joint information from these satellites we improved the system in many aspects. Data fusion allowed us to have images from more dates available. In this way we decreased the risk of misclassification due to missing data caused by high cloud coverage. Also, it allowed us to train a more accurate classifier compared to those trained on individual satellites. Finally, the spatial resolution of resulting maps was higher than the resolution of input images. This is extremely important for the observed region which is mostly constituted of small fields, 400 x 60 m in average. The database included more than 3 billion pixels with around 200 features each, i.e. all image channels from key dates between January and September. Classifiers were trained to distinguish between following crops: maize, wheat, sunflower, sugar beet and soybean, as well as forest and water bodies. Random forest proved to be the best classification algorithm, in terms of accuracy, speed and ability to deal with missing data. In classification of forest and water bodies, accuracy of up to 97 - 98% was achieved even without data fusion. However, since crop classification is a more difficult problem, performances of Sentinel and Landsat based classifiers could not match the performance of the joint classifier. Data fusion increased the overall system accuracy through the increase of average accuracy over all classes, as well as through more equal distribution of accuracy values over categories, in addition to higher spatial resolution of final decisions. The most significant improvement was observed in soybean classification, where Sentinel, Landsat and joint classifiers achieved accuracies of 84%, 87%, 89%, respectively. Other crops, such as sugar beet and wheat, which could be accurately classified with Sentinel and Landsat, were not improved further. This work is a step towards next year\u2019s case, when besides these two satellites, Sentinel-2b will be available. It will cut the revisit time of Sentinels to only 6 days meaning that there will be even more data available and even better classification performance can be expected. The system developed in this research is intended to be a part of a broader geo service. This service would offer solutions customised for a vast variety of users, utilising the full potential of land cover mapping.", "keywords": ["2. Zero hunger", "Land cover mapping", " Sentinel-2", "13. Climate action", "15. Life on land"], "contacts": [{"organization": "Lugonja, Predrag, Marko, Oskar, Pani\u0107, Marko, Brklja\u010d, Branko, Brdar, Sanja, Crnojevi\u0107, Vladimir,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7079709"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7079709", "name": "item", "description": "10.5281/zenodo.7079709", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7079709"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-03-16T00:00:00Z"}}, {"id": "10.5281/zenodo.7080114", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:03Z", "type": "Report", "title": "Detection of Double-Cropping Systems Using Machine Learning and Sentinel 2 Imagery - A Case Study of Ba\u010dka and Srem Regions, Serbia", "description": "Increasing agricultural production is inevitable in the future since population growth and climate change have led to significant pressure on global food security. One of the ways is to intensify the existing cropland by multi-cropping practice, allowing multiple uses of a single field during one year. This research aims to identify and map double-cropping land using multi-temporal Sentinel 2 imagery from 2021 and advanced machine learning models. The case study focus is on Ba\u010dka and Srem, regions located in the Autonomous Province of Vojvodina, Republic of Serbia. These regions are characterized by fertile land and widespread agriculture production. However, there is a low presence of double-cropping practice due to usually dry summers, but with a tendency to change as the number of irrigation systems increase. Considering the small amount of double-cropping fields, there is a need for direct ground truth data collection. For that reason, the first step was to reduce the area of interest to get insight into the locations of potential double-cropping land. This result was obtained by using the threshold method based on the phenology of crops during the year. The NDVI (Normalized Difference Vegetation Index) time series was utilized to define appropriate thresholds for feature two peak values to discriminate double-cropping within each pixel. The identification of the results was used on-site for collecting ground truth data. Based on the collected data and the analyzed NDVI time series, besides double-crop, three more classes of arable land were distincted and included in the classification: single winter crops, single summer crops and clover. The collected data contained 46 parcels of double crops, 43 single winter crops, 55 single summer crops and 27 parcels of clover. We used time-series images to create a dataset for training the pixel-based Random Forest classification. The results showed a very high overall accuracy of 99% and an F-score higher than 0.9 for each of the classes. This methodology is a suitable approach for detecting double-cropping systems, with further potential to identify exact crop types and the main practice of combining crops. The findings of this study showed that only about 2% of the study area was under this production. Except for positive economic outcomes, utilizing these systems brings significant environmental benefits and rational use of the soil without expanding physical cropland but with the same advantages. Therefore, the resulting geospatial datasets of double cropping croplands could help solve important questions relevant to food security, irrigation and climate change.", "keywords": ["2. Zero hunger", "13. Climate action", "15. Life on land"], "contacts": [{"organization": "Markovi\u0107, Miljana, Lugonja, Predrag, Brdar, Sanja, \u017divaljevi\u0107, Branislav, Crnojevi\u0107, Vladimir,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7080114"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7080114", "name": "item", "description": "10.5281/zenodo.7080114", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7080114"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-04-28T00:00:00Z"}}, {"id": "10.5281/zenodo.7080135", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:03Z", "type": "Report", "title": "Availability of Satellite Based Digital Surface Models \u2013 Comparison of ALOS-AW3D and ASTER-GDEM Data over Serbia", "description": "Quality of land cover mapping, as well as result of many other Earth observation studies can be highly affected by inherent terrain complexity. In order to cope with and overcome these difficulties there is a need for additional information, which can be incorporated in model formulation or preprocessing steps. An example of such application is topographic or terrain correction in case of multispectral measurements, where correct information about surface slope enables more precise computation of irradiance received from the Sun. This is achieved by taking into account the local geometry of the scene. In addition to direct irradiance that is mostly affected by the terrain slope and Sun position, specific geometry can also produce or influence the overall amount of diffuse irradiances from the sky and surrounding ground. As expected, such effect is present in regions with high terrain variability, which is the case in central and southern parts of Serbia. Therefore, availability of appropriate digital surface/elevation models (DSM/DEM) is highly desirable. Term appropriate suggests that in some cases there can be a compromise between quality of DSM, on one side, and the spatial resolution of satellite images, roughness of the terrain, and level of co-registration on the other. There are several parameters that determine quality of elevation data, such as: acquisition and production technique, which depends on instrument type (passive or active), spatial resolution and vertical accuracy of the measurements, presence of noise and production artifacts, rate and availability of data coverage. DEMs produced using digitized contours derived from topographic maps, or produced directly by photogrammetry from airphoto campaigns, are usually considered as very accurate. However, very often these data are not available for some regions, have high cost, or they are just outdated. On the other hand, satellite products usually have regional or global coverage, and recently many of these high quality products became freely available due to new open data policies. In this paper we focus on two global datasets that were produced by optical stereoscopic observations carried out by Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on board of NASA\u2019s Terra satellite and Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) on board of JAXA\u2019s Advanced Land Observing Satellite (ALOS). More exactly, we compare specificities of two freely available global-scale 30 m products: improved version of ASTER Global Digital Elevation Model (GDEMv2) and 30 m derivative of original 5 m ALOS World 3D Topographic Data (AW3D) in the case of Serbia. Two datasets are also statistically compared over the larger region between 40 and 50 deg N, and 15 and 25 deg E. Since AW3D does not have full coverage for the given region, possible combination of two dataset is considered. Applicability of combined DEM is demonstrated over Serbia. In addition, an overview of alternative DSM products is also given, including: Shuttle Radar Topography Mission (SRTM), Digital Elevation Model over Europe (EU-DEM), and commercial products like WorldDEM and NEXTMap. Possibilities of utilizing Sentinel-1 and new initiatives like 3DEP (by USGS) in the future are also considered.", "keywords": ["13. Climate action"], "contacts": [{"organization": "Brklja\u010d, Branko, Lugonja, Predrag, Mini\u0107, Vladan, Pani\u0107, Marko, Marko, Oskar, Brdar, Sanja, Crnojevi\u0107, Vladimir,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7080135"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7080135", "name": "item", "description": "10.5281/zenodo.7080135", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7080135"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-09-14T00:00:00Z"}}, {"id": "10.5281/zenodo.7124850", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:03Z", "type": "Report", "title": "Best4Soil Factsheets Soil Health", "description": "Best4Soil is a European Thematic Network for Soil Health. The factsheets were written about the 4 project best practices: Anaerobic soil disinfestation (ASD), (bio)solarisation, green manure crops, compost and other organic amendments", "keywords": ["2. Zero hunger", "13. Climate action", "11. Sustainability", "15. Life on land", "6. Clean water", "12. Responsible consumption"], "contacts": [{"organization": "Projectteam Best4Soil", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7124850"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7124850", "name": "item", "description": "10.5281/zenodo.7124850", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7124850"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-09-29T00:00:00Z"}}, {"id": "2164/10968", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:26:08Z", "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.7124851", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:03Z", "type": "Report", "title": "Best4Soil Factsheets Soil Health", "description": "Best4Soil is a European Thematic Network for Soil Health. The factsheets were written about the 4 project best practices: Anaerobic soil disinfestation (ASD), (bio)solarisation, green manure crops, compost and other organic amendments", "keywords": ["2. Zero hunger", "13. Climate action", "11. Sustainability", "15. Life on land", "6. Clean water", "12. Responsible consumption"], "contacts": [{"organization": "Projectteam Best4Soil", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7124851"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7124851", "name": "item", "description": "10.5281/zenodo.7124851", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7124851"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-09-29T00:00:00Z"}}, {"id": "10.5281/zenodo.7193829", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:04Z", "type": "Dataset", "title": "Fine-root biomass production, sedge root, sedge leaf, and moss shoot decomposition, soil water-table level, and temperature data from two sedge fens in Finland", "description": "Fine-root biomass production, sedge root, sedge leaf, and Sphagnum moss shoot mass loss data, along with environmental data (soil water-table level, air temperature, soil temperature at 5 cm, and soil temperature at 15 cm) from two sedge fens located in southern Finland (Lakkasuo, Orivesi, 61\u00b048' N 24\u00b019'E) and northern Finland (Lompoloj\u00e4nkk\u00e4, Kittil\u00e4, 68\u00b0N 24\u00b012'E). Data are from a climate change experiment, where warming was induced with open top chambers (OTCs) and drying with shallow ditching. Data are from years 2011-2013.", "keywords": ["decomposition", "fen", "fine roots", "carbon cycling", "15. Life on land", "6. Clean water", "wetland", "litter mass loss", "climate change", "root biomass production", "13. Climate action", "sedge", "peatland", "mire", "organic matter accumulation"], "contacts": [{"organization": "Bhuiyan, Rabbil, M\u00e4kiranta, P\u00e4ivi, Strakov\u00e1, Petra, Fritze, Hannu, Minkkinen, Kari, Penttil\u00e4, Timo, Tuittila, Eeva-Stiina, Laiho, Raija,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7193829"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7193829", "name": "item", "description": "10.5281/zenodo.7193829", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7193829"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-11-15T00:00:00Z"}}, {"id": "10.5281/zenodo.7572718", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:06Z", "type": "Dataset", "title": "Mangroves in the lagoon of the protected Aldabra Atoll: a dataset on species, structure, biomass and the environment", "description": "Open AccessMangroves are vital for climate change mitigation since they store vast quantities of carbon as biomass and in the soil. Global mangrove biomass estimates are derived from climate-based relationships of mangroves with precipitation and temperature. However, the carbon stored locally is highly variable depending on environmental conditions. This uncertainty highlights the importance of local mangrove surveys and the need to explore factors that regulate forest structure and, therefore, carbon storage. In this study, we investigate the mangrove forest structure, seedling growth, species composition, aboveground biomass, soil organic carbon, and local environmental factors related to variation in mangrove carbon in the lagoonal mangroves on the protected Aldabra Atoll, Seychelles. We present a database from an extensive field survey of Aldabra's mangrove ecosystem using 54 plots of 5 m x 5 m along a mangrove coverage gradient. From November 2019 to November 2020, we measured the structural attributes and identified six mangrove species from &gt;750 adult mangrove trees on Aldabra. We used the height and diameter of adult trees to derive aboveground biomass and carbon from a tropical allometric equation. We measured the height of 59 mangrove seedlings over three sampling periods. In addition, environmental factors were recorded for each plot. We measured soil salinity repeatedly along the soil column. From 90 soil samples, we measured the physical and chemical properties of the soil, including soil organic carbon and elemental concentrations for &gt;20 elements. Autonomous measures of the water level, temperature and conductivity were made every 10 minutes over 1 year in a subset of 36 plots. The database provides 60% more information that is currently available for Seychelles regarding mangrove forest structure and biomass and is essential for research on several globally threatened and endemic species that depend on the mangroves on Aldabra. Furthermore, the database allows the incorporation of data and insights for the Western Indian Ocean and lagoonal mangroves, where few studies have been conducted on mangrove aboveground biomass and soil organic carbon. No copyright restrictions apply to the use of this data set. Please cite this data paper when using the current data in publications.", "keywords": ["13. Climate action", "aboveground biomass", " blue carbon", " field survey", " islands", " lagoon", " one-year field period", " protected area", " Seychelles", " soil nutrients", " water level", " water temperature", " Western Indian Ocean.", "14. Life underwater", "15. Life on land"], "contacts": [{"organization": "Constance, Annabelle", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7572718"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7572718", "name": "item", "description": "10.5281/zenodo.7572718", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7572718"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-08-10T00:00:00Z"}}, {"id": "10.5281/zenodo.7229040", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:04Z", "type": "Software", "title": "Model code for \"Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)\"", "description": "500 m fire carbon emissions model code as part of the publication: 'Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)' Dave van Wees<sup>1</sup>, Guido R. van der Werf<sup>1</sup>, James T. Randerson<sup>2</sup>, Brendan M. Rogers<sup>3</sup>, Yang Chen<sup>2</sup>, Sander Veraverbeke<sup>1</sup>, Louis Giglio<sup>4</sup>, and Douglas C. Morton<sup>5</sup> <sup>1</sup>Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands<br> <sup>2</sup>Department of Earth System Science, University of California, Irvine, CA 92697, USA<br> <sup>3</sup>Woodwell Climate Research Center, Falmouth, MA 02540, USA<br> <sup>4</sup>Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA<br> <sup>5</sup>Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA DOI: https://doi.org/10.5194/gmd-15-8411-2022 Developed in Python version 2.7.16. Please note, this code is meant to give a general overview of the model structure and not to fully reproduce the model results with the push of one button. The full model code is much more complex to account for various simulation scenarios and relies on numerous large input datasets that all require extensive preprocessing. By omitting these complexities, we tried to make this script as understandable as possible. In case your goal is to reproduce the model in detail, please contact the first author to discuss the possibilities.", "keywords": ["13. Climate action", "7. Clean energy"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7229040"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7229040", "name": "item", "description": "10.5281/zenodo.7229040", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7229040"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-10-20T00:00:00Z"}}, {"id": "10.5281/zenodo.7229039", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:04Z", "type": "Software", "title": "Model code for \"Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)\"", "description": "500 m fire carbon emissions model code as part of the publication: 'Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)' Dave van Wees<sup>1</sup>, Guido R. van der Werf<sup>1</sup>, James T. Randerson<sup>2</sup>, Brendan M. Rogers<sup>3</sup>, Yang Chen<sup>2</sup>, Sander Veraverbeke<sup>1</sup>, Louis Giglio<sup>4</sup>, and Douglas C. Morton<sup>5</sup> <sup>1</sup>Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands<br> <sup>2</sup>Department of Earth System Science, University of California, Irvine, CA 92697, USA<br> <sup>3</sup>Woodwell Climate Research Center, Falmouth, MA 02540, USA<br> <sup>4</sup>Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA<br> <sup>5</sup>Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA DOI: https://doi.org/10.5194/gmd-15-8411-2022 Developed in Python version 2.7.16. Please note, this code is meant to give a general overview of the model structure and not to fully reproduce the model results with the push of one button. The full model code is much more complex to account for various simulation scenarios and relies on numerous large input datasets that all require extensive preprocessing. By omitting these complexities, we tried to make this script as understandable as possible. In case your goal is to reproduce the model in detail, please contact the first author to discuss the possibilities.", "keywords": ["13. Climate action", "7. Clean energy"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7229039"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7229039", "name": "item", "description": "10.5281/zenodo.7229039", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7229039"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-10-20T00:00:00Z"}}, {"id": "10.5281/zenodo.7229674", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:04Z", "type": "Dataset", "title": "Model data for \"Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)\"", "description": "500 m fire carbon emissions and burned area as part of the publication:  'Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)'  Dave van Wees1, Guido R. van der Werf1, James T. Randerson2, Brendan M. Rogers3, Yang Chen2, Sander Veraverbeke1, Louis Giglio4, and Douglas C. Morton5  1Department of Earth Sciences, Vrije Universiteit, Amsterdam, 1081 HV, The Netherlands2Department of Earth System Science, University of California, Irvine, CA 92697, USA3Woodwell Climate Research Center, Falmouth, MA 02540, USA4Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA5Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA  DOI: https://doi.org/10.5194/gmd-15-8411-2022  \u00a0  UPDATE OF DATASET TO 2022:  This dataset has now been extended to 2022 along with multiple updates to the model input data:  - Update from MODIS C6 to MODIS C6.1 for all MODIS input data, including MCD12Q1 land cover types, MCD14ML active fires, MCD15A2H fPAR, MOD44B VCF, MOD44W land-water mask, and MCD64A1 burned area.- Update of Hansen forest loss data from v1.9 to v1.11.- Update of GLEAM evaporative stress data from v3.6b to v3.7b.- Extension of ERA5-land data to 2022.  \u00a0  Files contain 500-m (per MODIS tile) and 0.25 degree aggregated (global grid) carbon emissions and burned area from biomass burning for 2002-2022, as part of the paper 'Global biomass burning fuel consumption and emissions at 500-m spatial resolution based on the Global Fire Emissions Database (GFED)' published in Geoscientific Model Development (https://doi.org/10.5194/gmd-15-8411-2022). 0.25 degree global grid files also include biome partitioning and accompanying biome fractional cover grids.  Zip archives with filenames '500m_YYYY.zip' contain annual files named 'Model500m_2002-2022yr_h##v##_YYYY.nc', which are the 500-meter resolution model results per MODIS tile using the MODIS sinusoidal projection. Carbon emission data layers are:  - Total biomass burning carbon emissions from aboveground; g C m-2 month-1 (/MOD_Grid/emissions/C_AG_TOT)  - Total biomass burning carbon emissions from belowground; g C m-2 month-1 (/MOD_Grid/emissions/C_BG_TOT)  - Fire-related forest loss carbon emissions from aboveground; g C m-2 month-1 (/MOD_Grid/emissions/C_AG_FL)  - Fire-related forest loss carbon emissions from belowground; g C m-2 month-1 (/MOD_Grid/emissions/C_BG_FL)  Total emissions are calculated as: C_AG_TOT + C_BG_TOT. Total fire-related forest loss emissions are calculated as: C_AG_FL + C_BG_FL.  Burned area data layers are:  - Total burned area; fraction of 500-m grid cell per month (/MOD_Grid/burned_area/BA_TOT)  - Burned area from fire-related forest loss; fraction of 500-m grid cell per month (/MOD_Grid/burned_area/BA_FL)  The Zip archive with filename '025d_2002_2022.zip' contains annual files named 'Model500m_2002-2022yr_025d_YYYY.nc', which are the 500-m model results aggregated to a 0.25 degree global lat-lon grid. These files contain the same variables as the 500-m files, but aggregated to 0.25 degree resolution (MOD_CMG025). Furthermore, these files include biome partitioning of emissions and burned area (MOD_CMG025BIOME) and provide accompanying biome fractional cover grids for all 20 biomes (variable 'biomes'). Biomes are listed in detail in Table S1 of the van Wees et al. (2022) paper. The biomes 'water', 'snow/ice' and 'barren' were excluded from Table S1 because of their negligible share, but are included in the files provided here for completeness.", "keywords": ["13. Climate action", "11. Sustainability", "15. Life on land", "7. Clean energy"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7229674"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7229674", "name": "item", "description": "10.5281/zenodo.7229674", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7229674"}, {"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.7398102", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:05Z", "type": "Other", "title": "Supporting the soil deal for Europe across national communities", "description": "Open AccessFunded by the European Union, Horizon Europe Programme. Grant Agreement N. 101090738", "keywords": ["13. Climate action", "EU Mission Soil", "Soil health", "11. Sustainability", "15. Life on land"], "contacts": [{"organization": "De Majo, Claudio, Drago, Federico,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7398102"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7398102", "name": "item", "description": "10.5281/zenodo.7398102", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7398102"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-12-05T00:00:00Z"}}, {"id": "10.5281/zenodo.7656722", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:06Z", "type": "Dataset", "title": "Data for: The effect of land-use change on soil C, N, P, and their stoichiometries: A global synthesis", "description": "Open Access<strong><em>Data description</em></strong> This dataset includes detailed information about five different types of land use change reported in \u201cThe effect of land-use change on soil C, N, P, and their stoichiometries: A global synthesis (Agriculture, Ecosystems and Environment; https://doi.org/10.1016/j.agee.2023.108402)\u201d. Lists of five different types of land use change 1) conversion of primary forest to cropland 2) conversion of primary forest to grassland 3) conversion of cropland to forest 4) conversion of grassland to forest 5) conversion of grassland to cropland Lists of detailed information Land use change (pre-LUC, post-LUC) Country, Location, Geographic position (Longitude, Latitude) Altitude (m) Climate zone Weather [rainfall (mm yr<sup>-1</sup>) and temperature (\u00b0C)] Reported time of change (years) Vegetation type (pre-LUC, post-LUC) Fertilizer (pre-LUC, post-LUC: type, application; change) Soil sampling depth (cm) Soil type [units, pre-LUC, post-LUC, change rate (%)] Soil pH, bulk density, CEC [units, pre-LUC, post-LUC, change rate (%)] Soil organic carbon [units, pre-LUC, post-LUC, change rate (%)] Soil total nitrogen [units, pre-LUC, post-LUC, change rate (%)] Soil total phosphorus [units, pre-LUC, post-LUC, change rate (%)] Soil C:N [units, pre-LUC, post-LUC, change rate (%)] Soil C:P [units, pre-LUC, post-LUC, change rate (%)] Soil N:P [units, pre-LUC, post-LUC, change rate (%)] Reference <em><strong>Data collection method</strong></em> We analyzed five different types of LUC: 1) conversion of primary forest to cropland, 2) conversion of primary forest to grassland, 3) conversion of cropland to forest, 4) conversion of grassland to forest, and 5) conversion of grassland to cropland. We classified primary forest as forest that had not previously been cleared and used for other land uses. The conversion of cropland or grassland to forest includes naturally generated and intentionally planted forest. Cropland is land used for growing agricultural crops and may include short pasture phases, and grassland is land used continuously for grazing purposes, but may include occasional and repeated pasture-renewal phases. While we tried to make categorical distinctions between these land-use types, land uses are often more fluid in practice, which may not always have been stated in the publications underlying our data compilation. When a paper reported both contents and stocks, we used the stock-based measure. We used reported stocks if the original work had already been corrected to equivalent soil mass (Ellert and Bettany, 1995) or if corrected stocks had been reported in previous reviews or meta-analyses (Don et al., 2011; Poeplau et al., 2011; Guo and Gifford, 2002). Where bulk-density correction had not been applied, we tried to make those corrections to estimate changes to equivalent soil mass if studies provided sufficient information on soil bulk density and depth, using the method of Zhang et al. (2004). If that was not possible, we used the reported SOC, TN, or TP contents. <em><strong>Acknowledgements</strong></em> We thank scientists who measured, analyzed, and published the data compiled for this study. We are especially grateful to Drs. Axel Don, Christopher Poeplau, Lex Bouwman, and Gaihe Yang, who provided their global meta-data through personal communication. D.-G.K. acknowledges support from the IAEA CRP D15020. M.U.F.K and L.L.L. were supported by the Strategic Science Investment Fund (SSIF) of New Zealand\u2019s Ministry of Business, Innovation and Employment.", "keywords": ["2. Zero hunger", "13. Climate action", "land-use change", " greenhouse gas emissions", " soil", " carbon", " nitrogen", " phosphorus", " stoichiometry", " time", " temperature", " rainfall", " forest type", "15. Life on land"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7656722"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7656722", "name": "item", "description": "10.5281/zenodo.7656722", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7656722"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-02-20T00:00:00Z"}}, {"id": "10.5281/zenodo.7462416", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:05Z", "type": "Report", "title": "SWAT+ modeling protocol for the assessment of water and nutrient retention measures in small agricultural catchments", "description": "This SWAT+ modelling protocol was designed for guiding model setup development and model calibration in 14 European case study sites participating in the modelling component of the EU funded research and innovation project OPtimal strategies to retAIN and re-use water and nutrients in small agricultural catchments across different soil-climatic regions in Europe (OPTAIN). These 14 case studies are small agricultural catchments (ranging in size from 21 to 254 km2 ) located in three biogeographical regions of Europe and 12 different countries. The main topic of OPTAIN are Natural/Small Water Retention Measures, which are a relatively new concept. These are small and multi-functional measures for the retention/management of water and nutrients in the landscape, thus addressing drought/flood control, management of water quality problems, climate change adaptation, biodiversity restoration, etc.", "keywords": ["modelling", "SWAT+", "13. Climate action", "NSWRMs", "11. Sustainability", "H2020", "OPTAIN", "protocol", "15. Life on land", "6. Clean water"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7462416"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7462416", "name": "item", "description": "10.5281/zenodo.7462416", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7462416"}, {"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.7464210", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:05Z", "type": "Dataset", "title": "CLSoilMaps: A national soil gridded product for Chile", "description": "unspecifiedFe de erratas: Available Water Capacity description had a minor error. We have updated the files description at the table below.       Soil att    File abreviation    Description    Units      Bulk density    Bulkd    Bulk density of the fine fraction    g/cm3      Clay    Clay    Clay content    %      Sand    Sand    Sand content    %      Silt    Silt    Silt content    %      Field Capacity    FC    Field capacity at 330kPa    cm3/cm3      Permanent Wilting Point    PWP    Permanent wilting point at 15000kPa    cm3/cm3      Available Water Capacity    AWC    Available water capacity as h*(FC-PWP), h = horizon depth in mm    mm      Total Available Water Capacity    Total_AWC    Sum of AWC across all depths    mm      Available Moisture    AvMoist    Available Moisture as FC-PWP    cm3/cm3    \u03b8r   theta_r    residual water content    cm3/cm3      \u03b8s    theta_s    saturated water content    cm3/cm3      \u03b1    alpha    'alpha' shape parameter    1/cm      npar    n    'n' shape parameter    -      Soil Hydraulic Conductivity    ksat    saturated hydraulic conductivity    cm/day", "keywords": ["2. Zero hunger", "13. Climate action", "Soil Physical properties", " Soil hydraulic parameters", " Digital Soil Mapping", " Chile", "15. Life on land", "6. Clean water"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7464210"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7464210", "name": "item", "description": "10.5281/zenodo.7464210", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7464210"}, {"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.7576836", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:06Z", "type": "Report", "title": "AI4SoilHealth project: accelerating collection and use of soil health information using AI technology to support the Soil Deal for Europe and EU Soil Observatory", "description": "The objective of AI4SoilHealth is to co-design, create and maintain an open access European-wide digital infrastructure, compiled using state-of-the-art Artificial Intelligence (AI) methods combined with new and deep soil health understanding and measures. The AI-based data infrastructure would function as a Digital Twin to the real-World biophysical system, a Soil Digital Twin, that could be then used for assessing and continuously monitoring Soil Health metrics by land use and/or management parcel, supporting the European Commission\u2019s objective of transitioning towards healthy soils by 2030. The project is divided into seven (7) work-packages including: (WP2) Policy and stakeholder engagement - networking and synchronising with EU and national programmes, (WP3) Soil health methodology and standards - developing/testing methodology to be used by WPs 4-6, (WP4) Soil health in-situ monitoring tools and data - developing field and laboratory solutions for Observations &amp; Measurements, (WP5) Harmonised EU-wide soil monitoring services - developing the final suite of tools, data and services, (WP6) Multi-actor engagement pilots - organizing field-works and collect users' feedback, (WP7) Soil literacy, capacity building and communication - organizing public campaigns and producing educational materials. Key deliverables of the project include: 1) Coherent Soil Health Index methodology, 2) Rapid Soil Health Assessment Toolbox, 3) AI4SoilHealth Data Cube for Europe, 4) Soil-Health-Soil-Degradation-Monitor, and 5) AI4SoilHealth API and Mobile phone App. Produced tools will be exposed to target-users (including farmer associations in &gt;10 countries), so their feedback is used to improve design/functionality. Produced high-resolution pan-European datasets will be distributed under an Open Data licence, allowing easy access by development communities.", "keywords": ["2. Zero hunger", "soil health", "13. Climate action", "AI", "soil monitoring", "11. Sustainability", "open data", "data cube", "15. Life on land", "6. Clean water", "12. Responsible consumption"], "contacts": [{"organization": "Wheeler, Ichsani", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7576836"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7576836", "name": "item", "description": "10.5281/zenodo.7576836", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7576836"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.7463395", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:05Z", "type": "Report", "title": "SWAT+ modeling protocol for the assessment of water and nutrient retention measures in small agricultural catchments", "description": "This SWAT+ modelling protocol was designed for guiding model setup development and model calibration in 14 European case study sites participating in the modelling component of the EU funded research and innovation project OPtimal strategies to retAIN and re-use water and nutrients in small agricultural catchments across different soil-climatic regions in Europe (OPTAIN). These 14 case studies are small agricultural catchments (ranging in size from 21 to 254 km2 ) located in three biogeographical regions of Europe and 12 different countries. The main topic of OPTAIN are Natural/Small Water Retention Measures, which are a relatively new concept. These are small and multi-functional measures for the retention/management of water and nutrients in the landscape, thus addressing drought/flood control, management of water quality problems, climate change adaptation, biodiversity restoration, etc.", "keywords": ["modelling", "SWAT+", "13. Climate action", "NSWRMs", "11. Sustainability", "H2020", "OPTAIN", "protocol", "15. Life on land", "6. Clean water"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7463395"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7463395", "name": "item", "description": "10.5281/zenodo.7463395", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7463395"}, {"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.7472652", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:05Z", "type": "Dataset", "title": "Soil gas concentration and flux data from two peatland forest harvesting experiments in Southern Finland", "description": "The dataset contains data analyzed in a scientific manuscript Peltoniemi et al., <em>Soil CO<sub>2</sub>, CH<sub>4</sub> and N<sub>2</sub>O concentrations and fluxes in peatland forests are associated with water table level - implications of selection harvesting on soil emissions</em>, in review. The data was collected from two peatland forests. The sites Lettosuo (60.63\u00b0 N, 23.95\u00b0 E ) and Paroninkorpi (61.01\u00b0N, 24.75\u00b0E) locate in southern Finland, and they are fertile drained spruce mires. Both sites have unharvested control and selection harvested treatments. The dataset contains CO<sub>2</sub>, CH<sub>4</sub>, N<sub>2</sub>O and O<sub>2</sub> concentrations analyzed by gas chromatograph from soil gas samples collected with silicon rubber tubes inserted into soil at different depths over the year 2018-2020. The dataset additionally contains soil gas flux measurements in 2020 with a portable gas analyzer. Air temperature and water table level were measured at both sites, rainfall in nearby meteorological stations, and soil redox potential at one of the sites (Paroninkorpi). More information about the research sites and data is available in the manuscript, and Laurila et al., 2020 (http://urn.fi/URN:ISBN:978-952-380-191-2).", "keywords": ["greenhouse gas", " soil", " peatland", " selection harvest", " climate smart forestry", "13. Climate action", "15. Life on land", "6. Clean water"], "contacts": [{"organization": "Peltoniemi, Mikko, Li, Qian, Turunen, Pauliina, M\u00fcller, Mitro, Anttila, Jani, Koskinen, Markku, Laiho, Raija, Lehtonen, Aleksi, M\u00e4kiranta, P\u00e4ivi, Sarkkola, Sakari, Tupek, Boris, M\u00e4kip\u00e4\u00e4, Raisa,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7472652"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7472652", "name": "item", "description": "10.5281/zenodo.7472652", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7472652"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-12-22T00:00:00Z"}}, {"id": "10.5281/zenodo.7472653", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:06Z", "type": "Dataset", "title": "Soil gas concentration and flux data from two peatland forest harvesting experiments in Southern Finland", "description": "The dataset contains data analyzed in a scientific manuscript Peltoniemi et al., <em>Soil CO<sub>2</sub>, CH<sub>4</sub> and N<sub>2</sub>O concentrations and fluxes in peatland forests are associated with water table level - implications of selection harvesting on soil emissions</em>, in review. The data was collected from two peatland forests. The sites Lettosuo (60.63\u00b0 N, 23.95\u00b0 E ) and Paroninkorpi (61.01\u00b0N, 24.75\u00b0E) locate in southern Finland, and they are fertile drained spruce mires. Both sites have unharvested control and selection harvested treatments. The dataset contains CO<sub>2</sub>, CH<sub>4</sub>, N<sub>2</sub>O and O<sub>2</sub> concentrations analyzed by gas chromatograph from soil gas samples collected with silicon rubber tubes inserted into soil at different depths over the year 2018-2020. The dataset additionally contains soil gas flux measurements in 2020 with a portable gas analyzer. Air temperature and water table level were measured at both sites, rainfall in nearby meteorological stations, and soil redox potential at one of the sites (Paroninkorpi). More information about the research sites and data is available in the manuscript, and Laurila et al., 2020 (http://urn.fi/URN:ISBN:978-952-380-191-2).", "keywords": ["greenhouse gas", " soil", " peatland", " selection harvest", " climate smart forestry", "13. Climate action", "15. Life on land", "6. Clean water"], "contacts": [{"organization": "Peltoniemi, Mikko, Li, Qian, Turunen, Pauliina, M\u00fcller, Mitro, Anttila, Jani, Koskinen, Markku, Laiho, Raija, Lehtonen, Aleksi, M\u00e4kiranta, P\u00e4ivi, Sarkkola, Sakari, Tupek, Boris, M\u00e4kip\u00e4\u00e4, Raisa,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7472653"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7472653", "name": "item", "description": "10.5281/zenodo.7472653", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7472653"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-12-22T00:00:00Z"}}, {"id": "2117/364526", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:26:06Z", "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": "<?xml version='1.0' encoding='UTF-8'?><article><p>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.</p></article>", "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.7576835", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:06Z", "type": "Report", "title": "AI4SoilHealth project: accelerating collection and use of soil health information using AI technology to support the Soil Deal for Europe and EU Soil Observatory", "description": "The objective of AI4SoilHealth is to co-design, create and maintain an open access European-wide digital infrastructure, compiled using state-of-the-art Artificial Intelligence (AI) methods combined with new and deep soil health understanding and measures. The AI-based data infrastructure would function as a Digital Twin to the real-World biophysical system, a Soil Digital Twin, that could be then used for assessing and continuously monitoring Soil Health metrics by land use and/or management parcel, supporting the European Commission\u2019s objective of transitioning towards healthy soils by 2030. The project is divided into seven (7) work-packages including: (WP2) Policy and stakeholder engagement - networking and synchronising with EU and national programmes, (WP3) Soil health methodology and standards - developing/testing methodology to be used by WPs 4-6, (WP4) Soil health in-situ monitoring tools and data - developing field and laboratory solutions for Observations &amp; Measurements, (WP5) Harmonised EU-wide soil monitoring services - developing the final suite of tools, data and services, (WP6) Multi-actor engagement pilots - organizing field-works and collect users' feedback, (WP7) Soil literacy, capacity building and communication - organizing public campaigns and producing educational materials. Key deliverables of the project include: 1) Coherent Soil Health Index methodology, 2) Rapid Soil Health Assessment Toolbox, 3) AI4SoilHealth Data Cube for Europe, 4) Soil-Health-Soil-Degradation-Monitor, and 5) AI4SoilHealth API and Mobile phone App. Produced tools will be exposed to target-users (including farmer associations in &gt;10 countries), so their feedback is used to improve design/functionality. Produced high-resolution pan-European datasets will be distributed under an Open Data licence, allowing easy access by development communities.", "keywords": ["2. Zero hunger", "soil health", "13. Climate action", "AI", "soil monitoring", "11. Sustainability", "open data", "data cube", "15. Life on land", "6. Clean water", "12. Responsible consumption"], "contacts": [{"organization": "Wheeler, Ichsani", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7576835"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7576835", "name": "item", "description": "10.5281/zenodo.7576835", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7576835"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.7625435", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:06Z", "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.7649034", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:06Z", "type": "Dataset", "title": "Electron microscopy of particles collected by different techniques from field measurements in the Moroccan Sahara during FRAGMENT 2019", "description": "An intensive field campaign between 4-30 September 2019 was conducted at a major source region on the edge of the Saharan desert in Morocco (29.83 \u00b0N 5.87 \u00b0W) in the context of the FRontiers in dust minerAloGical coMposition and its Effects upoN climaTe (FRAGMENT) project. Samples were collected with three different sampling techniques, namely: flat-plate sampler (FPS), free-wing impactor (FWI), and a micro-orifice uniform deposit impactor (MOUDI). Substrates in the MOUDI and FWI were collected two times a day with a typical sampling duration of a few minutes to avoid overloading the substrate for individual particle analysis. For the flat-plate sampler, the average exposure time was half a day. Here we present dataset of the elemental composition and morphology of more than 300,000 freshly emitted individual particles by performing offline analysis in the laboratory using Scanning Electron Microscopy (SEM) coupled with Energy-Dispersive X-ray Spectrometry (EDX).", "keywords": ["13. Climate action", "SEM-EDX; single particle analysis; Mineral dust"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7649034"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7649034", "name": "item", "description": "10.5281/zenodo.7649034", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7649034"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-02-22T00:00:00Z"}}, {"id": "10.5281/zenodo.7649033", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:06Z", "type": "Dataset", "title": "Electron microscopy of particles collected by different techniques from field measurements in the Moroccan Sahara during FRAGMENT 2019", "description": "An intensive field campaign between 4-30 September 2019 was conducted at a major source region on the edge of the Saharan desert in Morocco (29.83 \u00b0N 5.87 \u00b0W) in the context of the FRontiers in dust minerAloGical coMposition and its Effects upoN climaTe (FRAGMENT) project. Samples were collected with three different sampling techniques, namely: flat-plate sampler (FPS), free-wing impactor (FWI), and a micro-orifice uniform deposit impactor (MOUDI). Substrates in the MOUDI and FWI were collected two times a day with a typical sampling duration of a few minutes to avoid overloading the substrate for individual particle analysis. For the flat-plate sampler, the average exposure time was half a day. Here we present dataset of the elemental composition and morphology of more than 300,000 freshly emitted individual particles by performing offline analysis in the laboratory using Scanning Electron Microscopy (SEM) coupled with Energy-Dispersive X-ray Spectrometry (EDX).", "keywords": ["13. Climate action", "SEM-EDX; single particle analysis; Mineral dust"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7649033"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7649033", "name": "item", "description": "10.5281/zenodo.7649033", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7649033"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-02-22T00:00:00Z"}}, {"id": "10.5281/zenodo.7687513", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:07Z", "type": "Report", "title": "Effects of a fungal invasion on soil bacteria", "description": "<strong>Presentation by F.Pinzari at The World Congress of Soil Science 2022, which took place in Glasgow from 31st July - 5th August 2022</strong> Abstract: <strong>Effects of a fungal invasion on soil bacteria </strong> Pinzari F.<sup>1,2</sup>, Clark M.D.<sup>1</sup>, Misra R.<sup> 3</sup>, Chooneea D.<sup>3</sup>, Xu X.-M.<sup>4</sup>, Jungblut A.D.<sup>1</sup> <sup>1</sup>Life Sciences Department, Natural History Museum, Cromwell Road, SW7 5BD London, UK <sup>2</sup>Institute for Biological Systems, Council of National Research of Italy (CNR), Monterotondo (RM), Italy <sup>3</sup>Core Research Laboratories, Molecular Biology, Natural History Museum, London, United Kingdom <sup>4</sup>National Institute of Agricultural Botany, East Malling Research Station (EMR), East Malling, UK Fungal bioinoculants have a vast potential in agriculture because they can help increase crop yields and quality and reduce the application of chemicals. Their effectiveness has been widely tested (Malus\u00e0 et al., 2016). However, little is known about the effect of bioinoculants on microbial assemblages in non-rhizospheric soil. A sudden artificial introduction of a fungal species in soil could theoretically impact the biodiversity of local microbial communities and lead to changes in nutrient availability (van Elsas et al., 2012). We assessed the impact of a competitive fungal inoculum, the globally-used biofertiliser <em>Trichoderma afroharzianum </em>T22, on soil microcosms to understand 1) to what extent the native microbial community richness and relative abundance are influenced by a fungal strain introduced to soil; 2) whether microbial taxa are resilient to the disturbance caused by the fungus; 3) how far the bioinoculant impacts the soil microorganisms functions. We used bacterial 16S rRNA gene amplicon sequencing (Illumina) and a shotgun metagenomic analysis (Oxford Nanopore Sequencing) to analyse the microbial communities in bioreactors after seven weeks of incubation with and without the fungus. The presence of the fungus had a negative impact on the abundance of some groups of bacteria, such as the genus <em>Pseudomonas, </em>and it stimulated the presence of species metabolically linked to the fungus, including chitin degrading Chitinophagaceae. In conclusion, the results suggest that more than an impact on bacteria's overall biodiversity, the fungus has favoured some groups at the expense of others, even creating new food webs and trophic niches. <strong>References</strong> Malus\u00e0 E, Pinzari F, Canfora L (2016) Efficacy of Biofertilizers: Challenges to Improve Crop Production. In: D.P. Singh et al. (eds.), Microbial Inoculants in Sustainable Agricultural Productivity: Microbial Inoculants in Sustainable Agricultural Productivity, pp.17-40 Springer India doi.org/10.1007/978-81-322-2644-4_2 van Elsas JD, Chiurazzi M, Mallon CA, Elhottova D, Kristufek V, Salles JF. (2012) Microbial diversity determines the invasion of soil by a bacterial pathogen. Proc Natl Acad Sci U S A. 24;109(4):1159-64. doi: 10.1073/pnas.1109326109.", "keywords": ["2. Zero hunger", "soil", " Trichoderma", " invasion", " microbial community", " bioinoculants", " T22", "13. Climate action", "15. Life on land", "6. Clean water", "12. Responsible consumption"], "contacts": [{"organization": "Pinzari, Flavia, Jungblut, Anne D., Clark, M.D., Misra, R., Xu, X.-M., Chooneea, D.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7687513"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7687513", "name": "item", "description": "10.5281/zenodo.7687513", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7687513"}, {"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.7695462", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:07Z", "type": "Report", "title": "Knowedge needs and gaps on soil and land management", "description": "Soil health is vital for many ecosystem services. The Horizon Europe (HE) Mission \u201cA Soil Deal for Europe\u201d aims to accelerate the transition to sustainable soil and land management and healthy soils through an am-bitious transdisciplinary research and innovation (R&amp;I) programme, largely based on actor engagement, Liv-ing Labs and Lighthouses. The H2020 Soil Mission Support (SMS) project supported the implementation of the HE Mission, and aimed to improve the coordination of R&amp;I on sustainable soil and land management. Through a co-creation process together with actors, SMS collated available knowledge, actors R&amp;I needs and identified R&amp;I gaps that need to be addressed for successful transition towards sustainable soil and land management.<br> The first step was to identify existing R&amp;I knowledge through a keyword-based analysis of scientific literature published and peer reviewed, related to sustainable soil and land management. The literature analysis ad-dressed the full range of societal challenges, soil health objectives, land use types and knowledge domains necessary to capture the socio-ecological complexity of soil health. Covering some 15,700 scientific articles, this literature analysis represents the current peer reviewed knowledge stock on sustainable soil and land management. A textual analysis using the digital platform CorTexT was undertaken to explore the identified literature and submitted to project consortium internal experts, who analysed and processed the collected information of their respective area of expertise (Annex III). The literature analysis revealed that the societal challenges \u201creduce soil degradation\u201d and \u201cimprove disaster control\u201d have been studied extensively. Con-versely, the societal challenges \u201cmitigate land take\u201d and \u201cincrease biodiversity\u201d and the knowledge domains \u201cscience-based policy support\u201d and \u201cawareness, training &amp; education\u201d are less discussed. Factsheets present-ing the results of the literature analysis per societal challenge were developed and can be found in Annex VIII. Note that as the key-word based literature search was limited to Scopus-indexed scientific journals, other publishing formats such as conference papers, books, book chapters, non-digitalized articles, grey literature, reports, patents, etc., may be underrepresented or not included in the used data base. The exclusive use of Scopus-indexed scientific articles provided quality insurance of the material through the publication peer-review system. Nonetheless, important documents and knowledge have been incorporated by the consor-tium experts when analysing the collected literature.<br> The second step was to consult actors through online workshops and surveys in order to gain a practice-oriented \u2018real-life\u2019 picture of current knowledge and R&amp;I needs for swift implementation of sustainable soil and land management. This step was seen as complementary of the published and peer-reviewed literature.<br> Finally, after exploring our stocktaking of R&amp;I from existing knowledge evidenced by literature review and the actor\u2019s knowledge needs identified from actor consultations, we identified R&amp;I gaps. The main knowledge gaps across all Mission Objectives were of socio-economic nature: drivers and causes of land degradation, knowledge management, governance and policies for inciting improved management, and interaction with other sectors are not sufficiently understood. Second, the HE Missions\u2019 focus on improving soil literacy was supported by the literature analysis and by the actor consultation, which both revealed knowledge gaps re-lated to education and capacity building in all land use types and domains affecting soil health: production, consumption, trade, policy and governance. Thirdly, there is a gap in the long-term implementation of a new mode of knowledge co-design, where researchers and practitioners together develop solutions for sustaina-ble soil and land management in a real-world context. The HE Missions\u2019 focus on Living Labs and Lighthouses has the potential to close this gap. Finally, there is a need to define several concepts (e.g. soil health, soil degradation, footprint). Such definitions should be shared and will be a basis to identify relevant indicators and respective thresholds, and to develop guidelines to support monitoring programmes in order to translate knowledge into evidence for decision making.<br> The outcome of the deliverable is a list of validated R&amp;I gaps across all Mission Objectives which will feed into the SMS roadmap and the HE Mission.", "keywords": ["2. Zero hunger", "13. Climate action", "11. Sustainability", "15. Life on land", "6. Clean water", "12. Responsible consumption"], "contacts": [{"organization": "Mason, Elo\u00efse, L\u00f6bmann, Michael, Matt, Mireille, Sharif, Ibrat, Maring, Linda, Ittner, Sophie, Bispo, Antonio,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7695462"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7695462", "name": "item", "description": "10.5281/zenodo.7695462", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7695462"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-02-17T00:00:00Z"}}, {"id": "10.5281/zenodo.7695581", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:07Z", "type": "Report", "title": "Report on prioritization of actor needs and criteria for living lab and lighthouse identification", "description": "The EU Soil Mission \u201cA Soil Deal for Europe\u201d<br> Soil health is vital for a wide range of ecosystem services. The EU Soil Mission \u201cA Soil Deal for<br> Europe\u201d (EC, 2021) accelerates the transition to healthy soils through ambitious actions in 100 Living<br> Labs and Lighthouses within territorial settings, combined with an ambitious transdisciplinary R&amp;I<br> programme, a robust, harmonised soil monitoring framework and increased soil literacy and<br> communication to engage with citizens, all of this in synergy with relevant EU instruments and<br> policies. The H2020 Soil Mission Support (SMS) project supports the implementation of the Soil Mission. This<br> SMS deliverable reports the aspects of identification and prioritization of actor needs in relation to<br> the Soil Mission and elaborates on criteria for Soil Health Living Labs and Lighthouses (SH LL &amp; LH).<br> Actors are defined as stakeholders who actively engage in land and soil management, so who really<br> act. Actor needs are defined as that what increases the gains, or reliefs the pains that actors face in<br> their job to do. Living labs are understood as user-centred, place-based and transdisciplinary<br> research and innovation ecosystems between multiple partners established at regional or subregional<br> levels. Each living lab includes several sites at which systemic research and codesign,<br> testing, monitoring and evaluation of solutions take place. (EC, 2021). Lighthouses are rather<br> understood as one site for demonstration of solutions, training and communication that are<br> exemplary in their performance in terms of soil health improvement (EC, 2021).<br> LL &amp; LH and actor needs very much interrelate. Living Labs are efficient instruments for actors to<br> experiment in real life settings. Lighthouses have a real demonstrative potential to encourage and<br> engage larger communities. Hence, identification of the actor needs towards soil health is crucial to<br> enable their engagement in the activities of the \u201cSoil Deal for Europe\u201d i.e. to make the transitions<br> needed to significantly improve European Soil Health. <br> Actor needs<br> SMS anticipated that in order to be able to engage actors, it is needed to know and then express<br> \u2018what is in for them?\u2019 Therefore, SMS drafted value propositions tailored to different actor groups<br> (D3.3, Brils et al., 2021). To further refine these propositions, the next step was to specify and<br> prioritize the actor needs in relation to the objectives of the EU Soil Mission. Primary source of<br> information for achieving insight in the actor needs was the SMS survey on actor needs.<br> Furthermore, two workshops were held with actors to provide a supplementary source of<br> information.<br> The SMS survey was prepared and sent to approximately 550 soil professionals, all over Europe.<br> These professionals are expected to be knowledgeable about the topics that are raised in the survey.<br> Thus, the survey was not sent to citizens/general public/consumers as a specific actor group. In total<br> 93 respondents responded to the survey: 32 partly and 61 fully. Respondents were asked to<br> complete the survey from the perspective of their organization. Responding organizations are active<br> in most European Member States and in some non-EU countries. Most respondents were<br> researchers, policy makers and governmental organisations. The lowest response came from the<br> land-users, managers and owner\u2019s actor group.<br> <br> The workshops were held at the AquaConSoil conference, visited by service providers and<br> researchers with an urban/industrial background; and one with the SMS Expert Group, consisting of<br> representatives from relevant networks covering different actor and land use groups.<br> After a joint analysis and synthesis of the outcome of the survey as well as the workshops it is<br> concluded that the generic actor needs \u2013 i.e. applicable to all the actor groups \u2013 in order of priority<br> are: <br> 1. Funding: This appears the number one need for all the actor groups, but they need it for different<br> reasons, which are linked to their specific actor interests, e.g.: policy and governmental organisations<br> need it for capacity; the research community needs it for R&amp;I including (long-term) observation;<br> landowners and private sector need it for the implementation of measures/infrastructure.<br> 2. Legislation/policy frameworks: This appears the number two need for all the actor groups, but<br> legislation/policy needs vary between the various actor groups, e.g.: Policy and governmental<br> organisations need it to set clear boundaries and targets; Landowners and private sector need it to<br> create a level playing field, receive guidance and act.<br> 3. Knowledge/R&amp;I in order to inform policy and measures: For some of the Soil Mission objectives<br> there is a clear need expressed for knowledge/R&amp;I to inform policy making (legislation/frameworks)<br> as well as to inform the design or increase the effectiveness of measures (e.g. solutions, best practices<br> to improve land management, restore carbon stocks etc.)<br> 4. Awareness raising and communication: Different actor groups express different viewpoints regarding<br> awareness raising and communication. In general, the involvement of citizens is regarded as very<br> important for any actor interacting with society. Furthermore, reaching out to for example farmers<br> and project developers is also regarded as an important need. <br> It is now recommended to combine these results with the results from D3.3 for designing of a theory<br> of change or intervention logic for each specific Soil Mission objective. Important challenges that<br> need to be address in the steps are: Changing needs in the different phases of the planning and<br> implementation process; Being aware of short-term interests and long-term ambitions; Searching for<br> comparable themes between the groups (can be cross-cutting themes such as how to organize,<br> finance etc.). The results will be described and integrated in D2.4 which will provide an important<br> basis to prepare and deliver the Soil Mission roadmap in October 2022 (D3.5).<br> Criteria for Soil Health Living Labs and Lighthouses<br> SMS has built upon previous work done for LL &amp; LH (D3.1, Maring et al., 2021) and in interaction<br> with Working Group 5 (WG5) of the Mission Board on Soil Health and Food, that elaborated on living<br> labs and lighthouses for the EU Mission \u201cA soil deal for Europe\u201d and is in line with the recently<br> published implementation plan (EC, 2021).<br> Soil Health Living Labs being place-based, user-centred, transdisciplinary in which different actors<br> (practice, policy, science, citizens) codesign and cocreate approaches and measures in real-life<br> settings to improve soil health. These Living Labs act on landscape or ecosystem level and consist of<br> multiple sites; Soil Health Lighthouses are sites, with exemplary performance in terms of soil health<br> improvement. They can already exist or be a emerge from a LL site.<br> The list of current LL &amp; LH as collated by the Soil Mission Board and the SMS project has been<br> further elaborated. The list contains LL &amp; LH that qualify as SH LL &amp; LH \u2013 following the definition in<br> the implementation plan, but it also contains many initiatives that possibly better qualify as<br> \u2018awareness, education and exchange\u2019 initiatives and some on R&amp;I and monitoring. The list is<br> currently further complemented e.g. georeferencing is included to be able to map LL &amp; LH.<br> <br> Validation of what qualifies as a Soil Health (SH) LL &amp; LH is important for the Mission to setup 100 LL<br> and LH. Therefore, a limited set of manageable criteria, linked to the categories \u201cscale\u201d, \u201caim\u201d,<br> \u201cactivities, \u201cparticipants\u201d and \u201ccontext\u201d was proposed for both SH LL and LH. These validation<br> criteria were elaborated along the characteristics for SH LL and LH as proposed in the \u201cSoil Deal for<br> Europe\u201d. The actual full validation of the (SH) LL &amp; LH list is not part of the Soil Mission Support<br> project.<br> To establish a network of SH LL &amp; LH, it is of importance to find locations (e.g. in member states, on<br> a level fit for analyses to the basic regions for the application of regional policies (=NUTS21 level),<br> covering a broad range of geographic and geophysical conditions including soil types and socioeconomic<br> conditions) and the topics (covering different land uses, EU Soil Mission objectives) where<br> SH LL &amp; LH can be located. The current overview of LL and LH as mentioned above needs to be<br> validated first to see where suitable initiatives are located and which topics they cover. The overview<br> needs to be crosschecked after a more thorough inventory of specific soil needs as will be performed<br> by follow-up projects of SMS as set out in the SOIL-MISSION-2021 calls.<br> When setting up SH LL &amp; LH, or a network of these, it is important that next to the characteristics /<br> criteria some success factors are considered, such as the political context, available resources, a<br> clear outcome and collaboration. This will contribute to viability, sustainability and continuity of the<br> LL &amp; LH. Continuity is part of a set of principles that can help designing SH LL. The other principles<br> are openness, realism, empowerment/influence of the actors involved and spontaneity/adaptivity<br> within LL &amp; LH.<br> Within these characteristics/criteria, success factors and principles, the role of actors and<br> collaboration is crucial. The necessary but still untapped link between the soil health needs of actors<br> in different circumstances and SH LL &amp; LH is therewith supported. Related to this is the question<br> what effective business models for SH LL &amp; LH are. These should be further elaborated.<br> Social and economic scientists (such as policy scientists, economists, innovation science and<br> communication science experts) need to help to develop the connections between the operational<br> goals (R&amp;I, SH LL &amp; LH, monitoring, soil literacy) of the EU Mission \u201cA Soil deal for Europe\u201d. They can<br> help to increase the likelihood that activities of the EU mission will have impact, including that the<br> results will be adopted by land users and the public.<br> Finally, the governance and funding of a LL &amp; LH network should be further elaborated under the<br> activities of implementing the EU Soil Mission aimed at setting up a network of SH LL &amp; LH. Issues<br> and questions to resolve are: How to deal with \u2018existing\u2019 LL &amp; LH versus \u2018new\u2019 LL &amp; LH?; How to learn<br> and exchange between LL &amp; LH?; How to monitoring progress on their impact, outputs, outcomes?;<br> How to upscale both results as methods to setup successful LL &amp; LH? Experiences from existing<br> networks and initiatives can be helpful to answer these questions. This will be further covered by the<br> follow up projects of SMS as set out in the SOIL-MISSION-2021 calls.", "keywords": ["2. Zero hunger", "9. Industry and infrastructure", "13. Climate action", "11. Sustainability", "15. Life on land", "12. Responsible consumption"], "contacts": [{"organization": "Maring, Linda, Jan Ellen, Gerald, Brils, Jos,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7695581"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7695581", "name": "item", "description": "10.5281/zenodo.7695581", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7695581"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-04-28T00:00:00Z"}}, {"id": "10.5281/zenodo.7695607", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:07Z", "type": "Report", "title": "A European roadmap on soil and land management", "description": "Soil health is vital for many ecosystem services and crucial for achieving the Sustainable Development Goals and the European Green Deal's goals. The Horizon Europe (HE) Mission \u201cA Soil Deal for Europe\u201d aims to accelerate the transition to sustainable soil and land management, and healthy soils through an ambitious transdisciplinary research and innovation (R&amp;I) programme, largely based on actor engagement, Living Labs and Lighthouses. The H2020 Soil Mission Support (SMS) project supports the implementation of the HE Mission and aims to improve the coordination of R&amp;I on sustainable soil and land management. Through a co-creation process together with actors, SMS collates available knowledge, actors R&amp;I needs and identifies R&amp;I gaps that need to be addressed for successful transition towards sustainable soil and land management.<br> The presented R&amp;I roadmap on soils and land management is a strategic document outlining the actions and the pathway to address R&amp;I needs from different actors and sectors, as well as societal challenges. The roadmap presents the most important R&amp;I knowledge gaps for each Soil Mission Objective, which were iden-tified in collaboration with stakeholders from all relevant sectors and based on the key findings of the project. To ensure that the R&amp;I elements of the roadmap can effectively contribute to the desired transformation of land and soil management, the impact pathways approach was chosen as the analytical framework. The im-pact pathways are key component of the roadmap and describe several research actions that should be taken up to address the knowledge gaps and show what results we can expect from these actions that will lead to a short, medium- and finally long-term impact. The results show that to reach the eight Mission Objectives a common understanding of soil health is needed, as well as research and innovation in both natural sciences as well as social and economic spheres. The actions identified in the roadmap should be closely integrated. Future projects should focus more on finding systemic approaches to highlight synergies and minimise or avoid trade-offs while addressing the individual soil mission objectives.", "keywords": ["2. Zero hunger", "13. Climate action", "11. Sustainability", "15. Life on land", "12. Responsible consumption"], "contacts": [{"organization": "Ittner, Sophie, Naumann, Sandra, Bispo, Antonio, Wall, David, Mason, Elo\u00efse, Ellen, Gerald Jan, Prokop, Gundula, Brils, Jos, Helming, Katharina, Nougues, Laura, Maring, Linda, Doherty, Mary Kate, L\u00f6bmann, Michael, Keesstra, Saskia,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7695607"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7695607", "name": "item", "description": "10.5281/zenodo.7695607", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7695607"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-10-24T00:00:00Z"}}, {"id": "10.5281/zenodo.7695461", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:07Z", "type": "Report", "title": "Knowedge needs and gaps on soil and land management", "description": "Soil health is vital for many ecosystem services. The Horizon Europe (HE) Mission \u201cA Soil Deal for Europe\u201d aims to accelerate the transition to sustainable soil and land management and healthy soils through an am-bitious transdisciplinary research and innovation (R&amp;I) programme, largely based on actor engagement, Liv-ing Labs and Lighthouses. The H2020 Soil Mission Support (SMS) project supported the implementation of the HE Mission, and aimed to improve the coordination of R&amp;I on sustainable soil and land management. Through a co-creation process together with actors, SMS collated available knowledge, actors R&amp;I needs and identified R&amp;I gaps that need to be addressed for successful transition towards sustainable soil and land management.<br> The first step was to identify existing R&amp;I knowledge through a keyword-based analysis of scientific literature published and peer reviewed, related to sustainable soil and land management. The literature analysis ad-dressed the full range of societal challenges, soil health objectives, land use types and knowledge domains necessary to capture the socio-ecological complexity of soil health. Covering some 15,700 scientific articles, this literature analysis represents the current peer reviewed knowledge stock on sustainable soil and land management. A textual analysis using the digital platform CorTexT was undertaken to explore the identified literature and submitted to project consortium internal experts, who analysed and processed the collected information of their respective area of expertise (Annex III). The literature analysis revealed that the societal challenges \u201creduce soil degradation\u201d and \u201cimprove disaster control\u201d have been studied extensively. Con-versely, the societal challenges \u201cmitigate land take\u201d and \u201cincrease biodiversity\u201d and the knowledge domains \u201cscience-based policy support\u201d and \u201cawareness, training &amp; education\u201d are less discussed. Factsheets present-ing the results of the literature analysis per societal challenge were developed and can be found in Annex VIII. Note that as the key-word based literature search was limited to Scopus-indexed scientific journals, other publishing formats such as conference papers, books, book chapters, non-digitalized articles, grey literature, reports, patents, etc., may be underrepresented or not included in the used data base. The exclusive use of Scopus-indexed scientific articles provided quality insurance of the material through the publication peer-review system. Nonetheless, important documents and knowledge have been incorporated by the consor-tium experts when analysing the collected literature.<br> The second step was to consult actors through online workshops and surveys in order to gain a practice-oriented \u2018real-life\u2019 picture of current knowledge and R&amp;I needs for swift implementation of sustainable soil and land management. This step was seen as complementary of the published and peer-reviewed literature.<br> Finally, after exploring our stocktaking of R&amp;I from existing knowledge evidenced by literature review and the actor\u2019s knowledge needs identified from actor consultations, we identified R&amp;I gaps. The main knowledge gaps across all Mission Objectives were of socio-economic nature: drivers and causes of land degradation, knowledge management, governance and policies for inciting improved management, and interaction with other sectors are not sufficiently understood. Second, the HE Missions\u2019 focus on improving soil literacy was supported by the literature analysis and by the actor consultation, which both revealed knowledge gaps re-lated to education and capacity building in all land use types and domains affecting soil health: production, consumption, trade, policy and governance. Thirdly, there is a gap in the long-term implementation of a new mode of knowledge co-design, where researchers and practitioners together develop solutions for sustaina-ble soil and land management in a real-world context. The HE Missions\u2019 focus on Living Labs and Lighthouses has the potential to close this gap. Finally, there is a need to define several concepts (e.g. soil health, soil degradation, footprint). Such definitions should be shared and will be a basis to identify relevant indicators and respective thresholds, and to develop guidelines to support monitoring programmes in order to translate knowledge into evidence for decision making.<br> The outcome of the deliverable is a list of validated R&amp;I gaps across all Mission Objectives which will feed into the SMS roadmap and the HE Mission.", "keywords": ["[SDE] Environmental Sciences", "2. Zero hunger", "13. Climate action", "[SDE]Environmental Sciences", "11. Sustainability", "577", "15. Life on land", "6. Clean water", "12. Responsible consumption"], "contacts": [{"organization": "Mason, Elo\u00efse, L\u00f6bmann, Michael, Matt, Mireille, Sharif, Ibrat, Maring, Linda, Ittner, Sophie, Bispo, Antonio,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7695461"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7695461", "name": "item", "description": "10.5281/zenodo.7695461", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7695461"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.7695582", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:07Z", "type": "Report", "title": "Report on prioritization of actor needs and criteria for living lab and lighthouse identification", "description": "The EU Soil Mission \u201cA Soil Deal for Europe\u201d<br> Soil health is vital for a wide range of ecosystem services. The EU Soil Mission \u201cA Soil Deal for<br> Europe\u201d (EC, 2021) accelerates the transition to healthy soils through ambitious actions in 100 Living<br> Labs and Lighthouses within territorial settings, combined with an ambitious transdisciplinary R&amp;I<br> programme, a robust, harmonised soil monitoring framework and increased soil literacy and<br> communication to engage with citizens, all of this in synergy with relevant EU instruments and<br> policies. The H2020 Soil Mission Support (SMS) project supports the implementation of the Soil Mission. This<br> SMS deliverable reports the aspects of identification and prioritization of actor needs in relation to<br> the Soil Mission and elaborates on criteria for Soil Health Living Labs and Lighthouses (SH LL &amp; LH).<br> Actors are defined as stakeholders who actively engage in land and soil management, so who really<br> act. Actor needs are defined as that what increases the gains, or reliefs the pains that actors face in<br> their job to do. Living labs are understood as user-centred, place-based and transdisciplinary<br> research and innovation ecosystems between multiple partners established at regional or subregional<br> levels. Each living lab includes several sites at which systemic research and codesign,<br> testing, monitoring and evaluation of solutions take place. (EC, 2021). Lighthouses are rather<br> understood as one site for demonstration of solutions, training and communication that are<br> exemplary in their performance in terms of soil health improvement (EC, 2021).<br> LL &amp; LH and actor needs very much interrelate. Living Labs are efficient instruments for actors to<br> experiment in real life settings. Lighthouses have a real demonstrative potential to encourage and<br> engage larger communities. Hence, identification of the actor needs towards soil health is crucial to<br> enable their engagement in the activities of the \u201cSoil Deal for Europe\u201d i.e. to make the transitions<br> needed to significantly improve European Soil Health. <br> Actor needs<br> SMS anticipated that in order to be able to engage actors, it is needed to know and then express<br> \u2018what is in for them?\u2019 Therefore, SMS drafted value propositions tailored to different actor groups<br> (D3.3, Brils et al., 2021). To further refine these propositions, the next step was to specify and<br> prioritize the actor needs in relation to the objectives of the EU Soil Mission. Primary source of<br> information for achieving insight in the actor needs was the SMS survey on actor needs.<br> Furthermore, two workshops were held with actors to provide a supplementary source of<br> information.<br> The SMS survey was prepared and sent to approximately 550 soil professionals, all over Europe.<br> These professionals are expected to be knowledgeable about the topics that are raised in the survey.<br> Thus, the survey was not sent to citizens/general public/consumers as a specific actor group. In total<br> 93 respondents responded to the survey: 32 partly and 61 fully. Respondents were asked to<br> complete the survey from the perspective of their organization. Responding organizations are active<br> in most European Member States and in some non-EU countries. Most respondents were<br> researchers, policy makers and governmental organisations. The lowest response came from the<br> land-users, managers and owner\u2019s actor group.<br> <br> The workshops were held at the AquaConSoil conference, visited by service providers and<br> researchers with an urban/industrial background; and one with the SMS Expert Group, consisting of<br> representatives from relevant networks covering different actor and land use groups.<br> After a joint analysis and synthesis of the outcome of the survey as well as the workshops it is<br> concluded that the generic actor needs \u2013 i.e. applicable to all the actor groups \u2013 in order of priority<br> are: <br> 1. Funding: This appears the number one need for all the actor groups, but they need it for different<br> reasons, which are linked to their specific actor interests, e.g.: policy and governmental organisations<br> need it for capacity; the research community needs it for R&amp;I including (long-term) observation;<br> landowners and private sector need it for the implementation of measures/infrastructure.<br> 2. Legislation/policy frameworks: This appears the number two need for all the actor groups, but<br> legislation/policy needs vary between the various actor groups, e.g.: Policy and governmental<br> organisations need it to set clear boundaries and targets; Landowners and private sector need it to<br> create a level playing field, receive guidance and act.<br> 3. Knowledge/R&amp;I in order to inform policy and measures: For some of the Soil Mission objectives<br> there is a clear need expressed for knowledge/R&amp;I to inform policy making (legislation/frameworks)<br> as well as to inform the design or increase the effectiveness of measures (e.g. solutions, best practices<br> to improve land management, restore carbon stocks etc.)<br> 4. Awareness raising and communication: Different actor groups express different viewpoints regarding<br> awareness raising and communication. In general, the involvement of citizens is regarded as very<br> important for any actor interacting with society. Furthermore, reaching out to for example farmers<br> and project developers is also regarded as an important need. <br> It is now recommended to combine these results with the results from D3.3 for designing of a theory<br> of change or intervention logic for each specific Soil Mission objective. Important challenges that<br> need to be address in the steps are: Changing needs in the different phases of the planning and<br> implementation process; Being aware of short-term interests and long-term ambitions; Searching for<br> comparable themes between the groups (can be cross-cutting themes such as how to organize,<br> finance etc.). The results will be described and integrated in D2.4 which will provide an important<br> basis to prepare and deliver the Soil Mission roadmap in October 2022 (D3.5).<br> Criteria for Soil Health Living Labs and Lighthouses<br> SMS has built upon previous work done for LL &amp; LH (D3.1, Maring et al., 2021) and in interaction<br> with Working Group 5 (WG5) of the Mission Board on Soil Health and Food, that elaborated on living<br> labs and lighthouses for the EU Mission \u201cA soil deal for Europe\u201d and is in line with the recently<br> published implementation plan (EC, 2021).<br> Soil Health Living Labs being place-based, user-centred, transdisciplinary in which different actors<br> (practice, policy, science, citizens) codesign and cocreate approaches and measures in real-life<br> settings to improve soil health. These Living Labs act on landscape or ecosystem level and consist of<br> multiple sites; Soil Health Lighthouses are sites, with exemplary performance in terms of soil health<br> improvement. They can already exist or be a emerge from a LL site.<br> The list of current LL &amp; LH as collated by the Soil Mission Board and the SMS project has been<br> further elaborated. The list contains LL &amp; LH that qualify as SH LL &amp; LH \u2013 following the definition in<br> the implementation plan, but it also contains many initiatives that possibly better qualify as<br> \u2018awareness, education and exchange\u2019 initiatives and some on R&amp;I and monitoring. The list is<br> currently further complemented e.g. georeferencing is included to be able to map LL &amp; LH.<br> <br> Validation of what qualifies as a Soil Health (SH) LL &amp; LH is important for the Mission to setup 100 LL<br> and LH. Therefore, a limited set of manageable criteria, linked to the categories \u201cscale\u201d, \u201caim\u201d,<br> \u201cactivities, \u201cparticipants\u201d and \u201ccontext\u201d was proposed for both SH LL and LH. These validation<br> criteria were elaborated along the characteristics for SH LL and LH as proposed in the \u201cSoil Deal for<br> Europe\u201d. The actual full validation of the (SH) LL &amp; LH list is not part of the Soil Mission Support<br> project.<br> To establish a network of SH LL &amp; LH, it is of importance to find locations (e.g. in member states, on<br> a level fit for analyses to the basic regions for the application of regional policies (=NUTS21 level),<br> covering a broad range of geographic and geophysical conditions including soil types and socioeconomic<br> conditions) and the topics (covering different land uses, EU Soil Mission objectives) where<br> SH LL &amp; LH can be located. The current overview of LL and LH as mentioned above needs to be<br> validated first to see where suitable initiatives are located and which topics they cover. The overview<br> needs to be crosschecked after a more thorough inventory of specific soil needs as will be performed<br> by follow-up projects of SMS as set out in the SOIL-MISSION-2021 calls.<br> When setting up SH LL &amp; LH, or a network of these, it is important that next to the characteristics /<br> criteria some success factors are considered, such as the political context, available resources, a<br> clear outcome and collaboration. This will contribute to viability, sustainability and continuity of the<br> LL &amp; LH. Continuity is part of a set of principles that can help designing SH LL. The other principles<br> are openness, realism, empowerment/influence of the actors involved and spontaneity/adaptivity<br> within LL &amp; LH.<br> Within these characteristics/criteria, success factors and principles, the role of actors and<br> collaboration is crucial. The necessary but still untapped link between the soil health needs of actors<br> in different circumstances and SH LL &amp; LH is therewith supported. Related to this is the question<br> what effective business models for SH LL &amp; LH are. These should be further elaborated.<br> Social and economic scientists (such as policy scientists, economists, innovation science and<br> communication science experts) need to help to develop the connections between the operational<br> goals (R&amp;I, SH LL &amp; LH, monitoring, soil literacy) of the EU Mission \u201cA Soil deal for Europe\u201d. They can<br> help to increase the likelihood that activities of the EU mission will have impact, including that the<br> results will be adopted by land users and the public.<br> Finally, the governance and funding of a LL &amp; LH network should be further elaborated under the<br> activities of implementing the EU Soil Mission aimed at setting up a network of SH LL &amp; LH. Issues<br> and questions to resolve are: How to deal with \u2018existing\u2019 LL &amp; LH versus \u2018new\u2019 LL &amp; LH?; How to learn<br> and exchange between LL &amp; LH?; How to monitoring progress on their impact, outputs, outcomes?;<br> How to upscale both results as methods to setup successful LL &amp; LH? Experiences from existing<br> networks and initiatives can be helpful to answer these questions. This will be further covered by the<br> follow up projects of SMS as set out in the SOIL-MISSION-2021 calls.", "keywords": ["2. Zero hunger", "9. Industry and infrastructure", "13. Climate action", "11. Sustainability", "15. Life on land", "12. Responsible consumption"], "contacts": [{"organization": "Maring, Linda, Jan Ellen, Gerald, Brils, Jos,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7695582"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7695582", "name": "item", "description": "10.5281/zenodo.7695582", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7695582"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-04-28T00:00:00Z"}}, {"id": "10.5281/zenodo.7695608", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:07Z", "type": "Report", "title": "A European roadmap on soil and land management", "description": "Soil health is vital for many ecosystem services and crucial for achieving the Sustainable Development Goals and the European Green Deal's goals. The Horizon Europe (HE) Mission \u201cA Soil Deal for Europe\u201d aims to accelerate the transition to sustainable soil and land management, and healthy soils through an ambitious transdisciplinary research and innovation (R&amp;I) programme, largely based on actor engagement, Living Labs and Lighthouses. The H2020 Soil Mission Support (SMS) project supports the implementation of the HE Mission and aims to improve the coordination of R&amp;I on sustainable soil and land management. Through a co-creation process together with actors, SMS collates available knowledge, actors R&amp;I needs and identifies R&amp;I gaps that need to be addressed for successful transition towards sustainable soil and land management.<br> The presented R&amp;I roadmap on soils and land management is a strategic document outlining the actions and the pathway to address R&amp;I needs from different actors and sectors, as well as societal challenges. The roadmap presents the most important R&amp;I knowledge gaps for each Soil Mission Objective, which were iden-tified in collaboration with stakeholders from all relevant sectors and based on the key findings of the project. To ensure that the R&amp;I elements of the roadmap can effectively contribute to the desired transformation of land and soil management, the impact pathways approach was chosen as the analytical framework. The im-pact pathways are key component of the roadmap and describe several research actions that should be taken up to address the knowledge gaps and show what results we can expect from these actions that will lead to a short, medium- and finally long-term impact. The results show that to reach the eight Mission Objectives a common understanding of soil health is needed, as well as research and innovation in both natural sciences as well as social and economic spheres. The actions identified in the roadmap should be closely integrated. Future projects should focus more on finding systemic approaches to highlight synergies and minimise or avoid trade-offs while addressing the individual soil mission objectives.", "keywords": ["2. Zero hunger", "13. Climate action", "11. Sustainability", "15. Life on land", "12. Responsible consumption"], "contacts": [{"organization": "Ittner, Sophie, Naumann, Sandra, Bispo, Antonio, Wall, David, Mason, Elo\u00efse, Ellen, Gerald Jan, Prokop, Gundula, Brils, Jos, Helming, Katharina, Nougues, Laura, Maring, Linda, Doherty, Mary Kate, L\u00f6bmann, Michael, Keesstra, Saskia,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7695608"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7695608", "name": "item", "description": "10.5281/zenodo.7695608", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7695608"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-10-24T00:00:00Z"}}, {"id": "10.5281/zenodo.7695642", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:07Z", "type": "Report", "title": "Soil and land management ontology reference document", "description": "The Soil Mission Support (SMS) project supports the European Commission and the Mission Board of the Horizon Europe<br> Mission in the area of Soil Health and Food in delivering its objectives and related targets. It is assumed that the<br> Soil Mission and its related objectives and specific targets can only be achieved through healthy soils and for that,<br> stakeholder engagement is needed. Healthy soils are defined as soils that are in good chemical, biological and physical<br> condition and thus are able to continuously provide as many ecosystem services as possible (EC, 2021a). Stakeholders<br> are defined as those who are affected in their interest or concern by changes in soil and land management (Brils et al.,<br> 2022).<br> With multi-stakeholder processes, language and use of language is very important. The capability to understand each<br> other is critical. Communication difficulties originate to a large extent from the \u2018jargon\u2019 used in the different communities.<br> A common language facilitates \u2018learning together\u2019 which helps to build trust, develop a common view on the issues<br> at stake, resolve conflicts and arrive at joint solutions that are technically sound and that can be implemented in<br> practice. Ontology defines a common vocabulary for those who, for example, need to converse about a common issue<br> or share information in a specific domain.<br> In first instance the shared domain of discourse was defined and then at different levels of hierarchy:<br> \u00b7 Primary objects of relevance for the domain of discourse were selected;<br> \u00b7 The inter-relational links between these objects was conceptualized (conceptual model); and<br> \u00b7 These objects were defined in a representational vocabulary (a common language).<br> The domain of discourse covers soil and land management aimed to achieve the first six (of the eight) Soil Mission<br> objectives, which are: 1. reduce desertification, 2. conserve soil organic carbon stocks, 3. stop soil sealing and increase<br> re-use of urban soils, 4. reduce soil pollution and enhance restoration, 5. prevent erosion, and 6. improve soil structure<br> to enhance soil biodiversity.<br> The first level of hierarchy covers soil and land and its use. At this level the following objects have been selected, interrelated<br> in a conceptual model (i.e. visual of soil and land-use) and defined in a common language: soil, land, landuse<br> and land-use types (including: urban, industrial, agriculture, forest, nature and protected land).<br> The second level of hierarchy covers soil management. At his level the following objects have been selected, interrelated<br> in a conceptual soil management model and defined in a common language: soil management (including: soil<br> management strategy, measures, program of measures), soil ecosystems (including: ecosystem services, pressures,<br> healthy soil ecosystems), users (stakeholders) and information.<br> Lastly, the third level of hierarchy covers the achievement of the first six Soil Mission objectives. At this level the<br> most relevant objects related to each of these objectives are selected and interrelated to their position in the DPSIR<br> (Drivers-Pressures-State-Impact-Response) framework which is at this 3rd level superimposed on the soil management<br> model as used for level 2.<br> The remaining two Soil Mission objectives, i.e. 7. reduce the EU global footprint on soils and 8. improve soil literacy in<br> society, do not directly relate to the actual management of soil and land. However, also for these mission objectives<br> some important objects have been selected and defined in a common language.<br> Experts in the SMS project \u2013 jointly covering the fields of expertise related to all the 8 Soil Mission objectives \u2013 developed<br> this ontology. This ontology should now be used in soil policy and management practice, such as Living Labs. In<br> such settings, the ontology can be improved through interaction with stakeholders from different backgrounds, further<br> increasing its value.<br> The key-recommendations are:<br> \u00b7 use this ontology in soil policy and management practice (e.g. Living Labs)<br> \u00b7 soil policy makers and managers should promote its use in such practice<br> \u00b7 use the feedback from stakeholders to further improve the ontology<br> In support of the dissemination of this document a policy brief is prepared and attached as annex in this document.<br> Both documents are made publicly available via de SMS website: https://www.soilmissionsupport.eu/outputs", "keywords": ["2. Zero hunger", "13. Climate action", "11. Sustainability", "15. Life on land", "12. Responsible consumption"], "contacts": [{"organization": "Nougues, Laura, Brils, Jos,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7695642"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7695642", "name": "item", "description": "10.5281/zenodo.7695642", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7695642"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-02-04T00:00:00Z"}}, {"id": "10.5281/zenodo.7727569", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:07Z", "type": "Dataset", "title": "Predicted soil organic carbon stock at 30 m in t/ha for 0-100 cm depth global / update of the map of mangrove forest soil carbon", "description": "Open Access{'references': ['Hamilton, S. E., &amp; Casey, D. (2016). Creation of a high spatio u2010temporal resolution global database of continuous mangrove forest cover for the 21st century (CGMFC u201021). Global Ecology and Biogeography, 25(6), 729-738.', 'Murray, N. J., Worthington, T. A., Bunting, P., Duce, S., Hagger, V., Lovelock, C. E., ... &amp; Lyons, M. B. (2022). High-resolution mapping of losses and gains of Earth's tidal wetlands. Science, 376(6594), 744-749.', 'Rovai, A. S., Twilley, R. R., Casta u00f1eda-Moya, E., Riul, P., Cifuentes-Jara, M., Manrow-Villalobos, M., ... &amp; Pagliosa, P. R. (2018). Global controls on carbon storage in mangrove soils. Nature Climate Change, 8: 534 u2013538.', 'Sanderman, Jonathan, Tomislav Hengl, Greg Fiske, Kylen Solvik, Maria Fernanda Adame, Lisa Benson, Jacob J. Bukoski et al. (2018)  'A global map of mangrove forest soil carbon at 30 m spatial resolution. ' Environmental Research Letters, 13(5): 055002.']}", "keywords": ["machine learning", "13. Climate action", "mangroves", "14. Life underwater", "superlearner package", "15. Life on land", "soil carbon"], "contacts": [{"organization": "Hengl, Tomislav, Maxwell, Tania, Parente, Leandro,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7727569"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7727569", "name": "item", "description": "10.5281/zenodo.7727569", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7727569"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-10-23T00:00:00Z"}}, {"id": "10.5281/zenodo.7746495", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:08Z", "type": "Dataset", "title": "ELABORATION OF THE ITALIAN PORTION OF THE GLOBAL SOIL ORGANIC CARBON MAP (GSOCMAP)", "description": "Open Accessc_stock: mean value c_stock_cv: coefficient of variation c_stock_sd: standard deviation c_stock_se: standard error c_stock_minus: lower bound of the confidence interval c_stock_plus: upper limit of the confidence interval", "keywords": ["2. Zero hunger", "http://id.agrisemantics.org/gacs/C3841", "carbon sequestration", " common agricultural policy", " digital soil mapping", " land degradation neutrality", " national soil hub", " sustainable development goals", "15. Life on land", "national soil hub", "sustainable development goals", "carbon sequestration", "common agricultural policy", "6. Clean water", "https://www.geonames.org/countries/IT/italy.html", "13. Climate action", "digital soil mapping", "https://inspire.ec.europa.eu/theme/so", "land degradation neutrality", "https://lod.nal.usda.gov/nalt/67854"], "contacts": [{"organization": "Fantappi\u00e8, Maria, Calzolari, Costanza, Ungaro, Fabrizio, Ialina Vinci, Giandon, Paolo, Muscolo, Adele, Zaccone, Claudio, Dell'Abate, Maria Teresa, L'Abate, Giovanni, Pellegrini, Sergio, Brenna, Stefano, Staffilani, Francesca, Petrella, Fabio, Gardin, Lorenzo, Barbieri, Stefano, Pini, Stefano, Tiberi, Mauro, Paone, Raffaele, Scamarcio, Luigi, D'Antonio, Amedeo, Guaitoli, Fabio, Munaf\u00f2, Michele, Fumanti, Fiorenzo, Napoli, Rosario, D'Acqui, Luigi, Martal\u00f2, Paolo, Tarocco, Paola, Costantini, Edoardo A. C.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7746495"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7746495", "name": "item", "description": "10.5281/zenodo.7746495", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7746495"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-12-05T00:00:00Z"}}, {"id": "10.5281/zenodo.7716691", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:07Z", "type": "Dataset", "title": "Results of the SMS Survey on soil stakeholder interests related to the EU Soil Mission objectives", "description": "Soil Mission Support results of the online survey on soil stakeholder interests related to the EU Soil Mission objectives The processed survey results can be found in SMS D3.4 'Report on prioritization of actor needs and criteria for living lab and lighthouse identification' (https://zenodo.org/record/7695582#.ZAdE-NXMI2w)", "keywords": ["13. Climate action", "15. Life on land"], "contacts": [{"organization": "Maring, Linda, Jan Ellen, Gerald, Brils, Jos,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7716691"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7716691", "name": "item", "description": "10.5281/zenodo.7716691", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7716691"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-03-10T00:00:00Z"}}, {"id": "10.5281/zenodo.7729492", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:08Z", "type": "Dataset", "title": "Global mangrove soil carbon data set at 30 m resolution for year 2020 (0-100 cm)", "description": "Open AccessGlobal soil organic carbon stocks in mangrove forests at 30 m resolution, and predicted for 2020 using spatiotemporal ensemble machine learning. Soil organic carbon stock (t/ha) was derived using predictions of soil organic carbon content and bulk density (BD) to 1 m soil depth, which were then aggregated to calculate soil organic carbon stocks. The 'mangroves_tiles_SOC_predictions_2020.zip' file contains predictions of SOC content, Bulk Density (BD) and aggregated SOC stocks (t/ha) for 0\u2014100 cm depth interval. Example of a tile: 089E_21N (89E to 90E, 21N to 22N): sol_db.od_mangroves.typology_m_30m_s0..100cm_2020_global_v0.1.tif = predicted BD aggregated to 0\u2014100 cm; sol_soc.wpct_mangroves.typology_m_30m_s0..0cm_2020_global_v1.1.tif = predicted SOC content (%) at 0 cm depth (surface soil); sol_soc.wpct_mangroves.typology_m_30m_s0..100cm_2020_global_v1.1.tif = predicted SOC content (%) for 0\u2014100 cm; sol_soc.tha_mangroves.typology_m_30m_s0..100cm_2020_global_v0.1.tif = predicted SOC stocks in t/ha (mean value); sol_soc.tha_mangroves.typology_l.std_30m_s0..100cm_2020_global_v0.1.tif = predicted SOC stocks in t/ha lower 95% probability prediction interval; sol_soc.tha_mangroves.typology_u.std_30m_s0..100cm_2020_global_v0.1.tif = predicted SOC stocks in t/ha upper 95% probability prediction interval; Example of a tile: class : RasterLayer dimensions : 4004, 4004, 16032016 (nrow, ncol, ncell) resolution : 0.00025, 0.00025 (x, y) extent : 88.9995, 90.0005, 20.9995, 22.0005 (xmin, xmax, ymin, ymax) crs : +proj=longlat +datum=WGS84 +no_defs source : sol_db.od_mangroves.typology_m_30m_s0..0cm_2002_global_v0.1.tif To load global mosaics <strong><strong>Soil Carbon t/ha Maps (0\u2014100cm)</strong></strong> as COGs directly into QGIS or similar, best use: https://s3.eu-central-1.wasabisys.com/openlandmap/mangroves/sol/soc.tha_tnc.mangroves.typology_m_30m_b0..100cm_2019_2020_go_epsg.4326_v1.2.tif https://s3.eu-central-1.wasabisys.com/openlandmap/mangroves/sol/soc.tha_tnc.mangroves.typology_l.std_30m_b0..100cm_2019_2020_go_epsg.4326_v1.2.tif https://s3.eu-central-1.wasabisys.com/openlandmap/mangroves/sol/soc.tha_tnc.mangroves.typology_u.std_30m_b0..100cm_2019_2020_go_epsg.4326_v1.2.tif", "keywords": ["mangrove forests", "13. Climate action", "landsat", "coastal ecosystem", "15. Life on land", "soil carbon"], "contacts": [{"organization": "Hengl, T., Maxwell, T., Parente, L.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7729492"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7729492", "name": "item", "description": "10.5281/zenodo.7729492", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7729492"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-03-13T00:00:00Z"}}, {"id": "10.5281/zenodo.7735993", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:08Z", "type": "Software", "title": "HiLSS Project", "description": "The R script code was developed by dr. F. Brandolini (Newcastle University, UK) to accompany the paper: '<em>Brandolini, F., Kinnaird, T.C., Srivastava, A., Turner S. - Modelling the impact of historic landscape change on soil erosion and degradation. Sci Rep 13, 4949 (2023)</em>'. <strong>List of files included in <em>HLC_RUSLE.zip</em>:</strong> <em>R_script_code named 'HLC_RUSLE' in .rmd format</em> <em>Output folder: </em> <em>Figures folder: .png products of the R script code</em> <em>Rasters folder: .png products of the R script code</em> <em>Tables folder: .pdf products of the R script code</em> <em>GeoTiff folder (.TIFF file format): Regional RUSLE Data</em> <em>GPKG:</em> <em>HLC </em>dataset and <em>Region Of Interest file in .gpkg format.</em>", "keywords": ["13. Climate action", "Landscape Archaeology", "11. Sustainability", "RUSLE", "15. Life on land", "Historic Landscape Characterisation", "Soil Sustainability", "Soil Erosion Modelling", "12. Responsible consumption"], "contacts": [{"organization": "Brandolini Filippo", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7735993"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7735993", "name": "item", "description": "10.5281/zenodo.7735993", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7735993"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-10-10T00:00:00Z"}}, {"id": "10.5281/zenodo.7763668", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:08Z", "type": "Dataset", "title": "Sentinel-2 based maps of irrigated fields in Vojvodina, Serbia", "description": "RestrictedThis dataset consists of classified irrigated fields of the three most irrigated crops in Vojvodina (Serbia): maize, soybean, and sugar beet. Maps were generated for three years (2020, 2021, 2022), characterized by different climate conditions using Sentinel-2 satellite data and machine learning algorithms. All maps are in GIS format (.tiff) where label 0 corresponds to non-irrigated fields while label 1 corresponds to irrigated fields in Vojvodina and as if could be used for further research.", "keywords": ["2. Zero hunger", "13. Climate action", "15. Life on land", "Sentinel-2", "Irrigation"], "contacts": [{"organization": "Radulovi\u0107 Mirjana, Brdar Sanja, Pejak Branislav, Lugonja Predrag, Athanasiadis Ioannis, Pajevi\u0107 Nina, Pavi\u0107 Dragoslav, Crnojevi\u0107 Vladimir,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7763668"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7763668", "name": "item", "description": "10.5281/zenodo.7763668", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7763668"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.7777673", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:08Z", "type": "Software", "title": "HiLSS Project", "description": "The R script code was developed by dr. F. Brandolini (Newcastle University, UK) to accompany the paper: '<em>Brandolini, F., Kinnaird, T.C., Srivastava, A., Turner S. - Modelling the impact of historic landscape change on soil erosion and degradation. Sci Rep 13, 4949 (2023)</em>'. <strong>List of files included in <em>HLC_RUSLE.zip</em>:</strong> <em>R_script_code named 'HLC_RUSLE' in .rmd format</em> <em>Output folder: </em> <em>Figures folder: .png products of the R script code</em> <em>Rasters folder: .png products of the R script code</em> <em>Tables folder: .pdf products of the R script code</em> <em>GeoTiff folder (.TIFF file format): Regional RUSLE Data</em> <em>GPKG:</em> <em>HLC </em>dataset and <em>Region Of Interest file in .gpkg format.</em>", "keywords": ["13. Climate action", "Landscape Archaeology", "11. Sustainability", "RUSLE", "15. Life on land", "Historic Landscape Characterisation", "Soil Sustainability", "Soil Erosion Modelling", "12. Responsible consumption"], "contacts": [{"organization": "Filippo, Brandolini", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7777673"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7777673", "name": "item", "description": "10.5281/zenodo.7777673", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7777673"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-10-10T00:00:00Z"}}, {"id": "10.5281/zenodo.7781245", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:08Z", "type": "Dataset", "title": "The legacy of one hundred years of climate change for organic carbon stocks in global agricultural topsoils - full dataset", "description": "This zip folder contains a txt and a shp file with predicted soil organic carbon stocks for a total of 931149 points on agricultural land across the globe at three different timepoints. The initial value (2018) for the scenarios c (constant carbon input) and v (variable carbon input) was derived from the FAO GSP Global SOC map published in 2018. the values in 1969 and 1919 are the results of backwards modelling with RothC model to estimate past climate change effects on SOC stocks. Details can be found in the publication ' The legacy of one hundred years of climate change for organic carbon stocks in global agricultural topsoils' as published in Scientific Reports.", "keywords": ["2. Zero hunger", "13. Climate action", "15. Life on land"], "contacts": [{"organization": "Poeplau, Christopher, , Rene,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7781245"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7781245", "name": "item", "description": "10.5281/zenodo.7781245", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7781245"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-03-29T00:00:00Z"}}, {"id": "10.5281/zenodo.7787856", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:08Z", "type": "Dataset", "title": "Management-induced changes in soil organic carbon and related crop yield dynamics in China's cropland", "description": "Enhancing soil organic carbon (SOC) sequestration and food supply are vital for human survival when facing climate change. Site-specific best management practices (BMPs) are being promoted for adoption globally as solutions. However, how SOC and crop yield are related to each other in responding to BMPs remains unknown. Here, path analysis based on meta-analysis and machine learning was conducted to identify the effects and potential mechanisms of how the relationship between SOC and crop yield responds to site-specific BMPs in China. The results showed that BMPs could significantly enhance SOC and maintain or increase crop yield. The maximum benefits in SOC (30.6%) and crop yield (79.8%) occurred in mineral fertilizer combined with organic inputs (MOF). Specifically, the optimal SOC and crop yield would be achieved when the areas were arid, soil pH was \u22657.3, initial SOC content was \u226410 g kg-1, duration was >10 years, and the nitrogen (N) input level was 100-200 kg ha-1. Further analysis revealed that the original SOC level and crop yield change showed an inverted V-shaped structure. The association between the changes in SOC and crop yield might be linked to the positive role of the nutrient-mediated effect. The results generally suggested that improving the SOC can strongly support better crop performance. Limitations in increasing crop yield still exist due to low original SOC levels, and in regions where the excessive N inputs, inappropriate tillage or organic input is inadequate and could be diminished by optimizing BMPs in harmony with site-specific conditions.", "keywords": ["2. Zero hunger", "climate change", " soil organic carbon", " crop yield", " farming management", " nutrient-mediated effect", "13. Climate action", "15. Life on land"], "contacts": [{"organization": "Lin, Baijian, Zhang, Hailin,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7787856"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7787856", "name": "item", "description": "10.5281/zenodo.7787856", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7787856"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-03-31T00:00:00Z"}}, {"id": "10.5281/zenodo.7811348", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:08Z", "type": "Dataset", "title": "Novel cropping system strategies in China can increase plant protein with higher economic value but lower greenhouse gas emissions and water use", "description": "This database contains the average crop residue, manure nitrogen, manure organic carbon, net greenhouse gas emissions, and cropland area for 17 cropping systems at prefecture level during the period 2014-2018. In addition, it contained the changes of net greenhouse gas emissions caused by the optimization at prefecture level and province level. We also shared the key code for optimizing cropping systems at prefecture level and provided the all data. Users can run it the Matlab platform.", "keywords": ["2. Zero hunger", "Agricultural management", "Climate change mitigation", "Carbon budget", "13. Climate action", "Soil organic carbon", "11. Sustainability", "15. Life on land", "Agricultural system optimization", "Greenhouse gas", "12. Responsible consumption"], "contacts": [{"organization": "Lichang Yin, Fulu Tao, Yi Chen, Yicheng Wang, Philippe Ciais, Pete Smith,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7811348"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7811348", "name": "item", "description": "10.5281/zenodo.7811348", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7811348"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-04-08T00:00:00Z"}}, {"id": "10.5281/zenodo.7820796", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:08Z", "type": "Dataset", "title": "Global distribution of predicted soil types at 1 km resolution based on the WRB 2022 classification", "description": "Open Access{'references': ['Batjes, N. H., Ribeiro, E., &amp; Van Oostrum, A. (2020). Standardised soil profile data to support global mapping and modelling (WoSIS snapshot 2019). Earth System Science Data, 12(1), 299-320. https://doi.org/10.5194/essd-12-299-2020', 'FAO &amp; IIASA. 2023. Harmonized World Soil Database version 2.0. Rome and Laxenburg. https://doi.org/10.4060/cc3823en', 'Hengl, T., Mendes de Jesus, J., Heuvelink, G. B., Ruiperez Gonzalez, M., Kilibarda, M., Blagoti u0107, A., ... &amp; Kempen, B. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLoS one, 12(2), e0169748.', 'Krasilnikov, P., J. Ib u00e1 u00f1ez Mart u00ed, R.W. Arnold, and S. Shoba. (2009). A Handbook of Soil Terminology, Correlation and Classification. Edited by P. Krasilnikov, J. Ib u00e1 u00f1ez Mart u00ed, R.W. Arnold, and S. Shoba. London: Earthscan. 25 https://www.researchgate.net/profile/Juan_Ibanez3/publication/285586468_Handbook_of_Soil_Terminology_Corre lation_and_Classificati/links/5660452908ae4988a7bf10e4.pdf']}", "keywords": ["2. Zero hunger", "IUSS", "13. Climate action", "15. Life on land", "HWSD", "soil", "FAO"], "contacts": [{"organization": "Hengl, T., Minarik, R.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7820796"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7820796", "name": "item", "description": "10.5281/zenodo.7820796", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7820796"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-04-12T00:00:00Z"}}, {"id": "10.5281/zenodo.7820797", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:08Z", "type": "Dataset", "title": "Global distribution of predicted soil types at 1 km resolution based on the WRB 2022 classification", "description": "Open Access{'references': ['Batjes, N. H., Ribeiro, E., &amp; Van Oostrum, A. (2020). Standardised soil profile data to support global mapping and modelling (WoSIS snapshot 2019). Earth System Science Data, 12(1), 299-320. https://doi.org/10.5194/essd-12-299-2020', 'FAO &amp; IIASA. 2023. Harmonized World Soil Database version 2.0. Rome and Laxenburg. https://doi.org/10.4060/cc3823en', 'Hengl, T., Mendes de Jesus, J., Heuvelink, G. B., Ruiperez Gonzalez, M., Kilibarda, M., Blagoti u0107, A., ... &amp; Kempen, B. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLoS one, 12(2), e0169748.', 'Krasilnikov, P., J. Ib u00e1 u00f1ez Mart u00ed, R.W. Arnold, and S. Shoba. (2009). A Handbook of Soil Terminology, Correlation and Classification. Edited by P. Krasilnikov, J. Ib u00e1 u00f1ez Mart u00ed, R.W. Arnold, and S. Shoba. London: Earthscan. 25 https://www.researchgate.net/profile/Juan_Ibanez3/publication/285586468_Handbook_of_Soil_Terminology_Corre lation_and_Classificati/links/5660452908ae4988a7bf10e4.pdf']}", "keywords": ["2. Zero hunger", "IUSS", "13. Climate action", "15. Life on land", "HWSD", "soil", "FAO"], "contacts": [{"organization": "Hengl, T., Minarik, R.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7820797"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7820797", "name": "item", "description": "10.5281/zenodo.7820797", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7820797"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-04-12T00:00:00Z"}}, {"id": "10.5281/zenodo.7827912", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:08Z", "type": "Report", "title": "Effects of a fungal invasion on soil bacteria", "description": "Open AccessThe NHM-affiliated author Flavia Pinzari was funded by the H2020-MSCA-IF-EF-SE project 'AlienInSoil'. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 892048.", "keywords": ["2. Zero hunger", "13. Climate action", "15. Life on land", "Soil", " biofertiliser", " Trichoderma", " biodiversity", " impact", "12. Responsible consumption"], "contacts": [{"organization": "Pinzari, Flavia", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7827912"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7827912", "name": "item", "description": "10.5281/zenodo.7827912", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7827912"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.7829025", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:08Z", "type": "Report", "title": "Cross-continental importance of CH4 emissions from dry inland-waters", "description": "Dry inland waters are hotspots of C emission to the atmosphere. CH4 contributed 10\u201321% to total C emissions (in CO2-eq) of dry inland waters. The contribution of CH4 (total C emissions) did not differ between types of systems. Globally, dry inland waters emit 2.7 Tg CCH4 y\u22121 and emissions are likely rising. More CH4 emission data are needed to improve the global GHG budget of inland waters.", "keywords": ["13. Climate action"], "contacts": [{"organization": "Paranaiba, Jose, Aben, Ralf, Barros, Nathan, Quadra, Gabrielle, Linkhorst, Annika, Amado, Andre, Brothers, Soren, Catal\u00e1n, N\u00faria, et al.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7829025"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7829025", "name": "item", "description": "10.5281/zenodo.7829025", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7829025"}, {"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.7829026", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:08Z", "type": "Report", "title": "Cross-continental importance of CH4 emissions from dry inland-waters", "description": "Dry inland waters are hotspots of C emission to the atmosphere. CH4 contributed 10\u201321% to total C emissions (in CO2-eq) of dry inland waters. The contribution of CH4 (total C emissions) did not differ between types of systems. Globally, dry inland waters emit 2.7 Tg CCH4 y\u22121 and emissions are likely rising. More CH4 emission data are needed to improve the global GHG budget of inland waters.", "keywords": ["13. Climate action"], "contacts": [{"organization": "Paranaiba, Jose, Aben, Ralf, Barros, Nathan, Quadra, Gabrielle, Linkhorst, Annika, Amado, Andre, Brothers, Soren, Catal\u00e1n, N\u00faria, Et Al.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7829026"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7829026", "name": "item", "description": "10.5281/zenodo.7829026", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7829026"}, {"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.7948399", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:09Z", "type": "Report", "title": "Farm management information systems as tools for revealing management zones inside the fields", "description": "INTRODUCTION and OBJECTIVES: There is a huge need to increase the productivity in agriculture to feed the world\u2019s growing population. However, this increase needs to be achieved in a sustainable way, without jeopardising the ecosystem and environment. Innovations in AgTech are accelerating this process and providing adequate solutions for optimisation of on-field decision-making, but they are often isolated and inaccessible to the farmers. The objective of our work was to design a comprehensive farm management system that takes scientific achievements and enables farmers to use them in their daily operations. MATERIAL and METHOD: In order to digitally transform the Serbian agriculture, we designed AgroSense farm management information system. It was launched in 2017 and has since gathered more than 20,000 users, whose total area equals one fourth of all farmland in Serbia. The platform has a number of modules for weather forecast, historical weather records, digital field books, satellite image processing etc., while the newest addition is the drone image processing module. This module allows 3rd party drone services to scan the fields and upload the data to the platform, after which, the images are processed and analysed. The analysis is directed towards zone management delineation, which is the first step in application of precision agriculture technologies. Zones are detected within the field as areas with homogeneous soil and elevation properties. This is done by applying k-means, an unsupervised machine learning model for clusterisation of data, i.e. pixels in this case. This algorithm minimises the intra-class variance (variance of pixels within the zone) and maximises the inter-class variance (variance between pixels from different classes. This zone delineation can be done on a pixel-level if the objective of zone delineation is e.g. choosing the right locations for soil sampling, or on the level of the tractor swath if the goal is e.g. the variable-rate application of fertiliser. The number of zones and the swath width are variable parameters, left to the user to choose, according to the size of the field, type of the equipment and other factors. RESULTS and CONCLUSIONS: The resulting platform was deployed in 2021 and tested on a number of users. It yielded excellent results and served for optimising the route and sampling location of unmanned ground vehicles (UGVs), characterisation of fields and variable application of fertiliser. Future work includes development of other algorithms for more complex image recognition tasks, such as row detection, leaf area assessment and disease/weed mapping.", "keywords": ["2. Zero hunger", "13. Climate action", "15. Life on land", "drones; precision agriculture; image processing; machine learning"], "contacts": [{"organization": "Marko, Oskar, Brdar, Sanja, Pani\u0107, Marko, Mini\u0107, Vladan, Pejak, Branislav, Crnojevi\u0107, Vladimir,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7948399"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7948399", "name": "item", "description": "10.5281/zenodo.7948399", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7948399"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-06-16T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=13.+Climate+action&offset=6100&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=13.+Climate+action&offset=6100&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "prev", "title": "items (prev)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=13.+Climate+action&offset=6050", "hreflang": "en-US"}, {"rel": "next", "type": "application/geo+json", "title": "items (next)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=13.+Climate+action&offset=6150", "hreflang": "en-US"}], "numberMatched": 7487, "numberReturned": 50, "distributedFeatures": [], "timeStamp": "2026-04-05T05:32:33.790689Z"}