{"type": "FeatureCollection", "features": [{"id": "10.1071/sr13043", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:16:51Z", "type": "Journal Article", "created": "2013-12-20", "title": "Impact Of Carbon Farming Practices On Soil Carbon In Northern New South Wales", "description": "<p>This study sought to quantify the influence of \uffe2\uff80\uff98carbon farming\uffe2\uff80\uff99 practices on soil carbon stocks, in comparison with conventional grazing and cropping, in northern New South Wales. The study had two components: assessment of impacts of organic amendments on soil carbon and biological indicators in croplands on Vertosols of the Liverpool Plains; and assessment of the impact of grazing management on soil carbon in Chromosols of the Northern Tablelands. The organic amendment sites identified for the survey had been treated with manures, composts, or microbial treatments, while the conventional management sites had received only chemical fertilisers. The rotational grazing sites had been managed so that grazing was restricted to short periods of several days, followed by long rest periods (generally several months) governed by pasture growth. These were compared with sites that were grazed continuously. No differences in total soil carbon stock, or soil carbon fractions, were observed between sites treated with organic amendments and those treated with chemical fertiliser. There was some evidence of increased soil carbon stock under rotational compared with continuous grazing, but the difference was not statistically significant. Similarly, double-stranded DNA (dsDNA) stocks were not significantly different in either of the management contrasts, but tended to show higher values in organic treatments and rotational grazing. The enzymatic activities of \uffce\uffb2-glucosidase and leucine-aminopeptidase were significantly higher in rotational than continuous grazing but statistically similar for the cropping site treatments. Relative abundance and community structure, measured on a subset of the cropping sites, showed a higher bacteria\uffe2\uff80\uff89:\uffe2\uff80\uff89fungi ratio and provided evidence that microbial process rates were significantly higher in chemically fertilised sites than organic amendment sites, suggesting enhanced mineralisation of organic matter under conventional management. The higher enzyme activity and indication of greater efficiency of microbial populations on carbon farming sites suggests a greater potential to build soil carbon under these practices. Further research is required to investigate whether the indicative trends observed reflect real effects of management.</p>", "keywords": ["2. Zero hunger", "Land Capability and Soil Degradation", "550", "XXXXXX - Unknown", "0401 agriculture", " forestry", " and fisheries", "Carbon Sequestration Science", "04 agricultural and veterinary sciences", "15. Life on land", "Land capability and soil productivity"]}, "links": [{"href": "https://doi.org/10.1071/sr13043"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20Research", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1071/sr13043", "name": "item", "description": "10.1071/sr13043", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1071/sr13043"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2013-01-01T00:00:00Z"}}, {"id": "10.1098/rstb.2011.0313", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:17:09Z", "type": "Journal Article", "created": "2012-03-26", "title": "The Role Of N2o Derived From Crop-Based Biofuels, And From Agriculture In General, In Earth'S Climate", "description": "<p>             In earlier work, we compared the amount of newly fixed nitrogen (N, as synthetic fertilizer and biologically fixed N) entering agricultural systems globally to the total emission of nitrous oxide (N             2             O). We obtained an N             2             O emission factor (EF) of 3\uffe2\uff80\uff935%, and applied it to biofuel production. For \uffe2\uff80\uff98first-generation\uffe2\uff80\uff99 biofuels, e.g. biodiesel from rapeseed and bioethanol from corn (maize), that require N fertilizer, N             2             O from biofuel production could cause (depending on N uptake efficiency) as much or more global warming as that avoided by replacement of fossil fuel by the biofuel. Our subsequent calculations in a follow-up paper, using published life cycle analysis (LCA) models, led to broadly similar conclusions. The N             2             O EF applies to agricultural crops in general, not just to biofuel crops, and has made possible a top-down estimate of global emissions from agriculture. Independent modelling by another group using bottom-up IPCC inventory methodology has shown good agreement at the global scale with our top-down estimate. Work by Davidson showed that the rate of accumulation of N             2             O in the atmosphere in the late nineteenth and twentieth centuries was greater than that predicted from agricultural inputs limited to fertilizer N and biologically fixed N (Davidson, E. A. 2009             Nat. Geosci             .             2             , 659\uffe2\uff80\uff93662.). However, by also including soil organic N mineralized following land-use change and NO                            x                          deposited from the atmosphere in our estimates of the reactive N entering the agricultural cycle, we have now obtained a good fit between the observed atmospheric N             2             O concentrations from 1860 to 2000 and those calculated on the basis of a 4 per cent EF for the reactive N.           </p>", "keywords": ["2. Zero hunger", "Air Pollutants", "330", "Climate", "Nitrous Oxide", "Agriculture", "15. Life on land", "Nitrification", "01 natural sciences", "7. Clean energy", "630", "Soil", "13. Climate action", "Biofuels", "Denitrification", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1098/rstb.2011.0313"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Philosophical%20Transactions%20of%20the%20Royal%20Society%20B%3A%20Biological%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1098/rstb.2011.0313", "name": "item", "description": "10.1098/rstb.2011.0313", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1098/rstb.2011.0313"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2012-05-05T00:00:00Z"}}, {"id": "10.1111/j.1365-2486.2009.02121.x", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:17:27Z", "type": "Journal Article", "created": "2009-12-22", "title": "Functional Changes In The Control Of Carbon Fluxes After 3 Years Of Increased Drought In A Mediterranean Evergreen Forest?", "description": "Abstract<p>Our objective was to test how a long\uffe2\uff80\uff90term increased water limitation affects structural and functional properties of a Mediterranean ecosystem, and how these changes modify the response of the main carbon fluxes to climatic controls. In 2003, a 27% throughfall exclusion experiment was installed in a Quercus ilex L. forest in France. Gross primary production (GPP), ecosystem respiration (RECO) and net ecosystem exchange (NEE) were estimated in a control and a dry treatment. Decreasing throughfall decreased GPP by 14% and had a smaller effect on RECO (\uffe2\uff88\uff9212%), especially soil respiration RS (\uffe2\uff88\uff9211%). Interannual variability of GPP (29%) was higher than for RECO (12%). Error propagation was used to estimates uncertainties in the NEE fluxes, which ranged from 3% to 10% in the control treatment but up to 167% for NEE in the dry treatment because more steps and data types were involved in the scaling. After 3 years of throughfall exclusion, we found no acclimation of RS to climatic drivers. Functional properties of the response of RS to soil water, temperature and rain pulse remained similar in the control and the dry treatments. A diurnal clockwise hysteresis in RS was probably controlled by canopy photosynthesis with a 3\uffe2\uff80\uff83h lag. The proportion of diurnal variation of respiration due to photosynthesis was similar in all treatments (4\uffe2\uff80\uff935%). Because of the characteristic of rain in Mediterranean climates, a continuous decrease of water input in these environments have an effect on topsoil water and consequently on RS only during short periods when rainfall is characterized by infrequent and small events that does not allow the topsoil to reach field capacity and does not allow to dry completely. However, in the longer term, we expect a stronger decrease in RS in the dry treatment driven by the decrease in GPP.</p>", "keywords": ["0106 biological sciences", "550", "15. Life on land", "gross primary production", "soil respiration", "01 natural sciences", "630", "6. Clean water", "Quercus ilex", "throughfall exclusion", "13. Climate action", "rain pulse", "eddy-covariance", "Q(10)", "error propagation", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1111/j.1365-2486.2009.02121.x"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Global%20Change%20Biology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/j.1365-2486.2009.02121.x", "name": "item", "description": "10.1111/j.1365-2486.2009.02121.x", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/j.1365-2486.2009.02121.x"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2010-08-01T00:00:00Z"}}, {"id": "10.2139/ssrn.4556085", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:18:49Z", "type": "Journal Article", "created": "2023-08-29", "title": "A Laser Diffractometry Technique for Determining the Soil Water Stable Aggregates Index", "description": "Open AccessPeer reviewed", "keywords": ["Water stable aggregates index", "Laser diffractometry", "Wet sieving", "Soil aggregates"]}, "links": [{"href": "https://doi.org/10.2139/ssrn.4556085"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoderma", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.2139/ssrn.4556085", "name": "item", "description": "10.2139/ssrn.4556085", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.2139/ssrn.4556085"}, {"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": "10.23986/afsci.148486", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:18:54Z", "type": "Journal Article", "created": "2025-05-26", "title": "Defining critical SOC/clay thresholds for soil health in boreal croplands using satellite-based NDVI proxies for productivity and resilience", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The European Union\u2019s soil strategy underscores the necessity for establishing feasible criteria to assess the soil health condition. In this study, we developed a method to define a critical threshold value for SOC/clay ratio on the basis of crop productivity and resilience. The study integrated data from national soil monitoring (NSM) of Finnish cropland soils (n=505) with satellite-based normalized difference vegetation index (NDVI) obtained from the EcoDataCube (EDC) portal. The study area was confined to the boreal environmental zone to ensure consistent pedo-climatic conditions. The results show that the interannual variation in crop productivity increases rapidly below SOC/clay ratio of 0.09 (95% confidence intervals ranging from 0.07 to 0.16), whereas the corresponding threshold for mean productivity was 0.13 (0.09\u20130.16). The observed threshold values were found applicable for both cereals and temporary ley. The SOC/clay ratio of 1:13 (=0.08), regarded as a criterion for healthy soil in the current Soil Monitoring Law proposal, based on studies by Johannes et al. (2017) and Prout et al. (2021), is lower than the mean thresholds estimated in this study but aligns close to the lower bound of the 95% confidence intervals. In this research, Finnish agricultural land served as the case study area, but the method is easily applicable to various pedo-climatic regions and potentially to different land use types.</p></article>", "keywords": ["S", "Soil Monitoring Law", " SOC/clay ratio", " cropland", " NDVI", " satellite data", " national soil monitoring", "Agriculture (General)", "Agriculture", "S1-972"], "contacts": [{"organization": "Heikkinen, Jaakko, Keskinen, Riikka, Ylivainio, Kari,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.23986/afsci.148486"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agricultural%20and%20Food%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.23986/afsci.148486", "name": "item", "description": "10.23986/afsci.148486", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.23986/afsci.148486"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-05-26T00:00:00Z"}}, {"id": "10261/359343", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:21:52Z", "type": "Dataset", "title": "Plant affinity to extreme soils and foliar sulphur mediate species-specific responses to sheep grazing in gypsum systems [Dataset V2]", "description": "Open AccessPeer reviewed", "keywords": ["Semiarid systems", "Gypsophiles", "Elemental composition", "Gypsum soils", "Herbivory", "Functional traits"], "contacts": [{"organization": "Cera, Andreu, Montserrat-Mart\u00ed, Gabriel, Luzuriaga, Arantzazu L., Pueyo, Yolanda, Palacio, Sara,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10261/359343"}, {"rel": "self", "type": "application/geo+json", "title": "10261/359343", "name": "item", "description": "10261/359343", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10261/359343"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-01-01T00:00:00Z"}}, {"id": "10.3389/fmicb.2016.01446", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:19:03Z", "type": "Journal Article", "created": "2016-09-14", "description": "Soil management is fundamental to all agricultural systems and fertilization practices have contributed substantially to the impressive increases in food production. Despite the pivotal role of soil microorganisms in agro-ecosystems, we still have a limited understanding of the complex response of the soil microbiota to organic and mineral fertilization in the very long-term. Here, we report the effects of different fertilization regimes (mineral, organic and combined mineral and organic fertilization), carried out for more than a century, on the structure and activity of the soil microbiome. Organic matter content, nutrient concentrations, and microbial biomass carbon were significantly increased by mineral, and even more strongly by organic fertilization. Pyrosequencing revealed significant differences between the structures of bacterial and fungal soil communities associated to each fertilization regime. Organic fertilization increased bacterial diversity, and stimulated microbial groups (Firmicutes, Proteobacteria, and Zygomycota) that are known to prefer nutrient-rich environments, and that are involved in the degradation of complex organic compounds. In contrast, soils not receiving manure harbored distinct microbial communities enriched in oligotrophic organisms adapted to nutrient-limited environments, as Acidobacteria. The fertilization regime also affected the relative abundances of plant beneficial and detrimental microbial taxa, which may influence productivity and stability of the agroecosystem. As expected, the activity of microbial exoenzymes involved in carbon, nitrogen, and phosphorous mineralization were enhanced by both types of fertilization. However, in contrast to comparable studies, the highest chitinase and phosphatase activities were observed in the solely mineral fertilized soil. Interestingly, these two enzymes showed also a particular high biomass-specific activities and a strong negative relation with soil pH. As many soil parameters are known to change slowly, the particularity of unchanged fertilization treatments since 1902 allows a profound assessment of linkages between management and abiotic as well as biotic soil parameters. Our study revealed that pH and TOC were the majors, while nitrogen and phosphorous pools were minors, drivers for structure and activity of the soil microbial community. Due to the long-term treatments studied, our findings likely represent permanent and stable, rather than transient, responses of soil microbial communities to fertilization.", "keywords": ["Soil nutrients", "0301 basic medicine", "2. Zero hunger", "0303 health sciences", "long-term fertilization", "microbial biomass", "15. Life on land", "microbial activity", "Microbiology", "QR1-502", "03 medical and health sciences", "13. Climate action", "soil microbial communities", "soil nutrients", "454 pyrosequencing"]}, "links": [{"href": "https://doi.org/10.3389/fmicb.2016.01446"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Microbiology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3389/fmicb.2016.01446", "name": "item", "description": "10.3389/fmicb.2016.01446", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3389/fmicb.2016.01446"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-09-14T00:00:00Z"}}, {"id": "10.3390/proceedings2019030057", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:19:16Z", "type": "Journal Article", "created": "2020-05-20", "title": "Soil Structural Shifts Caused by Land Management Practices", "description": "Long-term agricultural practices have been shown to affect soil hydro-physical properties in multiple ways. They affect the stability and distribution of soil aggregates leading to changes in water retention, bulk density, hydraulic conductivity, and porosity. Aggregate stability is an indicator of the resilience of aggregates to external forces. Unstable aggregates can change rapidly under different land management practices and meteorological conditions. \u039cacro-aggregates (>250 \u03bcm) are formed more rapidly and are often more sensitive to management changes. Here, four different long-term experiments, run by the SoilCare Horizon 2020 Project partners, were sampled and analyzed, in order to evaluate the impact of different agricultural management practices in the water stability of soil aggregates and the fractions distribution. Different experiments selected, include control-conventional treatment and different treatments, which are considered soil improving. The treatments are about soil cultivation (conventional ploughing-control, zero tillage, minimum tillage, strip tillage, shallow tillage) and organic input (mineral fertilization-control, residue incorporation, farmyard manure) and are selected in areas with different climatic and soil conditions. Initial results indicate that treatments with less soil disturbance present more water stable aggregates (WSA) >250 \u03bcm and higher mean weight diameters (MWD), as well as the same trend following the treatments with increased organic input. According to Tukey\u2019s Honest Significance test (<i>p</i> < 0.05), management practices are shown to have a significant impact on the WSA and MWD in most cases, but not all similar treatments in the different areas present the same results. The large macro-aggregates (>2 mm) seem to be greatly sensitive to soil cultivation, whereas the results for the small macro-aggregates (250 \u03bcm\u20132 mm) are controversial among the different tillage experiments. The different organic inputs seems to affect more the small macro-aggregates than the larger. The initial results indicate that the shifts in the soil structure cannot only be justified by the different management practices. The interrelationships and potential links with other soil properties like texture, bulk density, particulate organic matter and climate will be taken into account in further steps in order to understand the mechanisms behind the aggregation shifts.", "keywords": ["long-term experiments", "2. Zero hunger", "13. Climate action", "soil cultivation", "A", "aggregates", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "soil structure", "SoilCare", "General Works", "6. Clean water"], "contacts": [{"organization": "Ioanna Panagea, Jan Diels, Guido Wyseure,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.3390/proceedings2019030057"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/TERRAenVISION%202019", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/proceedings2019030057", "name": "item", "description": "10.3390/proceedings2019030057", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/proceedings2019030057"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-05-19T00:00:00Z"}}, {"id": "10.3390/su17031093", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:19:20Z", "type": "Journal Article", "created": "2025-01-29", "title": "Microbial Bioindicators for Monitoring the Impact of Emerging Contaminants on Soil Health in the European Framework", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Antibiotic resistance (AR) is recognized by the World Health Organization as a major threat to human health, and recent studies highlight the role of microplastics (MPs) in its spread. MPs in the environment may act as vectors for antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs). Bacterial communities on the plastisphere, the surface of MPs, are influenced by plastic properties, allowing ARB to colonize and form biofilms. These biofilms facilitate the transfer of ARGs within microbial communities. This study analyzed data from the LUCAS soil dataset (885 soil samples across EU countries) using the Emu tool to characterize microbial communities at the genus/species level. Functional annotation via PICRUSt2, supported by a custom tool for Emu output formatting, revealed significant correlations between the genera Solirubrobacter, Bradyrhizobium, Nocardioides, and Bacillus with pathways linked to microplastic degradation and antibiotic resistance. These genera were consistently present in various soil types (woodland, grassland, and cropland), suggesting their potential as bioindicators of soil health in relation to MP pollution. The findings underscore MPs as hotspots for ARB and ARGs, offering new insights into the identification of bioindicators for monitoring soil health and the ecological impacts related to MP contamination.</p></article>", "keywords": ["microplastics; antibiotic resistance genes; soil microbiome; LUCAS soil"], "contacts": [{"organization": "Andrea Visca, Luciana Di Gregorio, Manuela Costanzo, Elisa Clagnan, Lorenzo Nolfi, Roberta Bernini, Alberto Orgiazzi, Arwyn Jones, Francesco Vitali, Stefano Mocali, Annamaria Bevivino,", "roles": ["creator"]}]}, "links": [{"href": "https://air.unimi.it/bitstream/2434/1142151/2/sustainability-17-01093.pdf"}, {"href": "https://www.mdpi.com/2071-1050/17/3/1093/pdf"}, {"href": "https://doi.org/10.3390/su17031093"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sustainability", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/su17031093", "name": "item", "description": "10.3390/su17031093", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/su17031093"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-01-29T00:00:00Z"}}, {"id": "10.5061/dryad.pb271", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:19:38Z", "type": "Dataset", "title": "Data from: Interactions among roots, mycorrhizae and free-living microbial communities differentially impact soil carbon processes", "description": "unspecifiedPlant roots, their associated microbial community and free-living soil  microbes interact to regulate the movement of carbon from the soil to the  atmosphere, one of the most important and least understood fluxes of  terrestrial carbon. Our inadequate understanding of how plant\u2013microbial  interactions alter soil carbon decomposition may lead to poor model  predictions of terrestrial carbon feedbacks to the atmosphere. Roots,  mycorrhizal fungi and free-living soil microbes can alter soil carbon  decomposition through exudation of carbon into soil. Exudates of simple  carbon compounds can increase microbial activity because microbes are  typically carbon limited. When both roots and mycorrhizal fungi are  present in the soil, they may additively increase carbon decomposition.  However, when mycorrhizas are isolated from roots, they may limit soil  carbon decomposition by competing with free-living decomposers for  resources. We manipulated the access of roots and mycorrhizal fungi to  soil in situ in a temperate mixed deciduous forest. We added 13C-labelled  substrate to trace metabolized carbon in respiration and measured  carbon-degrading microbial extracellular enzyme activity and soil carbon  pools. We used our data in a mechanistic soil carbon decomposition model  to simulate and compare the effects of root and mycorrhizal fungal  presence on soil carbon dynamics over longer time periods. Contrary to  what we predicted, root and mycorrhizal biomass did not interact to  additively increase microbial activity and soil carbon degradation. The  metabolism of 13C-labelled starch was highest when root biomass was high  and mycorrhizal biomass was low. These results suggest that mycorrhizas  may negatively interact with the free-living microbial community to  influence soil carbon dynamics, a hypothesis supported by our enzyme  results. Our steady-state model simulations suggested that root presence  increased mineral-associated and particulate organic carbon pools, while  mycorrhizal fungal presence had a greater influence on particulate than  mineral-associated organic carbon pools. Synthesis. Our results suggest  that the activity of enzymes involved in organic matter decomposition was  contingent upon root\u2013mycorrhizal\u2013microbial interactions. Using our  experimental data in a decomposition simulation model, we show that  root\u2013mycorrhizal\u2013microbial interactions may have longer-term legacy  effects on soil carbon sequestration. Overall, our study suggests that  roots stimulate microbial activity in the short term, but contribute to  soil carbon storage over longer periods of time.", "keywords": ["2. Zero hunger", "roots", "13. Climate action", "simulation model", "carbon dynamics", "Rhizosphere", "stable isotope", "plant-soil (belowground) interactions", "15. Life on land", "extra-cellular enzyme activity", "mycorrhizae"], "contacts": [{"organization": "Moore, Jessica A. M., Jiang, Jiang, Patterson, Courtney M., Wang, Gangsheng, Mayes, Melanie A., Classen, Aim\u00e9e T.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.pb271"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.pb271", "name": "item", "description": "10.5061/dryad.pb271", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.pb271"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-09-14T00:00:00Z"}}, {"id": "10.5281/zenodo.10060810", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:19:53Z", "type": "Dataset", "title": "SoilCompDB: Global soil compressive properties database. Version 1.0", "description": "Data collection and processing Our data collection comprised published journal articles sourced from Web of Science and Scopus databases, using search terms such as 'soil precompression stress,' 'soil compression index,' 'soil compaction index,' 'soil recompression index,' 'soil swelling index,' 'soil precompaction stress,' and 'preconsolidation pressure' for articles published up to February 2022. \u00a0A total of 1235 publications were found. Duplicate records were eliminated using the Endnote Web citation management application. The remaining references were exported to Rayyan software for title and abstract screening based on predefined criteria for full-text selection. \u00a0After a careful review, we identified 128 papers where the data on soil compressive properties (precompression stress, compression index, and swelling index) were reported in numerical format or legible graphical format and considered suitable for inclusion in the database. \u00a0We employed the WebPlotDigitizer software to extract data from figures within the original publications. For each chosen study, we systematically recorded data concerning soil compressive properties and collected information on soil properties, soil conditions, site characteristics, and experimental settings. We compiled 4,743 individual data entries. Time and place The database includes data from 128 independent studies published between 1992 and 2021. Each study reported between 1 and 360 measurements, with a study median of 14 measurements and a mean of 38 measurements, totalling 4743 database entries. Our database includes data from 20 countries, with a significant concentration of the data originating from Brazil, followed by Germany, Switzerland, Sweden, and Denmark. The majority of the data came from arable soils, representing approximately 72% of data entries.\u00a0\u00a0 Instruments The soil compressive properties included in the database were based on soil compressive tests performed in the laboratory by uniaxial method. The procedure used for stress application on soil samples was mainly the stepwise stress application method, while the constant strain rate method was applied in few studies (less than 2% of the data). The component of the compressive curve related to the soil packing state was represented by soil bulk density, void ratio, and strain. The stress component of the curve was represented in a logarithmic form in the entirety of the database. The database also comprised eight different methods for calculating precompresion stress: Casagrande (1936), Dias Junior and Pierce (1995), Lamand\u00e9 et al. (2017), Sullivan and Robertson (1996), Casini (2012), Culley and Larson (1987), Pacheco Silva (1990), Gregory et al. (2006). Resources Web of Science, Scopus \u2013 literature search Endnote Web \u2013 removal of duplicates Rayyan software \u2013 initial paper selection based on title and abstract WebPlotDigitizer \u2013 data extraction from figures Microsoft Access \u2013 database platform Description of the collected data (column, unit, and description) Sample ID-\u00a0\u00a0\u00a0 A unique identification number assigned to each individual sample within the database\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Study ID- Identification number assigned to each research study in the database Reference - Research paper reference Year - Year of research paper publication \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Language - Language of the research paper \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Soil classification (SiBCS) - Soil Classification according to the Brazilian System (SiBCS), as described in portuguese-language papers Soil classification (original in paper) - Soil classification described in research paper\u00a0 Soil classification (convertion to Soil Taxonomy orders) -\u00a0 Soil classification aligned with the Soil Taxonomy system developed by the United States Department of Agriculture (USDA)\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Location - Study location country\u00a0\u00a0\u00a0 Texture classification (USDA) -\u00a0Soil textural classification according USDA Texture\u00a0 classification USDA (letter code) - Letter code for soil textural classification according USDA: S=sand; LS=loamy sand; SL=sandy loam; SiL=silt loam; Si=silt; L=loam; SCL= Sandy clay loam; SiCL=Silty clay loam; CL=clay loam; SC=Sandy clay; SiC=Silty clay; C=clay Clay (USDA) - % - Soil clay content (weight based) - (<0.002 mm) \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Silt (USDA) - % - Soil silt content (weight based) - (0.002 < x < 0.05 mm, interpolated for European samples where needed using the k-nearest neighbor technique by Nemes et al. 2006)\u00a0 Sand (USDA) - % - Soil sand content (weight based)\u00a0 - (0.05 < x < 2 mm, interpolated for European samples where needed using the k-nearest neighbor technique by Nemes et al. 2006) USDA PSD interpolated - =0 if the data was NOT interpolated; =1 if the data was interpolated Published texture class - Texture classification provided in the source publication when the values for clay, silt and sand were not available Clay - g kg-1 - Soil clay content - original in the paper Clay class upper boundary - \u00b5m - The clay class upper boundary informed in source publication Silt - g kg-1 - Silt clay content - original in the paper Silt class upper boundary - \u00b5m - The silt class upper boundary informed in source publication Sand - Soil sand content - original in the paper Sand class upper boundary - \u00b5m - The sand class upper boundary informed in source publication Particle size data flag - =0 if no issues; =1 if there are issues (summing) Sum particle size- g kg-1 - Sum of clay, silt, and sand content Soil depth FROM \u2013 cm - When soil depth is presented as a range (e.g., 0-10cm), it indicates the minimum depth at which soil samples were collected\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Soil depth TO \u2013 cm - When soil depth is presented as a range (e.g., 0-10cm), it indicates the maximum depth at which soil samples were collected\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Depth \u2013 cm -Specific depth value as presented in paper, or when soil depth is showed as a range (e.g., 0-10cm), it indicates the average depth at which soil samples were collected (e.g 5cm) \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 SOC - g kg-1 - Soil organic carbon content informed in research paper or soil organic carbon content calculate from soil organic matter content by multiplying by 0,58\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 SOC converted from SOM - 1= yes for soil organic carbon derived from soil organic matter content calculations Particle density - Mg m-3 - Soil particle density\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Initial matric potential \u2013 hPa - Soil water matric potential before loading log Initial matric potential - Soil water matric potential expressed by log\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Wetness (based on initial matric potential) - \u00a01=if initial matric potential (MP)<100 hPa; 2= if 100<=initial MP<1000 hPa; 3= initial MP>=1000 hPa Initial gravimetric water content - g g-1 - Gravimetric soil water content before loading provided by source publication, or calculated by volumetric water content divided by soil bulk density Initial volumetric water content - m3 m-3 - Volumetric soil water content before loading, when the soil bulk density was not reported Initial water content data source -\u00a0Graph or table from where the data was collected, or explanation on calculation used Matric potential type - Compressive tests performed on soil samples under different conditions: 1= equilibrated at matric potential; 2= field matric potential; 3= air-dried samples\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Initial bulk density - Mg m-3 - Soil bulk density before loading\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Initial BD data source - Graph or table from where the data was collected, or explanation on calculation used\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Initial volumetric water content calculated - m3 m-3 - Soil volumetric water content calculated by multiplying soil gravimetric water content by soil bulk density Precompression stress \u2013 kPa - Precompression stress \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Precompression stress (SD) \u2013 kPa - Standard deviation for precompression stress values reported in paper\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Precompression stress data source - Graph or table from where the data was collected, or explanation on calculation used Compression index - Compression index \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Compression index (SD) - Standard deviation of compression index values reported in paper\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Compression index data source - Graph or table from where the data was collected, or explanation on calculation used Swelling index - Swelling index\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Swelling index (SD) - Standard deviation of swelling index values reported in paper\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Swelling index data source - Graph or table from where the data was collected, or explanation on calculation used N -\u00a0Number of replicates used for calculating precompression stress, compression index, and swelling index when mean values are reported Land use (paper) -\u00a0Land use described in the research paper Land use (categories) -\u00a0Land use categorized Land use standardized -\u00a0Land use classified as: arable, forest, grassland, and native vegetation. The latter includes forest, grassland, and savanna Land use (number code) -\u00a0Number code for land use: 1=Arable, 2= forest, 3= grassland, and 4= native vegetation Tillage system -\u00a0Tillage system Tillage system (arable soils) - Tillage system for arable soils classified as 'conventional' and 'conservation' Coordinates -\u00a0\u00a0Geographical coordinates\u00a0 of study location Climate -\u00a0Climatic region classification: temperate, tropical, subtropical Climatecod -\u00a0\u00a0\u00a0 Code number assigned to each climatic region: 1=temperate, 2=tropical, 3=subtropical Sampling position (paper) -\u00a0Field position where soil samples were collected with details described in the paper Sampling position -\u00a0Field position where soil samples were collected standardized Treatment -\u00a0Experimental treatment type where the soil samples were collected Stress rate - \u00a0kPa - Stress applied in compressive tests\u00a0 Minimum stress \u2013\u00a0kPa - Minimum stress applied in compressive tests Maximum stress \u2013\u00a0kPa - Maximum stress applied in compressive tests Number of stress rate steps -\u00a0Number of steps in stepwise stress application procedure Stess application type -\u00a01=Stepwise stress 2=one sample per stress 3=Strain controlled Stess application type \u2013\u00a0min - Time for stress application in each step in stepwise stress application procedure Degree of deformation at the end of loading -\u00a0% - Degree of deformation at the end of compressive test Sample diameter \u2013\u00a0cm - Diameter of the soil samples Sample height \u2013\u00a0cm - Height of the soil samples Ratio sample diameter and height\u00a0-\u00a0Ratio between diameter and height of the soil samples Sample volume -\u00a0cm3 -Sample volume when the sample diameter and height are nor presented Precompression stress calculation method -\u00a0Calculation method of precompression stress Precompression stress calculation method (number code) -\u00a0Number code for calculation method PC:1=Casagrande (1936); 2=Dias Junior and Pierce (1995); 3= Lamand\u00e9 et al. (2017); 4=O`Sullivan and Robertson (1996); 5=Casini (2012); 6=Culley and Larson (1987);7=ABNT (1990); 8=Gregory et al. (2006) Description of precompression stress calculation -\u00a0Brief explanation of precompression stress calculation Soil compressive curve components -\u00a0Component of the soil compression curve related to the soil packing state: soil bulk density, void ratio, and strain.\u00a0 Soil compressive curve components (number code) -\u00a0Number code for component of the soil compressive curve related to the soil packing state: 1= soil bulk density; 2= strain; 3= void ratio Curve components source -\u00a0Source of the component of the soil compressive curve related to the soil packing state: 1= showed in the paper, 2= according to original method for precompression stress calculation, 3= described in method, but not clear in the paper Compressive curve available -\u00a0Original soil compressive curve available in the paper: 1= No 2=Yes Comments -\u00a0Brief comments on the paper Issues and remarks We sought out important information not included in the paper by directly communicating with the authors whenever possible. In cases where multiple papers covered the same experiment, we prioritized the one offering more comprehensive details. If two papers complemented each other, we included both. When analyzing studies comparing various methods for calculating soil precompression stress, we exclusively gathered data calculated using the widely accepted Casagrande (1936) method. To ensure comparability across studies, we standardized the collected data by converting it to the same unit. The standardization process involved: i) assuming that 58% of soil organic matter (SOM) was soil organic carbon (SOC) when only SOM was reported, ii) calculating soil bulk density using a soil particle density of 2.65 Mg m-3 when only total porosity data were provided, and iii) harmonizing all texture data to the USDA classification system, which defines the silt/sand boundary as 50 \u03bcm, utilizing the k-nearest neighbor approach (referred to as 'similarity method' by Nemes et al. (1999). \u00a0 Reference Associa\u00e7\u00e3o Brasileira de Normas T\u00e9cnicas - ABNT. NBR 12007: Ensaio de adensamento unidimensional. Rio de Janeiro: 1990. Casagrande, A., 1936. Determination of the preconsolidation load and its practical significance. In: Proceedings of the International Conference on Soil Mechanics and Foundation Engineering, vol. III, Harvard University, Cambridge, MA, pp. 60\u201364.Casini, F. 2012. Deformation induced by wetting: A simple model. Can. Geotech. J. 49:954\u2013960 10.1139/T2012-054. doi:10.1139/t2012-054 Culley, J.L.B., Larson, W.E., 1987. Susceptibility to compression of a clay loam Haplaquoll. Soil Sci. Soc. Am. J. 51, 562\u2013567. Dias Junior, M.S., Pierce, F.J., 1995. A simple procedure for estimating preconsolidation pressure from soil compression curves. Soil Technology 8, 139\u2013151. doi:10.1016/0933-3630(95)00015-8 Gregory, A.S., Whalley, W.R., Watts, C.W., Bird, N.R.A., Hallett, P.D., Whitmore, A.P., 2006. Calculation of the compression index and pre-compression stress from soil compression test data. Soil Till Res. 89:45-57. doi:10.1016/j.still.2005.06.012 Lamand\u00e9, M., Schj\u00f8nning, P., Labouriau, R., 2017. A novel method for estimating soil precompression stress from uniaxial confined compression tests. Soil Sci. Soc. Am. J. 81 https://doi.org/10.2136/sssaj2016.09.0274. Nemes, A., \u00a0W\u00f6sten, J.H.M., Lilly, A., \u00a0Oude Voshaar, J.H., 1999. Evaluation of different procedures to interpolate the cumulative particle-size distribution to achieve compatibility within a soil database.\u00a0Geoderma 90: 187-202. 129\u00a0 O'Sullivan, M.F., Robertson, E.A.G., 1996. Critical state parameters from intact samples of two agricultural topsoils. Soil Tillage Res 39(3 \u2013 4):161 \u2013 173.", "keywords": ["2. Zero hunger", "soil compression curve", "precompression stress", "15. Life on land", "soil mechanical properties", "compression index", "soil moisture", "uniaxial compression test", "swelling index"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.10060810"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.10060810", "name": "item", "description": "10.5281/zenodo.10060810", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.10060810"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-11-06T00:00:00Z"}}, {"id": "10.5281/zenodo.10959077", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:20:04Z", "type": "Dataset", "created": "2023-10-30", "title": "Knowledge gaps on trade-offs of soil carbon sequestration related to soil management strategies", "description": "The database contains 87 unique literature items (29 reviews, 42 meta-analyses, 16 original papers) describing the effect of a soil management strategy (tillage management, cropping systems, water management, cover crops, crop residues, livestock manure, slurry, compost, biochar, liming) on the trade-offs between soil carbon sequestration or SOC change and N2O emission, CH4 emission and nitrogen leaching. Since some literature items describe effects of several SMS categories, the database_summary tab comprises a total of 112 unique inputs. For each input it is indicated in the Database_summary tab if it was used as input for the 'Soil management effect assessment' in Maenhout et al. (2024) [Maenhout, P., Di Bene, C., Cayuela, M. L., Diaz-Pines, E., Govednik, A., Keuper, F., Mavsar, S., Mihelic, R., O'Toole, A., Schwarzmann, A., Suhadolc, M., Syp, A., & Valkama, E. (2024). Trade-offs and synergies of soil carbon sequestration: Addressing knowledge gaps related to soil management strategies. European Journal of Soil Science, 75(3), e13515. https://doi.org/10.1111/ejss.13515] and/or to define knowledge gaps ('Knowledge gap in tab'-column). Knowledge gaps and research recommendations are gouped per soil management strategy in different tabs in this database. Per soil management strategy, knowledge gaps are clustered per theme in groups. These themes include: the specific soil management strategy, pedoclimatic conditions, establishment of experiments, other soil management strategies, meta-analysis, modelling and other", "keywords": ["Water management", "EJP SOIL", "Climate change mitigation", "Nitrogen leaching", "CH4", "Conservation agriculture", "Cropping systems", "SOMMIT", "N2O", "Organic matter inputs", "Tillage"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.10959077"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.10959077", "name": "item", "description": "10.5281/zenodo.10959077", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.10959077"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-05-13T00:00:00Z"}}, {"id": "10.5281/zenodo.13952097", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:20:14Z", "type": "Dataset", "created": "2024-01-01", "title": "SERENA EJPSoil Soil erosion control in Tuscany (Italy)", "description": "Open AccessThe internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.  One of the\u00a0objective of SERENA project was to develop methods to calculate and map soil-based ecosystem services and soil threats. The present data was prepared according to the methodology of the SERENA Soil erosion and soil erosion control cookbook.\u00a0 Soil loss was used as an indicator for soil erosion (ST). The map of soil mass not eroded was based on the RUSLE model. Soil erosion control was calculated as the difference between potential and actual soil erosion (SES, ecosystem service of soil erosion protection, i.e. soil eroded mass retained by vegetation, Mg/ha/y). For Italy, the cookbook was applied in the Tuscany region. \u00a0     To create the soil loss map we used:\u00a0       for R-factor, not freely available database of meteorological parameters spatialized at 250 m (minimum and maximum daily air temperature; cumulate daily precipitation) over Tuscany region (period 1990\u20132022, Lamma Consortium)\u00a0 and a local linear equation between R and mean annual precipitation (P);   for C -factor, Regional Land use map 1:10.000 (2018, freely available at:\u00a0https://www502.regione.toscana.it/geoscopio/usocoperturasuolo.html) and ESDAC method (https://doi.org/10.1016/j.landusepol.2015.05.021) ; \u00a0  for K-factor, sand, silt, clay, and O.C. (%) maps (built from 4.000 soil profiles, following FAO\u2019s methodology in GSP-GSOC map, Lamma Consortium), and Torri et al. (1997) function;   for LS-factor, DEM 10 m of Tuscany, (freely available at\u00a0https://www502.regione.toscana.it/geoscopio/cartoteca.html99) and Desmet & Govers (1996) SAGA tool (applied at 10 m and upscaled);   for P-factor, not freely available database 1:10.00 of terraced areas (Lamma Consortium, 2020) (for terraced areas a multiplication factor of\u00a0 0.5\u00a0 was considered, based on expert evaluation)   for P-factor, not freely available database 1:10.00 of terraced areas (Lamma Consortium, 2020) (for terraced areas a multiplication factor\u00a0of\u00a0 0.5\u00a0 was considered, based on expert evaluation)\u00a0      Maps was delivered in the GeoTIFF format in the resolution of 100m. \u00a0     Delivered data will be validated by stakeholders from Italy (scientist) in October, 2024.", "keywords": ["Italy", "Tuscany", "SERENA project", "Soil-based ecosystem service", "RUSLE", "soil erosion control", "Grant n. 862695", "EJP-Soil"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.13952097"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.13952097", "name": "item", "description": "10.5281/zenodo.13952097", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.13952097"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-10-18T00:00:00Z"}}, {"id": "10.5281/zenodo.14044657", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:20:17Z", "type": "Dataset", "title": "SERENA EJPSOIL BE Flanders soil sealing cookbook", "description": "Open AccessThe internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national and European scales.The data was prepared according to the Level 2 methodology of the SERENA soil sealing cookbook. For Belgium, the application was carried out at the regional scale for the Flanders region. \u00a0The automatically generated yearly soil sealing maps (1 m resolution GeoTIFF rasters)\u00a0combine \u201cknown\u201d sealing from administrative databases (buildings and transport infrastructure) with modelled sealing based on artificial intelligence. Administrative databases do not (adequately) cover parking lots, private driveways and garden terraces, which are a substantial part of the sealed area in Flanders. Hence, a machine learning model was built for deriving this remaining sealing from aerial imagery. For this purpose, an assessor manually labeled the sealed parts on a subset of the images. Based on this training set, a convolutional neural network model was used to produce a sealing probability map, which was converted to a binary modelled sealing map. Finally, a continuity correction was applied to ensure a temporally consistent result across the yearly maps. \u00a0The objective of the SERENA project was to develop methods to calculate and map soil-based ecosystem services and soil threats. The selected indicator was the degree of soil sealing. By evaluating this degree at two moments in time, the change in soil sealing can be determined. \u00a0\u00a0The following data were used:\u00a0         Large-scale Reference Database (Grootschalig Referentiebestand or Basiskaart), the digital topographic reference map for Flanders (vector)\u00a0           Medium-scale annual winter aerial images of Flanders (15 or 25 cm raster resolution)    This dataset is originally hosted at Geopunt (www.geopunt.be). For the most up-to-date version of the dataset, please access the data from the Geopunt repository.", "keywords": ["soil sealing", "remote sensing", "BELGIUM (FLANDERS)", "aerial images", "SERENA", "EJP-Soil", "photointerpretation"], "contacts": [{"organization": "Cockx, Kasper, Oorts, Katrien,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14044657"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14044657", "name": "item", "description": "10.5281/zenodo.14044657", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14044657"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-10-30T00:00:00Z"}}, {"id": "10.5281/zenodo.14833053", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:20:28Z", "type": "Dataset", "created": "2025-02-06", "title": "Surface soil moisture for Europe 2014-2024 at 1 km annual and quarterly aggregates", "description": "Copernicus Land Monitoring Services provides Surface Soil Moisture 2014-present (raster 1 km), Europe, daily \u2013 version 1. Each day covers only 5 to 10% of European land mask and shows lines of scenes (obvious artifacts). This is the long-term aggregates of daily images of soil moisture (0\u2013100%) based on two types of aggregation:    Long-term quarterly (qr.1 - winter, qr.2 - spring, qr.3 - summer and qr.4 - autumn),  Annual quantiles P.05, P.50 and P.95,   The soil moisture rasters are based on Sentinel 1 and described in detail in:\u00a0    Bauer-Marschallinger, B. ; Freeman, V. ; Cao, S. ; Paulik, C. ; Schaufler, S. ; Stachl, T. ; Modanesi, S. ; Massari, C. ; Ciabatta, L. ; Brocca, L. ; Wagner, W.\u00a0Toward Global Soil Moisture Monitoring With Sentinel-1: Harnessing Assets and Overcoming Obstacles.\u00a0IEEE Transactions on Geoscience and Remote Sensing\u00a02019, 1 - 20. DOI\u00a010.1109/TGRS.2018.2858004   You can access and download the original data as .nc files from: https://globalland.vito.be/download/manifest/ssm_1km_v1_daily_netcdf/.  The files with pattern 'soil.moisture_s1.clms.qr.*.p0.*.gf_m_1km_20140101_20241231_eu_epsg4326_v20250211.tif' are the gap-filled soil moisture quarterly estimates. For gap filling I build a model using cca 250k random training points and relationship with CHELSA climate bioclimatic variables, ESA CCI snow cover probability, ESA CCI forest and bare areas percent cover and Global Water Pack long-term surface water fraction. The gap-filling model had an R-square of 0.96 and RMSE of 6.5% of soil moisture.  Aggregation has been generated using the terra package in R in combination with the matrixStats::rowQuantiles function. Tiling system and land mask for pan-EU is also available.  library(terra) library(matrixStats) g1 = terra::vect('/mnt/inca/EU_landmask/tilling_filter/eu_ard2_final_status.gpkg') ## 1254 tiles tile = g1[534] nc.lst = list.files('/mnt/landmark/SM1km/ssm_1km_v1_daily_netcdf/', pattern = glob2rx('*.nc$'), full.names=TRUE) ## 3726 ## test it #r = terra::rast(nc.lst[100:210])  agg_tile = function(r, tile, pv=c(0.05,0.5,0.95), out.year='2015.annual'){   bb = paste(as.vector(ext(tile)), collapse = '.')   out.tif = paste0('./eu_tmp/', out.year, '/sm1km_', pv, '_', out.year, '_', bb, '.tif')   if(any(!file.exists(out.tif))){     r.t = terra::crop(r, ext(tile))     r.t = as.data.frame(r.t, xy=TRUE, na.rm=FALSE)     sel.c = grep(glob2rx('ssm$'), colnames(r.t))     t1s = cbind(data.frame(matrixStats::rowQuantiles(as.matrix(r.t[,sel.c]), probs = pv, na.rm=TRUE)),  data.frame(x=r.t$x,  y=r.t$y))     ## write to GeoTIFFs     r.o = terra::rast(t1s[,c('x','y','X5.','X50.','X95.')], type='xyz', crs='+proj=longlat +datum=WGS84 +no_defs')     for(k in 1:length(pv)){        terra::writeRaster(r.o[[k]], filename=out.tif[k], gdal=c('COMPRESS=DEFLATE'), datatype='INT2U', NAflag=32768, overwrite=FALSE)     }     rm(r.t); gc()     tmpFiles(remove=TRUE)   } }  ## quarterly values: lA = data.frame(filename=nc.lst) library(lubridate) lA$Date = ymd(sapply(lA$filename, function(i){substr(strsplit(basename(i), '_')[[1]][4], 1, 8)})) #summary(is.na(lA$Date)) #hist(lA$Date, breaks=60) lA$quarter = quarter(lA$Date, fiscal_start = 11) summary(as.factor(lA$quarter))  for(qr in 1:4){   #qr=1   pth = paste0('A.q', qr)   rs = terra::rast(lA$filename[lA$quarter==qr])   x = parallel::mclapply(sample(1:length(g1)), function(i){try( agg_tile(rs, tile=g1[i], out.year=pth) )}, mc.cores=20)   for(type in c(0.05,0.5,0.95)){     x <- list.files(path=paste0('./eu_tmp/', pth), pattern=glob2rx(paste0('sm1km_', type, '_*.tif$')), full.names=TRUE)     out.tmp <- paste0(pth, '.', type, '.sm1km_eu.txt')     vrt.tmp <- paste0(pth, '.', type, '.sm1km_eu.vrt')     cat(x, sep=' n', file=out.tmp)     system(paste0('gdalbuildvrt -input_file_list ', out.tmp, ' ', vrt.tmp))     system(paste0('gdal_translate ', vrt.tmp, ' ./cogs/soil.moisture_s1.clms.qr.', qr, '.p', type, '_m_1km_20140101_20241231_eu_epsg4326_v20250206.tif -ot 'Byte' -r 'near' --config GDAL_CACHEMAX 9216 -co BIGTIFF=YES -co NUM_THREADS=80 -co COMPRESS=DEFLATE -of COG -projwin -32 72 45 27'))   } }  ## per year ---- for(year in 2015:2023){   l.lst = nc.lst[grep(year, basename(nc.lst))]   r = terra::rast(l.lst)   pth = paste0(year, '.annual')   x = parallel::mclapply(sample(1:length(g1)), function(i){try( agg_tile(r, tile=g1[i], out.year=pth) )}, mc.cores=40)   ## Mosaics:   for(type in c(0.05,0.5,0.95)){     x <- list.files(path=paste0('./eu_tmp/', pth), pattern=glob2rx(paste0('sm1km_', type, '_*.tif$')), full.names=TRUE)     out.tmp <- paste0(pth, '.', type, '.sm1km_eu.txt')     vrt.tmp <- paste0(pth, '.', type, '.sm1km_eu.vrt')     cat(x, sep=' n', file=out.tmp)     system(paste0('gdalbuildvrt -input_file_list ', out.tmp, ' ', vrt.tmp))     system(paste0('gdal_translate ', vrt.tmp, ' ./cogs/soil.moisture_s1.clms.annual.', type, '_m_1km_', year, '0101_', year, '1231_eu_epsg4326_v20250206.tif -ot 'Byte' -r 'near' --config GDAL_CACHEMAX 9216 -co BIGTIFF=YES -co NUM_THREADS=80 -co COMPRESS=DEFLATE -of COG -projwin -32 72 45 27'))   } }", "keywords": ["Soil", "Soil moisture"], "contacts": [{"organization": "Hengl, Tomislav", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14833053"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14833053", "name": "item", "description": "10.5281/zenodo.14833053", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14833053"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-02-07T00:00:00Z"}}, {"id": "10.5281/zenodo.14252610", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:20:22Z", "type": "Dataset", "title": "Data from: Turfgrass pedogenesis under low maintenance: an experimental analysis with Festuca rubra subspecies at different fertilization levels", "description": "unspecifiedThatchMatThickeness.csv  (Frc = Festuca rubra commutata, Frt = Festuca rubra trichophylla, Frr = Festuca rubra rubra)    spec = subspecies  var = variety  rep = replicate number  Nfert = N fertilization (kg N ha-1)  thatch1 = thatch thickness at 18-5-2018 (cm)  thatch2 = thatch thickness at 26-10-2018 (cm)  thatch3 = thatch thickness at 17-5-2019 (cm)  thatch4 =\u00a0 thatch thickness at 29-10-2019 (cm)  thatch5 = thatch thickness at 10-6-2020 (cm)  thatch6 = thatch tcicknesss at 16-6-2021 (cm)  mat1 = mat thickness at 18-5-2018 (cm)  mat2 = mat thickness at 26-10-2018 (cm)  mat3 = mat thickness at 17-5-2019 (cm)  mat4 =\u00a0 mat thickness at 29-10-2019 (cm)  mat5 = mat thickness at 10-6-2020 (cm)  mat6 = mat tcicknesss at 16-6-2021 (cm)", "keywords": ["Festuca rubra", "festuca rubra", "fertilization", "carbon", "soil layers", "pedogenesis", "turfgrass", "microbes", "Turfgrass", "nitrogen", "Carbon", "organic matter"], "contacts": [{"organization": "Evers, Maurice, De Caluwe, Hannie, Visser, Eric J.W., De Kroon, Hans,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14252610"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14252610", "name": "item", "description": "10.5281/zenodo.14252610", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14252610"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-12-01T00:00:00Z"}}, {"id": "10.5281/zenodo.14875898", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:20:29Z", "type": "Other", "title": "Les mod\u00e8les de COS doivent \u00eatre valid\u00e9s par des s\u00e9ries temporelles ind\u00e9pendantes pour permettre une pr\u00e9diction fiable", "description": "Les efforts visant \u00e0 maintenir les jeux de donn\u00e9es sont imp\u00e9ratifs pour obtenir des projections et des pr\u00e9visions pr\u00e9cises en mati\u00e8re de COS.", "keywords": ["[SDV.SA.AGRO] Life Sciences [q-bio]/Agricultural sciences/Agronomy", "[SDV.SA.SDS] Life Sciences [q-bio]/Agricultural sciences/Soil study"], "contacts": [{"organization": "Le No\u00eb, Julia, Manzoni, Stefano, Abramoff, Rose, B\u00f6lscher, Tobias, Bruni, Elisa, Cardinael, R\u00e9mi, Ciais, Philippe, Chenu, Claire, Clivot, Hugues, Derrien, Delphine, Ferchaud, Fabien, Garnier, Patricia, Goll, Daniel, Lashermes, Gwena\u00eblle, Martin, Manuel, Rasse, Daniel, Rees, Fr\u00e9d\u00e9ric, Sainte-Marie, Julien, Salmon, \u00c9lodie, Schiedung, Marcus, Schimel, Josh, Wieder, William, Abiven, Samuel, Barr\u00e9, Pierre, C\u00e9cillon, Lauric, Guenet, Bertrand, Delahaie, Amicie,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14875898"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14875898", "name": "item", "description": "10.5281/zenodo.14875898", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14875898"}, {"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.14936177", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:20:30Z", "type": "Dataset", "title": "Precision Liming Soil Datasets (LimeSoDa) Zenodo Repository", "description": "Overview  Precision Liming Soil Datasets (LimeSoDa) is a collection of 31 datasets from a field- and farm-scale soil mapping context. These datasets are 'ready-to-use' for modeling purposes, as they include target soil properties and features in a tidy tabular format. Three target soil properties are present in every dataset: (1) soil organic matter (SOM) or soil organic carbon (SOC), (2) pH, and (3) clay content, while the features for modeling are dataset-specific. The primary goal of `LimeSoDa` is to enable more reliable benchmarking of machine learning methods in digital soil mapping and pedometrics. All the associated materials and data from LimeSoDa can be downloaded in this data repository. However, for a more in-depth analysis, we refer to the published paper 'LimeSoDa: A Dataset Collection for Benchmarking of Machine Learning Regressors in Digital Soil Mapping' by Schmidinger et al. (2025). You may also use our R\u00a0and Python package likewise called LimeSoDa.  \u00a0  Citation  Upon usage of datasets from LimeSoDa, please cite our associated paper:  Schmidinger, J., Vogel, S., Barkov, V., Pham, A.-D., Gebbers, R., Tavakoli, H., Correa, J., Tavares, T.R., Filippi, P., Jones, E. J., Lukas, V., Boenecke, E., Ruehlmann, J., Schroeter, I., Kramer, E., Paetzold, S., Kodaira, M., Wadoux, A.M.J.-C., Bragazza, L., Metzger, K., Huang, J., Valente, D.S.M., Safanelli, J.L., Bottega, E.L., Dalmolin, R.S.D., Farkas, C., Steiger, A., Horst, T. Z., Ramirez-Lopez, L., Scholten, T., Stumpf, F., Rosso, P., Costa, M.M., Zandonadi, R.S., Wetterlind, J. & Atzmueller, M. (2025). LimeSoDa: A Dataset Collection for Benchmarking of Machine Learning Regressors in Digital Soil Mapping.", "keywords": ["Environmental sciences", "Soil Organic Carbon", "Pedometrics", "pH", "Soil Organic Matter", "Clay", "Remote sensing", "Digital Soil Mapping"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14936177"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14936177", "name": "item", "description": "10.5281/zenodo.14936177", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14936177"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.15096788", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:20:32Z", "type": "Dataset", "title": "HWSD2_Climate_and_Socioeconomic_agriculturalsoil_dataset_mainland_portugal", "description": "The study uses the Harmonized World Soil Database (HWSD v2.0) developed by FAO and IIASA for biophysical models and agroecological queries. This database consolidates information from various sources, including the European Soil Database, the 1:1 million soil map of China, and national soil maps from Afghanistan, Ghana, and T\u00fcrkiye. It has a spatial resolution of around 1 km and is revised in 2013 and 2023. HWSD v2.0 includes detailed information on soil mapping units, general soil unit information, and specific physical and chemical soil unit characteristics across seven depth layers.  The database fields cover a wide range of attributes, such as soil texture, bulk density, organic carbon content, pH, and cation exchange capacity. The harmonization process ensures that data from different sources is standardized and integrated, providing a consistent and reliable dataset for various applications. However, the HWSD v2.0 has some limitations, such as combining soil inventories gathered at different times, scales, and precision, which may affect its reliability for national studies. It is recommended to use national-level harmonized soil databases for more accurate results in specific regions.  For Portugal's mainland, the data presented in the HWSD v2.0 dataset is sourced from the European Soil Data Centre (ESDAC), which contains various metrics of chemical and physical soil properties. Out of the 2882 Portuguese parishes, only 22 are left out, representing 0.76% percent of the total number of parishes.  The study uses several datasets to analyze land use and occupation in Portugal. The Land Use and Occupation Map (COS2007v3.0) is a detailed thematic map of land use and occupation for mainland Portugal, developed by the Directorate-General for Territory (DGT). The data is organized hierarchically and includes 83 classes of land use and occupation. The CHELSA database, maintained by the Swiss Federal Institute for Forest, Snow, and Landscape Research (WSL), provides bioclimatic indexes for precipitation and average temperature over various temporal intervals and variables.  The National Institute of Statistics (INE) provides data on agricultural machinery distribution across different geographical locations. The dataset covers the total number of agricultural machines, as well as specific categories such as wheeled and tracked tractors, motor cultivators, power hoes, motor mowers, and combine harvesters. The dataset also examines the distribution of farms with access to irrigation based on geographical location.  The burned land data from 1975 to 2023 provides a comprehensive overview of fire occurrences and their impact over time. This data is crucial for understanding long-term patterns, assessing the effectiveness of fire prevention measures, and informing future land management and policy decisions.  Lastly, the population density dataset from the 2021 Census and the 2011 Census provides a decennial comparison of total population density across different geographical regions. These data are essential for understanding the evolution of land use and occupation in Portugal and their implications for environmental and agricultural consequences.", "keywords": ["Soil", "Total organic carbon", "Land use", "Soil use", "Atmospheric precipitation", "Soil type", "Organic carbon", "Land surface temperature"], "contacts": [{"organization": "Almeida Santos, R. G. F.", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15096788"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15096788", "name": "item", "description": "10.5281/zenodo.15096788", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15096788"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-03-27T00:00:00Z"}}, {"id": "10.5281/zenodo.15328215", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:20:36Z", "type": "Dataset", "title": "1000 Soils Pilot Dataset, version 8, May 2025", "description": "This record hosts data generated by the 1000 Soils Pilot. Data will be updated as more become available. Please see the most recent data upload for current data.  A beta visualization tool is available for some data types at\u00a0https://shinyproxy.emsl.pnnl.gov/app/1000soils. Please submit any suggestions or comments through the 'contact' tab. We are actively working to improve visualizations and value all feedback.  Data completed include:    Geochemistry, texture, respiration, and enzyme activities  FTICR-MS organic matter chemistry  Microbial biomass C and N  TOC/TDN of water-extractable OM  X-ray computed tomography (derived metrics available here, raw data available upon request)  Metagenomes; a variety of data formats are available upon request  Soil hydraulic properties   Data in progress:    LC-MS/MS in development, timeline TBD, inquire for status   1000S_processed_BGC_summary.csv contains all available biogeochemical data; microbial biomass C and N; and TOC/TDN of water-extractable OM; and\u00a0  1000S_Tomography.xslx contains a summary of data generated via X-ray computed tomography.  icr_v2_corems2.csv contains FTICR-MS data processed by CoreMS version 2. These data are merged by formula across instrument runs to enable cross-sample comparisons. Technical replicates are merged by retaining peaks present in 2 out of 3 replicates.  1000Soils_Metadata_Site_Mastersheet_v1.csv contains site information.  Soil Hydraulics_corrected_02042025.xlsx contains soil hydraulics information.  Readme File_v4.xlsx is the readme file.  Please contact the MONet project (monet.emsl@pnnl.gov) or Emily Graham (emily.graham@pnnl.gov) with questions.  The following file and all raw data are\u00a0available upon request:  icr_by_mass_for_single_sample_analysis_only.csv\u00a0contains FTICR-MS data processed by CoreMS and is intended for usage in the calculation of biochemical transformations within samples only. These data are not acceptable for cross-sample comparison of masses because they are from multiple instrument runs.  For more information, please see: https://www.emsl.pnnl.gov/monet and https://sc-data.emsl.pnnl.gov/monet  Acknowledgment:\u00a0  Soil data were provided by the Molecular Observation Network (MONet) at the Environmental Molecular Sciences Laboratory (https://ror.org/04rc0xn13), a DOE Office of Science user facility sponsored by the Biological and Environmental Research program under Contract No. DE-AC05-76RL01830. The work (proposal: 10.46936/10.25585/60008970) conducted by the U.S. Department of Energy, Joint Genome Institute (https://ror.org/04xm1d337), a DOE Office of Science user facility, is supported by the Office of Science of the U.S. Department of Energy operated under Contract No. DE-AC02-05CH11231.\u00a0  The Molecular Observation Network (MONet) database is an open, FAIR, and publicly available compilation of the molecular and microstructural properties of soil. Data in the MONet open science database can be found at\u00a0https://sc-data.emsl.pnnl.gov/.", "keywords": ["2. Zero hunger", "decomposition", "13. Climate action", "FTICR-MS", "biogeochemistry", "carbon", "molecular", "15. Life on land", "6. Clean water", "soil"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15328215"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15328215", "name": "item", "description": "10.5281/zenodo.15328215", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15328215"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-05-02T00:00:00Z"}}, {"id": "10.5281/zenodo.15772619", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:20:42Z", "type": "Dataset", "title": "Dataset to: Foundation for an Austrian NIR Soil Spectral Library for Soil Health Assessments", "description": "Dataset description  This is the corresponding dataset to the publication 'Foundation for an Austrian NIR Soil Spectral Library for Soil Health Assessments' by Fohrafellner et al. (2025). In this publication, we created the first Near-Infrared (NIR) Austrian Soil Spectral Library (ASSL, 680 \u2013 2500 nm) using 2,129 legacy samples from all environmental zones of Austria. Additionally, we utilized partial least squares regression modeling to evaluate the dataset's current effectiveness for soil health assessments. The dataset contains three tabs, 'Document meta data', 'Legend' and 'Dataset'. Tab 'Document meta data' gives information on the authors, the data collection time frame, terms of use, etc. In 'Legend', each column of the 'Dataset' is described. The 'Dataset' contains information on the legacy soil samples including:\u00a0    meta data (e.g. sample number, sampling year, zip code, environmental zone, land use),   soil properties (soil organic carbon [SOC], SOC to clay ratio, total carbon, labile carbon, CaCO3, total nitrogen, plant available phosphorus, pH measured in CaCl2 and acetate, cation exchange capacity, texture [sand, silt, clay content], and clay content measured by density in suspension), and  measured NIR soil spectra, also for the standards.   Project description  This Austrian Soil Spectral Library was built within the ProbeField project (November 2021 \u2013 January 2025), which was part of the European Joint Program for SOIL \u2018Towards climate-smart sustainable management of agricultural soils\u2019 (EJP SOIL) funded by the European Union Horizon 2020 research and innovation programme (Grant Agreement N\u00b0 862695). The project aimed to create a protocol detailing procedures and methodologies for accurately estimating fertility-related properties in agricultural soils in the field. Additionally, the potential for extending this data to two- and three-dimensional mapping using co-variates was demonstrated. ProbeField further collected field spectra that closely match laboratory spectra, enabling the prediction of soil properties using models calibrated with soil spectral libraries.  References  Fohrafellner, J., Lippl, M., Bajraktarevic, A., Baumgarten, A., Spiegel, H., K\u00f6rner, R. and Sand\u00e9n, T.: Foundation for an Austrian NIR Soil Spectral Library for Soil Health Assessments, 2025, in review.", "keywords": ["EJP SOIL", "ProbeField", "Spectroscopy", " Near-Infrared", "data"], "contacts": [{"organization": "Fohrafellner, Julia", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15772619"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15772619", "name": "item", "description": "10.5281/zenodo.15772619", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15772619"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-07-07T00:00:00Z"}}, {"id": "10.5281/zenodo.15797289", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:20:43Z", "type": "Dataset", "title": "Database of soil characteristics under specific pesticide management", "description": "Database of soil characteristics under specific pesticide management  Contributors: Mark\u00e9ta Mayerov\u00e1 and Veronika \u0158ez\u00e1\u010dov\u00e1  Affiliation: Czech Agrifood Research Center, Drnovsk\u00e1 507/73, CZ-160 00 Prague 6, Czech Republic  Database of soil characteristics will contribute to the realization of the project\u00b4s goal to identify appropriate and inappropriate pesticides from the point of the view of their impact on the non-target organisms and soil characteristics.  Field I.  The field experiment was established in 2024 in the experimental area of the Czech Agrifood Research Center in Prague \u2013 Ruzyn\u011b (previously Crop Research Institute). The experiment took place at the site of the experiment that had been running there since 2013 and included 5 different herbicide treatments in 4 replications (Mayerov\u00e1 et al. 2018)  The new trial area was split into 20 randomised plots with 2 different herbicide treatments in 8 replications and control without herbicides in 4 replications. Herbicide treatments differed in the mode of action (Table 1)  Table 1. Summary of the herbicides and active ingredients used in the trial. Classification Group by Herbicide Resistance Action Committee (HRAC).       herbicide     dose    formulation    active ingredient    content of a.i.    HRAC group    target weeds      Agritox 50 SL    1.5 l/ha    EC    MCPA    500 g/l    O    dicot      Glean 75 PX    15 g/ha    WG    chlorsulfuron    750 g/l    B    dicot + annual grasses       \u00a0  The area of each plot was 100 m2 and the 10 by 10 m plots were separated from field boundaries and from each other by 2 m on all sides to eliminate interaction between plots. Herbicides were applied post emergency in spring (April 26, 2024) from the tillering crop stage to the beginning of stem elongation (BBCH 21\u201331) by the Agrio-Napa 12 sprayer. Winter wheat was grown in the experimental field in 2024. At the beginning of March, it was mineral fertilized with LAD (ammonium nitrate with dolomite - NH4NO3\u00a0+\u00a0CaMg (CO3)2; 27 % N) at a dose 100 kg/ha.   Mixed disturbed soil samples for microbiological and physicochemical analyses were taken from the 0-15cm upper soil layer in each replication before herbicide application (April 24, 2024), 14 days after herbicide application (May 9, 2024) and 7 weeks after herbicide application (June 14, 2024). \u00a0A total of 20 soil samples were collected at each sampling. The soil samples were subsequently dried and sieved through a 2 mm sieve, thus simultaneously homogenised. The following soil properties were determined: pH (H2O), electric conductivity, available P and K, concentration NH4 and NO3, soil organic carbon, and total organic nitrogen content. Available P and K were assessed according to the Mehlich III method (Mehlich, 1984) on an Agilant ICP-OES 5110 VDV instrument. NO3 and NH4 were determined using calcium chloride solution as extractant according to ISO 14255:1998 on automated chemistry analyser SKALAR. Soil organic carbon and soil organic matter content were determined by sulfochromic oxidation according to ISO 14235:1998.   Field II  The field experiment was established in 2024 in the experimental area of the Czech Agrifood Research Center in Prague \u2013 Ruzyn\u011b (previously Crop Research Institute). The total area of the experiment is about 11 ha including the protective area around the entire experiment. The experimental area is divided into two halves, 120m wide and 300m long.\u00a0 One half was treated on June 17, 2024, with insecticide Decis forte (active ingredient deltamethrin) at a dose 62.5ml/ha, the other half was without insecticide treatment. Both areas are further divided into other halves. One half was treated on May 15, 2024, with herbicide Agritox (active ingredient MCPA) at a dose 1.5l/ha, the other was treated with hoeing only. We thus obtained 4 strips 60m wide with following treatment combinations: (A) herbicide + insecticide; (B) hoeing + insecticide; (C) hoeing; (D) herbicide. Spring wheat was grown in the experimental field in 2024. It was fertilized with mineral nitrogen at a dose of 150 kg N/ha before sowing and with 39 kg N/ha (DAM 390 - ammonium nitrate with urea) in the tillering phenophase.  In the middle of each strip (i.e. treatment), 8 sampling sites were marked in a row, 20 m apart from each other. Mixed disturbed soil samples for microbiological and physicochemical analyses were taken from the 0-15cm upper soil layer at each sampling site 14 days after herbicide application and 14 days after insecticide application. A total of 32 soil samples were collected at each sampling. Further sample processing was the same as for Field I.  The database will be gradually supplemented in the following years.   Funding: Development for this work is funded primarily by the Technology Agency of the Czech Republic, project SS07020100: \u201cThe impact of plant protection products on non-target biodiversity: soil microorganisms, invertebrates and wild plants\u201d, and the Ministry of Agriculture of the Czech Republic, institutional support MZE-RO0425.  The database was approved on September 2, 2025, by the Ministry of Agriculture of the Czech Republic.  References:  Mayerov\u00e1 M., Mikulka J., Soukup J. (2018): Effects of selective herbicide treatment on weed community in cereal crop rotation. Plant Soil Environ., 64: 413\u2013420. https://doi.org/10.17221/289/2018-PSE  \u00a0Mehlich A. (1984): Mehlich 3 Soil Test Extractant. A Modification of the Mehlich 2 Extractant. Commun. Soil Sci. Plant Anal. 15, 1409-1416. http://dx.doi.org/10.1080/00103628409367568.", "keywords": ["field trial", " herbicides", " insecticides", " soil properties"], "contacts": [{"organization": "Mayerov\u00e1, Mark\u00e9ta, \u0158ez\u00e1\u010dov\u00e1, Veronika,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15797289"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15797289", "name": "item", "description": "10.5281/zenodo.15797289", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15797289"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-07-03T00:00:00Z"}}, {"id": "10.5281/zenodo.16017208", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:20:44Z", "type": "Dataset", "title": "Cashew orchard soil properties, Dodamarg, Northern Western Ghats, India", "description": "Soil properties of cashew orchards of the Northern Western Ghats, India  This project contains chemical properties of soil collected from cashew orchards of Dodamarg, Northern Western Ghats, for a study investigating the factors influencing the effects of forest cover, flower abundance, temperature and (potentially) soil composition on cashew pollinators.  Taxonomic Coverage:\u00a0Not applicable  Geographic Coverage: Dodamarg, Sindhudurg District, Maharashtra, India  Temporal Coverage: March 2025  \u00a0  Description of field and lab methods  Soil collection: Soil samples were collected from 30 cashew orchards, using soil core sampler. The diameter of the core sampler was measured before soil collection. All soil samples were collected from 10 cm depth after removing all the leaf litter from the ground. From each orchard, 10 soil columns were collected for analysis of chemical properties.  Chemical Properties: We estimated thirteen soil chemical properties for all soil samples collected. The following parameters were analyzed by Zuari Farmhubs Laboratory: pH, electrical conductivity (E.C.) at 25\u00b0C, organic carbon (O.C.), available phosphorus (P\u2082O\u2085), available potassium (K\u2082O), available calcium (Ca), magnesium (Mg), sulfur (S), boron (B), zinc (Zn), iron (Fe), copper (Cu), and manganese (Mn).  More details about the data can be obtained from Aditya Satish (adityasatish@ncf-india.org) and Rohit Naniwadekar (rohit@ncf-india.org) from the Nature Conservation Foundation (www.ncf-india.org).  File Descriptions:  Data file: Dodamarg_2025_Cashew_Soil_Properties.csv  We have also included a ReadMe.txt file that explains the data file, akin to the description in the metadata.  Description of the columns of the data file:    Sl no: Serial number  Site: Site ID  Code: Site code (General location)  Latitude: latitude co-ordinate of the plot (in decimal degrees, \u00b0N)  Longitude: longitude co-ordinate of the plot (in decimal degrees, \u00b0E)  pH: pH of the soil  E.C.: Electrical Conductivity at 25\u00b0C (in dS/m)  O.C.: Organic Carbon (in %)  P\u2082O\u2085: Available P\u2082O\u2085 (in Kg /acre)  K\u2082O: Available Potassium (in Kg /acre)  Ca: Available Calcium (in mg/Kg)  Mg: Available Magnesium (in mg/Kg)  S: Available Sulphur (in mg/Kg)  B: Available Boron (in mg/Kg)  Zn: Available Zinc (in mg/Kg)  Fe: Available Iron (in mg/Kg)  Cu: Available Copper (in mg/Kg)  Mn: Available Manganese (in mg/Kg)   Funding:\u00a0  Godrej Consumer Products Limited  Arvind Datar  Rohini Nilekani Philanthropies", "keywords": ["Soil chemical properties", "Cashew orchards", "Ecology", "FOS: Biological sciences", "Northern Western Ghats"], "contacts": [{"organization": "Sadekar, Vishal, Satish, Aditya, Naniwadekar, Rohit,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.16017208"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.16017208", "name": "item", "description": "10.5281/zenodo.16017208", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.16017208"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-07-17T00:00:00Z"}}, {"id": "10.5281/zenodo.16261617", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:20:44Z", "type": "Dataset", "title": "Dataset to: Austrian NIR Soil Spectral Library for Soil Health Assessments", "description": "Dataset description  This is the corresponding dataset to the publication 'Austrian NIR Soil Spectral Library for Soil Health Assessments' by Fohrafellner et al. (2025). In this publication, we created the first Austrian Near-Infrared (NIR) Soil Spectral Library (680 \u2013 2500 nm) using 2,129 legacy samples from all environmental zones of Austria. Additionally, we utilized partial least squares regression modeling to evaluate the dataset's current effectiveness for soil health assessments. The dataset contains three tabs, 'Document meta data', 'Legend' and 'Dataset'. Tab 'Document meta data' gives information on the authors, the data collection time frame, terms of use, etc. In 'Legend', each column of the 'Dataset' is described. The 'Dataset' contains information on the legacy soil samples including:\u00a0    meta data (e.g. sample number, sampling year, zip code, environmental zone, land use),   soil properties (soil organic carbon [SOC], SOC to clay ratio, total carbon, labile carbon, CaCO3, total nitrogen, plant available phosphorus, pH measured in CaCl2 and acetate, cation exchange capacity, texture [sand, silt, clay content], and clay content measured by density in suspension), and  measured NIR soil spectra, also for the standards.   Project description  This Austrian NIR Soil Spectral Library was built within the ProbeField project (November 2021 \u2013 January 2025), which was part of the European Joint Program for SOIL 'Towards climate-smart sustainable management of agricultural soils' (EJP SOIL) funded by the European Union Horizon 2020 research and innovation programme (Grant Agreement N\u00b0 862695). The project aimed to create a protocol detailing procedures and methodologies for accurately estimating fertility-related properties in agricultural soils in the field. Additionally, the potential for extending this data to two- and three-dimensional mapping using co-variates was demonstrated. ProbeField further collected field spectra that closely match laboratory spectra, enabling the prediction of soil properties using models calibrated with soil spectral libraries.  References  Fohrafellner, J., Lippl, M., Bajraktarevic, A., Baumgarten, A., Spiegel, H., K\u00f6rner, R. and Sand\u00e9n, T.: Austrian NIR Soil Spectral Library for Soil Health Assessments, 2025, in review.", "keywords": ["EJP SOIL", "ProbeField", "spectroscopy", "data", "near-infrared"], "contacts": [{"organization": "Fohrafellner, Julia, Lippl, Maximilian, Bajraktarevic, Armin, Baumgarten, Andreas, Spiegel, Heide, K\u00f6rner, Robert, Sand\u00e9n, Taru,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.16261617"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.16261617", "name": "item", "description": "10.5281/zenodo.16261617", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.16261617"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-07-21T00:00:00Z"}}, {"id": "10.5281/zenodo.17941270", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:20:48Z", "type": "Dataset", "title": "Dataset to: Austrian NIR Soil Spectral Library for Soil Health Assessments", "description": "Dataset description  This is the corresponding dataset to the publication 'Austrian NIR Soil Spectral Library for Soil Health Assessments' by Fohrafellner et al. (2025). In this publication, we created the first Austrian Near-Infrared (NIR) Soil Spectral Library (680 \u2013 2500 nm) using 2,129 legacy samples from all environmental zones of Austria. Additionally, we utilized partial least squares regression modeling to evaluate the dataset's current effectiveness for soil health assessments. The dataset contains three tabs, 'Document meta data', 'Legend' and 'Dataset'. Tab 'Document meta data' gives information on the authors, the data collection time frame, terms of use, etc. In 'Legend', each column of the 'Dataset' is described. The 'Dataset' contains information on the legacy soil samples including:\u00a0    meta data (e.g. sample number, sampling year, zip code, environmental zone, land use),   soil properties (soil organic carbon [SOC], SOC to clay ratio, total carbon, labile carbon, CaCO3, total nitrogen, plant available phosphorus, pH measured in CaCl2 and acetate, cation exchange capacity, texture [sand, silt, clay content], and clay content measured by density in suspension), and  measured NIR soil spectra, also for the standards.   Project description  This Austrian NIR Soil Spectral Library was built within the ProbeField project (November 2021 \u2013 January 2025), which was part of the European Joint Program for SOIL 'Towards climate-smart sustainable management of agricultural soils' (EJP SOIL) funded by the European Union Horizon 2020 research and innovation programme (Grant Agreement N\u00b0 862695). The project aimed to create a protocol detailing procedures and methodologies for accurately estimating fertility-related properties in agricultural soils in the field. Additionally, the potential for extending this data to two- and three-dimensional mapping using co-variates was demonstrated. ProbeField further collected field spectra that closely match laboratory spectra, enabling the prediction of soil properties using models calibrated with soil spectral libraries.  References  Fohrafellner, J., Lippl, M., Bajraktarevic, A., Baumgarten, A., Spiegel, H., K\u00f6rner, R. and Sand\u00e9n, T.: Austrian NIR Soil Spectral Library for Soil Health Assessments, 2025, in review.", "keywords": ["EJP SOIL", "ProbeField", "spectroscopy", "data", "near-infrared"], "contacts": [{"organization": "Fohrafellner, Julia, Lippl, Maximilian, Bajraktarevic, Armin, Baumgarten, Andreas, Spiegel, Heide, K\u00f6rner, Robert, Sand\u00e9n, Taru,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.17941270"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.17941270", "name": "item", "description": "10.5281/zenodo.17941270", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.17941270"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-07-21T00:00:00Z"}}, {"id": "10.5281/zenodo.3591992", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:20:51Z", "type": "Dataset", "title": "Organic matter content (om) soil maps of the Upper Colorado River Basin", "description": "UPDATE: WE FOUND A RENDERING ERROR IN MANY AREAS OF THE 5 CM MAP. WE HAVE RECREATED THE MAP AND INCLUDED IN A NEW VERSION OF THE REPOSITORY. Repository includes maps of organic matter content (% wt) as defined by United States soil survey program. These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data. This data should be used in combination with a soil depth or depth to restriction layer map (both layers that will be released soon as part of this project) to eliminate areas mapped at deeper depths than the soil actually goes. This is a limitation of this data which will hopefully be updated in future updates. The creation and interpretation of this data is documented in the following article. Please note this article has not been reviewed yet and this citation will be updated as the peer review process proceeds. Nauman, T. W., Duniway, M. C., In Preparation. Predictive reconstruction of soil survey property maps for field scale adaptive land management. Soil Science Society of America Journal. File Name Details: ACCURACY!! Please see manuscript and Github repository (https://github.com/naumi421/SoilReconProps) for full details on accuracy. We do provide cross validation (CV) accuracy plots in this repository for both the overall sample (_CV_plots.tif). These plots compare CV predictions with observed values relative to a 1:1 line. Values plotted near the 1:1 line are more accurate. Note that values are plotted in hex-bin density scatter plots because of the large number of observations (most are &gt;3000). Predictions are also evaluated with the U.S. soil survey laboratory database soil organic carbon (SOC) data. The SOC measurements were coverted to OM matter values using the common 1.724 conversion factor. The converted OM values are compared to predicted OM values using an accuracy plot (OM_SOC_plots.tif). Elements are separated by underscore (_) in the following sequence: property_r_depth_cm_geometry_model_additional_elements.extension Example: om_r_0_cm_2D_QRF_bt.tif Indicates soil organic matter content (om) at 0 cm depth using a 2D model (separate model for each depth) employing a quantile regression forest. This file is the raster prediction map for this model. There may be additional GIS files associated with this file (e.g. pyramids) that have the same file name, but different extensions. The _bt indicates that the map has been back transformed from ln or sqrt transformation used in modeling. The following elements may also exist on the end of filenames indicating other spatial files that characterize a given model's uncertainty (see below). _95PI_h: Indicates the layer is the upper 95% prediction interval value. _95PI_l: Indicates the layer is the lower 95% prediction interval value. _95PI_relwidth: Indicates the layer is the 95% relative prediction interval (RPI). The RPI is a standardization of the prediction interval that indicates that model is constraining uncertainty relative to the original sample. RPI values less than one represent uncertainty is being improved by the model relative to the original sample, and values less than 0.5 indicate low uncertainty in predictions. See paper listed above and also Nauman and Duniway (In revision) for more details on RPI. References Nauman, T. W., and Duniway, M. C., In Revision, Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data: Geoderma", "keywords": ["2. Zero hunger", "13. Climate action", "soil organic matter", "digital soil mapping", "15. Life on land", "6. Clean water", "predictive soil mapping", "soil property mapping"], "contacts": [{"organization": "Nauman, Travis", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.3591992"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.3591992", "name": "item", "description": "10.5281/zenodo.3591992", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.3591992"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-01-28T00:00:00Z"}}, {"id": "10.5281/zenodo.5574882", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:20:55Z", "type": "Report", "created": "2020-03-09", "title": "Hyperspectral imaging for high resolution mapping of soil profile organic carbon distribution in an Austrian Alpine landscape", "description": "<p>         &amp;lt;p&amp;gt;Studies on soil organic carbon (SOC) stocks mostly focus on topsoils (&amp;lt; 30 cm). However, 30 to 63% of the SOC are stored in the subsoils (30 to 100 cm), and the factors controlling SOC storage in subsoils may be substantially different than in topsoils. The low mean SOC content in subsoils makes its quantification and characterization challenging. Thus, new approaches are required to depict the SOC stocks distribution in full soil profile. Hyperspectral imaging of soil core samples can provide high spatial resolution of the vertical distribution of SOC in a soil profile. The main objective of the ongoing study, within the Horizon 2020 European Project Circular Agronomics, is to apply laboratory hyperspectral imaging with a variety of machine learning approaches for the mapping of OC distribution in undisturbed soil cores. Soil cores were collected down to a depth of one meter in grasslands of 15 organic farms located in the Lungau Valley, in Austria. Some samples were divided into five depths in the field for classical bulk soil measurements (total carbon and nitrogen, texture, pH, EC and bulk density) on disturbed samples. Undisturbed soil cores were sliced vertically for laboratory hyperspectral imaging in the range of Vis-NIR (400-1000 nm). We were able to reveal the hotspots of OC and map the OC distribution in soil profile by applying a variety of machine learning approaches (i.e. partial least square and random forest regression) as a function of spectral responses. A digital elevation model was further exploited to investigate the effects of topographical factors such as elevation, aspect and slope on SOC profile distribution. Landsat 8 data were also used to depict the spatial variability of land insensitive cover/vegetation in study area.&amp;lt;/p&amp;gt;         </p>", "keywords": ["2. Zero hunger", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "Vis-NIR imaging spectroscopy", " Alpine grassland", " Digital elevation model", " Subsoils"], "contacts": [{"organization": "YASER OSTOVARI, K\u00f6ppend\u00f6rfer, Baptist, Guigue, Julien, Van Groenigen, Jan Willem, Creamer, Rachel, Guggenberger, Thomas, Grassauer, Florian, Hobley, Eleanor, Ferron, Laura, Martens, Henk, K\u00f6gel-Knabner, Ingrid, Vidal, Alix,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5574882"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5574882", "name": "item", "description": "10.5281/zenodo.5574882", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5574882"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-03-23T00:00:00Z"}}, {"id": "10.5281/zenodo.7041393", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:21:02Z", "type": "Other", "title": "QBS-ar and QBS-ar_BF index toolbox for biodiversity assessment of microarthropods community in soil", "description": "This toolbox contains a proposal and detailed instructions for the standardization of the QBS-ar index procedure. The QBS-ar index (Parisi, 2001; Parisi et al., 2005) is an index aimed at assessing soil-dwelling microarthropod communities in relation to their soil adaptation level. The core of QBS-ar index principle is: the higher is soil quality, the higher will be the number of microarthropod groups well adapted to soil habitats living there. Soil quality here stands for good stability, high organic matter content, and good biodiversity level. Worth of notice, QBS-ar is not comprehensive of the whole soil biodiversity or whole soil quality.\u00a0  For each sample to be assessed with the QBS-ar method, three different samplings are performed 5-10 meters apart (Menta et al., 2018), obtaining three subsamples (replicates) that are considered representative of an area homogeneous for slope and vegetation. 1-page guidelines for sampling and extraction were detailed in file\u00a0\u20181.QBS-ar sampling and extraction guidelines.pdf\u2019.\u00a0  The functional characteristics for soil adaptation of microarthropods are the reduction of visual structures as microphthalmia or even anophthalmia, the reduction or loss of pigmentation and dehydration adaptations (thinner cuticle, shorter setas or chaeta loss), appendage reduction (as shorter and/or smaller antennae, legs, furca), miniaturization, streamlined body form. Starting from these principles, soil organisms could be divided into a discrete set of Biological Forms (BFs, eco-morpho-types) according to their morphological adaptation to soil (Menta et al., 2018); in the EXCALIBUR and EJP-Minotaur projects, CREA Research Centre for Agriculture and Environment, in collaboration with the National QBS-ar working group (D\u2019Avino et al., 2021), recognized no less than 54 different BFs, 16 of which were classified as less frequent. Each BF is shortly described and\u00a0 associated with a score, the so-called Eco-Morphological Index (EMI), which ranges from 1 to 20 in proportion to the increasing degree of the soil adaptation of microarthropods. As reported by Parisi et al. (2005), some taxa show only one single EMI value because all species belonging to these taxa report the same adaptation level to the soil. Other groups show a range of EMIs in relation to the different adaptation levels of species to the soil. In general, eu-edaphic forms get EMI = 20, hemi-edaphic get an EMI rating proportionate to their degree of soil adaptation, while epedaphic (epigean) forms get EMI score = 1 (Menta et al., 2018). In QBS-ar whenever two eco-morphological forms are present in the same group, the final score is determined by the higher EMI. In other words, the most highly adapted microarthropods belonging to a group determine the overall EMI score for that group (Parisi et al 2005). This statement is not valid for the QBS-ar_BF proposed here, for which every biological form concurs to the calculation of QBS-ar_BF index, regardless of whether or not it belongs to the same group (i.e. class or order). Moreover, to assess variability between subsamples (replicates) an index based on spectral analysis (D\u2019Avino, 2019) is proposed. The file\u00a0\u20182.Sheet for QBS-ar record and calculation_v2.xlsx\u2019\u00a0provides the selection of 54 most common BFs in European soils and should be used as a template for data registration during the microarthropods identification and count at the stereomicroscope. As outlined in the \u201cREADME\u201d sheet of the excel file, the user should register the abundance of each BF and other sampling-related information, and the template will automatically perform a series of calculations and will format several output sheets. In particular it calculates: mean abundance of microarthropods and relative class of abundance,\u00a0QBS-ar, QBS-ar_BF, variability index community based on spectral analysis.\u00a0  One example of the compilated sheet is reported as file\u00a0\u20182a.Filled_template_example_v2.pdf\u2019. The sampling was carried out during MINOTAUR project, the example was implemented starting from file\u00a0\u20182. Sheet for QBS-ar record and calculation.xlsx\u2019.\u00a0  \u00a0The file '2.1.Sheet for QBS-ar record and calculations_v2.1.xlsx' contains updates to the previous version:      In the \u2018QBS-ar sheet\u2019 the formulas for calculating Density n.m-2, Class n.m-2, Density n.kg-1 and S1 in Pauropoda_20 have been corrected     In the \u2018\u201dExport_Subsamples\u201d sheet, the formula of Pauropoda_20 has been corrected     In the \u2018Export_MINOTAUR\u2019 sheet the formulas for Hymenoptera_AB, Hymenoptera_Number_taxa, Diplopoda_AB and Diplopoda_Number_taxa have been corrected     In the \u2018Codelist\u2019 sheet, Pest_control_method_Insecticide was added between Main_Pest_control_methods    The file '2.Sheet for QBS-ar record and calculation. vers_3.xlsx' is the latest version of the sheet. It contains several updates regarding the metadata that can be entered, updated descriptions of the biological forms and their common name. The order of the biological forms has been changed from the previous version: Hymenoptera-L_10 has been moved between the rare forms and Symphyla_20 has been moved between Chilopoda_20 and Pauropoda_20. One example of the compilated sheet is reported as file\u00a0\u20182a.Filled_template_example_v3.pdf\u2019.  This file also contains a new printable sheet called 'Data_report' that automatically summarizes the values of the QBS indices, diversity indices (Shannon, Evenness, Simpson and Richness) and abundances per square meter of the different biological forms along with the graphs.  The collection, merging and organization of data from the precompiled sheet could be a cumbersome task, especially for surveys with many different samples. The user would need to copy-paste each row in the \u201cExport_Sample\u201d or \u201cExport_Subsample\u201d sheets (from file 2.) to a new file, to perform overall analysis; an error-prone operation. To ease this process, we have compiled a short R script that can be found in this submission as the third file\u00a0\u20183.QBS-ar data merging_v2.R\u2019. By using the script, the user would automatically obtain three files (csv and/or xlsx, that will be stored in a \u201cResults\u201d folder) resulting from the merging of any number of QBS precompiled templates collected in a folder:\u00a0\u00a0  1. The result of the merge of \u201cExport_Sample\u201d sheets\u00a0  2. The result of the merge of \u201cExport_Subsample\u201d sheets\u00a0\u00a0  3. The result of the merge of\u00a0 'Export_MINOTAUR' sheets\u00a0  The latest version of the script is \u201c3.QBS-ar data merging_v3.R\u201d  It is recommended to use this version for the file \u201c2.Sheet for QBS-ar record and calculation. vers_3.xlsx\u201d  \u00a0  Cited reference\u00a0  D\u2019Avino, L., Menta, C., Jacomini, C., Cassi, F., L\u2019Abate, G., La Terza, A., Staffilani, F., Pocaterra, F., Piazzi, M., Parisi, V. (2021). The Italian skill network of Soil Biological Quality assessed by microarthropods\u2019 community, in: FAO. 2021. Keep soil alive, protect soil biodiversity \u2013Global Symposium on Soil Biodiversity 19\u201322 April 2021. Proceedings. Rome. ISBN [978-92-5-135218-2] pages 182-188. Available at\u00a0https://doi.org/10.4060/cb7374en\u00a0(accessed 11/11/2024)\u00a0  D\u2019Avino 2019. Soil mesofauna QBS-ar index in: Malusa et al. Common guidelines for analytical methods.\u00a0\u00a0https://cordis.europa.eu/project/id/817946/results/it\u00a0(Accessed 11/11/2024)\u00a0  Menta, C., Conti, F. D., Pinto, S., Bodini, A. (2018). Soil Biological Quality index (QBS-ar): 15 years of application at global scale. Ecological Indicators, 85, 773-780.\u00a0  Parisi V. (2001). La qualit\u00e0 biologica del suolo. Un metodo basato sui microartropodi [In Italian] Acta Naturalia de L\u2019Ateneo Parmense 37, 97-106.\u00a0  Parisi, V., Menta, C., Gardi, C., Jacomini, C., Mozzanica, E. (2005). Microarthropod communities as a tool to assess soil quality and biodiversity:\u00a0 a new approach in Italy. Agriculture, Ecosystems & Environment, 105 (12), 323-333.", "keywords": ["Data manipulation", "Mesofauna", "QBS data template", "Soil biodiversity", "Soil quality"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7041393"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7041393", "name": "item", "description": "10.5281/zenodo.7041393", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7041393"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-11T00:00:00Z"}}, {"id": "10.5281/zenodo.7656722", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:21:05Z", "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.7856487", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:21:07Z", "type": "Dataset", "title": "HiLSS Project", "description": "This\u00a0repository is periodically updated.   Historic Landscape and Soil Sustainability (MSCA-IF-2019 - Individual Fellowships)   The HiLSS Project aims to investigate the relationships between sustainability and landscape heritage with particular reference to soil loss and degradation over the long term. The project will take a multidisciplinary approach that combines archaeology, Historical Landscape Characterisation (HLC), geosciences, and computer-based geospatial analysis (GIS - Geographical Information Systems) and modelling (RUSLE - Revisited Universal Soil Loss Equation). The research objectives of the HiLSS project are to quantify the impact of human activities during the Late Holocene in order to create spatial models which can inform the development of sustainable conservation strategies for rural landscape heritage. This project will focus on two mountainous regions that present historical and cultural similarities but located in different climatic zones of Europe (1- Tuscan-Emilian Apennines, Italy; 2- Northern-mid Galicia, Spain). In previous HLC studies, land-use has been evaluated from the perspective of cultural heritage, whereas RUSLE have used it as a proxy for the land-cover of an area and its effect on soil erosion. The HiLSS project will propose an innovative methodology that combines both the historic/cultural values and the environmental values of land-use to inform development of a model for the sustainable conservation. By considering the different agricultural land-use HLC types in GIS-RUSLE modelling, it will be possible to quantify the effect on soil loss for each HLC type and consequently to devise more environmentally sustainable management for each type. Environmental sustainability and historic landscape conservation are typically treated as two separate fields, but the HiLSS project will develop a transformative model for interdisciplinary research, proposing a new way to embrace both cultural and natural values as components of the same landscape management plans.     HLC_RUSLE.zip    The R script code was developed by dr. F. Brandolini (Newcastle University, UK) to accompany the paper: 'Brandolini, F., Kinnaird, T.C., Srivastava, A., Turner S. -\u00a0Modelling the impact of historic landscape change on soil erosion and degradation. Sci Rep 13, 4949 (2023)'.   List of files included in HLC_RUSLE.zip:      R_script_code named 'HLC_RUSLE'\u00a0in .rmd format   Output folder:        Figures folder: .png products of the R script code    Rasters\u00a0folder: .png products of the R script code    Tables\u00a0folder: .pdf\u00a0products of the R script code       GeoTiff folder (.TIFF file format): Regional RUSLE\u00a0Data   GPKG:\u00a0HLC dataset\u00a0and\u00a0Region Of Interest file in .gpkg format      Spatial statistics to reveal patterns and connections in the historic landscape    The R script code was developed by dr. F. Brandolini (Newcastle University, UK) to accompany the paper: '\u00a0F.\u00a0Brandolini & S.\u00a0Turner\u00a0(2022)\u00a0Revealing patterns and connections in the historic landscape of the northern Apennines (Vetto, Italy),\u00a0Journal of Maps,\u00a0DOI:\u00a010.1080/17445647.2022.2088305.\u00a0'.   It is available at:\u00a0https://doi.org/10.5281/zenodo.5907229     Supplementary material_Land _SI_Historic Landscape Evolution.zip    Supplementary Materials to accompaing\u00a0the paper:\u00a0The evolution of historic agroforestry landscape in the Northern Apennines (Italy) and its consequences for slope geomorphic processes, submitted to\u00a0Land,\u00a0Special Issue\u00a0Historic Landscape Transformation.     Project_Publications.zip    List of .pdf file included in the folder:\u00a0   1) Brandolini F, Domingo-Ribas G, Zerboni A and Turner S. A Google Earth Engine-enabled Python approach for the identification of anthropogenic palaeo-landscape features [version 2; peer review: 2 approved, 1 approved with reservations]. Open Res Europe 2021,\u00a01:22\u00a0(https://doi.org/10.12688/openreseurope.13135.2)   2) Brandolini F., Turner S.\u00a0 2022 - Revealing patterns and connections in the historic landscape of the northern Apennines (Vetto, Italy), \u00a0Journal of Maps,\u00a0 (https://doi.org/10.1080/17445647.2022.2088305)   3) Brandolini, F., Kinnaird, T.C., Srivastava, A., Turner S. 2023 -\u00a0Modelling the impact of historic landscape change on soil erosion and degradation. Sci Rep 13, 4949 (2023), (https://doi.org/10.1038/s41598-023-31334-z)   4)\u00a0Brandolini, F., Compostella, C., Pelfini, M., and Turner, S. 2023 - 'The Evolution of Historic Agroforestry Landscape in the Northern Apennines (Italy) and Its Consequences for Slope Geomorphic Processes' Land 12, no. 5: 1054. (https://doi.org/10.3390/land12051054)", "keywords": ["2. Zero hunger", "13. Climate action", "Landscape Archaeology", "11. Sustainability", "RUSLE", "USPED", "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.7856487"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7856487", "name": "item", "description": "10.5281/zenodo.7856487", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7856487"}, {"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.8089699", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:21:08Z", "type": "Journal Article", "created": "2019-11-28", "title": "High-resolution and three-dimensional mapping of soil texture of China", "description": "The lack of detailed three-dimensional soil texture information largely restricts many applications in agriculture, hydrology, climate, ecology and environment. This study predicted 90 m resolution spatial variations of sand, silt and clay contents at a national extent across China and at multiple depths 0\u20135, 5\u201315, 15\u201330, 30\u201360, 60\u2013100 and 100\u2013200 cm. We used 4579 soil profiles collected from a national soil series inventory conducted recently and currently available environmental covariates. The covariates characterized environmental factors including climate, parent materials, terrain, vegetation and soil conditions. We constructed random forest models and employed a parallel computing strategy for the predictions of soil texture fractions based on its relationship with the environmental factors. Quantile regression forest was used to estimate the uncertainty of the predictions. Results showed that the predicted maps were much more accurate and detailed than the conventional linkage maps and the SoilGrids250m product, and could well represent spatial variation of soil texture across China. The relative accuracy improvement was around 245\u2013370% relative to the linkage maps and 83\u2013112% relative to the SoilGrids250m product with regard to the R2, and it was around 24\u201326% and 14\u201319% respectively with regard to the RMSE. The wide range between 5% lower and 95% upper prediction limits may suggest that there was a substantial room to improve current predictions. Besides, we found that climate and terrain factors are major controllers for spatial patterns of soil texture in China. The heat and water-driven physical and chemical weathering and wind-driven erosion processes primarily shape the pattern of clay content. The terrain, wind and water-driven deposition, erosion and transportation sorting processes of soil particles primarily shape the pattern of silt. The findings provide clues for modeling future soil evolution and for national soil security management under the background of global and regional environmental changes.", "keywords": ["2. Zero hunger", "Digital soil mapping", "13. Climate action", "Large extent", "Machine learning", "Environmental factors", "Uncertainty", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8089699"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoderma", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8089699", "name": "item", "description": "10.5281/zenodo.8089699", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8089699"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-03-01T00:00:00Z"}}, {"id": "10.5281/zenodo.8109600", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:21:09Z", "type": "Dataset", "title": "Data on soil compounds, respiration and incorporation of 13C-labeled substrate", "description": "Open AccessSee Readme.pdf", "keywords": ["2. Zero hunger", "microdialysis", "respiration rates", "compound concentration in soil solution", "PLFA and NLFA", "13C isotopic labeling", "15. Life on land", "6. Clean water"], "contacts": [{"organization": "Wiesenbauer, Julia, Kaiser, Christina,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8109600"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8109600", "name": "item", "description": "10.5281/zenodo.8109600", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8109600"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-07-18T00:00:00Z"}}, {"id": "10.7910/DVN/GVNJAB", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:21:40Z", "type": "Dataset", "created": "2019-06-24", "title": "Physical topsoil  properties in Murugusi, Western Kenya", "description": "Open Access&lt;b&gt;General:&lt;/b&gt; Lab determined topsoil bulk density, contents of sand, clay and organic carbon in Murugusi, W. Kenya, together with spatial coordinates of where the soil samples were taken (rounded to the closest center point of a 250 m \u00d7 250 m raster). All lab analyses were carried out at the ILRI/CIAT lab in Nairob, Kenya.  &lt;br&gt;  &lt;b&gt;Soil sampling:&lt;/b&gt; At each sample location, one composite topsoil sample was taken; three cores of 7 cm in diameter taken within an area of one square meter. The soil was taken from 0-0.2 m depth below any organic (O) horizon.   &lt;br&gt;  &lt;b&gt;Determination of soil properties:&lt;/b&gt; The bulk density of the soil was determined by taking two undisturbed soil samples (0-10 cm and 10-20 cm depth) of known volume (100 cm2) and weighting them after air drying. Soil fractions of clay (&lt;0.002 mm) and sand (0.05-2 mm) were determined by the hydrometer method (Estefan et al., 2014), using 10% sodium hexametaphosphate as the dispersing agent. Soil pH was determined potentiometrically on a soil suspension of 1:2 (soil: water). Total carbon was measured after dry combustion using an elemental analyser (Elementar Vario max cube; ISO 10694, first edition 1995-03-01)  &lt;br&gt;  &lt;b&gt;Reference: &lt;/b&gt;Estefan G., Sommer R., Ryan J. (2014) Analytical Methods for Soil-Plant and Water in Dry Areas. A Manual of Relevance to the West Asia and North Africa Region. 3rd Edition, International Center for Agricultural Research in the Dry Areas, Aleppo, 255 pp. Available online at: http://repo.mel.cgiar.org:8080/handle/20.500.11766/7512?show=full. Verified: October 9, 2018.  &lt;br&gt;  &lt;b&gt;Acknowledgements: &lt;/b&gt; We are deeply thankful for the good services provided by John Mukulama (soil sampling), John Yumbya Mutua (soil sampling) and Francis Mungthu Njenga (lab analyses) The project was carried out within the CGIAR Research Program on Water, Land and Ecosystems (WLE).", "keywords": ["Soil organic matter", "Agricultural Sciences", "Soil organic carbon", "sand", "Kenya", "Carbon", "Latin America and the Caribbean", "soil", "Soil", "Soil bulk density", "Sand", "soil organic matter", "Earth and Environmental Sciences", "Soil texture", "Murugusi", "Africa", "Clay", "Texture", "Western Kenya", "Agroecosystems and Sustainable Landscapes - ASL"], "contacts": [{"organization": "Piikki, Kristin, S\u00f6derstr\u00f6m, Mats, Sommer, Rolf, Da Silva, Mayesse,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.7910/DVN/GVNJAB"}, {"rel": "self", "type": "application/geo+json", "title": "10.7910/DVN/GVNJAB", "name": "item", "description": "10.7910/DVN/GVNJAB", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.7910/DVN/GVNJAB"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-01-01T00:00:00Z"}}, {"id": "10.7910/DVN/HXAH87", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:21:40Z", "type": "Dataset", "title": "Arbuscular and ectomycorrhizal fungi diversity in the Indian subcontinent", "description": "Mycorrhizal fungi (MF) are below-ground organisms playing a key role in terrestrial ecosystems as they regulate nutrient and carbon cycles, and influence soil structure and ecosystem multifunctionality. Arbuscular and ectomycorrhizal fungi are the two mycorrhizal types most relevant to worldwide ecosystems, but areas like the Indian sub-continent remain under-represented in global maps. The dataset presented here reports the available information regarding arbuscular and ectomycorrhizal fungi diversity in cultivated and natural ecosystems of the Indian subcontinent. We have selected studies published in English in ISI Web of Science during the years 2005 - 2020 that provided a taxonomic classification of MF and their associated abundance in terms of percentage of root colonization or number of spores per quantity of soil. From the screening of 74 studies, we have recorded: i. the scientific or common name of the plant or the generic habitat sampled for MF identification; ii the MF genus and species; iii. the location of the study with associated altitude and geographic coordinates; iv. main soil physico-chemical properties (soil pH, texture, organic Carbon, Total Nitrogen, available Phosphorus); climatic variables such as mean annual precipitation and temperature.&lt;br&gt;&lt;br&gt;", "keywords": ["ecosystem management", "Asia", "Agricultural Sciences", "CGIAR Research Program on Water", " Land and Ecosystems", "Multifunctional Landscapes", "gesti\u00f3n de ecosistemas", "soil biology", "MYCORRHIZAE", "CGIAR Research Program", "Earth and Environmental Sciences", "SOIL BIOLOGY", "BIODIVERSITY", "mycorrhizae", "biolog\u00eda del suelo"], "contacts": [{"organization": "Beggi, Francesca, Dasgupta, Debarshi,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.7910/DVN/HXAH87"}, {"rel": "self", "type": "application/geo+json", "title": "10.7910/DVN/HXAH87", "name": "item", "description": "10.7910/DVN/HXAH87", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.7910/DVN/HXAH87"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-01T00:00:00Z"}}, {"id": "11369/372709", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:22:08Z", "type": "Journal Article", "created": "2018-09-07", "title": "Soil resources and element stocks in drylands to face global issues", "description": "Abstract<p>Drylands (hyperarid, arid, semiarid, and dry subhumid ecosystems) cover almost half of Earth\uffe2\uff80\uff99s land surface and are highly vulnerable to environmental pressures. Here we provide an inventory of soil properties including carbon (C), nitrogen (N), and phosphorus (P) stocks within the current boundaries of drylands, aimed at serving as a benchmark in the face of future challenges including increased population, food security, desertification, and climate change. Aridity limits plant production and results in poorly developed soils, with coarse texture, low C:N and C:P, scarce organic matter, and high vulnerability to erosion. Dryland soils store 646 Pg of organic C to 2\uffe2\uff80\uff89m, the equivalent of 32% of the global soil organic C pool. The magnitude of the historic loss of C from dryland soils due to human land use and cover change and their typically low C:N and C:P suggest high potential to build up soil organic matter, but coarse soil textures may limit protection and stabilization processes. Restoring, preserving, and increasing soil organic matter in drylands may help slow down rising levels of atmospheric carbon dioxide by sequestering C, and is strongly needed to enhance food security and reduce the risk of land degradation and desertification.</p", "keywords": ["2. Zero hunger", "0301 basic medicine", "Conservation of Natural Resources", "0303 health sciences", "Multidisciplinary", "Nitrogen", "Climate", "Climate Change", "Phosphorus", "15. Life on land", "Article", "Carbon", "Food Supply", "Soil", "03 medical and health sciences", "element cycles", "13. Climate action", "carbon cycle", "Life Science", "Humans", "Desert Climate", "Ecosystem", "geochemistry"]}, "links": [{"href": "https://iris.univr.it/bitstream/11562/1001390/1/Soil%20resources%20and%20element%20stocks%20in%20drylands%20to%20face%20global%20issues.pdf"}, {"href": "https://www.nature.com/articles/s41598-018-32229-0.pdf"}, {"href": "https://doi.org/11369/372709"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Scientific%20Reports", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "11369/372709", "name": "item", "description": "11369/372709", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/11369/372709"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-09-13T00:00:00Z"}}, {"id": "11379/629705", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:22:08Z", "type": "Journal Article", "created": "2025-07-19", "title": "Role of Biodegradable and Non-Biodegradable Microplastic in Modulating the toxicological Effects of Organic Pollutants in the Soil Organism Folsomia candida", "description": "Abstract                   <p>                     The ecotoxicological effects of microplastics in soil ecosystems are complex, particularly in areas of intensive agriculture and livestock production, where plant protection products and veterinary drugs commonly coexist with plastic residues. In this study, we investigated the impact, under laboratory conditions, of 3 MP types (non-biodegradable low-density polyethylene (LDPE) and biodegradable polybutylene adipate terephthalate-based (PBAT-based) and a starch-based polymer) on the soil-dwelling species                     Folsomia candida                     (Willem, 1902) in soils contaminated with the anthelmintic albendazole and the fungicide pyraclostrobin. These organic pollutants (OPs) are frequently found in areas of intensive agriculture and livestock production.                     F. candida                     individuals were exposed for 28\uffc2\uffa0days to soils contaminated by the OPs at 0.0001 w/w% (1\uffc2\uffa0mg/kg), with and without MPs at 0.01 and 0.1 w/w% concentrations (100 and 1000\uffc2\uffa0mg/kg respectively), under laboratory conditions (21\uffe2\uff80\uff89\uffc2\uffb1\uffe2\uff80\uff891 C\uffc2\uffb0, 80%\uffe2\uff80\uff89\uffc2\uffb1\uffe2\uff80\uff891 RH). Adults\uffe2\uff80\uff99 survival, egg production, and juveniles\uffe2\uff80\uff99 occurrence were recorded as endpoints. Our findings indicate that microplastics alone did not significantly affect the survival and reproductive outcomes of                     F. candida                     . However, in soils contaminated with albendazole and pyraclostrobin, the presence of biodegradable MPs resulted in significant effects compared to the control and the treatment with only microplastics. Specifically, PBAT-based MPs significantly impacted adult survival, juvenile occurrence, and egg counts, while starch-based MPs primarily affected egg counts. On the contrary, co-exposure to OPs and LDPE MPs did not show significant effects. These results suggest that different MPs influence the bioavailability and toxicity of co-occurring fungicides and veterinary drug in soil ecosystems in different ways, with implications for assessing the ecological risks of biodegradable and non-biodegradable plastics in contaminated soils. The potential of MPs to influence the spatial distribution and bioavailability of organic pollutants for soil mesofauna needs further investigation.                   </p", "keywords": ["Soil ecotoxicology", "Pyraclostrobin", "Microplastics (MPs)", "Organic pollutants (OPs)", "Albendazole", "Biodegradable plastics", "Microplastics (MPs)", " Biodegradable plastics", " Organic pollutants (OPs)", " Albendazole", " Pyraclostrobin", " Soil ecotoxicology", " Folsomia candida", "Folsomia candida"]}, "links": [{"href": "https://iris.unibs.it/bitstream/11379/629705/1/s11270-025-08351-x.pdf"}, {"href": "https://doi.org/11379/629705"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Water%2C%20Air%2C%20%26amp%3B%20Soil%20Pollution", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "11379/629705", "name": "item", "description": "11379/629705", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/11379/629705"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-07-19T00:00:00Z"}}, {"id": "14280e45-7eee-4f1c-93cd-9f00083ddcc8-envidat", "type": "Feature", "geometry": null, "properties": {"updated": "2020-08-20T13:49:14Z", "type": "Dataset", "language": "en", "title": "Anthropogenic change and soil net N mineralization", "description": "This dataset contains all data on which the following publication below is based.  Paper Citation:  Risch Anita C., Zimmermann, Stefan, Moser, Barbara, Sch\u00fctz, Martin, Hagedorn, Frank, Firn, Jennifer, Fay, Philip A., Adler, Peter B., Biederman, Lori A., Blair, John M., Borer, Elizabeth T., Broadbent, Arthur A.D., Brown, Cynthia S., Cadotte, Marc W., Caldeira, Maria C., Davies, Kendi F., di Virgilio, Augustina, Eisenhauer, Nico, Eskelinen, Anu, Knops, Johannes M.H., MacDougall, Andrew S., McCulley, Rebecca L., Melbourne, Brett A., Moore, Joslin L., Power, Sally A., Prober, Suzanne M., Seabloom, Eric W., Siebert, Julia, Silveira, Maria L. , Speziale, Karina L., Stevens, Carly J., Tognetti, Pedro M., Virtanen, Risto, Yahdjian, Laura, Ochoa-Hueso, Raul (accepted). Global impacts of fertilization and herbivore removal on soil net nitrogen mineralization are modulated by local climate and soil properties. Global Change Biology  Please cite this paper together with the citation for the datafile.  We assessed how the removal of mammalian herbivores (Fence) and fertilization with growth-limiting nutrients (N, P, K, plus nine essential macro- and micronutrients; NPK) individually, and in combination (NPK+Fence), affected potential and realized soil net Nmin across 22 natural and semi-natural grasslands on five continents. Our sites spanned a comprehensive range of climatic and edaphic conditions found across the grassland biome. We focused on grasslands, because they cover 40-50% of the ice-free land surface and provide vital ecosystem functions and services. They are particularly important for forage production and C sequestration. Worldwide, grasslands store approximately 20-30% of the Earth\u2019s terrestrial C, most of it in the soil (Schimel, 1995; White et al., 2000).", "formats": [{"name": "XLS"}], "keywords": ["ammonification", "ch", "climate", "fertilization", "global-change", "grassland", "herbivore", "mineralization", "nitrification", "nitrogen", "nutrient-network", "soil"], "contacts": [{"organization": "Anita C. Risch", "roles": ["creator"]}, {"organization": "https://envidat.ch/#/about", "roles": ["publisher"]}]}, "links": [{"href": "https://www.envidat.ch/#/metadata/anthropogenic-change-and-net-n-mineralization"}, {"href": "https://www.envidat.ch/dataset/anthropogenic-change-and-net-n-mineralization/resource/13089b78-5a54-47a5-abe2-243a1e32772d"}, {"href": "http://data.europa.eu/88u/dataset/14280e45-7eee-4f1c-93cd-9f00083ddcc8-envidat"}, {"rel": "self", "type": "application/geo+json", "title": "14280e45-7eee-4f1c-93cd-9f00083ddcc8-envidat", "name": "item", "description": "14280e45-7eee-4f1c-93cd-9f00083ddcc8-envidat", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/14280e45-7eee-4f1c-93cd-9f00083ddcc8-envidat"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"null": "date"}}, {"id": "17e5dafc-0450-41d3-ad4b-d698b0f1a9ad", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:24:51Z", "type": "Dataset", "title": "Atlas des d\u00e9partements fran\u00e7ais d'Outre-Mer : 4. La Guyane. 4 mosaics of 4 map sheets and a set of 32 map sheets. Scale of 1:40\u00a0000\u00a0000 to 1:7\u00a0100. Date of publication: 1978-1979.", "description": "4 mosaics of 4 map sheets and a set of 32 additional map sheets 1.   Administrations, banques : la Guyane : planche 29. Scale of 1:3\u00a0000\u00a0000 to 1:10\u00a0000. Date of publication: 1979. 2.   Arch\u00e9ologie, histoire des Am\u00e9rindiens : la Guyane : planche 17. Scale of 1:15\u00a0000\u00a0000 to 1:1\u00a0500\u00a0000. Date of publication: 1979. 3.   Cartes anciennes : la Guyane : planche 18. Scale of 1:2\u00a0500\u00a0000 to 1:630\u00a0000. Date of publication: 1979. 4.   Cayenne : la Guyane : planche 34. Scale of 1:60\u00a0000 to 1:10\u00a0000. Date of publication: 1979. 5.   Circulation, transports : la Guyane : planche 27. Scale of 1:14\u00a0000\u00a0000 to 1:100\u00a0000. Date of publication: 1979. 6.   Climatologie : la Guyane : planche 7. Scale of 1:3\u00a0500\u00a0000. Date of publication: 1979. 7.   Climatologie II : pluviom\u00e9trie annuelle moyenne : la Guyane : planche 8. Scale of 1:1\u00a0000\u00a0000. Date of publication: 1979. 8.   Commerce, industrie, p\u00eache, tourisme : la Guyane : planche 28. Scale of 1:2\u00a0000\u00a0000 to 1:1\u00a0500\u00a0000. Date of publication: 1979. 9.   Energie, eaux, PTT et radiot\u00e9l\u00e9vision : la Guyane : planche 30. Scale of 1:1\u00a0500\u00a0000 to 1:225\u00a0000. Date of publication: 1979. 10.  Exploitation foresti\u00e8re : la Guyane : planche 25. Scale of 1:1\u00a0000\u00a0000. Date of publication: 1979. 11.  Faunes originales : plaines c\u00f4ti\u00e8res : la Guyane : planche 15. Scale of 1:350\u00a0000. Date of publication: 1979. 12.  G\u00e9ologie : la Guyane : planche 4. Scale of 1:2\u00a0500\u00a0000 to 1:1\u00a0000\u00a0000. Date of publication: 1979. 13.  G\u00e9omorphologie : la Guyane : planche 5. Scale of 1:2\u00a0000\u00a0000 to 1:1\u00a0000\u00a0000. Date of publication: 1979. 14.  Groupes humains : la Guyane : planche 20. Scale of 1:2\u00a0000\u00a0000 to 1:300\u00a0000. Date of publication: 1979. 15.  Histoire coloniale : la Guyane : planche 19. Scale of 1:2\u00a0000\u00a0000 to 1:500\u00a0000. Date of publication: 1979. 16.  Hydrologie : la Guyane : planche 9. Scale of 1:1\u00a0000\u00a0000. Date of publication: 1978. 17.  Ile de Cayenne : la Guyane : planche 35. Scale of 1:100\u00a0000 to 1:60\u00a0000. Date of publication: 1978. 18.  Kourou, Saint-Laurent, Sinnamary : la Guyane : planche 36. Scale of 1:100\u00a0000 to 1:7\u00a0100. Date of publication: 1979. 19.  La r\u00e9gion des Guyanes : la Guyane : planche 2. Scale of 1:5\u00a0000\u00a0000. Date of publication: 1979. 20.  Logement : la Guyane : planche 33. Scale of 1:1\u00a0000\u00a0000 to 1:35\u00a0000. Date of publication: 1979. 21.  Milieu marin : la Guyane : planche 14. Scale of 1:1\u00a0500\u00a0000. Date of publication: 1979. 22.  Mouvements migratoires : la Guyane : planche 22. Scale of 1:1\u00a0000\u00a0000 to 1:100\u00a0000. Date of publication: 1979. 23.  Pathologie : la Guyane : planche 16. Scale of 1:2\u00a0000\u00a0000 to 1:150\u00a0000. Date of publication: 1979. 24.  P\u00e9dologie : la Guyane : planche 10. Scale of 1:1\u00a0000\u00a0000. Date of publication: 1979. 25.  P\u00e9dologie : plaines c\u00f4ti\u00e8res : la Guyane : planche 11. Scale of 1:350\u00a0000. Date of publication: 1979. 26.  Population : r\u00e9partition : la Guyane : planche 21. Scale of 1:2\u00a0000\u00a0000 to 1:100\u00a0000. Date of publication: 1979. 27.  Productions agricoles : la Guyane : planche 24. Scale of 1:2\u00a0000\u00a0000. Date of publication: 1979. 28.  Relief : la Guyane : planche 3. Scale of 1:1\u00a0000\u00a0000 to 1:100\u00a0000. Date of publication: 1979. 29.  Ressources mini\u00e8res : la Guyane : planche 26. Scale of 1:1\u00a0000\u00a0000 to 1:25\u00a0000. Date of publication: 1979. 30.  Sant\u00e9 et action sociale, scolarit\u00e9 : la Guyane : planche 31. Scale of 1:1\u00a0500\u00a0000 to 1:50\u00a0000. Date of publication: 1979. 31.  S\u00e9dimentologie : plaines c\u00f4ti\u00e8res : la Guyane : planche 6. Scale of 1:350\u00a0000. Date of publication: 1979. 32.  Situation : la Guyane : planche 1. Scale of 1:40\u00a0000\u00a0000. Date of publication: 1979. 33.  Sports et loisirs, cultes : la Guyane : planche 32. Scale of 1:1\u00a0500\u00a0000 to 1:20\u00a0000. Date of publication: 1979. 34.  Utilisation du sol : la Guyane : planche 23. Scale of 1:1\u00a0000\u00a0000 to 1:200\u00a0000. Date of publication: 1979. 35.  V\u00e9g\u00e9tation : la Guyane : planche 12. Scale of 1:1\u00a0000\u00a0000. Date of publication: 1979. 36.  V\u00e9g\u00e9tation : plaines c\u00f4ti\u00e8res : la Guyane : planche 13. Scale of 1:350\u00a0000. Date of publication: 1979.", "keywords": ["elevation", "fr", "geology", "hydrography", "illustration", "labex-ceba", "land-use", "local-coverage", "map-collection", "meteorological-geographical-features", "mineral-resources", "national-coverage", "oceanographic-geographical-features", "population-distribution-\u2014-demography", "precipitation", "project-numerisud", "regional-coverage", "relief-(land)", "settlements", "soil", "species-distribution", "thematic-map", "topography"]}, "links": [{"href": "http://data.europa.eu/88u/dataset/17e5dafc-0450-41d3-ad4b-d698b0f1a9ad"}, {"rel": "self", "type": "application/geo+json", "title": "17e5dafc-0450-41d3-ad4b-d698b0f1a9ad", "name": "item", "description": "17e5dafc-0450-41d3-ad4b-d698b0f1a9ad", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/17e5dafc-0450-41d3-ad4b-d698b0f1a9ad"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"null": "date"}}, {"id": "1854/LU-8732814", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:22:21Z", "type": "Journal Article", "created": "2021-11-09", "title": "Litter quality, mycorrhizal association, and soil properties regulate effects of tree species on the soil fauna community", "description": "Abstract   Forest management, including selection of appropriate tree species to mitigate climate change and sustain biodiversity, requires a better understanding of factors that affect the composition of soil fauna communities. These communities are an integral part of the soil ecosystem and play an essential role in forest ecosystem functioning related to carbon and nitrogen cycling. Here, by performing a field study across six common gardens in Denmark, we evaluated the effects of tree species identity and mycorrhizal association (i.e., arbuscular mycorrhiza (AM) and ectomycorrhiza (ECM)) on soil fauna (meso- and macrofauna) taxonomic and functional community composition by using diversity, abundance, and biomass as proxies. We found that (1) tree species identity and mycorrhizal association both showed significant effects on soil fauna communities, but the separation between community characteristics in AM and ECM tree species was not entirely consistent; (2) total soil fauna abundance, biomass, as well as taxonomic and functional diversity were generally significantly higher under AM tree species, as well as lime, with higher litter quality (high N and base cation and low lignin:N ratio); (3) tree species significantly influenced the properties of litter, forest floor, and soil, among which litter and/or forest floor N, P, Ca, and Mg concentrations, soil pH, and soil moisture predominantly affected soil fauna abundance, biomass, and taxonomic and functional diversity. Our results from this multisite common garden experiment provide strong and consistent evidence of positive effects of tree species with higher litter quality on soil fauna communities in general, which helps to better understand the effects of tree species selection on soil biodiversity and its functions related to forest soil carbon sequestration.", "keywords": ["DECOMPOSITION", "EARTHWORMS", "Diversity", "PH", "FOREST FLOOR", "Common garden experiment", "Soil meso- and macrofauna", "DIVERSITY", "Biology and Life Sciences", "04 agricultural and veterinary sciences", "15. Life on land", "NITROGEN", "CARBON", "Taxonomic group", "FUNCTIONAL TRAITS", "Abundance", "13. Climate action", "Earth and Environmental Sciences", "Functional group", "0401 agriculture", " forestry", " and fisheries", "BIODIVERSITY", "ABUNDANCE", "Biomass"]}, "links": [{"href": "https://doi.org/1854/LU-8732814"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoderma", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1854/LU-8732814", "name": "item", "description": "1854/LU-8732814", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1854/LU-8732814"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-02-01T00:00:00Z"}}, {"id": "1cd3d55f-db85-433b-a626-848ca4b4c6a3", "type": "Feature", "geometry": null, "properties": {"updated": "2025-06-13T11:22:13.577757", "type": "Dataset", "language": "it", "title": "MODELLO WRF-ARW a 3km - Temperatura del suolo (C) - (2025-06-13 ore 00 UTC).", "description": "Temperatura del suolo (C). Corsa del 2025-06-13 ore 00 UTC - Valido dalle ore 00 UTC del 2025-06-13 alle ore 00 UTC del 2025-06-16. Modello meteorologico WRF (Weather Research and Forecasting model), core ARW (versione 3.2) con risoluzione spaziale a 3km, risoluzione temporale 60 ore, intervallo 1 ora.", "formats": [{"name": "PNG"}], "keywords": ["000000", "2025-06-13", "20250613t000000000z", "3km", "arw", "below", "between", "depths", "it", "lamma", "layer", "soil", "surface", "temperature", "two"], "contacts": [{"organization": "regione-toscana", "roles": ["creator"]}]}, "links": [{"href": "http://geoportale.lamma.rete.toscana.it/geoserver/ARW_3KM_RUN00/ows"}, {"href": "http://www.lamma.rete.toscana.it/"}, {"href": "https://dati.toscana.it/dataset/modello-wrf-arw-a-3km-temperatura-del-suolo-c-2025-06-13-ore-00-utc#"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run00/arw_3km_Soil_temperature_layer_between_two_depths_below_surface_layer_20250613T000000000Z/arw_3km_Soil_temperature_layer_between_two_depths_below_surface_layer_20250613T000000000Z_150_0.zip"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run00/arw_3km_Soil_temperature_layer_between_two_depths_below_surface_layer_20250613T000000000Z/arw_3km_Soil_temperature_layer_between_two_depths_below_surface_layer_20250613T000000000Z_25_0.zip"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run00/arw_3km_Soil_temperature_layer_between_two_depths_below_surface_layer_20250613T000000000Z/arw_3km_Soil_temperature_layer_between_two_depths_below_surface_layer_20250613T000000000Z_5_0.zip"}, {"href": "https://geoportale.lamma.rete.toscana.it/download/arw_3km_run00/arw_3km_Soil_temperature_layer_between_two_depths_below_surface_layer_20250613T000000000Z/arw_3km_Soil_temperature_layer_between_two_depths_below_surface_layer_20250613T000000000Z_70_0.zip"}, {"href": "http://data.europa.eu/88u/dataset/1cd3d55f-db85-433b-a626-848ca4b4c6a3"}, {"rel": "self", "type": "application/geo+json", "title": "1cd3d55f-db85-433b-a626-848ca4b4c6a3", "name": "item", "description": "1cd3d55f-db85-433b-a626-848ca4b4c6a3", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1cd3d55f-db85-433b-a626-848ca4b4c6a3"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"null": "date"}}, {"id": "1F20D517-4F12-4194-B9EC-544A45676839", "type": "Feature", "geometry": null, "properties": {"updated": "2020-02-11T00:00:00Z", "type": "Dataset", "language": "de", "title": "Boden\u00fcbersichtskarte 1:200.000 (B\u00dcK200) - CC7110 Mannheim", "description": "Die Boden\u00fcbersichtskarte 1:200.000 (B\u00dcK200) wird von der Bundesanstalt f\u00fcr Geowissenschaften und Rohstoffe (BGR) in Zusammenarbeit mit den Staatlichen Geologischen Diensten (SGD) der Bundesl\u00e4nder im Blattschnitt der Topographischen \u00dcbersichtskarte 1:200.000 (T\u00dcK200) erarbeitet und in 55 einzelnen Kartenbl\u00e4ttern herausgegeben. Die digitale, blattschnittfreie Datenhaltung bildet eine detaillierte, bundesweit einheitliche und fl\u00e4chendeckende Informationsgrundlage f\u00fcr L\u00e4nder \u00fcbergreifende Aussagen zu Bodennutzung und Bodenschutz. \u00dcber den aktuellen Bearbeitungsstand des Kartenwerks informieren die Internetseiten der BGR zum Thema Boden. Die Verbreitung und Vergesellschaftung der B\u00f6den auf dem Gebiet dieses Kartenblattes wird anhand von 97 Legendeneinheiten (gegliedert nach Bodenregionen und Bodengro\u00dflandschaften) beschrieben. Jede Legendeneinheit beinhaltet bodensystematische Informationen (Bodensubtyp) und Informationen zum Bodenausgangsgestein sowohl f\u00fcr die Leitb\u00f6den als auch f\u00fcr deren Begleiter. Im Rahmen der Qualit\u00e4tssicherung wurde die Linienf\u00fchrung der Bodengrenzen im gesamten Blattgebiet verbessert (Stand 12. Juli 2016).", "formats": [{"name": "PDF"}], "keywords": ["ackerbauliches-ertragspotential", "baden-wu\u0308rttemberg", "boden", "bodenart", "bodenausgangsgestein", "bodenauslaugung", "bodenbearbeitung", "bodenbelastung", "bodenbildung", "bodenbiologie", "bodenchemie", "bodendegradation", "bodendekontamination", "bodeneigenschaften", "bodenerosion", "bodenfeuchtigkeit", "bodenform", "bodenfruchtbarkeit", "bodenfunktion", "bodengefu\u0308ge", "bodengesellschaft", "bodengestaltung", "bodengruppe", "bodengu\u0308te", "bodenhorizont", "bodeninformationssystem", "bodenkarte", "bodenluft", "bodenmechanik", "bodenmineralogie", "bodennutzbarkeit", "bodennutzung", "bodenphysikalische-eigenschaften", "bodenprofil", "bodenskelett", "bodensubstrat", "bodensystematik", "bodentyp", "bodenverbreitung", "de", "durchla\u0308ssigkeit", "effektive-durchwurzelungstiefe", "fachinformationssystem", "hessen", "mannheim", "opendata", "rheinland-pfalz", "saarland", "soil"], "contacts": [{"organization": "Bundesanstalt f\u00fcr Geowissenschaften und Rohstoffe (BGR)", "roles": ["creator"]}]}, "links": [{"href": "https://download.bgr.de/bgr/Boden/BUEK200/7110/jpg/buek200_7110.zip"}, {"href": "https://download.bgr.de/bgr/Boden/BUEK200/7110/pdf/buek200_7110.zip"}, {"href": "https://download.bgr.de/bgr/Boden/BUEK200/7110/png/buek200_7110.zip"}, {"href": "https://download.bgr.de/bgr/Boden/BUEK200/7110/shp/buek200_7110.zip"}, {"href": "https://download.bgr.de/bgr/Boden/BUEK200/7110/tiff/buek200_7110.zip"}, {"href": "http://data.europa.eu/88u/dataset/1f20d517-4f12-4194-b9ec-544a45676839"}, {"rel": "self", "type": "application/geo+json", "title": "1F20D517-4F12-4194-B9EC-544A45676839", "name": "item", "description": "1F20D517-4F12-4194-B9EC-544A45676839", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1F20D517-4F12-4194-B9EC-544A45676839"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"null": "date"}}, {"id": "10.1002/ldr.2158", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:14:10Z", "type": "Journal Article", "created": "2012-04-03", "title": "Changes in soil organic carbon under eucalyptus plantations in brazil: a comparative analysis", "description": "ABSTRACT<p>Proper assessment of environmental quality or degradation requires knowledge of how terrestrial C pools respond to land use change. Forest plantations offer a considerable potential to sequester C in aboveground biomass. However, their impact on initial levels of soil organic carbon (SOC) varies from strong losses to gains, possibly affecting C balances in afforestation or reforestation initiatives. We compiled paired\uffe2\uff80\uff90plot studies on how SOC stocks under native vegetation change after planting fast\uffe2\uff80\uff90growth Eucalyptus species in Brazil, where these plantations are becoming increasingly important. SOC changes for the 0\uffe2\uff80\uff9320 and 0\uffe2\uff80\uff9340\uffe2\uff80\uff89cm depths varied between \uffe2\uff88\uff9225 and 42\uffe2\uff80\uff89Mg\uffe2\uff80\uff89ha\uffe2\uff88\uff921, following a normal distribution centered near zero. After replacing native vegetation by Eucalyptus plantations, mean SOC changes were \uffe2\uff88\uff921\uffc2\uffb75 and 0\uffc2\uffb73\uffe2\uff80\uff89Mg\uffe2\uff80\uff89ha\uffe2\uff88\uff921 for the 0\uffe2\uff80\uff9320 and 0\uffe2\uff80\uff9340\uffe2\uff80\uff89cm depths, respectively. These are very low figures in comparison to C stocks usually sequestered in aboveground biomass and were statistically nonsignificant as demonstrated by a t\uffe2\uff80\uff90test at p\uffe2\uff80\uff89&lt;\uffe2\uff80\uff890\uffc2\uffb705. Similar low, nonsignificant SOC changes were estimated after data were stratified into first or second rotation cycles, soil texture and biome (savanna, rainforest or grassland). Although strong SOC losses or gains effectively occurred in some cases, their underpinning causes could not be generally identified in the present work and must be ascribed in a case basis, considering the full set of environmental and management conditions. We conclude that Eucalyptus spp. plantations in average have no net effect on SOC stocks in Brazil. Copyright \uffc2\uffa9 2012 John Wiley &amp; Sons, Ltd.</p>", "keywords": ["Soil organic matter", "Carbon stocks", "Tropical soils", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "Fast-growth tree plantations", "Land use change"]}, "links": [{"href": "https://doi.org/10.1002/ldr.2158"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Land%20Degradation%20%26amp%3B%20Development", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1002/ldr.2158", "name": "item", "description": "10.1002/ldr.2158", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/ldr.2158"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2012-04-03T00:00:00Z"}}, {"id": "23546339ad735a64e55426484b88fe14", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:22:53Z", "type": "Report", "title": "Global maps of soil temperature.", "description": "Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km &lt;sup&gt;2&lt;/sup&gt; resolution for 0-5 and 5-15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km &lt;sup&gt;2&lt;/sup&gt; pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10\u00b0C (mean = 3.0 \u00b1 2.1\u00b0C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 \u00b1 2.3\u00b0C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 \u00b1 2.3\u00b0C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.", "keywords": ["Climate Change; Ecosystem; Microclimate; Soil; Temperature; bioclimatic variables; global maps; microclimate; near-surface temperatures; soil temperature; soil-dwelling organisms; temperature offset; weather stations"], "contacts": [{"organization": "Lembrechts, J.J., van den Hoogen, J., Aalto, J., Ashcroft, M.B., De Frenne, P., Kemppinen, J., Kopeck\u00fd, M., Luoto, M., Maclean, IMD, Crowther, T.W., Bailey, J.J., Haesen, S., Klinges, D.H., Niittynen, P., Scheffers, B.R., Van Meerbeek, K., Aartsma, P., Abdalaze, O., Abedi, M., Aerts, R., Ahmadian, N., Ahrends, A., Alatalo, J.M., Alexander, J.M., Allonsius, C.N., Altman, J., Ammann, C., Andres, C., Andrews, C., Ard\u00f6, J., Arriga, N., Arzac, A., Aschero, V., Assis, R.L., Assmann, J.J., Bader, M.Y., Bahalkeh, K., Baran\u010dok, P., Barrio, I.C., Barros, A., Barthel, M., Basham, E.W., Bauters, M., Bazzichetto, M., Marchesini, L.B., Bell, M.C., Benavides, J.C., Benito Alonso, J.L., Berauer, B.J., Bjerke, J.W., Bj\u00f6rk, R.G., Bj\u00f6rkman, M.P., Bj\u00f6rnsd\u00f3ttir, K., Blonder, B., Boeckx, P., Boike, J., Bokhorst, S., Brum, BNS, Br\u016fna, J., Buchmann, N., Buysse, P., Camargo, J.L., Campoe, O.C., Candan, O., Canessa, R., Cannone, N., Carbognani, M., Carnicer, J., Casanova-Katny, A., Cesarz, S., Chojnicki, B., Choler, P., Chown, S.L., Cifuentes, E.F., \u010ciliak, M., Contador, T., Convey, P., Cooper, E.J., Cremonese, E., Curasi, S.R., Curtis, R., Cutini, M., Dahlberg, C.J., Daskalova, G.N., de Pablo, M.A., Della Chiesa, S., Dengler, J., Deronde, B., Descombes, P., Di Cecco, V., Di Musciano, M., Dick, J., Dimarco, R.D., Dolezal, J., Dorrepaal, E., Du\u0161ek, J., Eisenhauer, N., Eklundh, L., Erickson, T.E., Erschbamer, B., Eugster, W., Ewers, R.M., Exton, D.A., Fanin, N., Fazlioglu, F., Feigenwinter, I., Fenu, G., Ferlian, O., Fern\u00e1ndez Calzado, M.R., Fern\u00e1ndez-Pascual, E., Finckh, M., Higgens, R.F., Forte, TGW, Freeman, E.C., Frei, E.R., Fuentes-Lillo, E., Garc\u00eda, R.A., Garc\u00eda, M.B., G\u00e9ron, C., Gharun, M., Ghosn, D., Gigauri, K., Gobin, A., Goded, I., Goeckede, M., Gottschall, F., Goulding, K., Govaert, S., Graae, B.J., Greenwood, S., Greiser, C., Grelle, A., Gu\u00e9nard, B., Guglielmin, M., Guillemot, J., Haase, P., Haider, S., Halbritter, A.H., Hamid, M., Hammerle, A., Hampe, A., Haugum, S.V., Hederov\u00e1, L., Heinesch, B., Helfter, C., Hepenstrick, D., Herberich, M., Herbst, M., Hermanutz, L., Hik, D.S., Hoffr\u00e9n, R., Homeier, J., H\u00f6rtnagl, L., H\u00f8ye, T.T., Hrbacek, F., Hylander, K., Iwata, H., Jackowicz-Korczynski, M.A., Jactel, H., J\u00e4rveoja, J., Jastrz\u0119bowski, S., Jentsch, A., Jim\u00e9nez, J.J., J\u00f3nsd\u00f3ttir, I.S., Jucker, T., Jump, A.S., Juszczak, R., Kanka, R., Ka\u0161par, V., Kazakis, G., Kelly, J., Khuroo, A.A., Klemedtsson, L., Klisz, M., Kljun, N., Knohl, A., Kobler, J., Koll\u00e1r, J., Kotowska, M.M., Kov\u00e1cs, B., Kreyling, J., Lamprecht, A., Lang, S.I., Larson, C., Larson, K., Laska, K., le Maire, G., Leihy, R.I., Lens, L., Liljebladh, B., Lohila, A., Lorite, J., Loubet, B., Lynn, J., Macek, M., Mackenzie, R., Magliulo, E., Maier, R., Malfasi, F., M\u00e1li\u0161, F.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/23546339ad735a64e55426484b88fe14"}, {"rel": "self", "type": "application/geo+json", "title": "23546339ad735a64e55426484b88fe14", "name": "item", "description": "23546339ad735a64e55426484b88fe14", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/23546339ad735a64e55426484b88fe14"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-05-01T00:00:00Z"}}, {"id": "10.1016/j.soilbio.2008.11.003", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:16:02Z", "type": "Journal Article", "created": "2008-12-05", "title": "Functional Shifts Of Grassland Soil Communities In Response To Soil Warming", "description": "In terrestrial ecosystems most carbon (C) occurs below-ground, making the activity of soil decomposer organisms critical to the global carbon cycle. Temperate grassland ecosystems, contain large, diverse and active soil meso- and macrofauna decomposer communities. Understanding the effects of climate change on their ecology offers a first step towards meaningful predictions of changes in soil organic carbon mineralisation.    We examined the effects of soil warming on the abundance, diversity and ecology of temperate grassland soil fauna functional groups, ecosystem net CO2 flux and respiration and plant above- and below-ground productivity in a 2-year plant\u2013soil mesocosm experiment. Low voltage heating cable mounted on a framework of stainless steel mesh provided a constant 3.5 \u00b0C difference between control and warmed mesocosm soils.    Results showed that this temperature increment had little effect on soil respiration and above-ground plant biomass. There was, however, a significant effect on the soil fauna due to warmer conditions and increased root growth, with significant decreases in the numbers in the large oligochaete groups and Prostigmata mites and the re-distribution of enchytraeids to deeper soil layers. Functional groups exhibited individualistic responses to soil warming, with the total disappearance of epigeic species in the case of the ecosystem engineers and an increased diversity of fungivorous mites that, together, produced significant changes in the composition and trophic structure of the fauna community.    The observed switch towards a fungal driven food web has important implications for the fate of soil organic carbon in temperate ecosystems subjected to sustained warming. Accordingly, soil biology needs to be properly incorporated in C models to make better predictions of the fate of SOC under warmer scenarios.", "keywords": ["570", "Soil invertebrates", "13. Climate action", "Trophic food webs", "0401 agriculture", " forestry", " and fisheries", "SOC", "04 agricultural and veterinary sciences", "15. Life on land", "SOM", "Community structure"]}, "links": [{"href": "https://doi.org/10.1016/j.soilbio.2008.11.003"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20Biology%20and%20Biochemistry", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.soilbio.2008.11.003", "name": "item", "description": "10.1016/j.soilbio.2008.11.003", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.soilbio.2008.11.003"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2009-02-01T00:00:00Z"}}, {"id": "10.1007/s10533-012-9731-2", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:14:35Z", "type": "Journal Article", "created": "2012-04-05", "title": "Soil Volume And Carbon Storage Shifts In Drained And Afforested Wetlands Of The Parana River Delta", "description": "Wetland ecosystems have a high carbon storage potential as a result of high primary productivity and low decomposition rates dictated by water saturation. In the herbaceous wetlands of the Parana River Delta, drainage and afforestation with poplars represents one of the dominant land uses. We explored the effects of these interventions on the volume and carbon storage of the young sedimentary soils of the region. At three sites we identified paired stands occupying similar landscape positions and soil types but subject to natural flooding and covered by natural herbaceous communities or drainage and flood control by dikes and covered by poplar plantations established 12, 17 and 19\u00a0years ago. Soil sampling at these sites revealed a reduction of the litter compartment (\u221286\u00a0%) and decreasing volume and porosity of its underlying mineral layer (0\u201310\u00a0cm in the wetland reduced to 0\u20134\u00a0cm in the plantation). Our comparisons of carbon storage accounted for these volumetric shifts by using accumulated mineral mass rather than depth as a reference, showing that tree plantations gained in the mineral soil (22\u00a0Mg\u00a0C\u00a0ha\u22121) almost as much as what they lost in the litter. These gains were particularly large at intermediate depths (4\u201343\u00a0cm in the plantations) were soil porosity remained unaffected and C was raised by 64\u00a0% explained by (1) the pulse of inputs from overlaying litter and organic layers subject to rapid decomposition and mobilization after drainage and (2) root colonization, since tree plantations had 75\u00a0% of their fine root biomass at these intermediate soil depths, whereas roots in the wetlands did not explore the mineral soil profile and were completely confined to the organic layer. A neutral C balance following wetland drainage and afforestation resulted from the opposing effects of aeration, favoring decomposition in the organic layer, root colonization and organic matter stabilization, favoring its accumulation in the mineral soil.", "keywords": ["FLOOD CONTROL", "ARGENTINA", "POPULUS DELTOIDES", "CARBON SEQUESTRATION", "https://purl.org/becyt/ford/1.5", "SOIL COMPACTION", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "https://purl.org/becyt/ford/1", "6. Clean water"], "contacts": [{"organization": "Ceballos, Dar\u00edo S., Frangi, Jorge Luis, Jobbagy Gampel, Esteban Gabriel,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1007/s10533-012-9731-2"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Biogeochemistry", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/s10533-012-9731-2", "name": "item", "description": "10.1007/s10533-012-9731-2", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/s10533-012-9731-2"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2012-04-06T00:00:00Z"}}, {"id": "10.1016/j.agee.2006.03.013", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:14:57Z", "type": "Journal Article", "created": "2006-04-27", "title": "Analysis Of The Effects Of Rotational Woodlots On The Nutrition And Yield Of Maize Following Trees In Western Tanzania", "description": "Farmers in western Tanzania are establishing rotations of trees and crops in an attempt to overcome the shortage of wood, reverse deforestation of natural forests and improve soil fertility for food security enhancement. We compared fallows of Acacia crassicarpa, A. julifera, A. leptocarpa, Leucaena pallida and Senna siamea, with traditional bush fallow and continuous sole maize (Zea mays L.). The aim of the study was to analyze the effectiveness offallow types in terms of N, Pand K use by maize. Trees were intercropped with maize for the first 3 years. After 5 years, trees were harvested, wood components were removed, and leaves, twigs and grasses were incorporated into the soil. Factorial N,P,Ktrialswere carriedoutwithmaizegrown afterthefallowtypes. Parameters studiedweregrainyield,uptakeof N,PandK,and nutrient use efficiency. The effects offertiliser were much stronger than the effects offallow types. Therewas no clear effect of tree fallows on nutrient use efficiency of the following maize. Non-fertilized maize yielded more after acacia than after the other trees and natural fallow. Upon fertiliser application the influences of fallow types became weaker. Fertiliser N improved maize yields more than fertiliser P, and there was a positive NP interaction. Fertilizer K did not bring about clear effects. N recovery efficiency was improved by the application of P and vice versa. When fertilisers were applied, differences in average maize grain yields between tree fallows and natural fallow varied from 300 kg ha 1 (for A. julifera) to minus 250 kg ha 1 (for S. siamea). A yield increase of 300 kg maize grain could also be obtained by application of 10 kg fertiliser N or 8 kg fertiliser P. The best fallow type for soil fertility improvement was Acacia juliferasuggesting that this acacia is mining the soil for P and K. In conclusion, benefits of rotational woodlots seem larger in terms of wood production than in terms of soil fertility restoration. # 2006 Elsevier B.V. All rights reserved.", "keywords": ["0106 biological sciences", "2. Zero hunger", "fallow", "soil fertility", "quefts", "04 agricultural and veterinary sciences", "15. Life on land", "shifting cultivation", "01 natural sciences", "nitrogen", "agroforestry", "africa", "nutrients", "vegetation", "0401 agriculture", " forestry", " and fisheries", "management"], "contacts": [{"organization": "Nyadzi, G.I., Janssen, B.H., Oenema, O.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1016/j.agee.2006.03.013"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agriculture%2C%20Ecosystems%20%26amp%3B%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.agee.2006.03.013", "name": "item", "description": "10.1016/j.agee.2006.03.013", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.agee.2006.03.013"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2006-08-01T00:00:00Z"}}, {"id": "10.1002/ecy.1513", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:14:06Z", "type": "Journal Article", "created": "2016-07-02", "title": "Land Use Intensification In The Humid Tropics Increased Both Alpha And Beta Diversity Of Soil Bacteria", "description": "Abstract<p>Anthropogenic pressures on tropical forests are rapidly intensifying, but our understanding of their implications for biological diversity is still very limited, especially with regard to soil biota, and in particular soil bacterial communities. Here we evaluated bacterial community composition and diversity across a gradient of land use intensity in the eastern Amazon from undisturbed primary forest, through primary forests varyingly disturbed by fire, regenerating secondary forest, pasture, and mechanized agriculture. Soil bacteria were assessed by paired\uffe2\uff80\uff90end Illumina sequencing of 16S rRNA gene fragments (V4 region). The resulting sequences were clustered into operational taxonomic units (OTU) at a 97% similarity threshold. Land use intensification increased the observed bacterial diversity (both OTU richness and community heterogeneity across space) and this effect was strongly associated with changes in soil pH. Moreover, land use intensification and subsequent changes in soil fertility, especially pH, altered the bacterial community composition, with pastures and areas of mechanized agriculture displaying the most contrasting communities in relation to undisturbed primary forest. Together, these results indicate that tropical forest conversion impacts soil bacteria not through loss of diversity, as previously thought, but mainly by imposing marked shifts on bacterial community composition, with unknown yet potentially important implications for ecological functions and services performed by these communities.</p>", "keywords": ["Rios de composi\u00e7\u00e3o de comunidade bacteriana", "2. Zero hunger", "0301 basic medicine", "570", "0303 health sciences", "550", "Bacteria", "Biodiversidade subterr\u00e2nea", "Agriculture", "Biodiversity", "Forests", "15. Life on land", "Below\u2010ground biodiversity", "High\u2010throughput sequencing", "Soil", "03 medical and health sciences", "RNA", " Ribosomal", " 16S", "Sequenciamento de alto rendimento", "Rivers of bacterial community composition", "Soil Microbiology"]}, "links": [{"href": "https://eprints.lancs.ac.uk/id/eprint/82660/1/de_Carvalho_et_al_2016_raw_pdf.pdf"}, {"href": "https://doi.org/10.1002/ecy.1513"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Ecology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1002/ecy.1513", "name": "item", "description": "10.1002/ecy.1513", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/ecy.1513"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-09-09T00:00:00Z"}}, {"id": "10.1002/2017JD027346", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:14:04Z", "type": "Journal Article", "created": "2017-12-28", "title": "Soil Moisture-Temperature Coupling in a Set of Land Surface Models", "description": "Abstract<p>The land surface controls the partitioning of water and energy fluxes and therefore plays a crucial role in the climate system. The coupling between soil moisture and air temperature, in particular, has been shown to affect the severity and occurrence of temperature extremes and heat waves. Here we study soil moisture\uffe2\uff80\uff90temperature coupling in five land surface models, focusing on the terrestrial segment of the coupling in the warm season. All models are run off\uffe2\uff80\uff90line over a common period with identical atmospheric forcing data, in order to allow differences in the results to be attributed to the models' partitioning of energy and water fluxes. Coupling is calculated according to two semiempirical metrics, and results are compared to observational flux tower data. Results show that the locations of the global hot spots of soil moisture\uffe2\uff80\uff90temperature coupling are similar across all models and for both metrics. In agreement with previous studies, these areas are located in transitional climate regimes. The magnitude and local patterns of model coupling, however, can vary considerably. Model coupling fields are compared to tower data, bearing in mind the limitations in the geographical distribution of flux towers and the differences in representative area of models and in situ data. Nevertheless, model coupling correlates in space with the tower\uffe2\uff80\uff90based results (r = 0.5\uffe2\uff80\uff930.7), with the multimodel mean performing similarly to the best\uffe2\uff80\uff90performing model. Intermodel differences are also found in the evaporative fractions and may relate to errors in model parameterizations and ancillary data of soil and vegetation characteristics.</p>", "keywords": ["ENVIRONMENT SIMULATOR JULES", "FLUXES", "0207 environmental engineering", "02 engineering and technology", "01 natural sciences", "CO2 EXCHANGE", "models", "WATER", "SCALE", "Research Articles", "0105 earth and related environmental sciences", "land surface", "CARBON-DIOXIDE EXCHANGE", "eartH2Observe", "temperature", "15. Life on land", "DECIDUOUS FOREST", "CLIMATE", "EVAPORATION", "VARIABILITY", "13. Climate action", "Earth and Environmental Sciences", "BALANCE", "land surface models", "SENSIBLE HEAT", "land-atmosphere interactions", "soil moisture"]}, "links": [{"href": "https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2017JD027346"}, {"href": "https://doi.org/10.1002/2017JD027346"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Geophysical%20Research%3A%20Atmospheres", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1002/2017JD027346", "name": "item", "description": "10.1002/2017JD027346", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/2017JD027346"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-02-01T00:00:00Z"}}, {"id": "10.1002/2017JG004269", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:14:04Z", "type": "Journal Article", "created": "2017-12-18", "title": "Soil Carbon Dynamics in Soybean Cropland and Forests in Mato Grosso, Brazil", "description": "Abstract<p>Climate and land use models predict that tropical deforestation and conversion to cropland will produce a large flux of soil carbon (C) to the atmosphere from accelerated decomposition of soil organic matter (SOM). However, the C flux from the deep tropical soils on which most intensive crop agriculture is now expanding remains poorly constrained. To quantify the effect of intensive agriculture on tropical soil C, we compared C stocks, radiocarbon, and stable C isotopes to 2\uffc2\uffa0m depth from forests and soybean cropland created from former pasture in Mato Grosso, Brazil. We hypothesized that soil disturbance, higher soil temperatures (+2\uffc2\uffb0C), and lower OM inputs from soybeans would increase soil C turnover and deplete C stocks relative to nearby forest soils. However, we found reduced C concentrations and stocks only in surface soils (0\uffe2\uff80\uff9310\uffc2\uffa0cm) of soybean cropland compared with forests, and these differences could be explained by soil mixing during plowing. The amount and \uffce\uff9414C of respired CO2 to 50\uffc2\uffa0cm depth were significantly lower from soybean soils, yet CO2 production at 2\uffc2\uffa0m deep was low in both forest and soybean soils. Mean surface soil \uffce\uffb413C decreased by 0.5\uffe2\uff80\uffb0 between 2009 and 2013 in soybean cropland, suggesting low OM inputs from soybeans. Together these findings suggest the following: (1) soil C is relatively resistant to changes in land use and (2) conversion to cropland caused a small, measurable reduction in the fast\uffe2\uff80\uff90cycling C pool through reduced OM inputs, mobilization of older C from soil mixing, and/or destabilization of SOM in surface soils.</p>", "keywords": ["tropical forest", "2. Zero hunger", "Life on Land", "land use", "Agriculture", "04 agricultural and veterinary sciences", "15. Life on land", "Soil carbon", "Geophysics", "Tropical forest", "Isotopes", "13. Climate action", "Land use", "Earth Sciences", "0401 agriculture", " forestry", " and fisheries", "soil carbon", "Brazil", "isotopes", "Research Articles", "agriculture"]}, "links": [{"href": "https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2017JG004269"}, {"href": "https://escholarship.org/content/qt4jm295dz/qt4jm295dz.pdf"}, {"href": "https://doi.org/10.1002/2017JG004269"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Geophysical%20Research%3A%20Biogeosciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1002/2017JG004269", "name": "item", "description": "10.1002/2017JG004269", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/2017JG004269"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-01-01T00:00:00Z"}}, {"id": "10.1007/s10021-013-9690-z", "type": "Feature", "geometry": null, "properties": {"updated": "2026-08-24T16:14:30Z", "type": "Journal Article", "created": "2013-06-28", "title": "Pedogenic Thresholds And Soil Process Domains In Basalt-Derived Soils", "description": "Pedogenic thresholds occur where soil properties change abruptly and/or nonlinearly with a small increment in environmental forcing; soil process domains are the regions between thresholds where soils change much more gradually across a large range of environmental forcing. We evaluated thresholds and domains in basalt-derived soils on two rainfall gradients in Hawaii\u2014one from 260 to 3,540 mm/y precipitation on 150,000-year-old substrate, the other from 600 to 3,760 mm/y on 4,100,000-year-old substrate. We identified thresholds associated with the initiation of biological uplift of nutrients at about 700 mm/y on the younger substrate, the depletion of primary minerals at about 2,100 mm/y on the younger and about 900 mm/y on the older substrate, and the initiation of anoxic conditions and associated Fe mobility at about 2,500 mm/y on the older substrate. These thresholds delineated process domains characterized by pedogenic carbonate accumulation and wind erosion (dry young substrate); by weathering and biological uplift of nutrients (intermediate rainfall young substrate and dry old substrate); by surface Fe enrichment and nutrient depletion (wet young substrate and intermediate rainfall old substrate); and by Fe mobilization and loss (wet old substrate). Soils on the older substrate were more highly weathered, lower in total and available P, and characterized by more crystalline clays than otherwise comparable soils on the younger substrate. Prior to European contact, Hawaiian cultivators developed an intensive rainfed agricultural system in the weathering/biological uplift domain on the younger substrate; we suggest that only this domain could support indigenous agricultural intensification in upland soils.", "keywords": ["2. Zero hunger", "Ecology", "biological uplift", "pedogenic threshold", "04 agricultural and veterinary sciences", "Biological Sciences", "15. Life on land", "01 natural sciences", "Hawaii", "gradient", "weathering", "Environmental Chemistry", "0401 agriculture", " forestry", " and fisheries", "indigenous agriculture", "process domain", "Environmental Sciences", "Ecology", " Evolution", " Behavior and Systematics", "basalt-derived soil", "0105 earth and related environmental sciences"], "contacts": [{"organization": "Vitousek, Peter M, Chadwick, Oliver A,", "roles": ["creator"]}]}, "links": [{"href": "https://escholarship.org/content/qt0267w3mm/qt0267w3mm.pdf"}, {"href": "https://doi.org/10.1007/s10021-013-9690-z"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Ecosystems", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/s10021-013-9690-z", "name": "item", "description": "10.1007/s10021-013-9690-z", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/s10021-013-9690-z"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2013-06-29T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=soil&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=soil&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": "first", "title": "items (first)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=soil&", "hreflang": "en-US"}, {"rel": "next", "type": "application/geo+json", "title": "items (next)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=soil&offset=50", "hreflang": "en-US"}], "numberMatched": 10388, "numberReturned": 50, "distributedFeatures": [], "timeStamp": "2026-08-25T07:38:56.589911Z"}