{"type": "FeatureCollection", "features": [{"id": "10.5061/dryad.h3r16", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:19:30Z", "type": "Dataset", "title": "Data from: The impact of environmental heterogeneity and life stage on the hindgut microbiota of Holotrichia parallela larvae (Coleoptera: Scarabaeidae)", "description": "unspecifiedGut microbiota has diverse ecological and evolutionary effects on their  hosts. However, the ways in which it responds to environmental  heterogeneity and host physiology remain poorly understood. To this end,  we surveyed intestinal microbiota of Holotrichia parallela larvae at  different instars and from different geographic regions. Bacterial 16S  rRNA gene clone libraries were constructed and clones were subsequently  screened by DGGE and sequenced. Firmicutes and Proteobacteria were the  major phyla, and bacteria belonging to Ruminococcaceae, Lachnospiraceae,  Enterobacteriaceae, Desulfovibrionaceae and Rhodocyclaceae families were  commonly found in all natural populations. However, bacterial diversity  (Chao1 and Shannon indices) and community structure varied across host  populations, and the observed variation can be explained by soil pH,  organic carbon and total nitrogen, and the climate factors (e.g., mean  annual temperature) of the locations where the populations were sampled.  Furthermore, increases in the species richness and diversity of gut  microbiota were observed during larval growth. Bacteroidetes comprised the  dominant group in the first instar; however, Firmicutes composed the  majority of the hindgut microbiota during the second and third instars.  Our results suggest that the gut\u2019s bacterial community changes in response  to environmental heterogeneity and host\u2019s physiology, possibly to meet the  host\u2019s ecological needs or physiological demands.", "keywords": ["Holotrichia parallela", "Cenozoic era", "15. Life on land"], "contacts": [{"organization": "Huang, Shengwei, Zhang, Hongyu,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.h3r16"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.h3r16", "name": "item", "description": "10.5061/dryad.h3r16", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.h3r16"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2013-05-20T00:00:00Z"}}, {"id": "10.5061/dryad.pb271", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:19:31Z", "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": "10.5194/amt-2021-82", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:19:35Z", "type": "Journal Article", "created": "2021-03-22", "title": "An automated system for trace gas \ufb02ux measurements from plantfoliage and other plant compartments", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. Plant shoots can act as sources or sinks of trace gases including methane and nitrous oxide. Accurate measurementsof these trace gas fluxes require enclosing of shoots in closed non-steady state chambers. Due to plant physiological activity, this type of enclosures, however, lead to CO2 depletion in the enclosed air volume, condensation of transpired water, and warmingof the enclosures exposed to sunlight, all of which may bias the flux measurements. Here, we present PlasTraGAS, ab novel measurement system designed for continuous and automated measurements of trace gas and volatile organic compound (VOC) fluxes from plant shoots. The system uses transparent shoot enclosures equipped with Peltier cooling elements and automatically replaces fixated CO2 and removes transpired water from the enclosure. The system is designed for measuring trace gasfluxes over extended periods, capturing diurnal and seasonal variations and linking trace gas exchange to plant physiologicalfunctioning and environmental drivers. Initial measurements show daytime CH4 emissions two pine shoots of 0.056 and 0.089 nmol g\u22121 foliage d.w.h\u22121or 7.80 and 13.1 nmol m\u22122 h\u22121. Simultaneously measured CO2 uptake rates were 9.2 and 7.6 mmol m\u22122 sec\u22121 and transpiration rates of 1.24 and 0.90 mol m\u22122 h\u22121. Concurrent measurement of VOC emissionsdemonstrated that potential effects of spectral interferences on CH4 flux measurements were at least ten-fold smaller than themeasured CH4 fluxes. Overall, this new system solves multiple technical problems that so far prevented automated plant shoottrace gas flux measurements, and holds the potential for providing important new insights into the role of plant foliage in the global CH4 and N2O cycles.                         </p></article>", "keywords": ["Earthwork. Foundations", "13. Climate action", "TA715-787", "Environmental engineering", "TA170-171", "15. Life on land", "7. Clean energy", "01 natural sciences", "Geosciences", "EMISSIONS", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://amt.copernicus.org/articles/14/4445/2021/amt-14-4445-2021.pdf"}, {"href": "https://doi.org/10.5194/amt-2021-82"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Atmospheric%20Measurement%20Techniques", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/amt-2021-82", "name": "item", "description": "10.5194/amt-2021-82", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/amt-2021-82"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-22T00:00:00Z"}}, {"id": "10.5194/bg-11-6969-2014", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:19:36Z", "type": "Journal Article", "created": "2014-12-11", "title": "Meta-analysis of high-latitude nitrogen-addition and warming studies implies ecological mechanisms overlooked by land models", "description": "<p>Abstract. Accurate representation of ecosystem processes in land models is crucial for reducing predictive uncertainty in energy and greenhouse gas feedbacks with the climate. Here we describe an observational and modeling meta-analysis approach to benchmark land models, and apply the method to the land model CLM4.5 with two versions of belowground biogeochemistry. We focused our analysis on the aboveground and belowground responses to warming and nitrogen addition in high-latitude ecosystems, and identified absent or poorly parameterized mechanisms in CLM4.5. While the two model versions predicted similar soil carbon stock trajectories following both warming and nitrogen addition, other predicted variables (e.g., belowground respiration) differed from observations in both magnitude and direction, indicating that CLM4.5 has inadequate underlying mechanisms for representing high-latitude ecosystems. On the basis of observational synthesis, we attribute the model\uffe2\uff80\uff93observation differences to missing representations of microbial dynamics, aboveground and belowground coupling, and nutrient cycling, and we use the observational meta-analysis to discuss potential approaches to improving the current models. However, we also urge caution concerning the selection of data sets and experiments for meta-analysis. For example, the concentrations of nitrogen applied in the synthesized field experiments (average = 72 kg ha\uffe2\uff88\uff921 yr\uffe2\uff88\uff921) are many times higher than projected soil nitrogen concentrations (from nitrogen deposition and release during mineralization), which precludes a rigorous evaluation of the model responses to likely nitrogen perturbations. Overall, we demonstrate that elucidating ecological mechanisms via meta-analysis can identify deficiencies in ecosystem models and empirical experiments.                     </p>", "keywords": ["0301 basic medicine", "QE1-996.5", "Ecology", "Geology", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "03 medical and health sciences", "Life", "13. Climate action", "QH501-531", "0401 agriculture", " forestry", " and fisheries", "QH540-549.5", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.5194/bg-11-6969-2014"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Biogeosciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/bg-11-6969-2014", "name": "item", "description": "10.5194/bg-11-6969-2014", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/bg-11-6969-2014"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2014-08-18T00:00:00Z"}}, {"id": "10.5194/soil-2020-96", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:19:45Z", "type": "Report", "created": "2021-02-06", "title": "Controls on heterotrophic soil respiration and carbon cycling in geochemically distinct African tropical forest soils", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. Heterotrophic soil respiration is an important component of the global terrestrial carbon (C) cycle, driven by environmental factors acting from local to continental scales. For tropical Africa, these factors and their interactions remain largely unknown. Here, using samples collected along strong topographic and geochemical gradients in the East African Rift Valley, we study how soil chemistry and soil fertility, derived from the geochemical composition of soil parent material, can drive soil respiration even after many millennia of weathering and soil development. To address the drivers of soil respiration, we incubated soils from three regions with contrasting geochemistry (mafic, felsic, and mixed sedimentary) sampled along slope gradients. For three soil depths, we measured the potential maximum heterotrophic respiration under stable environmental conditions as well as the radiocarbon content (\u039414C) of the bulk soil and respired CO2. We found that soil microbial communities were able to mineralize C from fossil as well as other poor quality C sources under laboratory conditions representative of tropical topsoils. Furthermore, despite similarities in terms of climate, vegetation, and the size of soil C stocks, soil respiration showed distinct patterns with soil depth and parent material geochemistry. The topographic origin of our samples was not a main determinant of the observed respiration rates and \u039414C. In situ, however, soil hydrological conditions likely influence soil C stability by inhibiting decomposition in valley subsoils. Our study shows that soil fertility conditions are the main determinant of C stability in tropical forest soils. Further, in the presence of organic carbon sources of poor quality or the presence of strong mineral related C stabilization, microorganisms tend to discriminate against these sources in favor of more accessible forms of soil organic matter as energy sources, resulting in a slower rate of C cycling. Our results demonstrate that even in deeply weathered tropical soils, parent material has a long-lasting effect on soil chemistry that can influence and control microbial activity, the size of subsoil C stocks, and the turnover of C in soil. Soil parent material and its lasting control on soil chemistry need to be taken into account to understand and predict C stabilization and rates of C cycling in tropical forest soils.                         </p></article>", "keywords": ["2. Zero hunger", "13. Climate action", "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/10.5194/soil-2020-96"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/soil-2020-96", "name": "item", "description": "10.5194/soil-2020-96", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/soil-2020-96"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-02-04T00:00:00Z"}}, {"id": "10.5281/zenodo.10065971", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:19:46Z", "type": "Dataset", "title": "Database of topsoil  chemical and physical properties in Croatia", "description": "Sources Data for database is collected from four main sources:\u00a0  Data published in book 'Martinovi\u0107, J. and Vrankovi\u0107, A. (Editors), 1997. Baza podataka o hrvatskim tlima, I. Dr\u017eavna uprava za za\u0161titu prirode i okoli\u0161a, Zagreb' labeled as 'martinovic_1997' in the database.  This source consists of 2199 pedological profiles sampled from 1963 to 1996, most of which include depth to bedrock information. Data from project: 'Spatial variability of trace and toxic metals in agricultural soils of Croatia', Ministry of Science and Education and Croatian Waters. Project leader: prof.dr.sc. Marija Romi\u0107 from Faculty of Agriculture, Zagreb, labeled as 'agricultural_2013'.  Data are sampled from 'database of properties and quality of agricultural soils of Croatia' on 8x8 km grid and consists only from top soil samples (0-30 cm). There are 811 samples in this database. Data from the the project: 'Change in soil carbon stocks and calculation of trends in total nitrogen and organic carbon in soil and C: N ratio', from Ministry of Environmental Protection and Energy, carried on by Croatian Geological Institute (HGI), the Croatian Forestry Institute (H\u0160I) and the Agricultural Land Agency (APZ).  This dataset consists of two subsets:  'azo_2013' - 2519 samples of topsoil (0-25 cm), from 1994 to 2004 for making of Geochemistry Atlas of Croatia 'azo_2016' - 742 locations were revisited during 2015-2016 and new samples are taken and analyzed in horizons 0-10 cm, 10-20 cm, 20-30 cm. Network of piezometers Description of sources\u00a0 Martinovi\u0107, J. and Vrankovi\u0107, A. (Editors), 1997. Baza podataka o hrvatskim tlima (Database of Croatian Soils)\u00a0 The database contains data on soil profiles and covers the total area of the Republic of Croatia. Only data accepted by external control are entered in the Database, as well as those profiles for which there is a minimum data. External control of data reliability was performed by comparing the genetical-morphological characteristics of the soil determined by field research and the data of laboratory soil analyses. The profiles for which the field and laboratory analyses are found to differ are rejected. In addition to data on soil properties, basic data on pedogenetic factors are given. The soil profiles surveyed in the period 1963-1996 are entered in the database. The majority of data come from the Basic Pedological Map of Croatia (Osnovna pedolo\u0161ka karta Hrvatske - OPKH) project. The following are entered in the Database: 1347 profiles in volume I and 851 profiles in volume II, a total of 2198 pedological profiles Spatial variability of trace and toxic metals in agricultural soils of Croatia, Project Leader: Marija Romi\u0107\u00a0 The problem of exposure of agricultural soils to different anthropogenic inputs of toxic metals, but also of other potentially toxic substances, has acquired global dimensions in the last decades. Besides atmospheric deposition, environmental dispersion of chemicals used in agriculture is an important factor directly affecting the natural soil functions, or indirectly endangering the biosphere by bioaccumulation and inclusion into the food chain. Metal concentrations in soil can be generally predicted starting with the element abundance in the parent material. The extent to which pedogenesis affects heavy metals distribution varies according to the prevailing factors affecting soil processes. Because of the toxicity to plants and animals, it is important to determine their content, forms and distribution. Such hypotheses may be tested by total metal content determination, as well as other elements relevant for geochemical valorization of the agricultural soils of Croatia. Thus, the spatial variability and baseline of elements in soils will be determined by means of relevant statistical and geostatistical methods. The maps of toxic metal distribution will be produced and the suitability of soils for agriculture will be assessed. GIS is increasingly used in environmental assessment studies because of its ability to superimpose different spatial information and to combine them with the results of statistical analysis, enabling thus the detection of complex spatial relationships among different parameters. Geostatistics and multivariate statistics has been widely used in geochemical studies to identify pollution sources and to apportion natural vs. anthropogenic contribution, establishing a geochemical background as well. The main objectives of the investigation are: (i) to provide a geochemical database relevant to the agricultural soils in Croatia; (ii) to provide a detailed information about the natural variability of the geochemical background which is pertinent to administrative and legal issues as well as to safety food production and environmental protection; (iii) presenting the influence of human and other environmental activities on the soil quality mainly regarding the toxic and trace metal contents, and (iv) we are going to observe the influence of natural conditions on regional differences which have been widely neglected so far, and have not been taken into account while national regulations and guidelines on soil toxic metal contents have been established. Change in soil carbon stocks and calculation of trends in total nitrogen and organic carbon in soil and C: N ratio\u00a0 The project is funded by the Fund for Environmental Protection and Energy Efficiency within the Program 'Upgrading and Development of the Environmental Information System and Improving the Monitoring and Reporting System on the State of the Environment in the Republic of Croatia', Component 2: Improving the Monitoring and Reporting System on the State of the Environment Croatia; improving the system of data collection and exchange and developing methodologies for their processing in accordance with the guidelines of the UNFCCC and the Kyoto Protocol defined by the IPCC (Intergovernmental Panel on Climate Change).\u00a0 The project holder is the Ministry of Environmental Protection and Energy, and the executors are the Croatian Geological Institute, the Croatian Forestry Institute and the Agricultural Land Agency. In the period 2014-2017, field and laboratory research of soil conditions was conducted at 725 representative locations. General data on the location of sampling were collected, which contain administrative, locational, geographical and other data (relief, climatic and meteorological data, detailed data on land use and vegetation cover, description of surface soil properties). Field soil sampling for each LULUCF land use category was performed according to a modified methodology described in the EU DG JRC (Joint Research Center) 'Protocol for soil sampling to confirm changes in organic carbon stocks in the EU' by Stolbovoy et al. 2007 (Soil sampling protocol to certify the changes of organic carbon stock in mineral soil of the European Union - EU JRC). The protocol modifications aimed to ensure reporting under the UNFCCC and Kyoto protocols, i.e., to ensure compliance with the IPCC methodology. Soil sampling on forest land (FL) according to the JRC protocol is planned at two depths of 0-10 cm and 10 - 20 cm and an organic layer (list), but due to reporting requirements under the UNFCCC and Kyoto protocol, sampling was carried out at a depth of 20 - 30 cm. Land under crops (CL) was sampled at two depths (0-20 cm and 20-30 cm) and grasslands (GL), wetlands (WL), settlements (SL) and other land (OL) were sampled at three depths 0- 10, 10-20 and 20-30 cm. Geochemical analyzes were performed at depths of 0\u201310 and 20\u201330 cm for forest soils (FL) and for meadows and pastures (GL) while for soils under crops (CL) composite samples of 0\u201330 cm and 0\u201320 cm were analyzed. Network of piezometers To get a more accurate depth to bedrock parameter, positions of 812 piezometers are considered as they have at least 4 meters of depth to bedrock. Description of database Column names and descriptions: Metadata columns: site_key - unique identifier that identifies sample in source database\u00a0source_db - label of source database\u00a0source_sampled - label of organization/team who sampled and analyzed data\u00a0site_obsdate - year of taking sample\u00a0longitude_decimal_degrees - longitude in degrees in WGS84 geographical projection\u00a0latitude_decimal_degrees - latitude in degrees in WGS84 geographical projection\u00a0pedon_completeness_index - quality factor (0-100)\u00a0taxgrtgroup - classification of sample according to WBR 2014/2016 classification\u00a0 Soil properties columns: column name - property - measurement units - descriptionoc - Carbon, Organic - % wt - CMS analyte. Organic carbon is a measure of all organic forms of carbon in the soil, including organic carbon within minerals.\u00a0n_tot_ncs - Nitrogen, Total NCS - % wt - Total nitrogen is a measure of all organic and inorganic nitrogen, including that found in nitrogen minerals.ca_mehlich3 - Calcium, Mehlich3 Extractable \u00a0- mg/kg - The calcium extracted by the Mehlich III solution.\u00a0k_mehlich3 - Potassium, Mehlich3 Extractable - mg/kg - The potassium extracted by the Mehlich III solution.\u00a0mg_mehlich3 - Magnesium, Mehlich3 Extractable - mg/kg - The magnesium extracted by the Mehlich III solution.\u00a0p_mehlich3 - Phosphorus, Mehlich3 Extractable - mg/kg - The phosphorus extracted by the Mehlich III solution.\u00a0cec_sum - Cation Exchange Capacity, Summary \u00a0 cmol(+)/kg - The effective cation exchange capacity is calculated by BASE_SUM+AL_KCL. It is not calculated if soluble salts are present. It is reported as meq per 100 grams on a <2 mm base. CMS derived value default\u00a0ec_satp - Electrical Conductivity , Saturation Extract - dS/m - The electrical conductivity of the saturation extract is used to estimate the concentration of salts in a sample, and provides inferences on cation concentration in solution and osmotic pressure. It is reported as mmhos per centimeter.\u00a0caco3 - Carbonates - % wt - Carbonate in the < 2mm fraction is measured by CO2 evolution after acid treatment. It is reported as gravimetric percent CaCO3 on a <2 mm base, even though carbonates of Mg, Na, K, and Fe may be present and react with the acidph_h2o - pH, 1:1 Soil-Water Suspension - (NA) - The pH, 1:1 soil-water suspension is the pH of a sample measured in distilled water at a 1:1 soil:solution ratio. If wider ratios increase the pH, salts are indicated.\u00a0ph_kcl - The pH, 1:1 soil-KCl suspension - (NA) - The pH, 1:1 soil-KCl suspension is the pH of a sample measured in 1.0N KCl at a 1:1 soil:solution ratio. If the pH in KCl < pH in water, Al+++ is indicated.\u00a0total_clay - Clay, Total - % wt - Total clay is the soil separate with <0.002 mm particle diameter. Clay size carbonate is included. Total clay is reported as a weight percent of the <2 mm fraction.\u00a0total_silt - Silt, Total - % wt - Total silt is the soil separate with 0.002 to 0.05 mm particle size. It is reported as a gravimetric percent on a <2 mm base.\u00a0total_sand - Sand, Total - % wt - Total sand is the soil separate with 0.05 to 2.0 mm particle diameter. It is reported as a gravimetric percent on a <2 mm base.\u00a0wpg2 - Coarse fragments - % wt - The weight fraction of particles with >2 mm diameter is reported as a gravimetric percent on a whole soil base.\u00a0db_od Bulk Density, <2mm Fraction, Ovendry - g/cc - Bulk density, oven dry (105 C) is the weight per unit volume of the <2 mm fraction, with volume measured on oven dry (105 C) natural fabric (clods). It is reported as grams per cubic centimeter on a <2 mm base.\u00a0dbr - Depth to bedrock - cm - Depth to the R horizon or similar", "keywords": ["2. Zero hunger", "13. Climate action", "15. Life on land", "16. Peace & justice", "3. Good health"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.10065971"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.10065971", "name": "item", "description": "10.5281/zenodo.10065971", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.10065971"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-11-02T00:00:00Z"}}, {"id": "10.5281/zenodo.10404481", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:19:48Z", "type": "Report", "title": "D.4.1 \u2013 Coaching and Capacity  Building Report, Round #1", "description": "This deliverable reports on the work related to tasks 4.1 and 4.2, carried out by consortium partners from Department of Agroecology at Aarhus University and ENoLL (European Network of Living Labs), respectively. These partners provide applicants with tools and coaching (T4.1), to ease the application process and guide them through consortium building and to design sustainable and well-thought soil health improving living labs. To provide possible applicants from all over Europe with valuable advice, NATI00NS has found mentors in 18 European\u00a0countries, who can be consulted by possible applicants.  This deliverable is written to report on the implementation and execution of tasks 4.1,\u00a0Coaching Sessions, and 4.2, Capacity Building. The tasks feed into NATI00NS\u2019 main objective,\u00a0that is enhancing the possibilities of more viable and well-planned soil health improving living\u00a0lab applications under the Mission auspices, which hopefully will lead to the establishment of\u00a0well-functioning living labs in the near future. The deliverable will provide both the public and\u00a0the funding body, with knowledge on NATI00NS\u2019 initial progress and results.  In short, the function of T4.1 has been to identify candidate Soil Health Living Lab Mentors in\u00a0all EU member states and associated countries, followed up by a process aligning the\u00a0candidates' perceptions on the meaning of a living lab and understanding the topic description\u00a0in dept by participating in on-line training sessions. This concluded in mentor candidates\u00a0signing the Non-Disclosure Agreement (NDA) agreements to officially become mentors and\u00a0thereby be mandated to coach possible living lab applicants within the NATI00NS framework.  Alongside the coaching of mentors, NATI00NS\u2019 has carried out capacity building, prepared and\u00a0implemented by ENoLL, the European Network of Living Labs, a NATI00NS consortium partner,\u00a0that leads online support to bolster up stakeholders around the Soil Mission (hereafter only\u00a0described as the Mission) and broaden their understanding of what a LL is. ENoLL have for this\u00a0purpose, produced e-learning materials, including factsheets and webinars. The Capacity\u00a0Building in combination with Coaching Session activities, provide information and training,\u00a0that enhance the chances of well-conceived and relevant Soil Health Living Labs being created,\u00a0by making sure living lab applicants are not only trained well by mentors with knowledge on\u00a0living lab concepts; participants will also have capacity building material available to them in\u00a0order to design and create a strong Living Lab consortium. The materials include manuals\u00a0which they can use to design a living lab. The capacity building provided by NATI00NS does, in\u00a0general, provide applicants with hands-on capacities, whether it be factsheets or webinars on\u00a0specific living lab related questions.In supporting the applicants at national level identifying It has been important to associate\u00a0skilled mentors has been of the essence. Therefore, the NATI00NS consortium has mapped\u00a0stakeholders across EU Member States and Associated Countries during most of its first\u00a0\u2018introduction and pilot\u2019 phase, to get in contact with gatekeepers in each country.  In most countries, the National Contact Point (NCP) structure, and its responsible officers\u00a0appointed either for the Mission or the Food, Bioeconomy, Natural Resources, Agriculture and\u00a0Environment area, were primary contact points, since it is already an integrated part of their\u00a0job description, to support the Soil Health Mission calls. Consequently, many NCPs have taken\u00a0on the role of mentors themselves while others have tried assisting NATIOONS in finding\u00a0suitable mentor candidates, interested in acting as mentors within the NATI00NS framework.  NCPs are national structures associated to the framework programme. NCPs give personalised support on the spot and in applicants' own languages.  After reaching out to possible mentors, AU AGRO has continuously answered questions about\u00a0the scope of the mentoring work \u2013 such as the mentors\u2019 expected workload, responsibilities,\u00a0and for how long they are expected to commit to mentoring duties. In parallel, NATIOONS has\u00a0planned and implemented two training of trainers webinars that offered training to candidate\u00a0mentors, so they all could be aligned in terms of living lab concepts, practical circumstances\u00a0regarding the application process and confidentiality measures, after which they were able to\u00a0take an informed decision about becoming NATI00NS Mentors or not.All webinar participants, whether they joined for reasons of curiosity or already knew they\u00a0would commit to mentoring, were then briefed on, how it is necessary for them to read and\u00a0sign NDA-documents to officially become NATI00NS appointed Soil Health Living Lab mentors,\u00a0and thus appear on the NATIOONS website with name and contact details. NATIOONS have\u00a0since then continuously collected signed NDA documents and updated the website\u00a0accordingly, thereby expanding the number of mentors available to possible applicants.To carry out the work related to recruiting soil health living lab mentors and training them in\u00a0living lab-affiliated concepts, a number of Aarhus University\u2019s soil and farming systems\u00a0scientists and research support advisers, planned a training programme for mentors.  They have also been responsible for all communication and mapping of possible mentors, organising of the training of trainers event (I.e., training the mentors that will eventually offer\u00a0training to living lab applicants) webinars and gathering and handling Non-disclosure\u00a0Agreement (NDA) documents and FAQ by mentors and applicants. Content for webinars on\u00a0soil health and living labs, have been created and presented by the NATI00NS partners who\u00a0also produced the slides for the National Engagement Events \u2013 another NATIOONS activity\u00a0belonging to another work package, which will be described in its own deliverable.  Additionally, a senior officer from the Aarhus University\u2019s Research Support Office, with great\u00a0experience in providing support for framework programme applicants, provided webinar\u00a0attendants with important guidance on application practices.  The Capacity Building (CB) efforts plays a pivotal role in the NATI00NS project, to ensure the\u00a0success of the Mission. Its main objective is to guarantee the submission of high-quality\u00a0applications for the first two sets of topics aimed at establishing Living Labs (LLs) in 2023 and\u00a02024. These efforts are led by the European Network of Living Labs (ENoLL) as part of Work\u00a0Package 4, 'Supporting Proposal Applicants.'\u00a0NATI00NS\u2019 Capacity Building brings together a comprehensive range of essential training and\u00a0guidance activities tailored specifically for applicants interested in the LL topics related to the\u00a0Mission. At its core, Capacity Building provides online support materials for stakeholders\u00a0involved in the Mission. These materials include a series of e-learning resources, such as\u00a0Factsheets and recorded webinars, offering information about the criteria governing Soil\u00a0Health LLs and the objectives of the Missions within the context of various land use types. Thisinitiative sets the stage for prospective LL applicants in the future.", "keywords": ["2. Zero hunger", "9. Industry and infrastructure", "15. Life on land", "16. Peace & justice"], "contacts": [{"organization": "Krabbe, Kasper, Couture, Isabelle, Cavallo, Dolinda,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.10404481"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.10404481", "name": "item", "description": "10.5281/zenodo.10404481", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.10404481"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-12-19T00:00:00Z"}}, {"id": "10.5281/zenodo.13374006", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:20:04Z", "type": "Dataset", "title": "Microbial biomass and water-extractable carbon on Mt. Kilimanjaro", "description": "This dataset presents the value of microbial biomass carbon (MBC) and water-extractable carbon (WOC) at study plots under KiLi project.  Microbial biomass carbon (MBC) and water-extractable organic carbon (WOC) \u2013 as sensitive and important parameters for soil fertility and C turnover \u2013 are strongly affected by land-use changes all over the world. These effects are particularly distinct upon conversion of natural to agricultural ecosystems due to very fast carbon (C) and nutrient cycles and high vulnerability, especially in the tropics. The objective of this study was to use the unique advantage of Mt. Kilimanjaro \u2013 altitudinal gradient leading to different tropical ecosystems but developed all on the same soil parent material \u2013 to investigate the effects of land-use change and elevation on MBC and WOC contents during a transition phase from dry to wet season. Down to a soil depth of 50\u00a0cm, we compared MBC and WOC contents of 2 natural (Ocotea\u00a0and\u00a0Podocarpus forest), 3 seminatural (lower montane forest, grassland, savannah), 1 sustainably used (homegarden) and 2 intensively used (maize field, coffee plantation) ecosystems on an elevation gradient from 950 to 2850\u00a0m a.s.l.  The KiLi project (2010-2018) is a German Science Foundation (DFG) funded research unit (DFG research unit FOR1246) that focuses on biodiversity and ecosystem processes along altitudinal and disturbance gradients on Mt. Kilimanjaro (Tanzania, Africa), capitalizing on its world-wide unique range of climatic and vegetation zones. The research unit comprises 2 central projects and 7 subprojects from various disciplines. On a total of 60 study sites in both natural and human-disturbed ecosystems biodiversity (e.g. plants, soil arthropods, ants, bees, frogs, lizards, bats, birds), related ecosystem processes (decomposition, seed dispersal, pollination, herbivory, predation), and biogeochemical processes and properties of ecosystems (climate, soil properties and nutrient status, regulation of water and carbon fluxes, trace gas emissions, primary productivity, functional diversity) are analyzed.", "keywords": ["land-use change", "microbial carbon dynamics", "tropical ecosystem", "andosol", "elevation gradient", "water-extractable carbon"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.13374006"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.13374006", "name": "item", "description": "10.5281/zenodo.13374006", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.13374006"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-08-26T00:00:00Z"}}, {"id": "10.5281/zenodo.14044657", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:20:10Z", "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.14185189", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:20:13Z", "type": "Dataset", "title": "Dataset: experiments on volatile organic compounds uptake by the active layer soils of Greenlandic permafrost areas", "description": "This dataset is associated with a publication currently under peer review (DOI and link to the publication will be updated upon its publication).  Permafrost serves as a significant carbon reservoir, storing up to 1700 petagrams of carbon accumulated over millennia. As global warming accelerates permafrost thaw, this carbon can be mobilized, with a fraction being transformed into volatile organic compounds (VOCs). These VOCs can influence atmospheric oxidizing capacity and contribute to the formation of secondary organic aerosols.  In this study, active layer soils\u2014the seasonally unfrozen layer above the permafrost\u2014were collected from two contrasting Greenlandic permafrost locations (Disko Island, and Kangerlussuaq) and incubated to investigate their role in soil-atmosphere VOC exchange. Laboratory incubations were conducted under controlled conditions, where a VOC mixture gas was continuously purged through jars containing the soil samples. Gas concentrations were monitored at the inlet and outlet using a PTR-ToF-MS, allowing for the estimation of VOC uptake rates based on the differences in VOC concentrations.  The results demonstrated that these soils actively function as VOC sinks, despite variations in their physicochemical properties. Soils from upper active layers showed relatively higher uptake capacities, with soil moisture, organic matter, and microbial carbon content identified as key factors influencing uptake rates. Additionally, uptake coefficients for several major VOC species were calculated, providing valuable data for future model development. Correlation analysis and varying uptake coefficients suggest that the sink is likely biotic, with selective preferences for different VOCs.\u00a0The findings indicate that the development of a deeper active layer under climate change could enhance the soil\u2019s sink capacity and mitigate net VOC emissions from permafrost thaw.  Detailed methods and interpretations of the results can be found in the associated publication.", "keywords": ["volatile organic compounds", "Disko Island", "active layer", "sink", "Kangerlussuaq", "permafrost"], "contacts": [{"organization": "Jiao, Yi, Kramsh\u00f8j, Magnus, Davie-Martin, Cleo, Elberling, Bo, Rinnan, Riikka,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14185189"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14185189", "name": "item", "description": "10.5281/zenodo.14185189", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14185189"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-19T00:00:00Z"}}, {"id": "10.5281/zenodo.14761001", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:20:19Z", "type": "Software", "title": "PyretoClustR (executable version)", "description": "PyretoClustR (executable version)  Distilling the Pareto Optimal Front into Actionable Insights  ##Highlights:    Open-access tool to cluster and visualize complex multi-dimensional Pareto solutions  Bridging the gap between decision and objective space through intuitive visualizations  Implements k-means and k-medoids clustering without expert knowledge  Uses frequency maps to display hotspot locations for spatial optimization problems  Increasing stakeholder accessibility to Pareto solutions   Program languages: Python, RSoftware availability (source code version): https://github.com/SydneyEWhite/Pareto_ClusteringSoftware availability (executable version, with python and libraries implemented): Zenodo (10.5281/zenodo.14761001)  ## Overview    This framework performs k-means and k-medoids clustering on a set of Pareto optimal solutions derived from a multi-objective optimization algorithm.  (Optional) A correlation matrix of the input variables is returned (the goal is to help users reduce the input variable count).  Before clustering occurs, the data is cast onto principal component axes and extreme solutions are handled (if desired).  The code iterates through different possible inputs for the number of clusters, the number of principal components, and the variables that define 'extreme solutions'.  After these iterations, the best solution, as defined by silhouette score, is visualized in several ways:    Representative solutions are plotted on up to 4 dimensions  Distributions of values within the clusters are plotted in a violin plot  (optional) Maps with the frequency of a trait can be plotted, if locational data (*.shp file) is provided     ## Background: executable version  Unlike the version of PyretoClustR available on GitHub (https://github.com/SydneyEWhite/PyretoClustR), this version on Zenodo represents a standalone version of PyretoClustR not requiring a Python installation. The python_files folder contains three executables and one folder called _internal. The executables can be run by clicking on them. The _internal folder is crucial for the standalone operation of the executables and should not be modified or deleted.  ## Example Data  The data provided in the input folder (pareto_and_scen_solutions.csv and the shapefiles) are part of the BiodivERsA project TALE (\u2018Towards multifunctional agricultural landscapes in Europe\u2019). This project optimized four objectives, Agricultural gross margin (AY), Breeding habitat (BH), Low flow (LF), Nitrate load (NL) and four different land use scenarios (SQ, BAU, EXT, INT) were distinguished in the decision space.\u00a0The config.ini has been adapted to run with this dataset. To run it with your own data please alter the respective input values as outlined in config.ini.  ## Directory Structure  project_root/||\u2500\u2500 README.md||\u2500\u2500 input/|\u00a0 |\u2500\u2500 config.ini|\u00a0 |\u2500\u2500 [your_input_data].csv| \u2514\u2500\u2500 [shape_files]||\u2500\u2500 python_files/|\u00a0 |\u2500\u2500 _internal|\u00a0 |\u2500\u2500 correlation_matrix.exe|\u00a0 |\u2500\u2500 kmeans.exe| \u2514\u2500\u2500 kmedoid.exe||\u2500\u2500 r_files/| \u2514\u2500\u2500 plot_frequency_maps.R|\u2514\u2500\u2500 output/ (populated once code is run)\u00a0\u00a0 |\u2500\u2500 correlation_matrix.csv (if run)\u00a0\u00a0 |\u2500\u2500 kmeans_data_w_clusters_representativesolutions.csv (created with kmeans.exe when Extreme Solutions are not handled)\u00a0\u00a0\u00a0 |\u2500\u2500 kmeans_data_w_clusters_representativesolutions_outliers.csv (created with kmeans.exe when Extreme Solutions are handled)\u00a0\u00a0 |\u2500\u2500 kmedoid_data_w_clusters_representativesolutions.csv (created with kmedoid.exe when Extreme Solutions are not handled)\u00a0\u00a0 |\u2500\u2500 kmedoid_data_w_clusters_representativesolutions_outliers.csv (created with kmedoid.exe when Extreme Solutions are handled)\u00a0 \u2514\u2500\u2500 freq_map_cluster_X.png (if run)", "keywords": ["Multi-objective optimization", "Pareto pruning", "Land management", "Pareto optimal data", "visualization", "clustering"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14761001"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14761001", "name": "item", "description": "10.5281/zenodo.14761001", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14761001"}, {"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": "10.5281/zenodo.15017546", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:20:24Z", "type": "Dataset", "title": "Global Rice Land Suitability and Adaptation Strategies Under Climate Change", "description": "This study evaluated the land suitability of irrigated and rainfed rice cultivation under current and projected climate change scenarios for the year 2050 on a global scale. , the climate and soil factors utilized for this purpose were obtained from the WorldClim website (https://www.worldclim.org/) and represent the historical monthly weather data for the period between 2001 and 2021 (https://worldclim.org/data/monthlywth.html). Moreover, future climate projections were sourced from the same source for the period 2041 to 2060 (centered around 2050).The projections are based on mean value of ten Global Climate Models (GCMs) from the CMIP6 dataset and two scenarios (SSP245 and SSP585), including models such as ACCESS-CM2, CanESM5-CanOE, EC-Earth3-Veg, FIO-ESM-2-0, GISS-E2-1-H, HadGEM3-GC31-LL, INM-CM4-8, IPSL-CM6A-LR, MRI-ESM2-0, and UKESM1-0-LL (https://worldclim.org/data/cmip6/cmip6_clim2.5m.html). The future climate scenarios were developed using a new set of integrated assessment models (IAMs) that incorporate the Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs), as outlined by O'Neill et al. (2016). Two integrated scenarios were considered in this study. SSP245 (a combination of SSP2 with RCP4.5) and SSP585 (a combination of SSP5 with RCP8.5). SSP2 represents a scenario characterized by a continuation of existing social, economic and technological trends, with minimal deviation from historical patterns. In contrast, SSP5 depicts a pathway of fossil-fuelled development, characterized by rapid technological advancement and human capital growth. With regard to radiative forcing, RCP4.5 represents a medium scenario (4.5 W m\u207b\u00b2 by 2100), whereas RCP8.5 represents a high-emission scenario (8.5 W m\u207b\u00b2 by 2100). To ensure the highest possible spatial accuracy, 2.5 minutes (~21 km2 at the equator) spatial resolution was applied to all maps. Furthermore, maps of rice cultivation area was obtained from the MAP SPAM2020 (Spatial Production Allocation Model) website (https://www.mapspam.info), which provides detailed spatial data on global rice production.Soil parameters were then determined for each point using soil information obtained from the FAO soil map. The FAO soil map provided vital parameters such as organic carbon (OC), exchangeable sodium percentage (ESP), cation exchange capacity (CEC), CaCO3, pH, EC which extracted from the Harmonized World Soil Database (HWSD) at a scale of 1:5,000,000, sourced from the FAO Soil Portal website. (Supplementary Figure 2). The slope map was created using the digital elevation model (for more details, see Guerra et al., 2020). The SPAM2020 for rainfed and irrigated rice was used to intersect the climate zones and soil layers, focusing specifically on the areas dedicated to this area.\u00a0  The final land suitability map was generated by overlaying the suitability output with raster files representing both irrigated and rainfed harvested areas, thus creating a composite map of land suitability . This stage ensured the precise delineation of areas suitable for rice cultivation. Following the creation of land suitability maps for a variety of scenarios and adaptation strategies, the results were compared in order to identify the optimal strategy. The final map identifies the most efficacious adaptation strategy for enhancing land suitability in each region in the context of projected climate change. Also, The overview of the status of rainfed and irrigated rice worldwide.", "keywords": ["Adaptation strategy", "Climate chnage", "Rice", "land suitablity"], "contacts": [{"organization": "Dadrasi, Amir, Va\u010dk\u00e1\u0159ov\u00e1, Davina, Fatemeh, Salmani, Nguyen, Trong Can, Weinzettel, Jan,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15017546"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15017546", "name": "item", "description": "10.5281/zenodo.15017546", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15017546"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-02-15T00:00:00Z"}}, {"id": "10.5281/zenodo.15096788", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:20:25Z", "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": "2026-09-20T16:20:28Z", "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.3247592", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:20:42Z", "type": "Dataset", "title": "Daily cycles in soil carbon flux", "description": "<strong>Description: </strong> Measurements of 24 hour cycles in soil CO2 flux taken from soil collars in the Belian Carbon plot at Maliau. Measurements were taken from 12 subplots over four days at 5-hourly intervals, ensuring good coverage of the complete 24 hour cycle. Air and soil temperatures, soil moisture content and CO2 flux were taken from each plot at each visit. 9 subplots only have a single total soil respiration collar, but 3 subplots also have soil flux partitioning treatments to separate contributions to total respiration from soil organic matter, mycorrhizae and roots.<br> <br> This data was collected by the 2019 cohort of the Tropical Forest Ecology MRes at Imperial College London. <strong>Project: </strong>This dataset was collected as part of the following SAFE research project: <strong>MRes Tropical Forest Ecology Field Course</strong> <strong>XML metadata: </strong>GEMINI compliant metadata for this dataset is available here <strong>Files: </strong>This dataset consists of 2 files: Carbon_corrected_slopes.xlsx, raw_egm.zip <strong>Carbon_corrected_slopes.xlsx</strong> This file contains dataset metadata and 2 data tables: <strong>24 hour observations of CO2 flux patterns</strong> (described in worksheet Carbon_flux_data) Description: Estimated CO2 flux values, soil moisture and air and soil temperatures from Carbon subplots Number of fields: 14 Number of data rows: 296 Fields: <strong>record_no</strong>: EGM 'Plot' value - record number on EGM machine for this collar (Field type: ID) <strong>plot</strong>: Carbon subplot number (Field type: Location) <strong>date</strong>: Calendar date that measurements taken (Field type: Date) <strong>time</strong>: Time that measurements taken (Field type: Time) <strong>soil_wmc</strong>: Soil water moisture content (Field type: Numeric) <strong>soil_temp</strong>: Soil temperature (Field type: Numeric) <strong>air_temp</strong>: Air temperature (Field type: Numeric) <strong>treatment</strong>: Exclusion treatments for partitioning soil respiration components (Field type: Categorical) <strong>field_flux</strong>: CO2 flux reported in the field by EGM (Field type: Numeric) <strong>Source</strong>: EGM dat file of source data used for corrected fluxes where available (Field type: File) <strong>corrected_flux</strong>: Corrected flux measurements using by eye exclusion of raw flux data (Field type: Numeric) <strong>n_points</strong>: Number of points in EGM record (Field type: Numeric) <strong>n_used</strong>: Number of points used for corrected slope estimation (Field type: Numeric) <strong>flux</strong>: Final flux values, using corrected values where available (Field type: Numeric) <strong>EGM raw data</strong> (described in worksheet EGM_raw_data) Description: Duplicates key information from raw EGM files and indicates points excluded in calculation of corrected flux values Number of fields: 7 Number of data rows: 7114 Fields: <strong>Plot</strong>: EGM recorder 'plot' code, actually just the record sequence number. (Field type: ID) <strong>RecNo</strong>: EGM record number - time points of gas measurement at a single plot (Field type: ID) <strong>Datetime</strong>: Time of gas concentration measurement (Field type: Datetime) <strong>CO2.Ref</strong>: Measured CO2 (Field type: Numeric) <strong>Input.E</strong>: EGM internal variable used in slope estimation (Field type: Numeric) <strong>Source</strong>: Original EGM dat file containing the flux data (Field type: File) <strong>ignore</strong>: Indicates where points from raw data excluded from corrected slope calculations (Field type: Categorical) <strong>raw_egm.zip</strong> Description: Zipfile of raw EGM dat files <strong>Date range: </strong>2019-02-18 to 2019-02-21 <strong>Latitudinal extent: </strong>4.7467 to 4.7480 <strong>Longitudinal extent: </strong>116.9693 to 116.9704", "keywords": ["2. Zero hunger", "550", "soil CO2 flux", "24 hour cycle", "partitioning", "15. Life on land", "6. Clean water", "630"], "contacts": [{"organization": "Orme, C David L, Riutta, Terhi, Fraser, Adam, Mackintosh, Emma, Vollans, Maisie, Graves, Kristina, Roxby, Gabrielle, Baynham, Georgina, Noble, Ciar, Adzhar, Rahayu, MacLean, Catherine, Martin, Stephanie, McKinlay, Amelia, Poznansky, Frederica, Prairie, Aaron, Brasington, Ella, Wiederkehr, Fabienne,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.3247592"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.3247592", "name": "item", "description": "10.5281/zenodo.3247592", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.3247592"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-06-17T00:00:00Z"}}, {"id": "10.5281/zenodo.3591992", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:20:44Z", "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.4287780", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:20:46Z", "type": "Dataset", "title": "Forest carbon prospecting for climate change mitigation: Version 1.0", "description": "This data package includes the two 1-km resolution global maps (.tif) of tropical forests between ~23.44\u00b0N and 23.44\u00b0S produced from the study: 1) investible forest carbon (in tCO<sub>2</sub>e ha<sup>-1</sup>y<sup>-1</sup>) and 2) forest carbon return-on-investment (Net Present Value in USD ha<sup>-1</sup>y<sup>-1</sup>) over a 30-year timeframe. It also includes the R script to reproduce these layers and their uncertainties. <em><strong>Investible Forest Carbon</strong>: </em>The investible forest carbon map was produced based on the total volume of CO<sub>2</sub>e associated with the three main carbon pools in the tropics, namely aboveground carbon, belowground carbon and soil organic carbon. This is followed by the application of key Verified Carbon Standard (VCS) criteria including additionality, to determine the magnitude and areas of investible forest carbon across the tropics. <em>Aboveground carbon.</em> A stoichiometric factor of 0.475 was applied to recent spatial data on aboveground carbon biomass to obtain carbon stock based on established carbon accounting methodologies. An uncertainty analyses was also performed to account for potential variability in stoichiometric factor. Subsequently, a conversion factor of 3.67 was applied to the carbon stock layer to obtain the volume of CO<sub>2</sub>e associated with this carbon pool. <em>Belowground carbon</em>. Belowground carbon biomass was firstly derived by applying two allometric equations relating to root to shoot biomass to the most recent spatial dataset on aboveground carbon biomass following established carbon accounting methodologies. The two equations are: Belowground biomass = 0.489\u00d7aboveground biomass^0.89; and Belowground biomass = 0.26\u00d7aboveground biomass A stoichiometric factor of 0.475 was subsequently applied to the estimated belowground carbon biomass to obtain the carbon stock. An uncertainty analyses was then performed to determine the mean, minimum and maximum values for belowground carbon. Following that, a conversion factor of 3.67 was applied to the carbon stock layer to obtain the volume of CO<sub>2</sub>e associated with this carbon pool. <em>Soil Organic Carbon</em>. Organic carbon density of the topsoil layer (0-30 cm) was obtained from the European Soil Data Centre as it represented the best data available for soil organic carbon. A conversion factor of 3.67 was subsequently applied to derive the volume of CO<sub>2</sub>e associated with this carbon pool. <em>Applying VCS criteria</em>. The criterion of additionality is a pre-condition for carbon credits to be certified under the VCS. This implies that only the volume of forest carbon that are under imminent threat of decline or loss if left unprotected by a conservation intervention can be certified under the VCS. The volume of forest carbon under threat of loss was based on the best available data on predicted deforestation rates across the tropics (through to the year 2029), and annualized over predicted 15-year period. The estimated annual deforestation rates was then applied to the total volume of CO<sub>2</sub>e associated with tropical forests as estimated above, deriving the volume of CO<sub>2</sub>e that would be certifiable and thus investible under the VCS. In addition, a conservative 10-year decay estimate was assumed for the estimate of the belowground carbon pool, and lands that will likely not be certifiable for other reasons, including recently deforested areas (i.e. for the period of 2010-2017), a well as human settlements, were excluded. Lastly, the VCS requirement to set aside buffer credits of 20% was accounted for to consider the risk of non-permanence associated with Agriculture, Forestry and Other Land Use (AFOLU) projects. <strong><em>Return</em>-<em>on-Investment</em></strong>. From the investible forest carbon map, the relative profitability of these areas was then modelled to produce a global forest carbon return-on-investment map based on their NPV. The NPV of returns were based on several simplifying assumptions following established values from previous studies. <em>Cost of project establishment</em>. The cost of project establishment was estimated to be at $25 ha<sup>-1</sup>. This was based on a range of costs that are key to the development of a project, including but not limited to project design, governance and planning, enforcement, zonation, land tenure and acquisition, surveying and research. <em>Cost for annual maintenance</em>. The cost for annual maintenance was estimated to be $10 ha<sup>-1</sup>, which included aspects such as in education and communication, monitoring, sustainable livelihoods, marketing, finance and administration. <em>Carbon price</em>. A constant carbon price of $5.8 t<sup>-1</sup>CO\u00ad<sub>2</sub>e for the first five years was applied. This price was based on an average price of carbon for avoided deforestation projects reported recently by Forest Trends\u2019 Ecosystem Marketplace (i.e. for the period 2006 \u2013 2018). Subsequently, a 5% price appreciation was applied annually over a project timeframe of 30 years. <em>Discount rate</em>. We calculated NPV of annual and accumulated profits over 30 years based on a 10% risk-adjusted discount rate. Further details for these datasets and their uncertainties are presented in Koh et. al. For questions or issues on the spatial data layers, please contact Yiwen Zeng (zengyiwen@nus.edu.sg).", "keywords": ["Carbon stocks", "Climate change mitigation", "13. Climate action", "Carbon finance", "15. Life on land"], "contacts": [{"organization": "Koh, Lian Pin, Zeng, Yiwen, Sarira, Tasya Vadya, Siman, Kelly,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4287780"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4287780", "name": "item", "description": "10.5281/zenodo.4287780", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4287780"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-11-25T00:00:00Z"}}, {"id": "10.5281/zenodo.4487144", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:20:46Z", "type": "Dataset", "title": "Eddy Covariance data from ICOS-associated station IT-NIV \u2013 August-November 2019", "description": "RestrictedData stored here refer to Eddy Covariance (EC) data measured in 2019 between August and November at the Alpine CZO (Critical Zone Observatory, hereafter CZO@Nivolet) which was established at the Nivolet Plain (Piani del Nivolet) in the Gran Paradiso National Park (GPNP), located in the western Italian Alps. The EC site (IT-NIV) is an ICOS-associated station. CZO@Nivolet is aimed at investigating the cross-scale interactions between climatic shifts and ecosystem functions multiple scales, involving multidisciplinary studies. The main research questions that we aim to answer are concerning: (a) the effect of bedrock lithology, soil physics and chemisty, topographic hetereogenity, biotic components and meteo-climatic parameters in modulating CO<sub>2</sub> flux in alpine grassland; and (b) what are the controlling factors of organic C and weathering under geologic substrates and different topographic positions. The investigations started in 2017. In 2019, the EC tower was added to deeply study CO<sub>2</sub>, H<sub>2</sub>0, latent and sensible heat exchanges between soil, vegetation, and atmosphere. Carbon dioxide fluxes and environmental variables are recorded during the snow-free season to estimate carbon storage and explore CO<sub>2</sub> fluxes drivers in high-altitude grasslands. Further developments will regard the integration of different techniques (Eddy Covariance, Remote Sensing, Flux chambers) to improve both spatial and temporal extent of carbon fluxes estimates to finally assess grasslands' productivity.", "keywords": ["13. Climate action", "alpine grassland", "15. Life on land", "Gran Paradiso National Park", "Mountain", "EO_Data", "Eddy Covariance", "Net Ecosystem Exchange", "ecosystem-atmosphere carbon exchange"], "contacts": [{"organization": "Vivaldo, Gianna, Raco, Brunella, Baneschi, Ilaria, Giamberini, Maria Silvia,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4487144"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4487144", "name": "item", "description": "10.5281/zenodo.4487144", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4487144"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-20T00:00:00Z"}}, {"id": "10.5281/zenodo.5574882", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:20:48Z", "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.6202061", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:20:49Z", "type": "Dataset", "title": "Bias-corrected EURO-CORDEX RCM simulations for the OPTAIN case studies", "description": "Open AccessBias-corrected EURO-CORDEX RCM simulations are available on a daily timescale for: -period 1981-2099/2100, -6 RCM, -3 scenarios (RCPs 2.6, 4.5 and 8.5), -7 variables (mean, minimum and maximum temperature, precipitation, solar radiation, wind speed at 2 m and relative humidity) and -18 domains and 23 locations within these domains. Bias correction and further downscaling to 0.1\ufffd\ufffd was done using ERA5-Land reanalysis data with non-parametric empirical quantile mapping. Moreover, the interpolation of gridded bias-corrected climate model simulations to the locations was made using universal kriging. <strong>Organization of the data</strong> The name of the files are <em>domain</em>-<em>type</em>.zip, where <em>type</em> is gridded (NetCDF) or point (csv). Each zip file contains multiple files, organized in subfolders: <em>experiment</em>/<em>modelNumber</em>/<em>variable</em>.nc for gridded and <em>experiment</em>/<em>modelNumber</em>/<em>variable-pilotFieldNumber</em>.txt for point data, where <em>experiment </em>is rcp26, rcp45 or rcp85. <em>domain and pilotFieldNumber</em> <strong>domain</strong> <strong>domain </strong><strong>location (min and max. Longitude, min and max latitude</strong><strong>)</strong> <strong>pilotFieldNumber</strong> <strong>pilot field </strong><strong>location (longitude, latitude)</strong> <strong>case study</strong><strong> number</strong> <strong>country</strong> <strong>Name (OPTAIN case study)</strong> 01 50.95 51.45 14.55 15.05 1 DEU Schoeps 02 46.35 47.05 6.55 7.15 2 46.816667 6.95 2 CHE Petite Glane 02_1 46.75 47.25 7.25 7.75 1 46.983333 7.466667 02_34 47.35 47.85 8.35 3 4 47.433333 8.516667 47.683333 8.616667 02_5 46.15 46.65 5.95 6.45 5 46.4 6.233333 03a 46.65 47.15 17.45 17.95 1 2 3 4 46.92649 17.68246 46.9166 17.68976 46.91283 17.69754 46.91283 17.69723 3a HUN Csorsza 03b 46.45 46.95 16.65 17.15 3b HUN Felso Valicka 04 52.35 52.85 18.45 18.95 1 52.597469 18.728617 4 POL Upper Zglowiaczka 05 46.35 46.85 15.35 15.85 5 SVN Pesnica 06 46.45 46.95 16.15 16.65 6 HUN/SVN Kebele/Kobiljski 07 49.85 50.35 4.75 5.25 7 BEL La Wimbe 08 55.15 55.75 23.55 24.05 1 2 55.522057 23.799235 55.42233194 23.82580339 8 LTU Dotnuvele 09 45.45 45.95 9.65 10.15 9 ITA Cherio 10 59.45 59.95 10.75 11.25 1 2 3 4 5 6 7 8 59.71949 10.83576 59.6833306 10.8833298 59.6833306 10.8833298 59.665 10.9475 59.665 10.9475 59.841012 10.903597 59.757631 11.072031 59.539623 10.856447 10 NOR Krogstad 11 46.45 46.95 17.55 18.05 1 2 46.658333 17.75583 46.656944 17.75833 11 HUN Tetves 12 49.35 49.85 14.75 15.25 1 49.616837 15.078266 12 CZE Cechticky 13 55.85 56.35 25.85 26.45 13 LVA Dviete 14 59.75 60.25 17.55 18.05 14 SWE Ingvastaan Lehstaan <em>modelNumber</em> <strong>modelNumber</strong> <strong>Driving Model (GCM)</strong> <strong>Ensemble</strong> <strong>RCM </strong> <strong>End date</strong> 1 EC-EARTH r12i1p1 CCLM4-8-17 31.12.2100 2 EC-EARTH r3i1p1 HIRHAM5 31.12.2100 3 HadGEM2-ES r1i1p1 HIRHAM5 30.12.2099 4 HadGEM2-ES r1i1p1 RACMO22E 30.12.2099 5 HadGEM2-ES r1i1p1 RCA4 30.12.2099 6 MPI-ESM-LR r2i1p1 REMO2009 31.12.2100 <em>variable</em> <strong>variable</strong> <strong>description</strong> <strong>Unit</strong> Tmean Mean temperature \ufffd\ufffdC Tmin Min temperature \ufffd\ufffdC Tmax Max temperature \ufffd\ufffdC prec Precipitation mm solarRad Solar radiation MJ/m2 windSpeed Wind speed at 2m m/s relHum Relative humidity % <strong>Methodolody</strong> Bias correction was done using non-parametric empirical quantile mapping with modified method from R package qmap. Parameters selected were: corrections for each day of the year using a moving windows for a 31 days; 100 quantiles; wet days corrections for precipitation. The reference period is 1981-2010. The interpolation of gridded bias-corrected climate model simulations to the location was made using universal kriging with R packages automap and gstat with (external) variables x, y, x2, y2, x*y, z, where x is latitude, y is longitude, and z is elevation. For Digital Elevation Model Shuttle Radar Topography Mission was used. If there was an error using above mentioned variables, the number of variables was reduced to x, y, x*y, z and if there was still an error to x, y, z. <strong>Funding</strong> This project has received funding from the European Union\ufffd\ufffd\ufffds Horizon 2020 research and innovation programme under grant agreement No 862756.", "keywords": ["CORDEX", "13. Climate action", "RCM", "ERA5-Land", "OPTAIN", "EURO-CORDEX", "bias correction"], "contacts": [{"organization": "Honzak, Luka", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6202061"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6202061", "name": "item", "description": "10.5281/zenodo.6202061", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6202061"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-02-21T00:00:00Z"}}, {"id": "10.5281/zenodo.6320677", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:20:50Z", "type": "Dataset", "title": "MOSSO_SoilPhysics&Chemistry_AllSites", "description": "<strong>Abstract</strong> The dataset provides information about the soil physical and chemical properties at eight permanent LTER sites (named site 1, 2, 3, 6, 7, 8, 9, and 10, according to the LTER site numerations), located between 2686 (site 10) and 2854 m a.s.l. (site 6). Details: Site 1 (coordinates: 45\ufffd\ufffd52'22.43'N, 7\ufffd\ufffd52'25.84'E; elevation: 2840 m a.s.l.), Site 2 (coordinates: 45\ufffd\ufffd52'22.17'N, 7\ufffd\ufffd52'38.07'E; elevation: 2800 m a.s.l.), Site 3 (coordinates: 45\ufffd\ufffd52'13.52'N, 7\ufffd\ufffd52'35.01'E; elevation: 2770 m a.s.l.), Site 6 (coordinates: 45\ufffd\ufffd52'32.21'N, 7\ufffd\ufffd52'31.87'E; elevation: 2854 m a.s.l.), Site 7 (coordinates: 45\ufffd\ufffd52'29.13'N, 7\ufffd\ufffd52'44.71'E; elevation: 2813 m a.s.l.), Site 8 (coordinates: 45\ufffd\ufffd52'27.74'N, 7\ufffd\ufffd52'56.86'E; elevation: 2749 m a.s.l.), Site 9 (coordinates: 45\ufffd\ufffd52'23.80'N, 7\ufffd\ufffd53'3.96'E; elevation: 2720 m a.s.l.), and Site 10 (coordinates: 45\ufffd\ufffd52'21.76'N, 7\ufffd\ufffd53'9.32'E; elevation: 2686 m a.s.l.). The bedrock is primarily micaschists, with some inclusions of amphibolites and calcschists. The vegetation of the sites is included in the \ufffd\ufffd\ufffdSiliceous alpine and boreal grasslands\ufffd\ufffd\ufffd (habitat 6150, according to the EU Habitat Directive). Each study site consists of paired plots for soil and vegetation survey. Soils belongs mainly to Regosol (sites 1 and 8), Leptosol (site 2), Cambisol (sites 6 and 7), and Umbrisol (sites 3, 9, and 10) great groups (IUSS Working Group WRB, 2015, E-ISBN 978-92-5- 108370-3.). On soil samples the following analyses are performed: pH, Cation Exchange Capacity (CEC), Total Organic Carbon (TOC), Total Nitrogen (TN), available phosphorous (Olsen P), and particle-size. <strong>Method Description</strong> The field description of the soil profile is done according to FAO (2006, ISBN 92-5-105521-1) while soil classification is done according to WRB classification system IUSS Working Group WRB, (2015 E-ISBN 978-92-5- 108370-3.). Soil material is collected from every horizon in the soil pit. The soil samples are air-dried, sieved to 2 mm, and analysed following the standard methods reported by Van Reeuwijk (2002, ISBN: 90-6672-044-1.). pH is measured in water (soil:water=1:2.5). Particle-size analysis is performed by the pipette method after organic matter destruction with H2O2 followed by dispersion with Na-hexametaphosphate. CEC is determined in a BaCl2 solution buffered at pH 8.1. TOC and TN are analysed by dry combustion with a CN elemental analyzer (CE Instruments NA2100, Rodano, Italy). Available P (Olsen P) is extracted with NaHCO3 and determined colorimetrically by the ascorbic acid molybdate blue method (Murphy and Riley 1962, https://doi.org/10.1016/S0003-2670(00)88444-5). <strong>Instrumentation</strong> WTW-InoLab 7110 pH-meter equipped with Hamilton GelGlass electrode (pH) CN elemental analyzer CE Instruments NA2100, Rodano, Italy (TOC and TN) Spectrophotometer U-2000, Hitachi, Tokyo, Japan (Olsen P)", "keywords": ["2. Zero hunger", "15. Life on land"], "contacts": [{"organization": "Freppaz, Michele, Colombo, Nicola,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6320677"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6320677", "name": "item", "description": "10.5281/zenodo.6320677", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6320677"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-03-01T00:00:00Z"}}, {"id": "10.5281/zenodo.7307470", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:20:57Z", "type": "Dataset", "title": "Soil biological, chemical and physical parameters and herbage yield in a field experiment with organic and inorganic fertilizers on peat grassland in the Netherlands", "description": "Open AccessTo evaluate the performance of organic and inorganic fertilizers for regeneration of ecosystem services in peat grasslands with biodiversity goals, we carried out a field experiment in the western peat district in the Netherlands. The fertilizers tested represent the current practice and potential alternatives for regenerative grassland management on drained peat. <strong>Experimental setup</strong> The field experiment (2013 \u2013 2015) was conducted on a permanent grassland on peat soil (Terric Histosol; SOM 56 g 100 g<sup>\u22121</sup> and pH<sub>KCl</sub> of 4.5 in 0-10 cm) at the experimental dairy farm at Zegveld (the Netherlands). In March 2013, a randomized block experiment (six blocks) was laid out with six fertilizer treatments and a control treatment (no fertilizer: \u201cContr\u201d). The fertilizer used were: conventional dairy cattle slurry manure (\u201cSlurry\u201d), mature compost of kitchen and garden waste (\u201cComp\u201d), dairy cattle farmyard manure (\u201cFYM\u201d), solid fraction of the cattle slurry manure (\u201cSFrac\u201d, obtained by pressurized filtration), inorganic N fertilizer (\u201cIF\u201d; calcium ammonium nitrate, 27% N) and a combination of inorganic N fertilizer and sawdust (\u201cIF+SD\u201d). Plot size was 4 \u00d7 10 m; for the Slurry treatment plots were 5.2 \u00d7 10 m. Slurry was applied by slit injection, the other fertilizers were applied by hand. Target application rate was 120 kg total N ha<sup>\u22121</sup> yr<sup>\u22121</sup>, divided in two applications per year (February/March and May). This is relatively low for conventional grasslands but usual for grasslands with biodiversity goals (Kleijn et al., 2004). The amount of C<sub>total</sub> applied in Comp was taken for the rate of sawdust to be applied. All plots were fertilized with 200 kg K<sub>2</sub>O ha<sup>\u22121</sup> yr<sup>\u22121</sup> (applications in March and May) (Commissie Bemesting Grasland en Voedergewassen, 2019). Fertilizer application quantities and organic matter and nutrient inputs are provided in Fertilizer_intput.csv (dataset). The grassland had an history of conventional management with mainly cutting, winter grazing with sheep and a normal fertilization regime with both slurry manure and inorganic fertilizer. The normal cutting and grazing regime was continued in the first two years of the experiment; during 2015, the monitoring year, the plots were not grazed and only cut for herbage measurements. <strong>Measurements</strong> From April to October 2015, soil and aboveground measurements were carried out. Most soil parameters were measured in October. Earthworms and insect larvae are an important food source for meadow birds during the pre-breeding period in spring (Galbraith, 1989) and were therefore sampled in April. Soil moisture and penetration resistance were measured both in April and October. <em>Soil biological parameters</em> Earthworms and insect larvae were sampled in the top soil layer in two soil cubes (20 \u00d7 20 \u00d7 20 cm) per plot. Earthworms were hand-sorted, counted, weighed and fixed in alcohol prior to identification. Both adults and juveniles were identified to species (Sims and Gerard, 1985; St\u00f6p-Bowitz, 1969) and classified into functional groups (Bouch\u00e9, 1977). Crane flies (Tipulidae; leatherjackets) or click beetles (Elateridae; wireworms) larvae were counted. Phospholipid fatty acids (PLFA) were measured in October. PLFA were extracted from 4 g of fresh soil (Paloj\u00e4rvi, 2006), and analyzed by gas chromatography (Hewlett-Packard, USA). PLFA i15:0, a15:0, 15:0, i16:0, 16:1\u03c99, i17:0, a17:0, cy17:0, 18:1\u03c97 and cy19:0 were chosen to represent bacteria and PLFA 18:2\u03c96 was used as a marker of saprotrophic fungi (Hedlund, 2002). The neutral lipid fatty acid (NLFA) 16:1\u03c95 occurs in storage lipids of arbuscular mycorrhizal fungi (AMF) and was used as marker of AMF (Vestberg et al., 2012). PLFA i15:0, a15:0, i16:0, i17:0 and a17:0 were used as a measure of Gram-positive bacteria, and cy17:0 and cy19:0 for Gram-negative bacteria. PLFA 10Me16:0, 10Me17:0 and 10Me18:0 represented actinomycetes. <em>Soil chemical parameters</em> A soil sample from the 0\u221210 cm layer (c. 50 randomly taken soil cores) per experimental plot was collected in October (auger diameter 2.3 cm; Eijkelkamp grass plot sampler, Giesbeek, the Netherlands), was sieved (1 cm mesh size) and homogenized. One sub-sample was taken for analysis of hot water extractable carbon (HWC) according to Ghani et al. (2003) and one for chemical analysis. Prior to analysis of soil acidity (pH<sub>KCl</sub>), soil organic matter (SOM), total carbon (C<sub>total</sub>), total nitrogen (N<sub>total</sub>), total phosphorus (P<sub>total</sub>) and ammonium-lactate extractable P (P<sub>AL</sub>) by Eurofins Agro (Wageningen, the Netherlands), the sub sample was dried at 40\u00b0C. Soil pH<sub>KCl</sub> was measured according to NEN-ISO 10390 2005. SOM was determined by loss-on-ignition (NEN 5754 2005). C<sub>total</sub> was measured by incineration at 1150\u00b0C, and determination of the CO<sub>2</sub> produced by an infrared detector (LECO Corporation, St. Joseph, Mich., USA). For N<sub>total</sub>, evolved gasses after incineration were reduced to N<sub>2</sub> and measured with a thermal-conductivity detector (LECO Corporation, St. Joseph, Mich., USA). P<sub>total</sub> was analysed with Fleishmann acid (Houba et al., 1997). P<sub>AL</sub> is used to assess the P supply capacity of grassland soils (Reijneveld et al., 2014) and was determined according to Egn\u00e9r et al. (1960) (NEN 5793). <em>Soil physical parameters</em> Soil moisture was determined in April and October in a homogenized 0\u221210 cm soil sample after drying at 105\u00b0C for 24 hrs. Moisture content was expressed as percentage of fresh soil weight. Penetration resistance was measured (April and October) with a penetrologger (Eijkelkamp, Giesbeek, the Netherlands; cone of 2.0 cm<sup>2</sup> penetration surface and 60\u00b0 apex angle. Penetration resistance was expressed as an average of 7 penetrations per plot and per soil layer of 0\u221210, 10\u221220, and 20\u221230 cm. Soil structure and rooting density were assessed in October in the 0\u221210 cm and 10\u221225 cm layers. The percentage of crumbs, sub-angular blocky elements and angular blocky elements was estimated by one experienced person as described by Peerlkamp (1959) and Shepherd (2000), Root density was estimated by scoring visible roots (score 1\u201310; 1 for no roots and 10 for above average). Water infiltration rate was measured in October at three spots per experimental plot in 5 of the 6 blocks (35 plots). A PVC pipe (15 cm high, 15 cm diameter) was pushed into the soil to a depth of 10 cm. 500 ml water was poured into each pipe and the infiltration time was recorded. If the infiltration time exceeded 15 min, the remaining water volume was estimated to calculate the infiltration rate (mm min<sup>\u22121</sup>). <em>Grass yield and botanical composition</em> Grass dry matter (DM) and N yield were determined during 2015 with a Haldrup plot harvester (J. Haldrup a/s, L\u00f8gst\u00f8r, Denmark). The four harvest dates were May 15, June 29, August 19 and September 30. Fresh biomass, DM content (70\u00b0C for 24 hrs) and total N content (Kjeldahl) were determined for each harvest. Herbage DM yield (Mg DM ha<sup>\u22121</sup>) and herbage N yield (kg N ha<sup>\u22121</sup>) were calculated. Apparent N recovery (ANR; kg N.kg N<sup>\u22121</sup>) was calculated as (N yield<sub>(fertilized)</sub> \u2013 N yield<sub>(non-fertilized)</sub>)/(N fertilization rate) (Vellinga and Andr\u00e9, 1999). In June 2015, botanical composition was measured by visually estimating the relative soil cover of the sward and the proportion of each species therein (Sikkema, 1997). <strong>Data files</strong> <em><strong>Data_soil_grass.csv</strong></em> <em>Content:</em> Dataset with soil biological (earthworms, microbial PLFA), soil chemical, soil physical parameters, herbage dry matter and N yields, and botanical parameters. <em>Column names and units:</em> plot: Experimental plot number (1-42) treatment: Treatment code (see text) block: Block number (1-6) EW_species_number: Earthworm - number of species EW_totalnumber: Earthworm - total number per m2 EW_epigeic: Earthworm - number of epigeic adults and juveniles per m2 EW_endogeic: Earthworm - number of endogeic adults and juveniles per m2 EW_adults: Earthworm - number of adults per m2 EW_juveniles: Earthworm - number of juveniles per m2 EW_adult_epigeic: Earthworm - number of epigeic adults per m2 EW_adult_endogeic: Earthworm - number of endogeic adults per m2 EW_juven_epigeic: Earthworm - number of epigeic juveniles per m2 EW_juven_endogeic: Earthworm - number of endogeic juveniles per m2 EW_L_rubellus: Earthworm - number of L. rubellus adults and juveniles per m2 EW_A_chlorotica: Earthworm - number of A. chlorotica adults and juveniles per m2 EW_A_caliginosa: Earthworm - number of A. caliginosa adults and juveniles per m2 EW_O_lacteum: Earthworm - number of O. lacteum adults and juveniles per m2 EW_A_rosea: Earthworm - number of A. rosea adults and juveniles per m2 EW_O_cyaenum: Earthworm - number of O. cyaneum adults and juveniles per m2 EW_L_castaneus: Earthworm - number of L. castaneus adults and juveniles per m2 EW_D_rubida: Earthworm - number of D. rubida adults and juveniles per m2 EW_adult_L_rubellus: Earthworm - number of L. rubellus adults per m2 EW_adult_A_chlorotica: Earthworm - number of A. chlorotica adults per m2 EW_adult_A_caliginosa: Earthworm - number of A. caliginosa adults per m2 EW_adult_O_lacteum: Earthworm - number of O. lacteum adults per m2 EW_adult_A_rosea: Earthworm - number of A. rosea adults per m2 EW_adult_O_cyaenum: Earthworm - number of O. cyaneum adults per m2 EW_adult_L_castaneus: Earthworm - number of L. castaneus adults per m2 EW_adult_D_rubida: Earthworm - number of D. rubida adults per m2 EW_juven_L_rubellus: Earthworm - number of L. rubellus juveniles per m2 EW_juven_A_chlorotica: Earthworm - number of A. chlorotica juveniles per m2 EW_juven_A_caliginosa: Earthworm - number of A. caliginosa juveniles per m2 EW_non_determined: Earthworm - number of non determined individuals per m2 EW_total_biomass: Earthworm - total fresh biomass per m2 Leatherjackets: number of leatherjackets per m2 Wireworms: number of wireworms per m2 TOTmicrPLFA: total microbial PLFA in nmol.g-1 dry soil bactPLFA: bacterial PLFA in nmol.g-1 dry soil saprofungPLFA: saprotrophic fungal PLFA in nmol.g-1 dry soil Fung_bactPLAF_ratio: ratio of fungal to bacterial PLFA GramPLUSplfa: gram positive PLFA in nmol.g-1 dry soil GramMINplfa: gram negative PLFA in nmol.g-1 dry soil ratioGram_PLUS_MIN: ratio of gram positive to gram negative PLFA AMFsporNLFA: AMF spores NLFA in nmol.g-1 dry soil ActinomPLFA: Actinomycetes PLFA in nmol.g-1 dry soil ShannonPLFA: PLFA shannon diversity index SOM: soil organic matter in g.100 g-1 dry soil Ctotal: total C in g.100 g-1 dry soil HWC: hot water extractable C in \u03bcg.100 g-1 dry soil Ntotal: total N in g.100 g-1 dry soil Ptotal: total P2O5 in mg.100 g-1 dry soil P_AL: total P-AL in mg.100 g-1 dry soil pH_KCl: pH-KCl CN_ratio: C:N ratio C_SOM: C:SOM ratio Soilmoisture_April: soil moisture content in April in g.100g-1 fresh soil Penetrationresistance_April_cm010: penetration resistance in April in 10-20 cm in Newton Penetrationresistance_April_cm1020: penetration resistance in April in 20-30 cm in Newton Penetrationresistance_April_cm2030: penetration resistance in April in 0-10 cm in Newton Soilmoisture_October: soil moisture content in October in g.100g-1 fresh soil Penetrationresistance_October_cm010: penetration resistance in October in 10-20 cm in Newton Penetrationresistance_October_cm1020: penetration resistance in October in 20-30 cm in Newton Penetrationresistance_October_cm2030: penetration resistance in October in 0-10 cm in Newton crumb_struct_cm010: percentage of crumb elements in 0-10 cm round_struct_cm011: percentage of sub-angular elements in 0-10 cm rootdensity_cm010: score (1-10) of root density in 0-10 cm crumb_struct_cm1025: percentage of crumb elements in 10-25 cm round_struct_cm1025: percentage of sub-angular elements in 10-25 cm sharp_struct_cm1025: percentage of angular elements in 10-25 cm rootdensity_cm1025: score (1-10) of root density in 10-25 cm water_infiltration: water infiltration rate in mm per minute DM_yield_year: total herbage dry matter yield in kg.ha-1 per year DM_yield_H1: herbage dry matter yield of harvest 1 in kg.ha-1 DM_yield_H2: herbage dry matter yield of harvest 2 in kg.ha-1 DM_yield_H3: herbage dry matter yield of harvest 3 in kg.ha-1 DM_yield_H4: herbage dry matter yield of harvest 4 in kg.ha-1 N_yield_year: total herbage N yield in kg.ha-1 per year N_yield_H1: herbage N yield of harvest 1 in kg.ha-1 N_yield_H2: herbage N yield of harvest 2 in kg.ha-1 N_yield_H3: herbage N yield of harvest 3 in kg.ha-1 N_yield_H4: herbage N yield of harvest 4 in kg.ha-1 DMperc_yield_year: herbage dry matter content (per year; weighed average over the 4 harvests) in g.100g-1 fresh weight DMperc_yield_H1: herbage dry matter content of harvest 1 in g.100g-1 fresh weight DMperc_yield_H2: herbage dry matter content of harvest 2 in g.100g-1 fresh weight DMperc_yield_H3: herbage dry matter content of harvest 3 in g.100g-1 fresh weight DMperc_yield_H4: herbage dry matter content of harvest 4 in g.100g-1 fresh weight Ncontent_yield_year: herbage N content (per year; weighed average over the 4 harvests) in g.kg-1 dry matter Ncontent_yield_H1: herbage N content of harvest 1 in g.kg-1 dry matter Ncontent_yield_H2: herbage N content of harvest 2 in g.kg-1 dry matter Ncontent_yield_H3: herbage N content of harvest 3 in g.kg-1 dry matter Ncontent_yield_H4: herbage N content of harvest 4 in g.kg-1 dry matter fresh_yield_H1: herbvage fresh yield of harvest 1 in Mg.ha-1 ANR: apparent N recovery in kg N.kg N-1 productive_grasses: cover percentage of L. perenne and P trivialis monocotyledons: cover percentage of monocotyledons dicotyledons: cover percentage of dicotyledons plant_species: number of plant species monocot_species: number of monocotyledon species dicot_species: number of dicotyledon species Lolium_perenne: plant cover % Poa_trivialis: plant cover % Phleum_pratense: plant cover % Elytrigia_repens: plant cover % Poa_annua: plant cover % Agrostis_stolonifera: plant cover % Holcus_lanatus: plant cover % Alopecurus_pratensis: plant cover % Alopecurus_geniculatus: plant cover % Trifolium_repens: plant cover % Taraxacum_officinale: plant cover % Ranunculus_arvensis: plant cover % Rumex_obtusifolius: plant cover % Rumex_crispus: plant cover % Ranunculus_acris: plant cover % Stellaria_media: plant cover % Cardamine_pratensis: plant cover % Bellis_perennis: plant cover % Rumex_acetosa: plant cover % Ranunculus_sceleratus: plant cover % Polygonum_aviculare: plant cover % Capsella_bursa-pastoris: plant cover % Glechoma_hederacea: plant cover % Geranium_molle: plant cover % <em><strong>Fertilizer_input.csv</strong></em> <em>Content:</em> Application quantities of fertilizers and ash, organic matter, C and mineral inputs, and fertilizer C:N ratio. Total N input is the sum of mineral N (Nmin) and organic N (Norg). Average values per hectare and per year over the years 2013\u22122015. <em>Column names and units:</em> Treatment: Treatment code (see text) Fertilizer_fresh: Applied fertilizer in Mg.ha<sup>-1</sup> per year (fresh weight) Fertilizer_DM: Applied fertilizer in Mg.ha<sup>-1</sup> per year (dry matter weight); for IF+SD this is the sum of 2.72 Mg sawdust + 0.45 Mg N fertilizer Ash: Mineral fraction in kg.ha<sup>-1</sup> per year OM: Organic matter in kg.ha<sup>-1</sup> per year C: Total C in kg.ha<sup>-1</sup> per year Nmin: Mineral N in kg.ha<sup>-1</sup> per year Norg: Organic N in kg.ha<sup>-1</sup> per year P2O5: kg.ha<sup>-1</sup> per year C_N_ratio: C:N ratio", "keywords": ["2. Zero hunger", "herbage production", "manure", "PLFA", "Life Science", "earthworms", "soil quality", "15. Life on land", "regenerative farming", "6. Clean water"], "contacts": [{"organization": "Deru, Joachim, Bloem, Jaap, De Goede, Ron, Brussaard, Lijbert, Van Eekeren, Nick,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7307470"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7307470", "name": "item", "description": "10.5281/zenodo.7307470", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7307470"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.7353722", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:20:57Z", "type": "Software", "title": "Algorithm to harmonize soil particle size data to the FAO/USDA system", "description": "Different countries often measure and express soil particle-size distribution using different delineations between the main textural components, clay, silt and sand content. In order to harmonize such diverse data so that a uniform textural classification system can be used, interpolation of the data is necessary. Here we provide an example algorithm written in MATLAB that helps harmonize such data country-by-country to the FAO-USDA particle-size classification system that defines clay content as the mass of solids (individual particles) that are &lt;0.002 mm, silt as the mass of solids in the 0.002 \u2013 0.05 mm size range, and sand content as the mass of solids in the 0.05 \u2013 2 mm size range (USDA 1951; FAO 1990). This system considers particles sized above 2 mm as gravel or stones. The algorithm uses k-nearest neighbor type pattern recognition in a non-spatial context algorithm to achieve this goal (Nemes et al. 1999; Nemes et al. 2006). Note: The algorithm uses a pre-existing external reference data set to compare the current data with. That data set cannot be provided with the algorithm due to prior agreements about the use and availability of those data, but its description is provided on pages 125-127 in the report by Weynants et al. (2013), and the authors herein offer their collaboration with a future user in order to take advantage of this algorithm. <strong>References</strong> FAO, Food, and Agricultural Organization. 1990. <em>Guidelines for Soil Profile Description.</em> 3rd ed. Rome: FAO. Nemes, A., J. H. M. W\u00f6sten, A. Lilly, and JH Oude Voshaar. 1999. \u201cEvaluation of different procedures to interpolate particle-size distributions to achieve compatibility within soil databases.\u201d <em>Geoderma</em> 90: 187\u2013202. http://www.sciencedirect.com/science/article/pii/S0016706199000142. Nemes, A., W. J. Rawls, and Y. A. Pachepsky. 2006. \u201cUse of the Nonparametric Nearest Neighbor Approach to Estimate Soil Hydraulic Properties.\u201d <em>Soil Science Society of America Journal</em> 70 (2): 327\u201336. https://doi.org/10.2136/SSSAJ2005.0128. USDA, United States Department of Agriculture. 1951. <em>Soil survey manual, U.S. Dept. Agriculture Handbook No. 18.</em> Washington, DC. Weynants, M\u00e9lanie, Luca Montanarella, Gergely T\u00f3th, Arnold Arnoldussen, Mar\u00eda Anaya Romero, George Bilas, Trond Borresen, et al. 2013. \u201cEuropean HYdropedological Data Inventory (EU-HYDI).\u201d Luxembourg: European Commission EUR 26053 \u2013 Joint Research Centre \u2013 Institute for Environment; Sustainability; EUR \u2013 Scientific; Technical Research series \u2013 ISSN 1831-9424. https://doi.org/10.2788/5936.", "keywords": ["2. Zero hunger", "15. Life on land", "6. Clean water"], "contacts": [{"organization": "Nemes, Attila", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7353722"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7353722", "name": "item", "description": "10.5281/zenodo.7353722", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7353722"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-11-24T00:00:00Z"}}, {"id": "10.5281/zenodo.7353721", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:20:57Z", "type": "Software", "title": "Algorithm to harmonize soil particle size data to the FAO/USDA system", "description": "Different countries often measure and express soil particle-size distribution using different delineations between the main textural components, clay, silt and sand content. In order to harmonize such diverse data so that a uniform textural classification system can be used, interpolation of the data is necessary. Here we provide an example algorithm written in MATLAB that helps harmonize such data country-by-country to the FAO-USDA particle-size classification system that defines clay content as the mass of solids (individual particles) that are &lt;0.002 mm, silt as the mass of solids in the 0.002 \u2013 0.05 mm size range, and sand content as the mass of solids in the 0.05 \u2013 2 mm size range (USDA 1951; FAO 1990). This system considers particles sized above 2 mm as gravel or stones. The algorithm uses k-nearest neighbor type pattern recognition in a non-spatial context algorithm to achieve this goal (Nemes et al. 1999; Nemes et al. 2006). Note: The algorithm uses a pre-existing external reference data set to compare the current data with. That data set cannot be provided with the algorithm due to prior agreements about the use and availability of those data, but its description is provided on pages 125-127 in the report by Weynants et al. (2013), and the authors herein offer their collaboration with a future user in order to take advantage of this algorithm. <strong>References</strong> FAO, Food, and Agricultural Organization. 1990. <em>Guidelines for Soil Profile Description.</em> 3rd ed. Rome: FAO. Nemes, A., J. H. M. W\u00f6sten, A. Lilly, and JH Oude Voshaar. 1999. \u201cEvaluation of different procedures to interpolate particle-size distributions to achieve compatibility within soil databases.\u201d <em>Geoderma</em> 90: 187\u2013202. http://www.sciencedirect.com/science/article/pii/S0016706199000142. Nemes, A., W. J. Rawls, and Y. A. Pachepsky. 2006. \u201cUse of the Nonparametric Nearest Neighbor Approach to Estimate Soil Hydraulic Properties.\u201d <em>Soil Science Society of America Journal</em> 70 (2): 327\u201336. https://doi.org/10.2136/SSSAJ2005.0128. USDA, United States Department of Agriculture. 1951. <em>Soil survey manual, U.S. Dept. Agriculture Handbook No. 18.</em> Washington, DC. Weynants, M\u00e9lanie, Luca Montanarella, Gergely T\u00f3th, Arnold Arnoldussen, Mar\u00eda Anaya Romero, George Bilas, Trond Borresen, et al. 2013. \u201cEuropean HYdropedological Data Inventory (EU-HYDI).\u201d Luxembourg: European Commission EUR 26053 \u2013 Joint Research Centre \u2013 Institute for Environment; Sustainability; EUR \u2013 Scientific; Technical Research series \u2013 ISSN 1831-9424. https://doi.org/10.2788/5936.", "keywords": ["2. Zero hunger", "15. Life on land", "6. Clean water"], "contacts": [{"organization": "Nemes, Attila", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7353721"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7353721", "name": "item", "description": "10.5281/zenodo.7353721", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7353721"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-11-24T00:00:00Z"}}, {"id": "10.5281/zenodo.7656722", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:20:58Z", "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.7695641", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:20:59Z", "type": "Report", "title": "Soil and land management ontology reference document", "description": "The Soil Mission Support (SMS) project supports the European Commission and the Mission Board of the Horizon Europe<br> Mission in the area of Soil Health and Food in delivering its objectives and related targets. It is assumed that the<br> Soil Mission and its related objectives and specific targets can only be achieved through healthy soils and for that,<br> stakeholder engagement is needed. Healthy soils are defined as soils that are in good chemical, biological and physical<br> condition and thus are able to continuously provide as many ecosystem services as possible (EC, 2021a). Stakeholders<br> are defined as those who are affected in their interest or concern by changes in soil and land management (Brils et al.,<br> 2022).<br> With multi-stakeholder processes, language and use of language is very important. The capability to understand each<br> other is critical. Communication difficulties originate to a large extent from the \u2018jargon\u2019 used in the different communities.<br> A common language facilitates \u2018learning together\u2019 which helps to build trust, develop a common view on the issues<br> at stake, resolve conflicts and arrive at joint solutions that are technically sound and that can be implemented in<br> practice. Ontology defines a common vocabulary for those who, for example, need to converse about a common issue<br> or share information in a specific domain.<br> In first instance the shared domain of discourse was defined and then at different levels of hierarchy:<br> \u00b7 Primary objects of relevance for the domain of discourse were selected;<br> \u00b7 The inter-relational links between these objects was conceptualized (conceptual model); and<br> \u00b7 These objects were defined in a representational vocabulary (a common language).<br> The domain of discourse covers soil and land management aimed to achieve the first six (of the eight) Soil Mission<br> objectives, which are: 1. reduce desertification, 2. conserve soil organic carbon stocks, 3. stop soil sealing and increase<br> re-use of urban soils, 4. reduce soil pollution and enhance restoration, 5. prevent erosion, and 6. improve soil structure<br> to enhance soil biodiversity.<br> The first level of hierarchy covers soil and land and its use. At this level the following objects have been selected, interrelated<br> in a conceptual model (i.e. visual of soil and land-use) and defined in a common language: soil, land, landuse<br> and land-use types (including: urban, industrial, agriculture, forest, nature and protected land).<br> The second level of hierarchy covers soil management. At his level the following objects have been selected, interrelated<br> in a conceptual soil management model and defined in a common language: soil management (including: soil<br> management strategy, measures, program of measures), soil ecosystems (including: ecosystem services, pressures,<br> healthy soil ecosystems), users (stakeholders) and information.<br> Lastly, the third level of hierarchy covers the achievement of the first six Soil Mission objectives. At this level the<br> most relevant objects related to each of these objectives are selected and interrelated to their position in the DPSIR<br> (Drivers-Pressures-State-Impact-Response) framework which is at this 3rd level superimposed on the soil management<br> model as used for level 2.<br> The remaining two Soil Mission objectives, i.e. 7. reduce the EU global footprint on soils and 8. improve soil literacy in<br> society, do not directly relate to the actual management of soil and land. However, also for these mission objectives<br> some important objects have been selected and defined in a common language.<br> Experts in the SMS project \u2013 jointly covering the fields of expertise related to all the 8 Soil Mission objectives \u2013 developed<br> this ontology. This ontology should now be used in soil policy and management practice, such as Living Labs. In<br> such settings, the ontology can be improved through interaction with stakeholders from different backgrounds, further<br> increasing its value.<br> The key-recommendations are:<br> \u00b7 use this ontology in soil policy and management practice (e.g. Living Labs)<br> \u00b7 soil policy makers and managers should promote its use in such practice<br> \u00b7 use the feedback from stakeholders to further improve the ontology<br> In support of the dissemination of this document a policy brief is prepared and attached as annex in this document.<br> Both documents are made publicly available via de SMS website: https://www.soilmissionsupport.eu/outputs", "keywords": ["2. Zero hunger", "13. Climate action", "11. Sustainability", "15. Life on land", "12. Responsible consumption"], "contacts": [{"organization": "Nougues, Laura, Brils, Jos,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7695641"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7695641", "name": "item", "description": "10.5281/zenodo.7695641", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7695641"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-02-04T00:00:00Z"}}, {"id": "10.5281/zenodo.7856487", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:21:00Z", "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": "2026-09-20T16:21:01Z", "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": "2026-09-20T16:21:02Z", "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": "10.5281/zenodo.8320433", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:21:04Z", "type": "Dataset", "title": "Carbon storage and carbon-equivalent albedo impact for US forests, by age and forest type", "description": "These tables document estimates of carbon storage (Mg/ha +/- Standard Error) and carbon-equivalent albedo impacts (same units) of US forests by age and forest type (Healey et al., in review). Carbon estimates are derived from field measurements made by the USDA Forest Service on approximately 125,000 forested field plots (Domke et al., 2022). Soil organic carbon is omitted from these estimates, but all other above- and below-ground pools are included. Albedo impacts (time-dependent emissions equivalent, TDEE; Bright et al., 2016) were developed by applying atmospheric kernels (Bright and O'Halloran) to a new Landsat blue sky albedo product for the Landsat archive (Erb et al., 2022), as described by Healey et al. (in review). Standard error is supplied for each age/forest type bin for carbon storage, but upper and lower standard error bounds are specified for TDEE because log transformation creates an asymmetrical uncertainty envelope. Bright, Bogren, Bernier, Astrup, (2016). Carbon-equivalent metrics for albedo changes in land management contexts: Relevance of the time dimension. <em>Ecol. Appl.</em> 26, 1868\u20131880 Bright, R. M., &amp; O'Halloran, T. L. (2019). Developing a monthly radiative kernel for surface albedo change from satellite climatologies of Earth's shortwave radiation budget: CACK v1. 0. <em>Geoscientific Model Development, </em>12(9), 3975-3990. Domke, Walters, Nowak, Greenfield, Smith, Nichols, Ogle, Coulston, Wirth (2022). Greenhouse Gas Emissions and Removals From Forest Land, Woodlands, Urban Trees, and Harvested Wood Products in the United States, 1990\u20132020. (US Dept. Ag. For. Service, Madison, WI; https://doi.org/10.2737/FS-RU-382). Erb, Li, Sun, Paynter, Wang, &amp; Schaaf, (2022). Evaluation of the Landsat-8 Albedo Product across the Circumpolar Domain. <em>Remote Sensing</em>, <em>14</em>(21), 5320. Healey, Yang, Erb, Bright, Domke, Frescino, Schaaf, (in review) New satellite observations expose albedo dynamics offsetting half of carbon storage benefits in US forests.", "keywords": ["climate change", "forest carbon", "13. Climate action", "15. Life on land", "Landsat", "albedo"], "contacts": [{"organization": "Healey, Sean, Yang, Zhiqiang,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8320433"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8320433", "name": "item", "description": "10.5281/zenodo.8320433", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8320433"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-09-06T00:00:00Z"}}, {"id": "10.5846/stxb201107151055", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:21:22Z", "type": "Journal Article", "created": "2012-04-25", "title": "Effect Of Fertilization On Ammonia Volatilization From Paddy Fields In Chao Lake Basin", "description": "Ammonia(NH3) is formed constantly in soils because of the biological degradation of organic compounds and NH+4 yielding mineral and organic fertilizers.NH3 volatilization is one of the major losses of applied nitrogen fertilizers,which is affected by the local climate conditions(e.g.,temperature,sun illumination,and wind speed),soil moisture content,urease activity,fertilization time,and ammonium concentration in surface water of the paddy fields.In this study,A situ experiment combined with venting method was conducted to study the effects of different fertilization patterns on NH3 volatilization in Chao Lake Basin during the rice season in 2010.In the experiment,there were six treatments with the control,the conventional fertilization,the optimization fertilization(according to local test of fertilization recipe),the reduction by 30% of nitrogen fertilizer,the reduction by 50% of phosphorus fertilizer and the straw return.The results showed that the NH3 volatilization loss from paddy fields was affected by fertilization and ammonium nitrogen(NH+4-N)concentration in surface water of the paddy fields.The NH3 volatilization among different treatments lasted for one week and the volatilization peak occurred within one to three days after fertilization,then declined quickly.When the rate of phosphorus fertilizer remained the same(e.g.,he treatments of the optimization fertilization and the reduction by 30% of nitrogen fertilizer),The concentration of NH3 volatilization loss enhanced as the nitrogen application rate increased with the same rate of phosphorus fertilizer.The amount of NH3 volatilization ranged from 7.22 to 14.20 kg/hm2,Which accounted for 4.59% to 6.64% of the total applied N for the whole period of rice growth.Compared with the conventional fertilization,the NH3 volatilization loss was reduced by 1% to 2% respectively with the treatments of the reduction by 30% of nitrogen fertilizer\u3001by 50% of phosphorus fertilizer and the optimization fertilization,while the NH3 volatilization loss was reduced by 6.98 kg/hm2 with the treatment of the straw return,which total ammonia volatilize quantity was 54% of the conventional fertilization.The highest NH3 volatilization loss occurred at basal dressing,which amount for 60% of the total NH3 loss,while the lowest loss did at panicle stage.The concentration of NH+4-N in surface water reached the highest within one to two days after fertilization.The positive correlation was observed between NH3 loss and NH+4-N concentration in the surface water of the basal dressing and the first top-dressing.From the point of the water improvement of Chao Lake basin,the period within a week after the fertilizer application was a critical time to control N loss and leaching from flooded paddy fields.NH3 loss would be decreased when N and P ferlization was rational.It is necessary to apply rational and balanced fertilization for N and P.Combined with economic benefit and environmental risk,the straw return is recommended for rice production in Chao Lake basin.", "keywords": ["2. Zero hunger", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "6. Clean water", "0105 earth and related environmental sciences"], "contacts": [{"organization": "\u6731\u5c0f\u7ea2 Zhu Xiaohong, \u8096\u5723\u8f89 Xiao Shenghui, \u5f90\u5b8f\u519b Xu Hongjun, \u9a6c\u53cb\u534e Ma Youhua, \u9a6c\u4e2d\u6587 Ma Zhongwen, \u6c6a\u4e3d\u5a77 Wang Liting, \u5f20\u4e3d\u5a1f Zhang Lijuan,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5846/stxb201107151055"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Acta%20Ecologica%20Sinica", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5846/stxb201107151055", "name": "item", "description": "10.5846/stxb201107151055", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5846/stxb201107151055"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2012-01-01T00:00:00Z"}}, {"id": "10.6084/m9.figshare.19498606", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:21:26Z", "type": "Report", "created": "2022-04-02", "title": "Additional file 1 of Rhizosheath\u2013root system changes exopolysaccharide content but stabilizes bacterial community across contrasting seasons in a desert environment", "description": "Additional file 1. Result S1. Analysis of rhizosheath sand composition. Result S2. Analysis of cultivable bacteria. Table S1. Climatic conditions throughout the year in Ksar Ghilane, Sahara Desert (Tunisia); data elaborated from https://www.worldweatheronline.com ; data as reported as monthly average measurement from January 2019 to now. Table S2. List of FISH probes used and conditions applied; probe sequence (5\u2019-3\u2019), attached fluorochrome (fluor.), bacterial target group, percentage of formaldehyde (FA) used during treatment, and references are also reported. Table S3. Number of KTUs and sequences used for each sample. RH: rhizosphere, RS: rhizosheath, RT: root tissue, BS: bulk sand. Table S4. Humidity (RH%) and temperature (T\u00b0C) measured in summer (June, 2016) and winter (November, 2016) for bulk sand and belowground speargrasses (i.e., plant rhizosheath\u2013root system, RS). Table S5. Physico-chemical analyses conducted on bulk sand (BS) and rhizosheath matrix (RS) collected in summer (S) and winter (W). Table S6. PERMANOVA pair-wise comparison test of physico-chemical conditions (Table S5) in rhizosheath (RS) and bulk sand (BS) collected during summer (S) and winter (W) seasons. Table S7. Multivariate test (pairwise comparison) of beta-diversity associated with root tissue, rhizosheath, rhizosphere, and bulk sand. Table S8. Mean and standard error of multivariate dispersions from centroid calculated for each compartment (within-betadiversity). Table S9. Relative importance of different ecological processes in the assembly of bacterial community associated with the rhizosheath-root system compartments and bulk sand in two contrasting seasons. Table S10. Generalized linear model univariate test indicates the KTUs contributing to the difference in bulk soil among summer and winter. Table S11. Tukey\u2019s honest significance difference (TukeyHSD) pairwise comparison tests for the degree, betweenness and keystone species detected across the four co-occurrence networks, namely bulk sand winter, bulk sand winter summer, rhizosheath\u2013root system winter and rhizosheath\u2013root system summer. Table S12. List of bacterial isolates, PGP activity, and abiotic resistance tested in vitro. Figure S1. Stipagrostis pungens rhizosheath-root system. Figure S2. Rarefaction curves of bacterial reads obtained by pair-ends MiSeq Illumina sequencing in bulk sand, rhizosheath, rhizosphere, and root tissues. Figure S3. Visualization of Stipagrostis pungens rhizosheath with scanning electron microscopy (SEM). Figure S4. SEM images and electron micrographs of the sand grains and root tissue within the rhizosheath of Stipagrostis pungens using environmental scanning electron microscopy (ESEM) to reveal chemical composition. Figure S5. Localization of bacteria in rhizosheath-root system by confocal laser-scanning microscopy (CLSM) and fluorescence in situ hybridization (FISH). Figure S6. Bacterial KTUs distribution across samples. Figure S7. Venn diagram shows the distribution of bacterial KTUs across compartment categories. Figure S8. Alpha diversity expressed as richness (number of KTUs) and Shannon diversity across the compartment categories. Figure S9. Quantification of betadiversity components in bulk sand bacterial communities across seasons (summer and winter). Figure S10. Alpha diversity expressed as richness (number of KTUs) and Shannon diversity across the compartments in summer and winter. Figure S11. Venn diagrams showing the number of KTUs present in summer and/or in winter in each compartment category. Figure S12. Analysis of 2-fold change was performed to evaluate the KTUs that had a significantly (p &lt; 0.01) different relative abundance (2-fold change) over summer and winter. Figure S13. Taxonomy of co-occurrence network degrees in bulk sand and rhizosheath matrix across seasons. Figure S14. Microcosms to evaluate sand wettability in vitro. Evaluation of sand weight at 48 h and along the entire incubation (0, 24 and 48 h).", "keywords": ["2. Zero hunger", "13. Climate action", "15. Life on land", "6. Clean water"], "contacts": [{"organization": "Marasco, Ramona, Fusi, Marco, Mosqueira, Maria, Booth, Jenny Marie, Rossi, Federico, Cardinale, Massimiliano, Michoud, Gr\u00e9goire, Rolli, Eleonora, Mugnai, Gianmarco, Vergani, Lorenzo, Borin, Sara, De Philippis, Roberto, Cherif, Ameur, Daffonchio, Daniele,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.6084/m9.figshare.19498606"}, {"rel": "self", "type": "application/geo+json", "title": "10.6084/m9.figshare.19498606", "name": "item", "description": "10.6084/m9.figshare.19498606", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.6084/m9.figshare.19498606"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "10.6084/m9.figshare.7987250", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:21:28Z", "type": "Dataset", "created": "2019-04-12", "title": "Dataset S4 from Convergent evolution in Arabidopsis halleri and Arabidopsis arenosa on calamine metalliferous soils.", "description": "It is a plausible hypothesis that parallel adaptation events to the same environmental challenge should result in genetic changes of similar or identical effects, depending on the underlying fitness landscapes. However, systematic testing of this is scarce. Here we examine this hypothesis in two closely related plant species, <i>Arabidopsis halleri</i> and <i>Arabidopsis arenosa</i>, which co-occur at two calamine metalliferous (M) sites harbouring toxic levels of the heavy metals zinc and cadmium. We conduct individual genome resequencing alongside soil elemental analysis for 64 plants from eight populations on M and non-metalliferous (NM) soils, and identify genomic footprints of selection and local adaptation. Selective sweep and environmental association analyses indicate a modest degree of gene as well as functional network convergence, whereby the proximal molecular factors mediating this convergence mostly differ between site pairs and species. Notably, we observe repeated selection on identical single nucleotide poly-morphisms in several <i>A. halleri</i> genes at two independently colonized M sites. Our data suggest that species-specific metal handling and other biological features could explain a low degree of convergence between species. The parallel establishment of plant populations on calamine M soils involves convergent evolution, which will probably be more pervasive across sites purposely chosen for maximal similarity in soil composition.This article is part of the theme issue \u2018Convergent evolution in the genomics era: new insights and directions\u2019.", "keywords": ["2. Zero hunger", "15. Life on land"], "contacts": [{"organization": "Preite, Veronica, Sailer, Christian, Syllwasschy, Lara, Bray, Sian, Ahmadi, Hassan, Kr\u00e4mer, Ute, Yant, Levi,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.6084/m9.figshare.7987250"}, {"rel": "self", "type": "application/geo+json", "title": "10.6084/m9.figshare.7987250", "name": "item", "description": "10.6084/m9.figshare.7987250", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.6084/m9.figshare.7987250"}, {"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": "10.6084/m9.figshare.7987292", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:21:29Z", "type": "Dataset", "created": "2019-04-12", "title": "Dataset S1 from Convergent evolution in Arabidopsis halleri and Arabidopsis arenosa on calamine metalliferous soils.", "description": "It is a plausible hypothesis that parallel adaptation events to the same environmental challenge should result in genetic changes of similar or identical effects, depending on the underlying fitness landscapes. However, systematic testing of this is scarce. Here we examine this hypothesis in two closely related plant species, <i>Arabidopsis halleri</i> and <i>Arabidopsis arenosa</i>, which co-occur at two calamine metalliferous (M) sites harbouring toxic levels of the heavy metals zinc and cadmium. We conduct individual genome resequencing alongside soil elemental analysis for 64 plants from eight populations on M and non-metalliferous (NM) soils, and identify genomic footprints of selection and local adaptation. Selective sweep and environmental association analyses indicate a modest degree of gene as well as functional network convergence, whereby the proximal molecular factors mediating this convergence mostly differ between site pairs and species. Notably, we observe repeated selection on identical single nucleotide poly-morphisms in several <i>A. halleri</i> genes at two independently colonized M sites. Our data suggest that species-specific metal handling and other biological features could explain a low degree of convergence between species. The parallel establishment of plant populations on calamine M soils involves convergent evolution, which will probably be more pervasive across sites purposely chosen for maximal similarity in soil composition.This article is part of the theme issue \u2018Convergent evolution in the genomics era: new insights and directions\u2019.", "keywords": ["2. Zero hunger", "15. Life on land"], "contacts": [{"organization": "Preite, Veronica, Sailer, Christian, Syllwasschy, Lara, Bray, Sian, Ahmadi, Hassan, Kr\u00e4mer, Ute, Yant, Levi,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.6084/m9.figshare.7987292"}, {"rel": "self", "type": "application/geo+json", "title": "10.6084/m9.figshare.7987292", "name": "item", "description": "10.6084/m9.figshare.7987292", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.6084/m9.figshare.7987292"}, {"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": "11245.1/e982467a-6b87-4f88-8ac3-53d0fb37aeb2", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:21:53Z", "type": "Journal Article", "created": "2024-05-01", "title": "Arabinosylation of cell wall extensin is required for the directional response to salinity in roots", "description": "Abstract                   <p>Soil salinity is a major contributor to crop yield losses. To improve our understanding of root responses to salinity, we developed and exploited a real-time salt-induced tilting assay. This assay follows root growth upon both gravitropic and salt challenges, revealing that root bending upon tilting is modulated by Na+ ions, but not by osmotic stress. Next, we measured this salt-specific response in 345 natural Arabidopsis (Arabidopsis thaliana) accessions and discovered a genetic locus, encoding the cell wall-modifying enzyme EXTENSIN ARABINOSE DEFICIENT TRANSFERASE (ExAD) that is associated with root bending in the presence of NaCl (hereafter salt). Extensins are a class of structural cell wall glycoproteins known as hydroxyproline (Hyp)-rich glycoproteins, which are posttranslationally modified by O-glycosylation, mostly involving Hyp-arabinosylation. We show that salt-induced ExAD-dependent Hyp-arabinosylation influences root bending responses and cell wall thickness. Roots of exad1 mutant seedlings, which lack Hyp-arabinosylation of extensin, displayed increased thickness of root epidermal cell walls and greater cell wall porosity. They also showed altered gravitropic root bending in salt conditions and a reduced salt-avoidance response. Our results suggest that extensin modification via Hyp-arabinosylation is a unique salt-specific cellular process required for the directional response of roots exposed to salinity.</p", "keywords": ["580", "0301 basic medicine", "2. Zero hunger", "570", "Salinity", "0303 health sciences", "Glycosylation", "Arabidopsis Proteins", "Arabidopsis", "Breakthrough Report", "Sodium Chloride", "15. Life on land", "Arabinose", "Plant Roots", "Gravitropism", "03 medical and health sciences", "Cell Wall", "Gene Expression Regulation", " Plant", "Life Science", "Glycoproteins", "Plant Proteins"]}, "links": [{"href": "https://academic.oup.com/plcell/article-pdf/36/9/3328/59006321/koae135.pdf"}, {"href": "https://doi.org/11245.1/e982467a-6b87-4f88-8ac3-53d0fb37aeb2"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/The%20Plant%20Cell", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "11245.1/e982467a-6b87-4f88-8ac3-53d0fb37aeb2", "name": "item", "description": "11245.1/e982467a-6b87-4f88-8ac3-53d0fb37aeb2", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/11245.1/e982467a-6b87-4f88-8ac3-53d0fb37aeb2"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-05-01T00:00:00Z"}}, {"id": "11369/372709", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:21:55Z", "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": "11585/996230", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:21:58Z", "type": "Journal Article", "created": "2023-10-10", "title": "Beyond PLFA: Concurrent extraction of neutral and glycolipid fatty acids provides new insights into soil microbial communities", "description": "The analysis of phospholipid fatty acids (PLFAs) is one of the most common methods used to quantify the abundance, and analyse the community structure, of soil microbes. The PLFA extraction method can yield two additional lipid fractions\u2014neutral lipids and glycolipids\u2014which potentially hold additional, valuable information on soil microbial communities. Yet its quantitative sensitivity on complete neutral lipid (NLFA) and glycolipid fatty acid (GLFA) profiles has never been validated. In this study we tested (i) if the high-throughput PLFA method can be expanded to concurrently extract complete NLFA and GLFA profiles, as well as sterols, (ii) whether taxonomic specificities of signature fatty acids are retained across the three lipid fractions in pure culture strains, and (iii) whether NLFAs and GLFAs allow soil-specific fingerprinting to the same extent as PLFA analysis. By adjusting the polarity of chloroform with 2% ethanol for solid phase extraction, pure lipid standards were fully fractionated into neutral lipids, glycolipids, and phospholipids. Sterols eluted in the neutral lipid fraction, and a betaine lipid co-eluted with phospholipids. We found consistent taxonomic specificities of fatty acid markers across the three lipid fractions by analysing pure culture extracts representative of soil microbes. Fatty acid profiles from soil extracts, however, showed stronger differences between PLFAs, NLFAs, and GLFAs than between soil types. This indicates that PLFAs and NLFAs signify different community properties (biomass vs. carbon storage, putatively), and that GLFAs are sensitive markers for community traits which behave differently than PLFAs. Although we consistently found high abundances of characteristic sterols in fungal extracts, the PLFA extraction method only yielded miniscule amounts of ergosterol from soil extracts. We argue that concomitant measurement of fatty acid profiles from all three lipid fractions is a low-effort and potentially information-rich addition to the PLFA method, and discuss its applicability for soil microbial community analyses.", "keywords": ["0301 basic medicine", "2. Zero hunger", "106022 Mikrobiologie", "0303 health sciences", "15. Life on land", "Soil lipids", "03 medical and health sciences", "106026 \u00d6kosystemforschung", "NLFA", "Ergosterol", "Ergosterol; GLFA; NLFA; Phospholipid fatty acids; Soil lipids", "Phospholipid fatty acid", "soil lipids", "Phospholipid fatty acids", "106022 Microbiology", "GLFA", "106026 Ecosystem research"]}, "links": [{"href": "https://doi.org/11585/996230"}, {"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": "11585/996230", "name": "item", "description": "11585/996230", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/11585/996230"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-12-01T00:00:00Z"}}, {"id": "1854/LU-8732814", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:22:04Z", "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": "1871.1/0b041c5c-edd1-45f1-895d-546207d34a0a", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:22:05Z", "type": "Journal Article", "created": "2024-03-21", "title": "Environmental drivers and remote sensing proxies of post-fire thaw depth in Eastern Siberian larch forests", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. Boreal fire regimes are intensifying because of climate change and the northern parts of boreal forests are underlain by permafrost. Boreal fires combust vegetation and organic soils, which insulate permafrost, and as such deepen the seasonally thawed active layer and can lead to further carbon emissions to the atmosphere. Current understanding of the environmental drivers of post-fire thaw depth is limited but of critical importance. In addition, mapping thaw depth over fire scars may enable a better understanding of the spatial variability in post-fire responses of permafrost soils. We assessed the environmental drivers of post-fire thaw depth using field data from a fire scar in a larch-dominated forest in the continuous permafrost zone in Eastern Siberia. Particularly, summer thaw depth was deeper in burned (mean = 127.3 cm, standard deviation (sd) = 27.7 cm) than in unburned (98.1 cm, sd = 26.9 cm) landscapes one year after the fire, yet the effect of fire was modulated by landscape and vegetation characteristics. We found deeper thaw in well-drained landscape positions, in open larch forest often intermixed with Scots pine, and in high severity burns. The environmental drivers, site moisture, forest type and density, and fire severity explained 73.4 % of the measured thaw depth variability at the study sites. In addition, we evaluated the relationships between field-measured thaw depth and several remote sensing proxies. Albedo, the differenced Normalized Burn Ratio (dNBR), land surface temperature (LST), and pre-fire Normalized Difference Vegetation Index (NDVI) derived from Landsat 8 imagery together explained 66.3 % of the variability in field-measured thaw depth. Based on these remote sensing proxies and multiple linear regression analysis, we estimated thaw depth over the entire fire scar, and found that LST displayed particularly strong correlations with post-fire thaw depth (r = 0.65, p &lt; 0.01). Our study reveals some of the governing processes of post-fire thaw depth development and shows the capability of Landsat imagery to estimate thaw depth at a landscape scale.                         </p></article>", "keywords": ["Dynamic and structural geology", "QE1-996.5", "13. Climate action", "Science", "Q", "Geology", "QE500-639.5", "Deforestation", "15. Life on land", "Landsat", "Multiple linear regression", "Atmospheric temperature"]}, "links": [{"href": "https://esd.copernicus.org/articles/15/1459/2024/esd-15-1459-2024.pdf"}, {"href": "https://doi.org/1871.1/0b041c5c-edd1-45f1-895d-546207d34a0a"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Earth%20System%20Dynamics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1871.1/0b041c5c-edd1-45f1-895d-546207d34a0a", "name": "item", "description": "1871.1/0b041c5c-edd1-45f1-895d-546207d34a0a", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1871.1/0b041c5c-edd1-45f1-895d-546207d34a0a"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-03-21T00:00:00Z"}}, {"id": "1871.1/bbc7e25d-d1b9-4c7d-baa4-1a09012f06b2", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:22:05Z", "type": "Journal Article", "created": "2022-11-21", "title": "Global biomass burning fuel consumption and emissions at 500\u2009m spatial resolution based on the Global Fire Emissions Database (GFED)", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. In fire emission models, the spatial resolution of both the modelling framework and the satellite data used to quantify burned area can have considerable impact on emission estimates. Consideration of this sensitivity is especially important in areas with heterogeneous land cover and fire regimes and when constraining model output with field measurements. We developed a global fire emissions model with a spatial resolution of 500\u2009m using MODerate resolution Imaging Spectroradiometer (MODIS) data. To accommodate this spatial resolution, our model is based on a simplified version of the Global Fire Emissions Database (GFED) modelling framework. Tree mortality as a result of fire, i.e.\u00a0fire-related forest loss, was modelled based on the overlap between 30\u2009m forest loss data and MODIS burned area and active fire detections. Using this new 500\u2009m model, we calculated global average carbon emissions from fire of 2.1\u00b10.2 (\u00b11\u03c3 interannual variability, IAV)\u2009Pg\u2009C\u2009yr\u22121 during 2002\u20132020. Fire-related forest loss accounted for 2.6\u00b10.7\u2009% (uncertainty range =1.9\u2009%\u20133.3\u2009%) of global burned area and 24\u00b16\u2009% (uncertainty range =16\u2009%\u201331\u2009%) of emissions, indicating that fuel consumption in forest fires is an order of magnitude higher than the global average. Emissions from the combustion of soil organic carbon (SOC) in the boreal region and tropical peatlands accounted for 13\u00b14\u2009% of global emissions. Our global fire emissions estimate was higher than the 1.5\u2009Pg\u2009C\u2009yr\u22121 from GFED4 and similar to 2.1\u2009Pg\u2009C\u2009yr\u22121 from GFED4s. Even though GFED4s included more burned area by accounting for small fires undetected by the MODIS burned area mapping algorithm, our emissions were similar to GFED4s due to higher average fuel consumption. The global difference in fuel consumption could mainly be explained by higher SOC emissions from the boreal region as constrained by additional measurements. The higher resolution of the 500\u2009m model also contributed to the difference by improving the simulation of landscape heterogeneity and reducing the scale mismatch in comparing field measurements to model grid cell averages during model calibration. Furthermore, the fire-related forest loss algorithm introduced in our model led to more accurate and widespread estimation of high-fuel-consumption burned area. Recent advances in burned area detection at resolutions of 30\u2009m and finer show a substantial amount of burned area that remains undetected with 500\u2009m sensors, suggesting that global carbon emissions from fire are likely higher than our 500\u2009m estimates. The ability to model fire emissions at 500\u2009m resolution provides a framework for further improvements with the development of new satellite-based estimates of fuels, burned area, and fire behaviour, for use in the next generation of GFED.</p></article>", "keywords": ["QE1-996.5", "13. Climate action", "11. Sustainability", "Geology", "15. Life on land", "7. Clean energy", "01 natural sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/1871.1/bbc7e25d-d1b9-4c7d-baa4-1a09012f06b2"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoscientific%20Model%20Development", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1871.1/bbc7e25d-d1b9-4c7d-baa4-1a09012f06b2", "name": "item", "description": "1871.1/bbc7e25d-d1b9-4c7d-baa4-1a09012f06b2", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1871.1/bbc7e25d-d1b9-4c7d-baa4-1a09012f06b2"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-05-30T00:00:00Z"}}, {"id": "1887/4246123", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:22:06Z", "type": "Journal Article", "created": "2023-08-30", "title": "Inland Waters Increasingly Produce and Emit Nitrous Oxide", "description": "Nitrous oxide (N2O) is a long-lived greenhouse gas and currently contributes \u223c10% to global greenhouse warming. Studies have suggested that inland waters are a large and growing global N2O source, but whether, how, where, when, and why inland-water N2O emissions changed in the Anthropocene remains unclear. Here, we quantify global N2O formation, transport, and emission along the aquatic continuum and their changes using a spatially explicit, mechanistic, coupled biogeochemistry-hydrology model. The global inland-water N2O emission increased from 0.4 to 1.3 Tg N yr-1 during 1900-2010 due to (1) growing N2O inputs mainly from groundwater and (2) increased inland-water N2O production, largely in reservoirs. Inland waters currently contribute 7 (5-10)% to global total N2O emissions. The highest inland-water N2O emissions are typically in and downstream of reservoirs and areas with high population density and intensive agricultural activities in eastern and southern Asia, southeastern North America, and Europe. The expected continuing excessive use of nutrients, dam construction, and development of suboxic conditions in aging reservoirs imply persisting high inland-water N2O emissions.", "keywords": ["Inland waters", "N2O cycling", " long-term temporal changes", "long-term temporal changes", "Nitrous oxide", "Asia", " Southern", "Nitrous Oxide", "Integrated process-based modeling", "Water", "Agriculture", "General Chemistry", "15. Life on land", "N2O cycling", "6. Clean water", "Greenhouse gas emission", "13. Climate action", "Environmental Chemistry", "14. Life underwater", "Spatial distributions", "closed N2O budget"]}, "links": [{"href": "https://doi.org/1887/4246123"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Science%20%26amp%3B%20Technology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1887/4246123", "name": "item", "description": "1887/4246123", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1887/4246123"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-08-30T00:00:00Z"}}, {"id": "1959.7/uws:75008", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:22:08Z", "type": "Journal Article", "created": "2023-10-04", "title": "Plant footprint decreases the functional diversity of molecules in topsoil organic matter after millions of years of ecosystem development", "description": "AbstractAim<p>Theory suggests that the diversity of molecules in soil organic matter (SOM functional diversity) provides key insights on multiple ecosystem services. We aimed to investigate how and why SOM functional diversity and composition change as topsoils develop, and its implications for key soil functions (e.g., from nutrient pool to water regulation).</p>Location<p>We reported data on 16 soil chronosequences globally distributed in nine countries from six continents.</p>Time Period<p>2016\uffe2\uff80\uff932017.</p>Major Taxa Studied<p>Soil microbes (bacteria and fungi) and vascular plants.</p>Methods<p>SOM functional diversity and composition without mineral interference were measured using diffuse reflectance mid\uffe2\uff80\uff90infrared Fourier transform spectroscopy (DRIFT). We aimed to characterize the main environmental factors related to SOM functional diversity and composition. Also, we calculated the links among SOM functional diversity and key soil functions.</p>Results<p>We found that SOM functional diversity declines after millions of years of soil formation (pedogenesis). We further showed that increases in plant cover and productivity led to a higher ratio of reduced (e.g., alkanes) over oxidized carbon forms (i.e., C: O\uffe2\uff80\uff90functional groups ratio), which was positively correlated to SOM functional diversity as soils age. Our findings indicated that the plant footprint (i.e., the accumulation of plant\uffe2\uff80\uff90derived material promoting the C: O\uffe2\uff80\uff90functional group ratio) would explain the reduction of SOM functional diversity as ecosystems develop. Moreover, the dissimilarity in SOM composition consistently increased with soil age, with the soil development stage emerging as the main predictor of SOM dissimilarity across contrasting biomes.</p>Main Conclusions<p>Our global survey contextualized the natural history of SOM functional diversity and composition during long\uffe2\uff80\uff90term soil development. Together, we showed how plant footprint drives the losses of SOM functional diversity with increasing age, which might provide a novel mechanism to explain typically reported losses in ecosystem functions during ecosystem retrogression.</p", "keywords": ["2. Zero hunger", "0301 basic medicine", "03 medical and health sciences", "13. Climate action", "XXXXXX - Unknown", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land"]}, "links": [{"href": "https://doi.org/1959.7/uws:75008"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Global%20Ecology%20and%20Biogeography", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1959.7/uws:75008", "name": "item", "description": "1959.7/uws:75008", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1959.7/uws:75008"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-10-03T00:00:00Z"}}, {"id": "10.1016/j.actao.2006.05.010", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:14:56Z", "type": "Journal Article", "created": "2006-07-22", "title": "Post-Fire Regeneration In A Mediterranean Pine Forest With Historically Low Fire Frequency", "description": "Abstract   Species of Mediterranean vegetation are known to regenerate directly after fire. The phenomenon of autosuccession (direct regeneration) has been found to be often combined with an increase of species richness during the first years after fire due to the high abundance of short-lived herbaceous plants facilitated by plentiful nutrients and light. The high degree of vegetation resilience, which is expressed in terms of autosuccession, has been explained by the selective pressure of fire in historic times. According to existing palaeoecological data, however, the  Pinus halepensis  forests in the Ricote Mountains (Province of Murcia, SE Spain) did not experience substantial fire impact before the presence of man nor are they especially fire-prone today. Therefore, we studied post-fire regeneration to find out if direct succession is present or if species from pre-fire vegetation are absent during the post-fire regeneration stages. Patterns of succession were deduced from observations made in sample plots on sites of a known regeneration age as well as in adjacent unburnt areas. The results of the vegetation analyses, including a Detrended Correspondence Analysis, indicate that  Pinus halepensis  forest regeneration after fire resembles autosuccession. As regards the presence of woody species, there is a high percentage similarity on north (83%) and south (70%) facing slopes during the first year after fire vs. reference areas which is due, for example, to direct regeneration of the resprouting  Quercus coccifera  or seeders like  Pinus halepensis  or  Fumana laevipes . However, if herbaceous species are included in the comparison, the similarity on north-facing sites decreases (to 53%) with the presence of additional species, mainly ruderals like  Anagallis arvensis  or  Reseda phyteuma , and even woody species on the burnt plots. This effect indicates \u201cenhanced autosuccession\u201d, which was not found on south-facing sites where overall species richness was very high irrespective of the impact of fire. Locally we found limited regeneration of some species, for example  Pinus halepensis  at high altitudes (1000\u00a0m), even 22\u00a0years after fire. As we assume that historical fires did not play an important role in the area and direct succession is present nevertheless, our results support the theory that autosuccession is not a process restricted to fire-prone areas. Fire has been only one of several selective forces since human settlement that probably led to a set of species pre-adapted against recurrent disturbance.", "keywords": ["0106 biological sciences", "14. Life underwater", "15. Life on land", "01 natural sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.actao.2006.05.010"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Acta%20Oecologica", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.actao.2006.05.010", "name": "item", "description": "10.1016/j.actao.2006.05.010", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.actao.2006.05.010"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2006-11-01T00:00:00Z"}}, {"id": "10.1007/s10705-012-9492-2", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:14:37Z", "type": "Journal Article", "created": "2012-03-15", "title": "Soil Carbon Dynamics As Influenced By Tillage And Crop Residue Management In Loamy Sand And Sandy Loam Soils Under Smallholder Farmers\u2019 Conditions In Malawi", "description": "Conservation agriculture (CA) characterised by minimal soil disturbance, permanent soil surface cover by dead or living plants and crop rotations is one way of achieving higher soil organic carbon (C) in agricultural fields. Sandy loam and loamy soil samples from zero tillage (ZT) and conventional tillage (CT) plots were taken from farmers\u2019 fields during the dry season in August 2006. Soil organic carbon (SOC) and soil organic nitrogen (SON), microbial biomass carbon (MB-C) and microbial biomass nitrogen (MB-N), C mineralization and SOC distribution in particle size fractions in 0\u201320\u00a0cm layer were evaluated. Forty eight farmers\u2019 fields were randomly sampled at four different locations in Central and Northern Malawi, representing ZT plots maintained for a different number of years, and ten fields under CT with similar soil type and crop grown were selected. SOC and SON in ZT fields were 44 and 41\u00a0% (4\u00a0years ZT) and 75 and 77\u00a0% (5\u00a0years ZT) higher, respectively, than CT plots. MB-C and MB-N in ZT fields were 16 and 44\u00a0% (4\u00a0years ZT) and 20 and 38\u00a0% (5\u00a0years ZT) higher, respectively, than CT plots. However, MB-C and MB-N in ZT fields were 27 and 25\u00a0% (2\u00a0years ZT) and 17 and 9\u00a0% (3\u00a0years ZT) lower than in CT plots. The proportion of the total organic C as microbial biomass C was relatively higher under CT than ZT treatments. The higher SOC and MB-C content in the ZT fields resulted in 10, 62, 57\u00a0% higher C mineralization rate in ZT plots of 3, 4 and 5\u00a0years of loamy sand soils and 35\u00a0% higher C mineralization rate in ZT plot of 2\u00a0years than CT of sandy loam soils in undisturbed soils in the laboratory. Simulating plough from the undisturbed soils that were used for C mineralization experiment resulted in linear curves indicating that all organic C was already depleted during the first incubation period. The relative distribution of soil organic matter (SOM) in silt and clay size fractions was strongly correlated (r\u00a0=\u00a00.907 and P\u00a0\u2264\u00a00.01) with silt percentages. Easily degradable carbon pool (CA,f) was correlated (r\u00a0=\u00a00.867 and P\u00a0\u2264\u00a00.05) with organic carbon in sand size fraction. In developing viable conservation agriculture practices to optimize SOC content and long-term sustainability of maize production systems, priority should be given to the maintenance of C inputs, crop rotations and associations and also to reduced soil disturbance by tillage.", "keywords": ["2. Zero hunger", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land"]}, "links": [{"href": "https://doi.org/10.1007/s10705-012-9492-2"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Nutrient%20Cycling%20in%20Agroecosystems", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/s10705-012-9492-2", "name": "item", "description": "10.1007/s10705-012-9492-2", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/s10705-012-9492-2"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2012-03-16T00:00:00Z"}}, {"id": "10.1016/j.fcr.2019.107659", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:15:32Z", "type": "Journal Article", "created": "2019-10-31", "title": "Does crop rotation yield more in China? A meta-analysis", "description": "Abstract   Crop rotation has been widely used to mitigate weed, insect, and pathogen pressure, as well as to increase plant diversity. However, the magnitude of and variability in crop yield with rotation in China remain uncertain. We conducted a meta-analysis on 45 studies with 214 comparisons to examine the effects of crop rotation on yield and to explore the importance of environmental factors and field management in determining yield variability. Rotation increased crop yields by 20% on average when compared with continuous monoculture practices. Effects of rotation on crop yield were more pronounced in southwestern China (+38%) than in eastern China (+10%) and were notably less reliable and generally smaller in regions with moderate annual rainfall (400\u2013550\u202fmm). Rotation had greater yield benefits in soils with coarse or medium soil textures, intermediate levels of initial soil organic carbon (7\u201310\u202fg kg\u22121), and lower total nitrogen (\u2264 1.2\u202fg kg\u22121). Effect of rotation on crop yield also depends on the type of previously cultivated crops (hereinafter, pre-crops), ranging from 2% for Triticeae pre-crops to 27% for grain leguminous pre-crops. Yield increase under legume-based rotations was 14% higher than that without legumes. The mean yield response of the crop subsequent to rotation ranged from 6% (cereals, except for Triticeae crops) to 34% (root crops). The legacy benefit of rotation on subsequent crop yield persisted for 2\u20133 years, decreasing from +31% in the second year to +8% in the third year. Effect of rotation on crop yield under conventional tillage was greater than that under conservation tillage. The yield benefit of rotation on subsequent crops was much higher with a lower nitrogen fertilization rate (\u2264 120\u202fkg ha\u22121), indicating that fertilization can be reduced and an acceptable yield maintained with crop rotation. In conclusion, crop rotation largely increases agricultural production without extra inputs, although its design may need to consider diverse climates, soils, crops, and management practices to maximize its agronomic and environmental benefits.", "keywords": ["0106 biological sciences", "2. Zero hunger", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.fcr.2019.107659"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Field%20Crops%20Research", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.fcr.2019.107659", "name": "item", "description": "10.1016/j.fcr.2019.107659", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.fcr.2019.107659"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-01-01T00:00:00Z"}}, {"id": "10.1002/jgrg.20063", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:14:05Z", "type": "Journal Article", "created": "2013-04-25", "title": "Effect Of Continued Nitrogen Enrichment On Greenhouse Gas Emissions From A Wetland Ecosystem In The Sanjiang Plain, Northeast China: A 5 Year Nitrogen Addition Experiment", "description": "Abstract<p>Mounting evidence supports that wetland ecosystems, one of the largest carbon pools on the earth, are exposed to ample nitrogen (N) additions due to atmospheric deposition or N loading from upstream agricultural fertilizer application. However, our understanding of how N enrichment affects the fluxes of greenhouse gases (GHGs) in wetlands is weak. A 5\uffe2\uff80\uff89year N addition experiment was conducted to examine the responses of CH4 and N2O fluxes as well as ecosystem respiration from wetlands in the Sanjiang Plain, Northeast China, through 2005 to 2009. Four levels of N addition (control, 0\uffe2\uff80\uff89kg\uffe2\uff80\uff89N ha\uffe2\uff88\uff921\uffe2\uff80\uff89yr\uffe2\uff88\uff921; low\uffe2\uff80\uff90level, 60\uffe2\uff80\uff89kg\uffe2\uff80\uff89N ha\uffe2\uff88\uff921\uffe2\uff80\uff89yr\uffe2\uff88\uff921; medium\uffe2\uff80\uff90level, 120\uffe2\uff80\uff89kg\uffe2\uff80\uff89N ha\uffe2\uff88\uff921\uffe2\uff80\uff89yr\uffe2\uff88\uff921; high\uffe2\uff80\uff90level, 240\uffe2\uff80\uff89kg\uffe2\uff80\uff89N ha\uffe2\uff88\uff921\uffe2\uff80\uff89yr\uffe2\uff88\uff921) were designed in this study. Overall, our results show that medium and high levels of N addition increased ecosystem respiration by 28% and 69% (P\uffe2\uff80\uff89&lt;\uffe2\uff80\uff890.05), respectively, while low\uffe2\uff80\uff90level N addition has no effect on ecosystem respiration (P\uffe2\uff80\uff89&gt;\uffe2\uff80\uff890.05). High\uffe2\uff80\uff90level N fertilization exerted stronger effects on ecosystem respiration in the initial year than the following years. It indicated that the effects of high\uffe2\uff80\uff90level N fertilization on CO2 might be overestimated by short\uffe2\uff80\uff90term observations. High\uffe2\uff80\uff90level N fertilization increased N2O emissions by 396% over the 5\uffe2\uff80\uff89years (P\uffe2\uff80\uff89&lt;\uffe2\uff80\uff890.05), but the low\uffe2\uff80\uff90 and medium\uffe2\uff80\uff90level\uffe2\uff80\uff90N addition did not exert any apparent effect on N2O emissions (P\uffe2\uff80\uff89&gt;\uffe2\uff80\uff890.05). N2O emission under high\uffe2\uff80\uff90level N addition in the first and fifth years showed stronger pronounced responses to N addition compared with that from the third and fourth years, indicating the importance of long\uffe2\uff80\uff90term field observation. Over the 5\uffe2\uff80\uff89years, however, the low and medium\uffe2\uff80\uff90level N addition showed no effect on N2O emissions. The four levels of N addition exerted no effect on CH4 emissions (P\uffe2\uff80\uff89&gt;\uffe2\uff80\uff890.05). Furthermore, the relationship between GHGs and soil temperature or water table depth varied among different plots and experimental time. Our findings highlighted the importance of gas species, experimental time, and the amount of fertilizer N with regard to the responses of GHG emissions to N fertilization.</p>", "keywords": ["2. Zero hunger", "13. Climate action", "11. Sustainability", "15. Life on land", "01 natural sciences", "6. Clean water", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1002/jgrg.20063"}, {"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/jgrg.20063", "name": "item", "description": "10.1002/jgrg.20063", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/jgrg.20063"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2013-05-21T00:00:00Z"}}, {"id": "10.1002/ece3.9322", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:14:04Z", "type": "Journal Article", "created": "2022-09-22", "title": "Impacts of insect frass and cadavers on soil surface litter decomposition along a tropical forest temperature gradient", "description": "Abstract<p>Insect herbivores play important roles in shaping many ecosystem processes, but how climate change will alter the effects of insect herbivory are poorly understood. To address this knowledge gap, we quantified for the first time how insect frass and cadavers affected leaf litter decomposition rates and nutrient release along a highly constrained 4.3\uffc2\uffb0C mean annual temperature (MAT) gradient in a Hawaiian montane tropical wet forest. We constructed litterbags of standardized locally sourced leaf litter, with some amended with insect frass + cadavers to produce treatments designed to simulate ambient (Control\uffc2\uffa0=\uffc2\uffa0no amendment), moderate (Amended\uffe2\uff80\uff90Low\uffc2\uffa0=\uffc2\uffa02\uffe2\uff80\uff89\uffc3\uff97\uffe2\uff80\uff89Control level), or severe (Amended\uffe2\uff80\uff90High\uffc2\uffa0=\uffc2\uffa011\uffe2\uff80\uff89\uffc3\uff97\uffe2\uff80\uff89Control level) insect outbreak events. Multiple sets of these litterbags were deployed across the MAT gradient, with individual litterbags collected periodically over one\uffe2\uff80\uff89year to assess how rising MAT altered the effects of insect deposits on litter decomposition rates and nitrogen (N) release. Increased MAT and insect inputs additively increased litter decomposition rates and N immobilization rates, with effects being stronger for Amended\uffe2\uff80\uff90High litterbags. However, the apparent temperature sensitivity (Q10) of litter decomposition was not clearly affected by amendments. The effects of adding insect deposits in this study operated differently than the slower litter decomposition and greater N mobilization rates often observed in experiments which use chemical fertilizers (e.g., urea, ammonium nitrate). Further research is required to understand mechanistic differences between amendment types. Potential increases in outbreak\uffe2\uff80\uff90related herbivore deposits coupled with climate warming will accelerate litter decomposition and nutrient cycling rates with short\uffe2\uff80\uff90term consequences for nutrient cycling and carbon storage in tropical montane wet forests.</p", "keywords": ["Q 10", "Ekologi", "0106 biological sciences", "Ecology", "insect herbivory", "nutrient cycling", "15. Life on land", "Milj\u00f6vetenskap", "01 natural sciences", "13. Climate action", "nitrogen mineralization", "Environmental Sciences", "Research Articles"]}, "links": [{"href": "https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.9322"}, {"href": "https://doi.org/10.1002/ece3.9322"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Ecology%20and%20Evolution", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1002/ece3.9322", "name": "item", "description": "10.1002/ece3.9322", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1002/ece3.9322"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-09-01T00:00:00Z"}}, {"id": "10.1007/s003740050411", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:14:23Z", "type": "Journal Article", "created": "2002-08-25", "title": "Soil Microbial And Extractable C And N After Wildfire", "description": "Open AccessPeer reviewed", "keywords": ["13. Climate action", "Temperate humid zone", "Microbial biomass", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "Uncontrolled burning", "Forest soils", "15. Life on land", "Potassium-sulphate-soluble C and N", "6. Clean water"]}, "links": [{"href": "https://doi.org/10.1007/s003740050411"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Biology%20and%20Fertility%20of%20Soils", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/s003740050411", "name": "item", "description": "10.1007/s003740050411", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/s003740050411"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "1998-06-19T00:00:00Z"}}, {"id": "2591167526", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:22:29Z", "type": "Report", "created": "2017-02-21", "title": "Distinguishing between old and modern permafrost sources with compound-specific \u03b42H analysis", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. Pleistocene ice complex permafrost deposits contain roughly a quarter of the organic carbon (OC) stored in permafrost terrain. When permafrost thaws, its OC is remobilized into the (aquatic) environment where it is available for degradation, transport or burial. Aquatic or coastal environments contain sedimentary reservoirs that can serve as archives of past climatic change. As permafrost thaw is increasing throughout the Arctic, these reservoirs are important locations to assess the fate of remobilized permafrost OC. We here present compound-specific deuterium (\u03b42H) analysis on leaf waxes as a tool to distinguish between OC released from thawing Pleistocene permafrost (Ice Complex Deposits; ICD) and from thawing Holocene permafrost (from near-surface soils). Bulk geochemistry (%OC, \u03b413C, %total nitrogen; TN) was analyzed as well as the concentrations and \u03b42H signatures of long-chain n-alkanes (C21 to C33) and mid/long-chain n-alkanoic acids (C16 to C30) extracted from both ICD-PF samples (n\u2009=\u20099) and modern vegetation/O-horizon (Topsoil-PF) samples (n\u2009=\u20099) from across the northeast Siberian Arctic. Results show that these Topsoil-PF samples have higher %OC, higher OC/TN values, and more depleted \u03b413C-OC values than ICD-PF samples, suggesting that these former samples trace a fresher soil and/or vegetation source. Median concentrations of high-molecular weight n-alkanes (sum of C25-C27-C29-C31) were 210\u2009\u00b1\u2009350\u2009\u00b5g/gOC (median\u2009\u00b1\u2009IQR) for Topsoil-PF and 250\u2009\u00b1\u200981\u2009\u00b5g/gOC for ICD-PF samples. Long-chain n-alkanoic acids (sum of C22-C24-C26-C28) were more abundant than long-chain n-alkanes, both in Topsoil-PF samples (4700\u2009\u00b1\u20093400\u2009\u00b5g/gOC) and in ICD samples (6630\u2009\u00b1\u20093500\u2009\u00b5g/gOC). Whereas the two investigated sources differ on the bulk geochemical level, they are, however, virtually indistinguishable when using leaf wax concentrations and ratios. However, on the molecular-isotope level, leaf wax biomarker \u03b42H values are statistically different between Topsoil-PF and ICD-PF. The mean \u03b42H value of C29 n-alkane was \u2212246\u2009\u00b1\u200913\u2009\u2030 (mean\u2009\u00b1\u2009stdev) for Topsoil-PF and \u2212280\u2009\u00b1\u200912\u2009\u2030 for ICD-PF, whereas the C31 n-alkane was \u2212247\u2009\u00b1\u200923\u2009\u2030 for Topsoil-PF and \u2212297\u2009\u00b1\u200915\u2009\u2030 for ICD-PF. The C28 n-alkanoic acid \u03b42H value was \u2212220\u2009\u00b1\u200915\u2009\u2030 for Topsoil-PF and \u2212267\u2009\u00b1\u200916\u2009\u2030 for ICD-PF. With a dynamic isotopic range (difference between two sources) of 34 to 50\u2009\u2030, the isotopic fingerprints of individual, abundant, biomarker molecules from leaf waxes can thus serve as end-members to distinguish between these two sources. We tested this molecular \u03b42H tracer along with another source-distinguishing approach, dual-carbon (\u03b413C-\u03b414C) isotope composition of bulk OC, for a surface sediment transect in the Laptev Sea. Results show that general offshore patterns along the shelf-slope transect are similar, but the source apportionment between the approaches vary, which may highlight the advantages of either. The \u03b42H molecular approach has the advantage that it circumvents uncertainties related to a marine end-member, yet the \u03b413C-\u03b414C approach has the advantage that it represents the bulk OC fraction thereby avoiding issues related to the molecular-bulk upscaling challenge. This study indicates that the application of \u03b42H leaf wax values has potential to serve as a complementary quantitative measure of the source and differential fate of OC thawed out from different permafrost compartments.                         </p></article>", "keywords": ["13. Climate action", "14. Life underwater", "15. Life on land"]}, "links": [{"href": "https://doi.org/2591167526"}, {"rel": "self", "type": "application/geo+json", "title": "2591167526", "name": "item", "description": "2591167526", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2591167526"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-02-21T00:00:00Z"}}, {"id": "10.1007/s11104-010-0544-6", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-20T16:14:43Z", "type": "Journal Article", "created": "2010-09-01", "title": "Biochar Impact On Development And Productivity Of Pepper And Tomato Grown In Fertigated Soilless Media", "description": "The impact of additions (1\u20135% by weight) of a nutrient-poor, wood-derived biochar on pepper (Capsicum annuum L.) and tomato (Lycopersicum esculentum Mill.) plant development and productivity in a coconut fiber:tuff growing mix under optimal fertigation conditions was examined. Pepper plant development in the biochar-treated pots was significantly enhanced as compared with the unamended controls. This was reflected by a system-wide increase in most measured plant parameters: leaf area, canopy dry weight, number of nodes, and yields of buds, flowers and fruit. In addition to the observed increases in plant growth and productivity, the rhizosphere of biochar-amended pepper plants had significantly greater abundances of culturable microbes belonging to prominent soil-associated groups. Phylogenetic characterization of unique bacterial isolates based on 16S rRNA gene analysis demonstrated that of the 20 unique identified isolates from roots and bulk soil from the char-amended growing mix, 16 were affiliated with previously described plant growth promoting and/or biocontrol agents. In tomato, biochar treatments positively enhanced plant height and leaf size, but had no effect on flower and fruit yield. The positive impacts of biochar on plant response were not due to direct or indirect effects on plant nutrition, as there were no differences between control and treatments in leaf nutrient content. Nor did biochar affect the field capacity of the soilless mixture. A number of organic compounds belonging to various chemical classes, including n-alkanoic acids, hydroxy and acetoxy acids, benzoic acids, diols, triols, and phenols were identified in organic solvent extracts of the biochar. We conjecture two related alternatives to explain the improved plant performance under biochar treatment: (i) the biochar stimulated shifts in microbial populations towards beneficial plant growth promoting rhizobacteria or fungi, due to either chemical or physical attributes of the biochar; or (ii) low doses of biochar chemicals, many of which are phytotoxic or biocidal at high concentrations, stimulated plant growth at low doses (hormesis).", "keywords": ["2. Zero hunger", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "6. Clean water", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1007/s11104-010-0544-6"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Plant%20and%20Soil", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/s11104-010-0544-6", "name": "item", "description": "10.1007/s11104-010-0544-6", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/s11104-010-0544-6"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2010-09-02T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=land&offset=50&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=land&offset=50&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "prev", "title": "items (prev)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=land&offset=0", "hreflang": "en-US"}, {"rel": "next", "type": "application/geo+json", "title": "items (next)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=land&offset=100", "hreflang": "en-US"}], "numberMatched": 15602, "numberReturned": 50, "distributedFeatures": [], "timeStamp": "2026-09-21T07:44:03.952862Z"}