Photochemical degradation of dissolved organic matter (DOM) has been the subject of numerous studies; however, its regulation along the inland water continuum is still unclear. We aimed to unravel the DOM photoreactivity and concurrent DOM compositional changes across 30 boreal aquatic ecosystems including peat waters, streams, rivers, and lakes distributed along a water residence time (WRT) gradient. Samples were subjected to a standardized exposure of simulated sunlight. We measured the apparent quantum yield (AQY), which corresponds to DOM photomineralization per photon absorbed, and the compositional change in DOM at bulk and individual compound levels in the original samples and after irradiation. AQY increased with the abundance of terrestrially derived DOM and decreased at higher WRT. Additionally, the photochemical changes in both DOM optical properties and molecular composition resembled changes along the natural boreal WRT gradient at low WRT (<3\uffc2\uffa0years). Accordingly, mass spectrometry revealed that the abundance of photolabile and photoproduced molecules decreased with WRT along the boreal aquatic continuum. Our study highlights the tight link between DOM composition and DOM photodegradation. We suggest that photodegradation is an important driver of DOM composition change in waters with low WRT, where DOM is highly photoreactive.Microbial activity in drylands tends to be confined to rare and short periods of rain. Rapid growth should be key to the maintenance of ecosystem processes in such narrow activity windows, if desiccation and rehydration cause widespread cell death due to osmotic stress. Here, simulating rain with 2H2O followed by single-cell NanoSIMS, we show that biocrust microbial communities in the Negev Desert are characterized by limited productivity, with median replication times of 6 to 19 days and restricted number of days allowing growth. Genome-resolved metatranscriptomics reveals that nearly all microbial populations resuscitate within minutes after simulated rain, independent of taxonomy, and invest their activity into repair and energy generation. Together, our data reveal a community that makes optimal use of short activity phases by fast and universal resuscitation enabling the maintenance of key ecosystem functions. We conclude that desert biocrust communities are highly adapted to surviving rapid changes in soil moisture and solute concentrations, resulting in high persistence that balances limited productivity.This dataset corresponds to a datamart produced by the WP2 team of the Landmark H2020 project.
2 tables provided by France are available:
Both tables are connected with the same id attribute. To link soil data to management practices, yo", "keywords": ["2. Zero hunger", "Earth and Environmental Science", "Soils and soil sciences", "Agricultural Sciences", "Climate", "Life Sciences", "Agriculture", " Forestry", " Horticulture", " Aquaculture", "15. Life on land", "Farming Systems", "Soil functions; soil; management; climate;", "Farming Systems and Practices", "Earth and Environmental Sciences", "Soil Sciences", "Agriculture", " Forestry", " Horticulture", " Aquaculture and Veterinary Medicine", "Soil functions soil management climate", "Environmental Research", "Natural Sciences", "Agriculture", " Forestry", " Horticulture", "Geosciences"], "contacts": [{"organization": "Saby, Nicolas P.A., Chenu, Jean-Philippe, Szergi, Tamas, Csorba, Adam, Bertuzzi, Patrick, Toutain, Beno\u00eet, Picaud, Calypso, Gay, Laura, Creamer, R.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.15454/aiq9ws"}, {"rel": "self", "type": "application/geo+json", "title": "10.15454/aiq9ws", "name": "item", "description": "10.15454/aiq9ws", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.15454/aiq9ws"}, {"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": "10.15454/hwrhhx", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:19:48Z", "type": "Dataset", "title": "Replication Data for: Gap assessment in current soil monitoring networks across Europe for measuring soil functions", "description": "Open AccessThis dataset was produced during the Landmark project. We compared in this work a list of attributes to existing national (regional) and EU-wide soil monitoring networks. After establishing the ranked list of attributes we investigated the incorporation of these attributes in existing monitoring schemes throughout Europe. A standard Excel spreadsheet was sent to Landmark consortium members and contacts from 18 European countries requesting detailed information on national SMNs (including long-te", "keywords": ["Earth and Environmental Science", "Soils and soil sciences", "Agricultural Sciences", "Climate", "Life Sciences", "Agriculture", " Forestry", " Horticulture", " Aquaculture", "15. Life on land", "Farming Systems", "Soil functions; soil; management; climate;", "Farming Systems and Practices", "Earth and Environmental Sciences", "Soil Sciences", "Agriculture", " Forestry", " Horticulture", " Aquaculture and Veterinary Medicine", "Soil functions soil management climate", "Environmental Research", "Natural Sciences", "Agriculture", " Forestry", " Horticulture", "Geosciences"], "contacts": [{"organization": "Saby, Nicolas P.A., Van Leeuwen, Jeroen P., Creamer, R.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.15454/hwrhhx"}, {"rel": "self", "type": "application/geo+json", "title": "10.15454/hwrhhx", "name": "item", "description": "10.15454/hwrhhx", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.15454/hwrhhx"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-01-01T00:00:00Z"}}, {"id": "10.15454/10XUKC", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:19:47Z", "type": "Dataset", "title": "Caract\u00e9ristiques physico-chimiques des sols en semis direct sous couvert. Enqu\u00eate sur un r\u00e9seau d'agriculteurs en Bourgogne-Franche-Comt\u00e9: 2018-2019", "description": "Open AccessLes informations contenues dans ce jeu de donn\u00e9es correspondent aux r\u00e9sultats d'analyses de sol effectu\u00e9es sur des parcelles agricoles d\u2019un r\u00e9seau de 62 agriculteurs en agriculture de conservation de la r\u00e9gion Bourgogne-Franche-Comt\u00e9. Les pr\u00e9l\u00e8vements de sol ont \u00e9t\u00e9 r\u00e9alis\u00e9s sur une profondeur de 0 \u00e0 10 cm. Au total, le sol de 176 parcelles a \u00e9t\u00e9 analys\u00e9 soit en 2018, soit en 2019. Douze variables ont \u00e9t\u00e9 mesur\u00e9es ou calcul\u00e9es sur les pr\u00e9l\u00e8vements effectu\u00e9s : la granulom\u00e9trie (5 fractions), le carbone total et la mati\u00e8re organique, le calcaire total, l\u2019azote total, le rapport C/N, le pH eau et KCl.", "keywords": ["2. Zero hunger", "Earth and Environmental Science", "Soils and soil sciences", "Agricultural Sciences", "Life Sciences", "Agriculture", " Forestry", " Horticulture", " Aquaculture", "15. Life on land", "Farming Systems", "Farming Systems and Practices", "Earth and Environmental Sciences", "Soil Sciences", "Agriculture", " Forestry", " Horticulture", " Aquaculture and Veterinary Medicine", "Environmental Research", "Natural Sciences", "propri\u00e9t\u00e9 du sol", "sol agricole", "Agriculture", " Forestry", " Horticulture", "Geosciences"], "contacts": [{"organization": "Derrouch, Damien, Chauvel, Bruno, Dessaint, Fabrice,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.15454/10XUKC"}, {"rel": "self", "type": "application/geo+json", "title": "10.15454/10XUKC", "name": "item", "description": "10.15454/10XUKC", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.15454/10XUKC"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-01T00:00:00Z"}}, {"id": "10.15454/8DHNRM", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:19:47Z", "type": "Dataset", "title": "Inventory of plant vascular community diversity and large herbivore pressure of forest stands in 2013-2014, Sologne, France", "description": "This dataset contains data on vascular plant diversity and community composition of the understory in mature broadleaf and conifer forest stands in the Sologne natural region, Central France. The objectif of the project was to study the effects of large wild ungulates on forest biodiversity using a natural and anthropogenic gradient of ungulate pressure. Study area The study area was located in the Sologne Natural Region in the center of France. Sologne is characterized by flat topography and poor sandy soils on top of an impermeable clay layer, and the area is thus subject to frequent winter floodings and summer droughts are recurrent. The dominate land use is forest (53%), of which the majority is recent forests stemming from spontaneous colonization of abandoned land, but also the result of massive afforestation programs during the second half of the 19th century. Deciduous trees represent approximately 77% of the forest cover (Quercus robur: 39%, Quercus petraea: 14%, Betula pendula 9%), while the remaining 23% is made up of coniferous tree species, mainly Pinus sylvestris (13%). The three most common forest stand compositions are monospecific stands of Q. robur (23%), Q. petraea (9%) and P. sylvestris (9%). In Sologne, population densities of large ungulates (red deer, Cervus elaphus, roe deer, Capreaolus capreolus, and wild boar, Sus scrofa) have shown a marked increase in number during the past decades, as elsewhere in France and Europe. No absolute estimates of ungulate densities are available for the study area, but hunting statistics for the three species are among the highest in France, and hunting bags for the 2004/2005 hunting season for red deer, roe deer and wild boar averaged 0.4 \u00b1 0.5 (mean \u00b1 SD), 1.9 \u00b1 1.4 and 3.7 \u00b1 3.7 individuals per km\u00b2, respectively (source: the French national agency for wildlife, ONCFS). No wild large predators were present in the study area. Sampling design We made use of four fenced, five partially fenced and ten unfenced private forest properties to set up an experimental gradient of wild ungulate densities to test their effects on the structure, composition and diversity of plant communities. We selected private properties with at least 100 ha land and where forest was the dominant land use (60-100% forest). Average area of forest was 295 \u00b1 165 ha (mean \u00b1 SD; range = 91-703) per land property (Appendix 1: Table S1). A preselection of private properties all over the study area was established by contacting the Centre r\u00e9gional de la propri\u00e9t\u00e9 foresti\u00e8re (CRPF) d\u2019Ile-de-France et du Centre (Regional public organizations for private forest owners) who helped out to suggest potential land owners willing to participate to the study. We then set up a list of equal number of fenced and unfenced properties in different parts of the study area. Private land owners were then contacted by telephone in order to obtain their permission to carry out field observations on their land property. We stopped contacting landowners once we had obtained the permission from ten unfenced private properties and that were well spread over the study area. Due to difficulties in obtaining permission from land owners with fenced properties, we did not reach a completely balanced design. A completely random sample of private properties would not have been possible due to the high degree of reluctance among private landowners to give their permission to carry out observations on their land property. For each land property, we randomly assigned five study plots stratified according to the proportion of area of deciduous and coniferous forest stands. A buffer zone of at least 50 m was applied to each forest stand nearby roads and open areas. A field visit was made before final selection to assure that the study plot was not situated in recently harvested forest stands or nearby forest edges (<30 m). We also rejected coppice forest stands. Ungulate pressure data We used the observed intensity of major foraging activities by ungulates (browsing for deer and rooting for wild boar) to situate sampling points along a gradient of increasing ungulate densities. As mark-recapture data was not available for our study sites, we could not base our gradient on absolute ungulate densities, but situate the study sites on a relative scale based on the above-mentioned indices (and detailed below) of ungulate activities. Deer browsing pressure was quantified at each sampling point by comparing forage use and availability based on resource selection theory. Forage use and availability were estimated on winter browse (woody and semi-woody vegetation) accessible to deer (0-2 m) in late winter (March) before the start of the growing season. Forage use and availability were estimated on three 40 m2-circular subplots per study plot, each situated at a distance of 14 m from the center of the study plot. For each species, forage availability was quantified by estimating the percentage of plant cover (i.e. the horizontal projection of shoots, twigs and branches and thus a proxy of the total number of \u201cbites\u201d available), while forage use was quantified by estimating the percentage of available shoots browsed (i.e. the percentage of actual \u201cbites\u201d). Visual estimates of forage use and availability were then attributed to one of six classes (0-1%, 1-5%, 5-20%, 20-50%, 50-75%, 75-100%), converted to mid-point values for statistical analyses. For each circular plot (40 m2), we then calculated a browsing pressure index, B, based on the sum of the forage consumed weighted by forage availability. We then used the mean value of B for the three subplots as a representative measure of browsing pressure at study plot. Wild boar rooting was quantified at the sampling points by visually estimating the percentage of soil disturbed by wild boar rooting behavior. Observations of wild boar rooting were carried out in late winter at the same time as observations of deer browsing and were estimated on the same three circular plots used for estimating deer browsing pressure (40 m2). The mean percentage of wild boar rooting for the three subplots was used as a representative measure of wild boar rooting at the study plot level. Vegetation data At each sampling point, we recorded all vascular plant species according to their presence in two vertical understory vegetation layers. We defined the two vegetation layers in relation to their accessibility to one or both of the two deer species present in our study area: low understory layer accessible both to roe and red deer (up to 130 cm in height) and high understory layer accessible only to red deer (from 130 cm to 200 cm in height). However, the data for the two vegetation layers were merged (see below). We attributed plant cover values, to each species and for each vegetation layer, based on visual estimates to the nearest percent for common species (plant cover >1%) and to the nearest promille for rare species (plant cover <1%). Vegetation sampling was carried out by five experienced botanists (nBot1 = 75, nBot2 = 59, nBot2 = 26, nBot2 = 20, nBot2 = 10) that formed mixed teams composed of two observers (A and B) in order to minimize observer effects. A team was composed of either botanists A (n = 36) or B (n = 20) and any of the other botanists, or both of them (n = 39). In order to harmonize the sampling effort among study plots, teams spent at least 30 minutes of actively searching new species, excluding extra time that was added for species identification problems and estimations of plant cover values. We used a relatively large sample plot size (1 000 m2) as we were interested in capturing not only common but also rare species, while limiting the size in order to include only one forest habitat type. Plant functional traits We used a trait-based approach to determine any correlations between ungulate activities and understory plant community structure, composition and diversity. From the vegetation data, we derived three families of response variables: (i) species density and (ii) plant cover for qualitative traits (including plant functional groups and categorical habitat preferences), and (iii) community-weighted means (CWM) for quantitative trait values (including quantitative habitat preferences). Data on response variables were calculated at each sampling point for the overall plant community, and separately for four plant functional types (trees, shrubs, forbs and graminoids). Data on plant functional traits were extracted from four main sources: the LEDA and BiolFlor plant trait data bases, and the floras \u201cFlore Foresti\u00e8re Fran\u00e7aise\u201dand \u201cNouvelle flore de la Belgique, du Grand-Duch\u00e9 de Luxembourg, du Nord de la France et des R\u00e9gions voisines\u201d. Missing data was added by consulting the scientific literature. We used plant functional trait data of categorical traits of plants (plant life span, plant leaf vertical distribution and spiny plants) and seeds (seed life span, frugivory seed characteristics and seed appendages), quantitative traits of plants (specific leaf area, canopy height, plant leaf vertical distribution, plant life span) and seeds (seed mass, seed releasing height, seed longevity, seed shape), as well as qualitative and quantitative plant habitat preferences (forest history, successional stage, EUNIS main habitats, Ellenberg\u2019s indicator values and Grime\u2019s CSR-scheme). Observed differences at the community level of these plant and seed characteristics among study plots are likely to inform about the plant community\u2019s response to various ungulate activities related to trophic interactions (e.g. direct effects of grazing, browsing and frugivory) and engineering effects (e.g. direct effects of trampling, rooting, seed dispersal). Site characteristics In order to take into account possible confounding factors, known to be strong determinants of vegetation composition, we made a forest stand description and took soil samples at each sampling point. Forest stands were described by measuring the dominant tree height within a radius of 18 m from the sampling point, the basal area at 1.3 m (breast height, BAbh) and canopy openness. BAbh was estimated using point sampling methodology and by separately estimating BAbh for broadleaves (BAbroadleaves), conifers (BAconifers) and coppice (BAcoppice), which allowed us to calculate overall BAbh and canopy mixture (varying from 0%, pure stand, to 50% , equally mixed stand of broadleaves and coniferous tree species). We visually estimated canopy openness at sampling points along three radial transects (one measurement every two meter along the 16 m-long transects, a total of 27 measurements per sampling point). We also determined forest history at sampling points distinguishing between recent and ancient forests. Forest history was derived from three times series of historical maps (Carte d'\u00c9tat-Major) drawn between (i) 1820 and 1866 and aerial photographs for the periods (ii) 1947-1950 and (iii) 1975-1980. We classified forest stands at sampling points as ancient forests whenever continuous forest cover was observed for all three time series (i.e. forest as land use since at least 1820-1866), while stands were classified as recent forest whenever any other form of land use was described at the sampling points for any of the three time series. Soil samples of about 500 g were taken of the mineral soil at 20 cm depth at a distance of 10 m from the point center and in three different directions (0\u00b0, 120\u00b0 et 240\u00b0). Soil samples were sent to the Soil Analysis Laboratory of INRA, Arras France. Soil samples were analyzed for soil texture (particle-size fractions in percentage of sand, silt and clay), cation exchange capacity (CEC, cmol+/kg), organic carbon (C, g/kg), total nitrogen (N, g/kg), and extractable soil phosphorus (P2O5, g/kg). Total organic carbon and total nitrogen content in the soil was measured after dry combustion (ISO 10694, ISO 13878), and the cation exchange capacity (CEC) was determined by extracting exchangeable cations (Al3+, Ca2+, Fe2+, K+, Mg2+, Mn2+, Na+) using a hexamminecobalt trichloride solution (ISO 23470). Extractable soil phosphorus was determined using Duchaufour\u2019s method, which is a method appropriate for acidic forest soils. Soil pH was measured at our own laboratory using a pH-meter (Eutech Instruments Eco Scan 6+) in a 1:5 (volume fraction) suspension of soil in 1 mol/l potassium chloride solution (pHKCl) following the ISO 10390 standard. The majority of forest stands were recent forests (n = 76), while the remaining fifth were classified as ancient forests (n = 19). Two thirds of sampling points were situated in high stands dominanted by broadleafs (n = 63) composed of oak trees (Quercus petraea, Q. robur), while one third were in high stands of coniferous trees (n = 32) composed of pine trees (Pinus sylvestris, P. nigra subsp. laricio). Overall mean basal area was 22.6 \u00b1 0.9 m2/ha and the mean dominant tree height was 23.4 \u00b1 0.4 m. Coppice stools of Betula sp., Carpinus betulus, Castanea sativa, Corylus avellana and Quercus sp. were present in the understory at about one third (n = 35) of the sampling points with a mean basal area of 6.5 \u00b1 1.2 m2/ha. Soils were representative of the region characterized by low soil fertility of N (0.4 \u00b1 0.04 g/kg) and P2O5 (0.03 \u00b1 0.004 g/kg), low CEC (1.8 \u00b1 0.2), and high acidity (4.2 \u00b1 0.03).", "keywords": ["Earth and Environmental Science", "Cervidae", "Ecology", "plant community", "browsing", "Biospheric Sciences", "Biodiversity and Ecology", "Suidae", "Earth and Environmental Sciences", "ungulate", "species richness", "forest ecology", "Environmental Research", "Natural Sciences", "Geosciences", "biodiversity"], "contacts": [{"organization": "M\u00e5rell, Anders, Baltzinger, Christophe,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.15454/8DHNRM"}, {"rel": "self", "type": "application/geo+json", "title": "10.15454/8DHNRM", "name": "item", "description": "10.15454/8DHNRM", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.15454/8DHNRM"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-01-01T00:00:00Z"}}, {"id": "10.15454/9RDHIN", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:19:47Z", "type": "Dataset", "title": "French soil samples near infrared spectroscopy measurements and associated physico-chemical reference analysis.", "description": "This dataset presents near infrared spectra of soil samples from the experimental INRAE stations of the CAREX network including Auzeville, Epoisses, Crouel, Theix, Lusignan, Lusignan_Oasys and Ploudaniel sites (n=1040). Spectra data were acquired using a near infrared spectrometer BUCHI at Laboratoire d'Analyses des sols (LAS), Arras. The granulometric fractions and chemical properties measurements are available with their uncertainties. The tables of NIR spectra and chemical analysis and granulometry of soils from Is\u00e8re (n=28) and from Plaine_de_Versailles (n=99) locations were added. The details of the transformed NIR spectra table of Plaine_de_Versailles are available at https://doi.org/10.15454/LXKFAS.", "keywords": ["Earth and Environmental Science", "Soils and soil sciences", "Chemistry and chemical engineering", "Chemiometrics", "15. Life on land", "Construction Engineering and Architecture", "Chemistry", "Soil", "Engineering", "Earth and Environmental Sciences", "Soil Sciences", "Engineering Sciences", "Environmental Research", "Natural Sciences", "Geosciences", "Near Infrared spectroscopy"], "contacts": [{"organization": "Thoisy, Jeanne, Mistou, Marie-Noel, Latrille, Eric, Etayo, Amandine, Rossard, Virginie, Fouad, Youssef, Girardin, Cyril, Gog\u00e9, Fabien,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.15454/9RDHIN"}, {"rel": "self", "type": "application/geo+json", "title": "10.15454/9RDHIN", "name": "item", "description": "10.15454/9RDHIN", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.15454/9RDHIN"}, {"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": "10.15454/CFWBAA", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:19:47Z", "type": "Dataset", "title": "Donn\u00e9es de r\u00e9plication pour\u00a0: Agriculture increases the bioavailability of silicon, a beneficial element for crop, in temperate soils", "description": "These data concern the study 'Agriculture increases the bioavailability of Silicon, a beneficial element for crop, in temperate soils' published in nature communication. This work was performed in the frame of the French ANR BioSiSol project (ANR-14-CE01-0002) and is based on data from the RMQS program (French Soil Quality Monitoring Network). The French Soil Quality Monitoring Network (RMQS) is a national program for the assessment and long-term monitoring of the quality of French soils. This network is based on the monitoring of 2240 sites representative of French soils and their land use. These sites are spread over the whole French territory (metropolitan and overseas) along a systematic square grid of 16 km x 16 km cells. The network covers a broad spectrum of climatic, soil and land-use conditions (croplands, permanent grasslands, woodlands, orchards and vineyards, natural or scarcely anthropogenic land and urban parkland). The physical, chemical and biological properties of the soil are measured on each site. These soil analyses were carried out by the Soil Analysis Laboratory of INRAE (Arras, France). The spatial and temporal variability of soil properties are explained by biophysical variables, sources of contamination, history of land-use and management practices on each plot. The first sampling campaign in metropolitan France took place from 2000 to 2009 and the second campaign has begun in 2016. At each site, 25 core samples were taken by layer with an auger within a 20 m \u00d7 20 m plot and combined into a composite sample. Analyses used in this study only concern the surface layer (generally 0\u201330 cm layer) of samplings from the first campaign in metropolitan France. The analyses of total Si and available Si were made in 2016 on samples stored from the first sampling campaign. The dataset published contains all the raw data used in the statistical analysis in order to make them available for any further study. The table contains soil properties (total and available Si, particle size fraction, organic carbon content ...), observations about soil and land use, and spatial coordinates. We warn the user that coordinates published here are theoretical coordinates, the RMQS site can be located until 1 km around this point. Real coordinates can not be made publicly available because of confidential information.", "keywords": ["2. Zero hunger", "Earth and Environmental Science", "Agricultural Sciences", "silicon", "Life Sciences", "Agriculture", " Forestry", " Horticulture", " Aquaculture", "15. Life on land", "TER sciences du sol", "soil", "bioavailable silicon", "Earth and Environmental Sciences", "Agriculture", " Forestry", " Horticulture", " Aquaculture and Veterinary Medicine", "Environmental Research", "Natural Sciences", "RMQS", "Agriculture", " Forestry", " Horticulture", "Geosciences", "AGR farms and farming systems", "agriculture"], "contacts": [{"organization": "Saby, Nicolas P.A., Caubet, Manon, Cornu, Sophie, Meunier, Jean-Dominique, Boulonne, Line, Rati\u00e9, C\u00e9line, Jolivet, Claudy,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.15454/CFWBAA"}, {"rel": "self", "type": "application/geo+json", "title": "10.15454/CFWBAA", "name": "item", "description": "10.15454/CFWBAA", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.15454/CFWBAA"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-01T00:00:00Z"}}, {"id": "10.15454/OGJNIC", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:19:47Z", "type": "Dataset", "title": "Dataset for the Global Change Biology paper \"Feasibility of the 4 per 1000 aspirational target for soil carbon. A case study for France\", published as part of the French 4 per mille study.", "description": "Increasing soil organic carbon (SOC) stocks is a promising way to mitigate the increase in atmospheric CO2 concentration. Based on a simple ratio between CO2 anthropogenic emissions and SOC stocks worldwide, it has been suggested that a 0.4% (4 per 1000) yearly increase of SOC stocks could compensate for current anthropogenic CO2 emissions. Here, we used a reverse RothC modelling approach to estimate the amount of C inputs to soils required to sustain current SOC stocks and to increase them by 4\u2030 per year over a period of 30 years. We assessed the feasibility of this aspirational target first by comparing the amount of C inputs required with net primary productivity (NPP) flowing to the soil, and second by considering the SOC saturation concept. Calculations were performed for mainland France, at a 1 km grid cell resolution. This dataset gives the main results supporting these conclusions, as well as the uncertainty attached to these results.", "keywords": ["Earth and Environmental Science", "Soils and soil sciences", "Agricultural Sciences", "carbon", "Life Sciences", "Agriculture", " Forestry", " Horticulture", " Aquaculture", "15. Life on land", "7. Clean energy", "soil", "soil organic carbon", "13. Climate action", "Earth and Environmental Sciences", "Soil Sciences", "Forests and Forest Products", "Agriculture", " Forestry", " Horticulture", " Aquaculture and Veterinary Medicine", "net primary productivity", "Other", "Environmental Research", "Natural Sciences", "Silviculture", "Agriculture", " Forestry", " Horticulture", "Geosciences"], "contacts": [{"organization": "Martin, Manuel, Bassem Dimassi, Mercedes Rom\u00e0n Dobarco, Bertrand Guenet, Dominique Arrouays, Denis A. Angers, Fabrice Blache, Fr\u00e9d\u00e9ric Huard, Jean-Fran\u00e7ois Soussana, Sylvain Pellerin,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.15454/OGJNIC"}, {"rel": "self", "type": "application/geo+json", "title": "10.15454/OGJNIC", "name": "item", "description": "10.15454/OGJNIC", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.15454/OGJNIC"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-01T00:00:00Z"}}, {"id": "10.15454/OVA9SO", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:19:47Z", "type": "Dataset", "title": "Fine resolution map of top- and subsoil carbon sequestration potential in France", "description": "Although soils have a high potential to offset CO2 emissions through its conversion into soil organic carbon (SOC) with long turnover time, it is widely accepted that there is an upper limit of soil stable C storage, which is referred to SOC saturation. In this study we estimate SOC saturation in French topsoil (0\u201330 cm) and subsoil (30\u201350 cm), using the Hassink equation and calculate the additional SOC sequestration potential (SOCsp) by the difference between SOC saturation and fine fraction C on an unbiased sampling set of sites covering whole mainland France. We then map with fine resolution the geographical distribution of SOCsp over the French territory using a regression Kriging approach with environmental covariates. Results show that the controlling factors of SOCsp differ from topsoil and subsoil. The main controlling factor of SOCsp in topsoils is land use. Nearly half of forest topsoils are over-saturated with a SOCsp close to 0 (mean and standard error at 0.19 \u00b1 0.12) whereas cropland, vineyard and orchard soils are largely unsaturated with degrees of C saturation deficit at 36.45 \u00b1 0.68% and 57.10 \u00b1 1.64%, respectively. The determinant of C sequestration potential in subsoils is related to parent material. There is a large additional SOCsp in subsoil for all land uses with degrees of C saturation deficit between 48.52 \u00b1 4.83% and 68.68 \u00b1 0.42%. Overall the SOCsp for French soils appears to be very large (1008 Mt C for topsoil and 1360 Mt C for subsoil) when compared to previous total SOC stocks estimates of about 3.5 Gt in French topsoil. Our results also show that overall, 176 Mt C exceed C saturation in French topsoil and might thus be very sensitive to land use change.", "keywords": ["soil organic carbon", "Earth and Environmental Science", "Soils and soil sciences", "13. Climate action", "Earth and Environmental Sciences", "Soil Sciences", "top soil", "15. Life on land", "Environmental Research", "Natural Sciences", "carbon sequestration", "Geosciences"], "contacts": [{"organization": "Chen, Songchao, Martin, Manuel, P., Saby, Nicolas P.A., Walter, Christian, Angers, Denis A., Arrouays, Dominique,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.15454/OVA9SO"}, {"rel": "self", "type": "application/geo+json", "title": "10.15454/OVA9SO", "name": "item", "description": "10.15454/OVA9SO", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.15454/OVA9SO"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-01-01T00:00:00Z"}}, {"id": "10.15454/QSXKGA", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:19:47Z", "type": "Dataset", "title": "Analyses physico-chimiques des sites du R\u00e9seau de Mesures de la Qualit\u00e9 des Sols (RMQS) du territoire m\u00e9tropolitain pour la 1\u00e8re campagne (2000-2009), avec coordonn\u00e9es th\u00e9oriques", "description": "Le R\u00e9seau de mesures de la qualit\u00e9 des sols (RMQS) est un programme national d\u2019\u00e9valuation et de suivi \u00e0 long terme de la qualit\u00e9 des sols fran\u00e7ais. Ce r\u00e9seau repose sur le suivi de 2240 sites repr\u00e9sentatifs des sols fran\u00e7ais et de leurs occupations, r\u00e9partis sur l\u2019ensemble du territoire fran\u00e7ais (m\u00e9tropole et outre-mer) selon une grille syst\u00e9matique de 16 km de c\u00f4t\u00e9. Les sites recouvrent diverses occupations (grandes cultures, prairies permanentes, for\u00eats, vignes et vergers, milieux peu anthropis\u00e9s, parcs urbains). Des propri\u00e9t\u00e9s physiques, chimiques et biologiques des sols sont mesur\u00e9es sur chaque site, par campagne. Ces analyses sont associ\u00e9es \u00e0 la recherche des facteurs explicatifs de la variabilit\u00e9 spatiale et temporelle des propri\u00e9t\u00e9s des sols (variables biophysiques, sources de contamination, historique de l\u2019occupation et des pratiques de gestion de chaque site). La premi\u00e8re campagne de pr\u00e9l\u00e8vement en m\u00e9tropole s'est d\u00e9roul\u00e9e de 2000 \u00e0 2009. Cette campagne, ax\u00e9e sur la contamination des sols, a permis de cartographier les principaux param\u00e8tres p\u00e9dologiques (28 variables) ainsi que les teneurs en 12 \u00e9l\u00e9ments traces m\u00e9talliques (ETM) en extraction totale ou partielle et 70 polluants organiques persistants. L\u2019ensemble des pr\u00e9l\u00e8vements, mesures et observations r\u00e9alis\u00e9s sur chaque site durant cette campagne est d\u00e9taill\u00e9 dans le Manuel de la premi\u00e8re campagne du R\u00e9seau de Mesures de la Qualit\u00e9 des Sols . La deuxi\u00e8me campagne a d\u00e9marr\u00e9 en 2016 et devrait s\u2019\u00e9tendre sur 12 ans. Le jeu de donn\u00e9es fourni comprend les r\u00e9sultats d\u2019analyses issus de 2171 sites de m\u00e9tropole, r\u00e9partis sur 2146 cellules et correspondant \u00e0 la premi\u00e8re campagne RMQS (2000 -2009) et pour les param\u00e8tres suivants : granulom\u00e9trie 5 fractions, carbone et azote totaux, capacit\u00e9 d\u2019\u00e9change cationique et cations \u00e9changeables, calcaire total, pH eau, phosphore assimilable (P2O5), fer libre, \u00e9l\u00e9ments majeurs totaux et \u00e9l\u00e9ments traces m\u00e9talliques totaux et une partie des ETM en extraction partielle, conductivit\u00e9 \u00e9lectrique et \u00e9l\u00e9ments solubles \u00e0 l\u2019eau pour une s\u00e9lection de sites concern\u00e9s. Les analyses ont \u00e9t\u00e9 r\u00e9alis\u00e9es sur des \u00e9chantillons composites pr\u00e9lev\u00e9s \u00e0 la tari\u00e8re selon deux couches de pr\u00e9l\u00e8vement (0-30 cm ou couche travaill\u00e9e en sol cultiv\u00e9, appel\u00e9e composite de surface ou composite 1 et la couche sous-jacente jusqu\u2019\u00e0 50 cm, appel\u00e9e composite de sub-surface ou composite 2). Chaque \u00e9chantillon composite a \u00e9t\u00e9 constitu\u00e9 \u00e0 partir du m\u00e9lange de 25 pr\u00e9l\u00e8vements individuels pr\u00e9lev\u00e9s sur une surface d\u2019\u00e9chantillonnage de 400 m\u00b2 selon un plan d\u2019\u00e9chantillonnage al\u00e9atoire stratifi\u00e9 (voir description dans le manuel du RMQS). Une troisi\u00e8me couche de pr\u00e9l\u00e8vement issue d\u2019\u00e9chantillons composites a pu \u00eatre constitu\u00e9e en for\u00eat ou en prairie, \u00e0 partir des horizons holorganiques (correspondant aux horizons p\u00e9dologiques OF et OH) lorsque ces horizons \u00e9taient suffisamment \u00e9pais (au moins 1 cm) et continus sur la surface d\u2019\u00e9chantillonnage. English version The French Soil Quality Monitoring Network (RMQS) is a national program for the assessment and long-term monitoring of the quality of French soils. This network is based on the monitoring of 2240 sites representative of French soils and their land use. These sites are spread over the whole French territory (metropolitan and overseas) along a systematic square grid of 16 km x 16 km cells. The network covers a broad spectrum of climatic, soil and land-use conditions (croplands, permanent grasslands, woodlands, orchards and vineyards, natural or scarcely anthropogenic land and urban parkland). The physical, chemical and biological properties of the soil are measured on each site, during the first campaign et presently on the second campaign. The spatial and temporal variability of soil properties are explained by biophysical variables, sources of contamination, history of land-use and management practices on each plot. The first sampling campaign in metropolitan France took place from 2000 to 2009. This campaign focused on soil contamination assessment and made it possible to map key soil parameters (28 variables) as well as 12 trace metal elements and 70 persistent organic pollutants. The sampling method, measurements and observations on each site are described in the \u201cRMQS guidelines\u201d1 (in French: \u201cmanuel du RMQS\u201d). The second campaign started in 2016 and should last 12 years. The dataset includes the results of soil analyses from 2171 sites, spread over 2146 cells and corresponding to the first RMQS campaign (2000-2009). Analysed parameters are particle size analysis (5 fractions), total carbon and nitrogen, cation exchange capacity and exchangeable cations, total calcium carbonates, pH in water, available P2O5, free iron, total major elements and trace elements (total and partial extraction). The soil tests were carried out on composite samples collected with an auger at two sampling layers: 0-30 cm or cultivated layer named \u201cupper layer\u201d or \u201clayer 1\u201d and 30-50 cm named \u201csubsoil layer\u201d or \u201clayer 2\u201d. Each composite sample was made up of 25 individual sample cores taken on a sampling area of 400 m\u00b2, using an unaligned systematic sampling design (see description in the RMQS guidelines). In some cases a third layer was made up of holorganic layers in forests or meadows, corresponding to pedological horizons OF and OH, when these layers were sufficiently thick, (at least 1 cm) and continuous over the sampling area.", "keywords": ["Earth and Environmental Science", "sol", "pH du sol", "p\u00e9dologie (geosciences)", "TER sciences du sol", "capacit\u00e9 d \u00e9change cationique", "fer", "carbonate", "granulom\u00e9trie du sol", "Earth and Environmental Sciences", "conductivit\u00e9 \u00e9lectrique", "p\u00e9dologie geosciences", "carbone du sol", "phosphore du sol", "Environmental Research", "Natural Sciences", "capacit\u00e9 d'\u00e9change cationique", "azote du sol", "Geosciences"], "contacts": [{"organization": "Institut National de la Recherche Agronomique, Association marnaise de d\u00e9veloppement agricole et viticole, Association pour la Relance Agronomique en Alsace (ARAA), Chambre D\u00e9partementale d'Agriculture des Ardennes, Chambre D\u00e9partementale d'Agriculture de l'Aube, Chambre D\u00e9partementale d'Agriculture du Calvados, Chambre D\u00e9partementale d'Agriculture de Charente, Chambre D\u00e9partementale d'Agriculture de Charente-Maritime, Chambre D\u00e9partementale d'Agriculture du Cher, Chambre D\u00e9partementale d'Agriculture de Corr\u00e8ze, Chambre D\u00e9partementale d'Agriculture de la C\u00f4te d'Or, Chambre D\u00e9partementale d'Agriculture de Creuse, Chambre D\u00e9partementale d'Agriculture des Deux-S\u00e8vres, Chambre D\u00e9partementale d'Agriculture de l'Eure, Chambre D\u00e9partementale d'Agriculture de l'Eure-et-Loir, Chambre D\u00e9partementale d'Agriculture de Haute-Marne, Chambre D\u00e9partementale d'Agriculture de Haute-Vienne, Chambre D\u00e9partementale d'Agriculture de l'Indre, Chambre D\u00e9partementale d'Agriculture de Loire-Atlantique, Chambre D\u00e9partementale d'Agriculture de Mayenne, Chambre D\u00e9partementale d'Agriculture de la Manche, Chambre D\u00e9partementale d'Agriculture de Mayenne, Chambre D\u00e9partementale d'Agriculture de la Ni\u00e8vre, Chambre D\u00e9partementale d'Agriculture de l'Orne, Chambre D\u00e9partementale d'Agriculture de Sa\u00f4ne-et-Loire, Chambre D\u00e9partementale d'Agriculture de Seine-et-Marne, Chambre D\u00e9partementale d'Agriculture de Seine-Maritime, Chambre D\u00e9partementale d'Agriculture de Vend\u00e9e, Chambre D\u00e9partementale d'Agriculture de la Vienne, Chambre D\u00e9partementale d'Agriculture de l'Yonne, Chambre R\u00e9gionale d'Agriculture de Bretagne, Chambre R\u00e9gionale d'Agriculture de Lorraine, Chambre R\u00e9gionale d'Agriculture de Picardie, Chambre R\u00e9gionale d'Agriculture de Poitou-Charentes, Conseil G\u00e9n\u00e9ral de Mayenne, \u00c9cole nationale d'ing\u00e9nieurs des travaux agricoles de Bordeaux (ENITAB), EDIACARA, \u00c9tablissement national d'enseignement sup\u00e9rieur agronomique de Dijon (ENESAD), Genevois-Gomendy-Sol et Environnement, Groupe R\u00e9gional Agronomie P\u00e9dologie Environnement (GRAPE Franche-Comt\u00e9), Institut Forestier National (IFN), Institut National d'Horticulture d'Angers (INH), Institut National Polytechnique - Ecole Nationale Sup\u00e9rieure d'Agronomie de Toulouse (INP-ENSAT), Institut Sup\u00e9rieur d'Agriculture de Lille (ISA), SCITERRE, Soci\u00e9t\u00e9 du Canal de Provence et d'Am\u00e9nagement de la R\u00e9gion Proven\u00e7ale (SCP), Sol Info Rh\u00f4ne Alpes - Chambre R\u00e9gionale d'Agriculture de Rh\u00f4ne-Alpes (SIRA),", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.15454/QSXKGA"}, {"rel": "self", "type": "application/geo+json", "title": "10.15454/QSXKGA", "name": "item", "description": "10.15454/QSXKGA", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.15454/QSXKGA"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-01T00:00:00Z"}}, {"id": "10.15454/gxlrhg", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:19:48Z", "type": "Dataset", "title": "European climate indicators datasets", "description": "Open Access
This dataset corresponds to a datamart produced by the WP2 team of the Landmark H2020 project.
A specific request consists in the computation of a limited number of climate indicators for each grid cells and needed to run the dexi models. They are calculated for each year.
Those indicators was calculated for 2 periods:
Photochemical degradation of dissolved organic matter (DOM) has been the subject of numerous studies; however, its regulation along the inland water continuum is still unclear. We aimed to unravel the DOM photoreactivity and concurrent DOM compositional changes across 30 boreal aquatic ecosystems including peat waters, streams, rivers, and lakes distributed along a water residence time (WRT) gradient. Samples were subjected to a standardized exposure of simulated sunlight. We measured the apparent quantum yield (AQY), which corresponds to DOM photomineralization per photon absorbed, and the compositional change in DOM at bulk and individual compound levels in the original samples and after irradiation. AQY increased with the abundance of terrestrially derived DOM and decreased at higher WRT. Additionally, the photochemical changes in both DOM optical properties and molecular composition resembled changes along the natural boreal WRT gradient at low WRT (<3\uffc2\uffa0years). Accordingly, mass spectrometry revealed that the abundance of photolabile and photoproduced molecules decreased with WRT along the boreal aquatic continuum. Our study highlights the tight link between DOM composition and DOM photodegradation. We suggest that photodegradation is an important driver of DOM composition change in waters with low WRT, where DOM is highly photoreactive.Microbial activity in drylands tends to be confined to rare and short periods of rain. Rapid growth should be key to the maintenance of ecosystem processes in such narrow activity windows, if desiccation and rehydration cause widespread cell death due to osmotic stress. Here, simulating rain with 2H2O followed by single-cell NanoSIMS, we show that biocrust microbial communities in the Negev Desert are characterized by limited productivity, with median replication times of 6 to 19 days and restricted number of days allowing growth. Genome-resolved metatranscriptomics reveals that nearly all microbial populations resuscitate within minutes after simulated rain, independent of taxonomy, and invest their activity into repair and energy generation. Together, our data reveal a community that makes optimal use of short activity phases by fast and universal resuscitation enabling the maintenance of key ecosystem functions. We conclude that desert biocrust communities are highly adapted to surviving rapid changes in soil moisture and solute concentrations, resulting in high persistence that balances limited productivity.