{"type": "FeatureCollection", "features": [{"id": "10.14393/bj-v31n4a2015-26218", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:19:31Z", "type": "Journal Article", "created": "2015-07-01", "title": "Least Limiting Water Range And Degree Of Compactness Of Soils Under No- Tillage", "description": "<p>The least limiting water range (LLWR) and degree of compactness (DC) can be useful indicators of soil physical quality and crop yield. This study focused on assessing of LLWR, DC and evaluation of critical values to crop growth of an Alfisol and Oxisol under no-till management. Undisturbed soil cores were taken from the layer 0.00 - 0.20 m depth. Soil water retention curve, soil penetration resistance curve, air-filled porosity and bulk density (Bd) were measured. The range of LLWR variation was limited by volumetric water content at field capacity and penetration resistance. Values of LLWR varied from 0.00 - 0.14 m3 m-3 to Alfisol and 0.00 - 0.04 m3 m-3 to Oxisol. The critical values of the Bd and DC for crop development were 1.79 Mg m-3 and 1.35 Mg m-3 and 96% and 74% to Alfisol and Oxisol, respectively. Further researches relating LLWR, DC and crop response are still required in soils with different conditions and management.</p>", "keywords": ["2. Zero hunger", "bulk density", "porosity", "soil strength", "S", "QH301-705.5", "Soil strength", "Agriculture", "04 agricultural and veterinary sciences", "15. Life on land", "Soil quality", "Bulk density", "0401 agriculture", " forestry", " and fisheries", "soil quality", "Biology (General)", "Porosity"]}, "links": [{"href": "https://doi.org/10.14393/bj-v31n4a2015-26218"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Bioscience%20Journal", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.14393/bj-v31n4a2015-26218", "name": "item", "description": "10.14393/bj-v31n4a2015-26218", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.14393/bj-v31n4a2015-26218"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2015-06-30T00:00:00Z"}}, {"id": "10.1590/s0100-06832011000200028", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:19:44Z", "type": "Journal Article", "created": "2011-06-21", "title": "Sistemas De Preparo Do Solo E Culturas De Cobertura Na Produ\u00e7\u00e3o Org\u00e2nica De Feij\u00e3o E Milho: I - Atributos F\u00edsicos Do Solo", "description": "<p>H\uffc3\uffa1 necessidade de se avaliar a contribui\uffc3\uffa7\uffc3\uffa3o de culturas de cobertura e do seu manejo na manuten\uffc3\uffa7\uffc3\uffa3o da qualidade biol\uffc3\uffb3gica do solo em \uffc3\uffa1reas sob produ\uffc3\uffa7\uffc3\uffa3o org\uffc3\uffa2nica. Este trabalho objetivou determinar a influ\uffc3\uffaancia das plantas de cobertura crotal\uffc3\uffa1ria (Crotalaria juncea), guandu (Cajanus cajan (L.) Millsp), mucuna-preta (Mucuna aterrima), sorgo-vassoura (Sorghum technicum) e pousio nos atributos biol\uffc3\uffb3gicos de solo cultivado com feij\uffc3\uffa3o e milho org\uffc3\uffa2nicos, sob semeadura direta (SD) e preparo convencional (PC). O trabalho foi conduzido em Santo Ant\uffc3\uffb4nio de Goi\uffc3\uffa1s-GO, em Latossolo Vermelho distr\uffc3\uffb3fico, no delineamento de blocos ao acaso, com quatro repeti\uffc3\uffa7\uffc3\uffb5es. Em novembro de 2003 foram instalados quatro experimentos, dois em SD e dois em PC, sendo um com feij\uffc3\uffa3o e outro com milho em cada sistema. Amostragens de solo das parcelas e de uma mata pr\uffc3\uffb3xima aos experimentos foram realizadas em novembro de 2007, nas camadas de 0,00-0,10 e 0,10-0,20 m, para determina\uffc3\uffa7\uffc3\uffa3o do teor de C org\uffc3\uffa2nico total (COT), carbono da biomassa microbiana (CBM), respira\uffc3\uffa7\uffc3\uffa3o basal do solo (RBS), quociente metab\uffc3\uffb3lico (qCO2) e quociente microbiano (qMIC). As principais altera\uffc3\uffa7\uffc3\uffb5es nos atributos biol\uffc3\uffb3gicos com o uso agr\uffc3\uffadcola ocorreram na camada superficial, onde, de maneira geral, os valores de CBM foram menores que no solo sob mata, sendo esse fato mais pronunciado nas \uffc3\uffa1reas sob PC. O qCO2 mostrou-se sens\uffc3\uffadvel \uffc3\uffa0s altera\uffc3\uffa7\uffc3\uffb5es decorrentes do preparo do solo, apresentando valores mais favor\uffc3\uffa1veis na camada superficial do solo sob SD.</p>", "keywords": ["microbial quotient", "respira\u00e7\u00e3o basal do solo", "bulk density", "Agriculture (General)", "soil porosity", "quociente metab\u00f3lico", "Zea mays", "S1-972", "S index", "soil basal respiration", "Phaseolus vulgaris L", "densidade do solo", "2. Zero hunger", "04 agricultural and veterinary sciences", "\u00edndice S", "15. Life on land", "porosidade do solo", "6. Clean water", "soil organic carbon", "C org\u00e2nico do solo", "microbial biomass carbon", "carbono da biomassa microbiana", "0401 agriculture", " forestry", " and fisheries", "metabolic quotient", "quociente microbiano"]}, "links": [{"href": "https://doi.org/10.1590/s0100-06832011000200028"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Revista%20Brasileira%20de%20Ci%C3%AAncia%20do%20Solo", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1590/s0100-06832011000200028", "name": "item", "description": "10.1590/s0100-06832011000200028", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1590/s0100-06832011000200028"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2011-04-01T00:00:00Z"}}, {"id": "10.1590/s0100-06832012000600006", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:19:44Z", "type": "Journal Article", "created": "2013-02-02", "title": "Structural Sustainability Of Cambisol Under Different Land Use System", "description": "<p>Incongruous management techniques have been associated with some significant loss of agricultural land to degradation in many parts of the world. Land degradation results in the alteration of physical, chemical and biological properties of the soil, thereby posing a serious threat to sustainable agricultural development. In this study, our objective is to evaluate the changes in a Cambisol structure under six land use systems using the load bearing capacity model. Sampling was conducted in Amazonas Region, Brazil, in the following land use: a) young secondary forest; b) old secondary forest; c) forest; d) pasture; e) cropping, and f) agroforestry. To obtain the load bearing capacity models the undisturbed soil samples were collected in those land use systems and subjected to the uniaxial compression test. These models were used to evaluate which land use system preserved or degraded the Cambisol structure. The results of the bulk density and total porosity of the soil samples were not adequate to quantify structural degradation in Cambisol. Using the forest topsoil level (0-0.03 m) as a reference, it was observed that pasture land use system was most severe in the degradation of the soil structure while the structure were most preserved under old secondary forest, cropping system and forest. At the subsoil level (0.10-0.13 m depth), the soil structure was most degraded in the cropping land use system while it was most preserved in young secondary forest and pasture. At the 0.20-0.23 m depth, soil structure degradation was most severe in the old secondary forest system and well preserved in young secondary forest, cropping and agroforestry.</p>", "keywords": ["2. Zero hunger", "bulk density", "Agriculture (General)", "degrada\u00e7\u00e3o da estrutura", "precompression stress", "Amazonas", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "S1-972", "12. Responsible consumption", "press\u00e3o de pr\u00e9-consolida\u00e7\u00e3o", "structure degradation", "0401 agriculture", " forestry", " and fisheries", "densidade do solo", "0105 earth and related environmental sciences"], "contacts": [{"organization": "Martins, Paula Cristina Caruana, Dias Junior, Moacir de Souza, Ajayi, Ayodele Ebenezer, Moreira, F\u00e1tima Maria de Souza,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1590/s0100-06832012000600006"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Revista%20Brasileira%20de%20Ci%C3%AAncia%20do%20Solo", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1590/s0100-06832012000600006", "name": "item", "description": "10.1590/s0100-06832012000600006", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1590/s0100-06832012000600006"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2012-12-01T00:00:00Z"}}, {"id": "10.1590/s0100-06832008000400008", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:19:43Z", "type": "Journal Article", "created": "2008-10-15", "title": "Short And Long-Term Effects Of Tillage Systems And Nutrient Sources On Soil Physical Properties Of A Southern Brazilian Hapludox", "description": "<p>Soil tillage promotes changes in soil structure. The magnitude of the changes varies with the nature of the soil, tillage system and soil water content and decreases over time after tillage. The objective of this study was to evaluate short-term (one year period) and long-term (nine year period) effects of soil tillage and nutrient sources on some physical properties of a very clayey Hapludox. Five tillage systems were evaluated: no-till (NT), chisel plow + one secondary disking (CP), primary + two (secondary) diskings (CT), CT with burning of crop residues (CTb), and CT with removal of crop residues from the field (CTr), in combination with five nutrient sources: control without nutrient application (C); mineral fertilizers, according to technical recommendations for each crop (MF); 5 Mg ha-1 yr-1 of poultry litter (wetmatter) (PL); 60 m\uffc2\uffb3 ha-1 yr-1 of cattle slurry (CS) and; 40 m\uffc2\uffb3 ha-1 yr-1 of swine slurry (SS). Bulk density (BD), total porosity (TP), and parameters related to the water retention curve (macroporosity, mesoporosity and microporosity) were determined after nine years and at five sampling dates during the tenth year of the experiment. Soil physical properties were tillage and time-dependent. Tilled treatments increased total porosity and macroporosity, and reduced bulk density in the surface layer (0.00-0.05 m), but this effect decreased over time after tillage operations due to natural soil reconsolidation, since no external stress was applied in this period. Changes in pore size distribution were more pronounced in larger and medium pore diameter classes. The bulk density was greatest in intermediate layers in all tillage treatments (0.05-0.10 and 0.12-0.17 m) and decreased down to the deepest layer (0.27-0.32 m), indicating a more compacted layer around 0.05-0.20 m. Nutrient sources did not significantly affect soil physical and hydraulic properties studied.</p>", "keywords": ["2. Zero hunger", "distribui\u00e7\u00e3o de tamanho de poros", "bulk density", "pore size distribution", "manure", "dejetos animais", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "densidade do solo", "6. Clean water"]}, "links": [{"href": "https://doi.org/10.1590/s0100-06832008000400008"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Revista%20Brasileira%20de%20Ci%C3%AAncia%20do%20Solo", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1590/s0100-06832008000400008", "name": "item", "description": "10.1590/s0100-06832008000400008", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1590/s0100-06832008000400008"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2008-08-01T00:00:00Z"}}, {"id": "10.1590/s0100-06832014000400021", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:19:44Z", "type": "Journal Article", "created": "2014-10-04", "title": "The Effects Of Land Use And Soil Management On The Physical Properties Of An Oxisol In Southeast Brazil", "description": "<p>Soils of the tropics are prone to a decrease in quality after conversion from native forest (FO) to a conventional tillage system (CT). However, the adoption of no-tillage (NT) and complex crop rotations may improve soil structural quality. Thus, the aim of this study was to evaluate the physical properties of an Oxisol under FO, CT, and three summer crop sequences in NT: continuous corn (NTcc), continuous soybean (NTcs), and a soybean/corn rotation (NTscr). Both NT and CT decreased soil organic carbon (SOC) content, SOC stock, water stable aggregates (WSA), geometric mean diameter (GMD), soil total porosity (TP), macroporosity (MA), and the least limiting water range (LLWR). However they increased soil bulk density (BD) and tensile strength (TS) of the aggregates when compared to soil under FO. Soil under NT had higher WSA, GMD, BD, TS and microporosty, but lower TP and MA than soil under CT. Soil under FO did not attain critical values for the LLWR, but the lower limit of the LLWR in soils under CT and NT was resistance to penetration (RP) for all values of BD, while the upper limit of field capacity was air-filled porosity for BD values greater than 1.46 (CT), 1.40 (NTscr), 1.42 (NTcc), and 1.41 (NTcs) kg dm-3. Soil under NTcc and NTcs decreased RP even with the increase in BD because of the formation of biopores. Furthermore, higher critical BD was verified under NTcc (1.62 kg dm-3) and NTcs (1.57 kg dm-3) compared to NTscr and CT (1.53 kg dm-3).</p>", "keywords": ["bulk density", "Agriculture (General)", "No-tillage", "Soil aggregate", "no-tillage", "Solo tropical", "Agregado do solo", "solo tropical", "carbono org\u00e2nico do solo", "S1-972", "soil aggregate", "densidade do solo", "2. Zero hunger", "Soil organic carbon", "Cerrado", "04 agricultural and veterinary sciences", "Plantio direto", "15. Life on land", "Bulk density", "soil organic carbon", "plantio direto", "agregado do solo", "tropical soil", "0401 agriculture", " forestry", " and fisheries", "Densidade do solo", "Carbono org\u00e2nico do solo", "Tropical soil"], "contacts": [{"organization": "Seben Junior, Getulio de Freitas, Cor\u00e1, Jos\u00e9 Eduardo, Lal, Rattan,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1590/s0100-06832014000400021"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Revista%20Brasileira%20de%20Ci%C3%AAncia%20do%20Solo", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1590/s0100-06832014000400021", "name": "item", "description": "10.1590/s0100-06832014000400021", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1590/s0100-06832014000400021"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2014-08-01T00:00:00Z"}}, {"id": "10.1594/pangaea.194648", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:19:46Z", "type": "Dataset", "title": "Physical oceanography at station JCR2_CTD24", "description": "Plymouth Marine Laboratory. Authorship was originally 'BODC' and was changed by request of the BODC.", "keywords": ["Salinity", "potential", "Density", " sigma-theta (0)", "water", "James Clark Ross", "Fluorometer", "Density", "Irradiance", " downward PAR", "CTD/Rosette", "Fluorescence", "hemispherical photodiode", "Temperature", " water", "Irradiance", "Pressure", "Calculated", "chlorophyll", "14. Life underwater", "Fluorometer", " in-situ", "Temperature", " water", " potential", "Fluorescence", " chlorophyll", "Sigma theta (Computed by UNESCO SVAN function)", "DEPTH", " water", "downward PAR", "Temperature", "in situ", "CTD Rosette", "Sigma theta Computed by UNESCO SVAN function", "Light meter", " hemispherical photodiode", "Light meter", "Joint Global Ocean Flux Study JGOFS", "sigma theta 0", "CTD", "JR19921026", "660 nm", "Optical beam attenuation coefficient", "DEPTH", "Earth System Research", "Optical beam attenuation coefficient", " 660 nm", "Transmissometer", "Pressure", " water", "Joint Global Ocean Flux Study (JGOFS)"], "contacts": [{"organization": "Owens, Nick", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1594/pangaea.194648"}, {"rel": "self", "type": "application/geo+json", "title": "10.1594/pangaea.194648", "name": "item", "description": "10.1594/pangaea.194648", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1594/pangaea.194648"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2004-01-01T00:00:00Z"}}, {"id": "10.1594/pangaea.261535", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:19:46Z", "type": "Dataset", "title": "Physical properties of sediment core GIK23291-2", "keywords": ["wet bulk", "M7/3", "Gravity corer (Kiel type)", "Density", "DEPTH", " sediment/rock", "Quaternary Environment of the Eurasian North QUEEN", "Water content", " dry mass", "dry mass", "M7 3", "Density", " dry bulk", "Calculated", "Pore number", "Meteor 1986", "Water content", "Meteor (1986)", "dry bulk", "Calculated from mass volume", "Global Environmental Change: The Northern North Atlantic (SFB313)", "sediment rock", "Gravity corer Kiel type", "Density", " wet bulk", "Calculated from weight/volume", "Quaternary Environment of the Eurasian North (QUEEN)", "DEPTH", "Global Environmental Change The Northern North Atlantic SFB313", "Earth System Research", "Calculated from mass/volume", "Porosity"], "contacts": [{"organization": "Holler, Peter R, Kassens, Heidemarie,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1594/pangaea.261535"}, {"rel": "self", "type": "application/geo+json", "title": "10.1594/pangaea.261535", "name": "item", "description": "10.1594/pangaea.261535", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1594/pangaea.261535"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2005-01-01T00:00:00Z"}}, {"id": "10.1594/pangaea.594544", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:19:46Z", "type": "Dataset", "title": "Tree-ring measurements of Picea abies (Norway spruce) from sample LOETPCAB-2092", "description": "Species PCAB; No of rings 213; Begin 1786; End 1998", "keywords": ["minimum", "Latewood width", "Approximate age of pith", "Density", " minimum", "Density", "Density", " maximum", "Northern Hemispheric Dendroclimatological Network (NHD/WSL)", "Northern Hemispheric Dendroclimatological Network NHD WSL", "WDD", "AGE", "Age", "Tree ring sampling", "Earth System Research", "Earlywood width", "Earlywood density", "maximum", "Tree ring width", "Latewood density"], "contacts": [{"organization": "Schweingruber, Fritz Hans, Neuwirth, Burkhard,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1594/pangaea.594544"}, {"rel": "self", "type": "application/geo+json", "title": "10.1594/pangaea.594544", "name": "item", "description": "10.1594/pangaea.594544", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1594/pangaea.594544"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2007-01-01T00:00:00Z"}}, {"id": "10.1594/pangaea.922724", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:19:46Z", "type": "Report", "title": "Yedoma domain Mineral Concentrations Assessment (YMCA)", "description": "Mineral elements play a crucial role for organic carbon stabilization, which is key for organic carbon mineralization rates in soils. With thawing permafrost, especially in ice-rich regions such as the Yedoma domain, vast amounts of organic carbon previously stored in deep frozen deposits are unlocked and therefore available to undergo microbial mineralization leading to potential carbon dioxide and methane emissions. Mineral elements interfere with organic carbon degradation through various processes: i) mineral protection (aggregation, adsorption, and complexation) stabilizes organic carbon and mitigates its mineralization, and ii) change in mineral nutrients availability affects microorganisms growth and metabolic activity. Despite huge efforts to assess organic carbon stocks and lability in permafrost regions, there is a lack of studies on the mineral component assessment, which we aim to close with this dataset. Here, we provide a large-scale Yedoma domain Mineral Concentrations Assessment (YMCA) dataset of never thawed (since deposition) ice-rich Yedoma permafrost and previously thawed and partly refrozen Alas deposits. We used a portable X-ray fluorescence device (pXRF) for Si, Al, Fe, Ca, K, Ti, Mn, Zn, Sr and Zr concentration measurements on 1,292 sediment samples. Portable XRF measured concentrations trueness was calibrated using standard alkaline fusion and ICP-OES measurement from a subset of 144 samples (R\u00b2 from 0.725 to 0.996). This methodology lead to the creation of the Yedoma domain Mineral Concentration Assessment (YMCA) dataset, a necessary step to estimate mineral element stocks in never thawed Yedoma and previously thawed Alas deposits. Practically, the YMCA dataset is organized as follow: (i) all site and sample properties: sample ID, type of deposit, site location, profile ID, GPS coordinates, country, lithology, unconsolidated sediment type, geological epoch, samples depth below surface level (b.s.l) or height above sea/river level (a.s.l), sediment characteristics, bulk density, gravimetric and absolute ice content, total organic carbon content; (ii) the Si, Al, Fe, Ca, K, Ti, Mn, Zn, Sr and Zr concentrations (corrected based on linear regressions) in Yedoma and Alas deposits (n=1292).", "keywords": ["Density", "Permafrost", "Profile ID", "gravimetric", "Density", " bulk", " permafrost", "Aluminium", "total", "Sample code/label", "Portable X ray fluorescence device", "Titanium", "Mineral element", "Yedoma", "Portable X-ray fluorescence device", "Description", "Number", "Lithology/composition/facies", "Sample code label", "6. Clean water", "Deposit type", "Country", "sediment rock", "Zinc", "Earth System Research", "Alas", "Profile", "Silicon", "Lithology composition facies", "Height above sea level", "organic", "Iron", "Site", "DEPTH", " sediment/rock", "bulk", "Ice content", " gravimetric", "LONGITUDE", "Organic carbon", "Manganese", "Sediment type", "organic carbon", "15. Life on land", "Ice content", "Carbon", "Epoch", "Sample ID", "13. Climate action", "Strontium", "DEPTH", "LATITUDE", "Potassium", "Calcium", "Zirconium", "permafrost", "Carbon", " organic", " total"]}, "links": [{"href": "https://doi.org/10.1594/pangaea.922724"}, {"rel": "self", "type": "application/geo+json", "title": "10.1594/pangaea.922724", "name": "item", "description": "10.1594/pangaea.922724", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1594/pangaea.922724"}, {"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": "76f1bae3-cee1-4bc7-98b2-beb036d88d2b", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[-173.2, -78.5], [-173.2, 80.0], [178.5, 80.0], [178.5, -78.5], [-173.2, -78.5]]]}, "properties": {"themes": [{"concepts": [{"id": "geoscientificInformation"}], "scheme": "https://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_TopicCategoryCode"}, {"concepts": [{"id": "Soil science"}], "scheme": "Stratum"}, {"concepts": [{"id": "Global"}], "scheme": "Region"}], "updated": "2023-12-08T11:18:44", "type": "Dataset", "language": "eng", "title": "WoSIS snapshot - July 2016", "description": "The aim of the World Soil Information Service (WoSIS) is to serve quality-assessed, geo-referenced soil data (point, polygon, and grid) to the international community upon their standardisation and harmonisation. So far, the focus has been on developing procedures for legacy point data with special attention to the selection of soil analytical and physical properties considered in the GlobalSoilMap specifications (e.g. organic carbon, soil pH, soil texture (sand, silt, and clay), coarse fragments (\u2009greater than\u2009\u202f2\u202fmm), cation exchange capacity, electrical conductivity, bulk density, and water holding capacity). Profile data managed in WoSIS were contributed by a wide range of soil data providers; the data have been described, sampled, and analysed according to methods and standards in use in the originating countries. Hence, special attention was paid to measures for soil data quality and the standardisation of soil property definitions, soil property values, and soil analytical method descriptions.\n\nAt the time of writing, the full WoSIS database contained some 118\u202f400 unique shared soil profiles, of which some 96\u202f000 are geo-referenced within defined limits. In total, this corresponds with over 31 million soil records, of which some 20\u202f% have so far been quality-assessed and standardised using the sequential procedure discussed in this paper.\n\nThe number of measured data for each property varies between profiles and with depth, generally depending on the purpose of the initial studies. Overall, the data lineage strongly determined which data could be standardised with acceptable confidence in accord with WoSIS procedures, corresponding to over 4 million records for 94\u202f441 profiles.\n\nThe downloadable ZIP file has the data in TSV (tab separated values). It contains the following files:\n- ReadmeFirst_WoSIS_2016.pdf (148.1 KB)\n- wosis_201607_attributes.txt (4.1 KB)\n- wosis_201607_layers.txt (679.1 MB)\n- wosis_201607_profiles.txt (8.8 MB)\n\nCitation:\nBatjes NH, Ribeiro E, van Oostrum A, Leenaars J, and Mendes de Jesus J 2016. Standardised soil profile data for the world (WoSIS, July 2016 snapshot), doi:10.17027/isric-wdcsoils.20160003.\nThe dataset accompanies the following data paper: Batjes NH, Ribeiro E, van Oostrum A, Leenaars J, Hengl T, and Mendes de Jesus J 2017. WoSIS: Providing standardised soil profile data for the world, Earth System Science Data 9, 1-14, doi:10.5194/essd-9-1-2017.", "formats": [{"name": "Niels H. Batjes"}, {"name": "WWW:DOWNLOAD-1.0-ftp--download"}, {"name": "WWW:LINK-1.0-http--related"}], "keywords": ["bulk density", "cation exchange capacity", "soil classification", "coarse fragments", "clay", "effective cation exchange capacity", "electrical conductivity", "organic carbon", "pH", "sand", "silt", "calcium carbonate", "texture", "water retention", "soil profiles", "Soil science", "Global"], "contacts": [{"name": "Ad van Oostrum", "organization": "ISRIC - World Soil Information", "position": "Guest researcher", "roles": ["Author"], "phones": [{"value": null}], "emails": [{"value": null}], "addresses": [{"deliveryPoint": ["PO Box 353"], "city": "Wageningen", "administrativeArea": null, "postalCode": "6700AJ", "country": "Netherlands"}], "links": [{"href": null}]}, {"name": "Niels Batjes", "organization": "ISRIC - World Soil Information", "position": "Senior Soil Scientist", "roles": ["Author"], "phones": [{"value": null}], "emails": [{"value": "niels.batjes@isric.org"}], "addresses": [{"deliveryPoint": ["PO Box 353"], "city": "Wageningen", "administrativeArea": null, "postalCode": "6700AJ", "country": "Netherlands"}], "links": [{"href": null}]}, {"name": "Eloi Ribeiro", "organization": "ISRIC - World Soil Information", "position": null, "roles": ["author"], "phones": [{"value": null}], "emails": [{"value": null}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": null}]}, {"name": "Data infodesk", "organization": "ISRIC - World Soil Information", "position": null, "roles": ["pointOfContact"], "phones": [{"value": null}], "emails": [{"value": "data@isric.org"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": null}]}, {"organization": "ISRIC - World Soil Information", "roles": ["contributor"]}], "denominator": "100000"}, "links": [{"href": "https://files.isric.org/public/wosis_snapshot/WoSIS_2016_July.zip", "name": "Download zip", "protocol": "WWW:DOWNLOAD-1.0-ftp--download", "rel": "download"}, {"href": "https://doi.org/10.5194/essd-9-1-2017", "name": "Scientific paper", "protocol": "WWW:LINK-1.0-http--related", "rel": "information"}, {"href": "https://www.isric.org/explore/wosis/faq-wosis", "name": "Project webpage", "protocol": "WWW:LINK-1.0-http--related", "rel": "information"}, {"href": "https://files.isric.org/public/thumbnails/wosis_snapshot/wosis_snapshot.png", "name": "preview", "description": "Web image thumbnail (URL)", "protocol": "WWW:LINK-1.0-http--image-thumbnail", "rel": "preview"}, {"rel": "self", "type": "application/geo+json", "title": "76f1bae3-cee1-4bc7-98b2-beb036d88d2b", "name": "item", "description": "76f1bae3-cee1-4bc7-98b2-beb036d88d2b", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/76f1bae3-cee1-4bc7-98b2-beb036d88d2b"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"interval": ["1918-01-01T00:00:00Z", "2013-02-12T00:00:00Z"]}}, {"id": "ca880bd4-cff8-11e9-8046-0cc47adaa92c", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[-173.2, -78.5], [-173.2, 80.0], [178.5, 80.0], [178.5, -78.5], [-173.2, -78.5]]]}, "properties": {"themes": [{"concepts": [{"id": "geoscientificInformation"}], "scheme": "https://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_TopicCategoryCode"}, {"concepts": [{"id": "Soil science"}], "scheme": "Stratum"}, {"concepts": [{"id": "Global"}], "scheme": "Region"}], "updated": "2023-12-08T11:13:11", "type": "Dataset", "language": "eng", "title": "WoSIS snapshot - September 2019", "description": "The World Soil Information Service (WoSIS) provides quality-assessed and standardised soil profile data to support digital soil mapping and environmental applications at broad scale levels. Since the release of the first \u2018WoSIS snapshot\u2019, in July 2016, many new soil data were shared with us, registered in the ISRIC data repository, and subsequently standardised in accordance with the licences specified by the data providers. Soil profile data managed in WoSIS were contributed by a wide range of data providers, therefore special attention was paid to measures for soil data quality and the standardisation of soil property definitions, soil property values (and units of measurement), and soil analytical method descriptions.\n\nWe presently consider the following soil chemical properties (organic carbon, total carbon, total carbonate equivalent, total Nitrogen, Phosphorus (extractable-P, total-P, and P-retention), soil pH, cation exchange capacity, and electrical conductivity) and physical properties (soil texture (sand, silt, and clay), bulk density, coarse fragments, and water retention), grouped according to analytical procedures (aggregates) that are operationally comparable.\n\nFurther, for each profile, we provide the original soil classification (FAO, WRB, USDA, and version) and horizon designations insofar as these have been specified in the source databases. Measures for geographical accuracy (i.e. location) of the point data as well as a first approximation for the uncertainty associated with the operationally defined analytical methods are presented, for possible consideration in digital soil mapping and subsequent earth system modelling.\n\nThe present snapshot, referred to as \u2018WoSIS snapshot - September 2019\u2019, comprises 196,498 geo-referenced profiles originating from 173 countries. They represent over 832 thousand soil layers (or horizons), and over 6 million records. The actual number of observations for each property varies (greatly) between pro\ufb01les and with depth, this generally depending on the objectives of the initial soil sampling programmes.\n\nThe downloadable ZIP file has the data in TSV (tab separated values) and GeoPackage format. It contains the following files:\n- ReadmeFirst_WoSIS_2019dec04.pdf (546.7 KB)\n- wosis_201909.gpkg (2.2 GB, same data as in the tsv)\n- wosis_201909_attributes.tsv (8.7 KB)\n- wosis_201909_layers_chemical.tsv (893.5 MB)\n- wosis_201909_layers_physical.tsv (890.7 MB)\n- wosis_201909_profiles.tsv (18.8 MB)\n\nTo read the data in R, please, uncompress the ZIP file and specify the uncompressed folder. Then use read_tsv to read the TSV files, specifying the data types for each column (c = character, i = integer, n = number, d = double, l = logical, f = factor, D = date, T = date time, t = time).\n\nsetwd(\"/YourFolder/WoSIS_2019_September/\")\nattributes = readr::read_tsv('wosis_201909_attributes.tsv', col_types='cccciicd')\nprofiles = readr::read_tsv('wosis_201909_profiles.tsv', col_types='icccdddiicccciccccicccc')\nchemical = readr::read_tsv('wosis_201909_layers_chemical.tsv', col_types='iiddclcdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccc')\nphysical = readr::read_tsv('wosis_201909_layers_physical.tsv', col_types='iiddclcdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccccdccccc')\n\nFor more detailed instructions on how to read the data with R, please visit https://www.isric.org/accessing-wosis-using-r.\n\nCitation:\nBatjes N.H, Ribeiro E, and van Oostrum A.J.M, 2019. Standardised soil profile data for the world (WoSIS snapshot - September 2019), https://doi.org/10.17027/isric-wdcsoils.20190901.\nThe dataset accompanies the following data paper: Batjes N.H., Ribeiro E., and van Oostrum A.J.M., 2019. Standardised soil profile data to support global mapping and modelling (WoSIS snapshot - 2019). Earth System Science Data, https://doi.org/10.5194/essd-12-299-2020.", "formats": [{"name": "Niels H. Batjes"}, {"name": "WWW:DOWNLOAD-1.0-ftp--download"}, {"name": "WWW:LINK-1.0-http--related"}], "keywords": ["bulk density", "cation exchange capacity", "soil classification", "coarse fragments", "clay", "effective cation exchange capacity", "electrical conductivity", "organic carbon", "pH", "sand", "silt", "calcium carbonate", "texture", "water retention", "soil profiles", "Soil science", "Global"], "contacts": [{"name": "Niels Batjes", "organization": "ISRIC - World Soil Information", "position": "Guest researcher", "roles": ["Author"], "phones": [{"value": null}], "emails": [{"value": "niels.batjes@isric.org"}], "addresses": [{"deliveryPoint": ["PO Box 353"], "city": "Wageningen", "administrativeArea": null, "postalCode": "6700AJ", "country": "Netherlands"}], "links": [{"href": null}]}, {"name": "Ad van Oostrum", "organization": "ISRIC - World Soil Information", "position": "Senior Soil Scientist", "roles": ["Author"], "phones": [{"value": null}], "emails": [{"value": "ad.vanoostrum@isric.org"}], "addresses": [{"deliveryPoint": ["PO Box 353"], "city": "Wageningen", "administrativeArea": null, "postalCode": "6700AJ", "country": "Netherlands"}], "links": [{"href": null}]}, {"name": "Eloi Ribeiro", "organization": "ISRIC - World Soil Information", "position": "Geoinformatic", "roles": ["Author"], "phones": [{"value": null}], "emails": [{"value": null}], "addresses": [{"deliveryPoint": ["P.O. Box 47"], "city": "Wageningen", "administrativeArea": null, "postalCode": "6708 PB", "country": "Netherlands"}], "links": [{"href": null}]}, {"name": "Data infodesk", "organization": "ISRIC - World Soil Information", "position": null, "roles": ["pointOfContact"], "phones": [{"value": null}], "emails": [{"value": "data@isric.org"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": null}]}], "denominator": "100000"}, "links": [{"href": "https://files.isric.org/public/wosis_snapshot/WoSIS_2019_September.zip", "name": "Download zip", "protocol": "WWW:DOWNLOAD-1.0-ftp--download", "rel": "download"}, {"href": "https://doi.org/10.5194/essd-12-299-2020", "name": "Scientific paper", "protocol": "WWW:LINK-1.0-http--related", "rel": "information"}, {"href": "https://www.isric.org/explore/wosis/faq-wosis", "name": "Project webpage", "protocol": "WWW:LINK-1.0-http--related", "rel": "information"}, {"href": "https://files.isric.org/public/thumbnails/wosis_snapshot/wosis_snapshot_201909.png", "name": "preview", "description": "Web image thumbnail (URL)", "protocol": "WWW:LINK-1.0-http--image-thumbnail", "rel": "preview"}, {"rel": "self", "type": "application/geo+json", "title": "ca880bd4-cff8-11e9-8046-0cc47adaa92c", "name": "item", "description": "ca880bd4-cff8-11e9-8046-0cc47adaa92c", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/ca880bd4-cff8-11e9-8046-0cc47adaa92c"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"interval": ["1918-01-01T00:00:00Z", "2016-07-05T00:00:00Z"]}}, {"id": "e50f84e1-aa5b-49cb-bd6b-cd581232a2ec", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[-173.2, -78.5], [-173.2, 80.0], [178.5, 80.0], [178.5, -78.5], [-173.2, -78.5]]]}, "properties": {"themes": [{"concepts": [{"id": "geoscientificInformation"}], "scheme": "https://standards.iso.org/iso/19139/resources/gmxCodelists.xml#MD_TopicCategoryCode"}, {"concepts": [{"id": "Soil science"}], "scheme": "Stratum"}, {"concepts": [{"id": "Global"}], "scheme": "Region"}], "updated": "2025-02-05T09:26:02", "type": "Dataset", "language": "eng", "title": "WoSIS snapshot - December 2023", "description": "ABSTRACT:\n\nThe World Soil Information Service (WoSIS) provides quality-assessed and standardized soil profile data to support digital soil mapping and environmental applications at broad scale levels. Since the release of the \u2018WoSIS snapshot 2019\u2019 many new soil data were shared with us, registered in the ISRIC data repository, and subsequently standardized in accordance with the licenses specified by the data providers. The source data were contributed by a wide range of data providers, therefore special attention was paid to the standardization of soil property definitions, soil analytical procedures and soil property values (and units of measurement).\n\nWe presently consider the following soil chemical properties (organic carbon, total carbon, total carbonate equivalent, total Nitrogen, Phosphorus (extractable-P, total-P, and P-retention), soil pH, cation exchange capacity, and electrical conductivity) and physical properties (soil texture (sand, silt, and clay), bulk density, coarse fragments, and water retention), grouped according to analytical procedures (aggregates) that are operationally comparable.\n\nFor each profile we provide the original soil classification (FAO, WRB, USDA, and version) and horizon designations as far as these have been specified in the source databases. \n\nThree measures for 'fitness-for-intended-use' are provided: positional uncertainty (for site locations), time of sampling/description, and a first approximation for the uncertainty associated with the operationally defined analytical methods. These measures should be considered during digital soil mapping and subsequent earth system modelling that use the present set of soil data. \n\n\nDATA SET DESCRIPTION:\n\nThe 'WoSIS 2023 snapshot' comprises data for 228k profiles from 217k geo-referenced sites that originate from 174 countries. The profiles represent over 900k soil layers (or horizons) and over 6 million records. The actual number of measurements for each property varies (greatly) between pro\ufb01les and with depth, this generally depending on the objectives of the initial soil sampling programmes. \n\nThe data are provided in TSV (tab separated values) format and as GeoPackage. The zip-file (446 Mb) contains the following files: \n\n- Readme_WoSIS_202312_v2.pdf: Provides a short description of the dataset, file structure, column names, units and category values (this file is also available directly under 'online resources'). The pdf includes links to tutorials for downloading the TSV files into R respectively Excel. See also 'HOW TO READ TSV FILES INTO R AND PYTHON' in the next section. \n \n- wosis_202312_observations.tsv: This file lists the four to six letter codes for each observation, whether the observation is for a site/profile or layer (horizon), the unit of measurement and the number of profiles respectively layers represented in the snapshot. It also provides an estimate for the inferred accuracy for the laboratory measurements.\n\n- wosis_202312_sites.tsv: This file characterizes the site location where profiles were sampled.\n\n- wosis_2023112_profiles: Presents the unique profile ID (i.e. primary key), site_id, source of the data, country ISO code and name, positional uncertainty, latitude and longitude (WGS 1984), maximum depth of soil described and sampled, as well as information on the soil classification system and edition. Depending on the soil classification system used, the number of fields will vary .\n\n- wosis_202312_layers: This file characterises the layers (or horizons) per profile, and lists their upper and lower depths (cm). \n\n- wosis_202312_xxxx.tsv : This type of file presents results for each observation (e.g. \u201cxxxx\u201d = \u201cBDFIOD\u201d ), as defined under \u201ccode\u201d in file wosis_202312_observation.tsv. (e.g. wosis_202311_bdfiod.tsv). \n\n- wosis_202312.gpkg: Contains the above datafiles in GeoPackage format (which stores the files within an SQLite database).\n\n\nHOW TO READ TSV FILES INTO R  AND PYTHON:\n\nA) To read the data in R, please uncompress the ZIP file and specify the uncompressed folder. \n\nsetwd(\"/YourFolder/WoSIS_2023_December/\")       ## For example: setwd('D:/WoSIS_2023_December/')\n\nThen use read_tsv to read the TSV files, specifying the data types for each column (c = character, i = integer, n = number, d = double, l = logical, f = factor, D = date, T = date time, t = time).\n\nobservations = readr::read_tsv('wosis_202312_observations.tsv', col_types='cccciid')  \nobservations          ## show columns and first 10 rows    \n\nsites = readr::read_tsv('wosis_202312_sites.tsv', col_types='iddcccc')\nsites   \n\nprofiles = readr::read_tsv('wosis_202312_profiles.tsv', col_types='icciccddcccccciccccicccci')\nprofiles \n\nlayers = readr::read_tsv('wosis_202312_layers.tsv', col_types='iiciciiilcc')\nlayers  \n\n## Do this for each observation 'XXXX', e.g. file 'Wosis_202312_orgc.tsv':\norgc = readr::read_tsv('wosis_202312_orgc.tsv', col_types='iicciilccdccddccccc')   \norgc\n\n\nNote: One may also use the following R code (example is for file 'observations.tsv'):\nobservations <- read.table(\"wosis_202312_observations.tsv\",\n sep = \"\\t\",\n header = TRUE,\n quote = \"\",\n comment.char = \"\",\n stringsAsFactors = FALSE\n )\n\n\nB) To read the files into python first decompress the files to your selected folder.  Then in python: \n\n# import the required library\nimport pandas as pd\n\n# Read the observations data\nobservations = pd.read_csv(\"wosis_202312_observations.tsv\", sep=\"\\t\")\n    # print the data frame header and some rows\n      observations.head()\n\n# Read the sites data\nsites = pd.read_csv(\"wosis_202312_sites.tsv\", sep=\"\\t\")\n\n# Read the profiles data\nprofiles = pd.read_csv(\"wosis_202312_profiles.tsv\", sep=\"\\t\")\n\n# Read the layers data\nlayers = pd.read_csv(\"wosis_202312_layers.tsv\", sep=\"\\t\")\n\n# Read the soil property data, e.g. 'cfvo' (do this for each observation)\ncfvo = pd.read_csv(\"wosis_202312_cfvo.tsv\", sep=\"\\t\")\n\n\nCITATION:\nCalisto, L., de Sousa, L.M., Batjes, N.H., 2023. Standardised soil profile data for the world (WoSIS snapshot \u2013 December 2023), https://doi.org/10.17027/isric-wdcsoils-20231130\n\nSupplement to:\nBatjes N.H., Calisto, L. and de Sousa L.M., 2023. Providing quality-assessed and standardised soil data to support global mapping and modelling (WoSIS snapshot 2023). Earth System Science Data,  https://doi.org/10.5194/essd-16-4735-2024.", "formats": [{"name": "TSV and Geopackage"}, {"name": "WWW:DOWNLOAD-1.0-ftp--download"}, {"name": "WWW:LINK-1.0-http--link"}, {"name": "WWW:LINK-1.0-http--related"}], "keywords": ["bulk density", "cation exchange capacity", "soil classification", "coarse fragments", "clay", "effective cation exchange capacity", "electrical conductivity", "organic carbon", "pH", "sand", "silt", "calcium carbonate", "texture", "soil profiles", "water retention", "total nitrogen", "Soil science", "Global"], "contacts": [{"name": "Luis Calisto", "organization": "ISRIC - World Soil Information", "position": "Database expert", "roles": ["Author"], "phones": [{"value": null}], "emails": [{"value": "luis.calisto@isric.org"}], "addresses": [{"deliveryPoint": ["PO Box 353"], "city": "Wageningen", "administrativeArea": null, "postalCode": "6700AJ", "country": "Netherlands"}], "links": [{"href": null}]}, {"name": "Niels Batjes", "organization": "ISRIC - World Soil Information", "position": "Senior Soil Scientist", "roles": ["Author"], "phones": [{"value": null}], "emails": [{"value": "niels.batjes@isric.org"}], "addresses": [{"deliveryPoint": ["PO Box 353"], "city": "Wageningen", "administrativeArea": null, "postalCode": "6700AJ", "country": "Netherlands"}], "links": [{"href": null}]}, {"name": "Luis M. de Sousa", "organization": "ISRIC - World Soil Information", "position": "Geoinformatic", "roles": ["Author"], "phones": [{"value": null}], "emails": [{"value": "luis.deSousa@isric.org"}], "addresses": [{"deliveryPoint": ["P.O. Box 47"], "city": "Wageningen", "administrativeArea": null, "postalCode": "6708 PB", "country": "Netherlands"}], "links": [{"href": null}]}, {"name": "Data infodesk", "organization": "ISRIC - World Soil Information", "position": null, "roles": ["pointOfContact"], "phones": [{"value": null}], "emails": [{"value": "data@isric.org"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": null}]}], "denominator": "100000"}, "links": [{"href": "https://files.isric.org/public/wosis_snapshot/WoSIS_2023_December.zip", "name": "Download zipped dataset", "description": "Zip file with the WoSIS December 2023 snapshot", "protocol": "WWW:DOWNLOAD-1.0-ftp--download", "rel": "download"}, {"href": "https://doi.org/10.5194/essd-16-4735-2024", "name": "Scientific paper", "description": "Goes to landing page for ESSD snapshot paper", "protocol": "WWW:LINK-1.0-http--link", "rel": "download"}, {"href": "https://www.isric.org/explore/wosis/faq-wosis", "name": "Project webpage (FAQ)", "description": "Provides answers to frequently asked questions about WoSIS", "protocol": "WWW:LINK-1.0-http--related", "rel": "information"}, {"href": "https://www.isric.org/sites/default/files/Readme_WoSIS_202312.pdf", "name": "ReadMe file for 'wosis_snapshot_2023'", "description": "This pdf report describes the 'wosis snapshot 2023' dataset and includes links to guidelines on how to import the TSV files into R resp. Excel.", "protocol": "WWW:LINK-1.0-http--link", "rel": "download"}, {"href": "https://www.isric.org/sites/default/files/wosis_latest_2023may.png", "name": "preview", "description": "Web image thumbnail (URL)", "protocol": "WWW:LINK-1.0-http--image-thumbnail", "rel": "preview"}, {"rel": "self", "type": "application/geo+json", "title": "e50f84e1-aa5b-49cb-bd6b-cd581232a2ec", "name": "item", "description": "e50f84e1-aa5b-49cb-bd6b-cd581232a2ec", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/e50f84e1-aa5b-49cb-bd6b-cd581232a2ec"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"interval": ["1918-01-01T00:00:00Z", "2022-12-01T00:00:00Z"]}}, {"id": "10.17221/245/2014-pse", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:19:49Z", "type": "Journal Article", "created": "2018-02-10", "title": "Impact Of Tillage On Physical Characteristics In A Mollisol Of Northeast China", "description": "Soil management is aimed at the maintenance of optimal soil physical quality for crop production. In order to explore the effects of tillage practices on soil physical properties, a study was conducted to compare the effects of no tillage (NT), moldboard plow (MP) and ridge tillage (RT) on soil bulk density (BD), soil penetration resistance (SPR), soil water content (SWC), soil macroporosity (MAC) and soil air-filled porosity (AFP) in Northeast China. Results showed that both NT and RT led to significant BD increment than MP at 0-20 cm (P &lt; 0.05). Compared with MP, NT and RT increased SPR at the depths of 2.5-17.5 cm (P &lt; 0.05). SWC of 0-10 cm layer was significantly higher in NT and RT than MP soils (P &lt; 0.05). NT showed a significantly lower MAC than MP and RT at 0-20 cm soil depths (P &lt; 0.05). All AFP values were above the limit of 0.10 cm3/cm3 under all tillage treatments. RT improved the soil physical quality as evidenced by decreased BD and SPR, and increased SWC, MAC and AFP relative to NT.", "keywords": ["2. Zero hunger", "soil air-filled porosity", "Plant culture", "0401 agriculture", " forestry", " and fisheries", "soil water content", "04 agricultural and veterinary sciences", "15. Life on land", "soil macroporosity", "6. Clean water", "soil bulk density", "soil penetration resistance", "SB1-1110"], "contacts": [{"organization": "Wei Shuangshi, Xuewen Chen, Shuxia Jia, Xiao-Ping Zhang, Aizhen Liang,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.17221/245/2014-pse"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Plant%2C%20Soil%20and%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.17221/245/2014-pse", "name": "item", "description": "10.17221/245/2014-pse", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.17221/245/2014-pse"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2014-07-31T00:00:00Z"}}, {"id": "10.17221/846/2012-pse", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:19:50Z", "type": "Journal Article", "created": "2018-02-10", "title": "Long-Term Effects Of Returning Wheat Straw To Croplands On Soil Compaction And Nutrient Availability Under Conventional Tillage", "description": "To investigate the effects of returning wheat straw to croplands on soil compaction and nutrient availability, this trial was designed: (1) planted crops without fertilization (NF); (2) natural land without human activities (CT); (3) applied mineral fertilizers in combination with 7500 kg/ha wheat straw (WS-NPK); (4) applied mineral fertilizers in combination with 3750 kg/ha wheat straw (1/2WS-NPK); and (5) applied mineral fertilizers alone (NPK). It is found that, compared with NPK, the soil bulk density in 1/2WS-NPK and WS-NPK both decreased by more than 10% in the 0 cm to 15 cm layer, and by 6.93% and 9.14% in the 15 cm to 20 cm, respectively. Furthermore, in contrast to NPK, the soil available nitrogen in the 0 cm to 25 cm layer in 1/2WS-NPK and WS-NPK were higher by 17.43% and 35.19%, and the soil available potassium were higher by 7.66% and 17.47%, respectively. For soil available phosphorus in the depth of 5 cm to 25 cm, it was higher by 18.51% in 1/2WS-NPK and by 56.97% in WS-NPK, respectively. Therefore, returning wheat straw to croplands effectively improves soil compaction and nutrients availability, and the improvement in soil nitrogen and phosphorus availability is closely related to the amount of wheat straw.", "keywords": ["2. Zero hunger", "soil organic matter", "soil nitrogen", "soil phosphorus", "Plant culture", "0401 agriculture", " forestry", " and fisheries", "soil water content", "04 agricultural and veterinary sciences", "15. Life on land", "6. Clean water", "soil bulk density", "SB1-1110"], "contacts": [{"organization": "D. Z. Wang, Z. Guo,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.17221/846/2012-pse"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Plant%2C%20Soil%20and%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.17221/846/2012-pse", "name": "item", "description": "10.17221/846/2012-pse", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.17221/846/2012-pse"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2013-06-30T00:00:00Z"}}, {"id": "10.19061/iochem-bd-6-18", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:19:57Z", "type": "Dataset", "created": "2019-08-20", "title": "Dataset \u2013 Untangling Cooperative Effects of Pyridinic and Graphitic Nitrogen Sites at Metal-Free N-Doped Carbon Electrocatalysts for the Oxygen Reduction Reaction", "description": "This dataset contains the raw data for the published article 'Untangling Cooperative Effects of Pyridinic and Graphitic Nitrogen Sites at Metal\u2010Free N\u2010Doped Carbon Electrocatalysts for the Oxygen Reduction Reaction'. The dataset contains Electrochemistry, RAMAN and Xray photoelectron spectroscopy measures. This publication has emanated from research conducted with the financial support of Science Foundation Ireland under Grant No. 13/CDA/2213. J.A.B. acknowledges support from the Irish Research Council under Grant No. GOIPG/2014/399. This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under the Marie Sk\u0142odowska\u2010Curie grant agreements No. 748968 (FREMAB) and 799175 (HiBriCarbon). The results of this publication reflect only the authors' view and the Commission is not responsible for any use that may be made of the information it contains.", "keywords": ["Synergistic", "Electocatalysis", "N-doped carbon", "Nanoscience & Materials", "Density functional theory", "Oxygen reduction reaction"], "contacts": [{"organization": "Behan A., James, Mates-Torres, Eric, Stamatin N., Serban, Dom\u00ednguez, Carlota, Iannaci, Alessandro, Fleischer, Karsten, Hoque, Md. Khairul, S. Perova, Tatiana, Garc\u00eda\u2010Melchor, Max, E. Colavita, Paula,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.19061/iochem-bd-6-18"}, {"rel": "self", "type": "application/geo+json", "title": "10.19061/iochem-bd-6-18", "name": "item", "description": "10.19061/iochem-bd-6-18", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.19061/iochem-bd-6-18"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-08-20T00:00:00Z"}}, {"id": "10.21203/rs.3.rs-5128244/v2", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:20:00Z", "type": "Journal Article", "created": "2025-07-14", "title": "Spatiotemporal prediction of soil organic carbon density in Europe (2000\u20132022) using earth observation and machine learning", "description": "<p>This article describes a comprehensive framework for soil organic carbon density (SOCD, kg/m3) modeling and mapping, based on spatiotemporal random forest (RF) and quantile regression forests (QRF). A total of 45,616 SOCD observations and various Earth observation (EO) feature layers were used to produce 30 m SOCD maps for the EU at four-year intervals (2000\uffe2\uff80\uff932022) and four soil depth intervals (0\uffe2\uff80\uff9320 cm, 20\uffe2\uff80\uff9350 cm, 50\uffe2\uff80\uff93100 cm, and 100\uffe2\uff80\uff93200 cm). Per-pixel 95% probability prediction intervals (PIs) and extrapolation risk probabilities are also provided. Model evaluation indicates good overall accuracy (R2 = 0.63 and CCC = 0.76 for hold-out independent tests). Prediction accuracy varies by land cover, depth interval and year of prediction with the worst accuracy for shrubland and deeper soils 100\uffe2\uff80\uff93200 cm. The PI validation confirmed effective uncertainty estimation, though with reduced accuracy for higher SOCD values. Shapley analysis identified soil depth as the most influential feature, followed by vegetation, long-term bioclimate, and topographic features. While pixel-level uncertainty is substantial, spatial aggregation reduces uncertainty by approximately 66%. Detecting SOCD changes remains challenging but offers a baseline for future improvements. Maps, based primarily on topsoil data from cropland, grassland, and woodland, are best suited for applications related to these land covers and depths. We recommend that users interpret the maps in conjunction with local knowledge and consider the accompanying uncertainty and extrapolation risk layers. All data and code are available under an open license at https://doi.org/10.5281/zenodo.13754343 and https://github.com/AI4SoilHealth/SoilHealthDataCube/.</p", "keywords": ["Model interpretability", "Earth observation", "Time series", "QH301-705.5", "Uncertainty", "R", "Soil organic carbon density", "Soil Science", "Data transformation", "Spatial aggregation", "Machine learning", "Medicine", "Shapley value", "Biology (General)", "Random forest"]}, "links": [{"href": "https://doi.org/10.21203/rs.3.rs-5128244/v2"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PeerJ", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.21203/rs.3.rs-5128244/v2", "name": "item", "description": "10.21203/rs.3.rs-5128244/v2", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.21203/rs.3.rs-5128244/v2"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-05-01T00:00:00Z"}}, {"id": "10.3390/agronomy14071536", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:20:51Z", "type": "Journal Article", "created": "2024-07-15", "title": "Mechanism of Interaction between Earthworms and Root Parameters on Cambisol", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Plants respond to their environment through adaptations; for example, earthworms that create heterogeneity can lead to local adaptation of roots. This research identifies a mechanism to explain plant responses to earthworms and how these mechanisms are related. Our results show that tillage intensity has a negative effect on earthworms and root volume. The mean root volume and earthworm biomass under conventional tillage were lower than in reduced tillage and no-tillage. The root volume and the root diameter in the field with residues were higher than in the field without residues, while the root length density and earthworm biomass in the field with residues were lower than in the field without residues. This study demonstrates that the mean of the root length density and biomass of the earthworms were higher in sandy loam than in loam. Therefore, sand content had a positive effect on root length density (R2 = 0.72, p &lt; 0.01) and earthworm biomass (R2 = 0.74, p &lt; 0.01). Earthworm biomass had a positive effect on root volume (R2 = 0.54, p &lt; 0.05) and length density (R2 = 0.88, p &lt; 0.01). This confirms our hypothesis on the effect of earthworms on root systems.</p></article>", "keywords": ["2. Zero hunger", "root volume", "sandy loam", "S", "13. Climate action", "<i>Cambisol</i>", "Agriculture", "earthworms", "15. Life on land", "loam", "root length density"]}, "links": [{"href": "https://doi.org/10.3390/agronomy14071536"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agronomy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/agronomy14071536", "name": "item", "description": "10.3390/agronomy14071536", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/agronomy14071536"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-07-15T00:00:00Z"}}, {"id": "10.3390/agriculture12030432", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:20:49Z", "type": "Journal Article", "created": "2022-03-20", "title": "Comparison of Soil Organic Carbon Stocks Evolution in Two Olive Orchards with Different Planting Systems in Southern Spain", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>This study presents an evaluation of soil organic carbon (SOC) and stock (SOCstock) for the whole rooting depth (60 cm), spaced 55 months in two adjacent olive orchards with similar conditions but different tree densities: (i) intensive, planted in 1996 at 310 tree ha\u22121; (ii) superintensive, planted in 2000 at 1850 tree ha\u22121. This was carried out to test the hypothesis that olive orchards at different plant densities will have different rates of accumulation of SOC in the whole soil rooting depth. SOC increased significantly in the superintensive orchard during the 55-month period, from 1.1 to 1.6% in the lane area, and from 1.2 to 1.7% in the tree area (average 0\u201360 cm), with a significant increase in SOCstock from 4.7 to 6.1 kg m\u22122. In the intensive orchard, there was not a significant increase in SOCstock in 0\u201360 cm, average of 4.06 and 4.16 kg m\u22122 in 2013 and 2018, respectively. Results indicate a potential for a significant increase in SOC and SOCstock in olive orchards at higher tree densities when combined with temporary cover crops and mulch of chopped pruning residues. The increase is associated with an increase in SOC, mainly at a 0\u201315 cm depth. Results also point to the need for improve our monitoring capabilities to detect moderate increases in SOC.</p></article>", "keywords": ["2. Zero hunger", "bulk density", "intensive orchard", "deficit irrigation", "Agriculture (General)", "0401 agriculture", " forestry", " and fisheries", "tree density; intensive orchard; superintensive orchard; deficit irrigation; bulk density", "04 agricultural and veterinary sciences", "tree density", "15. Life on land", "superintensive orchard", "S1-972"]}, "links": [{"href": "http://www.mdpi.com/2077-0472/12/3/432/pdf"}, {"href": "https://www.mdpi.com/2077-0472/12/3/432/pdf"}, {"href": "https://doi.org/10.3390/agriculture12030432"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agriculture", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/agriculture12030432", "name": "item", "description": "10.3390/agriculture12030432", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/agriculture12030432"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-03-20T00:00:00Z"}}, {"id": "10.3389/fmicb.2019.02904", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:20:45Z", "type": "Journal Article", "created": "2020-01-09", "title": "Fungal Traits Important for Soil Aggregation", "description": "Soil structure, the complex arrangement of soil into aggregates and pore spaces, is a key feature of soils and soil biota. Among them, filamentous saprobic fungi have well-documented effects on soil aggregation. However, it is unclear what properties, or traits, determine the overall positive effect of fungi on soil aggregation. To achieve progress, it would be helpful to systematically investigate a broad suite of fungal species for their trait expression and the relation of these traits to soil aggregation. Here, we apply a trait-based approach to a set of 15 traits measured under standardized conditions on 31 fungal strains including Ascomycota, Basidiomycota, and Mucoromycota, all isolated from the same soil. We find large differences among these fungi in their ability to aggregate soil, including neutral to positive effects, and we document large differences in trait expression among strains. We identify biomass density, i.e., the density with which a mycelium grows (positive effects), leucine aminopeptidase activity (negative effects) and phylogeny as important factors explaining differences in soil aggregate formation (SAF) among fungal strains; importantly, growth rate was not among the important traits. Our results point to a typical suite of traits characterizing fungi that are good soil aggregators, and our findings illustrate the power of employing a trait-based approach to unravel biological mechanisms underpinning soil aggregation. Such an approach could now be extended also to other soil biota groups. In an applied context of restoration and agriculture, such trait information can inform management, for example to prioritize practices that favor the expression of more desirable fungal traits.", "keywords": ["saprobic fungi", "0301 basic medicine", "2. Zero hunger", "ddc:500", "570", "0303 health sciences", "Saprobic fungi", "500 Naturwissenschaften und Mathematik::570 Biowissenschaften; Biologie::570 Biowissenschaften; Biologie", "15. Life on land", "Traits", "leucine amino peptidases", "Microbiology", "QR1-502", "soil aggregation", "03 medical and health sciences", "traits", "biomass density", "Soil aggregation", "Biomass density", "Leucine amino peptidases", "Institut f\u00fcr Biochemie und Biologie", "random forest", "Random forest"]}, "links": [{"href": "https://doi.org/10.3389/fmicb.2019.02904"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Microbiology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3389/fmicb.2019.02904", "name": "item", "description": "10.3389/fmicb.2019.02904", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3389/fmicb.2019.02904"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-01-09T00:00:00Z"}}, {"id": "10.3390/land11020223", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:20:57Z", "type": "Journal Article", "created": "2022-02-03", "title": "Opportunities for Mitigating Soil Compaction in Europe\u2014Case Studies from the SoilCare Project Using Soil-Improving Cropping Systems", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Soil compaction (SC) is a major threat for agriculture in Europe that affects many ecosystem functions, such as water and air circulation in soils, root growth, and crop production. Our objective was to present the results from five short-term (&lt;5 years) case studies located along the north\u2013south and east\u2013west gradients and conducted within the SoilCare project using soil-improving cropping systems (SICSs) for mitigating topsoil and subsoil SC. Two study sites (SSs) focused on natural subsoil (\u02c325 cm) compaction using subsoiling tillage treatments to depths of 35 cm (Sweden) and 60 cm (Romania). The other SSs addressed both topsoil and subsoil SC (\u02c325 cm, Norway and United Kingdom; \u02c330 cm, Italy) using deep-rooted bio-drilling crops and different tillage types or a combination of both. Each SS evaluated the effectiveness of the SICSs by measuring the soil physical properties, and we calculated SC indices. The SICSs showed promising results\u2014for example, alfalfa in Norway showed good potential for alleviating SC (the subsoil density decreased from 1.69 to 1.45 g cm\u22121) and subsoiling at the Swedish SS improved root penetration into the subsoil by about 10 cm\u2014but the effects of SICSs on yields were generally small. These case studies also reflected difficulties in implementing SICSs, some of which are under development, and we discuss methodological issues for measuring their effectiveness. There is a need for refining these SICSs and for evaluating their longer-term effect under a wider range of pedoclimatic conditions.</p></article>", "keywords": ["2. Zero hunger", "S", "degree of compaction", "Soil Science", "straw incorporation", "Agriculture", "04 agricultural and veterinary sciences", "910", "15. Life on land", "6. Clean water", "soil penetration resistance", "Environmental Sciences related to Agriculture and Land-use", "degree of compaction; soil penetration resistance; relative normalised density; air-filled porosity; tillage; straw incorporation; bio-drilling crops; subsoiling; crop productivity", "relative normalised density", "13. Climate action", "tillage", "0401 agriculture", " forestry", " and fisheries", "S Agriculture (General)", "910 Geography & travel", "air-filled porosity", "550 Earth sciences & geology"]}, "links": [{"href": "http://www.mdpi.com/2073-445X/11/2/223/pdf"}, {"href": "https://pub.epsilon.slu.se/27668/1/piccoli-i-et-al-220502.pdf"}, {"href": "https://boris.unibe.ch/165197/1/Opportunities_for_Mitigating_Soil_Compaction_in_Europe_Case.pdf"}, {"href": "https://www.research.unipd.it/bitstream/11577/3462067/1/land-11-00223-v2.pdf"}, {"href": "https://rau.repository.guildhe.ac.uk/id/eprint/16542/1/land-11-00223-v2.pdf"}, {"href": "https://doi.org/10.3390/land11020223"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Land", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/land11020223", "name": "item", "description": "10.3390/land11020223", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/land11020223"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-02-02T00:00:00Z"}}, {"id": "10.3390/f9010004", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-05-01T16:20:55Z", "type": "Journal Article", "created": "2018-01-03", "title": "Drought-Induced Changes in Wood Density Are Not Prevented by Thinning in Scots Pine Stands", "description": "<p>Density is an important wood mechanical property and an indicator of xylem architecture and hydraulic conductivity. It can be influenced by forest management and climate. We studied the impact of thinning and climate variables on annual stem radial growth (ring width and ring density, and their earlywood and latewood components) in two contrasting Scots pine (Pinus sylvestris L.) stands in northern Spain (one continental, one Mediterranean). At each site, three thinning regimes (control or T0, removing 20% basal area or T20, and removing 30% or T30) were randomly applied to nine plots per site (three plots per treatment) in 1999. Thinning was repeated at the Mediterranean site in 2009 (increasing thinning intensity in T30 to 40%). Eight trees per plot were cored in spring 2014. Second thinning at the Mediterranean site and first thinning at the continental site generally caused significantly wider ring (RW), earlywood (EW) and latewood (LW) widths, although no differences between T20 and T30/40 were found, supporting in part the common observation that radial growth is enhanced following thinning as competition for water and nutrients is reduced. At the Mediterranean site, values of latewood density (LD) and maximum density (Dmax) relative to pre-thinning conditions were significantly lower in T0 than in T30. However, at the continental site, relative changes of ring density (RD) and LD were significantly higher in T0 than in T20 and T30. Climate significantly affected not only RW but also RD, with significant RD drops during or right after unusually warm-dry years (e.g., 2003, 2011), which were characterized by LD reductions between 5.4 and 8.0%. Such RD decreases were quickly followed by recovery of pre-drought density values. These results indicate trees temporarily reduce LD as a way to enhance hydraulic conductivity during dry summers. However, climate effects on wood density were site-dependent. We also detected that the thinning effect was not intense enough to prevent drought-induced changes in wood density by altering water availability, but it could help to reduce wood properties fluctuations and therefore maintain more homogeneous wood mechanic features.</p>", "keywords": ["0106 biological sciences", "Drought", "04 agricultural and veterinary sciences", "15. Life on land", "Tree-ring width", "01 natural sciences", "Dendroecology", "6. Clean water", "X-ray", "Scots pine", "0401 agriculture", " forestry", " and fisheries", "dendroecology; Scots pine; tree-ring width; wood density; X-ray densitometry; drought", "Wood density", "Densitometry"]}, "links": [{"href": "http://www.mdpi.com/1999-4907/9/1/4/pdf"}, {"href": "https://doi.org/10.3390/f9010004"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Forests", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/f9010004", "name": "item", "description": "10.3390/f9010004", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/f9010004"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-01-02T00:00:00Z"}}, {"id": "10.3390/land10121397", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:20:57Z", "type": "Journal Article", "created": "2021-12-19", "title": "Comparison of Compaction Alleviation Methods on Soil Health and Greenhouse Gas Emissions", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Soil compaction can occur due to trafficking by heavy equipment and be exacerbated by unfavourable conditions such as wet weather. Compaction can restrict crop growth and increase waterlogging, which can increase the production of the greenhouse gas nitrous oxide. Cultivation can be used to alleviate compaction, but this can have negative impacts on earthworm abundance and increase the production of the greenhouse gas carbon dioxide. In this study, a field was purposefully compacted using trafficking, then in a replicated plot experiment, ploughing, low disturbance subsoiling and the application of a mycorrhizal inoculant were compared as methods of compaction alleviation, over two years of cropping. These methods were compared in terms of bulk density, penetration resistance, crop yield, greenhouse gas emissions and earthworm abundance. Ploughing alleviated topsoil compaction, as measured by bulk density and penetrometer resistance, and increased the crop biomass in one year of the study, although no yield differences were seen. Earthworm abundance was reduced in both years in the cultivated plots, and carbon dioxide flux increased significantly, although this was not significant in summer months. Outside of the summer months, nitrous oxide production increased in the non-cultivated treatments, which was attributed to increased denitrifying activity under compacted conditions.</p></article>", "keywords": ["CO<sub>2</sub>", "2. Zero hunger", "nitrous oxide", "S", "nitrous oxide; N<sub>2</sub>O; carbon dioxide; CO<sub>2</sub>; greenhouse gas; compaction; earthworms; direct drilling; bulk density", "carbon dioxide", "Agriculture", "04 agricultural and veterinary sciences", "15. Life on land", "N<sub>2</sub>O", "12. Responsible consumption", "greenhouse gas", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "compaction", "S Agriculture (General)"]}, "links": [{"href": "http://www.mdpi.com/2073-445X/10/12/1397/pdf"}, {"href": "https://rau.repository.guildhe.ac.uk/id/eprint/16544/1/land-10-01397.pdf"}, {"href": "https://doi.org/10.3390/land10121397"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Land", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/land10121397", "name": "item", "description": "10.3390/land10121397", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/land10121397"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-12-17T00:00:00Z"}}, {"id": "10.3390/land11050645", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:20:57Z", "type": "Journal Article", "created": "2022-04-27", "title": "Soil Compaction Prevention, Amelioration and Alleviation Measures Are Effective in Mechanized and Smallholder Agriculture: A Meta-Analysis", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Background: The compaction of subsoils in agriculture is a threat to soil functioning. Measures aimed at the prevention, amelioration, and/or impact alleviation of compacted subsoils have been studied for more than a century, but less in smallholder agriculture. Methods: A meta-analysis was conducted to quantitatively examine the effects of the prevention, amelioration, and impact alleviation measures in mechanized and small-holder agriculture countries, using studies published during 2000~2019/2020. Results: Mean effect sizes of crop yields were large for controlled traffic (+34%) and irrigation (+51%), modest for subsoiling, deep ploughing, and residue return (+10%), and negative for no-tillage (\u22126%). Mean effect sizes of soil bulk density were small (&lt;10%), suggesting bulk density is not a sensitive \u2018state\u2019 indicator. Mean effect sizes of penetration resistance were relatively large, with large variations. Controlled traffic had a larger effect in small-holder farming than mechanized agriculture. Conclusion: We found no fundamental differences between mechanized and smallholder agriculture in the mean effect sizes of the prevention, amelioration, and impact alleviation measures. Measures that prevent soil compaction are commonly preferred, but amelioration and alleviation are often equally needed and effective, depending on site-specific conditions. A toolbox of soil compaction prevention, amelioration, and alleviation measures is needed, for both mechanized and smallholder agriculture.</p></article>", "keywords": ["2. Zero hunger", "S", "tillage", "0401 agriculture", " forestry", " and fisheries", "Agriculture", "04 agricultural and veterinary sciences", "crop yield", "15. Life on land", "compacted subsoils", "mechanized agriculture", "smallholder agriculture", "soil bulk density", "soil penetration resistance"]}, "links": [{"href": "https://doi.org/10.3390/land11050645"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Land", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/land11050645", "name": "item", "description": "10.3390/land11050645", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/land11050645"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-04-27T00:00:00Z"}}, {"id": "10.3390/su1020268", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:21:07Z", "type": "Journal Article", "created": "2009-06-05", "description": "<p>The aim of this study was to evaluate microbial activity in soils under conventional and organic agricultural system management regimes. Soil samples were collected from plots under conventional management (CNV), organic management (ORG) and native vegetation (AVN). Soil microbial activity and biomass was significantly greater in ORG compared with CNV. Soil bulk density decreased three years after adoption of organic system. Soil organic carbon (SOC) was higher in the ORG than in the CNV. The soil under organic agricultural system presents higher microbial activity and biomass and lower bulk density than the conventional agricultural system.</p>", "keywords": ["2. Zero hunger", "jel:O13", "jel:Q", "0401 agriculture", " forestry", " and fisheries", "jel:Q0", "04 agricultural and veterinary sciences", "jel:Q2", "jel:Q56", "15. Life on land", "jel:Q3", "jel:Q5", "microbial activity; microbial biomass; soil organic matter; bulk density"], "contacts": [{"organization": "Ademir S.F. Ara\u00fajo, Luiz F.C. Leite, Valdinar B. Santos, Romero F.V. Carneiro,", "roles": ["creator"]}]}, "links": [{"href": "http://www.mdpi.com/2071-1050/1/2/268/pdf"}, {"href": "https://doi.org/10.3390/su1020268"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sustainability", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/su1020268", "name": "item", "description": "10.3390/su1020268", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/su1020268"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2009-06-04T00:00:00Z"}}, {"id": "10.5061/dryad.0k6djhb5k", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:21:23Z", "type": "Dataset", "created": "2023-08-29", "title": "Empirical data and model simulations of the effect of repeated hurricanes on soil carbon dynamics in a humid tropical forest", "description": "unspecified<em>Site description</em> Soils  were sampled from the Bisley Experimental Watershed of the LEF, Puerto  Rico (18.3157 deg. N, 65.7487 deg W), a Long-Term Ecological Research and  Critical Zone Observatory and Network site (https://luq.lter.network). The  mean maximum daily temperature at Bisley was 27 \u00baC between 1993 and 2010  (Gonzales, 2020), with little seasonality. The mean annual precipitation  at Bisley was 3883 (\u00b1 864 s.d.) mm y<sup>-1</sup> from 1988  through 2014 (Gonz\u00e1lez, 2017; Murphy et al., 2017). Rainfall occurs all  year, though January through April experience slightly less precipitation  than other months (Heartsill-Scalley et al., 2007). The site is a humid  tropical forest with a diverse tree community of approximately 170 species  &gt; 4 cm diameter at breast height (Weaver &amp; Murphy, 1990),  and dominated by tabonuco (<em>Dacryodes excelsa</em>  Vahl<em>)</em>. Elevation of Bisley spans from 261 m a.s.l. at  the base to 450 m a.s.l. on the ridges (Scatena, 1989).  Soils in Bisley are derived from volcaniclastic sediments of  andesitic parent material (Scatena, 1989).\u00a0 Ridge soils are classified as  Ultisols (Typic Haplohumults), while slope soils are classified as Oxisols  (inceptic and Aquic Hapludox), and valley soils are classified as  Inceptisols (Typic Epiaquaepts) (Hall et al., 2015; McDowell et al., 2012;  Scatena, 1989). Detailed site descriptions can be found in Scatena (1989),  Heartsill-Scalley et al (2010), and McDowell et al (2012). Here we refer  to soil organic C (SOC) and soil C interchangeably because there is no  detectable inorganic C in these soils.  <em>Hurricane occurrence\u00a0</em>  <strong>Figure 1: Timeline of major hurricanes that have  affected Luquillo Experimental Forest between sampling dates.  </strong> Nine major hurricanes (category 3 or  higher) have impacted Puerto Rico between 1851 and 2019 (L\u00f3pez-Marrero et  al., 2019), and five of these hurricanes have impacted the LEF. Until  1998, hurricanes had historically directly impacted the LEF approximately  every 60 years (Scatena &amp; Larsen, 1991). Before the initial  sampling campaign of this study, Hurricane San Cipri\u00e1n in 1932 was the  most recent storm to cause major disturbance to the LEF (Scatena &amp;  Larsen, 1991).\u00a0 However, since sampling in 1988, four major hurricanes  have impacted the forest (Figure 1). Hurricane Hugo (Category 3-4) in  1989, Hurricane Georges (Category 3) in 1998, and Hurricanes Irma and  Maria (Categories 5 and 4, respectively) within two weeks in 2017. The  trajectory and windspeeds of all these hurricanes caused widespread  defoliation. Litterfall historically takes over five years to return to  pre-hurricane levels (Scatena et al., 1996).\u00a0  <em>Sampling</em> Sample  collection occurred in 1988 and again in 2018. In both years, samples were  collected from three depths: 0\u201310 cm (the A horizon), 10\u201335 cm (all of the  B1 horizon and part of B2), and 35\u201360 cm (B2 to C) using an 8 cm diameter  soil auger. Soils in this study were sampled at three separate sites at  least 40 m from one another for each of three topographic locations,  ridge, slope, and upland valley. Two separate cores were taken from a  fourth topographic location in the riparian valley, that characterized a  smaller proportion of the area of these watersheds (Scatena &amp;  Lugo, 1995). Riparian valley sites were ephemeral streambeds with a high  boulder presence that limited sampling to less than 25 cm depth in one  case. Sampling sites from 1988 were marked with flags, and samples from  2018 were collected from within 15 m of the same locations as the  replicates from 1988, for consistency. Samples  collected in 1988 were analyzed for bulk density, pH, soil moisture, and a  suite of soil chemical properties (see Silver <em>et al</em>.  1994). Samples were then air-dried and stored in closed Ziploc bags within  paper bags in a storage facility in Richmond, CA, USA before density  fractionation in 2018. Fresh samples collected in 2018 were also  characterized for pH, soil moisture, and soil chemistry. Approximately 3 g  subsamples from each fresh sample in 2018 were immediately extracted with  45 mL of 0.2 M sodium citrate/0.5 M ascorbate solution, shaken for 16  hours, then centrifuged and the supernatant decanted to measure  concentrations of poorly crystalline iron (Fe) oxides. Within two days of  being double-bagged in Ziploc bags, fresh samples were further subsampled  and analyzed for pH in a 1:1 soil-to-water slurry (Thomas, 1996) and for  gravimetric soil moisture by oven-drying ~10 g subsamples at 105 \u00baC until  a constant weight. Soil samples were air-dried before further processing  and analysis. Air-dried soils from both sampling years were sieved to 2 mm  and large roots were sorted out. <em>Soil Density  fractionation</em> Soil was fractionated by  density following the method of Swanston et al. (2005), as modified by  Marin-Spiotta et al., (2009). Approximately 20 g of air-dried soil was  added to centrifuge tubes. Sodium polytungstate (SPT, Na6 [H2W12O40]  TC-Tungsten Compounds, Bavaria, Germany) in solution of density 1.85 g  cm<sup>-3</sup> was added to centrifuge tubes and agitated  before centrifuging. The density of the SPT followed previous studies from  this and nearby sites to allow direct comparison (Guti\u00e9rrez del Arroyo  &amp; Silver, 2018; Hall et al., 2015). Particulate organic matter  floating at the surface after centrifugation, the free light fraction  (FLF), was aspirated and then rinsed with 100 ml of deionized water 5  times on a 0.8 \u00b5m pore polycarbonate filter (Whatman Nuclepore Track Etch  Membrane, Darmstadt, Germany). Rinsed FLF was oven-dried at 65 \u00baC until  weight had stabilized. The remainder of the sample was combined with 70 ml  of additional SPT and mixed using an electric benchtop mixer (G3U05R,  Lightning, New York, NY, USA) at 1700 rpm for 1 min and sonicated in an  ice bath for 3 min at 70% pulse (Branson 450 Sonifier, Danbury, CT, USA).  Sonication is intended to disrupt soil structure and liberate organic  matter that has been occluded in aggregates. The sonicated slurry was  centrifuged again, and the light fraction at the surface, the occluded  light fraction (OLF), was aspirated, rinsed, and dried using the same  method as for the FLF. The remaining soil pellet was considered the heavy  fraction (HF), or mineral-associated organic matter fraction. The HF was  rinsed by thoroughly mixing with 150 ml of deionized water in the  centrifuge tube, centrifuging, and removing the supernatant repeatedly  until the fraction had been rinsed 5 times. The rinsed HF was oven-dried  at 105 \u00baC until weight stabilized. The average mass recovery was  98%. <em>Soil C and N and  \u03b4<sup>13</sup>C</em> Dried bulk and  HF soils were homogenized separately using a Spex Ball mill (SPEX Sample  Prep Mixer Mill 8000D, Metuchen, NJ). The FLF and OLF were homogenized  separately by hand using a mortar and pestle. All homogenized samples were  then analyzed at U. C. Berkeley for C and N concentrations on the CE  Elantech elemental analyzer (Lakewood, NJ) and for  \u03b4<sup>13</sup>C in the Stable Isotope Laboratory at UC  Berkeley, using a CHNOS Elemental Analyzer interfaced to an IsoPrime 100  mass spectrometer (Cheadle Hulme, UK), with a long-term external precision  of 0.10 %. \u00a0Soil C stocks were calculated by multiplying the C  concentrations (%) by the oven-dry mass of bulk soil (&lt; 2 mm) and  dividing by depth and the bulk density as measured in 1988 (Silver et al.,  1994; Throop et al., 2012).  <em>Radiocarbon</em> Homogenized  soil samples were combusted to CO<sub>2</sub> in sealed glass  tubes along with silver (Ag) and copper oxide (CuO) at the Center for  Accelerator Mass Spectrometry at Lawrence Livermore National Lab. The  CO<sub>2 </sub>was then graphitized on Fe powder under  pressurized hydrogen gas (Vogel et al., 1984). Graphite was pressed into  aluminum targets and run on the Compact Accelerator Mass Spectrometer for  radiocarbon analysis (Broek et al., 2021). Radiocarbon is reported in  \u0394<sup>14</sup>C, following Stuiver &amp; Polach (1977),  and calculated based on the fraction of modern isotope composition,  corrected for the year of sampling, and corrected for mass-dependent  fractionation with observed \u03b413C values of the sample. The compact AMS had  an average \u0394<sup>14</sup>C precision of 3.2 %. We report the  corrected \u0394<sup>14</sup>C value and  \u0394\u0394<sup>14</sup>C, which is calculated as  \u0394<sup>14</sup>C of the sample minus  \u0394<sup>14</sup>C of the atmosphere, to account for rapidly  changing atmospheric \u0394<sup>14</sup>C during the study period.  Atmospheric radiocarbon has been decaying nonlinearly since the peak of  weapons testing in the 1950s. Radiocarbon signatures in the soil are  strongly influenced by the atmospheric D<sup>14</sup>C  signature, making them useful for modeling soil C age and transit time,  especially since the 1950s. To compare the contribution of modern C  between 1988 and 2018, it is useful to take the difference between soil  and atmospheric D<sup>14</sup>C values, or  DD<sup>14</sup>C, because atmospheric  D<sup>14</sup>C declined between 1988 (98 %) and 2018 (4.4 %)  in Northern Hemisphere Zone 2 (Hua et al., 2013). We note that the decline  in atmospheric D<sup>14</sup>C is nonlinear, and thus the  DD<sup>14</sup>C in 2018 soil will be less sensitive to  short-term shifts in D<sup>14</sup>C inputs than the samples  from 1988. <em>Carbon age and transit time  modeling</em> Transit times and ages of C were  modeled with the package \u201cSoilR\u201d (Sierra et al., 2012, 2014) in R, version  4.0.2. The change in C density fractions over time, termed C flow, was  modeled using a 3-pool structure with a series flow matrix, under the  simplifying assumption that C flows from the litter pool to the FLF, where  it is sequentially transferred into the OLF and HF pools (Figure 2). The  model structure is depicted in basic form in equation 1,  \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0  \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 (1)\u00a0 dC(t)/dt = Inputs - k*C \u00a0in  matrix form with explicit pools in equation 2,  <em>\u00a0</em> <em>\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0  \u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 </em>(2)\u00a0 dC(t)/dt = [Litter Inputs; 0; 0] +  [-<em>k</em><sub>FLF</sub>, 0, 0 ;  a<sub>21</sub>,\u00a0-<em>k</em><sub>OLF</sub>, 0; 0, a<sub>32</sub>, -<sup>k</sup><sub>HRF</sub>] * [C<sub>FLF</sub>; C<sub>OLF</sub>; C<sub>HF</sub>] where <em>k</em><strong> </strong>is the first-order decay constant for each pool, <em>a</em> is the C transfer rate between pools (<em>i.e. a<sub>21</sub> </em>is the transfer from FLF (pool 1) to OLF (pool 2) and <em>a<sub>32</sub></em> is the transfer from OLF (pool 2) to HF (pool 3)), and <em>C </em>is the C stock of each pool.<strong> </strong>The transitTime and systemAge functions within the \u201csoilR\u201d package use this model structure to solve for the distribution of ages (time since entry) of each pool, and the distribution of transit times (times between entry and exit from the bulk soil) (Sierra et al 2016). Distributions of age and transit time were time-independent and did not assume a specific distribution (Sierra et al., 2014, 2017). <strong>Figure 2: Hypothesized flow of C in soils. </strong> Free light fraction (FLF) C (pink) is either decomposed (at cycling rate -<em>k<sub>FLF </sub>* FLF</em>) or transferred to the occluded light fraction pool (OLF, blue) with the transfer proportion defined by <em>a<sub>21</sub></em>. Carbon transfer between the OLF and heavy fraction (HF, purple) is defined by transfer coefficient <em>a<sub>32</sub></em>, and is respired from these pools at cycling rates -<em>k<sub>OLF</sub>* OLF</em> and <em>-k<sub>HF</sub>* HF</em>, respectively. Figure adapted from Sierra et al. (2012). Soil D<sup>14</sup>C and C stock mean and standard deviations from each time point, depth, and fraction were used to constrain the matrix model describing the movement of C through three soil pools and losses of C from each pool. Topography was not a strong predictor of patterns in D<sup>14</sup>C, C stocks, or C fractions, so samples from all topographies were aggregated for model simulations. The model used mean observed C content in each pool for each depth in 1988 as initial conditions for SOC stocks. Above and belowground litter inputs at 0\u201310 cm were assumed to be 900 g C m<sup>-2</sup> in non-hurricane or hurricane recovery years, based on observations from the same site (Liu et al., 2018; Scatena et al., 1996; Silver et al., 1996; Vogt et al., 1996). Inputs to the 10\u201335 cm and 35\u201360 cm depths were estimated using observations of live fine roots on the surface and typical root distribution in the forest (Silver &amp; Vogt, 1993). Total root input is approximately threefold the input of fine roots alone (McCormack et al., 2015; Yaffar &amp; Norby, 2020), and live fine roots in the 0\u201310 cm depth had a mean biomass of 80 - 250 g C m<sup>-2 \u00a0</sup>(Hall et al., 2015), suggesting that total root C inputs of approximately 450 g C m<sup>-2 </sup>to the surface would be well within the expected range. Root inputs below 0\u201310 cm were estimated assuming that inputs follow the typical distribution of root biomass in Puerto Rican tropical forests, with 60\u201370% of root biomass in 0\u201310 cm, an additional 20-30% of biomass in 10\u201335 cm (~135 g C m<sup>-2\u00ad</sup>), and 5\u20138% of biomass is in the 35\u201360 cm depth (~40 g C m<sup>-2\u00ad</sup>) (Silver &amp; Vogt, 1993; Yaffar &amp; Norby, 2020). The model was parameterized under two scenarios for each depth: 1) constant inputs, assuming a steady-state undisturbed forest, and 2) hurricane inputs, which simulated the input fluxes from defoliation during the three major hurricanes, followed by a subsequent reduction in litter inputs and then litterfall increasing linearly to pre-hurricane inputs over 6 years (Scatena et al., 1996; Silver et al., 1996; Vogt et al., 1996). Hurricane inputs were imposed as an additional pulse of litter inputs to each depth interval, declining with depth. \u00a0The 0\u201310 cm interval received 100% of the surface input pulse, the 10\u201335 cm depth received a pulse of root inputs equivalent to 30% of the surface input pulse, and the 35\u201360 cm depth received root inputs equal to 10% of the surface input pulse. Surface litter pulses under hurricanes were specified according to measured litterfall values and were 42.5 g C m<sup>-2\u00ad</sup> to the surface in 1989 (Hurricane Hugo) and 1998 (Hurricane Georges) (Scatena et al., 1993; Silver et al., 1996) and 1611 g C m<sup>-2 \u00a0</sup>in 2017 (Hurricanes Irma and Maria) (Liu et al. 2018a). The same soil D<sup>14</sup>C and C stock observations were used to constrain the model under each scenario, with only the input regime varying. Parameters of the transfer matrix (<em>-k\u00ad\u00ad<sub>FLF</sub>,</em><sub> </sub><em>-k\u00ad\u00ad<sub>OLF</sub>,<sub> </sub>-k\u00ad\u00ad<sub>HF</sub>,<sub> </sub>a<sub>21</sub>, a<sub>32</sub></em>) were constrained using a cost function to accept or reject potential parameter sets over 1000 iterations, based on observed D<sup>14</sup>C and C stock means and standard errors from both time points (1988 and 2018). A Markov chain Monte Carlo (MCMC) simulation initialized with cost-optimized parameters was run to assimilate observed data and optimize parameter choices to the observations using function <em>modMCMC() </em>from R package \u201cFME\u201d (Sierra et al., 2014; Soetaert &amp; Petzoldt, 2010). The MCMC was iterated over at least 20,000 simulations or until parameter solutions converged according to the trace, which was over 100,000 iterations at the 35\u201360 cm depth. The first half of the iterations was considered the burn-in period before the chain started to converge near an equilibrium, and these iterations were discarded in calculations of optimal parameters. The model output for the surface soils of the HF pool was validated using published radiocarbon values from the mineral-associated fraction (the only fraction analyzed) of samples from the site taken in 2012 (Hall et al., 2015).\u00a0 Bulk and pool soil C age and transit time density distributions and mean values were calculated using the <em>systemAge() </em>and <em>transitTime()</em> functions from the \u201cSoilR\u201d package. Mean density distributions were calculated using the mean parameter set given from the MCMC analysis. Standard deviation from the mean was calculated using the <em>systemAge() </em>and <em>transitTime()</em> functions on 200 sets of five parameters selected randomly within one standard deviation of the mean of each parameter given as output from the MCMC. Lower and upper limits of SOC ages and transit times were calculated using the upper and lower ranges of these iterations. <em>Statistics</em> Statistics were run in R, version 4.0.2 (R Core Team, 2020). The statistical model selection followed the recommendations of Zuur et al (2009). Statistical models were chosen using a linear mixed effects model in package \u201clme4\u201d, with random slopes accounting for the influence each core, or sampling site, had on the response variable values as they varied with depth. This random effect of the core site on the depth effect was evaluated using a restricted maximum likelihood approach and was included in the initial evaluation of all model comparisons. Linear mixed effect models included year, topographic position, depth, and interactions as fixed factors, and the depth effect of each core as a random factor for each of the response variables: C concentration, N concentration, d<sup>13</sup>C, DD<sup>14</sup>C. In evaluations of some response variables with AIC and BIC criteria, the random effect no longer enhanced the model, and model comparison proceeded using ANOVAs of linear models without random effects. Topographic effects on C concentrations are discussed in the supplemental information. Model assumptions were evaluated using the check_model function in R package \u201cperformance\u201d, to check for multicollinearity, normality of residuals, homoscedasticity, homogeneity of variance, influential observations, and normality of random effects. In the cases when random effects were significant (bulk soil d<sup>13</sup>C and DD<sup>14</sup>C, FLF DD<sup>14</sup>C and HF C and N concentrations), fixed effects were chosen using ANOVA of subsequent models using maximum likelihood estimation, with the random effects held constant. Once fixed effects were established, the model was re-fitted using a restricted maximum likelihood approach to report model estimates, and an ANOVA was run to determine the significance of the response variable. In all cases, P-values were estimated using Tukey\u2019s honest significant post-hoc test to assess significant differences between variables, in the package \u201cagricolae\u201d in R, and contrasts and standard errors of contrasts were estimated using lsmeans() function in package \u201clsmeans\u201d in R. Values of\u00a0<em>P</em> &lt; 0.10 were reported as significant unless otherwise specified. The topographic position was not a significant predictor for most variables, so results are reported as means aggregated across positions.", "keywords": ["soil organic carbon", "Transit time", "Tropical forest soil", "FOS: Earth and related environmental sciences", "Soil R", "density fractions", "Radiocarbon"], "contacts": [{"organization": "Mayer, Allegra", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.0k6djhb5k"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.0k6djhb5k", "name": "item", "description": "10.5061/dryad.0k6djhb5k", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.0k6djhb5k"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-04-01T00:00:00Z"}}, {"id": "10.4141/cjss81-026", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:21:18Z", "type": "Journal Article", "created": "2010-03-24", "description": "<p> The amounts of organic matter in native prairie and in an adjacent cultivated field were compared with the output from a simulation model describing organic matter dynamics. The effects of past and possible future soil management practices, and the loss of organic C through rainfall erosion were incorporated into the simulation study. Seventy years of cultivation increased the bulk density of the A horizon by an average of 16% along the catena of a Black Chernozemic soil. Organic C had decreased by 36% in the soil profile at the mid-slope position. Losses of organic N were 5\uffe2\uff80\uff9310% less. Depletion of organic C and N from the Ah horizon accounted for &gt;\uffe2\uff80\uff8290% of the total loss from the soil profile. Therefore, extrapolation of data from surface soil, based solely on changes in the concentration of organic C and N, could result in an overestimation of organic matter losses from soils. Microbial biomass in the Ap horizon of the crop-summer-fallow site was 30% less than in the Ah horizon of the native prairie. The model predicted an immediate rise in microbial biomass C upon cultivation of the native prairie due to a large initial input of grassland litter and roots. Subsequently, the microbial biomass C decreased and approached a steady-state level which was 25% less than in the native prairie. The model indicates that large quantities of N released during the initial years of cultivation would not have been totally utilized by the cultivated crops, therefore resulting in major losses to the environment. However, now the organic matter is reaching a steady-state level and only small net release of N can be expected; external N sources are required for optimum crop production. Management practices such as straw removal and cropping sequence have short-term effects on the rate of depletion of soil organic C. Similar equilibrium levels of soil organic matter were predicted after 100\uffe2\uff80\uff82yr of cultivation in simulation studies that did not consider erosion losses. The inclusion of rainfall erosion losses indicated that major organic C and other nutrient losses will occur in management practices that include significant portions of fallow in the cropping sequence. </p>", "keywords": ["2. Zero hunger", "550", "soil organic matter", "soil organic C", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "N", "15. Life on land", "soil bulk density"], "contacts": [{"organization": "Paul, E. A., author, Voroney, R. P., author, Van Veen, J. A., author, Agricultural Institute of Canada, publisher,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.4141/cjss81-026"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Canadian%20Journal%20of%20Soil%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.4141/cjss81-026", "name": "item", "description": "10.4141/cjss81-026", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.4141/cjss81-026"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "1981-05-01T00:00:00Z"}}, {"id": "10.4141/cjss95-075", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:21:19Z", "type": "Journal Article", "created": "2011-04-24", "title": "Calculation Of Organic Matter And Nutrients Stored In Soils Under Contrasting Management Regimes", "description": "<p> Assessments of management-induced changes in soil organic matter depend on the methods used to calculate the quantities of organic C and N stored in soils. Chemical analyses in the laboratory indicate the concentrations of elements in soils, but the thickness and bulk density of the soil layers in the field must be considered to estimate the quantities of elements per unit area. Conventional methods that calculate organic matter storage as the product of concentration, bulk density and thickness do not fully account for variations in soil mass. Comparisons between the quantities of organic C, N, P and S in Gray Luvisol soils under native aspen forest and various cropping systems were hampered by differences in the mass of soil under consideration. The influence of these differences was eliminated by calculating the masses of C, N, P and S in an 'equivalent soil mass' (i.e. the mass of soil in a standard or reference surface layer). Reassessment of previously published data also indicated that estimates of organic matter storage depended on soil mass. Appraisals of organic matter depletion or accumulation usually were different for comparisons among element masses in an equivalent soil mass than for comparisons among element masses in genetic horizons or in fixed sampling depths. Unless soil erosion or deposition had altered the mass of topsoil per unit area, comparisons among unequal soil masses were unjustified and erroneous. For management-induced changes in soil organic matter and nutrient storage to be assessed reliably, the masses of soil being compared must be equivalent. Key words: Soil carbon, soil nitrogen, soil phosphorus, soil sulfur, carbon cycle, carbon storage, bulk density effects, Gray Luvisol, soil erosion </p>", "keywords": ["Gray Luvisol", "soil sulfur", "soil erosion", "soil nitrogen", "soil phosphorus", "carbon cycle", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "carbon storage", "15. Life on land", "Soil carbon", "bulk density effects", "Forest Sciences"]}, "links": [{"href": "https://doi.org/10.4141/cjss95-075"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Canadian%20Journal%20of%20Soil%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.4141/cjss95-075", "name": "item", "description": "10.4141/cjss95-075", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.4141/cjss95-075"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "1995-11-01T00:00:00Z"}}, {"id": "10.5061/dryad.3bk3j9kt3", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:21:24Z", "type": "Dataset", "created": "2024-03-31", "title": "Data from: Burrowing crab effects on the properties and functions of coastal soft sediments", "description": "unspecified# Data from: Burrowing crab effects on the properties and functions of  coastal soft sediments  [https://doi.org/10.5061/dryad.3bk3j9kt3](https://doi.org/10.5061/dryad.3bk3j9kt3) Effect size calculations (including means, sample sizes, and standard deviation) of crab burrowing effects (i.e., high density vs low density) on the properties, nutrient stocks, and functions of coastal sediments. Data comes from studies conducted across Africa, Asia, Australia, North America, and South America. ## Description of the data and file structure **File list:** 1. Rinehart_et_al.202X_Effectsizes CSV file containing the Hedges d effect size calculations (including the raw means, sample sizes, and standard deviations) for each extracted comparison/study from all 59 manuscripts. Additional extracted data (e.g., crab taxa, experimental conditions, habitat, burrow density) are also included for each comparison/study. 2. Rinehart_et_al.202X_Publicationbias CSV file containing the pooled standard deviation and the Hedges d effect size calculation for each comparison/study. This datafile was used to conduct analyses of publication bias for a resulting systematic meta-analysis. **Data-specific information for:** (1) Rinehart_et_al.202X_Effectsizes **Number of variables:** 47 **Number of cases/rows:** 1423 Variable List:\u00a0 1. id: the unique code assigned to each data row. 2. reference: author, year, and journal for each data source. 3. pub_year: year of reference publication. One in preparation study was included in the dataset (Rinehart et al. 20XX), it's publication year is denoted as 20XX. 4. paper id: the unique code assigned to each manuscript included in the dataset. 5. continent: the continent where the data was collected. 6. country: the country where the data was collected. 7. state: the state (united states only) where the data was collected. 8. estuary: the name of the estuary where the data was collected. 9. latitude_dd: the latitude associated with the data collected in decimal degrees (dd). 10. longitude_dd: the longitude associated with the data collected in decimal degrees (dd). 11. ecosystem: the type of ecosystem (e.g., salt marsh, mangrove forest, tidal flat) associated with the collected data. 12. vegetation: categorical variable noting the presence (vegetated) or absence (not unvegetated) of any vegetation. 13. ecosystem_type: categorical variable noting if the ecosystem was restored, created, or natural. 14. relative_salinity: categorical variable noting the relative salinity in the ecosystem where the data was collected. 15. tidal_amplitude_m: the tidal amplitude (in meters) in the ecosystem where the data was collected. 16. tidal_cycle: categorical variable noting the type of tidal cycle (e.g., diurnal) in the ecosystem where the data was collected. 17. soil_type: categorical variable noting the soil type (e.g., sand) in the ecosystem where the data was collected. 18. elevation_m: the elevation (in meters) of the ecosystem where the data was collected. 19. study_duration_d: the length of time (in days) that the study ran (applies mainly to manipulative studies). 20. study_timing: the seasons or months during which the study was run. 21. dominant_plant_genus: the genus of the dominant plant present in the ecosystem where the data was collected. 22. dominant_plant_species: the species of the dominant plant present in the ecosystem where the data was collected. 23. dominant_plant_functional_group: categorical variable noting the functional group (e.g., grass) of the dominant plant species in the ecosystem where the data was collected. 24. crab_genus: the genus of the dominant burrowing crab used in the study. Studies with mixed crab communities are denoted with by 'mixed'. 25. crab_species: the species of the dominant burrowing crab used in the study. Studies with mixed crab communities are denoted with by 'mixed'. 26. crab_diet: categorical variable noting the main feeding strategy (e.g., herbivore, detritivore) used by the dominant crab species. 27. crab_superfamily: the superfamily of the dominant burrowing crab used in the study. Studies with mixed crab communities are denoted with by 'mixed'. 28. mean_burrow_diameter_high_crab_treatment_mm: the mean burrow diameter in the study's high crab treatment in mm. 29. mean_burrow_diameter_low_crab_treatment_mm: the mean burrow diameter in the study's low crab treatment in mm. 30. mean_burrow_depth_cm: the mean burrow depth in cm reported by the study. 31. burrow_density_high_crab_m^2: the mean crab burrow density per meter-squared reported in the study's high crab treatment. 32. burrow_density_low_crab_m^2: the mean crab burrow density per meter-squared reported in the study's low crab treatment. 33. experiment_type: categorical variable noting if the study used observational or manipulative methodologies. 34. experiment_setting: categorical variable noting if the study was conducted in a laboratory or field setting. Laboratory studies also include outdoor mesocosm studies. 35. field_location: categorical variable noting where studies conducted in the field placed their study relative to the shoreline. Specifically, we noted if studied sampled in the ecosystem interior (far from shoreline) or at the ecosystem edge (adjacent to the shoreline). 36. soil_depth_cm: the depth, in cm, within the soil profile from which the sediment samples were collected. 37. soil_characteristic_measured: categorical variable identifying the specific sediment property, nutrient stock, or function that was quantified by the study. 38. soil_characteristic_units: the original units used to quantify the soil characteristic within the study. 39. mean_low_crab: the mean value of the soil characteristic measured in the low crab treatment within the study. 40. sd_low_crab: the standard deviation of the soil characteristic measured in the low crab treatment within the study. 41. n_low_crab: the sample size of the soil characteristic measured in the low crab treatment within the study. 42. mean_high_crab: the mean value of the soil characteristic measured in the high crab treatment within the study. 43. sd_high_crab: the standard deviation of the soil characteristic measured in the high crab treatment within the study. 44. n_high_crab: the sample size of the soil characteristic measured in the high crab treatment within the study. 45. crab_density: categorical variable noting if the study documented relative burrowing crab density within their study using burrow density (burrow) or counts of individuals (individuals). 46. hedges_d: the hedges d effect size calculated for the effects of burrowing crabs on the measured sediment characteristic. Hedges d values were calculated in OpenMee software (see code/software below). Positive effect sizes indicate that burrowing crabs increased the value of the sediment measurement, while negative effect sized indicate that burrowing crabs decreased the value of the sediment measurement. 47. hedges_d_var: the variation of the hedges d effect size calculated for the effects of burrowing crabs on the measured sediment characteristic. Hedges d variation values were calculated in OpenMee software (see code/software below). **Missing data codes:** na Data-specific information for: (2) Rinehart_et_al.202X_Publicationbias ***Number of variables:*** 22 ***Number of cases/rows:*** 1423 Variable List:\u00a0 1. id: the unique code assigned to each data row. 2. reference: author, year, and journal for each data source. 3. pub_year: year of reference publication. One in preparation study was included in the dataset (Rinehart et al. 20XX), it's publication year is denoted as 20XX. 4. paper id: the unique code assigned to each manuscript included in the dataset. 5. ecosystem: the type of ecosystem (e.g., salt marsh, mangrove forest, tidal flat) associated with the collected data. 6. vegetation: categorical variable noting the presence (vegetated) or absence (not unvegetated) of any vegetation. 7. crab_superfamily: the superfamily of the dominant burrowing crab used in the study. Studies with mixed crab communities are denoted with by 'mixed'. 8. burrow_density_high_crab_m^2: the mean crab burrow density per meter-squared reported in the study's high crab treatment. 9. experiment_type: categorical variable noting if the study used observational or manipulative methodologies. 10. experiment_setting: categorical variable noting if the study was conducted in a laboratory or field setting. Laboratory studies also include outdoor mesocosm studies. 11. soil_characteristic_measured: categorical variable identifying the specific sediment property, nutrient stock, or function that was quantified by the study. 12. soil_characteristic_units: the original units used to quantify the soil characteristic within the study. 13. mean_low_crab: the mean value of the soil characteristic measured in the low crab treatment within the study. 14. sd_low_crab: the standard deviation of the soil characteristic measured in the low crab treatment within the study. 15. n_low_crab: the sample size of the soil characteristic measured in the low crab treatment within the study. 16. mean_high_crab: the mean value of the soil characteristic measured in the high crab treatment within the study. 17. sd_high_crab: the standard deviation of the soil characteristic measured in the high crab treatment within the study. 18. n_high_crab: the sample size of the soil characteristic measured in the high crab treatment within the study. 19. pooled_sd: the pooled standard deviation of the high and low crab treatments for each study. 20. crab_density: categorical variable noting if the study documented relative burrowing crab density within their study using burrow density (burrow) or counts of individuals (individuals). 21. hedges_d: the hedges d effect size calculated for the effects of burrowing crabs on the measured sediment characteristic. Hedges d values were calculated in OpenMee software (see code/software below). Positive effect sizes indicate that burrowing crabs increased the value of the sediment measurement, while negative effect sized indicate that burrowing crabs decreased the value of the sediment measurement. 22. hedges_d_var: the variation of the hedges d effect size calculated for the effects of burrowing crabs on the measured sediment characteristic. Hedges d variation values were calculated in OpenMee software (see code/software below). **Missing data codes:** na ## Sharing/Access information All data are included in the provided datafiles. ## Code/Software Hedges\u2019 *d* (hereafter, *d*) effect sizes were calculated using meta-analysis using OpenMEE software (Build date: 26 July 2016; Wallace et al. 2017). Wallace, B. C., M. J. Lajeunesse, G. Dietz, I. J. Dahabreh, T. A. Trikalinos, C. H. Schmid, and J. Gurevitch. 2017. OpenMEE: Intuitive, open-source software for meta-analysis in ecology and evolutionary biology. Methods in Ecology and Evolution 8:941\u2013947.", "keywords": ["coastal wetlands", "density-dependance", "bioturbation", "animal effects", "Burrowing", "functional traits", "FOS: Earth and related environmental sciences", "habitat effects", "zoogeochemistry"], "contacts": [{"organization": "Rinehart, Shelby", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.3bk3j9kt3"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.3bk3j9kt3", "name": "item", "description": "10.5061/dryad.3bk3j9kt3", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.3bk3j9kt3"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-04-29T00:00:00Z"}}, {"id": "10.5061/dryad.3xsj3txc0", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:21:24Z", "type": "Dataset", "title": "Data from: Megafauna biogeography explains plant functional trait variation in the tropics", "description": "unspecifiedThe dataset that is made  available here cosists of two files in .csv format. The first is the  complete trait dataset for specific leaf area (sla;  mm<sup>2</sup>.mg<sup>-1</sup>), wood density  (woo; g.cm<sup>-3</sup>), HMax (m) and Spines (yes/no). The  list of reference sources for trait data is presentes in the end of this  note. Other abreviations in this file are: ref.sla: reference sources for  sla data; ref.woo: reference sources for wood density data; ref.hmax:  reference sources for hmax data; mat:\u00a0 mean annual temperature; map: mean  annual precipitation; rs: rainfall seasonality; nfires5: number of fires  per 5 km area (our proxy for fire frequency); avgfrp: average fire  radiative power (our proxy for fire intensity); cec: soil cation exchange  capacity; orc: soil organic carbon content; cly: weight percentage of clay  particles (&lt;0.0002 mm) in the soil; slt: weight percentage of silt  particles (0.0002\u20130.05 mm) in the soil; snd: weight percentage of the sand  particles (0.05\u20132 mm) in the soil; crf: volumetric percentage of coarse  fragments (&gt;2 mm) in the soil. The second file attached is a  dataset of Geoxyle species (geox; y(yes)/n(no)) for a subset of the  Brazilian Cerrado species. \u00a0 <strong>Complete Reference  Sources for the Funcitonal Trait Data</strong> \u00a0 Abbot, P., Lowore, J., Khofi, C. &amp; Werren, M. (1997). Defining firewood quality: A comparison of quantitative and rapid appraisal techniques to evaluate firewood species from a Southern African Savanna. <i>Biomass and Bioenergy</i>, <b>12</b>, 429\u2013437. Abe, N., Miatto, R.C. &amp; Batalha, M.A. (2018). Relationships among functional traits define primary strategies in woody species of the Brazilian \u201ccerrado.\u201d <i>Revista Brasileira de Botanica</i>, <b>41</b>, 351\u2013360. African Plant Database (version 3.4.0). Conservatoire et Jardin botaniques de la Ville de Gen\u00e8ve and South African National Biodiversity Institute, Pretoria, 'Retrieved in january 2020', from &lt;http://www.ville-ge.ch/musinfo/bd/cjb/africa/&gt;. Balch, J.K., Nepstad, D.C., Curran, L.M., Brando, P.M., Portela, O., Guilherme, P., Reuning-Scherer, J.D. &amp; de Carvalho, O. (2011). Size, species, and fire behavior predict tree and liana mortality from experimental burns in the Brazilian Amazon. <i>Forest Ecology and Management</i>, <b>261</b>, 68\u201377. Barbosa, R.I. &amp; Fearnside, P.M. (2004). Wood density of trees in open savannas of the Brazilian Amazon. <i>Forest Ecology and Management</i>, <b>199</b>, 115\u2013123. Batalha, M.A., Silva, I.A., Cianciaruso, M.V., Fran\u00e7a, H. &amp; de Carvalho, G.H. (2011). Phylogeny, traits, environment, and space in cerrado plant communities at Emas National Park (Brazil).. <i>Flora - Morphology, Distribution, Functional Ecology of Plants</i>, <b>206</b>, 949\u2013956. Borchert, R. (1994). Soil and stem water storage determine phenology and distribution of tropical dry forest trees. <i>Ecology</i>, <b>75</b>, 1437\u20131449. Bucci, S.J., Goldstein, G., Meinzer, F.C., Scholz, F.G., Franco,\u00a0 a C. &amp; Bustamante, M. (2004). Functional convergence in hydraulic architecture and water relations of tropical savanna trees: from leaf to whole plant. <i>Tree physiology</i>, <b>24</b>, 891\u20139. Bucci, S.J., Scholz, F.G., Goldstein, G., Meinzer, F.C., Franco, A.C., Campanello, P.I., Villalobos-Vega, R., Bustamante, M. &amp; Miralles-Wilhelm, F. (2006). Nutrient availability constrains the hydraulic architecture and water relations of savannah trees. <i>Plant, cell &amp; environment</i>, <b>29</b>, 2153\u201367. Cianciaruso, M. V., Silva, I.A., Manica, L.T. &amp; Souza, J.P. (2013). Leaf habit does not predict leaf functional traits in cerrado woody species. <i>Basic and Applied Ecology</i>, <b>14</b>, 404\u2013412. Costa, T.G., Bianchi, M.L., Prot\u00e1sio, T. de P., Trugilho, P.F. &amp; Pereira, A.J. (2014). Wood quality of five species from cerrado for production of charcoal. <i>Cerne</i>, <b>20</b>, 37\u201345. Dantas, V.L. &amp; Batalha, M.A. (2012). Can antiherbivory resistance explain the abundance of woody species in a Neotropical savanna? <i>Botany</i>, <b>90</b>, 93\u201399. Dantas, V.L., Batalha, M.A. &amp; Pausas, J.G. (2013). Fire drives functional thresholds on the savanna\u2013forest transition. <i>Ecology</i>, <b>94</b>, 2454\u20132463. Domingues, T.F., Meir, P., Feldpausch, T.R., Saiz, G., Veenendaal, E.M., Schrodt, F., Bird, M., Djagbletey, G., Hien, F., Compaore, H., Diallo, A., Grace, J. &amp; Lloyd, J. (2010). Co-limitation of photosynthetic capacity by nitrogen and phosphorus in West Africa woodlands. <i>Plant, Cell and Environment</i>, <b>33</b>, 959\u2013980. Flora do Brasil 2020 in construction. Jardim Bot\u00e2nico do Rio de Janeiro. Available at: &lt; http://floradobrasil.jbrj.gov.br/ &gt;. Accessed in January 2020 Hao, G.Y., Hoffmann, W.A., Scholz, F.G., Bucci, S.J., Meinzer, F.C., Franco, A.C., Cao, K.F. &amp; Goldstein, G. (2008). Stem and leaf hydraulics of congeneric tree species from adjacent tropical savanna and forest ecosystems. <i>Oecologia</i>, <b>155</b>, 405\u2013415. Higgins, S.I., Bond, W.J., Combrink, H., Craine, J.M., February, E.C., Govender, N., Lannas, K., Moncreiff, G. &amp; Trollope, W.S.W. (2012). Which traits determine shifts in the abundance of tree species in a fire-prone savanna? <i>Journal of Ecology</i>, <b>100</b>, 1400\u20131410. Kitajima, K. &amp; Poorter, L. (2010). Tissue-level leaf toughness, but not lamina thickness, predicts sapling leaf lifespan and shade tolerance of tropical tree species. <i>New Phytologist</i>, <b>186</b>, 708\u2013721. Markesteijn, L. &amp; Poorter, L. (2009). Seedling root morphology and biomass allocation of 62 tropical tree species in relation to drought- and shade-tolerance. <i>Journal of Ecology</i>, <b>97</b>, 311\u2013325. Markesteijn, L., Poorter, L., Paz, H., Sack, L. &amp; Bongers, F. (2011). Ecological differentiation in xylem cavitation resistance is associated with stem and leaf structural traits. <i>Plant, Cell and Environment</i>, <b>34</b>, 137\u2013148. Meir, P., Levy, P.E., Grace, J. &amp; Jarvis, P.G. (2007). Photosynthetic parameters from two contrasting woody vegetation types in West Africa. <i>Plant Ecology</i>, <b>192</b>, 277\u2013287. Miatto, R.C. (2011). A inclus\u00e3o da abund\u00e2ncia na diversidade funcional aumenta o seu poder de previs\u00e3o?: teste em uma comunidade de cerrado. 37. Miatto, R.C., Wright, I.J. &amp; Batalha, M. a. (2016). Relationships between soil nutrient status and nutrient-related leaf traits in Brazilian cerrado and seasonal forest communities. <i>Plant and Soil</i>. Nygard, R. &amp; Elfving, B. (2000). Stem basic density and bark proportion of 45 woody species in young savanna coppice forests in Burkina Faso. <i>Annals of Forest Science</i>, <b>57</b>, 143\u2013153. Oliveira-filho, A.T. (2017). NeoTropTree, Flora arb\u00f3rea da Regi\u00e3o Neotropical: Um banco de dados envolvendo biogeografia, diversidade e conserva\u00e7\u00e3o. <i>Universidade Federal de Minas Gerais</i>. Van der Plas, F., Howison, R., Reinders, J., Fokkema, W. &amp; Olff, H. (2013). Functional traits of trees on and off termite mounds: Understanding the origin of biotically-driven heterogeneity in savannas. <i>Journal of Vegetation Science</i>, <b>24</b>, 227\u2013238. Poorter, L., McDonald, I., Alarcon, A., Fichtler, E., Licona, J.-C., Pe\u00f1a-Carlos, M., Sterck, F., Villegas, Z. &amp; Sass-klaassen, U. (2010). The importance of wood traits and hydraulic conductance for the performance and life history strategies of 42 rainforest tree species - Poorter - 2009 - New Phytologist - Wiley Online Library. <i>New Phytologist</i>, 481\u2013492. Ribeiro, S.C., Fehrmann, L., Soares, C.P.B., Jacovine, L.A.G., Kleinn, C. &amp; de Oliveira Gaspar, R. (2011). Above- and belowground biomass in a Brazilian Cerrado. <i>Forest Ecology and Management</i>, <b>262</b>, 491\u2013499. Santiago, L.S., Goldstein, G., Meinzer, F.C., Fisher, J.B., Machado, K., Woodruff, D. &amp; Jones, T. (2004). Leaf photosynthetic traits scale with hydraulic conductivity and wood density in Panamanian forest canopy trees. <i>Oecologia</i>, <b>140</b>, 543\u2013450. Scogings, P.F., Taylor, R.W. &amp; Ward, D. (2012). Inter and intra-plant variations in nitrogen, tannins and shoot growth of Sclerocarya birrea browsed by elephants. <i>Plant Ecology</i>, <b>213</b>, 483\u2013491. Vale, A.T., Dias, I.S. &amp; Santana, M.A.E. (2010). Rela\u00e7\u00f5es entre propriedades qu\u00edmicas, f\u00edsicas e energ\u00e9ticas da madeira em cinco esp\u00e9cies de cerrado. <i>Ci\u00eanc ia Florestal</i>, <b>20</b>, 137\u2013145. Vinya, R., Malhi, Y., Brown, N. &amp; Fisher, J.B. (2012). Functional coordination between branch hydraulic properties and leaf functional traits in miombo woodlands: Implications for water stress management and species habitat preference. <i>Acta Physiologiae Plantarum</i>, <b>34</b>, 1701\u20131710. Yeaton, R. (1988). Porcupines , Fires and the Dynamics of the Tree Layer of the Burkea Africana Savanna. <i>Journal of Ecology</i>, <b>76</b>, 1017\u20131029. Zanne, A.E., Lopez-Gonzalez, G., Coomes, D.A., Ilic, J., Jansen, S., Lewis, S.L., Miller, R.B., Swenson, N.G., Wiemann, M.C. &amp; Chave, J. 2009. Global wood density database. Dryad. Identifier: http://hdl.handle.net/10255/dryad.235 Zizka, A., Govender, N. &amp; Higgins, S.I. (2014). How to tell a shrub from a tree: A life-history perspective from a South African savanna. <i>Austral Ecology</i>, <b>39</b>, 767\u2013778.", "keywords": ["megafauna", "specific leaf area (SLA)", "spines", "15. Life on land", "Maximum tree height", "Wood density", "geoxylic suffrutex"], "contacts": [{"organization": "Dantas, Vin\u00edcius, Pausas, Juli,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.3xsj3txc0"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.3xsj3txc0", "name": "item", "description": "10.5061/dryad.3xsj3txc0", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.3xsj3txc0"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-04-29T00:00:00Z"}}, {"id": "10.5061/dryad.9ghx3ffkj", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:21:27Z", "type": "Dataset", "title": "Sacred groves of Central India: Diversity status, carbon storage and conservation strategies", "description": "Sacred groves (SGs) play an important role in the conservation of local  biodiversity and provide numerous ecosystem services worldwide. We studied  how the ecological status of Central Indian SGs contributes to regional  tree diversity and carbon (C) storage. We inventoried the trees in  fifty-nine SGs of Madhya Pradesh and recorded a total of 109 tree species  (90 genera, 40 families). The most species-rich families were Fabaceae,  Combretaceae, Malvaceae and Moraceae. The tree density ranged from 75 to  925 individuals ha-1 (mean: 398 \u00b1 32 individuals ha-1), while the basal  area varied from 2.5 to 69.2 m2 ha-1 (mean: 24.2 \u00b1 1.9 m2 ha-1). The total  C stock {tree C + soil organic C (SOC; 0-30 cm)} ranged from 44.7 to 455.4  Mg C ha-1 (mean: 153.8 \u00b1 9.6 Mg C ha-1) across the SGs. The studied SGs  represented 74.7% of the total tree diversity and contained 33.1% higher  total C stock than the forests of the state. Tree C stock was  significantly positively correlated with tree basal area (r57 = 0.965, P  &lt; 0.0001), distance from the nearest village (r57 = 0.432, P  &lt; 0.001) and number of years of existence (r57 = 0.615, P &lt;  0.0001). The present study highlighted the crucial role of the studied SGs  in sustaining regional biodiversity and storing significant amounts of C  in biomass and soil. Continued conservation efforts and contained  anthropogenic interferences are necessary in order to maintain the current  role of these SGs as biodiversity and carbon reservoirs of Central India.", "keywords": ["2. Zero hunger", "tree diversity", "carbon density", "Tropical dry deciduous forests", "FOS: Earth and related environmental sciences", "15. Life on land", "Madhya Pradesh", "Protected forests", "Soil carbon"], "contacts": [{"organization": "Khan, Mohammed Latif, Dar, Javid Ahmad, Kothandaraman, Subashree, Khare, Pramod Kumar,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.9ghx3ffkj"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.9ghx3ffkj", "name": "item", "description": "10.5061/dryad.9ghx3ffkj", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.9ghx3ffkj"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-05-13T00:00:00Z"}}, {"id": "10.5281/zenodo.14039385", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:22:26Z", "type": "Dataset", "title": "Maps of topsoil (0-30 cm) properties of Tuscany (Italy)", "description": "Open AccessThe internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.  The topsoil (0-30 cm) properties maps are prepared to evaluate soil ecosystem services in SERENA/EJP-Soil and for applying SOC loss Cookbook and SOIL Loss Cookbook. In particular Soil Organic Carbon content map was directly considered as an application of SOC loss Cookbook (DOI: 10.5281/zenodo.13951265\u00a0Version 3).  They are based on Tuscany Region soil database available at Geoscopio (https://www502.regione.toscana.it/geoscopio/pedologia.html) and on point soil data not freely available (Lamma Consortium). More information and requests to:\u00a0info@lamma.toscana.it.  In accordance with the methodology reported in the Soil Organic Carbon Mapping Cookbook (Yigini et al., 2018), the following soil properties were mapped for all Tuscany Region:    soil organic carbon content (dag/kg),  soil organic carbon stock (t/ha),  textural fractions (sand, silt and clay, USDA limits, dag/kg),  rock fragments (vol/vol),  pH in water,  bulk density (g/cm3).   They were obtained through Digital Soil Mapping (DSM) approach, based on correlations with numerous environmental factors and using Random Forest algorithm.  All the maps have a 100 m spatial resolution.", "keywords": ["silt", "bulk density", "pH", "soil organic carbon content", "sand", "clay", "Grant n. 862695", "Digital Soil Mapping", "textural fractions", "Italy", "topsoil properties", "Tuscany", "soil organic carbon stock", "EJP-SOIL", "SERENA Project"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14039385"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14039385", "name": "item", "description": "10.5281/zenodo.14039385", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14039385"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-05T00:00:00Z"}}, {"id": "10.5281/zenodo.14230855", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:22:32Z", "type": "Dataset", "title": "Maps of topsoil (0-30 cm) properties of Tuscany (Italy)", "description": "Open AccessThe internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.  The topsoil (0-30 cm) properties maps are prepared to evaluate soil ecosystem services in SERENA/EJP-Soil and for applying SOC loss Cookbook and SOIL Loss Cookbook. In particular Soil Organic Carbon content map was directly considered as an application of SOC loss Cookbook (DOI: 10.5281/zenodo.13951265\u00a0Version 3).  They are based on Tuscany Region soil database available at Geoscopio (https://www502.regione.toscana.it/geoscopio/pedologia.html) and on point soil data not freely available (Lamma Consortium). More information and requests to:\u00a0info@lamma.toscana.it.  In accordance with the methodology reported in the Soil Organic Carbon Mapping Cookbook (Yigini et al., 2018), the following soil properties were mapped for all Tuscany Region:    soil organic carbon content (dag/kg),  soil organic carbon stock (t/ha),  textural fractions (sand, silt and clay, USDA limits, dag/kg),  rock fragments (vol/vol),  pH in water,  bulk density (g/cm3).   They were obtained through Digital Soil Mapping (DSM) approach, based on correlations with numerous environmental factors and using Random Forest algorithm.  All the maps have a 100 m spatial resolution.", "keywords": ["silt", "bulk density", "pH", "soil organic carbon content", "sand", "clay", "Grant n. 862695", "Digital Soil Mapping", "textural fractions", "Italy", "topsoil properties", "Tuscany", "soil organic carbon stock", "EJP-SOIL", "SERENA Project"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14230855"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14230855", "name": "item", "description": "10.5281/zenodo.14230855", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14230855"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-05T00:00:00Z"}}, {"id": "10.5281/zenodo.17923249", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:23:07Z", "type": "Dataset", "title": "Soil carbon stocks, bulk density, texture, and carbon concentration data from improved Urochloa humidicola pastures and native savannas in the Colombian Llanos", "description": "This dataset contains all original field and laboratory measurements used in the manuscript:  \u201cLarge-scale assessment of the contribution of improved Urochloa humidicola pastures for enhancing soil organic carbon stocks in the Colombian Llanos\u201d (manuscript under peer review).  The repository includes:1) Soil organic carbon (SOC) concentration (g kg\u207b\u00b9),2) Bulk density (g cm\u207b\u00b3),3) Soil texture composition (sand, silt, clay %),4) SOC stocks by individual soil layers,5) Total SOC stocks for 0\u2013100 cm,6) Sampling-site coordinates and associated SOC values.  All samples were collected across improved Urochloa humidicola pastures of different ages and conventionally burned savannas at Hacienda San Jos\u00e9, Vichada, Colombia. These are the same primary data used in the analysis for the manuscript.  Only original measurements are included; no intermediate calculations, scripts, or derived datasets are provided. A README file describing each file and variable is included.  If these data are used, please cite the manuscript once published.", "keywords": ["soil organic carbon", "Carbon sequestration", "Colombian Llanos", "Soil texture", "Urochloa humidicola", "SOC stocks", "Regenerative grazing", "Bulk density", "Tropical forages"], "contacts": [{"organization": "Bastidas, Mike, Mart\u00edn-L\u00f3pez, Javier M., Loaiza, Sandra, Arango, Jacobo, DA SILVA, MAYESSE, Rodriguez, Leonardo, Matiz-Rubio, Natalia, Arias, Juliana, Rao, Idupulapati M., costa junior, ciniro,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.17923249"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.17923249", "name": "item", "description": "10.5281/zenodo.17923249", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.17923249"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-12-13T00:00:00Z"}}, {"id": "11336/146044", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:24:46Z", "type": "Journal Article", "created": "2020-08-12", "title": "Relationship Between Soil Properties and Banana Productivity in the Two Main Cultivation Areas in Venezuela", "description": "To identify the main edaphic variables most correlated to banana productivity in Venezuela and explore the development of an empirical correlation model to predict this productivity based on soil characteristics. Six agricultural fields located in two of the main banana production areas of Venezuela were selected. The experimental sites were in large farms (\u2265\u200950 ha) with four productivity levels in \u201cGran Nain\u201d bananas, with an area of 4 ha for each of four productive levels: High - High, High - Low, Low - High, and Low - Low. Sixty sampling points were used to characterize the soils under study. Additionally, a Productivity Index (PI) based on three different biometric data on plant productivity was proposed. Through hierarchical statistical analysis, the first 16 soil variables that best explained the PI were selected. Thus, five multiple linear regression models were estimated, using the stepwise regression method. Subsequently, a performance analysis was used to compare the prediction quality range and the error associated with the number of soil variables selected for the proposed models. The selected model included the following soil variables: Mg, penetration resistance, total microbial respiration, bulk density, and omnivorous free-living nematodes. These variables explain the PI with an R2 of 0.55, the mean absolute error (MAE) of 0.8, and the root of the mean squared error (RMSE) of 1.0. The five selected variables are proposed to characterize the soil Productivity Index in banana and could be used in a site-specific soil management program for the banana areas of Venezuela. The authors recognize the financial support for international mobility of the Ibero-American scholarship program (2018\u20132019) of Banco Santander. Also, by project \u201cTechnological innovations for the management and improvement of the quality and health of banana soils in Latin America and the Caribbean\u201d financed by FONTAGRO and coordinated by Bioversity International (before INIBAP) and project SHui (European Commission Grant Agreement number: 773903).", "keywords": ["2. Zero hunger", "0106 biological sciences", "Penetration resistance", "Musaceae", "BULK DENSITY", "SOIL QUALITY", "Total microbial respiration", "04 agricultural and veterinary sciences", "15. Life on land", "TOTAL MICROBIAL RESPIRATION", "01 natural sciences", "Bulk density", "Soil quality", "FREE-LIVING NEMATODES", "MUSACEAE", "https://purl.org/becyt/ford/4.1", "0401 agriculture", " forestry", " and fisheries", "https://purl.org/becyt/ford/4", "Free-living nematodes", "PENETRATION RESISTANCE"]}, "links": [{"href": "https://link.springer.com/content/pdf/10.1007/s42729-020-00317-8.pdf"}, {"href": "https://doi.org/11336/146044"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Soil%20Science%20and%20Plant%20Nutrition", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "11336/146044", "name": "item", "description": "11336/146044", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/11336/146044"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-08-12T00:00:00Z"}}, {"id": "10.5281/zenodo.4091029", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:23:12Z", "type": "Dataset", "title": "Mapping the irrecoverable carbon in Earth's ecosystems", "description": "These datasets provide global maps of carbon density (aboveground, belowground biomass carbon and soil organic carbon stocks) for the year 2010 and 2018 at ~300-m spatial resolution in Mg ha-1 (Coordinate System: WGS 1984, float format). Input maps were collected from published literature, and where necessary, updated to cover the focal time period. These updates were applied to the manageable carbon, vulnerable carbon and irrecoverable carbon maps. Manageable carbon is carbon in terrestrial and coastal ecosystems that could experience an anthropogenic land-use conversion event . Vulnerable carbon is the carbon that would be that would be released in a typical land-use conversion. Irrecoverable carbon is the carbon that, if lost, would not recover by mid-century. Datasets are disaggregated for carbon density in biomass or soils. To view these datasets, go to: https://irrecoverable.resilienceatlas.org/map.", "keywords": ["13. Climate action", "14. Life underwater", "15. Life on land", "carbon density", " manageable carbon", " vulnerable carbon", " irrecoverable carbon"], "contacts": [{"organization": "Noon, Monica, Goldstein, Allie, Ledezma, Juan Carlos, Roehrdanz, Patrick, Cook-Patton, Susan C., Spawn-Lee, Seth A., Wright, Timothy Maxwell, Gonzalez-Roglich, Mariano, Hole, David G., Rockstr\u00f6m, Johan, Turner, Will R.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4091029"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4091029", "name": "item", "description": "10.5281/zenodo.4091029", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4091029"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-09-13T00:00:00Z"}}, {"id": "10.5281/zenodo.4291855", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:23:13Z", "type": "Dataset", "title": "EstSoil-EH: A high-resolution eco-hydrological modelling parameters dataset for Estonia (dataset)", "description": "Open AccessThis research has been supported by the Marie Sk\u0142odowska-Curie Actions individual fellowships under the Horizon 2020 Programme grant agreement number 795625, the Mobilitas Pluss postdoctoral researcher grant number MOBJD233 and grant numbers PRG352, PRG609, and PRG874 of the Estonian Research Council (ETAG), the European Regional Development Fund (Centre of Excellence EcolChange), the NUTIKAS programme of the Archimedes foundation, and by the Estonian Environmental Investment Centre.", "keywords": ["https://www.eionet.europa.eu/gemet/en/theme/35", "13. Climate action", "https://www.eionet.europa.eu/gemet/en/concept/4855", "soil", " texture", " FAO", " WRB", " available water capacity", " AWC", " estonia", " soilmap", " hydraulic properties", " soil organic carbon", " SOC", " bulk density", " saturated hydraulic conductivity", " ecosystem services", "https://www.eionet.europa.eu/gemet/en/concept/15138", "15. Life on land", "https://www.eionet.europa.eu/gemet/en/group/4856"], "contacts": [{"organization": "Kmoch, Alexander, Kanal, Arno, Astover, Alar, Kull, Ain, Virro, Holger, Helm, Aveliina, P\u00e4rtel, Meelis, Ostonen, Ivika, Uuemaa, Evelyn,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4291855"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4291855", "name": "item", "description": "10.5281/zenodo.4291855", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4291855"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-11-26T00:00:00Z"}}, {"id": "10.5281/zenodo.6033551", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:23:18Z", "type": "Dataset", "title": "Data to support the publication \"The Impact of Soil-Improving Cropping Practices on Erosion Rates: A Stakeholder-Oriented Field Experiment Assessment\" https://doi.org/10.3390/land10090964", "description": "Underlying data of soil measurements and analysis by TUC team for the publication \ufffd\ufffd\ufffdThe Impact of Soil-Improving Cropping Practices on Erosion Rates: A Stakeholder-Oriented Field Experiment Assessment\ufffd\ufffd\ufffd https://doi.org/10.3390/land10090964 from the SoilCare project study sites in Crete. Abstract: The risk of erosion is particularly high in Mediterranean areas, especially in areas that are subject to a not so effective agricultural management\ufffd\ufffd\ufffdor with some omissions\ufffd\ufffd\ufffd, land abandonment or wildfires. Soils on Crete are under imminent threat of desertification, characterized by loss of vegetation, water erosion, and subsequently, loss of soil. Several large-scale studies have estimated average soil erosion on the island between 6 and 8 Mg/ha/year, but more localized investigations assess soil losses one order of magnitude higher. An experiment initiated in 2017, under the framework of the SoilCare H2020 EU project, aimed to evaluate the effect of different management practices on the soil erosion. The experiment was set up in control versus treatment experimental design including different sets of treatments, targeting the most important cultivations on Crete (olive orchards, vineyards, fruit orchards). The minimum-to-no tillage practice was adopted as an erosion mitigation practice for the olive orchard study site, while for the vineyard site, the cover crop practice was used. For the fruit orchard field, the crop-type change procedure (orange to avocado) was used. The experiment demonstrated that soil-improving cropping techniques have an important impact on soil erosion, and as a result, on soil water conservation that is of primary importance, especially for the Mediterranean dry regions. The demonstration of the findings is of practical use to most stakeholders, especially those that live and work with the local land.", "keywords": ["2. Zero hunger", "13. Climate action", "15. Life on land", "6. Clean water", "Soil erosion", " bulk density", " Mineral Nitrogen", " Exchangeable Mg", " Available P", " SOC"], "contacts": [{"organization": "Tsanis, Ioannis, Seiradakis, Konstantinos, Sarchani, Sofia, Panagea, Ioanna S, Alexakis, Dimitrios D, Koutroulis, Aristeidis G,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6033551"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6033551", "name": "item", "description": "10.5281/zenodo.6033551", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6033551"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-09-12T00:00:00Z"}}, {"id": "10.5281/zenodo.6033552", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:23:18Z", "type": "Dataset", "title": "Data to support the publication \"The Impact of Soil-Improving Cropping Practices on Erosion Rates: A Stakeholder-Oriented Field Experiment Assessment\" https://doi.org/10.3390/land10090964", "description": "Underlying data of soil measurements and analysis by TUC team for the publication \ufffd\ufffd\ufffdThe Impact of Soil-Improving Cropping Practices on Erosion Rates: A Stakeholder-Oriented Field Experiment Assessment\ufffd\ufffd\ufffd https://doi.org/10.3390/land10090964 from the SoilCare project study sites in Crete. Abstract: The risk of erosion is particularly high in Mediterranean areas, especially in areas that are subject to a not so effective agricultural management\ufffd\ufffd\ufffdor with some omissions\ufffd\ufffd\ufffd, land abandonment or wildfires. Soils on Crete are under imminent threat of desertification, characterized by loss of vegetation, water erosion, and subsequently, loss of soil. Several large-scale studies have estimated average soil erosion on the island between 6 and 8 Mg/ha/year, but more localized investigations assess soil losses one order of magnitude higher. An experiment initiated in 2017, under the framework of the SoilCare H2020 EU project, aimed to evaluate the effect of different management practices on the soil erosion. The experiment was set up in control versus treatment experimental design including different sets of treatments, targeting the most important cultivations on Crete (olive orchards, vineyards, fruit orchards). The minimum-to-no tillage practice was adopted as an erosion mitigation practice for the olive orchard study site, while for the vineyard site, the cover crop practice was used. For the fruit orchard field, the crop-type change procedure (orange to avocado) was used. The experiment demonstrated that soil-improving cropping techniques have an important impact on soil erosion, and as a result, on soil water conservation that is of primary importance, especially for the Mediterranean dry regions. The demonstration of the findings is of practical use to most stakeholders, especially those that live and work with the local land.", "keywords": ["2. Zero hunger", "13. Climate action", "15. Life on land", "6. Clean water", "Soil erosion", " bulk density", " Mineral Nitrogen", " Exchangeable Mg", " Available P", " SOC"], "contacts": [{"organization": "Tsanis, Ioannis, Seiradakis, Konstantinos, Sarchani, Sofia, Panagea, Ioanna S, Alexakis, Dimitrios D, Koutroulis, Aristeidis G,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6033552"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6033552", "name": "item", "description": "10.5281/zenodo.6033552", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6033552"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-09-12T00:00:00Z"}}, {"id": "10.5281/zenodo.6574829", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:23:20Z", "type": "Dataset", "title": "Soil bulk density [10x kg/m3] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe", "description": "Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl &amp; MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale. The short description of currently available soil properties: db_od = bulk density over dry [kg/m3 \u2a09 10]; Soil properties were predicted at fixed depths: Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm. To produce estimates for depth intervals e.g. 0\u201330 cm, 0\u2013100 cm best use the trapezoidal rule formula. Periods: 2000 (2000\u20132003), 2004 (2004\u20132007), 2008 (2008\u20132011), 2012 (2012\u20132015), 2016 (2016\u20132019), 2020; The bulk density maps are also provided in 10 kg / m-cubic to reduce total data size; to convert values to kg / m-cubic multiply by 10 e.g. 120 = 1200 kg / m-cubic = 1.2 t / m-cubic.", "keywords": ["2. Zero hunger", "Europe", "15. Life on land", "pedometrics", "soil bulk density"], "contacts": [{"organization": "Hengl, T., Parente, L.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6574829"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6574829", "name": "item", "description": "10.5281/zenodo.6574829", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6574829"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-05-23T00:00:00Z"}}, {"id": "10.5281/zenodo.7075158", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:23:25Z", "type": "Dataset", "title": "Global Soil Bulk Density DataBase (GSBDDB)", "description": "We complied the Global Soil Bulk Density DataBase (GSBDDB). This database inlcudes 162,470 soil samples (35,805 sampling sites) with bulk density (BD) and soil organic cabron (SOC) for the globle. Among them, 96,705 soil samples have soil particle size fractions (i.e. clay, silt and sand) as well. In addtion, this dataset also records spatial coordinates, elevation, mean annual precipitation, mean annual temperature, potential evapotranspiration and aridity index. This dataset is asscoated to the 'Towards improved pedotransfer functions for estimating soil bulk density using the global soil bulk density database (DSBDDB)' by Chen et al. (in preparation). Manuscript citation: Chen, S., Dai, L, Shuai Q., Xue, J., Zhang, X., Xiao, Y., et al. Towards improved pedotransfer functions for estimating soil bulk density using the global soil bulk density database (DSBDDB). In preparation. When using the data, please cite repositories as well as the original manuscript. For any questions on the data, please contact Dr. Songchao Chen (chensongchao@zju.edu.cn).", "keywords": ["2. Zero hunger", "soil organic carbon", "13. Climate action", "environmental covariates", "soil depth", "soil particle size fractions", "15. Life on land", "6. Clean water", "spatial coordinates", "soil bulk density"], "contacts": [{"organization": "Songchao Chen, Lingju Dai, Shuai, Qi, Xue, Jie, Xianglin Zhang, Xiao, Yi, Shi, Zhou,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7075158"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7075158", "name": "item", "description": "10.5281/zenodo.7075158", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7075158"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-11-09T00:00:00Z"}}, {"id": "10.5281/zenodo.7552468", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:23:28Z", "type": "Dataset", "title": "Dataset for the publication: Moisture-driven divergence in mineral-associated soil carbon persistence", "description": "This data accompanies the manuscript titled, 'Moisture-driven divergence in mineral-associated soil carbon persistence'. Descriptions of data sources and measurement methods are given in the manuscript.", "keywords": ["Soil organic carbon", " radiocarbon", " density fractionation", " climate change"], "contacts": [{"organization": "Heckman, Katherine, Possinger, Angela, Badgley, Brian, Bowman, Maggie, Gallo, Adrian, Hatten, Jeff, Nave, Lucas, SanClements, Michael, Swanston, Christopher, Weiglein, Tyler, Wieder, William, Strahm, Brian,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7552468"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7552468", "name": "item", "description": "10.5281/zenodo.7552468", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7552468"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-01-19T00:00:00Z"}}, {"id": "10.5683/SP3/D8KCYZ", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:23:40Z", "type": "Dataset", "created": "2022-01-05", "title": "Soil organic carbon stock and uncertainties, 30cm and 1m depth, at 250m spatial resolution in Canada, version 3.0", "description": "Open AccessThis project aimed to produce the first wall-to-wall estimate of C stocks in plants and soils of Canada at 250 m spatial resolution. This dataset contains the map with the soil organic carbon (SOC) in kg/m\u00b2 for entire Canada in 30cm and 1m depth, and the uncertainty in SOC predictions. The SOC stock map was produced using 39,323 ground samples of soil organic carbon concentration (g/kg) distributed in 6,533 sites, 11,068 ground samples of bulk density (kg/dm3) distributed in 2,157 sites, long-term climate data, remote sensing observations and a machine learning model. The soil samples containing the x and y coordinates, depth and SOC (in g/kg) information were overlaid with the stacked covariates (soil forming factors) to compose the regression matrix. Random forest models were trained using a recursive feature elimination scheme and a cross-validation assessment. The best model was used for spatial prediction of SOC over Canada in intermediate depths between 0 and 1 m (0cm, 5cm, 15cm, 30cm, 60cm, 100cm). Afterwards, the SOC stock of each depth increment was computed using SOC concentration and bulk density maps, and corrected with coarse fragment information. The depth increments have been added to compose the 0-30cm and 0-1m depth intervals multiplied by rooting depths fraction to discount shallow soils. Water and ice/snow areas were removed using a mask based on the Land Cover of Canada map. Ground ice in permafrost areas was discounted according to ice abundance using the ground ice map of Canada. The SOC stock uncertainty map is the difference between the first and third quantiles of a quantile regression forest approach of SOC concentration and bulk density prediction (90% confidence interval).", "keywords": ["Canada soil carbon stock", "13. Climate action", "FOS: Agriculture", " forestry and fisheries", "Earth and Environmental Sciences", "soil carbon storage", "Soil Sciences", "Soils", "15. Life on land", "soil carbon stock", "soil carbon density"], "contacts": [{"organization": "Gonsamo, Alemu, Sothe, Camile, Snider, James, Finkelstein, Sarah, Arabian, Joyce, Kurz, Werner,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5683/SP3/D8KCYZ"}, {"rel": "self", "type": "application/geo+json", "title": "10.5683/SP3/D8KCYZ", "name": "item", "description": "10.5683/SP3/D8KCYZ", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5683/SP3/D8KCYZ"}, {"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": "10.57745/3QFT2T", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:24:00Z", "type": "Dataset", "title": "French maps for the Global Soil Nutrient and Nutrient Budget Map (GSNmap)", "description": "This set of maps presents digital maps of soil properties on agricultural lands in France within the FAO framework \u201cGlobal Soil Nutrient and Nutrient Budgets maps\u201d. The spatial predictions of ten soil properties, namely Total N, available P, CEC, pH (water), Clay, Silt, Sand, Soil Organic Carbon, Bulk density and available K were generated with a 250 m spatial resolution. Random forest machine learning approach in combination with environmental variables was used for spatial distribution assessment of properties. Additionally, uncertainty maps expressed as the standard deviation of spatial predictions were produced. All maps are provided in a raster geotiff format. the identifier of the spatial reference system (srid) is 4326.", "keywords": ["Earth and Environmental Science", "bulk density", "cation exchange capacity", "available phosphorus content", "Agriculture", " Forestry", " Horticulture", " Aquaculture", "sand", "cropland", "potassium content", "cation-exchange capacity", "Agriculture", " Forestry", " Horticulture", "2. Zero hunger", "silt", "Agricultural Sciences", "pH", "nutrient", "EAR soil sciences", "soil property", "Life Sciences", "clay", "15. Life on land", "6. Clean water", "soil organic carbon", "13. Climate action", "Earth and Environmental Sciences", "digital soil mapping", "Agriculture", " Forestry", " Horticulture", " Aquaculture and Veterinary Medicine", "Environmental Research", "Natural Sciences", "random forest", "Geosciences", "nitrogen content"], "contacts": [{"organization": "Suleymanov, Azamat, Saby, Nicolas, Bispo, Antonio,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.57745/3QFT2T"}, {"rel": "self", "type": "application/geo+json", "title": "10.57745/3QFT2T", "name": "item", "description": "10.57745/3QFT2T", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.57745/3QFT2T"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-01-01T00:00:00Z"}}, {"id": "10261/184998", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:24:26Z", "type": "Journal Article", "created": "2017-09-19", "title": "Speciation below ground: Tempo and mode of diversification in a radiation of endogean ground beetles", "description": "Abstract<p>Dispersal is a critical factor determining the spatial scale of speciation, which is constrained by the ecological characteristics and distribution of a species\uffe2\uff80\uff99 habitat and the intrinsic traits of species. Endogean taxa are strongly affected by the unique qualities of the below\uffe2\uff80\uff90ground environment and its effect on dispersal, and contrasting reports indicate either high dispersal capabilities favoured by small body size and mediated by passive mechanisms, or low dispersal due to restricted movement and confinement inside the soil. We studied a species\uffe2\uff80\uff90rich endogean ground beetle lineage, Typhlocharina, including three genera and more than 60 species, as a model for the evolutionary biology of dispersal and speciation in the deep soil. A time\uffe2\uff80\uff90calibrated molecular phylogeny generated from &gt;400 individuals was used to delimit candidate species, to study the accumulation of lineages through space and time by species\uffe2\uff80\uff93area\uffe2\uff80\uff93age relationships and to determine the geographical structure of the diversification using the relationship between phylogenetic and geographic distances across the phylogeny. Our results indicated a small spatial scale of speciation in Typhlocharina and low dispersal capacity combined with sporadic long distance, presumably passive dispersal events that fuelled the speciation process. Analysis of lineage growth within Typhlocharina revealed a richness plateau correlated with the range of distribution of lineages, suggesting a long\uffe2\uff80\uff90term species richness equilibrium mediated by density dependence through limits of habitat availability. The interplay of area\uffe2\uff80\uff90 and age\uffe2\uff80\uff90dependent processes ruling the lineage diversification in Typhlocharina may serve as a general model for the evolution of high species diversity in endogean mesofauna.</p", "keywords": ["2. Zero hunger", "0106 biological sciences", "Geography", "Genetic Speciation", "Geographic speciation", "Endogean", "Density dependence", "15. Life on land", "Anillini", "01 natural sciences", "Coleoptera", "Long\u2010distance dispersal (LDD)", "Animals", "Microendemism", "Typhlocharina", "Ecosystem", "Phylogeny"]}, "links": [{"href": "https://doi.org/10261/184998"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Molecular%20Ecology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10261/184998", "name": "item", "description": "10261/184998", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10261/184998"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-10-16T00:00:00Z"}}, {"id": "10261/266138", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:24:27Z", "type": "Journal Article", "created": "2022-03-20", "title": "Comparison of Soil Organic Carbon Stocks Evolution in Two Olive Orchards with Different Planting Systems in Southern Spain", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>This study presents an evaluation of soil organic carbon (SOC) and stock (SOCstock) for the whole rooting depth (60 cm), spaced 55 months in two adjacent olive orchards with similar conditions but different tree densities: (i) intensive, planted in 1996 at 310 tree ha\u22121; (ii) superintensive, planted in 2000 at 1850 tree ha\u22121. This was carried out to test the hypothesis that olive orchards at different plant densities will have different rates of accumulation of SOC in the whole soil rooting depth. SOC increased significantly in the superintensive orchard during the 55-month period, from 1.1 to 1.6% in the lane area, and from 1.2 to 1.7% in the tree area (average 0\u201360 cm), with a significant increase in SOCstock from 4.7 to 6.1 kg m\u22122. In the intensive orchard, there was not a significant increase in SOCstock in 0\u201360 cm, average of 4.06 and 4.16 kg m\u22122 in 2013 and 2018, respectively. Results indicate a potential for a significant increase in SOC and SOCstock in olive orchards at higher tree densities when combined with temporary cover crops and mulch of chopped pruning residues. The increase is associated with an increase in SOC, mainly at a 0\u201315 cm depth. Results also point to the need for improve our monitoring capabilities to detect moderate increases in SOC.</p></article>", "keywords": ["2. Zero hunger", "bulk density", "intensive orchard", "deficit irrigation", "Agriculture (General)", "tree density; intensive orchard; superintensive orchard; deficit irrigation; bulk density", "04 agricultural and veterinary sciences", "15. Life on land", "superintensive orchard", "Bulk density", "S1-972", "Tree density", "Superintensive orchard", "0401 agriculture", " forestry", " and fisheries", "tree density", "Deficit irrigation", "Intensive orchard"]}, "links": [{"href": "http://www.mdpi.com/2077-0472/12/3/432/pdf"}, {"href": "https://www.mdpi.com/2077-0472/12/3/432/pdf"}, {"href": "https://doi.org/10261/266138"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agriculture", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10261/266138", "name": "item", "description": "10261/266138", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10261/266138"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-03-20T00:00:00Z"}}, {"id": "10.6086/D1TX0T", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:24:13Z", "type": "Dataset", "title": "Mangrove sediment blue carbon estimates", "description": "Carbon accumulation in coastal wetlands is normally assessed by extracting  a sediment core and estimating its carbon content and bulk density.  Because carbon content and bulk density are functionally related, the  latter can be estimated gravimetrically from a section of the core or,  alternatively, from the carbon content in the sample using the Mixing  Model equation from soil science. We analyzed the effect that the choice  of corer and the method used to estimate bulk density could have on the  final estimates of carbon storage in the sediments. The choice of corer  did not have much influence on the final estimates of carbon density; the  main factor in selecting a corer is the operational difficulties that each  corer may have in different types of sediments. Because of the  multiplication of errors in a product of two variables subject to random  sampling error, when using gravimetric estimates of bulk density, the  dispersion of the data points in the estimation of total carbon density  rises rapidly as the amount of carbon in the soil increases. For this  reason, the estimation of carbon densities in peaty soils with this method  can be very imprecise in peaty sediments. In contrast, the estimation of  total carbon density using only the carbon fraction as a predictor is very  precise, especially in sediments rich in organic matter. This method,  however, depends critically on an accurate estimation of the two  parameters of the Mixing Model (the bulk density of pure peat and the bulk  density of pure mineral sediment). If these parameters are not estimated  accurately, the calculation of total carbon density can be biased.", "keywords": ["Sediment Core", "mangrove", "bulk density", "precision and accuracy", "13. Climate action", "blue carbon", "FOS: Earth and related environmental sciences", "15. Life on land"], "contacts": [{"organization": "Ezcurra, Exequiel", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.6086/D1TX0T"}, {"rel": "self", "type": "application/geo+json", "title": "10.6086/D1TX0T", "name": "item", "description": "10.6086/D1TX0T", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.6086/D1TX0T"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-12-13T00:00:00Z"}}, {"id": "10.7717/peerj.10375", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:24:15Z", "type": "Journal Article", "created": "2020-12-01", "title": "Effects of plastic mulching on the accumulation and distribution of macro and micro plastics in soils of two farming systems in Northwest China", "description": "Background <p>Inappropriate disposal of the plastic mulching debris could create macroplastics (MaPs) and microplastics (MiPs) pollution in agricultural soil.</p>   Methods <p>To study the effects of farming systems on accumulation and distribution of agricultural plastic debris, research was carried out on two farming systems in Northwest China. Farming in Wutong Village (S1) is characterized by small plots and low-intensity machine tillage while farming in Shihezi (S2) is characterized by large plots and high-intensity machine tillage. In September 2017, we selected six fields in S1, three fields with 6\uffe2\uff80\uff938 years of continuous plastic mulching (CM) as well as three fields with over 30 years of intermittent mulching (IM). In S2, we selected five cotton fields with 6, 7, 8, 15 and 18 years of continuous mulching. In both regions, MaPs and MiPs from soil surface to 30 cm depth (0\uffe2\uff80\uff9330 cm) were sampled.</p>   Results <p>The results showed that in S1, MaPs mass in fields with 6\uffe2\uff80\uff938 years CM (i.e., 97.4kg\uffc2\uffb7ha\uffe2\uff88\uff921) were significantly higher than in fields with 30 years IM (i.e., 53.7 kg\uffc2\uffb7ha\uffe2\uff88\uff921). MaPs in size category of 10\uffe2\uff80\uff9350 cm2 accounted for 46.9% in fields of CM and 44.5% in fields of IM of total collected MaPs number. In S2, MaPs mass ranged from 43.5 kg\uffc2\uffb7ha\uffe2\uff88\uff921 to 148 kg\uffc2\uffb7ha\uffe2\uff88\uff921. MaPs in size category of 2\uffe2\uff80\uff9310 cm2 account for 41.1% of total collected MaPs number while 0.25\uffe2\uff80\uff932 cm2 accounted for 40.6%. MiPs in S1 were mainly detected in fields with over 30 years of intermittent mulching (up to 2,200 particles\uffc2\uffb7kg\uffe2\uff88\uff921 soil), whereas in S2 were detected in all fields (up to 900 particles\uffc2\uffb7kg\uffe2\uff88\uff921 soil). The results indicated farming systems could substantially affect the accumulation and distribution of agricultural plastic debris. Continuous plastic mulching could accumulate higher amount of MaPs than intermittent plastic mulching. High-intensity machine tillage could lead to higher fragmentation of MaPs and more severe MiPs pollution. These results suggest that agricultural plastic regulations are needed.</p", "keywords": ["2. Zero hunger", "Plastic film mulching", "13. Climate action", "Microplastics", "Soil pollution", "Farming systems", "0401 agriculture", " forestry", " and fisheries", "Low-density polyethylene", "04 agricultural and veterinary sciences", "15. Life on land", "Agricultural Science", "01 natural sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.7717/peerj.10375"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PeerJ", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.7717/peerj.10375", "name": "item", "description": "10.7717/peerj.10375", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.7717/peerj.10375"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-12-01T00:00:00Z"}}, {"id": "10.7910/DVN/HMRZID", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:24:17Z", "type": "Dataset", "created": "2014-01-01", "title": "Explaining rice yields and yield gaps in Central Luzon, Philippines: An application of stochastic frontier analysis and crop modelling", "description": "Open AccessThe objective of the study was to decompose the rice yield gap into an efficiency, resource and technology yield gaps and to explain those using information related to crop management, farmers' objectives and constraints and production technology employed. Soil samples were collected to assess the influence of key soil properties on the efficiency yield gap.", "keywords": ["soil pH", "Agricultural Sciences", "Olsen-P", "organic carbon", "Social Sciences", "exchangeable potassium", "soi density (bulk density)"], "contacts": [{"organization": "Silva, Joao Vasco", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.7910/DVN/HMRZID"}, {"rel": "self", "type": "application/geo+json", "title": "10.7910/DVN/HMRZID", "name": "item", "description": "10.7910/DVN/HMRZID", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.7910/DVN/HMRZID"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-01-01T00:00:00Z"}}, {"id": "10.7910/DVN/XZIRK0", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:24:18Z", "type": "Dataset", "title": "Baselines for land degradation neutrality indicators in the Omusati region, Namibia", "description": "This data was collected to develop baselines for three Land Degradation Neutrality (LDN) indicators: land use and land cover change (LUC) for the period 2001-2017, soil organic carbon (SOC) stocks for 2017 and bush density for 2017 as a baseline for bush encroachment in Omusati region, Namibia.", "keywords": ["SDG 15.3", "Land cover", "sustainable development", "UNCCD", "Land degradation neutrality", "Agricultural Sciences", "land degradation", "carbon", "Namibia", "Soil carbon", "Carbon", "soil", "Soil", "land cover", "Omusati", "Earth and Environmental Sciences", "Sustainable development", "Africa", "Bush density", "Land degradation", "Agroecosystems and Sustainable Landscapes - ASL"], "contacts": [{"organization": "Hengari, Simeon, Angombe, Simon, Katjioungua, Georgina, Fabiano, Ezequiel, Zauisomue, Erlich, Nakashona, Natalia, Ipinge, Selma, Andreas, Amon, Muhoko, Edward, Emvula, Emerit, Mutua, John, Kempen, Bas, Nijbroek, Ravic,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.7910/DVN/XZIRK0"}, {"rel": "self", "type": "application/geo+json", "title": "10.7910/DVN/XZIRK0", "name": "item", "description": "10.7910/DVN/XZIRK0", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.7910/DVN/XZIRK0"}, {"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": "10261/349362", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:24:30Z", "type": "Journal Article", "created": "2023-02-25", "title": "Subsoiling for planting trees in dehesa system: long-term effects on soil organic carbon", "description": "Abstract<p>Incorporating trees into agricultural systems, including grasslands, increases the soil organic carbon sequestration and contributes to climate change mitigation. Site preparation for tree establishment is a common practice that can involve a variety of techniques and agricultural implements such as subsoiling. This study aimed to evaluate the long-term effects of subsoiling on soil organic carbon (SOC) concentrations and stocks in a Mediterranean grassland afforested with holm oaks 22\uffc2\uffa0years ago and now converted into a Dehesa agroforestry system. The study was conducted in a dehesa farm in Southwest Spain. Soil samples were taken at six depths under 10 tree canopies within and outside the original subsoiling line. Subsoiling significantly decreased SOC concentration. Mean SOC concentration in the first 20\uffc2\uffa0cm was 30% lower at the subsoiling line. SOC stocks for the first 60\uffc2\uffa0cm were 2660\uffc2\uffa0g\uffc2\uffa0m\uffe2\uff88\uff922 within the subsoiling line and 4320\uffc2\uffa0g\uffc2\uffa0m\uffe2\uff88\uff922 outside the line. There was a clear reduction in SOC concentration and stock with increasing depth. Root abundance and deeper rooting increased with subsoiling but did not translate into sufficient carbon accumulation in the soil, which is moderate even after 22\uffc2\uffa0years. This study reveals that, in the long term, there is a trade-off in CO2 sequestration between tree planting and soil subsoiling, highlighting the need for further research into the potential benefits and detriments of subsoiling.</p", "keywords": ["2. Zero hunger", "570", "Agroforestry system", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "Soil condition", "Tilling", "04 agricultural and veterinary sciences", "15. Life on land", "630", "Bulk density", "Holm oak"]}, "links": [{"href": "https://link.springer.com/content/pdf/10.1007/s10457-023-00820-8.pdf"}, {"href": "https://doi.org/10261/349362"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agroforestry%20Systems", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10261/349362", "name": "item", "description": "10261/349362", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10261/349362"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-02-25T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=DENSITY&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=DENSITY&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=DENSITY&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=DENSITY&offset=100", "hreflang": "en-US"}], "numberMatched": 182, "numberReturned": 50, "distributedFeatures": [], "timeStamp": "2026-05-02T08:29:50.916041Z"}