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52 results for “Soil database”
Database on soil properties of the CS1 Study case
<p>Soil properties dataset of the CS1 Study Case corresponding to the article "The combination of crop diversification and no tillage enhances key 1 soil quality parameters related to soil functioning without compromising crop yields in a low-input rainfed almond orchard under semiarid Mediterranean conditions" by Almagro et al.</p>
Guatemala Soil Organic Carbon Database (0 to 30 cm, 1965-2010).
The soil organic carbon database is a harmonized and structured data set with information about soil organic carbon (SOC), soil bulk density (BD), and coarse fragments (CF). The SOC, BD, and CF data were fitted to one standard depth of 0-30 cm using a mass preservation spline implemented in R programmation language. The data was obtained from Unidad de Planificación Geográfica y Gestión de Riesgo of Ministerio de Agricultura Ganadería y Alimentación (UPGGR-MAGA), Facultad de Agronomía of Universidad de San Carlos de Guatemala (CEDIA-FAUSAC), and the World Soil Information Service (WoSIS). This database covers the entire Guatemalan territory, although its distribution does not represent all ecosystems, soil types, and coverages/land use. It contains 910 observations of SOC, 704 of them have BD information, and 8 have CF information. The entire data set has information on the year of observation, and its temporal distribution goes from 1965 to 2010. Nearly 50% is in the decade 2000-2010, and 41% is in the 2010 year. In addition, the analytical methods for quantifying SOC, BD, and CF are available in 99, 25, and 0% of the data, respectively. SOC contents range from 1.45 to 162 g*kg^-1, BD values from 0.42 to 1.69 g*cm^-3, and CF values from 0 to 21%wt.
SOils DAta Harmonization database (SoDaH): an open-source synthesis of soil data from research networks
This SOils DAta Harmonization (SoDaH) database is designed to bring together soil carbon data from diverse research networks into a harmonized dataset that can be used for synthesis activities and model development. The research network sources for SoDaH span different biomes and climates, encompass multiple ecosystem types, and have collected data across a range of spatial, temporal, and depth gradients. The rich data sets assembled in SoDaH consist of observations from monitoring efforts and long-term ecological experiments. The SoDaH database also incorporates related environmental covariate data pertaining to climate, vegetation, soil chemistry, and soil physical properties. The data are harmonized and aggregated using open-source code that enables a scripted, repeatable approach for soil data synthesis.
AVATAR-Soils Database: A Database of 137Cs and 239+240Pu in Equatorial and Southern Hemisphere Reference Soils
<p>A total of 1122 reference soil profiles with <sup>137</sup>Cs and <sup>239+240</sup>Pu data from 135 publications were included to build a database under the AVATAR Project (“A reVised dATing framework for quantifying geomorphological processes during the AnthRopocene”), with a focus on compiling available data from the Equatorial and Southern Hemisphere regions. The AVATAR-Soils Database covers parts of the continents of Asia and the Sub-Saharan Africa, and the whole of Oceania and South America. The <sup>137</sup>Cs (decay-corrected to 2024) and <sup>239+240</sup>Pu data extracted from the literature include the inventory (in Bq/m<sup>2</sup>) and average activities (in Bq/kg) of the soil profile collected, and the <sup>137</sup>Cs/<sup>239+240</sup>Pu activity and <sup>239</sup>Pu/<sup>240</sup>Pu atomic ratios. In addition to the <sup>137</sup>Cs and <sup>239+240</sup>Pu data, the associated spatial, climatic, and topographic parameters and sampling details were also added in the database. The database contains the metadata describing the column names, separate tabs for <sup>137</sup>Cs and <sup>239+240</sup>Pu, and the list of publications.</p> <p> To cite the AVATAR-Soils Database, please use:</p> <p>Dicen, G., Guillevic, F., Gupta, S., Chaboche, P.-A., Meusburger, K., Sabatier, P., Evrard, O., and Alewell, C.: <strong>Distribution and sources of fallout <sup>137</sup>Cs and <sup>239+240</sup>Pu in equatorial and Southern Hemisphere reference soils</strong>, Earth Syst. Sci. Data, 17, 1529–1549, https://doi.org/10.5194/essd-17-1529-2025, 2025.</p> <p>***NOTES TO RESEARCHERS***</p> <p>Researchers who wish to add data to the AVATAR-Soils Database may do so through the following link: https://docs.google.com/spreadsheets/d/15R-rDMH6zW65B3ok3YRb0cdtyY67X_mdlC0H1t1atoY/edit?usp=drive_link</p>
Database of experimental determinations of soil respiration in pine and spruce forests during the growing season of 2023 at the Lyalsky test site (middle taiga, northeast of the East European Plain)
<p>The data set includes the results of 342 measurements of CO2 emissions (SR, gС/m2/day) from the soil surface of coniferous ecosystems at the Lyalsky test site during the growing season (May-September) of 2023. The objects of the study were: pine forest (PS, Gleyic Folic Albic Podzol (Arenic)) and spruce forest (SM, Albic Retisols). The values of temperature (Ts, at a depth of 10 cm), moisture (Ms, in a layer of 0-5 cm) of the soil and air temperature (Ta) are also given.</p><p>The research was carried out by employees of the Institute of Biology of Komi Science Centre of the Ural Branch of the Russian Academy of Sciences as part of the most important innovative project of national importance "Development of a system for ground-based and remote monitoring of carbon pools and greenhouse gas fluxes in the territory of the Russian Federation, ensuring the creation of recording data systems on the fluxes of climate-active substances and the carbon budget in forests and other terrestrial ecological systems" (Registration number: 123030300031-6). </p>
European soil bulk density and organic carbon stock database using LUCAS Soil 2018
<p>We complied the European topsoil bulk density and organic carbon stock database (0-20 cm) using LUCAS Soil 2018. This database inlcudes 18,945 and 15,389 soil samples (0-20 cm) with bulk density in fine fraction (Bdfine) and soil organic cabron stock (SOCS) for the EU and UK using the best traditional pedotransfer function (T-PTF-4) and machine leanring based PTFs (Local-RFFRFS). It also contains the POINTID linked to LUCAS Soil 2018, coarse fragements in volume (coarse_vol) and coordinates (GPS_LAT, GPS_LONG). For more information, please refer to LUCAS 2018 TOPSOIL data (https://esdac.jrc.ec.europa.eu/content/lucas-2018-topsoil-data).</p> <p>This dataset is asscoated to the "European soil bulk density and organic carbon stock database using machine learning based pedotransfer function" by Chen et al. (2024).</p> <p>Manuscript citation: Chen, S., Chen, Z., Zhang, X., Luo, Z., Schillaci, C., Arrouays, D., Richer-de-Forges, A.C., Shi, Z. , 2024. European topsoil bulk density and organic carbon stock database (0-20 cm) using machine learning based pedotransfer functions. Earth System Science Data, 16, 2367–2383.</p> <p>When using the data, please cite repositories as well as the original manuscript.</p> <p>For any questions on the data, please contact Dr. Songchao Chen (chensongchao@zju.edu.cn).</p>
Aggregated database of forest soil chemical properties in the Czech Republic based on surveys from 2000-2020.
<p>The dataset includes data from forest soil surveys conducted in the period 2000–2020. It provides soil and site variables from 8,269 locations. Data are aggregated in three basic soil layers: upper organic soil horizon (FH, 6,876 locations), upper mineral layer 0–30 cm (M03, 8,051 locations) and deeper mineral soil layer (30–80 cm, M38, 2,260 locations).</p> <p>This dataset is linked to the following article published in Annals of Forest Science:</p> <p>Neudertová Hellebrandová, K., Fadrhonsová, V. & Šrámek, V. Aggregated database of forest soil chemical properties in the Czech Republic based on surveys from 2000 to 2020. Annals of Forest Science 81, 17 (2024). <a href="https://doi.org/10.1186/s13595-024-01225-y">https://doi.org/10.1186/s13595-024-01225-y</a></p>
Database to: Effectiveness of soil management strategies for mitigation of N2O emissions in European arable land: A meta-analysis
<p>Database to a meta-analysis studing the effects of adding different organic matter inputs (crop residues, green manure, livestock manure, slurry, digestate, compost or biochar) to soils on N2O emissions. Database consists of over 50 field experiments conducted in 15 European countries. Diverse arable crops, mainly cereals, were cultivated in monoculture or in crop rotations on mineral soils. Cumulative N2O emissions per unit land area were monitored during periods of 30 to 1,070 days in treatments, which received organic matter inputs, alone or in combination with mineral N fertiliser; and in controls fertilised with mineral N. The original results appeared in 46 articles published between 1993 and 2022 in peer-reviewed scientific journals, as well as a project report, and a PhD thesis.</p>
Database of Soil Organic Carbon (SOC) stock up to 20 cm depth: A collection of studies from Uruguay
<p>This database comprises 667 soil organic carbon (SOC) stock measurements, collected from various studies conducted across Uruguay, with sampling depths extending up to 20 cm. The database aggregates data from 14 studies, including undergraduate and postgraduate theses and research projects, sampled between 2002 and 2024. It provides comprehensive details on the geographic location of sampling sites (latitude and longitude coordinates), the date of sample collection, the methodology employed for SOC determination, the technique used to estimate SOC stock values at the specified depths, and a description of land use at the moment of sampling.</p> <p>This data collection is particularly relevant for evaluating SOC stocks modeling exercises, as it contributes to a more comprehensive understanding of carbon dynamics across various soil types, environmental conditions, land use, and land cover types. The database is available in shapefile format and is expected to serve as a valuable resource for researchers and professionals in the field.</p> <p><strong> </strong></p> <h3>Data Table Description</h3> <p>The associated data table from this database contains the following key variables:</p> <ul> <li> <p>ID: Unique identifier for each sampling point.</p> </li> <li> <p>Latitude & Longitude: Geographic coordinates of the sampling site.</p> </li> <li> <p>SOC Stock: Soil Organic Carbon (SOC) stock calculated at a fixed depth using the equation SOC stock (MgC/ha) = C × Bd × d, where C represents carbon concentration (%C), Bd is the bulk density of the soil (g/cm³), and d is the depth (cm).</p> </li> <li> <p>Depth: Depth (cm) used for SOC stock calculation. Data were harmonized to a depth of 20 cm; however, lower values are reported for sites where the sampling scheme was conducted at depths less than 20 cm or where bedrock was encountered before reaching 20 cm.</p> </li> <li> <p>Land Use: Simplified land use classification with “grassland”, “agriculture”, or “forest” categories</p> </li> <li> <p>Land Use Extended: If available, the responsible party provides a more detailed explanation of land use.</p> </li> <li> <p>Month & Year: Month and year of sample collection.</p> </li> <li> <p>SOC Method: Methodology used for SOC determination, such as Walkley-Black or dry combustion.</p> </li> <li> <p>Published: Indicates whether the data have been published, with a URL provided if available. If not published, the data are marked as unpublished.</p> </li> <li> <p>SOC Calc: The approach used for calculating SOC stock at a fixed depth, detailed in two possible methods:</p> </li> <ul> <li> <p>Real: For sampling schemes that include a segment ending at 20 cm depth, SOC stock was calculated directly up to 20 cm. This method was also applied when the maximum soil sampling depth was less than 20 cm.</p> </li> <li> <p>Spline: When the sampling depth did not include 20 cm but extended beyond it, a spline interpolation was applied. This method employs all available cumulative values and their associated depths to estimate the SOC stock at 20 cm.</p> </li> </ul> </ul> <h3>Additional Observations</h3> <ul> <li> <p>For sites listed in rows 200-216, the GPS position represents the plot´s centroid, as no specific location was reported.</p> </li> <li> <p>For sites listed in rows 344-633, sampling was conducted at 0-7.5 cm, 7.5-15 cm, and 15-30 cm depth. In sites where the total depth was 15 cm, bulk density was measured only in the first layer (0-7.5 cm), and this value was also applied to the 7.5-15 cm layer. In sites where the total depth was 20 cm, bulk density was measured in all layers and calculated for 20 cm as a weighted average.</p> </li> <li> <p>For sites listed in rows 102-134, bulk density measurements were not taken directly. Instead, data were retrieved from the "SoilGrids" website (<a href="https://soilgrids.org/">https://soilgrids.org/</a>). Bulk density values for the 0-5 cm and 5-15 cm layers were downloaded, and a weighted average was calculated before determining the SOC stock.</p> </li> </ul> <h3> </h3> <h3>Funding:</h3> <p>Funded were provided by ANII (FSDA_1_2018_1_154817, Procesos Inductivos para generación de buenas prácticas agropecuarias: Compilación y análisis de bases de datos a nivel predial; FSA_1_2022_1_175272, Evaluación multiescalar del desempeño ambiental de sistemas agropecuarios con diferente nivel de intensificación a partir de indicadores derivados de sensores remotos); INIA (FPTA-515, Indicadores de sostenibilidad ambiental para el sector agropecuario de Uruguay basados en información derivada de sensores remotos y modelos biofísicos); and IDB (URUGUAY/UR-T1277, Adopción de prácticas Agroecológicas y Huella de Carbono en la Agricultura Uruguaya).</p> <p> </p>
EJPSOIL CarboSeq crop and soil management database
<p><span><strong>CarboSeq crop and soil management database :</strong> </span>The database of the EJPSOIL CarboSeq WP2 with (meta)data of long term agricultural field experiments for Tier2 (SOC) and where available Tier 3 (carbon input) from Europe</p>
GSHP: Global database of soil hydraulic properties
<p>A total of 15,259 SWCCs from 2,702 sites were assembled from published literature and other sources, standardized, and quality-checked to obtain global database of soil hydraulic properties (GSHP). The GSHP database covers most regions across the globe, with the highest number of curves from North America followed by Africa, Europe, Asia, South America, Australia/Oceania. In addition to SWCCs, other soil variables such as soil texture (12,233 measurements), bulk density (15,125 measurements), and soil organic carbon (2,255 measurements) are also listed in the database.</p> <p>The R code used for this study is available here: https://github.com/ETHZ-repositories/GSHP-database</p> <p>For more details / to cite this dataset please use:</p> <ul> <li>Gupta, S., Papritz, A., Lehmann, P., Hengl, T., Bonetti, S., and Or, D., (2022): Global Soil Hydraulic Properties dataset based on legacy site observations and robust parameterization”. Manuscript accepted to <strong>Scientific Data.</strong></li> </ul> <p>Examples of using the GSHP database to generate van Genuchten parameters maps can be found in <a href="https://doi.org/10.5281/zenodo.6343570">10.5281/zenodo.6343570</a>.</p> <p><strong>Description of the files</strong>:</p> <p>The datasets in this repository include: </p> <p><strong>WRC_dataset_surya_et_al_2021_final </strong>provides a global compilation of soil hydraulic properties and the information described in the<strong> Readme_GSHP file</strong>. <strong>Dataset_notebook </strong>shows the graphical representation of the GSHP database. </p> <p>The study was supported by ETH Zurich (Grant ETH-18 18-1). </p>
Database for the Priority Program 2322 SoilSystems – Soils and substrates used in the first phase (2021-2024)
<p><span>The presented data set represents a comprehensive characterization of soil samples that were used in the German joint research priority program 2322 ”SoilSystems: Systems ecology of soils – energy discharge modulated by microbiome and boundary conditions” funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation). The joint research program <em><span>SoilSystems</span></em> aims to integrate a thermodynamic description of the soil system for the first time in order to gain a systemic view on energy and matter fluxes and their interactions with living and non-living soil components. </span><span>These include numerous aspects and parameters of soil (organic matter) chemistry, soil microbiology (including faunal trophic levels) and the new discipline of soil thermodynamics. In order to have a joint focus in the individual research activities, a ‘Common Experimental Platform’ has been set up. The provision of shared soil samples, substrates and incubation conditions should enable coherent and interlinked data to be obtained. </span><span>More information can be found here: <a href="https://soilsystems.net/">https://soilsystems.net/</a>. </span></p>
Permafrost soil database with information on site, topography, geomorphology, hydrology, soil stratigraphy, soil carbon, ground ice isotopes, and vegetation at thermokarst features near Toolik and Noatak River, 2009-2013
This database contains soil and permafrost stratigraphy associated with thermokarst features near Toolik Lake and the Noatak River collected by Torre Jorgenson and Andrew Balser during summers 2009-2011. The Access Database has main data tables (tbl_) for site (environmental), soil stratigraphy, soil physical data, soil chemical data, soil isotopes (ground ice), soil radiocarbon dates, topography and bathymetry, and vegetation cover. The site data includes information of location, observers, geomorphology, topography, hydrology, soil summary characteristics, pH and EC, soil classification, and vegetation cover by species. Soil stratigrapy has information on soil texture and ground ice. Soil physical and chemical data includes lab data on bulk density, moisture, carbon, and nitrogen. The database has 37 reference tables (REF_) that have codes and descriptions for variables used in site, soil stratigraphy, and vegetation cover tables.
SoilCompDB: Global soil compressive properties database. Version 1.0
<p><strong>Data collection and processing</strong></p><p>Our data collection comprised published journal articles sourced from Web of Science and Scopus databases, using search terms such as 'soil precompression stress,' 'soil compression index,' 'soil compaction index,' 'soil recompression index,' 'soil swelling index,' 'soil precompaction stress,' and 'preconsolidation pressure' for articles published up to February 2022. A total of 1235 publications were found. Duplicate records were eliminated using the Endnote Web citation management application. The remaining references were exported to Rayyan software for title and abstract screening based on predefined criteria for full-text selection. After a careful review, we identified 128 papers where the data on soil compressive properties (precompression stress, compression index, and swelling index) were reported in numerical format or legible graphical format and considered suitable for inclusion in the database. We employed the WebPlotDigitizer software to extract data from figures within the original publications. For each chosen study, we systematically recorded data concerning soil compressive properties and collected information on soil properties, soil conditions, site characteristics, and experimental settings. We compiled 4,743 individual data entries.</p><p><strong>Time and place</strong></p><p>The database includes data from 128 independent studies published between 1992 and 2021. Each study reported between 1 and 360 measurements, with a study median of 14 measurements and a mean of 38 measurements, totalling 4743 database entries. Our database includes data from 20 countries, with a significant concentration of the data originating from Brazil, followed by Germany, Switzerland, Sweden, and Denmark. The majority of the data came from arable soils, representing approximately 72% of data entries. </p><p><strong>Instruments</strong></p><p>The soil compressive properties included in the database were based on soil compressive tests performed in the laboratory by uniaxial method. The procedure used for stress application on soil samples was mainly the stepwise stress application method, while the constant strain rate method was applied in few studies (less than 2% of the data). The component of the compressive curve related to the soil packing state was represented by soil bulk density, void ratio, and strain. The stress component of the curve was represented in a logarithmic form in the entirety of the database. The database also comprised eight different methods for calculating precompresion stress: Casagrande (1936), Dias Junior and Pierce (1995), Lamandé et al. (2017), Sullivan and Robertson (1996), Casini (2012), Culley and Larson (1987), Pacheco Silva (1990), Gregory et al. (2006).</p><p><strong>Resources</strong></p><p>Web of Science, Scopus – literature search</p><p>Endnote Web – removal of duplicates</p><p>Rayyan software – initial paper selection based on title and abstract</p><p>WebPlotDigitizer – data extraction from figures</p><p>Microsoft Access – database platform</p><p><strong>Description of the collected data (column, unit, and description)</strong></p><p>Sample ID- A unique identification number assigned to each individual sample within the database </p><p>Study ID- Identification number assigned to each research study in the database</p><p>Reference - Research paper reference</p><p>Year - Year of research paper publication </p><p>Language - Language of the research paper </p><p>Soil classification (SiBCS) - Soil Classification according to the Brazilian System (SiBCS), as described in portuguese-language papers</p><p>Soil classification (original in paper) - Soil classification described in research paper </p><p>Soil classification (convertion to Soil Taxonomy orders) - Soil classification aligned with the Soil Taxonomy system developed by the United States Department of Agriculture (USDA) </p><p>Location - Study location country </p><p>Texture classification (USDA) - Soil textural classification according USDA</p><p>Texture classification USDA (letter code) - Letter code for soil textural classification according USDA: S=sand; LS=loamy sand; SL=sandy loam; SiL=silt loam; Si=silt; L=loam; SCL= Sandy clay loam; SiCL=Silty clay loam; CL=clay loam; SC=Sandy clay; SiC=Silty clay; C=clay</p><p>Clay (USDA) - % - Soil clay content (weight based) - (<0.002 mm) </p><p>Silt (USDA) - % - Soil silt content (weight based) - (0.002 < x < 0.05 mm, interpolated for European samples where needed using the k-nearest neighbor technique by Nemes et al. 2006) </p><p>Sand (USDA) - % - Soil sand content (weight based) - (0.05 < x < 2 mm, interpolated for European samples where needed using the k-nearest neighbor technique by Nemes et al. 2006)</p><p>USDA PSD interpolated - =0 if the data was NOT interpolated; =1 if the data was interpolated</p><p>Published texture class - Texture classification provided in the source publication when the values for clay, silt and sand were not available</p><p>Clay - g kg-1 - Soil clay content - original in the paper</p><p>Clay class upper boundary - µm - The clay class upper boundary informed in source publication</p><p>Silt - g kg-1 - Silt clay content - original in the paper</p><p>Silt class upper boundary - µm - The silt class upper boundary informed in source publication</p><p>Sand - Soil sand content - original in the paper</p><p>Sand class upper boundary - µm - The sand class upper boundary informed in source publication</p><p>Particle size data flag - =0 if no issues; =1 if there are issues (summing)</p><p>Sum particle size- g kg-1 - Sum of clay, silt, and sand content</p><p>Soil depth FROM – cm - When soil depth is presented as a range (e.g., 0-10cm), it indicates the minimum depth at which soil samples were collected </p><p>Soil depth TO – cm - When soil depth is presented as a range (e.g., 0-10cm), it indicates the maximum depth at which soil samples were collected </p><p>Depth – cm -Specific depth value as presented in paper, or when soil depth is showed as a range (e.g., 0-10cm), it indicates the average depth at which soil samples were collected (e.g 5cm) </p><p>SOC - g kg-1 - Soil organic carbon content informed in research paper or soil organic carbon content calculate from soil organic matter content by multiplying by 0,58 </p><p>SOC converted from SOM - 1= yes for soil organic carbon derived from soil organic matter content calculations</p><p>Particle density - Mg m-3 - Soil particle density </p><p>Initial matric potential – hPa - Soil water matric potential before loading</p><p>log Initial matric potential - Soil water matric potential expressed by log </p><p>Wetness (based on initial matric potential) - 1=if initial matric potential (MP)<100 hPa; 2= if 100<=initial MP<1000 hPa; 3= initial MP>=1000 hPa</p><p>Initial gravimetric water content - g g-1 - Gravimetric soil water content before loading provided by source publication, or calculated by volumetric water content divided by soil bulk density</p><p>Initial volumetric water content - m3 m-3 - Volumetric soil water content before loading, when the soil bulk density was not reported</p><p>Initial water content data source - Graph or table from where the data was collected, or explanation on calculation used</p><p>Matric potential type - Compressive tests performed on soil samples under different conditions: 1= equilibrated at matric potential; 2= field matric potential; 3= air-dried samples </p><p>Initial bulk density - Mg m-3 - Soil bulk density before loading </p><p>Initial BD data source - Graph or table from where the data was collected, or explanation on calculation used </p><p>Initial volumetric water content calculated - m3 m-3 - Soil volumetric water content calculated by multiplying soil gravimetric water content by soil bulk density</p><p>Precompression stress – kPa - Precompression stress </p><p>Precompression stress (SD) – kPa - Standard deviation for precompression stress values reported in paper </p><p>Precompression stress data source - Graph or table from where the data was collected, or explanation on calculation used</p><p>Compression index - Compression index </p><p>Compression index (SD) - Standard deviation of compression index values reported in paper </p><p>Compression index data source - Graph or table from where the data was collected, or explanation on calculation used</p><p>Swelling index - Swelling index </p><p>Swelling index (SD) - Standard deviation of swelling index values reported in paper </p><p>Swelling index data source - Graph or table from where the data was collected, or explanation on calculation used</p><p>N - Number of replicates used for calculating precompression stress, compression index, and swelling index when mean values are reported</p><p>Land use (paper) - Land use described in the research paper</p><p>Land use (categories) - Land use categorized</p><p>Land use standardized - Land use classified as: arable, forest, grassland, and native vegetation. The latter includes forest, grassland, and savanna</p><p>Land use (number code) - Number code for land use: 1=Arable, 2= forest, 3= grassland, and 4= native vegetation</p><p>Tillage system - Tillage system</p><p>Tillage system (arable soils) - Tillage system for arable soils classified as "conventional" and "conservation"</p><p>Coordinates - Geographical coordinates of study location</p><p>Climate - Climatic region classification: temperate, tropical, subtropical</p><p>Climatecod - Code number assigned to each climatic region: 1=temperate, 2=tropical, 3=subtropical</p><p>Sampling position (paper) - Field position where soil samples were collected with details described in the paper</p><p>Sampling position - Field position where soil samples were collected standardized</p><p>Treatment - Experimental treatment type where the soil samples were collected</p><p>Stress rate - kPa - Stress applied in compressive tests </p><p>Minimum stress – kPa - Minimum stress applied in compressive tests</p><p>Maximum stress – kPa - Maximum stress applied in compressive tests</p><p>Number of stress rate steps - Number of steps in stepwise stress application procedure</p><p>Stess application type - 1=Stepwise stress 2=one sample per stress 3=Strain controlled</p><p>Stess application type – min - Time for stress application in each step in stepwise stress application procedure</p><p>Degree of deformation at the end of loading - % - Degree of deformation at the end of compressive test</p><p>Sample diameter – cm - Diameter of the soil samples</p><p>Sample height – cm - Height of the soil samples</p><p>Ratio sample diameter and height - Ratio between diameter and height of the soil samples</p><p>Sample volume - cm3 -Sample volume when the sample diameter and height are nor presented</p><p>Precompression stress calculation method - Calculation method of precompression stress</p><p>Precompression stress calculation method (number code) - Number code for calculation method PC:1=Casagrande (1936); 2=Dias Junior and Pierce (1995); 3= Lamandé et al. (2017); 4=O`Sullivan and Robertson (1996); 5=Casini (2012); 6=Culley and Larson (1987);7=ABNT (1990); 8=Gregory et al. (2006)</p><p>Description of precompression stress calculation - Brief explanation of precompression stress calculation</p><p>Soil compressive curve components - Component of the soil compression curve related to the soil packing state: soil bulk density, void ratio, and strain. </p><p>Soil compressive curve components (number code) - Number code for component of the soil compressive curve related to the soil packing state: 1= soil bulk density; 2= strain; 3= void ratio</p><p>Curve components source - Source of the component of the soil compressive curve related to the soil packing state: 1= showed in the paper, 2= according to original method for precompression stress calculation, 3= described in method, but not clear in the paper</p><p>Compressive curve available - Original soil compressive curve available in the paper: 1= No 2=Yes</p><p>Comments - Brief comments on the paper</p><p><strong>Issues and remarks</strong></p><p>We sought out important information not included in the paper by directly communicating with the authors whenever possible. In cases where multiple papers covered the same experiment, we prioritized the one offering more comprehensive details. If two papers complemented each other, we included both. When analyzing studies comparing various methods for calculating soil precompression stress, we exclusively gathered data calculated using the widely accepted Casagrande (1936) method. To ensure comparability across studies, we standardized the collected data by converting it to the same unit. The standardization process involved: i) assuming that 58% of soil organic matter (SOM) was soil organic carbon (SOC) when only SOM was reported, ii) calculating soil bulk density using a soil particle density of 2.65 Mg m-3 when only total porosity data were provided, and iii) harmonizing all texture data to the USDA classification system, which defines the silt/sand boundary as 50 μm, utilizing the k-nearest neighbor approach (referred to as "similarity method" by Nemes et al. (1999). </p><p><strong>Reference</strong></p><p>Associação Brasileira de Normas Técnicas - ABNT. NBR 12007: Ensaio de adensamento unidimensional. Rio de Janeiro: 1990.</p><p>Casagrande, A., 1936. Determination of the preconsolidation load and its practical significance. In: Proceedings of the International Conference on Soil Mechanics and Foundation Engineering, vol. III, Harvard University, Cambridge, MA, pp. 60–64.Casini, F. 2012. Deformation induced by wetting: A simple model. Can. Geotech. J. 49:954–960 10.1139/T2012-054. doi:10.1139/t2012-054</p><p>Culley, J.L.B., Larson, W.E., 1987. Susceptibility to compression of a clay loam Haplaquoll. Soil Sci. Soc. Am. J. 51, 562–567.</p><p>Dias Junior, M.S., Pierce, F.J., 1995. A simple procedure for estimating preconsolidation pressure from soil compression curves. Soil Technology 8, 139–151. doi:10.1016/0933-3630(95)00015-8</p><p>Gregory, A.S., Whalley, W.R., Watts, C.W., Bird, N.R.A., Hallett, P.D., Whitmore, A.P., 2006. Calculation of the compression index and pre-compression stress from soil compression test data. Soil Till Res. 89:45-57. doi:10.1016/j.still.2005.06.012</p><p>Lamandé, M., Schjønning, P., Labouriau, R., 2017. A novel method for estimating soil precompression stress from uniaxial confined compression tests. Soil Sci. Soc. Am. J. 81 https://doi.org/10.2136/sssaj2016.09.0274.</p><p>Nemes, A., Wösten, J.H.M., Lilly, A., Oude Voshaar, J.H., 1999. Evaluation of different procedures to interpolate the cumulative particle-size distribution to achieve compatibility within a soil database. Geoderma 90: 187-202. 129 </p><p>O'Sullivan, M.F., Robertson, E.A.G., 1996. Critical state parameters from intact samples of two agricultural topsoils. Soil Tillage Res 39(3 – 4):161 – 173.</p>
Database for meta-analysis of herbivore impacts on plant-soil feedbacks
<p class="MsoNormal"><span>We conducted a meta-analysis to test for an interaction between plant-soil feedbacks and herbivory, including effects on the magnitude and direction of feedbacks, herbivore consumption and herbivore growth.</span></p> <p class="MsoNormal"><em><span> </span></em><span>We identified 197 studies to address herbivore impacts on plant-soil feedbacks and 189 studies to address plant-soil impacts on herbivores. We calculated Hedge's D values to assess three questions: 1) What is the plant-soil feedback value of plants exposed to herbivory or no herbivory? 2) What is the growth or biomass of herbivores feeding on plants exposed to home or away soils in plant-soil feedback studies? 3) How much plant tissue is consumed by herbivores on plants grown in home or away soils?</span></p> <p class="paragraph"><em> </em></p> <p class="paragraph"><span class="normaltextrun">We found an overall significant weak negative effect of herbivory on plant-soil feedbacks that varied by plant functional type. In legumes herbivory drove plant-soil feedbacks from positive to negative, but herbivory on forbs further decreased </span><span class="eop">negative feedbacks</span><span class="normaltextrun">.</span><span class="eop"> </span><span class="normaltextrun"> Herbivore consumption was generally greater on plants grown in away soils. However, herbivore consumption was greater in home soils conditioned by legumes but lower in home soils conditioned by forbs.</span></p> <p class="paragraph"><em> </em></p> <p class="paragraph"><span class="eop">Therefore plant functional type determines the impact of conditioned soil on feedbacks, and herbivore consumption explains these results for legumes but not forbs. </span></p>
Description of Mont-Vert hillslope database: measured properties along soil cores and associated metadata
<p>This dataset includes data and metadata of twenty soil cores collected in Mont-Vert banana plantation (Le Robert, Martinique, France) in February 2023. These data were collected in order to study the chlordecone (an organochlorine insecticide) redistribution along a cultivated hillslope. To this end, radiocesium activities (artificial radionuclide) and chlordecone concentrations were measured in soil core subsamples (5-cm increments). </p>
Description of suspended sediment and soil samples database: measured properties associated metadata
<p>From the perspective of open science, this dataset includes data and metadata of soil and suspended sediment samples collected across three catchments in Martinique (French West Indies) : Galion river catchment, Lézarde river catchment and Salée river catchment. During one year (June 2022 - July 2023), suspended sediment samplers were installed in the three river networks. In order to determine the signature of the potential erosion sources, soil samples were collected across the three catchments. In order to quantify suspended sediment source contributions and chlordecone contamination of suspended matter transiting in river, a set of different properties were measured in soil and suspended sediment samples: radionuclide activities, carbon and nitrogen isotopes, elemental geochemistry, visible colorimetry, chlordecone and its degradation products concentrations</p>
Database of clustered vOTUs recovered from a viromics prescribed burn study of forest soil
<p>Database of dereplicated viral operational taxonomic units (vOTUs) recovered from a viromics (viral-size fraction metagenomics) prescribed burn study of forest soil</p>
Database for meta-analysis of herbivore impacts on plant-soil feedbacks
Open the record for dataset details and reuse information.
Best Practice Tool: Soil Database
<p>The database contains the hydrological properties of the soils within the regions of interest presented in the article on the LandSupport <em>Best practice tool</em></p>
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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