{"type": "FeatureCollection", "features": [{"id": "10.15454/SVDTOU", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:19:29Z", "type": "Dataset", "title": "Statistiques spatio-temporelles sur les propri\u00e9t\u00e9s agronomiques des sols agricoles en France issues de la Base de Donn\u00e9es d'Analyses de Terre (BDAT)", "description": "In France, farmers commission about 250,000 soil-testing analyses per year to assist them managing soil fertility. The number and diversity of origin of the samples make these analyses an interesting and original information source regarding cultivated topsoil variability. Moreover, these analyses relate to several parameters strongly influenced by human activity (macronutrient contents, pH...), for which existing cartographic information is not very relevant. Compiling the results of these analyses into a database makes it possible to re-use these data within both a national and temporal framework. A database compilation relating to data collected over the period 1990-2014 has been recently achieved. So far, commercial soil-testing laboratories approved by the Ministry of Agriculture have provided analytical results from more than 3,600,000 samples. After the initial quality control stage, analytical results from more than 1,900,000 samples were available in the database. The anonymity of the landholders seeking soil analyses is perfectly preserved, as the only identifying information stored is the location of the nearest administrative city to the sample site. We present in this dataset a set of statistical parameters of the spatial distributions for several agronomic soil properties. These statistical parameters are calculated for 4 different nested spatial entities (administrative areas: e.g. regions, departments, counties and agricultural areas) and for 5 time periods (1990-1994, 1995-1999, 2000-2004, 2005-2009, 2010-2014). Two kinds of agronomic soil properties are available: the first one correspond to the quantitative variables like the organic carbon content, and the second one corresponds to the qualitative variables like the texture class. For each spatial unit and temporal period, we calculated the following statistics sets: the first set is calculated for the quantitative variables and corresponds to the number of samples, the mean, the standard deviation and, the 2-,4-,10-quantiles; the second set is calculated for the qualitative variables and corresponds to the number of samples, the value of the dominant class, the number of samples of the dominant class, the second dominant class, the number of samples of the second dominant class.", "keywords": ["2. Zero hunger", "Earth and Environmental Science", "Soils and soil sciences", "Earth and Environmental Sciences", "Soil Sciences", "soil texture", "15. Life on land", "soil analysis", "Environmental Research", "Natural Sciences", "Geosciences"], "contacts": [{"organization": "Saby, Nicolas P.A., Lemercier, Blandine, Arrouays, Dominique, Walter, Christian, Gouny, Laetitia, Swidersky, Chlo\u00e9, Toutain, Beno\u00eet, Bispo, Antonio,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.15454/SVDTOU"}, {"rel": "self", "type": "application/geo+json", "title": "10.15454/SVDTOU", "name": "item", "description": "10.15454/SVDTOU", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.15454/SVDTOU"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-01-01T00:00:00Z"}}, {"id": "10.7910/DVN/GVNJAB", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:24:26Z", "type": "Dataset", "created": "2019-06-24", "title": "Physical topsoil properties in Murugusi, Western Kenya", "description": "Open Access<b>General:</b> Lab determined topsoil bulk density, contents of sand, clay and organic carbon in Murugusi, W. Kenya, together with spatial coordinates of where the soil samples were taken (rounded to the closest center point of a 250 m \u00d7 250 m raster). All lab analyses were carried out at the ILRI/CIAT lab in Nairob, Kenya. <br> <b>Soil sampling:</b> At each sample location, one composite topsoil sample was taken; three cores of 7 cm in diameter taken within an area of one square meter. The soil was taken from 0-0.2 m depth below any organic (O) horizon. <br> <b>Determination of soil properties:</b> The bulk density of the soil was determined by taking two undisturbed soil samples (0-10 cm and 10-20 cm depth) of known volume (100 cm2) and weighting them after air drying. Soil fractions of clay (<0.002 mm) and sand (0.05-2 mm) were determined by the hydrometer method (Estefan et al., 2014), using 10% sodium hexametaphosphate as the dispersing agent. Soil pH was determined potentiometrically on a soil suspension of 1:2 (soil: water). Total carbon was measured after dry combustion using an elemental analyser (Elementar Vario max cube; ISO 10694, first edition 1995-03-01) <br> <b>Reference: </b>Estefan G., Sommer R., Ryan J. (2014) Analytical Methods for Soil-Plant and Water in Dry Areas. A Manual of Relevance to the West Asia and North Africa Region. 3rd Edition, International Center for Agricultural Research in the Dry Areas, Aleppo, 255 pp. Available online at: http://repo.mel.cgiar.org:8080/handle/20.500.11766/7512?show=full. Verified: October 9, 2018. <br> <b>Acknowledgements: </b> We are deeply thankful for the good services provided by John Mukulama (soil sampling), John Yumbya Mutua (soil sampling) and Francis Mungthu Njenga (lab analyses) The project was carried out within the CGIAR Research Program on Water, Land and Ecosystems (WLE).", "keywords": ["Soil organic matter", "Agricultural Sciences", "Soil organic carbon", "sand", "Kenya", "Carbon", "Latin America and the Caribbean", "soil", "Soil", "Soil bulk density", "Sand", "soil organic matter", "Earth and Environmental Sciences", "Soil texture", "Murugusi", "Africa", "Clay", "Texture", "Western Kenya", "Agroecosystems and Sustainable Landscapes - ASL"], "contacts": [{"organization": "Piikki, Kristin, S\u00f6derstr\u00f6m, Mats, Sommer, Rolf, Da Silva, Mayesse,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.7910/DVN/GVNJAB"}, {"rel": "self", "type": "application/geo+json", "title": "10.7910/DVN/GVNJAB", "name": "item", "description": "10.7910/DVN/GVNJAB", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.7910/DVN/GVNJAB"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-01-01T00:00:00Z"}}, {"id": "10.1016/j.catena.2021.105718", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:15:35Z", "type": "Journal Article", "created": "2021-09-11", "title": "Correlation of banana productivity levels and soil morphological properties using regularized optimal scaling regression", "description": "Soil morphological properties described in the field, such as texture, consistence or structure, provide a valuable tool for the evaluation of soil productivity potential. In this study, we developed a regression model between the soil morphological variables of banana plantations and a crop Productivity Index (PI) previously developed for the same areas in Venezuela. For this, we implemented categorical regression, an optimal scaling procedure in which the morphological variables are transformed into a numerical scale, and can thus be entered in a multiple regression analysis. The model was developed from data from six plantations growing \u201cGran Nain\u201d bananas, each with two productivity levels (high and low), in two 4-ha experimental plots, one for each productivity level. Sixty-three A horizons in thirty-six soils were described using 15 field morphological variables on a nominal scale for structure type, texture and hue, and an ordinal scale for the rest (structure grade, structure size, wet and dry consistence, stickiness, plasticity, moist value, chroma, root abundance, root size, biological activity and reaction to HCl). The optimum model selected included biological activity, texture, dry consistence, reaction to HCl and structure type variables. These variables explained the PI with an R2 of 0.599, an expected prediction error (EPE) of 0.645 and a standard error (SE) of 0.135 using bootstrapping, and EPE of 0.662 with a SE of 0.236 using 10-fold cross validation. Our study showed how soil quality is clearly related to productivity on commercial banana plantations, and developed a way to correlate soil quality indicators to yield by using indicators based on easily measured soil morphological parameters. The methodology used in this study might be further expanded to other banana-producing areas to help identify the soils most suitable for its cultivation, thereby enhancing its environmental sustainability and profitability.", "keywords": ["2. Zero hunger", "55 Geolog\u00eda y ciencias afines", "63 Agricultura.", "Biological activity", "Biological activities", "63 Agricultura", "04 agricultural and veterinary sciences", "15. Life on land", "55 Geolog\u00eda y ciencias afines.", "01 natural sciences", "630", "Dry consistence", "Sustainability", "Qualitative soil indicators", "Soil structure", "0401 agriculture", " forestry", " and fisheries", "Texture", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.catena.2021.105718"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/CATENA", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.catena.2021.105718", "name": "item", "description": "10.1016/j.catena.2021.105718", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.catena.2021.105718"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "10.1016/j.agwat.2021.106827", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:15:21Z", "type": "Journal Article", "created": "2021-02-27", "title": "Implementing a new texture-based soil evaporation reduction coefficient in the FAO dual crop coefficient method", "description": "Abstract Crop evapotranspiration (ET) is a fundamental component of the hydrological cycle, especially in arid/semi-arid regions. The FAO-56 offers an operational method for deriving ET from the reduction (dual crop coefficient Kc) of the atmospheric evaporative demand (ET0). The dual coefficient approach (FAO-2Kc) is intended to improve the daily estimation of ET by separating the contribution of bare soil evaporation (E) and crop transpiration components. The FAO-2Kc has been a well-known reference for the operational monitoring of crop water needs. However, its performance for estimating the water use efficiency is limited by uncertainties in the modeled evaporation/transpiration partitioning. This paper aims at improving the soil module of the FAO-2Kc by modifying the E reduction coefficient (Kr) according to soil texture information and state-of-the-art formulations, hence, to amend the mismatch between FAO-2Kc and field-measured data beyond standard conditions. In practice this work evaluates the performance of two evaporation models, using the classical Kr (Kr,FAO) and a new texture-based Kr (Kr,text) over 33 bare soil sites under different evaporative demand and soil conditions. An offline validation is investigated by forcing both models with observed soil moisture ( \u03b8 s ) data as input. The Kr,text methodology provides more accurate E estimations compared to the Kr,FAO method and systematically reduces biases. Using Kr,text allows reaching the lowest root means square error (RMSE) of 0.16\u2009mm/day compared to the Kr,FAO where the lowest RMSE reached is 0.88\u2009mm/day. As a step further in the assessment of the proposed methodology, ET was estimated in three wheat fields across the entire agricultural season. Both approaches were thus inter-compared in terms of ET estimates forced by SM estimated as a residual of the water balance model (online validation). Compared to ET measurements, the new formulation provided more accurate results. The RMSE was 0.66\u2009mm/day (0.71\u2009mm/day) and the R2 was 0.83 (0.78) for the texture-based (classical) Kr.", "keywords": ["0106 biological sciences", "2. Zero hunger", "570", "Evapotranspiration", "Soil texture", "FAO-2Kc", "0207 environmental engineering", "Soil moisture", "02 engineering and technology", "15. Life on land", "Soil evaporation", "01 natural sciences", "6. Clean water"]}, "links": [{"href": "https://doi.org/10.1016/j.agwat.2021.106827"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agricultural%20Water%20Management", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.agwat.2021.106827", "name": "item", "description": "10.1016/j.agwat.2021.106827", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.agwat.2021.106827"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-01T00:00:00Z"}}, {"id": "04e12ad1-77b4-45a2-9ed2-2f9aedcc013c", "type": "Feature", "geometry": null, "properties": {"updated": "2025-09-03T08:42:09Z", "type": "Dataset", "language": "de", "title": "INSPIRE-WCS Soil / Konzeptbodenkarten BB", "description": "Der interoperable INSPIRE-WCS ist ein Downloaddienst, der Daten im INSPIRE-Zielschema Boden (abgeleitet aus der origin\u00e4ren Serie: Konzeptbodenkarten Brandenburg) bereitstellt. Er gibt einen \u00dcberblick zum Fl\u00e4chentyp Bodenartgruppe und Fl\u00e4chentyp Genese der B\u00f6den Brandenburgs und ist aktuell das hochaufl\u00f6sendste blattschnittfreie bodenkundliche Kartenwerk des LBGR im Rasterformat, mit einer Aufl\u00f6sung von 10 x 10 m. Datengrundlagen der Karten sind Fl\u00e4chendaten der Bodensch\u00e4tzung, Geologie und verschiedene bodenkundliche Punkt- und Fl\u00e4chendaten. Die Auswertungen liefern die fachliche Basis sowohl f\u00fcr die Planung und Umsetzung bodenkundlicher Kartierungen, als auch f\u00fcr die Synthese und Ableitung der Bodenfunktions- und -gef\u00e4hrdungsbewertungen. Die G\u00fcte der zugrundeliegenden Konzeptbodenkarten (KBK) hat somit einen hohen Einfluss auf die Qualit\u00e4t der abgeleiteten Produkte. Im Land Brandenburg werden die KBK mit einem regelbasierten Ansatz weitgehend automatisiert softwaretechnisch abgeleitet. Das Verfahren implementiert die bislang analog durchgef\u00fchrten Arbeitsschritte und bildet als \u201eExpertensystem\u201c das Verst\u00e4ndnis \u00fcber die Genese und die Verbreitung der B\u00f6den in Brandenburg weitgehend ab. Derzeit wird ein robustes Verfahren zum Abgleich laufender Erhebungen mit den \u201ePrognosen\u201c der Konzeptkarten und darauf fu\u00dfend deren systematische \u00dcberpr\u00fcfung und Bewertung erarbeitet. Zuk\u00fcnftig sollen in diesem Verfahren erkannte Defizite systematisch in einem modifizierten Ableitungsregelwerk ber\u00fccksichtigt und die Konzeptbodenkarten fortlaufend aktualisiert werden. Eine ausf\u00fchrliche Legende sowie die Erl\u00e4uterungen der Werte ist zu finden unter: https://isk.geobasis-bb.de/geodienste/lbgr/KBK_Erlaeuterungen.pdf. Gem\u00e4\u00df der INSPIRE-Datenspezifikation f\u00fcr Soil liegen die Inhalte INSPIRE-konform vor. Der WCS beinhaltet die folgenden Coverages: - SO.SoilTextureClasses:bft, - SO.SoilGenesis:gft, - SO.SoilPedogenesis:pft. --- The compliant INSPIRE-WCS is a download service that delivers data in the INSPIRE target schema Soil (derived from the original data series: Concept pedological maps in the State of Brandenburg). It provides an overview of the soil texture classes and soil genesis in the State of Brandenburg and is currently the highest-resolution pedological map of the LBGR in raster format with a resolution of 10 x 10 meters. The maps are based on data from German Soil taxonomy classification, geology and various pedological spatial data. The evaluations provide the technical basis for the planning and implementation of soil surveys as well as for the synthesis and derivation of soil function and risk assessments. The quality of the underlying Concept pedological maps (KBK) therefore has a major influence on the quality of the derived products. In the State of Brandenburg, the KBK is derived using a largely automated software-based approach. The procedure implements the work steps that have been carried out analogously to date and, as an 'expert system', largely reflects the understanding of the genesis and distribution of soils in Brandenburg. A robust procedure for comparing current surveys with the 'forecasts' of the KBK and, based on this, their systematic review and evaluation is currently being developed. In the future, deficits identified in this procedure will be systematically taken into account in a modified derivation set of rules and the KBK will be continuously updated. A detailed legend and explanations can be found at: https://isk.geobasis-bb.de/geodienste/lbgr/KBK_Erlaeuterungen.pdf. The content is compliant to the INSPIRE Data Specification on Soil. The WCS includes the following coverages: - SO.SoilTextureClasses:bft, - SO.SoilGenesis:gft, - SO.SoilPedogenesis:pft.", "formats": [{"name": "HTML"}], "keywords": ["boden", "bodenart", "bodendaten", "bodenform", "bodengeologie", "bodengesellschaft", "bodenkarte", "bodenkunde", "bodenscha\u0308tzung", "bodensystematische-einheit", "bodentyp", "brandenburg", "de", "fla\u0308chenbodenform", "genese", "geologie", "infocoverageaccessservice", "inspireidentifiziert", "interoperabel", "interoperability", "interoperable-daten", "konzeptbodenkarte", "konzeptkarte", "opendata", "organisch", "pedogenese", "process", "regional", "soil", "soillayer", "soiltexturegroup", "soiltype", "substratsystematische-einheit", "terrestrisch", "wcs"], "contacts": [{"organization": "Landesamt f\u00fcr Bergbau, Geologie und Rohstoffe Brandenburg (LBGR)", "roles": ["creator"]}]}, "links": [{"href": "https://geoportal.brandenburg.de/detailansichtdienst/render?view=gdibb&url=https%3A%2F%2Fgeoportal.brandenburg.de%2Fgs-json%2Fxml%3Ffileid%3D04e12ad1-77b4-45a2-9ed2-2f9aedcc013c"}, {"href": "https://inspire.brandenburg.de/services/so_kbk_wcs?REQUEST=GetCapabilities&SERVICE=WCS"}, {"href": "https://isk.geobasis-bb.de/geodienste/Sonstiges/Hilfe_Nutzung_Downloaddienst.pdf"}, {"href": "http://data.europa.eu/88u/dataset/04e12ad1-77b4-45a2-9ed2-2f9aedcc013c~~1"}, {"rel": "self", "type": "application/geo+json", "title": "04e12ad1-77b4-45a2-9ed2-2f9aedcc013c", "name": "item", "description": "04e12ad1-77b4-45a2-9ed2-2f9aedcc013c", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/04e12ad1-77b4-45a2-9ed2-2f9aedcc013c"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"null": "date"}}, {"id": "0f85c381-e496-47d9-89d8-f1fe2ee1a517", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[-180.0, -90.0], [-180.0, 90.0], [180.0, 90.0], [180.0, -90.0], [-180.0, -90.0]]]}, "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"}], "license": "Attribution 3.0 International (CC BY 3.0)", "updated": "2025-02-05T10:34:26", "type": "Dataset", "language": "eng", "title": "A homogenized soil data file for global environmental research: A subset of FAO, ISRIC and NRCS profiles", "description": "A homogenized, global set of 1,125 soil profiles is presented. These profiles have been extracted from the database developed at ISRIC for a project on \"World Inventory of Soil Emission Potentials\" (WISE), as a contribution to the activities of the Global Soils Data Task Group of IGBP-DIS. The subset consists of a selection of 665 profiles originating from digital data files released by the Natural Resources Conservation Service (NRCS, Lincoln), 250 profiles obtained from the Food and Agriculture Organization (FAO, Rome), and 210 profiles from the reference collection of the International Soil Reference and Information Centre (ISRIC, Wageningen). All profiles are georeferenced and classified in the FAO-Unesco Legend whereby they can be linked to the edited and digital version of the FAO-Unesco Soil Map of the World. This data set is being released in the public domain for use by global modellers and other interested scientists. It is envisaged that the data set will be expanded by ISRIC when new, uniform soil profile data become available.\n\nNote: \na) A more recent version (some 10,000 profiles) of WISE profiles is available at: http://data.isric.org/geonetwork/srv/eng/catalog.search#/metadata/a351682c-330a-4995-a5a1-57ad160e621c (2009)\nb) For a larger compilation see the WoSIS database: http://isric.org/explore/wosis (2017)", "formats": [{"name": "zip"}, {"name": "WWW:DOWNLOAD-1.0-ftp--download"}, {"name": "WWW:LINK-1.0-http--related"}], "keywords": ["calcium", "carbon", "cation exchange capacity", "electrical conductivity", "nitrogen", "organic carbon", "bulk density", "soil classification", "soil depth", "soil profiles", "pH", "salinity", "texture", "water holding capacity", "soil profiles", "nutrients", "Soil science", "Global"], "contacts": [{"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 (WDC - Soils)", "position": "Geoinformatic", "roles": ["author"], "phones": [{"value": null}], "emails": [{"value": "data@isric.org"}], "addresses": [{"deliveryPoint": ["P.O. Box 47"], "city": "Wageningen", "administrativeArea": null, "postalCode": "6708 PB", "country": "Netherlands"}], "links": [{"href": null}]}, {"name": "Data info desk", "organization": "ISRIC - World Soil Information (WDC - Soils)", "position": null, "roles": ["custodian"], "phones": [{"value": null}], "emails": [{"value": "data@isric.org"}], "addresses": [{"deliveryPoint": [null], "city": null, "administrativeArea": null, "postalCode": null, "country": null}], "links": [{"href": null}]}, {"name": "Data info desk", "organization": "ISRIC - World Soil Information (WDC - Soils)", "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 (WDC - Soils)", "roles": ["contributor"]}], "distancevalue": "30", "distanceuom": "arc-second"}, "links": [{"href": "https://files.isric.org/public/wise/ISRIC_report_1995_10b.zip", "name": "Download", "protocol": "WWW:DOWNLOAD-1.0-ftp--download", "rel": "download"}, {"href": "https://www.isric.org/documents/document-type/isric-report-199510b-homogenized-soil-data-file-global-environmental", "name": "Project webpage", "protocol": "WWW:LINK-1.0-http--related", "rel": "information"}, {"href": "https://files.isric.org/public/thumbnails/wise/ISRIC-WISE_Internat_Soil_Prof_Data_Set_c.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": "0f85c381-e496-47d9-89d8-f1fe2ee1a517", "name": "item", "description": "0f85c381-e496-47d9-89d8-f1fe2ee1a517", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/0f85c381-e496-47d9-89d8-f1fe2ee1a517"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"interval": ["1950-01-01T00:00:00Z", "1995-07-01T00:00:00Z"]}}, {"id": "10.1002/2015wr018233", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:13:55Z", "type": "Journal Article", "created": "2016-04-20", "title": "Modeling soil evaporation efficiency in a range of soil and atmospheric conditions using a meta\u2010analysis approach", "description": "Abstract

A meta\uffe2\uff80\uff90analysis data\uffe2\uff80\uff90driven approach is developed to represent the soil evaporative efficiency (SEE) defined as the ratio of actual to potential soil evaporation. The new model is tested across a bare soil database composed of more than 30 sites around the world, a clay fraction range of 0.02\uffe2\uff80\uff930.56, a sand fraction range of 0.05\uffe2\uff80\uff930.92, and about 30,000 acquisition times. SEE is modeled using a soil resistance (rss) formulation based on surface soil moisture (\uffce\uffb8) and two resistance parameters and \uffce\uffb8efolding. The data\uffe2\uff80\uff90driven approach aims to express both parameters as a function of observable data including meteorological forcing, cut\uffe2\uff80\uff90off soil moisture value at which SEE=0.5, and first derivative of SEE at , named . An analytical relationship between and is first built by running a soil energy balance model for two extreme conditions with rss\uffe2\uff80\uff89=\uffe2\uff80\uff890 and using meteorological forcing solely, and by approaching the middle point from the two (wet and dry) reference points. Two different methods are then investigated to estimate the pair either from the time series of SEE and \uffce\uffb8 observations for a given site, or using the soil texture information for all sites. The first method is based on an algorithm specifically designed to accomodate for strongly nonlinear relationships and potentially large random deviations of observed SEE from the mean observed . The second method parameterizes as a multi\uffe2\uff80\uff90linear regression of clay and sand percentages, and sets to a constant mean value for all sites. The new model significantly outperformed the evaporation modules of ISBA (Interaction Sol\uffe2\uff80\uff90Biosph\uffc3\uffa8re\uffe2\uff80\uff90Atmosph\uffc3\uffa8re), H\uffe2\uff80\uff90TESSEL (Hydrology\uffe2\uff80\uff90Tiled ECMWF Scheme for Surface Exchange over Land), and CLM (Community Land Model). It has potential for integration in various land\uffe2\uff80\uff90surface schemes, and real calibration capabilities using combined thermal and microwave remote sensing data.The relationship between the spatial variability of soil multifunctionality (i.e., the capacity of soils to conduct multiple functions; SVM) and major climatic drivers, such as temperature and aridity, has never been assessed globally in terrestrial ecosystems. We surveyed 236 dryland ecosystems from six continents to evaluate the relative importance of aridity and mean annual temperature, and of other abiotic (e.g., texture) and biotic (e.g., plant cover) variables as drivers of SVM, calculated as the averaged coefficient of variation for multiple soil variables linked to nutrient stocks and cycling. We found that increases in temperature and aridity were globally correlated to increases in SVM. Some of these climatic effects on SVM were direct, but others were indirectly driven through reductions in the number of vegetation patches and increases in soil sand content. The predictive capacity of our structural equation\uffc2\uffa0modelling was clearly higher for the spatial variability of N\uffe2\uff80\uff90 than for C\uffe2\uff80\uff90 and P\uffe2\uff80\uff90related soil variables. In the case of N cycling, the effects of temperature and aridity were both direct and indirect via changes in soil properties. For C and P, the effect of climate was mainly indirect via changes in plant attributes. These results suggest that future changes in climate may decouple the spatial availability of these elements for plants and microbes in dryland soils. Our findings significantly advance our understanding of the patterns and mechanisms driving SVM in drylands across the globe, which is critical for predicting changes in ecosystem functioning in response to climate change.Soil carbon losses to the atmosphere, via soil heterotrophic respiration, are expected to increase in response to global warming, resulting in a positive carbon-climate feedback. Despite the well-known suite of abiotic and biotic factors controlling soil respiration, much less is known about how the magnitude of soil respiration responses to temperature changes over soil development and across contrasting soil properties. Here, we investigated the role of soil development stage and soil properties in driving the responses of soil heterotrophic respiration to increasing temperatures. We incubated soils from eight chronosequences ranging in soil age from hundreds to million years, and encompassing a wide range of vegetation types, climatic conditions, and chronosequences origins, at three assay temperatures (5, 15 and 25\uffc2\uffb0C). We found a consistent positive effect of assay temperature on soil respiration rates across the eight chronosequences evaluated. However, soil properties such as organic carbon concentration, texture, pH, phosphorus content, and microbial biomass determined the magnitude of temperature effects on soil respiration. Finally, we observed a positive effect of soil development stage on soil respiration that did not alter the magnitude of assay temperature effects. Our work reveals that key soil properties alter the magnitude of the positive effect of temperature on soil respiration found across ecosystem types and soil development stages. This information is essential to better understand the magnitude of the carbon-climate feedback, and thus to establish accurate greenhouse gas emission targets.\u00a0110\u00a0cm in all treatments regardless of the tempering influence of nitrate based fertilizers. Soil organic carbon levels over the study period showed a 6 fold increase at >\u00a030\u00a0cm from the initial values, suggesting significant carbon sequestration. Whilst the study demonstrates the positive impact of a grass or legume ley in producing forage for livestock in a cut and carry system under rainfed conditions in Northeast Thailand, along with positive contributions to soil organic carbon sequestration, a precautionary approach should be adopted. Significant accelerated soil acidification has occurred to depths\u00a0>\u00a0110\u00a0cm that brings into question the sustainability of these systems on these soil types.", "keywords": ["2. Zero hunger", "carbon", "soil texture", "04 agricultural and veterinary sciences", "15. Life on land", "pastures", "6. Clean water", "acidification", "nitrogen fertilizers", "soil properties", "feeds", "stylosanthes guianensis", "0401 agriculture", " forestry", " and fisheries", "sandy soils", "andropogon gayanus"], "contacts": [{"organization": "Noble, A.D., Suzuki, S., Soda, Wannipa, Ruaysoongnern, Sawaeng, Berthelsen, S.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1016/j.geoderma.2007.11.019"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoderma", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.geoderma.2007.11.019", "name": "item", "description": "10.1016/j.geoderma.2007.11.019", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.geoderma.2007.11.019"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2008-03-01T00:00:00Z"}}, {"id": "10.1016/j.jenvman.2019.109391", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:16:20Z", "type": "Journal Article", "created": "2019-08-21", "title": "Towards ecologically functional riparian zones: A meta-analysis to develop guidelines for protecting ecosystem functions and biodiversity in agricultural landscapes", "description": "Riparian zones contribute with biodiversity and ecosystem functions of fundamental importance for regulating flow and nutrient transport in waterways. However, agricultural land-use and physical changes made to improve crop productivity and yield have resulted in modified hydrology and displaced natural vegetation. The modification to the hydrology and natural vegetation have affected the biodiversity and many ecosystem functions provided by riparian zones. Here we review the literature to provide state-of-the-art recommendations for riparian zones in agricultural landscapes. We analysed all available publications since 1984 that have quantified services provided by riparian zones and use this information to recommend minimum buffer widths. We also analysed publications that gave buffer width recommendations to sustain different groups of organisms. We found that drainage size matters for nutrient and sediment removal, but also that a 3\u202fm wide buffer zone acts as a basic nutrient filter. However, to maintain a high floral diversity, a 24\u202fm buffer zone is required, while a 144\u202fm buffer is needed to preserve bird diversity. Based on the analysis, we developed the concept of 'Ecologically Functional Riparian Zones' (ERZ) and provide a step-by-step framework that managers can use to balance agricultural needs and environmental protection of waterways from negative impacts. By applying ERZ in already existing agricultural areas, we can better meet small targets and move towards the long-term goal of achieving a more functional land management and better environmental status of waterways.", "keywords": ["Riparian zone", "river", "nutrient uptake", "hydrology", "Review", "water quality", "01 natural sciences", "Ecological functional riparian zones", "waterway transport", "freshwater environment", "biodiversity", "agriculture", "2. Zero hunger", "filter", "hydrological regime", "Agriculture", "Biodiversity", "Milj\u00f6vetenskap", "functional role", "6. Clean water", "riparian ecosystem", "agricultural land", "Aves", "Environmental Monitoring", "sandy loam", "crop production", "rural area", "12. Responsible consumption", "Buffer zone", "water temperature", "Rivers", "ecosystem function", "controlled study", "human", "14. Life underwater", "environmental protection", "Ecosystem", "environmental monitoring", "0105 earth and related environmental sciences", "ecosystem", "Agricultural", "Vegetation", "practice guideline", "species composition", "land management", "Water", "land use", "soil property", "soil texture", "landscape", "15. Life on land", "13. Climate action", "Environmental Sciences", "meta analysis"]}, "links": [{"href": "https://doi.org/10.1016/j.jenvman.2019.109391"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Environmental%20Management", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.jenvman.2019.109391", "name": "item", "description": "10.1016/j.jenvman.2019.109391", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.jenvman.2019.109391"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-11-01T00:00:00Z"}}, {"id": "10.1016/j.scitotenv.2023.168249", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:16:37Z", "type": "Journal Article", "created": "2023-10-31", "title": "An advanced global soil erodibility (K) assessment including the effects of saturated hydraulic conductivity", "description": "USLE-type models are widely used to estimate average annual soil loss at large scales, with the erodibility factor (K) being the sole component that accounts for soil's susceptibility to erosion. The factor includes the information on permeability in the equation, however, most definitions of the K factor consider the soil hydrological influence only very crudely and indirectly. Thus, the direct impact of surface runoff infiltration and drainage on soil erosion is largely neglected. The objective of this study is to incorporate soil hydraulic properties in the K factor map by merging available global-scale measured saturated hydraulic conductivity (Ksat) data with soil texture and organic carbon information into a modified K factor. To achieve this, the Wischmeier and Smith (1978) soil texture- and permeability-based equation (KWischmeier factor) was modified to include Ksat, called Kksat factor. Using the Random Forest machine learning algorithm, the KWischmeier factor and the Kksat factor were each correlated with soil and remote sensing covariates for spatial extrapolation of two independent K factor maps at 1\u00a0km spatial resolution. We noted a clear decrease in the mean value of the Kksat factor (0.023\u00a0t\u00a0ha\u00a0h\u00a0ha-1\u00a0MJ-1\u00a0mm-1) compared to the mean value of the KWischmeier factor (0.027\u00a0t\u00a0ha\u00a0h\u00a0ha-1\u00a0MJ-1\u00a0mm-1). The reduction in Kksat factor values was most pronounced in tropical regions reflecting the difference in soil properties (e.g., clay and iron), whereas other climate regions showed relatively minor changes in comparison to the KWischmeier factor as well as to the recent global modeling of Borrelli et al. (2017) (KGloSEM factor maps). As many studies discussed an overall overestimation of (R)USLE based erosion rates compared to measurements, this reduction in the K factor might improve modeled erosion rates in the right direction. The Kksat marks an important initial step in integrating hydraulic properties into the K factor of USLE-type models and can prove their significance in future studies.", "keywords": ["K factor; Random Forest; Soil hydraulic properties; Soil texture; Tropical regions; USLE", "15. Life on land", "6. Clean water"]}, "links": [{"href": "https://doi.org/10.1016/j.scitotenv.2023.168249"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Science%20of%20The%20Total%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.scitotenv.2023.168249", "name": "item", "description": "10.1016/j.scitotenv.2023.168249", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.scitotenv.2023.168249"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-01-01T00:00:00Z"}}, {"id": "10.1016/j.soilbio.2021.108497", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:16:50Z", "type": "Journal Article", "created": "2021-11-28", "title": "Co-localised phosphorus mobilization processes in the rhizosphere of field-grown maize jointly contribute to plant nutrition", "description": "Abstract Understanding phosphorus (P) dynamics in the rhizosphere is crucial for sustainable crop production. P mobilization processes in the rhizosphere include the release of plant and microbially-derived protons and extracellular phosphatases. We investigated the effect of root hairs and soil texture on the spatial distribution and intensity of P mobilizing processes in the rhizosphere of Zea mays L. root-hair defective mutant (rth3) and wild-type (WT) grown in two substrates (loam, sand). We applied 2D-chemical imaging methods in custom-designed root windows installed in the field to visualize soil pH (optodes), acid phosphatase activity (zymography), and labile P and Mn fluxes (diffusive gradients in thin films, DGT). The average rhizosphere extent for phosphatase activity and pH was greater in sand than in loam, while the presence of root-hairs had no impact. Acidification was significantly stronger at young root tissue ( 4\u202fcm from root cap) and stronger in WT than rth3. Accompanied with stronger acidification, higher P flux was observed mainly around young, actively growing root tissues for both genotypes. Our results indicate that acidification was linked to root growth and created a pH optimum for acid phosphatase activity, i.e., mineralization of organic P, especially at young root tissues which are major sites of P uptake. Both genotypes grew better in loam than in sand; however, the presence of root hairs generally resulted in higher shoot P concentrations and greater shoot biomass of WT compared to rth3. We conclude that soil substrate had a larger impact on the extent and intensity of P solubilization processes in the rhizosphere of maize than the presence of root hairs. For the first time, we combined 2D-imaging of soil pH, phosphatase activity, and nutrient gradients in the field and demonstrated a novel approach of stepwise data integration revealing the interplay of various P solubilizing processes in situ.", "keywords": ["[SDV.SA]Life Sciences [q-bio]/Agricultural sciences", "580", "2. Zero hunger", "0106 biological sciences", "[SDV.SA] Life Sciences [q-bio]/Agricultural sciences", "Soil zymography", "04 agricultural and veterinary sciences", "Diffusive gradients in thin films (DGT)", "15. Life on land", "01 natural sciences", "630", "Planar pH optodes", "Root window", "Soil texture", "[SDV.BV]Life Sciences [q-bio]/Vegetal Biology", "0401 agriculture", " forestry", " and fisheries", "Root hairs", "[SDV.BV] Life Sciences [q-bio]/Vegetal Biology"]}, "links": [{"href": "https://doi.org/10.1016/j.soilbio.2021.108497"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20Biology%20and%20Biochemistry", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.soilbio.2021.108497", "name": "item", "description": "10.1016/j.soilbio.2021.108497", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.soilbio.2021.108497"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-02-01T00:00:00Z"}}, {"id": "10.1016/j.still.2024.106125", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:17:03Z", "type": "Journal Article", "created": "2024-04-26", "title": "On the impact of soil texture on local scale organic carbon quantification: From airborne to spaceborne sensing domains", "description": "Soil organic carbon (SOC) distribution and interaction with light is influenced by soil texture parameters (clay, silt and sand), which makes SOC prediction complicated, especially in samples with considerable pedological variability. Hence, understanding the relationship between SOC and soil texture is important within the context of SOC prediction using remote sensing data. The main objective of this study was to find the impact of soil texture on the performance of local SOC prediction models that were developed on Sentinel-2 (S2) multispectral and CASI/SASI (CS) hyperspectral airborne data as the main predictor variables. One approach to that objective was to lower the texture variance by stratification of the samples. Therefore, soil samples collected from four agricultural sites in the Czech Republic were segregated based on the i) site-based and ii) texture-based stratification strategies. Random forest (RF) models were then developed on all stratified classes with and without considering the soil texture parameters as predictor variables and results were compared with those obtained by the RF models developed on the non-stratified (NS) samples. Both stratification strategies provided more homogeneous classes, which enhanced the accuracy of SOC prediction, compared to using the NS samples. In addition, the texture-based RF models yielded higher accuracy predictions than the site-based ones. Except for sand, adding texture parameters to the main predictors improved the accuracy of the models, so that the highest prediction performance was obtained by a texture-based model developed on clay-added CS data. Overall, texture-based stratification could significantly enhance the SOC prediction, when the texture parameters were added to the S2 and CS data as the main predictor variables.", "keywords": ["EJP SOIL", "550", "Airborne hyperspectral data", "STEROPES", "Soil organic carbon", "Soil texture", "EJPSOIL", "Sentinel-2", "Stratification"]}, "links": [{"href": "https://doi.org/10.1016/j.still.2024.106125"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20and%20Tillage%20Research", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.still.2024.106125", "name": "item", "description": "10.1016/j.still.2024.106125", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.still.2024.106125"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-09-01T00:00:00Z"}}, {"id": "10.1023/a:1013359319380", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:17:19Z", "type": "Journal Article", "created": "2002-12-23", "title": "Soil Organic Carbon Sequestration In Tropical Areas. General Considerations And Analysis Of Some Edaphic Determinants For Lesser Antilles Soils", "description": "Some general notions on soil organic carbon (SOC) sequestration and the difficulties to evaluate this process globally are presented. Problems of time- and space- scales are emphasized. SOC erosion, which is generally difficult to evaluate in relation to land use changes, is discussed in detail. Different aspects of SOC sequestration on the Lesser Antilles are presented for a wide range of soil types. Comparisons between soils revealed that the SOC stocks in the Lesser Antilles are highly dependent upon the mineralogy: higher stocks for allophanic (ALL) soils than for low activity clay (LAC) and high activity clay (HAC) soils. But in terms of potential of SOC sequestration (pSeq-SOC, differences between permanent vegetation and continuous cultivation situations), there are no differences between ALL and LAC soils (22.9 and 23.3 tC. ha\u22121, respectively). On the other hand, the potentials of SOC sequestration were higher for HAC soils (30.8 \u2013 59.4 tC. ha\u22121, with the higher levels in the less Mg- and Na-affected Vertisol). Sheet erosion is a serious problem for Vertisol with high Mg and Na on exchange complex, causing high dispersability of fine elements. Thus, the lower SOC levels in these soils may be partly due to erosion losses. Laboratory incubations have shown that 37 \u2013 53% of the protected SOC in these soils was located in aggregates larger than 0.2 mm. The effect of agricultural practices on SOC sequestration was studied for the Vertisols. Intensification of pastures led to higher plant productivity and higher organic matter restitutions and SOC sequestration. The gain was 53.5 and 25.4 tC. ha\u22121 for the low and high-Mg Vertisol, respectively (0\u201320 cm layer). SOC sequestration with pastures also depends upon the plot history with lower mean annual increase in SOC for the initially eroded (1.0 gC . kg\u22121 soil . yr\u22121) than for the non-degraded (1.5 gC . kg\u22121 soil . yr\u22121) Vertisol. Loss of SOC in a pasture-market gardening rotation was 22.2 tC . ha\u22121 with deep (30\u201340 cm) and 10.7 tC . ha\u22121 with surface (10\u201315 cm) tillage. It was unclear whether the differences in SOC losses were due to mineralization and/or to erosion.", "keywords": ["[SDE] Environmental Sciences", "2. Zero hunger", "SOL", "550", "[SDV]Life Sciences [q-bio]", "PATURAGE", "04 agricultural and veterinary sciences", "AGREGAT", "15. Life on land", "CARBONE ORGANIQUE", "PRATIQUE CULTURALE", "MINERALOGIE", "[SDV] Life Sciences [q-bio]", "STOCK ORGANIQUE", "13. Climate action", "[SDE]Environmental Sciences", "0401 agriculture", " forestry", " and fisheries", "UTILISATION DU SOL", "TEXTURE DU SOL"]}, "links": [{"href": "https://doi.org/10.1023/a:1013359319380"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Nutrient%20Cycling%20in%20Agroecosystems", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1023/a:1013359319380", "name": "item", "description": "10.1023/a:1013359319380", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1023/a:1013359319380"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2001-01-01T00:00:00Z"}}, {"id": "10.1093/aob/mcaa181", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:18:11Z", "type": "Journal Article", "created": "2020-10-07", "title": "Significance of root hairs for plant performance under contrasting field conditions and water deficit", "description": "AbstractBackground and Aims

Previous laboratory studies have suggested selection for root hair traits in future crop breeding to improve resource use efficiency and stress tolerance. However, data on the interplay between root hairs and open-field systems, under contrasting soils and climate conditions, are limited. As such, this study aims to experimentally elucidate some of the impacts that root hairs have on plant performance on a field scale.

Methods

A field experiment was set up in Scotland for two consecutive years, under contrasting climate conditions and different soil textures (i.e. clay loam vs. sandy loam). Five barley (Hordeum vulgare) genotypes exhibiting variation in root hair length and density were used in the study. Root hair length, density and rhizosheath weight were measured at several growth stages, as well as shoot biomass, plant water status, shoot phosphorus (P) accumulation and grain yield.

Key Results

Measurements of root hair density, length and its correlation with rhizosheath weight highlighted trait robustness in the field under variable environmental conditions, although significant variations were found between soil textures as the growing season progressed. Root hairs did not confer a notable advantage to barley under optimal conditions, but under soil water deficit root hairs enhanced plant water status and stress tolerance resulting in a less negative leaf water potential and lower leaf abscisic acid concentration, while promoting shoot P accumulation. Furthermore, the presence of root hairs did not decrease yield under optimal conditions, while root hairs enhanced yield stability under drought.

Conclusions

Selecting for beneficial root hair traits can enhance yield stability without diminishing yield potential, overcoming the breeder\uffe2\uff80\uff99s dilemma of trying to simultaneously enhance both productivity and resilience. Therefore, the maintenance or enhancement of root hairs can represent a key trait for breeding the next generation of crops for improved drought tolerance in relation to climate change.We examine the influence of climate, soil properties and vegetation characteristics on soil organic carbon (SOC) along a transect of West African ecosystems sampled across a precipitation gradient on contrasting soil types stretching from Ghana (15\uffc2\uffb0N) to Mali (7\uffc2\uffb0N). Our findings derive from a total of 1108 soil cores sampled over 14 permanent plots. The observed pattern in SOC stocks reflects the very different climatic conditions and contrasting soil properties existing along the latitudinal transect. The combined effects of these factors strongly influence vegetation structure. SOC stocks in the first 2\uffc2\uffa0m of soil ranged from 20\uffc2\uffa0Mg\uffc2\uffa0C\uffc2\uffa0ha\uffe2\uff88\uff921 for a Sahelian savanna in Mali to over 120\uffc2\uffa0Mg\uffc2\uffa0C\uffc2\uffa0ha\uffe2\uff88\uff921 for a transitional forest in Ghana. The degree of interdependence between soil bulk density (SBD) and soil properties is highlighted by the strong negative relationships observed between SBD and SOC (r2\uffc2\uffa0>\uffc2\uffa00.84). A simple predictive function capable of encompassing the effect of climate, soil properties and vegetation type on SOC stocks showed that available water and sand content taken together could explain 0.84 and 0.86 of the total variability in SOC stocks observed to 0.3 and 1.0\uffc2\uffa0m depth respectively. Used in combination with a suitable climatic parameter, sand content is a good predictor of SOC stored in highly weathered dry tropical ecosystems with arguably less confounding effects than provided by clay content. There was an increased contribution of resistant SOC to the total SOC pool for lower rainfall soils, this likely being the result of more frequent fire events in the grassier savannas of the more arid regions. This work provides new insights into the mechanisms determining the distribution of carbon storage in tropical soils and should contribute significantly to the development of robust predictive models of biogeochemical cycling and vegetation dynamics in tropical regions.

", "keywords": ["550", "Tropical ecosystems", "biotic controls", "West africa", "01 natural sciences", "forest soils", "land-use change", "Precipitation gradient", "Soil bulk density", "senegal", "cycle feedback", "Life Science", "Resistant organic carbon", "organic-matter", "0105 earth and related environmental sciences", "2. Zero hunger", "info:eu-repo/classification/ddc/550", "savanna soils", "ddc:550", "Soil organic carbon", "sequestration", "04 agricultural and veterinary sciences", "15. Life on land", "stabilization", "Earth sciences", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "texture", "Soil carbon stocks"]}, "links": [{"href": "https://doi.org/10.1111/j.1365-2486.2012.02657.x"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Global%20Change%20Biology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/j.1365-2486.2012.02657.x", "name": "item", "description": "10.1111/j.1365-2486.2012.02657.x", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/j.1365-2486.2012.02657.x"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2012-03-02T00:00:00Z"}}, {"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": "10.17221/118/2024-swr", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:19:40Z", "type": "Journal Article", "created": "2024-11-20", "title": "How to measure soil quality? A case study conducted on cropland in the Czech Republic", "description": "This work presents the advantages and risks of selected soil quality criteria using data from the monitoring of agricultural soils in the Czech Republic. Soil samples were taken from 71 sites covering various soil types. Basic soil parameters and mid-infrared spectra were measured. Indicators describing the quality of soil organic matter (SOM), and soil were calculated. The results show that soil types differ significantly in the qualitative indicators of soil organic matter. More acidic soils with lower clay content contain lower proportions of aromatic and higher proportions of aliphatic organic compounds than neutral soils with higher clay particles content. These soils differ little in total carbon content and C/N ratio but considerably in C/clay ratio. Cambisols are the least degraded soils in the Czech Republic in terms of C/clay ratio, which is controversial in many respects. The results indicate that more aliphatic organic matter is important for the SOM content in the upper part of the agricultural soil, and more aromatic organic matter is mainly bound to the clay fraction. The results raise questions about the suitability of uniform C/clay target values proposed in European legislation as a criterion for assessing soil degradation due to carbon loss.", "keywords": ["soil organic carbon", "S", "0401 agriculture", " forestry", " and fisheries", "soil texture", "Agriculture", "04 agricultural and veterinary sciences", "agricultural soils", "infrared spectroscopy", "01 natural sciences", "0105 earth and related environmental sciences"], "contacts": [{"organization": "Lenka Pavl\u016f, Ji\u0159\u00ed Bal\u00edk, Simona Proch\u00e1zkov\u00e1, Ivana Galu\u0161kov\u00e1, Lubo\u0161 Bor\u016fvka,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.17221/118/2024-swr"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20and%20Water%20Research", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.17221/118/2024-swr", "name": "item", "description": "10.17221/118/2024-swr", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.17221/118/2024-swr"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-27T00:00:00Z"}}, {"id": "10.3390/ma14144036", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:20:50Z", "type": "Journal Article", "created": "2021-07-20", "title": "Effects of Application of Recycled Chicken Manure and Spent Mushroom Substrate on Organic Matter, Acidity, and Hydraulic Properties of Sandy Soils", "description": "

Soil organic matter is a key resource base for agriculture. However, its content in cultivated soils is low and often decreases. This study aimed at examining the effects of long-term application of chicken manure (CM) and spent mushroom substrate (SMS) on organic matter accumulation, acidity, and hydraulic properties of soil. Two podzol soils with sandy texture in Podlasie Region (Poland) were enriched with recycled CM (10 Mg ha\u22121) and SMS (20 Mg ha\u22121), respectively, every 1\u20132 years for 20 years. The application of CM and SMS increased soil organic matter content at the depths of 0\u201320, 20\u201340, and 40\u201360 cm, especially at 0\u201320 cm (by 102\u2013201%). The initial soil pH increased in the CM- and SMS-amended soil by 1.7\u20132.0 units and 1.0\u20131.2 units, respectively. Soil bulk density at comparable depths increased and decreased following the addition of CM and SMS, respectively. The addition of CM increased field water capacity (at \u2013100 hPa) in the range from 45.8 to 117.8% depending on the depth within the 0\u201360 cm layer. In the case of the SMS addition, the value of the parameter was in the range of 42.4\u201348.5% at two depths within 0\u201340 cm. Depending on the depth, CM reduced the content of transmission pores (>50 \u00b5m) in the range from 46.3 to 82.3% and increased the level of residual pores (<0.5 \u00b5m) by 91.0\u2013198.6%. SMS increased the content of residual pores at the successive depths by 121.8, 251.0, and 30.3% and decreased or increased the content of transmission and storage pores. Additionally, it significantly reduced the saturated hydraulic conductivity at two depths within 0\u201340 cm. The fitted unsaturated hydraulic conductivity at two depths within the 0\u201340 cm layer increased and decreased in the CM- and SMS-amended soils, respectively. The results provide a novel insight into the application of recycled organic materials to sequester soil organic matter and improve crop productivity by increasing soil water retention capacity and decreasing acidity. This is of particular importance in the case of the studied low-productivity sandy acidic soils that have to be used in agriculture due to limited global land resources and rising food demand.

", "keywords": ["2. Zero hunger", "soil pH", "organic amendments", "soil water retention", "soil organic matter", "coarse textured soils", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "soil hydraulic conductivity", "15. Life on land", "Article", "6. Clean water", "12. Responsible consumption"]}, "links": [{"href": "http://www.mdpi.com/1996-1944/14/14/4036/pdf"}, {"href": "https://www.mdpi.com/1996-1944/14/14/4036/pdf"}, {"href": "https://doi.org/10.3390/ma14144036"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Materials", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/ma14144036", "name": "item", "description": "10.3390/ma14144036", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/ma14144036"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-07-19T00:00:00Z"}}, {"id": "10.3390/rs14030714", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:20:57Z", "type": "Journal Article", "created": "2022-02-07", "title": "Evaluation of Agricultural Bare Soil Properties Retrieval from Landsat 8, Sentinel-2 and PRISMA Satellite Data", "description": "

The PRISMA satellite is equipped with an advanced hyperspectral Earth observation technology capable of improving the accuracy of quantitative estimation of bio-geophysical variables in various Earth Science Applications and in particular for soil science. The purpose of this research was to evaluate the ability of the PRISMA hyperspectral imager to estimate topsoil properties (i.e., organic carbon, clay, sand, silt), in comparison with current satellite multispectral sensors. To investigate this expectation, a test was carried out using topsoil data collected in Italy following two approaches. Firstly, PRISMA, Sentinel-2 and Landsat 8 spectral simulated datasets were obtained from the spectral resampling of a laboratory soil library. Subsequently, bare soil reflectance data were obtained from two experimental areas in Italy, using real satellites images, at dates close to each other. The estimation models of soil properties were calibrated employing both Partial Least Square Regression and Cubist Regression algorithms. The results of the study revealed that the best accuracies in retrieving topsoil properties were obtained by PRISMA data, using both laboratory and real datasets. Indeed, the resampled spectra of the hyperspectral imager provided the best Ratio of Performance to Inter-Quartile distance (RPIQ) for clay (4.87), sand (3.80), and organic carbon (2.59) estimation, for the spectral soil library datasets. For the bare soil reflectance obtained from real satellite imagery, a higher level of prediction accuracy was obtained from PRISMA data, with RPIQ \u00b1 SE values of 2.32 \u00b1 0.07 for clay, 3.85 \u00b1 0.19 for silt, and 3.51 \u00b1 0.16 for soil organic carbon. The results for the PRISMA hyperspectral satellite imagery with the Cubist Regression provided the best performance in the prediction of silt, sand, clay and SOC. The same variables were better estimated using PLSR models in the case of the resampled hyperspectral data. The statistical accuracy in the retrieval of SOC from real and resampled PRISMA data revealed the potential of the actual hyperspectral satellite. The results supported the expected good ability of the PRISMA imager to estimate topsoil properties.

", "keywords": ["Landsat 8", "Sentinel\u20102", "Multispectral", "multispectral", "Science", "hyperspectral; multispectral; PRISMA; soil properties; bare soil; SOC; soil texture; Sentinel-2; Landsat 8; PLSR; Cubist", "Q", "Bare soil", "Cubist", "PRISMA", "04 agricultural and veterinary sciences", "15. Life on land", "hyperspectral", "Hyperspectral", "PLSR", "bare soil", "soil properties", "Soil texture", "Bare soil; Cubist; Hyperspectral; Landsat 8; Multispectral; PLSR; PRISMA; Sentinel\u20102; SOC; Soil properties; Soil texture", "0401 agriculture", " forestry", " and fisheries", "SOC", "Soil properties", "Sentinel-2"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/14/3/714/pdf"}, {"href": "https://iris.cnr.it/bitstream/20.500.14243/413305/1/prod_473291-doc_192827_compressed.pdf"}, {"href": "https://www.iris.unina.it/bitstream/11588/948571/1/Evaluation%20of%20Agricultural%20Bare%20Soil%20Properties%20Retrieval%20from%20Landsat%208%2c%20Sentinel-2%20and%20PRISMA%20Satellite%20Data%20Enhanced%20Reader.pdf"}, {"href": "https://www.mdpi.com/2072-4292/14/3/714/pdf"}, {"href": "https://doi.org/10.3390/rs14030714"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs14030714", "name": "item", "description": "10.3390/rs14030714", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs14030714"}, {"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/drones9020129", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:20:44Z", "type": "Journal Article", "created": "2025-02-11", "title": "Unmanned Aerial Vehicle-Based Hyperspectral Imaging and Soil Texture Mapping with Robust AI Algorithms", "description": "

This paper explores the integration of UAV-based hyperspectral imaging and advanced AI algorithms for soil texture mapping and stress detection in agricultural settings. The primary focus lies on leveraging multi-modal sensor data, including hyperspectral imaging, thermal imaging, and gamma-ray spectroscopy, to enable precise monitoring of abiotic and biotic stressors in crops. An innovative algorithm combining vegetation indices, path planning, and machine learning methods is introduced to enhance the efficiency of data collection and analysis. Experimental results demonstrate significant improvements in accuracy and operational efficiency, paving the way for real-time, data-driven decision-making in precision agriculture.

", "keywords": ["precision agriculture", "UAV-based hyperspectral imaging", "TL1-4050", "soil texture mapping", "artificial intelligence (AI) in agriculture", "Motor vehicles. Aeronautics. Astronautics"]}, "links": [{"href": "https://www.mdpi.com/2504-446X/9/2/129/pdf"}, {"href": "https://doi.org/10.3390/drones9020129"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Drones", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/drones9020129", "name": "item", "description": "10.3390/drones9020129", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/drones9020129"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-02-11T00:00:00Z"}}, {"id": "10.3390/rs13214195", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:20:56Z", "type": "Journal Article", "created": "2021-10-20", "title": "Sentinel-2 Recognition of Uncovered and Plastic Covered Agricultural Soil", "description": "

Medium resolution satellite data, such as Sentinel-2 of the Copernicus programme, offer great new opportunities for the agricultural sector, and provide insights on soil surface characteristics and their management. Soil monitoring requires a high-quality dataset of uncovered and plastic covered agricultural soil. We developed a methodology to identify uncovered soil pixels in agricultural parcels during seedbed preparation and considered the impacts of clouds and shadows, vegetation cover, and artificial covers, such as those of greenhouses and plastic mulch films. We preserved the spatial and temporal integrity of parcels in the process and analysed spectral anomalies and their sources. The approach is based on freely available tools, namely Google Earth Engine and R Programming packages. We tested the methodology on the northern region of Belgium, which is characterised by small, fragmented parcels. We selected a period between mid-April to end-May, when active agricultural management practices leave the soil bare in preparation for the main cropping season. The spectral angle mapper was used to identify soil covered by non-plastic greenhouses or temporary soil covers, such as plastic mulch films. The effect of underlying soil on temporary covers was considered. The retrogressive plastic greenhouse index was used for detecting plastic greenhouses. The result was a high quality dataset of potential bare uncovered agricultural soil that allows further soil surface characterisation. This offered an improved understanding of the use of artificial covers, their spatial distribution, and their corresponding crops during the considered period. Artificial covers occurred most frequently in maize parcels. The approach resulted in precision values exceeding 0.9 for the detection of temporary covers and non-plastic greenhouses and a sensitivity value exceeding 0.95 for non-plastic and plastic greenhouses.

", "keywords": ["Technology", "SURFACE", "Science", "Environmental Sciences & Ecology", "TEXTURE", "artificial cover", "ALMERIA", "0203 Classical Physics", "soil", "Remote Sensing", "SUPPORT", "0909 Geomatic Engineering", "Geosciences", " Multidisciplinary", "Imaging Science & Photographic Technology", "agriculture", "2. Zero hunger", "plastic mulch", "Science & Technology", "IDENTIFICATION", "soil; agriculture; Sentinel-2; artificial cover; plastic mulch", "Q", "Geology", "04 agricultural and veterinary sciences", "15. Life on land", "CLOUD", "REFLECTANCE", "RESOLUTION", "13. Climate action", "Physical Sciences", "0401 agriculture", " forestry", " and fisheries", "4013 Geomatic engineering", "Sentinel-2", "GREENHOUSE", "0406 Physical Geography and Environmental Geoscience", "Life Sciences & Biomedicine", "3701 Atmospheric sciences", "Environmental Sciences", "3709 Physical geography and environmental geoscience"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/21/4195/pdf"}, {"href": "https://www.mdpi.com/2072-4292/13/21/4195/pdf"}, {"href": "https://doi.org/10.3390/rs13214195"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs13214195", "name": "item", "description": "10.3390/rs13214195", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs13214195"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-10-20T00:00:00Z"}}, {"id": "10.5061/dryad.32ms0", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-07-26T16:21:20Z", "type": "Dataset", "title": "Data from: Convergence of soil nitrogen isotopes across global climate gradients", "description": "unspecifiedUnsummarized soil 15N datasoil 15N data for individual samples. not summarized. includes excluded data for further reference.CraineSoil15N.csvData Sourcesdata sources for soil 15N dataReferences.csv15NDataSummarizedMineral soil 15N data summarized to 0.1\u00b0 latitude and longitude", "keywords": ["2. Zero hunger", "nitrogen isotopes", "13. Climate action", "Soil texture", "15N", "soil organic matter", "Anthropocene", "15. Life on land", "6. Clean water"]}, "links": [{"href": "https://doi.org/10.5061/dryad.32ms0"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.32ms0", "name": "item", "description": "10.5061/dryad.32ms0", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.32ms0"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2015-03-23T00:00:00Z"}}, {"id": "10.5061/dryad.9w0vt4bk0", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:21:24Z", "type": "Dataset", "title": "The influence of inherent soil factors and agricultural management on soil organic matter", "description": "unspecifiedField descriptions and sampling.\u00a0 Soil samples were collected from 218 farm fields across Wisconsin (n=212) and Minnesota (n=6) (Fig. 1) between 2015 and 2017. The fields represent a range of cropping systems common in the Upper Midwest. Six distinct regions were sampled and identified by either general region of a state (northeast Wisconsin, southeast Wisconsin, and southern Minnesota) or by watershed (Dry Run, Elk Creek, Jersey Valley) (Fig. 1). Elk Creek and Jersey Valley exist within the Driftless Region, an area characterized by steep slopes and flash flood events. All fields were planted into corn the season soil samples were collected. In each field, three composite soil samples were collected that consisted of five 0- to 15-cm soil cores collected with a probe of 2.5- or 7.5-cm internal diameter. Most soil samples (194) were collected prior to fertilizer application and corn planting (mid-April); 24 samples were in late June (2017 only). Soil sampling was conducted with an area of 36 m2 within the dominant soil map unit as identified by the USDA NRCS Web Soil Survey (Soil Survey Staff, 2019) and from an area identified by the farmer where average crop yields were obtained. The composite samples were stored cold and transferred into a freezer with 1 to 6 hours of sampling to stagnate microbial metabolism and organic matter mineralization. Within 30 days, soil samples were thawed and dried for 1 week at 32\u02daC in a forced-air drier, ground to pass through a 2-mm sieve, and stored at room temperature until analysis. Inherent soil properties such as texture class, sand and clay content of the surface horizon, and drainage class were obtained from the USDA NRCS Web Soil Survey (Soil Survey Staff, 2019). Agronomic management information regarding crop rotation, tillage practices, cover crop use, tile drainage, and manure and fertilizer applications were obtained directly from each farmer through an in-person interview. Long-term crop management practices were difficult to obtain for all farms; for example, it was difficult to get accurate information on how long a field had received manure. The dataset constructed uses recent cropping history (past 5 years) as a representation of specific management practices (that often have occurred much longer than just the past 5 years). Based on the collected data, four categories for crop rotation (continuous corn, corn-soybean, corn with small grain, and corn with alfalfa) and five categories for previous crop were created (Supplementary Table 2). Two categorical data were developed for cover crops: if there was a cover crop planted last fall (yes or no) and the number of times a cover crop was planted in the past 5 years. Tillage practices were categorized by practice [no tillage, minimum tillage (including vertical tillage or strip tillage), and conventional tillage (chisel, disk or moldboard)] and by the number of tillage passes that occurred between harvest of the previous year\u2019s crop and the planting of the current year\u2019s crop (0 to 4). Tillage was only considered no-till or minimum tillage if practiced for more than 4 years. Manure was categorized based on the number of manure applications that occurred in the past 5 years (0 to 5), when manure was applied in the past year (none, summer, fall, winter, or spring), and manure type (species and if solid or liquid). Tile drainage presence was also noted (yes or no). The manure N, fertilizer N, and total N input (which includes manure, fertilizer, and legume N inputs) (kg ha-1) to the previous corn crop were also collected. If farmers did not have manure analysis, estimates of available N were used (Laboski & Peters, 2012); N input from alfalfa biomass was assumed to be 101 kg ha-1 (Laboski & Peters, 2012).\u00a0 Soil analysis.\u00a0 Soil pH and SOM were analyzed by the University of Wisconsin Soil and Forage Analysis Laboratory (Marshfield, Wisconsin). Soil pH was calculated using a 1:1 slurry of 10 g soil and 10 mL of deionized water and measured with a glass electrode (Peters et al., 2015). Soil organic matter values were determined through loss on ignition by heating the soil to 360\u02daC for 2 hours (Combs et al., 2015). Total C (TC) and total N (TN) levels were determined via the dry combustion method using a Flash EA 1112CN Automatic Elemental Analyzer (Thermo Finnigan, Milan, Italy). Between 8 to 10 mg of finely ground soil were packed into a 5 mm by 9 mm tin capsule prior to combustion at temperatures exceeding 1000\u00b0C. Soils with pH greater than 7.0 were tested for effervescence using 5% HCl as an indicator if carbonates were present. If carbonates were not observed, TC was assumed to be TOC; if carbonates were observed, they were subject to acid-fumigation prior to dry combustion (Harris et al., 2001). Only 25 samples were analyzed for carbonates and 13 of those had carbonate concentrations above the detection limit. There were 218 samples for SOM, but only 2016 for TOC and TN because two samples were accidently discarded.", "keywords": ["2. Zero hunger", "Alfalfa", "FOS: Agricultural sciences", "15. Life on land", "Total nitrogen", "Zea mays", "soil", "Tillage", "Maize", "soil organic carbon", "loss on ignition", "corn", "crop rotation", "Wisconsin", "soil organic matter", "manure", "Soil texture", "drainage", "Medicago sativa"], "contacts": [{"organization": "Ruark, Matt, Richardson, Greg, Radatz, Timothy, Radatz, Amber, Cooley, Eric, Augarten, Abigail,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.9w0vt4bk0"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.9w0vt4bk0", "name": "item", "description": "10.5061/dryad.9w0vt4bk0", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.9w0vt4bk0"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-02-02T00:00:00Z"}}, {"id": "10.5061/dryad.f542t16", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:21:26Z", "type": "Dataset", "title": "Data from: Grazing and resource availability control soil nematode body size and abundance-mass relationship in semi-arid grassland", "description": "unspecified1. Body size is a central functional trait in ecological communities. Despite recognition of the importance of above-belowground interactions, effects of aboveground herbivores on size and abundance-size relationships in soil fauna are almost uncharted. Depending on climate and soil properties, herbivores may increase basal resources of soil food webs, or reduce pore space, mechanisms expected to have contrasting effects on soil animal body size. 2. We investigated how body size and shape of soil nematodes responded to mammalian grazers in three semi-arid grassland sites, along a gradient of soil texture and organic matter (OM) in a long-term herbivore removal study. We analysed nematode mass, length, diameter, body size distribution, and biomass distribution. We formulated two mechanistic hypotheses to assess whether resource availability or pore space was the dominant abiotic control and modulated the effects of grazing. 3. In ungrazed soils, average and maximum nematode size, as well as abundance and biomass of large nematodes, were greater in the high-OM than in the low-OM soil, and intermediate in the medium-OM soil. Grazing promoted larger sizes in the low-OM soil, where it had been shown to increase organic matter and microbial biomass, and led to more homogeneous average size and body size distribution across sites. The results support the hypothesis that nematode size was controlled by basal resource availability rather than by pore space. However, body shape might have been constrained by small pores in the fine-texture, high-OM soil, where nematodes were more elongated. 4. Grazing may facilitate larger sizes in soil nematode communities by boosting basal resources where these are limiting, with important implications for estimations of nematode biomass and contribution to carbon and nutrient cycling. These findings contribute to the insofar-limited mechanistic understanding of how herbivores can shape functional traits of soil fauna, and demonstrate that animals at one trophic level may control patterns in body size and abundance-size relationships in other trophic levels without a direct predator-prey or competitive linkage between them.", "keywords": ["2. Zero hunger", "Individual size distribution", "Soil texture", "Aboveground-belowground interactions", "15. Life on land", "Mammalian herbivores", "organic matter", "Soil fauna"], "contacts": [{"organization": "Andriuzzi, Walter S., Wall, Diana H.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.f542t16"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.f542t16", "name": "item", "description": "10.5061/dryad.f542t16", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.f542t16"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-05-17T00:00:00Z"}}, {"id": "10.5281/zenodo.14134736", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:22:32Z", "type": "Dataset", "title": "Soil grid dataset of agricultural sites in the Czech Republic", "description": "The current dataset includes 320 topsoil samples (0\u201320\u202fcm depth) collected from four agricultural sites in the Czech Republic. The samples were gathered from P\u0159estavlky, Klu\u010dov, Nov\u00e1 Ves nad Popelkou, and Udrnice (80 samples from each site) in June 2021. It contains sample coordinates and some soil parameters including SOC and texture, prepared and stored in MS Excel (.xlsx) format. The data were used in STEROPES WP1 (basic local model development), WP3 (effect of texture), and WP4 (effect of vegetation and plant residues).", "keywords": ["EJP SOIL", "STEROPES", "Soil Organic Carbon", "Soil sampling", "SOC", "Texture", "Agricultural sites"], "contacts": [{"organization": "Czech University of Life Sciences Prague", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14134736"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14134736", "name": "item", "description": "10.5281/zenodo.14134736", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14134736"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-13T00:00:00Z"}}, {"id": "10.5281/zenodo.14134737", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:22:32Z", "type": "Dataset", "title": "Soil grid dataset of agricultural sites in the Czech Republic", "description": "The current dataset includes 320 topsoil samples (0\u201320\u202fcm depth) collected from four agricultural sites in the Czech Republic. The samples were gathered from P\u0159estavlky, Klu\u010dov, Nov\u00e1 Ves nad Popelkou, and Udrnice (80 samples from each site) in June 2021. It contains sample coordinates and some soil parameters including SOC and texture, prepared and stored in MS Excel (.xlsx) format. The data were used in STEROPES WP1 (basic local model development), WP3 (effect of texture), and WP4 (effect of vegetation and plant residues).", "keywords": ["EJP SOIL", "STEROPES", "Soil Organic Carbon", "Soil sampling", "SOC", "Texture", "Agricultural sites"], "contacts": [{"organization": "Czech University of Life Sciences Prague", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14134737"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14134737", "name": "item", "description": "10.5281/zenodo.14134737", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14134737"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-13T00:00:00Z"}}, {"id": "10.5281/zenodo.14137529", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:22:33Z", "type": "Dataset", "title": "Soil laboratory spectra of agricultural sites in the Czech Republic", "description": "The dataset includes VNIR-SWIR spectra of dried, ground, and sieved (< 2 mm) soil samples collected from three agricultural sites in the Czech Republic. The spectra\u00a0were recorded in the laboratory using an\u00a0ASD FieldSpec 3 instrument and under\u00a0the standard protocol.\u00a0The samples were gathered from Klu\u010dov, Nov\u00e1 Ves nad Popelkou, and Udrnice sites (80 samples from each site) in June 2021. The dataset also contains sample coordinates and some parameters including SOC and texture, prepared and stored in MS Excel (.xlsx) format. The data were used in STEROPES WP1 (basic local models development).", "keywords": ["EJP SOIL", "Soil spectra", "Field scale", "STEROPES", "Soil Organic Carbon", "SOC", "Texture"], "contacts": [{"organization": "Czech University of Life Sciences Prague", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14137529"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14137529", "name": "item", "description": "10.5281/zenodo.14137529", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14137529"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-13T00:00:00Z"}}, {"id": "10.5281/zenodo.14137530", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:22:33Z", "type": "Dataset", "title": "Soil laboratory spectra of agricultural sites in the Czech Republic", "description": "The dataset includes VNIR-SWIR spectra of dried, ground, and sieved (< 2 mm) soil samples collected from three agricultural sites in the Czech Republic. The spectra\u00a0were recorded in the laboratory using an\u00a0ASD FieldSpec 3 instrument and under\u00a0the standard protocol.\u00a0The samples were gathered from Klu\u010dov, Nov\u00e1 Ves nad Popelkou, and Udrnice sites (80 samples from each site) in June 2021. The dataset also contains sample coordinates and some parameters including SOC and texture, prepared and stored in MS Excel (.xlsx) format. The data were used in STEROPES WP1 (basic local models development).", "keywords": ["EJP SOIL", "Soil spectra", "Field scale", "STEROPES", "Soil Organic Carbon", "SOC", "Texture"], "contacts": [{"organization": "Czech University of Life Sciences Prague", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14137530"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14137530", "name": "item", "description": "10.5281/zenodo.14137530", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14137530"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-13T00:00:00Z"}}, {"id": "10.5281/zenodo.14161808", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:22:33Z", "type": "Dataset", "title": "SensRes dataset for downscaling soil maps", "description": "This dataset contains soil information and sensor data from agricultural fields in Denmark, Lithuania, Northern Ireland, the Netherlands, and Turkey to downscale coarse-resolution maps to high resolution in the SensRes project (EJP SOIL). \u00a0It includes 1455 soil samples with data on soil organic carbon and soil texture fractions (clay, silt, and sand). For each sampling site, the dataset also contains rasters of Sentinel-2 bare soil images, aerial images (RGB), and maps from Electromagnetic Induction and Gamma sensors. The soil data is provided in .txt file format, while the sensor data is available in .tif format. There are also shapefiles from each field in .shp format.\u00a0 The SensRes project developed a framework for downscaling soil maps, which was published as an R package (https://github.com/anbm-dk/soilscaler/tree/main), and this dataset contains the required local inputs to apply the downscaling process. Part of the soil information present in this dataset has also been used in the STEROPES EJP SOIL project.", "keywords": ["soil organic carbon", "STEROPES", "SensRes", "High resolution maps", "Sentinel 2", "soil texture"], "contacts": [{"organization": "Carvalho Gomes, Lucas, M\u00f8ller, Anders, Koganti, Triven, Higgins, Suzanne, \u017dydelis, Renaldas, Volungevi\u010dius, Jonas, Kavaliauskas, Ardas, van Egmond, Fenny, Kramer, Henk, Teuling, Kees, \u00c7inkaya, \u0130smail, Greve, Mogens H,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14161808"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14161808", "name": "item", "description": "10.5281/zenodo.14161808", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14161808"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-14T00:00:00Z"}}, {"id": "10.5281/zenodo.14161809", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:22:33Z", "type": "Dataset", "title": "SensRes dataset for downscaling soil maps", "description": "This dataset contains soil information and sensor data from agricultural fields in Denmark, Lithuania, Northern Ireland, the Netherlands, and Turkey to downscale coarse-resolution maps to high resolution in the SensRes project (EJP SOIL). \u00a0It includes 1455 soil samples with data on soil organic carbon and soil texture fractions (clay, silt, and sand). For each sampling site, the dataset also contains rasters of Sentinel-2 bare soil images, aerial images (RGB), and maps from Electromagnetic Induction and Gamma sensors. The soil data is provided in .txt file format, while the sensor data is available in .tif format. There are also shapefiles from each field in .shp format.\u00a0 The SensRes project developed a framework for downscaling soil maps, which was published as an R package (https://github.com/anbm-dk/soilscaler/tree/main), and this dataset contains the required local inputs to apply the downscaling process. Part of the soil information present in this dataset has also been used in the STEROPES EJP SOIL project.", "keywords": ["soil organic carbon", "STEROPES", "SensRes", "High resolution maps", "Sentinel 2", "soil texture"], "contacts": [{"organization": "Carvalho Gomes, Lucas, M\u00f8ller, Anders, Koganti, Triven, Higgins, Suzanne, \u017dydelis, Renaldas, Volungevi\u010dius, Jonas, Kavaliauskas, Ardas, van Egmond, Fenny, Kramer, Henk, Teuling, Kees, \u00c7inkaya, \u0130smail, Greve, Mogens H,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14161809"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14161809", "name": "item", "description": "10.5281/zenodo.14161809", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14161809"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-14T00:00:00Z"}}, {"id": "2919b1e3-6a79-4162-9d3a-e640a1dc5aef", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[73.6, 18.2], [73.6, 53.6], [134.8, 53.6], [134.8, 18.2], [73.6, 18.2]]]}, "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": "Asia"}, {"id": "China"}], "scheme": "Region"}], "license": "Attribution 3.0 International (CC BY 3.0)", "updated": "2021-07-14T11:52:00", "type": "Dataset", "language": "eng", "title": "Soil and Terrain Database (SOTER) for China", "description": "The Soil and Terrain database for China primary data (version 1.0), at scale 1:1 million (SOTER_China), was compiled of enhanced soil information within the framework of the FAO's program of Land Degradation Assessment in Drylands (LADA). The primary database was compiled using the SOTER methodology. The SOTER unit delineation was based on a raster format of the soil map of China, correlated and converted to FAO\u2019s Revised Legend (1988), in combination with a SOTER landform characterization derived from Shuttle Radar Topographic Mission (SRTM) 90 m digital elevation model (DEM). Reference profiles for the dominant soil of the SOTER units has been directly linked to the polygons. \n\nSOTER forms a part of the ongoing activities of ISRIC, FAO and UNEP to update the world's baseline information on natural resources.The project involved collaboration with national soil institutes from the countries in the region as well as individual experts.", "formats": [{"name": "zip"}, {"name": "WWW:DOWNLOAD-1.0-ftp--download"}, {"name": "WWW:LINK-1.0-http--related"}], "keywords": ["calcium", "carbon", "cation exchange capacity", "electrical conductivity", "nitrogen", "organic matter", "bulk density", "soil profiles", "pH", "salinity", "texture", "water holding capacity", "nutrients", "Soil science", "Asia", "China"], "contacts": [{"name": "Bas Kempen", "organization": "ISRIC - World Soil Information", "position": "Soil mapping specialist", "roles": ["pointOfContact"], "phones": [{"value": null}], "emails": [{"value": "bas.kempen@wur.nl"}], "addresses": [{"deliveryPoint": ["PO Box 353"], "city": "Wageningen", "administrativeArea": null, "postalCode": "6700AJ", "country": "Netherlands"}], "links": [{"href": null}]}, {"name": "J.A. Dijkshoorn", "organization": "ISRIC - World Soil Information", "position": null, "roles": ["Author"], "phones": [{"value": null}], "emails": [{"value": "None"}], "addresses": [{"deliveryPoint": ["PO Box 353"], "city": "Wageningen", "administrativeArea": null, "postalCode": "6700AJ", "country": "Netherlands"}], "links": [{"href": null}]}, {"name": "Jan R.M. Huting", "organization": "ISRIC - World Soil Information", "position": null, "roles": ["Author"], "phones": [{"value": null}], "emails": [{"value": "None"}], "addresses": [{"deliveryPoint": ["PO Box 353"], "city": "Wageningen", "administrativeArea": null, "postalCode": "6700AJ", "country": "Netherlands"}], "links": [{"href": null}]}], "denominator": "1000000"}, "links": [{"href": "https://files.isric.org/public/soter/CN-SOTER.zip", "name": "Download", "protocol": "WWW:DOWNLOAD-1.0-ftp--download", "rel": "download"}, {"href": "https://isric.org/index.php/projects/soil-and-terrain-soter-database-programme", "name": "Project webpage", "protocol": "WWW:LINK-1.0-http--related", "rel": "information"}, {"href": "https://isric.org/sites/default/files/isric_report_2008_06.pdf", "name": "Report", "protocol": "WWW:LINK-1.0-http--related", "rel": "information"}, {"href": "https://files.isric.org/public/thumbnails/soter/SOTER_CN.jpg", "name": "preview", "description": "Web image thumbnail (URL)", "protocol": "WWW:LINK-1.0-http--image-thumbnail", "rel": "preview"}, {"rel": "self", "type": "application/geo+json", "title": "2919b1e3-6a79-4162-9d3a-e640a1dc5aef", "name": "item", "description": "2919b1e3-6a79-4162-9d3a-e640a1dc5aef", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2919b1e3-6a79-4162-9d3a-e640a1dc5aef"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"interval": ["1986-01-01T00:00:00Z", "2008-10-01T00:00:00Z"]}}, {"id": "2a7d2fb8-e0db-4a4b-9661-4809865aaccf", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[-18.0, -35.0], [-18.0, 37.0], [51.0, 37.0], [51.0, -35.0], [-18.0, -35.0]]]}, "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": "Africa"}], "scheme": "Region"}], "license": "Attribution-NonCommercial 3.0 International (CC BY-NC 3.0), https://creativecommons.org/licenses/by-nc/3.0/", "updated": "2021-07-14T11:52:34", "type": "Dataset", "language": "eng", "title": "Africa SoilGrids - Texture", "description": "Textural class (defined according to USDA system) at 6 depth intervals derived from sand, silt and clay contents predicted using the Africa Soil Profiles Database (AfSP) v1.2. For details see published paper here below (Hengl T., G.B.M. Heuvelink, B. Kempen, J.G.B. Leenaars, M.G. Walsh, K.D. Shepherd, A. Sila, R.A. MacMillan, J. Mendes de Jesus, L.T. Desta, J.E. Tondoh, 2015. Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions. PLoS ONE 10(6)\n\nTexture classes of the USDA system / triangle used in this map:\ncode, name\n1 clay\n2 silty clay\n3 sandy clay\n4 clay loam\n5 silty clayloam\n6 sandy clay loam\n7 loam\n8 silty loam\n9 sandy loam\n10 silt \n11 loamy sand\n12 sand", "formats": [{"name": "GTiff"}, {"name": "WWW:DOWNLOAD-1.0-ftp--download"}, {"name": "WWW:LINK-1.0-http--related"}], "keywords": ["texture", "digital soil mapping", "Soil science", "Africa"], "contacts": [{"name": "Johan Leenaars", "organization": "ISRIC - World Soil Information", "position": "Senior soil scientist", "roles": ["Author"], "phones": [{"value": null}], "emails": [{"value": "johan.leenaars@wur.nl"}], "addresses": [{"deliveryPoint": ["PO Box 353"], "city": "Wageningen", "administrativeArea": null, "postalCode": "6700AJ", "country": "Netherlands"}], "links": [{"href": null}]}, {"name": "Tom Hengl", "organization": "ISRIC - World Soil Information", "position": "Former staff", "roles": ["Author"], "phones": [{"value": null}], "emails": [{"value": "None"}], "addresses": [{"deliveryPoint": ["PO Box 353"], "city": "Wageningen", "administrativeArea": null, "postalCode": "6700AJ", "country": "Netherlands"}], "links": [{"href": null}]}], "distancevalue": "250", "distanceuom": "m"}, "links": [{"href": "https://files.isric.org/public/afsis250m/af_TEXMHT_T__M_sd1_250m.tif", "name": "Download GeoTIFF at depth 0-5 cm", "protocol": "WWW:DOWNLOAD-1.0-ftp--download", "rel": "download"}, {"href": "https://files.isric.org/public/afsis250m/af_TEXMHT_T__M_sd2_250m.tif", "name": "Download GeoTIFF at depth 5-15 cm", "protocol": "WWW:DOWNLOAD-1.0-ftp--download", "rel": "download"}, {"href": "https://files.isric.org/public/afsis250m/af_TEXMHT_T__M_sd3_250m.tif", "name": "Download GeoTIFF at depth 15-30 cm", "protocol": "WWW:DOWNLOAD-1.0-ftp--download", "rel": "download"}, {"href": "https://files.isric.org/public/afsis250m/af_TEXMHT_T__M_sd4_250m.tif", "name": "Download GeoTIFF at depth 30-60 cm", "protocol": "WWW:DOWNLOAD-1.0-ftp--download", "rel": "download"}, {"href": "https://files.isric.org/public/afsis250m/af_TEXMHT_T__M_sd5_250m.tif", "name": "Download GeoTIFF at depth 60-100 cm", "protocol": "WWW:DOWNLOAD-1.0-ftp--download", "rel": "download"}, {"href": "https://files.isric.org/public/afsis250m/af_TEXMHT_T__M_sd6_250m.tif", "name": "Download GeoTIFF at depth 100-200 cm", "protocol": "WWW:DOWNLOAD-1.0-ftp--download", "rel": "download"}, {"href": "https://isric.org/projects/soil-property-maps-africa-250-m-resolution", "name": "Project webpage", "protocol": "WWW:LINK-1.0-http--related", "rel": "information"}, {"href": "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0125814", "name": "Scientific paper", "protocol": "WWW:LINK-1.0-http--related", "rel": "information"}, {"href": "https://files.isric.org/public/thumbnails/afsis250m/texmht.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": "2a7d2fb8-e0db-4a4b-9661-4809865aaccf", "name": "item", "description": "2a7d2fb8-e0db-4a4b-9661-4809865aaccf", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2a7d2fb8-e0db-4a4b-9661-4809865aaccf"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"interval": ["1960-01-01T00:00:00Z", "2015-12-31T00:00:00Z"]}}, {"id": "10.5281/zenodo.16040138", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-07-26T16:23:08Z", "type": "Dataset", "created": "2024-11-29", "title": "Full soil particle size fingerprints determined by laser diffraction of 48 selected test samples from Flanders (Belgium)", "description": "A set of 48 soil test samples was selected for a study (1) comparing two common methods for granulometric analysis: pipette-sieving method versus laser diffractometry (LD),\u00a0 (2) comparing the results among various LD instruments and (3) analysing the effect size of various settings on LD instruments (e.g. effect of diffraction models & optical parameters).\u00a0 All 48 samples originated from soils of Flanders (North of Belgium) and were taken in 4 landuses (croplands (n=8), grasslands (n=18), gardens (n=13) and natural habitats (n=9)) on the most common soil types and textures. The samples widely varied in organic carbon content and pH. The datasets are the output of the full soil textural \u2018fingerprint\u2019 (particle size range from 0,4 - 2000 \u00b5m) of the test samples analysed by a Beckman COULTER LS13320 laser diffraction instrument. Pretreatment of the soil samples was conducted according to ISO 11464 and the removal of organic matter and carbonates was done according to ISO 11272 as for the Sieving and Sedimentation method. Background information is given in the presentation (PDF): \u201cShould laser diffraction become the new standard for soil particle size analysis ?\u201d The equivalent LD diameters applied to convert the pipette based standard limits for clay fraction (2 \u00b5m) were set to 6 \u00b5m when using LD particle sizes\u00a0 and for silt (50 \u00b5m) to 63 \u00b5m (LD size).\u00a0 For each test sample the full textural fingerprint is given according to size bins showing for each bin the mean fraction (in vol%) of 5 replicated measurements along with the standard deviation (sd) under repeatability conditions. Based on the full fingerprint various fractions can be derived and statistically compared. The full fingerprint is also useful for calibrating\u00a0pedotransfer functions for estimating other soil physical properties and for in silico\u00a0modeling purposes.", "keywords": ["Granulometry", "Soil texture", "Textural fingerprint", "Laser diffraction", "Equivalent diameter"], "contacts": [{"organization": "De Vos, Bruno", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.16040138"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.16040138", "name": "item", "description": "10.5281/zenodo.16040138", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.16040138"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-07-17T00:00:00Z"}}, {"id": "10.5281/zenodo.16316472", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-07-26T16:23:08Z", "type": "Dataset", "title": "Poultry Manure Application Enhances Hydraulic Properties and Aggregate Stability of Nigerian Soils", "description": "Farmers in the tropics usually incorporate organic manure to improve the low levels of soil organic matter. However, little attention is given to how this incorporated manure affects soil physical properties. This study assessed the hydraulic properties and aggregate stability of five Nigerian benchmark soils of varied textures under different rates of poultry manure application. The soil types were Iwo, Apomu, Egbeda, Oba, and Itagunmodi series, as well as continuously cropped land with extensively degraded soil. The soils were sampled at 0-15 cm soil depth (plough-layer) and subsequently air-dried and sieved through a 20 mm mesh. For each soil type, 0, 2.5, 5, 7.5, and 10 tons ha-1 of cured poultry manure were applied, and the experiment was replicated three times, resulting in a total of 90 samples (6 soil types x 5 manure levels x 3 replicates). The samples were arranged in completely randomized design and maintained at 75% field capacity for six weeks. At the end of week six, bulk density, aggregate stability, unsaturated soil hydraulic conductivity, sorptivity, pH, organic carbon, and total nitrogen were analyzed. Data were analyzed using analysis of variance and means were separated using Duncan's multiple range test at p\u22640.05. Results revealed that the soils\u2019 texture ranged from sandy loam, loamy sand and clay loam. Soil bulk density decreased with increased incorporation of poultry manure. Generally, unsaturated hydraulic conductivity increased with the incorporation of poultry manure across all the soil series except for Apomu and Oba series. Sorptivity and aggregate stability increased with the incorporation of poultry manure. Soil pH increased with poultry manure incorporation in Apomu, Oba, Iwo, Itagunmodi and the highly cultivated Iwo series, but reduced in Egbeda series. As expected, organic carbon and total nitrogen increased with poultry manure application. Conclusively, poultry manure improved soil hydraulic properties and soil aggregation, which has positive implications on water quality and availability, groundwater recharge, erosion control, fertilizer use efficiency, plant growth, biodiversity and sustainable soil use", "keywords": ["Water stable aggregate", "Soil texture", "Unsaturated hydraulic conductivity", "Sorptivity", "Tropical soil", "Poultry manure"], "contacts": [{"organization": "Akinde, Bamikole, Tijani, Fatai Oladapo, Ibitoye, Rotimi George, Oyedele, Durodoluwa Joseph,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.16316472"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.16316472", "name": "item", "description": "10.5281/zenodo.16316472", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.16316472"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-07-22T00:00:00Z"}}, {"id": "10.5281/zenodo.17923249", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-07-26T16:23:14Z", "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": "10.5281/zenodo.4291855", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-07-26T16:23:21Z", "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.7079582", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:23:34Z", "type": "Report", "title": "Satellite based estimation of the arable topsoil texture at regional scale using Sentinel-2 data", "description": "Although satellite imaging has been present as a source of valuable spatial data for a long time, it was not until very recently that high quality satellite imagery products produced by high resolution multispectral instruments became affordable and broadly available. On the other hand, information contained in such measurements proved to have significant impact on the overall improvement of the best practices in agricultural production and environmental monitoring. One of the applications that could benefit from the large scale satellite based measurements is characterization of topsoil properties of arable land. More exactly, bare soil spectra acquired by multispectral instruments can directly provide information about soil texture, represented by the content of clay, sand, or silt, over the observed vegetation free area. There have been a few attempts to investigate such possibilities in the context of the current and forthcoming multispectral and hyperspectral imagers. In a recently published study, a comprehensive evaluation of the capabilities of several imagers in the task of soil texture estimation was performed. However, those findings were based only on the simulated and resampled spectral responses derived from the soil spectral signature libraries acquired under controlled laboratory conditions using high precision hyperspectral instruments. Among the simulated imagers was also Sentinel-2 MSI. In line with these efforts, aim of this paper is to further investigate applicability of this instrument in the real working environment, characterized by the challenging factors introduced by the atmosphere, tillage and plant remains, missing data due to cloud coverage, variable soil moisture as a consequence of climate and volatile weather conditions, as well as natural soil spatial variability, due to the large spatial extent of the performed analysis.", "keywords": ["2. Zero hunger", "13. Climate action", "Sentinel-2", " Satellite imaging", " Topsoil texture", " Estimation", "15. Life on land"], "contacts": [{"organization": "Predrag Lugonja, Branko Brklja\u010d, Vladimir \u0106iri\u0107, Pavel Benka, Vladimir Crnojevi\u0107,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7079582"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7079582", "name": "item", "description": "10.5281/zenodo.7079582", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7079582"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-09-28T00:00:00Z"}}, {"id": "10.5281/zenodo.7079583", "type": "Feature", "geometry": null, "properties": {"updated": "2026-07-26T16:23:34Z", "type": "Report", "title": "Satellite based estimation of the arable topsoil texture at regional scale using Sentinel-2 data", "description": "Although satellite imaging has been present as a source of valuable spatial data for a long time, it was not until very recently that high quality satellite imagery products produced by high resolution multispectral instruments became affordable and broadly available. On the other hand, information contained in such measurements proved to have significant impact on the overall improvement of the best practices in agricultural production and environmental monitoring. One of the applications that could benefit from the large scale satellite based measurements is characterization of topsoil properties of arable land. More exactly, bare soil spectra acquired by multispectral instruments can directly provide information about soil texture, represented by the content of clay, sand, or silt, over the observed vegetation free area. There have been a few attempts to investigate such possibilities in the context of the current and forthcoming multispectral and hyperspectral imagers. In a recently published study, a comprehensive evaluation of the capabilities of several imagers in the task of soil texture estimation was performed. However, those findings were based only on the simulated and resampled spectral responses derived from the soil spectral signature libraries acquired under controlled laboratory conditions using high precision hyperspectral instruments. Among the simulated imagers was also Sentinel-2 MSI. In line with these efforts, aim of this paper is to further investigate applicability of this instrument in the real working environment, characterized by the challenging factors introduced by the atmosphere, tillage and plant remains, missing data due to cloud coverage, variable soil moisture as a consequence of climate and volatile weather conditions, as well as natural soil spatial variability, due to the large spatial extent of the performed analysis.", "keywords": ["2. Zero hunger", "13. Climate action", "Sentinel-2", " Satellite imaging", " Topsoil texture", " Estimation", "15. 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The data derived from a long-term field trial for the year 2016 at the Asendorf field station 70 km north of Hanover, Germany (49 m above sea level, 52\u00b045\u203248.4\u2032\u2032N 9\u00b001\u203224.3\u2032\u2032E) and a field site in Triesdorf, located in Northern Bavaria (450 m a.s.l., 49\u00b012'36.5'N 10\u00b038'33.9'E). Data includes soil (OC, bulk density, texture, pH-value) and plant data (grain yield, thousand grain weight, tillers per m\u00b2, spikes per m\u00b2). All methods and data will be described in an upcoming journal article in the Journal Plant and Soil (DOI:10.1007/s111104-023-06212-2).", "keywords": ["2. Zero hunger", "soil organic carbon", "tillers per m\u00b2", "spikes per m\u00b2", "soil depths", "grain yield", "thousand grain weight", "soil texture", "15. 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