[Note: Integrated as part of FoodData Central, April 2019.] The USDA National Nutrient Database for Standard Reference (SR) is the major source of food composition data in the United States and provides the foundation for most food composition databases in the public and private sectors. This is the last release of the database in its current format. SR-Legacy will continue its preeminent role as a stand-alone food composition resource and will be available in the new modernized system currently under development. SR-Legacy contains data on 7,793 food items and up to 150 food components that were reported in SR28 (2015), with selected corrections and updates. This release supersedes all previous releases. Resources in this dataset:Resource Title: USDA National Nutrient Database for Standard Reference, Legacy Release. File Name: SR-Leg_DB.zipResource Description: Locally stored copy - The USDA National Nutrient Database for Standard Reference as a relational database using AcessResource Title: USDA National Nutrient Database for Standard Reference, Legacy Release. File Name: SR-Leg_ASC.zipResource Description: ASCII files containing the data of the USDA National Nutrient Database for Standard Reference, Legacy Release.Resource Title: USDA National Nutrient Database for Standard Reference, Legacy Release. File Name: SR-Leg_ASC.zipResource Description: Locally stored copy - ASCII files containing the data of the USDA National Nutrient Database for Standard Reference, Legacy Release.

Resource Description: Locally stored copy - The USDA National Nutrient Database for Standard Reference as a relational database using AcessResource Title: USDA National Nutrient Database for Standard Reference, Legacy Release. File Name: SR-Leg_ASC.zipResource Description: ASCII files containing the data of the USDA National Nutrient Database for Standard Reference, Legacy Release.Resource Title: USDA National Nutrient Database for Standard Reference, Legacy Release. File Name: SR-Leg_ASC.zipResource Description: Locally stored copy - ASCII files containing the data of the USDA National Nutrient Database for Standard Reference, Legacy Release.

[Note: Integrated as part of FoodData Central, April 2019.] The database consists of several sets of data: food descriptions, nutrients, weights and measures, footnotes, and sources of data. The Nutrient Data file contains mean nutrient values per 100 g of the edible portion of food, along with fields to further describe the mean value. Information is provided on household measures for food items. Weights are given for edible material without refuse. Footnotes are provided for a few items where information about food description, weights and measures, or nutrient values could not be accommodated in existing fields. Data have been compiled from published and unpublished sources. Published data sources include the scientific literature. Unpublished data include those obtained from the food industry, other government agencies, and research conducted under contracts initiated by USDA’s Agricultural Research Service (ARS). Updated data have been published electronically on the USDA Nutrient Data Laboratory (NDL) web site since 1992. Standard Reference (SR) 28 includes composition data for all the food groups and nutrients published in the 21 volumes of "Agriculture Handbook 8" (US Department of Agriculture 1976-92), and its four supplements (US Department of Agriculture 1990-93), which superseded the 1963 edition (Watt and Merrill, 1963). SR28 supersedes all previous releases, including the printed versions, in the event of any differences. Attribution for photos: Photo 1: k7246-9 Copyright free, public domain photo by Scott Bauer Photo 2: k8234-2 Copyright free, public domain photo by Scott Bauer Resources in this dataset:Resource Title: READ ME - Documentation and User Guide - Composition of Foods Raw, Processed, Prepared - USDA National Nutrient Database for Standard Reference, Release 28. File Name: sr28_doc.pdfResource Software Recommended: Adobe Acrobat Reader,url: http://www.adobe.com/prodindex/acrobat/readstep.html Resource Title: ASCII (6.0Mb; ISO/IEC 8859-1). File Name: sr28asc.zipResource Description: Delimited file suitable for importing into many programs. The tables are organized in a relational format, and can be used with a relational database management system (RDBMS), which will allow you to form your own queries and generate custom reports.Resource Title: ACCESS (25.2Mb). File Name: sr28db.zipResource Description: This file contains the SR28 data imported into a Microsoft Access (2007 or later) database. It includes relationships between files and a few sample queries and reports.Resource Title: ASCII (Abbreviated; 1.1Mb; ISO/IEC 8859-1). File Name: sr28abbr.zipResource Description: Delimited file suitable for importing into many programs. This file contains data for all food items in SR28, but not all nutrient values--starch, fluoride, betaine, vitamin D2 and D3, added vitamin E, added vitamin B12, alcohol, caffeine, theobromine, phytosterols, individual amino acids, individual fatty acids, or individual sugars are not included. These data are presented per 100 grams, edible portion. Up to two household measures are also provided, allowing the user to calculate the values per household measure, if desired.Resource Title: Excel (Abbreviated; 2.9Mb). File Name: sr28abxl.zipResource Description: For use with Microsoft Excel (2007 or later), but can also be used by many other spreadsheet programs. This file contains data for all food items in SR28, but not all nutrient values--starch, fluoride, betaine, vitamin D2 and D3, added vitamin E, added vitamin B12, alcohol, caffeine, theobromine, phytosterols, individual amino acids, individual fatty acids, or individual sugars are not included. These data are presented per 100 grams, edible portion. Up to two household measures are also provided, allowing the user to calculate the values per household measure, if desired.Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/ Resource Title: ASCII (Update Files; 1.1Mb; ISO/IEC 8859-1). File Name: sr28upd.zipResource Description: Update Files - Contains updates for those users who have loaded Release 27 into their own programs and wish to do their own updates. These files contain the updates between SR27 and SR28. Delimited file suitable for import into many programs.

Several USDA food composition databases, including the Food and Nutrient Database for Dietary Studies (FNDDS), Standard Reference (SR) Legacy, and the USDA Branded Food Products Database, have transitioned to FoodData Central, a new and harmonized USDA food and nutrient data system. FoodData Central also includes expanded nutrient content information as well as links to diverse data sources that offer related agricultural, environmental, food, health, dietary supplement, and other information. The new system is designed to strengthen the capacity for rigorous research and policy applications through its search capabilities, downloadable datasets, and detailed documentation. Application developers can incorporate the information into their applications and web sites through the application programming interface (API) REST access. The constantly changing and expanding food supply is a challenge to those who are interested in using food and nutrient data. Including diverse types of data in one data system gives researchers, policymakers, and other audiences a key resource for addressing vital nutrition and health issues. FoodData Central: Includes five distinct types of data containing information on food and nutrient profiles, each with a unique purpose: Foundation Foods; Experimental Foods; Standard Reference; Food and Nutrient Database for Dietary Studies; USDA Global Branded Food Products Database. Provides a broad snapshot in time of the nutrients and other components found in a wide variety of foods and food products. Presents data that come from a variety of sources and are updated as new information becomes available. Includes values that are derived through a variety of analytic and computational approaches, using state-of-the-art methodologies and transparent presentation. FoodData Central is managed by the Agricultural Research Service and hosted by the National Agricultural Library. Resources in this dataset:Resource Title: Website Pointer for FoodData Central. File Name: Web Page, url: https://fdc.nal.usda.gov/index.html Includes Search, Download data, API Guide, Data Type Documentation, and Help pages.

The RVCAT database contains data that have been collected on various vessel operations on the Great Lakes and select connecting waterways. This section of Reference Tables specifically handles repetitive or standardized information that is called upon in the main tables of the RVCAT database. Reference tables are used in database design in order to standardize often used values and to make the data file efficient. All of the terms defined in the reference tables have been determined by the United States Geological Survey, Great Lakes Science Center and it’s partners. Data Quality: Note that the following data release is a snapshot of the database at the time of release. Some data quality checks are still being undertaken after the time of release. Also, a large section of this database includes legacy data that if issues arise for cannot be addressed, but nevertheless adds great value to the database. When approaching the following data release, it is strongly suggested to approach the Great Lakes Science Center's researchers for input. Distribution Liability Statement: Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty.

AFRAA Data Upgrade & Transition Plan for 2025 Fire Season

Given the mission-critical nature of AFRAA dataset and federal mandates, the FS Enterprise Data Warehouse (EDW) and AFRAA data stewards will fully support the legacy map services throughout the 2025 fire season before retirement. The latest 2025 data, approved by Forest Service Regional Forester and GIS Coordinators, has been integrated into legacy schemas, ensuring authoritative data while maintaining backward compatibility for applications relying on legacy endpoints.

What You Need to KnowWhere is the New Data

Most users will use the new map image service REST endpoints:

https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_AerialFireRetardantAvoidanceAreas_Aquatic_01/MapServer https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_AerialFireRetardantAvoidanceAreas_Terrestrial_01/MapServer

Metadata is available in these ArcGIS items that reference the new map services:

Fire_AerialFireRetardantAvoidanceAreas_Aquatic (Landing Page) Fire_AerialFireRetardantAvoidanceAreas_Terrestrial (Landing Page)

Sync Enable ArcGIS Online Hosted Feature Services:

Aquatic Hosted Feature Service Terrestrial Hosted Feature Service

Internal FS users can make direct SDE connections (see detailed instructions on the AFRAA SharePoint ). Downloadable Data from the FS GeoData Clearinghouse:

Fire_AerialFireRetardantAvoidanceAreas_Aquatic.gdb.zip Fire_AerialFireRetardantAvoidanceAreas_Terrestrial.gdb.zip

When Will the Legacy Product Be Retired?

March 14, 2025 – New terrestrial map service available, and a deprecation watermark is added to the legacy service. March 14, 2025 - Region 3 approved 2025 aquatic data via the legacy service endpoint. April 1, 2025 – All 2025 approved data loaded into both new and legacy services. April 1, 2025 – New aquatic service published, and a deprecation watermark is added to the legacy service. April 1, 2025 – Legacy download products removed from the Clearinghouse and replaced with the new versions. April 7, 2025 – Sync enabled Hosted Feature Services published to ArcGIS Online October 1, 2025 – Legacy services stopped. Fire personnel can contact SM.FS.afraa@usda.gov during regular business hours if unknown dependencies are discovered. The services will be restarted if needed. November 1, 2025 – Unless an extension is requested, legacy services will be deleted, and all feature classes will be removed from EDW, making the data inaccessible.

What Products Are Being Deprecated?

Terrestrial Map Services & Associated Feature ClassesLegacy Terrestrial:

https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_AerialFireRetardantAvoidanceAreas_01/MapServerS_USA.AerialFireRetardantAvoidance

New Authoritative Terrestrial:

https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_AerialFireRetardantAvoidanceAreas_Terrestrial_01/MapServerS_USA.Fire_AerialFireRetardantAvoidanceAreas_Terrestrial

Aquatic Map Services & Associated Feature Classes

Legacy Aquatic:

https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_AerialFireRetardantHydrographicAvoidanceAreas_01/MapServer

S_R01.AFRAA_Hydro S_R02.AFRAA_Hydro S_R03.AFRAA_Hydro S_R04.AFRAA_Hydro S_R05.AFRAA_Hydro S_R06.AFRAA_Hydro S_R08.AFRAA_Hydro S_R09.AFRAA_Hydro S_R10.AFRAA_Hydro

New Authoritative Aquatic Map Service and Feature Class:

https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_AerialFireRetardantAvoidanceAreas_Aquatic_01/MapServerS_USA.Fire_AerialFireRetardantAvoidanceAreas_AquaticThis record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

The Marine Environment Monitoring and Assessment National database (MERMAN) is a national database which holds and provides access to data collected under the Clean Safe Seas Environmental Monitoring Programme (CSEMP) formerly the National Marine Monitoring Programme (NMMP). The data collected are the responsibility of the Competent Monitoring Authorities (CMAs) who collect the samples from stations in UK waters using water sampling techniques, trawls, nets or grabs. The CMAs then send the collected samples to accredited laboratories where they are analysed. A weighting is calculated, based on the quality of the analysis. The weighting score incorporates the laboratory accreditation, reference material, inter-laboratory comparisons, detection limits, uncertainties and standard deviations. Where data do not meet a threshold score they are given a status of ‘FAIL’ and although they are stored they are not made available to external users. The MERMAN contaminants, nutrients, biological and eutrophication effects in water data start in 1999. Data are submitted by the CMAs annually and an annual submission may include updates to legacy data to provide additional data or improve data/metadata. The data held in MERMAN fulfils the UK's mandatory monitoring requirements under the Oslo and Paris Convention (OSPAR) Joint Assessments and Monitoring Programme (JAMP). These data are used in support of European Commission (EC) directives and national assessments, such as Charting Progress 2 and are also supplied to the European Marine Observation and Data Network (EMODNET).

The Africa Soil Profiles Database, Version 1.2, is compiled by ISRIC - World Soil Information (World Data Center for Soils) as a project activity for the Globally integrated- Africa Soil Information Service (AfSIS) project (www.africasoils.net/data/legacyprofile). It replaces version 1.1.

The Africa Soil Profiles Database is a compilation of georeferenced and standardised legacy soil profile data for Sub-Saharan Africa. Version 1.2 (November 2014) identifies 18,532 unique soil profiles inventoried from a wide variety of data sources and includes profile site and layer attribute data. Soil analytical data are available for 15,564 profiles of which 14,197 are georeferenced, including the attributes as specified by GlobalSoilMap.net. Soil attribute values are standardized according to SOTER conventions and are validated according to routine rules. Odd values are flagged. The degree of validation, and associated reliability of the data, varies because reference soil profile data, that are previously and thoroughly validated, are compiled together with non-reference soil profile data of lesser inherent representativeness.

ISRIC World Soil Information is compiling legacy soil profile data of Sub Saharan Africa, as a project activity of the AfSIS project (Globally integrated Africa Soil Information Service). http://africasoils.net/services/data/soil-databases/

Africa Soil Profiles database, version. 1.0 (April 2012) identifies less than 15700 unique soil profiles inventoried from a wide variety of data sources. From the less than 14600 profiles that are geo-referenced, soil layer attribute data are available for less than 12500 and soil analytical data for less than 10000 profiles. The database includes, but is not limited, to the soil attributes specified by GlobalSoilMap.net. Soil attribute values are standardized according to e-SOTER conventions and validated according to routine rules. Odd values are flagged. The degree of validation, and associated reliability of the data, varies because reference soil profile data, that are previously and thoroughly validated, are compiled together with non-reference soil profile data of lesser inherent representativeness.

Updated milestone versions of the dataset have been posted online and made available to the project serving as input to the soil property maps generated by AfSIS. The continuously growing dataset will also be made available through the World Soil Information Service upon continuation of the project activity. The version is released here is version 1.0., the latest version is 1.1.

This data portrays the FSTopo quad footprint. Quadrangles with a Vintage (greater than zero) make up the FSTopo area of interest.Within the FSTopo database, features are represented as lines, points, or polygons, with descriptive subtype attribute codes attached to describe the cartographic symbology characteristics of features. Annotation features are represented as stand-alone map text collected relative to the scale of the topographic quadrangle. The FSTopo database was originally populated with Cartographic Feature File (CFF) data which was digitized from either the Primary Base Series (PBS) quadrangles or U.S. Geological Survey (USGS) topographic map series quadrangles. Over time, the legacy CFF data is being replaced (at least partially) with data from nationally standardized sources. Data completeness reflects the content of the original source graphic, digital correction guide information, stereoscopic source, monoscopic source, supplemented with cadastral source information. Forests and Quadrangles may have undergone revision at varying dates. The update revision uses a variety of sources, including Digital Orthophoto Quad (DOQ) imagery, NAIP imagery, cadastral information, other vector data sources, and field-prepared correction guides in hardcopy or digital format.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService OGC WMS CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.

The Marine Environment Monitoring and Assessment National database (MERMAN) is a national database which holds and provides access to data collected under the Clean Safe Seas Environmental Monitoring Programme (CSEMP) formerly the National Marine Monitoring Programme (NMMP). The data collected are the responsibility of the Competent Monitoring Authorities (CMAs) who collect the samples from stations in UK waters using water sampling techniques, trawls, nets or grabs. The CMAs then send the collected samples to accredited laboratories where they are analysed. A weighting is calculated, based on the quality of the analysis. The weighting score incorporates the laboratory accreditation, reference material, inter-laboratory comparisons, detection limits, uncertainties and standard deviations. Where data do not meet a threshold score they are given a status of ‘FAIL’ and although they are stored they are not made available to external users. The contaminants and biological effects in biota data start in 1987 with greater use of the database occurring from 1997 onwards. Data are submitted by the CMAs annually and an annual submission may include updates to legacy data to provide additional data or improve data/metadata. The data held in MERMAN fulfils the UK's mandatory monitoring requirements under the Oslo and Paris Convention (OSPAR) Joint Assessments and Monitoring Programme (JAMP). These data are used in support of European Commission (EC) directives and national assessments, such as Charting Progress 2 and are also supplied to the European Marine Observation and Data Network (EMODNET).

The data has been collected during the Nansen Legacy Seasonal Study Q3 from 5 - 27 August 2019 on research vessel RV Kronprins Haakon (cruise number 2019706), along a transect in the northern Barents Sea from 76N to 82N. The dataset contains abundance of pelagic marine protists, including phytoplankton (autotrophic) and protozooplankton (heterotrophic). Protists were identified and counted with light microscopy using the Utermöhl method and the result are given as cells per liter (cells/L) called organismQuantity.

Quality

Sampling method:

The samples were collected with Niskin bottles attached to a CTD rosette at the following depths: 5, 10, 30, 60, 90 m and deep chlorophyll max (DCM). The samples were preserved using an aldehyde mixture of glutaraldehyde and hexamethylenetetramine-buffered formalin at final concentrations of 0.1% and 1% respectively.

Analyse method:

All samples have been analysed at Institute of Oceanology of the Polish Academy of Sciences (IOPAN). The organisms were identified and counted under an inverted microscope according to the Utermöhl method.

Header name index - events

• expedition: cruise number for R/V Kronprins Haakon
• eventID: UUID for the sample
• parentID: UUID for the gear deployment (each Niskin has a unique parentID)
• eventDate: the date-time when an event occurred, using ISO 8601-1:2019 format (2020-07-27T07:16:03.446Z).
• fieldNumber: human-readable sample ID (e.g. PHT-001)
• locationID: station name
• decimalLongitude: geographic latitude (in decimal degrees, using the spatial reference system given in geodetic datum)
• decimalLatitude: geographic longitude (in decimal degrees, using the spatial reference system given in geodeticDatum)
• bottomDepthInMeters: bottom depth in meters
• eventRemarks: comments or remarks about the event (free text field)
• gearType: the gear used to take the sample e.g. Niskin bottle
• samplingDepthInMeters: depth sampled
• sampleType: description of the sample type according to a standard list
• recordedBy: name of the person who took the samples
• principalInvestigatorName: name of the person in charge of the sample collection
• principalInvestigatorEmail: email address of the person in charge of the sample collection
• principalInvestigatorInstitution: affiliated institution of the person in charge of the sample collection

Header name index - occurrence

• scientificName: full scientific name of the identified organism at the lowest taxonomic level that can be ascertained. The scientificName should be selected from a drop-down menu linked to the list in taxonomy sheet. (e.g Thalassiosira hyalina).
• identificationQualifier: A standard term (sp., spp., and indet.) to express uncertainty in identification.
• lifeStage: the life stage (e.g. resting spore) of the organism.
• sizeGroupOperator: describes if the size group is less than or greater than a value (It = less than, gte = greater or equal to)
• sizeGroup: the size group in µm.
• organismRemark: indicates e.g. varieties, colony type
• identificationRemarks: a free text field for adding information relevant to the analysis
• identifiedBy: person who did the lab-analyse
• fieldsInCount: Number of fields counted in the microscope
• magnificationMicroscope: The magnification setting used during analysis. Selected from a drop-down menu linked to vocab-sheet
• maxFields: Number of fields in the entire sedimentation chamber (Related to magnification used)
• takenVolumeML: The volume taken for sedimentation in the Utermöhl chamber (the sub-sample taken for analysis)
• identifiedBy: Drop-down menu linked to list in people-sheet
• dateIdentified: Date for the analysis
• sampleSizeValue=(fieldsInCount/maxFields)*(takenVolumeML/convertionMLtoL)*dilutionFactorFormaldehyde), dilutionFactorFormaldehyde = 0.95
• sampleSizeUnit: liter (l)
• organismQuantity: the quantity of the organism per volume water in the environment (organismQuantity = individualCount/sampleSizeValue)
• organismQuantityType: cells/l

Funding:

The Nansen Legacy is funded by the Research Council of Norway and the Norwegian Ministry of Education and Research. They provide 50% of the budget while the participating institutions contribute 50% in-kind. The total budget for the Nansen Legacy project is 740 mill. NOK.

Legacy Expenses and Benefits Return to HMRC including - Employee name, national insurance number, pay reference number, Bank Account, Tax Details, Deductions, Date of Birth etc

This data release includes GIS datasets supporting the Colorado Legacy Mine Lands Watershed Delineation and Scoring tool (WaDeS), a web mapping application available at https://geonarrative.usgs.gov/colmlwades/. Water chemistry data were compiled from the U.S. Geological Survey (USGS) National Water Information System (NWIS), U.S. Environmental Protection Agency (EPA) STORET database, and the USGS Central Colorado Assessment Project (CCAP) (Church and others, 2009). The CCAP study area was used for this application. Samples were summarized at each monitoring station and hardness-dependent chronic and acute toxicity thresholds for aquatic life protections under Colorado Regulation No. 31 (CDPHE, 5 CCR 1002-31) for cadmium, copper, lead, and/or zinc were calculated. Samples were scored according to how metal concentrations compared with acute and chronic toxicity thresholds. The results were used in combination with remote sensing derived hydrothermal alteration (Rockwell and Bonham, 2017) and mine-related features (Horton and San Juan, 2016) to identify potential mine remediation sites within the headwaters of the central Colorado mineral belt. Headwaters were defined by watersheds delineated from a 10-meter digital elevation dataset (DEM), ranging in 5-35 square kilometers in size. Python and R scripts used to derive these products are included with this data release as documentation of the processing steps and to enable users to adapt the methods for their own applications. References Church, S.E., San Juan, C.A., Fey, D.L., Schmidt, T.S., Klein, T.L. DeWitt, E.H., Wanty, R.B., Verplanck, P.L., Mitchell, K.A., Adams, M.G., Choate, L.M., Todorov, T.I., Rockwell, B.W., McEachron, Luke, and Anthony, M.W., 2012, Geospatial database for regional environmental assessment of central Colorado: U.S. Geological Survey Data Series 614, 76 p., https://doi.org/10.3133/ds614. Colorado Department of Public Health and Environment (CDPHE), Water Quality Control Commission 5 CCR 1002-31. Regulation No. 31 The Basic Standards and Methodologies for Surface Water. Effective 12/31/2021, accessed on July 28, 2023 at https://cdphe.colorado.gov/water-quality-control-commission-regulations. Horton, J.D., and San Juan, C.A., 2022, Prospect- and mine-related features from U.S. Geological Survey 7.5- and 15-minute topographic quadrangle maps of the United States (ver. 8.0, September 2022): U.S. Geological Survey data release, https://doi.org/10.5066/F78W3CHG. Rockwell, B.W. and Bonham, L.C., 2017, Digital maps of hydrothermal alteration type, key mineral groups, and green vegetation of the western United States derived from automated analysis of ASTER satellite data: U.S. Geological Survey data release, https://doi.org/10.5066/F7CR5RK7.

This application was created to support the Mapping Existing Vegetation on Prince of Wales Island story map.

The Prince of Wales Existing Vegetation mapping project encompasses over 4.2 million acres of Southeastern Alaska—2.3 million of which are terrestrial. This map was designed to be consistent with the standards established in the Existing Vegetation Classification and Technical Guide (Nelson et al. 2015), and to provide baseline information to support project planning and inform land management of the Prince of Wales and surrounding islands. The final map comprises seven distinct, integrated feature layers: 1) vegetation type; 2) tree canopy cover; 3) trees per acre (TPA) for trees ≥ 1’ tall; 4) trees per acre for trees ≥ 6” diameter at breast height (dbh); 5) quadratic mean diameter (QMD) for trees ≥ 2” dbh; 6) quadratic mean diameter for trees ≥ 9” dbh; and 7) thematic tree size. The dominance type map consists of 18 classes, including 15 vegetation classes and 3 other land cover types. Continuous tree canopy cover, TPA, QMD, and thematic tree size was developed for areas classified as forest on the final vegetation type map layer. Geospatial data, including remotely sensed imagery, topographic data, and climate information, were assembled to classify vegetation and produce the maps. A semi-automated image segmentation process was used to develop the modeling units (mapping polygons), which delineate homogeneous areas of land cover. Field plots containing thematic vegetation type and tree size information were used as reference for random forest prediction models. Important model drivers included 30 cm orthoimagery collected during the height of the 2019 growing season, in addition to Sentinel 2 and Landsat 8 satellite imagery, for vegetation type prediction. Additionally, detailed tree inventory data were collected at precise field locations to develop forest metrics for Quality Level 1 (QL1) Light Detection and Ranging (LiDAR) data. LiDAR information was acquired across approximately 80% of the project’s land area. Continuous tree canopy cover and 2^nd order forest metrics (TPA and QMD) were modeled across the LiDAR coverage area, and subsequently, extrapolated to the full project extent using Interferometric Synthetic Aperture Radar (IfSAR) as the primary topographic data source.

This harmonized set of soil parameter estimates for Tunisia. It has been derived from the 1:1 million scale Soil and Terrain Database for the country (SOTER_TN, ver. 1.0) and the ISRIC-WISE soil profile database, using standardized taxonomy-based pedotransfer (taxotransfer) procedures. The land surface of Tunisia, covering some 164,150 km2, has been characterized in SOTER_TN using 250 unique SOTER units. Each map unit consists of up to four different soil components. In so far as possible, each soil component has been characterized by a regionally representative profile, selected and classified by national soil experts (see Dijkshoorn et al. 2008). Conversely, in the absence of any measured legacy data, soil components were characterized using synthetic profiles for which only the FAO-Unesco (1988) classification is known. Soil components in SOTER_TN have been characterized using 100 profiles of which 44 are synthetic. The latter represent some 59 per cent of the territory. Comprehensive sets of measured attribute data are not available for most of the measured profiles (56) collated in SOTER_TN, as these were not considered in the source materials. Consequently, to permit modelling, gaps in the soil analytical data have been filled using consistent taxotransfer procedures. Modal soil property estimates necessary to populate the taxotransfer procedure were derived from statistical analyses of soil profiles held in the ISRIC-WISE database ― the current taxotransfer procedure only considers profiles in WISE that: (a) have FAO soil unit names identical to those mapped for Tunisia in SOTER, and (b) originate from regions having similar Köppen climate zones (n= 3566). Property estimates are presented for 18 soil variables by soil unit for fixed depth intervals of 0.2 m to 1 m depth: organic carbon, total nitrogen, pH(H2O), CECsoil, CECclay, base saturation, effective CEC, aluminium saturation, CaCO3 content, gypsum content, exchangeable sodium percentage (ESP), electrical conductivity (ECe), bulk density, content of sand, silt and clay, content of coarse fragments (less than 2 mm), and volumetric water content (-33 kPa to -1.5 MPa). These attributes have been identified as being useful for agro-ecological zoning, land evaluation, crop growth simulation, modelling of soil carbon stocks and change, and studies of global environmental change. The soil property estimates can be linked to the spatial data (map), using GIS, through the unique SOTER-unit code; database applications should consider the full map unit composition and depth range. The derived data presented here may be used for exploratory assessments at national scale or broader (greater than 1:1 000 000). They should be seen as best estimates based on the current, still limited, selection of soil profiles in SOTER_TN and data clustering procedure ― the type of taxotransfer rules used to fill gaps in the measured data has been flagged to provide an indication of confidence in the derived data

The lack of extreme water level fluctuations in managed, non-peat forming wetland ecosystems can result in decreased productivity through the loss of heterogeneity of these ecosystems. Stochastic disruption, such as a water level drawdown, can effectively reverse this effect and return the wetland to a more productive state, associated with higher biodiversity through new vegetation development. Yet, aside from the effect on vegetation dynamics, little is known about longer-term effects (30 years) of a water level drawdown, hereafter referred to as legacy effects, and how this may impact future water level drawdowns. Here, we aim to unravel the legacy effects of a water level drawdown, stand alone and along a water level gradient, on seed bank properties and nutrient availability in a eutrophic clay wetland. To identify these, we studied the hydrologically managed nature reserve Oostvaardersplassen in the Netherlands. Here, one section was subjected to a multi-year water level drawdown and another section was kept inundated. We determined seed bank properties in both areas, spatially and along a soil elevation gradient (20 cm). Nutrient availability was measured by taking sediment samples along the water level gradient and through experimental manipulation of the water level in an indoor mesocosm experiment. Germination was higher in locations with a water level drawdown history, especially at relatively high elevations. Additionally, the proportion of pioneer species in the seed bank was higher in the water level drawdown area. Overall, nutrient concentrations were higher compared to other systems. Nutrient availability was higher in the inundated area and did not respond to the water level gradient. We conclude that 30 years after an induced water level drawdown there is no depletion of nutrients, while we still observe a legacy effect in the number of viable seeds in the seed bank. Methods 2.1 Study site The study was conducted in Oostvaardersplassen in the Netherlands (coordinates: 52.456857, 5.355935). This eutrophic clay wetland of about 5600 ha consists of a 3600 ha marsh and a 2000 ha dryer border zone. This study took place in the marsh part. The marsh is characterized by large water bodies, reed vegetation and willow forests. Oostvaardersplassen is part of the polder Zuidelijk Flevoland, which is located in the former Zuiderzee estuary, a marine habitat (see van Leeuwen et al., 2021 for a detailed description). For water safety reasons the decision was made to separate the inland Zuiderzee from the North Sea through the construction of a dike, named the Afsluitdijk. After completion of the construction and within five years, the Zuiderzee transformed into a freshwater lake, IJsselmeer. In this freshwater lake, several polders were established to create land for agriculture; Zuidelijk Flevoland was reclaimed in 1968. Since Oostvaardersplassen is located in, what was then, the lowest part of the polder, it remained wet during the first years after reclamation and no actions were taken to develop this area into the industrial site as it was planned to be (Cornelissen et al., 2014). The marine clay soil and its associated high nutrient concentrations (eutrophic) in combination with the unmanaged and wet conditions, led nature to develop quickly. This made the area into an important breeding and resting area for many wetland birds and therefore became a protected wetland nature reserve in 1974. In 1989 it became a protected area within the European Bird directive and under the Ramsar agreement. Additionally, it was qualified as a Natura 2000 area in 2009. Later on, the relatively high water levels at the end of winter, due to the height of the weir, in combination with high grazing pressure by moulting greylag geese (Anser anser) from May to July, resulted in the loss of reed cover (Phragmites australis) (Vulink and Van Eerden, 1998). This in turn resulted in decreasing bird numbers due to lower food and habitat availability (Beemster et al., 2010). To restore reed-dominated wetlands and to increase food and habitat availability for birds, a complete multi-year water level drawdown was induced in the western part of the marsh from 1987 till 1991 (Vulink and Van Eerden, 1998). The eastern part was hydrologically separated from the western part by a low dike and water levels and dynamics remained unchanged in this area. The implemented water level drawdown resulted in the development of c. 600 ha of reed-dominated vegetation in the western part, after which typical wetland birds, e.g., bearded reedling (Paranrus biarmicus), marsh harrier (Circus aeruginosus) and Eurasian bittern (Botaurus stellaris), increased in numbers (Beemster et al., 2012; Vulink and Van Eerden, 1998). The study area experiences seasonal variation in water level, but lacks long-term dynamics in water level that would be caused by extreme climatological periods. As the marsh is rainwater fed, natural water level dynamics occur with a high water level at the end of winter (March) and low levels at the end of summer (September;). The surplus of water in winter leaves the marsh via a weir. The average difference in water level between summer and winter is approximately 30 cm. During ‘dry’ summers the water level can drop 50 cm at the end of the growing season. Due to both the climate conditions in combination with the height of the weir, set as to pertain high water levels in the reed beds during late winter and spring, these naturally occurring ‘dry’ summers did not result in enough mudflat exposure throughout the area to allow extensive marsh recovery. At the time of sampling, both the water level drawdown and the non-water level drawdown area were characterized by a sharp border between vegetation and open water. The vegetation on the shores was similar in both areas and dominated by Phragmites australis, Salix spp. and, to a lesser extent, Convolvulus spp.. At drier sites, with greater proximity to the lake, Urtica dioica and Carduus spp. were present in higher abundances. The shores of the lake, that sometimes fall dry during dry summers, are colonized quickly by species among which Tephroseris palustris (also known as Senecio congestus), Epilobium hirsutum and Ranunculus sceleratus. 2.2 Experimental design We examined the legacy effects of a water level drawdown, a water level gradient and water level fluctuations on seed bank germination and nutrient availability using field sampling and mesocosm experiment. The unique field situation consisting of areas with and without a water level drawdown history allows to explore legacy effects on seed bank properties (Part 1.1) and nutrient availability (Part 2.1). This approach focusses on the long-term effects of inducing a four-year water level drawdown, in this case 30 years after the event, by sampling 20 locations in each subarea that have been inundated since the last water level drawdown. In addition, soil samples have been taken in these two hydrologically distinct areas, along a water level gradient that is dictated by elevational differences of about 20 cm. With this approach, we used the elevational gradient to distinguish between higher locations, that would fall dry more often due to for example dry summers, and lower locations. The latter had not fallen dry for 30 years in case of the water level drawdown area and 50 years in case of the non–water level drawdown area. By taking soil samples on 7 (germination) or 5 (nutrient) locations along this water level gradient, we were able to research how changes in water level alter seed bank properties (Part 2.1) and nutrient availability (Part 2.2) on a smaller seasonal time scale. In addition to the above two sampling campaigns, a mesocosm experiment was conducted to study the effects of water level on germination (Part 3.1) and nutrient availability (Part 3.2) . With this approach it was possible to determine effects of a specified water level (inundated, saturated, dry) on an even smaller time scale of weeks/months and how such a response might be influenced by events in the past, in this case drawdown history. 2.2.1 Part 1: Water level drawdown history To investigate the legacy effects of a previously induced water level drawdown on the seed bank (part 1.1) and on nutrient availability (part 1.2), we compared seed bank properties (density, diversity, species composition) and sediment nutrient concentrations between an area with water level drawdown history and an area without. For the method on sediment nutrient concentrations we would like to refer to the section on water level gradient (2.2.2) for field sampling and lab protocols. 2.2.1.1 Seed bank properties (part 1.1) We collected sediment samples from both areas in Oostvaardersplassen in June 2021, when both areas were still inundated. To cover the spatial heterogeneity of the area, 40 locations were sampled. 20 Sample points were located in the area that was continuously inundated for 50 years (non-water level drawdown history, n = 20) and 20 in the area that had undergone a water level drawdown from 1987 till 1991 and was subsequently inundated for 30 years (water level drawdown history, n = 20). In June 2021, we took ten sediment cores of 23.8 cm2 (diameter = 5.5 cm) to a depth of 10 cm and pooled the 0-5 cm and 5-10 cm depth in separate plastic bags at each location (Verhofstad et al., 2017). The bags were stored in the dark at 4°C for approximately one month to allow seed stratification, after which the sediment was sieved (mesh width: 150 µm) and the residue, containing the seeds, was spread across a tray (37×27 cm) containing sediment for propagation and germination (Lensli substrates; pH = ~5.3; electrical conductivity = ~0.5mS/cm). The trays were placed in a greenhouse with supplementary light from 6:00-22:00h so that light conditions on plant level corresponded with 250 μmol.m2/s. The temperature in the greenhouse was on average 21°C between

Summary:

The data has been collected during the Nansen Legacy Joint Cruise 3, 19th February – 11th March 2022 on the research vessel RV Kronprins Haakon (cruise number 2022702), along a transect from 76N to 82N east of Svalbard. The dataset contains mesozooplankton occurrence. It has been sampled using a BongoNet, HydroBios 60 cm. Small mesozooplankton were collected with a mesh-size 64 µm and large mesozooplankton with a mesh-size 180 µm. All specimens are identified to the lowest taxonomical level and the occurrence is given for a specific species and stage or size group as ind/m3.

Sampling method:

The sampling covers a transect from 76 N to 82 N in the northern Barents Sea and Arctic Ocean. Zooplankton has been collected using a BongoNet 60 cm (HydroBios, opening: 0.2827 m2, net length: 250 cm). Small mesozooplankton were collected with a mesh-size 64 µm and large mesozooplankton with a mesh-size 180 µm. All samples were added 4 % formaldehyde free from acid.

PLEASE NOTE: THIS DATASET CONTAINS TWO COMPLETE DATASETS OF ZOOPLANKTON: ONE FOR SMALL MESOZOOPLANKTON (APPROX BODY SIZE BELOW 2 MM) COLLECTED WITH MULTINET 64 µM AND ONE FOR LARGE MESOZOOPLANKTON (APPROX BODY SIZE ABOVE 2 MM) COLLECTED WITH MULTINET 180 µM MESH SIZE. THE INFO ABOUT WHICH NET IS USED CAN BE FOUND IN gearType, USE EITHER 64 UM OR 180 UM DEPENDING ON WHETHER THE FOCUS IS SMALL OR LARGE MESOZOOPLANKTON

Analyse method:

All samples have been analysed at Institute of Oceanology of the Polish Academy of Sciences (IOPAN). The organisms were identified and counted under a stereomicroscope equipped with an ocular micrometer, according to standard procedures (Harris et al. 2000). Small-sized zooplankters (most of Copepoda, juvenile stages of Pteropoda, Euphausiacea, Ostracoda, Amphipoda and Chaetognatha) were identified and counted in sub-samples obtained from the fixed sample volume by automatic pipette (approximately 500 individuals). Large zooplankters (big Copepoda, Pteropoda, Euphausiacea, Ostracoda, Amphipoda, Decapoda, Appendicularia, Chaetognatha, and Pisces larvae) were sorted out and identified from the whole sample. Representatives of Calanus spp. were identified at the species level based on morphology and prosome lengths of individual copepodid stages (Kwasniewski et al. 2003).

Data structure:

The data is following Darwin Core nomenclature as far as possible but also include variables that aren’t supported by Darwin Core. All information about the sampling such as eventDate, latitude, longitude, depts etc is located in event file while the result such as scientificName, lifeStage, occurrence etc. are found in the occurrence file

Header name index - events

• expedition: cruise number for R/V Kronprins Haakon
• eventID: UUID for the sampel
• parentID: UUID for the gear deployment (each MultiNet deployment has a unique parentID)
• eventDate: the date-time when an event occurred, using ISO 8601-1:2019 format (2020-07-27T07:16:03.446Z).
• fieldNumber: human-readable sample ID (e.g. ZOT-001)
• locationID: station name
• decimalLongitude: geographic latitude (in decimal degrees, using the spatial reference system given in geodetic datum)
• decimalLatitude: geographic longitude (in decimal degrees, using the spatial reference system given in geodeticDatum)
• bottomDepthInMeters: bottom depth in meters
• eventRemarks: comments or remarks about the event (free text field)
• gearType: the gear used to take the sample e.g. MultiNet 200 µm
• maximumDepthInMeters: bottom depth of the sampled layer
• minimumDepthInMeters: top depth of the sampled layer
• sampleType: description of the sample type according to a standard list
• fieldSplit: info about whether the sample is splitted. If the sample was split in 2 then fieldSplit = 2
• initialSampleVolume: The volume of water filtered through the plankton net. (initialSampleVolume = (netOpeningArea * (maximumDepthInMeters – minimumDepthInMeters)/field Split), Bongonet opening area: 3.14*(0.3)^2=0.2826 m2
• initialSampleVolumeUnit: unit used for volume (e.g. m3)
• samplingProtocol: reference to the sampling protocol
• recordedBy: name of the person who took the samples
• principalInvestigatorName: name of the person in charge of the sample collection
• principalInvestigatorEmail: email address of the person in charge of the sample collection
• principalInvestigatorInstitution: affiliated institution of the person in charge of the sample collection # Header name index - occurrence
• analysedFraction: fraction of the sampled volume that is examined for organism counted
• individualCount: the number of individuals present in the analysed volume (see extra information below)
• phylum, class, order, family, genus & taxonKey-LSID: Taxonomical information for given species according to Worms
• scientificName: full scientific name of the identified organism at the lowest taxonomic level that can be ascertained. The scientificName should be selected from a drop-down menu linked to the list in taxonomy sheet. (e.g Calanus finmarchicus).
• identificationQualifier: A standard term (sp., spp., and indet.) to express the determiner’s doubts about the Identification.
• lifeStage: the age class, life stage, or life form/morph of the organism.
• sizeGroupOperator: describes if the size group is less than or greater than a value (It = less than, gte = greater or equal to)
• sizeGroup: the size group in mm.
• organismRemark: indicates whether it is mesozooplankton, macrozooplankton, rare species
• identificationRemarks: a free text field for adding information relevant to the analysis. Used to indicate the speciemen that were dead. When nothing was remarked they were alive.
• identifiedBy: person who did the lab-analyse
• sampleSizeValue: the sample volume used to calculate the organismQuantity (sampleSizeValue 0 initialSampleVolume *analysedFraction)
• sampleSizeUnit: m3
• organismQuantity: the quantity of the organism per volume water in the environment (organismQuantity = individualCount/sampleSizeValue)
• organismQuantityType: ind/m3

Additional information for some of the fields

individualCount: The number of (all) organisms found in the sample examined - for “mesozooplankton”, the number of mesozooplankton (medium size zooplankton organisms) encountered in all sub-samples - for “macrozooplankton”, the number of macrozooplankton (large size zooplankton organisms, total length > 5 mm) encountered, identified in the entire sample - for “rare” zooplankton, we only enter information about the finding of “rare” zooplankton in the database template, and its absolute number (“organismQuantity”) is not estimated

Funding:

The Nansen Legacy is funded by the Research Council of Norway and the Norwegian Ministry of Education and Research. They provide 50% of the budget while the participating institutions contribute 50% in-kind. The total budget for the Nansen Legacy project is 740 mill. NOK.

The source to concept map table is a legacy data structure within the OMOP Common Data Model, recommended for use in ETL processes to maintain local source codes which are not available as Concepts in the Standardized Vocabularies, and to establish mappings for each source code into a Standard Concept as target_concept_ids that can be used to populate the Common Data Model tables. The SOURCE_TO_CONCEPT_MAP table is no longer populated with content within the Standardized Vocabularies published to the OMOP community.

Sub-dataset: soil type probability - suborder: Xererts

Description

Global annual maps of soil properties for 2000—2022 produced within the scope of the Land & Carbon Lab, integrating Digital surface/terrain model, vegetation/tillage indices, climatic/bioclimatic variables, and based on tree-based spatiotemporal Machine Learning. While the primary focus is on improving monitoring in global soil properties, the dataset provides wall-to-wall coverage across all terrestrial ecosystems and is organized into 300+ global mosaics in COG (Cloud Optimized GeoTIFF) format. Data are presented at 5-year intervals, across 3 standard depth intervals (0–30 cm, 30–60 cm, 60–100 cm), and cover 79 USDA soil taxonomy suborders. Original layers use the WGS84 Coordinate System (EPSG:4326) at a pixel resolution of 0.00025 degrees, and 0.00075 degrees with uncertainty (STAC and GEE). Layers archived on Zenodo are at 0.00075 degrees with uncertainty but include only the initial and final periods (2000–2005 & 2020–2022), including:

• Soil Organic Carbon Content (g/kg)
  As a key indicator of soil fertility, structure, and microbial activity, it represents the concentration of organic carbon in the fine earth fraction of the soil. Standard method of measurement is dry combustion using elemental analyzers (e.g., ISO 10694).
• Soil Organic Carbon Density (kg/m³)
  Represents the mass of organic carbon per unit volume of soil. It is derived as: SOC content × bulk density × (1 − coarse fragment volume fraction). This value is critical for estimating total carbon stocks and monitoring soil carbon changes over time.
• Soil pH
  Indicates the acidity or alkalinity of soil, affecting nutrient availability and microbial processes. Reported as pH measured in water solution (pH in H₂O).
• Bulk Density (g/cm³)
  Refers to the mass of dry fine earth (<2 mm) per unit volume, excluding coarse fragments. It reflects soil compaction and porosity, influencing water retention and root penetration. Commonly determined using the core method or calculated from pedotransfer functions.
• Soil Texture Fraction
  Defines the relative proportions of mineral particles by size. Texture influences water movement, nutrient holding capacity, and plant growth.
  • Clay content (%): Proportion of particles <0.002 mm in diameter.
  • Sand content (%): Proportion of particles between 0.05–2.0 mm (some definitions use 0.063 mm as lower threshold).
  • Silt content (%): Particles sized between 0.002–0.05 mm or up to 0.063 mm depending on classification system.
  Textural fractions follow USDA or FAO particle size classifications.
• Soil Type Probability
  Probabilistic classification of soils based on USDA Soil Taxonomy at the subgroup level. Each pixel is assigned a probability distribution across potential soil types, based on legacy point data and environmental covariates.

30m layers can be accessed through STAC and Google Earth Engine GEE) through:

• OpenLandMap STAC
  https://stac.openlandmap.org
• Google Earth Engine
  https://code.earthengine.google.com/?asset=projects/global-pasture-watch/assets/gsm-30m

All modeling framework is publicly available at OpenLandMap GitHub - soildb

Data Detail

1. Time period: 2000-2022, in 5-year intervals (last period covers 2020–2022) for soil properties ; 2000-2022 static for soil type
2. Type of data: Spatiotemporal soil data base, with depth ranges and weighted percentage data for soil assessments and static soil type classification.
3. How the data was collected or derived: The data was derived using machine learning models.
4. Statistical methods used: Tree-based spatiotemporal machine learning
5. Depth reference: b30cm..60cm = below ground at 30-60cm interval
6. Limitations or exclusions in the data: no Antarctica; masking out permanent ice and deserts
7. Coordinate reference system: EPSG:4326
8. Bounding box (Xmin, Ymin, Xmax, Ymax): (-180, -56, 180, 76)
9. Spatial resolution: 0.00075 degree (~120m)
10. Image size: 360,000P, 132,000L
11. File format: Cloud Optimized Geotiff (COG) format

Dataset Contents

This dataset includes:

• soil type probability - suborder: Xererts

Related Identifiers

• SOC density:
  below ground 0cm-30cm 2000-2005 , below ground 0cm-30cm 2020-2022 , below ground 30cm-60cm 2000-2005 , below ground 30cm-60cm 2020-2022 , below ground 60cm-100cm 2000-2005 , below ground 60cm-100cm 2020-2022 ,
• SOC content:
  below ground 0cm-30cm 2000-2005 - part 1 , below ground 0cm-30cm 2000-2005 - part 2 , below ground 0cm-30cm 2020-2022 - part 1 , below ground 0cm-30cm 2020-2022 - part 2 , below ground 30cm-60cm 2000-2005 , below ground 30cm-60cm 2020-2022 , below ground 60cm-100cm 2000-2005 , below ground 60cm-100cm 2020-2022 ,
• Bulk density:
  below ground 0cm-30cm 2000-2005 , below ground 0cm-30cm 2020-2022 , below ground 30cm-60cm 2000-2005 , below ground 30cm-60cm 2020-2022 , below ground 60cm-100cm 2000-2005 , below ground 60cm-100cm 2020-2022 ,
• Soil ph of water:
  below ground 0cm-30cm 2000-2005 , below ground 0cm-30cm 2020-2022 , below ground 30cm-60cm 2000-2005 , below ground 30cm-60cm 2020-2022 , below ground 60cm-100cm 2000-2005 , below ground 60cm-100cm 2020-2022 ,
• Soil textures fraction:
  clay below ground 0cm-30cm 2000-2005 , clay below ground 0cm-30cm 2020-2022 , clay below ground 30cm-60cm 2000-2005 , clay below ground 30cm-60cm 2020-2022 , clay below ground 60cm-100cm 2000-2005 , clay below ground 60cm-100cm 2020-2022 , sand below ground 0cm-30cm 2000-2005 , sand below ground 0cm-30cm 2020-2022 , sand below ground 30cm-60cm 2000-2005 , sand below ground 30cm-60cm 2020-2022 , sand below ground 60cm-100cm 2000-2005 , sand below ground 60cm-100cm 2020-2022 , silt below ground 0cm-30cm 2000-2005 , silt below ground 0cm-30cm 2020-2022 , silt below ground 30cm-60cm 2000-2005 , silt below ground 30cm-60cm 2020-2022 , silt below ground 60cm-100cm 2000-2005 , silt below ground 60cm-100cm 2020-2022 ,
• Soil type (Suborder):
  Uderts , Calcids , Xerands , Orthents , Cryands , Ustalfs , Cryalfs , Aquepts , <a