A Web Map displaying the property ownership boundaries within Natrona County, as well as the municipal boundaries, addresses, Improvement Service District boundaries, streets, roads, Township, Range, and Section Boundaries and zoning boundaries.

Access the Wyoming Property Tax Division's website in order to view and download their maps and GIS data in the form of .zip files and .pdf files.

The Subsurface Mineral Estate (SSME) dataset depicts Federal mineral interest in the State of Wyoming and classifies these lands by subsurface mineral ownership, Federal or Indian trust, types of minerals owned, and percent ownership within the State of Wyoming. Federal Mineral Estate refers to the mineral estate reserved to the Federal government or a Federally recognized tribe. The SSME data depicts which minerals are reserved, whether they are reserved to the Federal government or held in trust for a tribe, and the percentage of said minerals reserved. The SSME data does not depict private mineral ownership patterns. This data set is a result of compiling differing source materials of various vintages. Source material examples used to create and maintain this dataset include BLM 100k Subsurface Maps, Oil and Gas Plats, Coal Plats, Public Land Survey GIS Data (CadNSDI v.2.0), Field Office GIS Data, Compiled 24k USGS Maps, and Land Records.

A map displaying the property ownership for all the municipalities in Natrona County, as well as zoning and addresses.

The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public open space and voluntarily provided, private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastral Theme (http://www.fgdc.gov/ngda-reports/NGDA_Datasets.html). PAD-US is an ongoing project with several published versions of a spatial database of areas dedicated to the preservation of biological diversity, and other natural, recreational or cultural uses, managed for these purposes through legal or other effective means. The geodatabase maps and describes public open space and other protected areas. Most areas are public lands owned in fee; however, long-term easements, leases, and agreements or administrative designations documented in agency management plans may be included. The PAD-US database strives to be a complete “best available” inventory of protected areas (lands and waters) including data provided by managing agencies and organizations. The dataset is built in collaboration with several partners and data providers (http://gapanalysis.usgs.gov/padus/stewards/). See Supplemental Information Section of this metadata record for more information on partnerships and links to major partner organizations. As this dataset is a compilation of many data sets; data completeness, accuracy, and scale may vary. Federal and state data are generally complete, while local government and private protected area coverage is about 50% complete, and depends on data management capacity in the state. For completeness estimates by state: http://www.protectedlands.net/partners. As the federal and state data are reasonably complete; focus is shifting to completing the inventory of local gov and voluntarily provided, private protected areas. The PAD-US geodatabase contains over twenty-five attributes and four feature classes to support data management, queries, web mapping services and analyses: Marine Protected Areas (MPA), Fee, Easements and Combined. The data contained in the MPA Feature class are provided directly by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas Center (MPA, http://marineprotectedareas.noaa.gov ) tracking the National Marine Protected Areas System. The Easements feature class contains data provided directly from the National Conservation Easement Database (NCED, http://conservationeasement.us ) The MPA and Easement feature classes contain some attributes unique to the sole source databases tracking them (e.g. Easement Holder Name from NCED, Protection Level from NOAA MPA Inventory). The "Combined" feature class integrates all fee, easement and MPA features as the best available national inventory of protected areas in the standard PAD-US framework. In addition to geographic boundaries, PAD-US describes the protection mechanism category (e.g. fee, easement, designation, other), owner and managing agency, designation type, unit name, area, public access and state name in a suite of standardized fields. An informative set of references (i.e. Aggregator Source, GIS Source, GIS Source Date) and "local" or source data fields provide a transparent link between standardized PAD-US fields and information from authoritative data sources. The areas in PAD-US are also assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant "GAP Status Code" and global "IUCN Category" standard. A wealth of attributes facilitates a wide variety of data analyses and creates a context for data to be used at local, regional, state, national and international scales. More information about specific updates and changes to this PAD-US version can be found in the Data Quality Information section of this metadata record as well as on the PAD-US website, http://gapanalysis.usgs.gov/padus/data/history/.) Due to the completeness and complexity of these data, it is highly recommended to review the Supplemental Information Section of the metadata record as well as the Data Use Constraints, to better understand data partnerships as well as see tips and ideas of appropriate uses of the data and how to parse out the data that you are looking for. For more information regarding the PAD-US dataset please visit, http://gapanalysis.usgs.gov/padus/. To find more data resources as well as view example analysis performed using PAD-US data visit, http://gapanalysis.usgs.gov/padus/resources/. The PAD-US dataset and data standard are compiled and maintained by the USGS Gap Analysis Program, http://gapanalysis.usgs.gov/ . For more information about data standards and how the data are aggregated please review the “Standards and Methods Manual for PAD-US,” http://gapanalysis.usgs.gov/padus/data/standards/ .

This dataset represents the portion of Surface and Mineral Ownership for the State of Wyoming within the Wyoming portions of the Utah Sub-Region for the BLM Greater Sage-Grouse Land Use Planning Strategy. This data was used during preparation of a draft and final environmental impact statement and the record of decision to consider amendments to 14 BLM land use plans throughout the State of Utah, as well as consideration of 6 Forest Service land use plans, including portions of two that extended into Wyoming. This planning process was initiated through issuance of a Notice of Intent published on December 6, 2011. This dataset is associated with the Record of Decision and Approved Resource Management Plan Amendments for the Great Basin Region, released to the public via a Notice of Availability on September 24, 2015. The purpose of the planning process was to address protection of greater sage-grouse, in partial response to a March 2010 decision by the U.S. Fish and Wildlife Service (FWS) that found the greater sage-grouse was eligible for listing under the authorities of the Endangered Species Act. The planning process resulted in preparation of a draft environmental impact statement (DEIS) and final environmental impact statement (FEIS) in close coordination with cooperating agencies for the planning effort. The planning effort addressed the adequacy of regulatory mechanisms found in the land use plans, as well as addressing the myriad threats to grouse and their habitat that were identified by the FWS. This dataset is intended to represent the ownership information on Master Title Plats (MTPs). Surface ownership will be identified by the Agency of Jurisdiction, when the surface is Federal. All other parcels will be identified as either Private or State. Private parcels do not identify the name of the individual owner.

From https://www.usgs.gov/core-science-systems/national-geospatial-program/national-map :"The National Map is a suite of products and services that provide access to base geospatial information to describe the landscape of the United States and its territories. The National Map embodies 11 primary products and services and numerous applications and ancillary services. The National Map supports data download, digital and print versions of topographic maps, geospatial data services, and online viewing. Customers can use geospatial data and maps to enhance their recreational experience, make life-saving decisions, support scientific missions, and for countless other activities. Nationally consistent geospatial data from The National Map enable better policy and land management decisions and the effective enforcement of regulatory responsibilities. The National Map is easily accessible for display on the Web through such products as topographic maps and services and as downloadable data. The geographic information available from The National Map includes boundaries, elevation, geographic names, hydrography, land cover, orthoimagery, structures, and transportation. The majority of The National Map effort is devoted to acquiring and integrating medium-scale (nominally 1:24,000 scale) geospatial data for the eight base layers from a variety of sources and providing access to theresulting seamless coverages of geospatial data. The National Map also serves as the source of base mapping information for derived cartographic products, including 1:24,000 scale US Topo maps and georeferenced digital files of scanned historic topographic maps. Data sets and products from The National Map are intended for use by government, industry, and academia—focusing on geographic information system (GIS) users—as well as the public, especially in support of recreation activities. Other types of georeferenced or mapping information can be added within The National Map Viewer or brought in with The National Map data into a GIS to create specific types of maps or map views and (or) to perform modeling or analyses."

A web map to display the Land Corner Records as filed with the Natrona County Clerk. Land Corner Records are viewed in the Records Department.

This data set depicts federal lands having restrictions on access or activities -- that is, lands mangaed by the National Park Service, Defense Department, or Energy Department -- in western North America. The data set was created by reformatting and merging state- and province-based ownership data layers originally acquired from diverse sources (including state GAP programs, USBLM state offices and other sources). For each original dataset 3 additional fields, "Pub_Pvt", "CA_OWN", and "SOURCE" were added and populated based on the specific ownership information contained in the source data. The original coverages were then merged based on the "CA_OWN" field. Finally, NPS, DOD, and DOE lands were selected out of the ownership layer. All work was completed in AcMap 8.3. This product and all source data are available online from SAGEMAP: http://sagemap.wr.usgs.gov.

These data were compiled to demonstrate new predictive mapping approaches and provide comprehensive gridded 30-meter resolution soil property maps for the Colorado River Basin above Hoover Dam. Random forest models related environmental raster layers representing soil forming factors with field samples to render predictive maps that interpolate between sample locations. Maps represented soil pH, texture fractions (sand, silt clay, fine sand, very fine sand), rock, electrical conductivity (ec), gypsum, CaCO3, sodium adsorption ratio (sar), available water capacity (awc), bulk density (dbovendry), erodibility (kwfact), and organic matter (om) at 7 depths (0, 5, 15, 30, 60, 100, and 200 cm) as well as depth to restrictive layer (resdept) and surface rock size and cover. Accuracy and error estimated using a 10-fold cross validation indicated a range of model performances with coefficient of variation (R2) for models ranging from 0.20 to 0.76 with mean of 0.52 and a standard deviation of 0.12. Models of pH, om and ec had the best accuracy (R2 > 0.6). Most texture fractions, CaCO3, and SAR models had R2 values from 0.5-0.6. Models of kwfact, dbovendry, resdept, rock models, gypsum and awc had R2 values from 0.4-0.5 excepting near surface models which tended to perform better. Very fine sands and 200 cm estimates for other models generally performed poorly (R2 from 0.2-0.4), and sample size for the 200 cm models was too low for reliable model building. More than 90% of the soils data used was sampled since 2000, but some older samples are included. Uncertainty estimates were also developed by creating relative prediction intervals, which allow end users to evaluate uncertainty easily.

These data were compiled to demonstrate new predictive mapping approaches and provide comprehensive gridded 30-meter resolution soil property maps for the Colorado River Basin above Hoover Dam. Random forest models related environmental raster layers representing soil forming factors with field samples to render predictive maps that interpolate between sample locations. Maps represented soil pH, texture fractions (sand, silt clay, fine sand, very fine sand), rock, electrical conductivity (ec), gypsum, CaCO3, sodium adsorption ratio (sar), available water capacity (awc), bulk density (dbovendry), erodibility (kwfact), and organic matter (om) at 7 depths (0, 5, 15, 30, 60, 100, and 200 cm) as well as depth to restrictive layer (resdept) and surface rock size and cover. Accuracy and error estimated using a 10-fold cross validation indicated a range of model performances with coefficient of variation (R2) for models ranging from 0.20 to 0.76 with mean of 0.52 and a standard deviation of 0.12. Models of pH, om and ec had the best accuracy (R2 > 0.6). Most texture fractions, CaCO3, and SAR models had R2 values from 0.5-0.6. Models of kwfact, dbovendry, resdept, rock models, gypsum and awc had R2 values from 0.4-0.5 excepting near surface models which tended to perform better. Very fine sands and 200 cm estimates for other models generally performed poorly (R2 from 0.2-0.4), and sample size for the 200 cm models was too low for reliable model building. More than 90% of the soils data used was sampled since 2000, but some older samples are included. Uncertainty estimates were also developed by creating relative prediction intervals, which allow end users to evaluate uncertainty easily.

State lands information - ownership, easements, and easement applications - as provided by the Office of State Lands and Investments. Provided via a map server.

These data were compiled to demonstrate new predictive mapping approaches and provide comprehensive gridded 30-meter resolution soil property maps for the Colorado River Basin above Hoover Dam. Random forest models related environmental raster layers representing soil forming factors with field samples to render predictive maps that interpolate between sample locations. Maps represented soil pH, texture fractions (sand, silt clay, fine sand, very fine sand), rock, electrical conductivity (ec), gypsum, CaCO3, sodium adsorption ratio (sar), available water capacity (awc), bulk density (dbovendry), erodibility (kwfact), and organic matter (om) at 7 depths (0, 5, 15, 30, 60, 100, and 200 cm) as well as depth to restrictive layer (resdept) and surface rock size and cover. Accuracy and error estimated using a 10-fold cross validation indicated a range of model performances with coefficient of variation (R2) for models ranging from 0.20 to 0.76 with mean of 0.52 and a standard deviation of 0.12. Models of pH, om and ec had the best accuracy (R2 > 0.6). Most texture fractions, CaCO3, and SAR models had R2 values from 0.5-0.6. Models of kwfact, dbovendry, resdept, rock models, gypsum and awc had R2 values from 0.4-0.5 excepting near surface models which tended to perform better. Very fine sands and 200 cm estimates for other models generally performed poorly (R2 from 0.2-0.4), and sample size for the 200 cm models was too low for reliable model building. More than 90% of the soils data used was sampled since 2000, but some older samples are included. Uncertainty estimates were also developed by creating relative prediction intervals, which allow end users to evaluate uncertainty easily.

This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the Northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the Southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe’s Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe’s Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS.

These data were compiled to demonstrate new predictive mapping approaches and provide comprehensive gridded 30-meter resolution soil property maps for the Colorado River Basin above Hoover Dam. Random forest models related environmental raster layers representing soil forming factors with field samples to render predictive maps that interpolate between sample locations. Maps represented soil pH, texture fractions (sand, silt clay, fine sand, very fine sand), rock, electrical conductivity (ec), gypsum, CaCO3, sodium adsorption ratio (sar), available water capacity (awc), bulk density (dbovendry), erodibility (kwfact), and organic matter (om) at 7 depths (0, 5, 15, 30, 60, 100, and 200 cm) as well as depth to restrictive layer (resdept) and surface rock size and cover. Accuracy and error estimated using a 10-fold cross validation indicated a range of model performances with coefficient of variation (R2) for models ranging from 0.20 to 0.76 with mean of 0.52 and a standard deviation of 0.12. Models of pH, om and ec had the best accuracy (R2 > 0.6). Most texture fractions, CaCO3, and SAR models had R2 values from 0.5-0.6. Models of kwfact, dbovendry, resdept, rock models, gypsum and awc had R2 values from 0.4-0.5 excepting near surface models which tended to perform better. Very fine sands and 200 cm estimates for other models generally performed poorly (R2 from 0.2-0.4), and sample size for the 200 cm models was too low for reliable model building. More than 90% of the soils data used was sampled since 2000, but some older samples are included. Uncertainty estimates were also developed by creating relative prediction intervals, which allow end users to evaluate uncertainty easily.

This TNC Lands spatial dataset represents the lands and waters in which The Nature Conservancy (TNC) currently has, or historically had, an interest, legal or otherwise in Wyoming. The system of record for TNC Lands is the Legal Records Management (LRM) system, which is TNC’s database for all TNC land transactions.TNC properties should not be considered open to the public unless specifically designated as being so. TNC may change the access status at any time at its sole discretion. It's recommended to visit preserve-specific websites or contact the organization operating the preserve before any planned visit for the latest conditions, notices, and closures. TNC prohibits redistribution or display of the data in maps or online in any way that misleadingly implies such lands are universally open to the public.The types of current land interests represented in the TNC Lands data include: Fields and Attributes included in the public dataset:Field NameField DefinitionAttributesAttribute Definitions Public NameThe name of the tract that The Nature Conservancy (TNC) Business Unit (BU) uses for public audiences.Public name of tract if applicableN/A TNC Primary InterestThe primary interest held by The Nature Conservancy (TNC) on the tractFee OwnershipProperties where TNC currently holds fee-title or exclusive rights and control over real estate. Fee Ownership can include TNC Nature Preserves, managed areas, and properties that are held for future transfer. Conservation EasementProperties on which TNC holds a conservation easement, which is a legally binding agreement restricting the use of real property for conservation purposes (e.g., no development). The easement may additionally provide the holder (TNC) with affirmative rights, such as the rights to monitor species or to manage the land. It may run forever or for an expressed term of years. Deed RestrictionProperties where TNC holds a deed restriction, which is a provision placed in a deed restricting or limiting the use of the property in some manner (e.g., if a property goes up for sale, TNC gets the first option). TransferProperties where TNC historically had a legal interest (fee or easement), then subsequently transferred the interest to a conservation partner. AssistProperties where TNC assisted another agency/entity in protecting. Management Lease or AgreementAn agreement between two parties whereby one party allows the other to use their property for a certain period of time in exchange for a periodic fee. Grazing Lease or PermitA grazing lease or permit held by The Nature Conservancy Right of WayAn access easement or agreement held by The Nature Conservancy. OtherAnother real estate interest or legal agreement held by The Nature Conservancy Fee OwnerThe name of the organization serving as fee owner of the tract, or "Private Land Owner" if the owner is a private party. If The Nature Conservancy (TNC) primary interest is a "Transfer" or "Assist", then this is the fee owner at the time of the transaction.Fee Owner NameN/A Fee Org TypeThe type of organization(s) that hold(s) fee ownership. Chosen from a list of accepted values.Organization Types for Fee OwnershipFED:Federal, TRIB:American Indian Lands, STAT:State,DIST:Regional Agency Special District, LOC:Local Government, NGO:Non-Governmental Organization, PVT:Private, JNT:Joint, UNK:Unknown, TERR:Territorial, DESG:Designation Other Interest HolderThe name of the organization(s) that hold(s) a different interest in the tract, besides fee ownership or TNC Primary Interest. This may include TNC if the Other Interest is held or co-held by TNC. Multiple interest holders should be separated by a semicolon (;).Other Interest Holder NameN/A Other Interest Org TypeThe type of organization(s) that hold(s) a different interest in the tract, besides fee ownership. This may include TNC if the Other Interest is held or co-held by TNC. Chosen from a list of accepted values.Organization Types for interest holders:FED:Federal, TRIB:American Indian Lands, STAT:State,DIST:Regional Agency Special District, LOC:Local Government, NGO:Non-Governmental Organization, PVT:Private, JNT:Joint, UNK:Unknown, TERR:Territorial, DESG:Designation Other Interest TypeThe other interest type held on the tract. Chosen from a list of accepted values.​Access Right of Way; Conservation Easement; Co-held Conservation Easement; Deed Restriction; Co-held Deed Restriction; Fee Ownership; Co-held Fee Ownership; Grazing Lease or Permit; Life Estate; Management Lease or Agreement; Timber Lease or Agreement; OtherN/A Preserve NameThe name of The Nature Conservancy (TNC) preserve that the tract is a part of, this may be the same name as the as the "Public Name" for the tract.Preserve Name if applicableN/APublic AccessThe level of public access allowed on the tract.Open AccessAccess is encouraged on the tract, trails are maintained, signage is abundant, and parking is available. The tract may include regular hours of availability.Open with Limited AccessThere are no special requirements for public access to the tract, the tract may include regular hours of availability with limited amenities.Restricted AccessThe tract requires a special permit from the owner for access, a registration permit on public land, or has highly variable times or conditions to use.Closed AccessNo public access is allowed on the tract.UnknownAccess information for the tract is not currently available.Gap CategoryThe Gap Analysis Project (GAP) code for the tract. Gap Analysis is the science of determining how well we are protecting common plants and animals. Developing the data and tools to support that science is the mission of the Gap Analysis Project (GAP) at the US Geological Survey. See their website for more information, linked in the field name.1 - Permanent Protection for BiodiversityPermanent Protection for Biodiversity2 - Permanent Protection to Maintain a Primarily Natural StatePermanent Protection to Maintain a Primarily Natural State3 - Permanently Secured for Multiple Uses and in natural coverPermanently Secured for Multiple Uses and in natural cover39 - Permanently Secured and in agriculture or maintained grass coverPermanently Secured and in agriculture or maintained grass cover4 - UnsecuredUnsecured (temporary easements lands and/or municipal lands that are already developed (schools, golf course, soccer fields, ball fields)9 - UnknownUnknownProtected AcresThe planar area of the tract polygon in acres, calculated by the TNC Lands geographic information system (GIS).Total geodesic area of polygon in acresProjection: WGS 1984 Web Mercator Auxiliary SphereOriginal Protection DateThe original protection date for the tract, from the Land Resource Management (LRM) system record.Original protection dateN/AStateThe state within the United States of America or the Canadian province where the tract is located.Chosen from a list of state names.N/ACountryThe name of the country where the tract is located.Chosen from a list of countries.N/ADivisionThe name of the TNC North America Region Division where the tract is located. Chosen from a list of TNC North America DivisionsN/A

This dataset contains land use authorization leases, permits and easement cases derived from Legal Land Descriptions (LLD) contained in the US Bureau of Land Management's, BLM, Mineral and Land Record System(MLRS) and geocoded (mapped) using the Public Land Survey System (PLSS) derived from the most accurate survey data available through BLM Cadastral Survey workforce. The minimum data entry requirement for legal description for linear authorizations is to the nearest 40 acre aliquot level (e.g.,NENW). Legal descriptions for non-linear authorizations are as described on the authorizing document. Geospatial representations might be missing for some cases that can not be geocoded using the MLRS algorithm. This data set contains cases with the dispositions of 'Authorized', 'Pending','Closed', and 'Interim'.

This map shows the surficial geology of Kent County, Delaware, at a scale of 1:100,000. Maps at this scale are useful for viewing general geologic framework on a county-wide basis, determining the geology of watersheds, and recognizing the relationship of geology to regional or county-wide environmental or land-use issues. The map was compiled from topographic and geologic maps, aerial photographs, geologists' and drillers' logs, geophysical logs, soils maps, and sample descriptions. Samples from drill holes and outcrops were examined for comparison with previous descriptions. Descriptions of geologic units, unless otherwise referenced, were generated by the author after examination of cores, outcrops, and samples from the Delaware Geological Survey Core and Sample Repository.

A map displaying the property ownership for all the municipalities in Natrona County, as well as zoning and addresses.

These data were compiled to demonstrate new predictive mapping approaches and provide comprehensive gridded 30-meter resolution soil property maps for the Colorado River Basin above Hoover Dam. Random forest models related environmental raster layers representing soil forming factors with field samples to render predictive maps that interpolate between sample locations. Maps represented soil pH, texture fractions (sand, silt clay, fine sand, very fine sand), rock, electrical conductivity (ec), gypsum, CaCO3, sodium adsorption ratio (sar), available water capacity (awc), bulk density (dbovendry), erodibility (kwfact), and organic matter (om) at 7 depths (0, 5, 15, 30, 60, 100, and 200 cm) as well as depth to restrictive layer (resdept) and surface rock size and cover. Accuracy and error estimated using a 10-fold cross validation indicated a range of model performances with coefficient of variation (R2) for models ranging from 0.20 to 0.76 with mean of 0.52 and a standard deviation of 0.12. Models of pH, om and ec had the best accuracy (R2 > 0.6). Most texture fractions, CaCO3, and SAR models had R2 values from 0.5-0.6. Models of kwfact, dbovendry, resdept, rock models, gypsum and awc had R2 values from 0.4-0.5 excepting near surface models which tended to perform better. Very fine sands and 200 cm estimates for other models generally performed poorly (R2 from 0.2-0.4), and sample size for the 200 cm models was too low for reliable model building. More than 90% of the soils data used was sampled since 2000, but some older samples are included. Uncertainty estimates were also developed by creating relative prediction intervals, which allow end users to evaluate uncertainty easily.