Type:	Package
Title:	Soil Database Interface
Version:	2.8.11
Maintainer:	Andrew Brown <andrew.g.brown@usda.gov>
Description:	A collection of functions for reading soil data from U.S. Department of Agriculture Natural Resources Conservation Service (USDA-NRCS) and National Cooperative Soil Survey (NCSS) databases.
License:	GPL (≥ 3)
LazyLoad:	yes
Depends:	R (≥ 3.5.0)
Imports:	grDevices, graphics, stats, utils, methods, aqp (≥ 2.1.0), data.table, DBI, curl
Suggests:	jsonlite, xml2, httr, rvest, odbc, RSQLite, sf, wk, terra, raster, knitr, rmarkdown, testthat
Repository:	CRAN
URL:	https://github.com/ncss-tech/soilDB/, https://ncss-tech.github.io/soilDB/, https://ncss-tech.github.io/AQP/
BugReports:	https://github.com/ncss-tech/soilDB/issues
RoxygenNote:	7.3.2
Encoding:	UTF-8
Language:	en-US
LazyData:	false
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2025-07-11 00:13:28 UTC; Andrew.G.Brown
Author:	Dylan Beaudette [aut], Jay Skovlin [aut], Stephen Roecker [aut], Andrew Brown [aut, cre]
Date/Publication:	2025-07-11 03:40:02 UTC

Soil Database Interface

Description

A collection of functions for reading soil data from U.S. Department of Agriculture Natural Resources Conservation Service (USDA-NRCS) and National Cooperative Soil Survey (NCSS) databases

Details

This package provides methods for extracting soils information from local NASIS databases (MS SQL Server), local PedonPC and AKSite databases (MS Access format), Soil Data Access, and other soil-related web services.

Author(s)

J.M. Skovlin, D.E. Beaudette, S.M Roecker, A.G. Brown

See Also

fetchNASIS, SDA_query, loafercreek

Get 800m gridded soil properties from SoilWeb ISSR-800 Web Coverage Service (WCS)

Description

Intermediate-scale gridded (800m) soil property and interpretation maps from aggregated SSURGO and STATSGO data. These maps were developed by USDA-NRCS-SPSD staff in collaboration with UCD-LAWR. Originally for educational use and interactive thematic maps, these data are a suitable alternative to gridded STATSGO-derived thematic soil maps. The full size grids can be downloaded here.

Usage

ISSR800.wcs(aoi, var, res = 800, quiet = FALSE)

Arguments

Details

aoi should be specified as a SpatRaster, Spatial*, RasterLayer, SpatRaster/SpatVector, sf, sfc, or bbox object or a list containing:

aoi

bounding-box specified as (xmin, ymin, xmax, ymax) e.g. c(-114.16, 47.65, -114.08, 47.68)

crs

coordinate reference system of BBOX, e.g. 'OGC:CRS84' (EPSG:4326, WGS84 Longitude/Latitude)

The WCS query is parameterized using a rectangular extent derived from the above AOI specification, after conversion to the native CRS (EPSG:5070) of the ISSR-800 grids.

Variables available from this WCS can be queried using WCS_details(wcs = 'ISSR800').

Value

A SpatRaster (or RasterLayer) object containing indexed map unit keys and associated raster attribute table or a try-error if request fails. By default, spatial classes from the terra package are returned. If the input object class is from the raster or sp packages a RasterLayer is returned.

Note

There are still some issues to be resolved related to the encoding of NA Variables with a natural zero (e.g. SAR) have 0 set to NA.

Author(s)

D.E. Beaudette and A.G. Brown

Examples

## Not run: 
library(terra)

# see WCS_details() for variable options
WCS_details(wcs = 'ISSR800')

# get wind erodibility group
res <- ISSR800.wcs(list(aoi = c(-116, 35, -115.5, 35.5), crs = "EPSG:4326"), 
                   var = 'weg', res = 800)
plot(res)

## End(Not run)

Develop a Water Retention Curve from KSSL Data

Description

Water retention curve modeling via van Genuchten model and KSSL data.

Usage

KSSL_VG_model(VG_params, phi_min = 10^-6, phi_max = 10^8, pts = 100)

Arguments

Details

This function was developed to work with measured or estimated parameters of the van Genuchten model, as generated by the Rosetta model. As such, VG_params should have the following format and conventions:

theta_r

saturated water content, values should be in the range of {0, 1}

theta_s

residual water content, values should be in the range of {0, 1}

alpha

related to the inverse of the air entry suction, function expects log10-transformed values with units of 1/cm

npar

index of pore size distribution, function expects log10-transformed values (dimensionless)

Value

A list with the following components:

VG_curve

estimated water retention curve: paired estimates of water potential (phi) and water content (theta)

VG_function

spline function for converting water potential (phi, units of kPa) to estimated volumetric water content (theta, units of percent, range: {0, 1})

VG_inverse_function

spline function for converting volumetric water content (theta, units of percent, range: {0, 1}) to estimated water potential (phi, units of kPa)

Note

A practical example is given in the fetchSCAN tutorial.

Author(s)

D.E. Beaudette

References

water retention curve estimation

Examples

# basic example
d <- data.frame(
  theta_r = 0.0337216, 
  theta_s = 0.4864061, 
  alpha = -1.581517, 
  npar = 0.1227247
)

vg <- KSSL_VG_model(d)

str(vg)

Work with NASIS Choice Lists

Description

Create (ordered) factors and interchange between choice names, values and labels for lists of input vectors.

Usage

NASISChoiceList(
  x = NULL,
  colnames = names(x),
  what = "ColumnPhysicalName",
  choice = c("ChoiceName", "ChoiceValue", "ChoiceLabel"),
  obsolete = FALSE,
  factor = TRUE,
  droplevels = FALSE,
  ordered = TRUE,
  simplify = TRUE,
  dsn = NULL
)

Arguments

Value

A list of "choices" based on the input x that have been converted to a consistent target set of levels (specified by choice) via NASIS 7 metadata.

When factor=TRUE the result is a factor, possibly ordered when ordered=TRUE and the target domain is a "ranked" domain (i.e. ChoiceSequence has logical meaning).

When factor=FALSE the result is a character or numeric vector. Numeric vectors are always returned when choice is "ChoiceValue".

Examples

NASISChoiceList(1:3, "texcl")

NASISChoiceList(1:3, "pondfreqcl")

NASISChoiceList("Clay loam", "texcl", choice = "ChoiceValue")

NASISChoiceList("Silty clay loam", "texcl", choice = "ChoiceName")

Get/Set Options for Encoding NASIS Domains as Factors

Description

Set package option soilDB.NASIS.DomainsAsFactor for returning coded NASIS domains as factors.

Usage

NASISDomainsAsFactor(x = NULL)

Arguments

Value

logical, result of getOption("soilDB.NASIS.DomainsAsFactor")

Examples

## Not run: 
NASISDomansAsFactor(TRUE)

## End(Not run)

NASIS Local Database

Description

This is a guide on using databases that follow the NASIS schema. Most of the time users are querying an instance of the Microsoft SQL Server NASIS local transactional database running on their computer. It is possible to create file-based "snapshots" of a local instance of the NASIS database using SQLite. See ⁠[createStaticNASIS()]⁠ for details. These file-based snapshots, or other custom connections, can generally be specified to NASIS-related functions via the dsn argument.

Working With Coded Values and Decoding

Some values (choice lists) in NASIS are conventionally stored using numeric codes. The codes are defined by "domain" and allow for both "names" and "labels" as well as other descriptive information to be provided for each choice list element. See get_NASIS_column_metadata() for details.

Many soilDB functions call the function uncode() internally to handle conversion to human-readable values using official NASIS domains. If writing queries directly against the database source, such as a connection created with NASIS() or query run with dbQueryNASIS(), you call uncode() on the data.frame result of your query. Conversion of internal values to choice list names is based on domains associated with result column names.

When using a custom SQLite database, sometimes values in the database are delivered pre-decoded to make the database more directly usable. An example of this would be the Kellogg Soil Survey Laboratory morphologic database, the NASIS data corresponding to the laboratory analyses available through the Lab Data Mart (LDM).

To avoid issues with offsets between internal storage value and external readable value (for data such as farmland classification or Munsell color value and chroma), you should not call uncode() multiple times. Also, you can disable the "decoding" behavior made internally in soilDB functions by setting options(soilDB.NASIS.skip_uncode = TRUE).

NASIS 7 Tables, Columns and Foreign Keys

Description

This dataset contains NASIS 7 Tables, Columns and Foreign Keys

Search full text of Official Series Description on SoilWeb

Description

This is the R interface to OSD search by Section and OSD Search APIs provided by SoilWeb.

OSD records are searched with the PostgreSQL fulltext indexing and query system (syntax details). Each search field (except for the "brief narrative" and MLRA) corresponds with a section header in an OSD. The results may not include every OSD due to formatting errors and typos. Results are scored based on the number of times search terms match words in associated sections.

Usage

OSDquery(
  everything = NULL,
  mlra = "",
  taxonomic_class = "",
  typical_pedon = "",
  brief_narrative = "",
  ric = "",
  use_and_veg = "",
  competing_series = "",
  geog_location = "",
  geog_assoc_soils = ""
)

Arguments

Details

See this webpage for more information.

• family level taxa are derived from SC database, not parsed OSD records

• MLRA are derived via spatial intersection (SSURGO x MLRA polygons)

• MLRA-filtering is only possible for series used in the current SSURGO snapshot (component name)

• logical AND: &

• logical OR: |

• wildcard, e.g. rhy-something rhy:*

• search terms with spaces need doubled single quotes: ”san joaquin”

• combine search terms into a single expression: (grano:* | granite)

Related documentation can be found in the following tutorials

• overview of all soil series query functions

• competing soil series

• siblings

Value

a data.frame object containing soil series names that match patterns supplied as arguments.

Note

SoilWeb maintains a snapshot of the Official Series Description data.

Author(s)

D.E. Beaudette

References

USDA-NRCS OSD search tools: https://soilseries.sc.egov.usda.gov/

See Also

fetchOSD, siblings, fetchOSD

Examples

  # find all series that list Pardee as a geographically associated soil.
  s <- OSDquery(geog_assoc_soils = 'pardee')

  # get data for these series
  x <- fetchOSD(s$series, extended = TRUE, colorState = 'dry')

  # simple figure
  par(mar=c(0,0,1,1))
  aqp::plotSPC(x$SPC)

Query USDA-ARS ROSETTA Model API

Description

A simple interface to the ROSETTA model for predicting hydraulic parameters from soil properties. The ROSETTA API was developed by Dr. Todd Skaggs (USDA-ARS) and links to the work of Zhang and Schaap, (2017). See the related tutorial for additional examples.

Usage

ROSETTA(
  x,
  vars,
  v = c("1", "2", "3"),
  include.sd = FALSE,
  chunkSize = 10000,
  conf = NULL
)

Arguments

Details

Soil properties supplied in x must be described, in order, via vars argument. The API does not use the names but column ordering must follow: sand, silt, clay, bulk density, volumetric water content at 33kPa (1/3 bar), and volumetric water content at 1500kPa (15 bar).

The ROSETTA model relies on a minimum of 3 soil properties, with increasing (expected) accuracy as additional properties are included:

• required, sand, silt, clay: USDA soil texture separates (percentages) that sum to 100 percent

• optional, bulk density (any moisture basis): mass per volume after accounting for >2mm fragments, units of gm/cm3

• optional, volumetric water content at 33 kPa: roughly "field capacity" for most soils, units of cm^3/cm^3

• optional, volumetric water content at 1500 kPa: roughly "permanent wilting point" for most plants, units of cm^3/cm^3

The Rosetta pedotransfer function predicts five parameters for the van Genuchten model of unsaturated soil hydraulic properties

• theta_r : residual volumetric water content

• theta_s : saturated volumetric water content

• log10(alpha) : retention shape parameter ⁠[log10(1/cm)]⁠

• log10(npar) : retention shape parameter

• log10(ksat) : saturated hydraulic conductivity ⁠[log10(cm/d)]⁠

Column names not specified in vars are retained in the output.

Three versions of the ROSETTA model are available, selected using "v = 1", "v = 2", or "v = 3".

• version 1 - Schaap, M.G., F.J. Leij, and M.Th. van Genuchten. 2001. ROSETTA: a computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions. Journal of Hydrology 251(3-4): 163-176. doi: doi:10.1016/S0022-1694(01)00466-8.

• version 2 - Schaap, M.G., A. Nemes, and M.T. van Genuchten. 2004. Comparison of Models for Indirect Estimation of Water Retention and Available Water in Surface Soils. Vadose Zone Journal 3(4): 1455-1463. doi: doi:10.2136/vzj2004.1455.

• version 3 - Zhang, Y., and M.G. Schaap. 2017. Weighted recalibration of the Rosetta pedotransfer model with improved estimates of hydraulic parameter distributions and summary statistics (Rosetta3). Journal of Hydrology 547: 39-53. doi: doi:10.1016/j.jhydrol.2017.01.004.

Author(s)

D.E. Beaudette, Todd Skaggs (ARS), Richard Reid

References

Consider using the interactive version, with copy/paste functionality at: https://www.handbook60.org/rosetta.

Rosetta Model Home Page: https://www.ars.usda.gov/pacific-west-area/riverside-ca/agricultural-water-efficiency-and-salinity-research-unit/docs/model/rosetta-model/.

Python ROSETTA model: https://pypi.org/project/rosetta-soil/.

Yonggen Zhang, Marcel G. Schaap. 2017. Weighted recalibration of the Rosetta pedotransfer model with improved estimates of hydraulic parameter distributions and summary statistics (Rosetta3). Journal of Hydrology. 547: 39-53. doi:10.1016/j.jhydrol.2017.01.004.

Kosugi, K. 1999. General model for unsaturated hydraulic conductivity for soils with lognormal pore-size distribution. Soil Sci. Soc. Am. J. 63:270-277.

Mualem, Y. 1976. A new model predicting the hydraulic conductivity of unsaturated porous media. Water Resour. Res. 12:513-522.

Schaap, M.G. and W. Bouten. 1996. Modeling water retention curves of sandy soils using neural networks. Water Resour. Res. 32:3033-3040.

Schaap, M.G., Leij F.J. and van Genuchten M.Th. 1998. Neural network analysis for hierarchical prediction of soil water retention and saturated hydraulic conductivity. Soil Sci. Soc. Am. J. 62:847-855.

Schaap, M.G., and F.J. Leij, 1998. Database Related Accuracy and Uncertainty of Pedotransfer Functions, Soil Science 163:765-779.

Schaap, M.G., F.J. Leij and M. Th. van Genuchten. 1999. A bootstrap-neural network approach to predict soil hydraulic parameters. In: van Genuchten, M.Th., F.J. Leij, and L. Wu (eds), Proc. Int. Workshop, Characterization and Measurements of the Hydraulic Properties of Unsaturated Porous Media, pp 1237-1250, University of California, Riverside, CA.

Schaap, M.G., F.J. Leij, 1999, Improved prediction of unsaturated hydraulic conductivity with the Mualem-van Genuchten, Submitted to Soil Sci. Soc. Am. J.

van Genuchten, M.Th. 1980. A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci. Am. J. 44:892-898.

Schaap, M.G., F.J. Leij, and M.Th. van Genuchten. 2001. ROSETTA: a computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions. Journal of Hydrology 251(3-4): 163-176. doi: doi:10.1016/S0022-1694(01)00466-8.

Schaap, M.G., A. Nemes, and M.T. van Genuchten. 2004. Comparison of Models for Indirect Estimation of Water Retention and Available Water in Surface Soils. Vadose Zone Journal 3(4): 1455-1463. doi: doi:10.2136/vzj2004.1455.

Zhang, Y., and M.G. Schaap. 2017. Weighted recalibration of the Rosetta pedotransfer model with improved estimates of hydraulic parameter distributions and summary statistics (Rosetta3). Journal of Hydrology 547: 39-53. doi: doi:10.1016/j.jhydrol.2017.01.004.

USDA-NRCS Station Metadata for SCAN, CSCAN, SNOTEL, SNOWLITE Networks

Description

These metadata are a work in progress.

Format

A data.frame with 1186 SCAN, CSCAN, SNOTEL, and SNOWLITE station metadata records

Query Soil Data Access

Description

Submit a query to the Soil Data Access (SDA) REST/JSON web-service and return the results as a data.frame. There is a 100,000 record and 32Mb JSON serialization limit per query. Queries should contain a WHERE clause or JOIN condition to limit the number of rows affected / returned. Consider wrapping calls to SDA_query() in a function that can iterate over logical chunks (e.g. areasymbol, mukey, cokey, etc.). The function makeChunks() can help with such iteration. All usages of SDA_query() should handle the possibility of a try-error result in case the web service connection is down or if an invalid query is passed to the endpoint.

Usage

SDA_query(q, dsn = NULL)

Arguments

Details

The SDA website can be found at https://sdmdataaccess.nrcs.usda.gov and query examples can be found at https://sdmdataaccess.nrcs.usda.gov/QueryHelp.aspx. A library of query examples can be found at https://nasis.sc.egov.usda.gov/NasisReportsWebSite/limsreport.aspx?report_name=SDA-SQL_Library_Home.

SSURGO (detailed soil survey) and STATSGO (generalized soil survey) data are stored together within SDA. This means that queries that don't specify an area symbol may result in a mixture of SSURGO and STATSGO records. See the examples below and the SDA Tutorial for details.

Value

A data.frame result for queries that return a single table. A list of data.frame for queries that return multiple tables. NULL if result is empty, and try-error on error.

Note

This function requires the httr, jsonlite, and xml2 packages

Author(s)

D.E. Beaudette, A.G Brown

See Also

SDA_spatialQuery()

Examples

  ## get SSURGO export date for all soil survey areas in California
  # there is no need to filter STATSGO
  # because we are filtering on SSURGO area symbols
  q <- "SELECT areasymbol, saverest FROM sacatalog WHERE areasymbol LIKE 'CA%';"
  x <- SDA_query(q)
  head(x)


  ## get SSURGO component data associated with the
  ## Amador series / major component only
  # this query must explicitly filter out STATSGO data
  q <- "SELECT cokey, compname, comppct_r FROM legend
    INNER JOIN mapunit mu ON mu.lkey = legend.lkey
    INNER JOIN component co ON mu.mukey = co.mukey
    WHERE legend.areasymbol != 'US' AND compname = 'Amador';"

  res <- SDA_query(q)
  str(res)

  ## get component-level data for a specific soil survey area (Yolo county, CA)
  # there is no need to filter STATSGO because the query contains
  # an implicit selection of SSURGO data by areasymbol
  q <- "SELECT
    component.mukey, cokey, comppct_r, compname, taxclname,
    taxorder, taxsuborder, taxgrtgroup, taxsubgrp
    FROM legend
    INNER JOIN mapunit ON mapunit.lkey = legend.lkey
    LEFT OUTER JOIN component ON component.mukey = mapunit.mukey
    WHERE legend.areasymbol = 'CA113' ;"

  res <- SDA_query(q)
  str(res)

  ## get tabular data based on result from spatial query
  # there is no need to filter STATSGO because
  # SDA_Get_Mukey_from_intersection_with_WktWgs84() implies SSURGO
  p <- wk::as_wkt(wk::rct(-120.9, 37.7, -120.8, 37.8))
  q <- paste0("SELECT mukey, cokey, compname, comppct_r FROM component
      WHERE mukey IN (SELECT DISTINCT mukey FROM
      SDA_Get_Mukey_from_intersection_with_WktWgs84('", p,
       "')) ORDER BY mukey, cokey, comppct_r DESC")

   x <- SDA_query(q)
   str(x)

Query Soil Data Access by spatial intersection with supplied geometry

Description

Query SDA (SSURGO / STATSGO) records via spatial intersection with supplied geometries. Input can be SpatialPoints, SpatialLines, or SpatialPolygons objects with a valid CRS. Map unit keys, overlapping polygons, or the spatial intersection of geom + SSURGO / STATSGO polygons can be returned. See details.

Usage

SDA_spatialQuery(
  geom,
  what = "mukey",
  geomIntersection = FALSE,
  geomAcres = TRUE,
  db = c("SSURGO", "STATSGO", "SAPOLYGON"),
  byFeature = FALSE,
  idcol = "gid",
  query_string = FALSE,
  as_Spatial = getOption("soilDB.return_Spatial", default = FALSE)
)

Arguments

Details

Queries for map unit keys are always more efficient vs. queries for overlapping or intersecting (i.e. least efficient) features. geom is converted to GCS / WGS84 as needed. Map unit keys are always returned when using what = "mupolygon".

SSURGO (detailed soil survey, typically 1:24,000 scale) and STATSGO (generalized soil survey, 1:250,000 scale) data are stored together within SDA. This means that queries that don't specify an area symbol may result in a mixture of SSURGO and STATSGO records. See the examples below and the SDA Tutorial for details.

Value

A data.frame if what = 'mukey', otherwise an sf object. A try-error in the event the request cannot be made or if there is an error in the query.

Note

Row-order is not preserved across features in geom and returned object. Use byFeature argument to iterate over features and return results that are 1:1 with the inputs. Polygon area in acres is computed server-side when what = 'mupolygon' and geomIntersection = TRUE.

Author(s)

D.E. Beaudette, A.G. Brown, D.R. Schlaepfer

See Also

SDA_query

Examples

## Not run: 
library(aqp)
library(sf)

## query at a point

# example point
p <- sf::st_as_sf(data.frame(x = -119.72330, y = 36.92204),
                  coords = c('x', 'y'),
                  crs = 4326)

# query map unit records at this point
res <- SDA_spatialQuery(p, what = 'mukey')

# convert results into an SQL "IN" statement
# useful when there are multiple intersecting records
mu.is <- format_SQL_in_statement(res$mukey)

# composite SQL WHERE clause
sql <- sprintf("mukey IN %s", mu.is)

# get commonly used map unit / component / chorizon records
# as a SoilProfileCollection object
# request that results contain `mukey` with `duplicates = TRUE`
x <- fetchSDA(sql, duplicates = TRUE)

# safely set texture class factor levels
# by making a copy of this column
# this will save in lieu of textures in the original
# `texture` column
aqp::horizons(x)$texture.class <- factor(x$texture, levels = aqp::SoilTextureLevels())

# graphical depiction of the result
aqp::plotSPC(
  x,
  color = 'texture.class',
  label = 'compname',
  name = 'hzname',
  cex.names = 1,
  width = 0.25,
  plot.depth.axis = FALSE,
  hz.depths = TRUE,
  name.style = 'center-center'
)

## query mukey + geometry that intersect with a bounding box

# define a bounding box: xmin, xmax, ymin, ymax
#
#         +-------------------(ymax, xmax)
#         |                        |
#         |                        |
#     (ymin, xmin) ----------------+
b <- c(-119.747629, -119.67935, 36.912019, 36.944987)

# convert bounding box to WKT
bbox.sp <- sf::st_as_sf(wk::rct(
  xmin = b[1],
  xmax = b[2],
  ymin = b[3],
  ymax = b[4],
  crs = sf::st_crs(4326)
))

# results contain associated map unit keys (mukey)
# return SSURGO polygons, after intersection with provided BBOX
ssurgo.geom <- SDA_spatialQuery(bbox.sp,
                                what = 'mupolygon',
                                db = 'SSURGO',
                                geomIntersection = TRUE)

# return STATSGO polygons, after intersection with provided BBOX
statsgo.geom <- SDA_spatialQuery(bbox.sp,
                                 what = 'mupolygon',
                                 db = 'STATSGO',
                                 geomIntersection = TRUE)

# inspect results
par(mar = c(0, 0, 3, 1))
plot(sf::st_geometry(ssurgo.geom), border = 'royalblue')
plot(
  sf::st_geometry(statsgo.geom),
  lwd = 2,
  border = 'firebrick',
  add = TRUE
)
plot(sf::st_geometry(bbox.sp), lwd = 3, add = TRUE)
legend(
  x = 'topright',
  legend = c('BBOX', 'STATSGO', 'SSURGO'),
  lwd = c(3, 2, 1),
  col = c('black', 'firebrick', 'royalblue'),
)

# quick reminder that STATSGO map units often contain many components
# format an SQL IN statement using the first STATSGO mukey
mu.is <- format_SQL_in_statement(statsgo.geom$mukey[1])

# composite SQL WHERE clause
sql <- sprintf("mukey IN %s", mu.is)

# get commonly used map unit / component / chorizon records
# as a SoilProfileCollection object
x <- fetchSDA(sql)

# tighter figure margins
par(mar = c(0, 0, 3, 1))

# organize component sketches by national map unit symbol
# color horizons via awc
# adjust legend title
# add alternate label (vertical text) containing component percent
# move horizon names into the profile sketches
# make profiles wider
aqp::groupedProfilePlot(
  x,
  groups = 'nationalmusym',
  label = 'compname',
  color = 'awc_r',
  col.label = 'Available Water Holding Capacity (cm / cm)',
  alt.label = 'comppct_r',
  name.style = 'center-center',
  width = 0.3
)

mtext(
  'STATSGO (1:250,000) map units contain a lot of components!',
  side = 1,
  adj = 0,
  line = -1.5,
  at = 0.25,
  font = 4
)

## End(Not run)

Graphical Description of US Soil Taxonomy Soil Temperature Regimes

Description

Graphical Description of US Soil Taxonomy Soil Temperature Regimes

Usage

STRplot(mast, msst, mwst, permafrost = FALSE, pt.cex = 2.75, leg.cex = 0.85)

Arguments

Details

Soil Temperature Regime Evaluation Tutorial

Author(s)

D.E. Beaudette

References

Soil Survey Staff. 2015. Illustrated guide to soil taxonomy. U.S. Department of Agriculture, Natural Resources Conservation Service, National Soil Survey Center, Lincoln, Nebraska.

See Also

estimateSTR

Examples

par(mar=c(4,1,0,1))
STRplot(mast = 0:25, msst = 10, mwst = 1)

Get SSURGO Data via Spatial Query

Description

Get SSURGO Data via Spatial Query to SoilWeb

Data are currently available from SoilWeb. These data are a snapshot of the "official" data. The snapshot date is encoded in the "soilweb_last_update" column in the function return value. Planned updates to this function will include a switch to determine the data source: "official" data via USDA-NRCS servers, or a "snapshot" via SoilWeb.

Usage

SoilWeb_spatial_query(
  bbox = NULL,
  coords = NULL,
  what = "mapunit",
  source = "soilweb"
)

Arguments

Value

The data returned from this function will depend on the query style. See examples below.

Note

SDA now supports spatial queries, consider using SDA_spatialQuery() instead.

Author(s)

D.E. Beaudette

Examples

    # query by bbox
    SoilWeb_spatial_query(bbox=c(-122.05, 37, -122, 37.05))
    
    # query by coordinate pair
    SoilWeb_spatial_query(coords=c(-121, 38))

Web Coverage Services Details

Description

List variables or databases provided by soilDB web coverage service (WCS) abstraction. These lists will be expanded in future versions.

Usage

WCS_details(wcs = c("mukey", "ISSR800", "soilColor"))

Arguments

Value

a data.frame

Examples

WCS_details(wcs = 'ISSR800')

Create a database from SSURGO Exports

Description

The following database types are tested and fully supported:

• SQLite or Geopackage

• DuckDB

• Postgres or PostGIS

Usage

createSSURGO(
  filename = NULL,
  exdir,
  conn = NULL,
  pattern = NULL,
  include_spatial = TRUE,
  include_tabular = TRUE,
  dissolve_field = NULL,
  maxruledepth = 0,
  overwrite = FALSE,
  header = FALSE,
  quiet = TRUE,
  ...
)

Arguments

Details

In theory any other DBI-compatible data source can be used for output. See conn argument. If you encounter issues using specific DBI connection types, please report in the soilDB issue tracker.

Value

Character. Vector of layer/table names in filename.

See Also

downloadSSURGO()

Examples

## Not run: 
 downloadSSURGO("areasymbol IN ('CA067', 'CA077', 'CA632')", destdir = "SSURGO_test")
 createSSURGO("test.gpkg", "SSURGO_test")

## End(Not run)

Create a memory or file-based instance of NASIS database

Description

Create a memory or file-based instance of NASIS database for selected tables.

Usage

createStaticNASIS(
  tables = NULL,
  new_names = NULL,
  SS = TRUE,
  dsn = NULL,
  output_path = NULL,
  verbose = FALSE
)

Arguments

Value

A named list of results from calling dbQueryNASIS for all columns in each NASIS table.

Create local NASIS database connection

Description

Create a connection to a local NASIS database with DBI

Usage

dbConnectNASIS(dsn = NULL)

NASIS(dsn = NULL)

Arguments

Value

A DBIConnection object, as returned by DBI::dbConnect(). If dsn is a DBIConnection, the attribute isUserDefined of the result is set to TRUE. If the DBIConnection is created by the internal NASIS connection process, isUserDefined is set to FALSE.

Query a NASIS DBIConnection

Description

Send queries to a NASIS DBIConnection

Usage

dbQueryNASIS(conn, q, close = TRUE, ...)

Arguments

Value

Result of DBI::dbGetQuery

Get SSURGO ZIP files from Web Soil Survey 'Download Soils Data'

Description

Download ZIP files containing spatial (ESRI shapefile) and tabular (TXT) files with standard SSURGO format; optionally including the corresponding SSURGO Template Database with include_template=TRUE.

Usage

downloadSSURGO(
  WHERE = NULL,
  areasymbols = NULL,
  destdir = tempdir(),
  exdir = destdir,
  include_template = FALSE,
  db = c("SSURGO", "STATSGO"),
  extract = TRUE,
  remove_zip = FALSE,
  overwrite = FALSE,
  quiet = FALSE
)

Arguments

Details

To specify the Soil Survey Areas you would like to obtain data you use a WHERE clause for query of sacatalog table such as areasymbol = 'CA067', "areasymbol IN ('CA628', 'CA067')" or ⁠areasymbol LIKE 'CT%'⁠.

When db="STATSGO" the WHERE argument is not supported. Allowed areasymbols include "US" and two-letter state codes e.g. "WY" for the Wyoming general soils map.

Pipe-delimited TXT files are found in /tabular/ folder extracted from a SSURGO ZIP. The files are named for tables in the SSURGO schema. There is no header / the files do not have column names. See the Soil Data Access Tables and Columns Report: https://sdmdataaccess.nrcs.usda.gov/documents/TablesAndColumnsReport.pdf for details on tables, column names and metadata including the default sequence of columns used in TXT files. The function returns a try-error if the WHERE/areasymbols arguments result in

Several ESRI shapefiles are found in the /spatial/ folder extracted from a SSURGO ZIP. These have prefix soilmu_ (mapunit), soilsa_ (survey area), soilsf_ (special features). There will also be a TXT file with prefix soilsf_ describing any special features. Shapefile names then have an a_ (polygon), l_ (line), p_ (point) followed by the soil survey area symbol.

Value

character. Paths to downloaded ZIP files (invisibly). May not exist if remove_zip = TRUE.

See Also

createSSURGO()

Estimate color mixtures using weighted average of CIELAB color coordinates

Description

Estimate color mixtures using weighted average of CIELAB color coordinates

Usage

estimateColorMixture(x, wt = "pct", backTransform = FALSE)

Arguments

Value

A data.frame containing estimated color mixture

Note

See aqp::mixMunsell() for a more realistic (but slower) simulation of subtractive mixing of pigments. An efficient replacement for this function (wt. mean in CIELAB coordinates) is implemented in aqp::mixMunsell(..., mixingMethod = 'estimate').

Author(s)

D.E. Beaudette

Estimate Soil Temperature Regime

Description

Estimate soil temperature regime (STR) based on mean annual soil temperature (MAST), mean summer temperature (MSST), mean winter soil temperature (MWST), presence of O horizons, saturated conditions, and presence of permafrost. Several assumptions are made when O horizon or saturation are undefined.

Usage

estimateSTR(
  mast,
  mean.summer,
  mean.winter,
  O.hz = NA,
  saturated = NA,
  permafrost = FALSE
)

Arguments

Details

Soil Temperature Regime Evaluation Tutorial

Value

Vector of soil temperature regimes.

Author(s)

D.E. Beaudette

References

Soil Survey Staff. 2015. Illustrated guide to soil taxonomy. U.S. Department of Agriculture, Natural Resources Conservation Service, National Soil Survey Center, Lincoln, Nebraska.

See Also

STRplot

Examples

# simple example
estimateSTR(mast=17, mean.summer = 22, mean.winter = 12)

Fetch Harmonized World Soil Database Data

Description

Creates a local cache of FAO Harmonized World Soil Database (HWSD) information. Source raster map in ESRI Grid format (.bil) is converted to GeoTIFF. The source tabular database in Microsoft Access (.mdb) format is converted to SQLite.

Usage

fetchHWSD(
  x = NULL,
  hwsd_url = "https://s3.eu-west-1.amazonaws.com/data.gaezdev.aws.fao.org/HWSD/",
  hwsd_version = 2L,
  force = FALSE
)

get_HWSD_path(what = c("sqlite", "mdb", "raster", "path"), hwsd_version = 2L)

Arguments

Value

A SpatRaster object with mapunit-level aggregate information stored as categories.

Source

Food and Agriculture Organization of the United Nations (FAO), Soils Portal, Harmonized World Soil Database (HWSD) v2.0 https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v20/en/

Examples

## Not run: 
  x <- terra::vect(system.file("ex", "lux.shp", package = "terra"))
  res <- fetchHWSD(x)
  
  # categorical data (WRB class)
  terra::activeCat(res) <- "WRB4"
  
  # view WRB4 map
  terra::plot(res)
  terra::lines(x, col = "white")
  
  # convert categories containing numeric data to numeric values
  res2 <- terra::catalyze(res)
  
  # view AWC map
  terra::plot(res2$AWC, main = "Available Water Capacity, mm")
  terra::lines(x, col = "white")
  
  # access tabular data from cached SQLite database
  SDA_query("SELECT * FROM HWSD2_SMU LIMIT 1", dsn = get_HWSD_path())

## End(Not run)

Get Kellogg Soil Survey Laboratory Data from SoilWeb snapshot

Description

Download soil characterization and morphologic data via BBOX, MLRA, or soil series name query, from the KSSL database.

Usage

fetchKSSL(
  series = NA,
  bbox = NA,
  mlra = NA,
  pedlabsampnum = NA,
  pedon_id = NA,
  pedon_key = NA,
  returnMorphologicData = FALSE,
  returnGeochemicalData = FALSE,
  simplifyColors = FALSE,
  progress = TRUE
)

Arguments

Details

This interface has largely been superseded by the Soil Data Access snapshot of the Laboratory Data Mart, available via fetchLDM().

Series-queries are case insensitive. Series name is based on the "correlated as" field (from KSSL snapshot) when present. The "sampled as" classification was promoted to "correlated as" if the "correlated as" classification was missing.

When returnMorphologicData is TRUE, the resulting object is a list. The standard output from fetchKSSL (SoilProfileCollection object) is stored in the named element "SPC". The additional elements are basic morphologic data: soil color, rock fragment volume, pores, structure, and redoximorphic features. There is a 1:many relationship between the horizon data in "SPC" and the additional dataframes in morph. See examples for ideas on how to "flatten" these tables.

When returnGeochemicalData is TRUE, the resulting object is a list. The standard output from fetchKSSL (SoilProfileCollection object) is stored in the named element "SPC". The additional elements are geochemical and mineralogy analysis tables, specifically: geochemical/elemental analyses "geochem", optical mineralogy "optical", and X-ray diffraction / thermal "xrd_thermal". returnGeochemicalData will include additional dataframes geochem, optical, and xrd_thermal in list result.

Setting simplifyColors=TRUE will automatically flatten the soil color data and join to horizon level attributes.

Function arguments (series, mlra, etc.) are fully vectorized except for bbox.

Value

a SoilProfileCollection object when returnMorphologicData is FALSE, otherwise a list.

Note

SoilWeb maintains a snapshot of these KSSL and NASIS data. The SoilWeb snapshot was developed using methods described here: https://github.com/dylanbeaudette/process-kssl-snapshot. Please use the link below for the live data.

Author(s)

D.E. Beaudette and A.G. Brown

References

http://ncsslabdatamart.sc.egov.usda.gov/

See Also

fetchOSD

Examples

    library(aqp)

    # search by series name
    s <- fetchKSSL(series='auburn')

    # search by bounding-box
    # s <- fetchKSSL(bbox=c(-120, 37, -122, 38))

    # how many pedons
    length(s)

    # plot
    aqp::plotSPC(s, name='hzn_desgn', max.depth=150)

    ##
    ## morphologic data
    ##

    # get lab and morphologic data
    s <- fetchKSSL(series='auburn', returnMorphologicData = TRUE)

    # extract SPC
    pedons <- s$SPC

Query data from Kellogg Soil Survey Laboratory Data Mart via Soil Data Access or local SQLite snapshot

Description

This function provides access to the Kellogg Soil Survey Laboratory Data Mart via Soil Data Access or a local SQLite snapshot. See details and examples for additional usage instructions.

Usage

fetchLDM(
  x = NULL,
  what = "pedlabsampnum",
  bycol = "pedon_key",
  tables = c("lab_physical_properties", "lab_chemical_properties",
    "lab_calculations_including_estimates_and_default_values", "lab_rosetta_Key"),
  WHERE = NULL,
  chunk.size = 1000,
  ntries = 3,
  layer_type = c("horizon", "layer", "reporting layer"),
  area_type = c("ssa", "country", "state", "county", "mlra", "nforest", "npark"),
  prep_code = c("S", ""),
  analyzed_size_frac = c("<2 mm", ""),
  dsn = NULL
)

Arguments

Details

You can download SQLite or GeoPackage snapshots here: https://ncsslabdatamart.sc.egov.usda.gov/database_download.aspx. Specify the dsn argument to use a local copy of the lab data rather than Soil Data Access web service.

Lab Data Mart model diagram: https://jneme910.github.io/Lab_Data_Mart_Documentation/Documents/SDA_KSSL_Data_model.html If the chunk.size parameter is set too large and the Soil Data Access request fails, the algorithm will re-try the query with a smaller (halved) chunk.size argument. This will be attempted up to 3 times before returning NULL

The default behavior joins the lab_area tables only for the "Soil Survey Area" related records. You can specify alternative area records for use in x, what or WHERE arguments by setting area_type to a different value.

When requesting data from "lab_major_and_trace_elements_and_oxides", "lab_mineralogy_glass_count", or "lab_xray_and_thermal" multiple preparation codes (prep_code) or size fractions (analyzed_size_frac) are possible. The default behavior of fetchLDM() is to attempt to return a topologically valid (minimal overlaps) SoilProfileCollection. This is achieved by setting prep_code="S" ("sieved") and analyzed_size_frac="<2 mm". You may specify alternate or additional preparation codes or fractions as needed, but note that this may cause "duplication" of some layers where measurements were made with different preparation or on fractionated samples

Value

a SoilProfileCollection for a successful query, a try-error if no site/pedon locations can be found or NULL for an empty lab_layer (within sites/pedons) result

Examples

## Not run: 
  
  # fetch by Soil Survey Area area symbol (area_code using default "ssa" area_type)
  res <- fetchLDM("CA630", what = "area_code")
  
  # fetch by Major Land Resource area symbol (area_code using "mlra" area_type)
  res <- fetchLDM("22A", what = "area_code", area_type = "mlra")
  
  # fetch by multiple case-insensitive taxon name
  # (correlated or sampled as Musick or Holland series)
  res <- fetchLDM(WHERE = "(CASE WHEN corr_name IS NOT NULL 
                                THEN LOWER(corr_name) 
                                ELSE LOWER(samp_name) 
                            END) IN ('musick', 'holland')")

  # physical properties of soils correlated as taxonomic subgroup "Typic Argialbolls"
  res <- fetchLDM(x = "Typic Argialbolls", 
                  what = "corr_taxsubgrp", 
                  tables = "lab_physical_properties")


## End(Not run)

Get a pedon or component data SoilProfileCollection from NASIS

Description

Fetch commonly used site/pedon/horizon or mapunit component data from NASIS, returned as a SoilProfileCollection object.

This function imports data from NASIS into R as a SoilProfileCollection object. It "flattens" NASIS pedon and component tables, including their child tables, into several more manageable data frames. Primarily these functions access the local NASIS database using an ODBC connection. The dsn argument allows you to specify a path or DBIConnection to an SQLite database. The argument from = "pedon_report", data can be read from the NASIS Report 'fetchNASIS', from either text file or URL (specified as url). The primary purpose of fetchNASIS(from = "pedon_report") is importing datasets larger than 8000+ pedons/components.

Tutorials:

• fetchNASIS Columns

• fetchNASIS Pedons Tutorial

• fetchNASIS Components Tutorial

Usage

fetchNASIS(
  from = "pedons",
  url = NULL,
  SS = TRUE,
  rmHzErrors = FALSE,
  nullFragsAreZero = TRUE,
  soilColorState = "moist",
  mixColors = FALSE,
  lab = FALSE,
  fill = FALSE,
  dropAdditional = TRUE,
  dropNonRepresentative = TRUE,
  duplicates = FALSE,
  stringsAsFactors = NULL,
  dsn = NULL
)

get_concentrations_from_NASIS_db(
  SS = TRUE,
  stringsAsFactors = NULL,
  dsn = NULL
)

get_phfmp_from_NASIS_db(SS = TRUE, stringsAsFactors = NULL, dsn = NULL)

Arguments

Details

The value of nullFragsAreZero will have a significant impact on the rock fragment fractions returned by fetchNASIS. Set nullFragsAreZero = FALSE in those cases where there are many data-gaps and NULL rock fragment values should be interpreted as NULL. Set nullFragsAreZero = TRUE in those cases where NULL rock fragment values should be interpreted as 0.

This function attempts to do most of the boilerplate work when extracting site/pedon/horizon or component data from a local NASIS database. Pedon IDs that are missing horizon data, or have errors in their horizonation are printed on the console. Pedons with combination horizons (e.g. B/C) are erroneously marked as errors due to the way in which they are stored in NASIS as two overlapping horizon records.

Value

A SoilProfileCollection object

Author(s)

D. E. Beaudette, J. M. Skovlin, S.M. Roecker, A.G. Brown

See Also

get_component_data_from_NASIS()

Get NCSS Pedon laboratory data from NASIS

Description

Fetch KSSL laboratory pedon/horizon layer data from a local NASIS database, return as a SoilProfileCollection object.

Usage

fetchNASISLabData(SS = TRUE, dsn = NULL)

Arguments

Value

a SoilProfileCollection object

Author(s)

J.M. Skovlin and D.E. Beaudette

See Also

get_labpedon_data_from_NASIS_db

Get component tables from NASIS Web Reports

Description

Get component tables from NASIS Web Reports

Usage

fetchNASISWebReport(
  projectname,
  rmHzErrors = FALSE,
  fill = FALSE,
  stringsAsFactors = NULL
)

get_component_from_NASISWebReport(projectname, stringsAsFactors = NULL)

get_chorizon_from_NASISWebReport(
  projectname,
  fill = FALSE,
  stringsAsFactors = NULL
)

get_legend_from_NASISWebReport(
  mlraoffice,
  areasymbol,
  droplevels = TRUE,
  stringsAsFactors = NULL
)

get_lmuaoverlap_from_NASISWebReport(
  areasymbol,
  droplevels = TRUE,
  stringsAsFactors = NULL
)

get_mapunit_from_NASISWebReport(
  areasymbol,
  droplevels = TRUE,
  stringsAsFactors = NULL
)

get_projectmapunit_from_NASISWebReport(projectname, stringsAsFactors = NULL)

get_projectmapunit2_from_NASISWebReport(
  mlrassoarea,
  fiscalyear,
  projectname,
  stringsAsFactors = NULL
)

get_project_from_NASISWebReport(mlrassoarea, fiscalyear)

get_progress_from_NASISWebReport(mlrassoarea, fiscalyear, projecttypename)

get_project_correlation_from_NASISWebReport(
  mlrassoarea,
  fiscalyear,
  projectname
)

get_cosoilmoist_from_NASISWebReport(
  projectname,
  impute = TRUE,
  stringsAsFactors = NULL
)

get_sitesoilmoist_from_NASISWebReport(usiteid)

Arguments

Value

A data.frame or list with the results.

Author(s)

Stephen Roecker

Get Official Series Descriptions and summaries from SoilWeb API

Description

This function fetches a variety of data associated with named soil series, extracted from the USDA-NRCS Official Series Description text files and detailed soil survey (SSURGO). These data are updated quarterly and made available via SoilWeb. Set extended = TRUE and see the soilweb.metadata list element for information on when the source data were last updated.

Usage

fetchOSD(soils, colorState = "moist", extended = FALSE)

Arguments

Details

• overview of all soil series query functions

• competing soil series

• siblings

The standard set of "site" and "horizon" data are returned as a SoilProfileCollection object (extended = FALSE). The "extended" suite of summary data can be requested by setting extended = TRUE. The resulting object will be a list with the following elements:

SPC

SoilProfileCollection containing standards "site" and "horizon" data

competing

competing soil series from the SC database snapshot

geog_assoc_soils

geographically associated soils, extracted from named section in the OSD

geomcomp

empirical probabilities for geomorphic component, derived from the current SSURGO snapshot

hillpos

empirical probabilities for hillslope position, derived from the current SSURGO snapshot

mtnpos

empirical probabilities for mountain slope position, derived from the current SSURGO snapshot

terrace

empirical probabilities for river terrace position, derived from the current SSURGO snapshot

flats

empirical probabilities for flat landscapes, derived from the current SSURGO snapshot

shape_across

empirical probabilities for surface shape (across-slope) from the current SSURGO snapshot

shape_down

empirical probabilities for surface shape (down-slope) from the current SSURGO snapshot

pmkind

empirical probabilities for parent material kind, derived from the current SSURGO snapshot

pmorigin

empirical probabilities for parent material origin, derived from the current SSURGO snapshot

mlra

empirical MLRA membership values, derived from the current SSURGO snapshot

ecoclassid

area cross-tabulation of ecoclassid by soil series name, derived from the current SSURGO snapshot, major components only

climate

climate summaries from PRISM stack (CONUS only)

NCCPI

select quantiles of NCCPI and Irrigated NCCPI, derived from the current SSURGO snapshot

metadata

metadata associated with SoilWeb cached summaries

When using extended = TRUE, there are a couple of scenarios in which series morphology contained in SPC do not fully match records in the associated series summary tables (e.g. competing).

1. A query for soil series that exist entirely outside of CONUS (e.g. PALAU).

- Climate summaries are empty data.frames because these summaries are currently generated from PRISM. We are working on a solution that uses DAYMET.

2. A query for data within CONUS, but OSD morphology missing due to parsing error (e.g. formatting, typos).

- Extended summaries are present but morphology missing from SPC. A warning is issued.

These last two cases are problematic for analysis that makes use of morphology and extended data, such as outlined in this tutorial on competing soil series.

Value

a SoilProfileCollection object containing basic soil morphology and taxonomic information.

Note

Requests to the SoilWeb API are split into batches of 100 series names from soils via makeChunks().

Author(s)

D.E. Beaudette, A.G. Brown

References

USDA-NRCS OSD search tools: https://soilseries.sc.egov.usda.gov/

See Also

OSDquery(), siblings()

Examples

  library(aqp)
  # soils of interest
  s.list <- c('musick', 'cecil', 'drummer', 'amador', 'pentz',
              'reiff', 'san joaquin', 'montpellier', 'grangeville', 'pollasky', 'ramona')

  # fetch and convert data into an SPC
  s.moist <- fetchOSD(s.list, colorState='moist')
  s.dry <- fetchOSD(s.list, colorState='dry')

  # plot profiles
  # moist soil colors
  par(mar=c(0,0,0,0), mfrow=c(2,1))
  aqp::plotSPC(
    s.moist,
    name = 'hzname',
    cex.names = 0.85,
    depth.axis = list(line = -4)
  )
  aqp::plotSPC(
    s.dry,
    name = 'hzname',
    cex.names = 0.85,
    depth.axis = list(line = -4)
  )

  # extended mode: return a list with SPC + summary tables
  x <- fetchOSD(s.list, extended = TRUE, colorState = 'dry')

  par(mar=c(0,0,1,1))
  aqp::plotSPC(x$SPC)
  str(x, 1)

Get a SoilProfileCollection from a PedonPC v.5 database

Description

Fetch commonly used site/horizon data from a version 5.x PedonPC database, return as a SoilProfileCollection object.

Usage

fetchPedonPC(dsn)

getHzErrorsPedonPC(dsn, strict = TRUE)

Arguments

Value

a SoilProfileCollection class object

Note

This function attempts to do most of the boilerplate work when extracting site/horizon data from a PedonPC or local NASIS database. Pedons that have errors in their horizonation are excluded from the returned object, however, their IDs are printed on the console. See getHzErrorsPedonPC for a simple approach to identifying pedons with problematic horizonation. Records from the 'taxhistory' table are selected based on 1) most recent record, or 2) record with the least amount of missing data.

Author(s)

D. E. Beaudette and J. M. Skovlin

See Also

get_hz_data_from_pedon_db

Get Rapid Carbon Assessment (RaCA) data

Description

NOTICE: The SoilWeb snapshot of the RaCA data has been deprecated. The latest version of the data, including values measured by the Kellogg Soil Survey Laboratory, and supporting documentation, are available here: https://www.nrcs.usda.gov/resources/data-and-reports/rapid-carbon-assessment-raca. Download link on National Agricultural Library Ag Data Commons: https://data.nal.usda.gov/dataset/rapid-carbon-assessment-raca

Get Rapid Carbon Assessment (RaCA) data by state, geographic bounding-box, RaCA site ID, or soil series query from the SoilWeb API. This interface to the data was an experimental delivery service that does not include the latest soil organic carbon (SOC) measurements.

Please use current RaCA distribution if you need lab measured SOC rather than SOC estimated by VNIR.

This interface will be updated sometime calendar year 2022 to include the latest soil morphology, taxonomic classification, and measured SOC values. More detailed coordinates for sample sites should also be available.

Usage

fetchRaCA(
  series = NULL,
  bbox = NULL,
  state = NULL,
  rcasiteid = NULL,
  get.vnir = FALSE
)

Arguments

Details

The VNIR spectra associated with RaCA data are quite large (each gzip-compressed VNIR spectra record is about 6.6kb), so requests for these data are disabled by default. Note that VNIR spectra can only be queried by soil series or geographic BBOX.

Value

pedons:

a SoilProfileCollection object containing site/pedon/horizon data

trees:

a data.frame object containing tree DBH and height

veg:

a data.frame object containing plant species

stock:

a data.frame object containing carbon quantities (stocks) at standardized depths

sample:

a data.frame object containing sample-level bulk density and soil organic carbon values

spectra:

a numeric matrix containing VNIR reflectance spectra from 350–2500 nm

Author(s)

D.E. Beaudette, USDA-NRCS staff

References

https://data.nal.usda.gov/dataset/rapid-carbon-assessment-raca

See Also

fetchOSD

Get Daily Climate Data from USDA-NRCS SCAN (Soil Climate Analysis Network) Stations

Description

Query soil/climate data from USDA-NRCS SCAN Stations.

Usage

fetchSCAN(
  site.code = NULL,
  year = NULL,
  report = "SCAN",
  timeseries = c("Daily", "Hourly"),
  tz = "US/Central",
  ...
)

SCAN_sensor_metadata(site.code)

SCAN_site_metadata(site.code = NULL)

Arguments

Details

Possible above and below ground sensor types include: 'SMS' (soil moisture), 'STO' (soil temperature), 'SAL' (salinity), 'TAVG' (daily average air temperature), 'TMIN' (daily minimum air temperature), 'TMAX' (daily maximum air temperature), 'PRCP' (daily precipitation), 'PREC' (daily precipitation), 'SNWD' (snow depth), 'WTEQ' (snow water equivalent),'WDIRV' (wind direction), 'WSPDV' (wind speed), 'LRADT' (solar radiation/langley total).

This function converts below-ground sensor depth from inches to cm. All temperature values are reported as degrees C. Precipitation, snow depth, and snow water content are reported as inches.

The datetime column in sensor data results is converted to the target time zone specified in tz argument, the default is "US/Central". Use tz = "UTC" (or other OlsonNames() that do not use daylight savings, e.g. "US/Arizona") to avoid having a mix of time offsets due to daylight savings time.

SCAN Sensors

All Soil Climate Analysis Network (SCAN) sensor measurements are reported hourly.

SNOTEL Sensors

All Snow Telemetry (SNOTEL) sensor measurements are reported daily.

See the fetchSCAN tutorial for additional usage and visualization examples.

Value

a list of data.frame objects, where each element name is a sensor type, plus a metadata table; different report types change the types of sensor data returned. SCAN_sensor_metadata() and SCAN_site_metadata() return a data.frame. NULL on bad request.

Author(s)

D.E. Beaudette, A.G. Brown, J.M. Skovlin

References

See the Soil Climate Analysis Network home page for more information on the SCAN program, and links to other associated programs such as SNOTEL, at the National Weather and Climate Center. You can get information on available web services, as well as interactive maps of snow water equivalent, precipitation and streamflow.

Examples

## Not run: 
    # get data
    x <- try(fetchSCAN(site.code = c(356, 2072), year = c(2015, 2016)))
    str(x, 1)

    # get sensor metadata
    m <- SCAN_sensor_metadata(site.code = c(356, 2072))
    m
    
    # get site metadata
    m <- SCAN_site_metadata(site.code = c(356, 2072))
    m
    
    # # get hourly data (warning, result is large ~11MB) 
    # x <- try(fetchSCAN(site.code = c(356, 2072), 
    #                    year = 2015, 
    #                    timeseries = "Hourly"))
    #
    # # data are in US/Central time, standard or daylight savings time based on day of year
    # unique(format(x$SMS$datetime, '%Z'))
    #
    # # the site metadata indicate timeseries data time zone (dataTimeZone)
    # # for site 356 the timezone is offset of 8 hours behind UTC
    #
    # # to obtain all datetime data with a consistent offset use ETC GMT offset
    # # e.g. "Etc/GMT+8". note the sign is inverted ("GMT+8" vs. `dataTimeZone=-8`)
    # x <- try(fetchSCAN(site.code = c(356, 2072), 
    #          year = 2015, 
    #          timeseries = "Hourly", 
    #          tz = "Etc/GMT+8"))
    

## End(Not run)

Get Spatial Data from Soil Data Access by mukey, nationalmusym or areasymbol

Description

This method facilitates queries to Soil Data Access (SDA) mapunit and survey area geometry. Queries are generated based on map unit key (mukey) and national map unit symbol (nationalmusym) for mupolygon (SSURGO) or gsmmupolygon (STATSGO) geometry OR legend key (lkey) and area symbols (areasymbol) for sapolygon (Soil Survey Area; SSA) geometry).

A Soil Data Access query returns geometry and key identifying information about the map unit or area of interest. Additional columns from the map unit or legend table can be included; see add.fields argument.

Usage

fetchSDA_spatial(
  x,
  by.col = "mukey",
  method = "feature",
  geom.src = "mupolygon",
  db = "SSURGO",
  add.fields = NULL,
  chunk.size = 10,
  verbose = TRUE,
  as_Spatial = getOption("soilDB.return_Spatial", default = FALSE)
)

Arguments

Details

This function automatically "chunks" the input vector (using makeChunks()) of map unit identifiers to minimize the likelihood of exceeding the SDA data request size. The number of chunks varies with the chunk.size setting and the length of your input vector. If you are working with many map units and/or large extents, you may need to decrease this number in order to have more chunks.

Querying regions with complex mapping may require smaller chunk.size. Numerically adjacent IDs in the input vector may share common qualities (say, all from same soil survey area or region) which could cause specific chunks to perform "poorly" (slow or error) no matter what the chunk size is. Shuffling the order of the inputs using sample() may help to eliminate problems related to this, depending on how you obtained your set of MUKEY/nationalmusym to query. One could feasibly use muacres as a heuristic to adjust for total acreage within chunks.

Note that STATSGO data are fetched where CLIPAREASYMBOL = 'US' to avoid duplicating state and national subsets of the geometry.

A prototype interface, geom.src="mlrapolygon", is provided for obtaining Major Land Resource Area (MLRA) polygon boundaries. When using this geometry source x is a vector of MLRARSYM (MLRA Symbols). The geometry source is the MLRA Geographic Database v5.2 (2022) which is not (yet) part of Soil Data Access. Instead of SDA, GDAL utilities are used to read a zipped ESRI Shapefile from a remote URL: https://www.nrcs.usda.gov/sites/default/files/2022-10/MLRA_52_2022.zip. Therefore, most additional fetchSDA_spatial() arguments are not currently supported for the MLRA geometry source. In the future a mlrapolygon table may be added to SDA (analogous to mupolygon and sapolygon), and the function will be updated accordingly at that time.

Value

an sf data.frame corresponding to SDA spatial data for all symbols requested. If as_Spatial=TRUE returns a Spatial*DataFrame from the sp package via sf::as_Spatial() for backward compatibility. Default result contains geometry with attribute table containing unique feature ID, symbol and area symbol plus additional fields in result specified with add.fields.

Author(s)

Andrew G. Brown, Dylan E. Beaudette

Examples

 
    # get spatial data for a single mukey
    single.mukey <- try(fetchSDA_spatial(x = "2924882"))

    # demonstrate fetching full extent (multi-mukey) of national musym
    full.extent.nmusym <- try(fetchSDA_spatial(x = "2x8l5", by = "nmusym"))

    # compare extent of nmusym to single mukey within it
    if (!inherits(single.mukey, 'try-error') && 
        !inherits(full.extent.nmusym, 'try-error')) {
        
        if (requireNamespace("sf")) {
      
         plot(sf::st_geometry(full.extent.nmusym), col = "RED", border = 0)
         plot(sf::st_geometry(single.mukey), add = TRUE, col = "BLUE", border = 0)
       
        }
        
    }

    # demo adding a field (`muname`) to attribute table of result
    head(try(fetchSDA_spatial(x = "2x8l5", by="nmusym", add.fields="muname")))

Fetch Soil Landscapes of the United States (SOLUS) Grids

Description

This tool creates a virtual raster or downloads data for an extent from Cloud Optimized GeoTIFFs (COGs) from the Soil Landscapes of the United States 100-meter (SOLUS100) soil property maps project repository.

Usage

fetchSOLUS(
  x = NULL,
  depth_slices = c(0, 5, 15, 30, 60, 100, 150),
  variables = c("anylithicdpt", "caco3", "cec7", "claytotal", "dbovendry", "ec", "ecec",
    "fragvol", "gypsum", "ph1to1h2o", "resdept", "sandco", "sandfine", "sandmed",
    "sandtotal", "sandvc", "sandvf", "sar", "silttotal", "soc"),
  output_type = c("prediction", "relative prediction interval",
    "95% low prediction interval", "95% high prediction interval"),
  grid = TRUE,
  samples = NULL,
  method = c("linear", "constant", "fmm", "natural", "monoH.FC", "step", "slice"),
  max_depth = 151,
  filename = NULL,
  overwrite = FALSE
)

Arguments

Details

If the input object x is not specified (NULL or missing), a SpatRaster object using the virtual URLs is returned. The full extent and resolution data set can be then downloaded and written to file using terra::writeRaster() (or any other processing step specifying an output file name). When input object x is specified, a SpatRaster object using in memory or local (temporary file or filename) resources is returned after downloading the data only for the target extent. In the case where x is a SoilProfileCollection or an sf or SpatVector object containing point geometries, the result will be a SoilProfileCollection for values extracted at the point locations. To return both the SpatRaster and SoilProfileCollection object output in a list, use grid = NULL.

Value

A SpatRaster object containing SOLUS grids for specified extent, depths, variables, and product types.

Author(s)

Andrew G. Brown

References

Nauman, T. W., Kienast-Brown, S., Roecker, S. M., Brungard, C., White, D., Philippe, J., & Thompson, J. A. (2024). Soil landscapes of the United States (SOLUS): developing predictive soil property maps of the conterminous United States using hybrid training sets. Soil Science Society of America Journal, 88, 2046–2065. doi:10.1002/saj2.20769

Examples

## Not run: 
b <- c(-119.747629, -119.67935, 36.912019, 36.944987)

bbox.sp <- sf::st_as_sf(wk::rct(
  xmin = b[1], xmax = b[2], ymin = b[3], ymax = b[4],
  crs = sf::st_crs(4326)
))

ssurgo.geom <- soilDB::SDA_spatialQuery(
  bbox.sp,
  what = 'mupolygon',
  db = 'SSURGO',
  geomIntersection = TRUE
)

# grid output
res <- fetchSOLUS(
  ssurgo.geom,
  depth_slices = "0",
  variables = c("sandtotal", "silttotal", "claytotal", "cec7"),
  output_type = "prediction"
)

terra::plot(res)

# SoilProfileCollection output, using linear interpolation for 1cm slices
# site-level variables (e.g. resdept) added to site data.frame of SPC
res <- fetchSOLUS(
  ssurgo.geom,
  depth_slices = c("0", "5", "15", "30", "60", "100", "150"),
  variables = c("sandtotal", "silttotal", "claytotal", "cec7", "resdept"),
  output_type = "prediction",
  method = "linear",
  grid = FALSE,
  samples = 10
)

# plot, truncating each profile to the predicted restriction depth
aqp::plotSPC(trunc(res, 0, res$resdept_p), color = "claytotal_p", divide.hz = FALSE)

## End(Not run)

Fetch Soil Inventory Resource (SRI) for USFS Region 6

Description

This is a higher level wrapper around the get_SRI and get_SRI_layers functions. This function can fetch multiple File Geodatabases (GDB) and returns all the layers within the GDB.

Usage

fetchSRI(gdb, ...)

Arguments

Value

A list.

Author(s)

Josh Erickson

See Also

get_SRI() get_SRI_layers()

Examples

## Not run: 

# fetch Willamette and Winema SRI

sri <- fetchSRI(gdb = c('will', 'win'), quiet = TRUE)


## End(Not run)

Get SoilGrids 2.0 Property Estimates for Points or Spatial Extent

Description

This function obtains SoilGrids 2.0 properties information (250m raster resolution) given a data.frame containing site IDs, latitudes and longitudes, or a spatial extent (see grid=TRUE argument).

SoilGrids API and maps return values as whole (integer) numbers to minimize the storage space used. These values have conversion factors applied by fetchSoilGrids() to produce conventional units shown in the table below (see Details).

Usage

fetchSoilGrids(
  x,
  loc.names = c("id", "lat", "lon"),
  depth_intervals = c("0-5", "5-15", "15-30", "30-60", "60-100", "100-200"),
  variables = c("bdod", "cec", "cfvo", "clay", "nitrogen", "phh2o", "sand", "silt",
    "soc", "ocd", "wv0010", "wv0033", "wv1500"),
  grid = FALSE,
  filename = NULL,
  overwrite = TRUE,
  target_resolution = c(250, 250),
  summary_type = c("Q0.05", "Q0.5", "Q0.95", "mean"),
  endpoint = ifelse(!grid, "https://rest.isric.org/soilgrids/v2.0/properties/query",
    "https://files.isric.org/soilgrids/latest/data/"),
  ...,
  verbose = FALSE,
  progress = FALSE
)

Arguments

Details

Properties

SoilGrids predictions are made for the six standard depth intervals specified in the GlobalSoilMap IUSS working group and its specifications. The default depth intervals returned are (in centimeters): "0-5", "5-15", "15-30", "30-60", "60-100", "100-200" for the properties "bdod", "cec", "cfvo", "clay", "nitrogen", "phh2o", "sand", "silt", "soc", "ocd", "wv0010", "wv0033", "wv1500"–each with percentiles (5th, 50th, 95th), mean and uncertainty values. The summary statistic name will be appended to the abbreviate variable name for each depth interval returned. Soil organic carbon stocks (0-30cm) (variables="ocs") are returned only for depth_intervals="0-30". The uncertainty values are the ratio between the inter-quantile range (90% prediction interval width) and the median : (Q0.95-Q0.05)/Q0.50. All values are converted from "mapped" to "conventional" based on above table conversion factors. Point data requests are made through "properties/query" endpoint of the SoilGrids v2.0 REST API. Please check ISRIC's data policy, disclaimer and citation: https://www.isric.org/about/data-policy.

Find out more information about the SoilGrids and GlobalSoilMap products here:

• https://www.isric.org/explore/soilgrids/faq-soilgrids

• https://www.isric.org/sites/default/files/GlobalSoilMap_specifications_december_2015_2.pdf

Value

A SoilProfileCollection (or SpatRaster when grid=TRUE). Returns try-error if all requests fail. Any error messages resulting from parsing will be echoed when verbose=TRUE.

Author(s)

Andrew G. Brown

References

• Common soil chemical and physical properties: Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., and Rossiter, D.: SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty, SOIL, 7, 217–240, 2021. DOI: doi:10.5194/soil-7-217-2021

• Soil water content at different pressure heads: Turek, M.E., Poggio, L., Batjes, N. H., Armindo, R. A., de Jong van Lier, Q., de Sousa, L.M., Heuvelink, G. B. M. : Global mapping of volumetric water retention at 100, 330 and 15000 cm suction using the WoSIS database, International Soil and Water Conservation Research, 11-2, 225-239, 2023. DOI: doi:10.1016/j.iswcr.2022.08.001

Examples

## Not run: 
  library(aqp)
  
  your.points <- data.frame(id  = c("A", "B"), 
                            lat = c(37.9, 38.1), 
                            lon = c(-120.3, -121.5), 
                            stringsAsFactors = FALSE)
  x <- try(fetchSoilGrids(your.points))
 
  if (!inherits(x, 'try-error'))
   aqp::plotSPC(x, name = NA, color = "socQ50")
 
  # organic carbon stocks use 0-30cm interval
  y <- try(fetchSoilGrids(your.points[1, ], 
                          depth_interval = c("0-5", "0-30", "5-15", "15-30"),
                          variables = c("soc", "bdod", "ocd", "ocs")))
                          
  # extract horizons from a SoilProfileCollection where horizon 2 overlaps 1, 3, and 4
  h <- aqp::horizons(y)
  
  # "ocs" (organic carbon stock 0-30cm interval)
  h[2, ]
  
  h$thickness_meters <- ((h$hzdepb - h$hzdept) / 100)

  # estimate "ocs" from modeled organic carbon and bulk density in 0-5, 5-15, 15-30 intervals
  #  (sum the product of soc, bdod, and thickness in meters)
  #  (1 gram per cubic decimeter = 1 kilogram per cubic meter)
  sum(h$socmean * h$bdodmean * h$thickness_meters, na.rm = TRUE)
  
  # estimate "ocs" from modeled organic carbon density in 0-5, 5-15, 15-30 intervals
  #  (sum the product of "ocd" and thickness in meters)
  sum(h$ocdmean * h$thickness_meters, na.rm = TRUE)
 

## End(Not run)

Get vegetation plot data from local NASIS database

Description

Convenience function for loading most commonly used Vegetation Plot information from local NASIS database.

Usage

fetchVegdata(
  SS = TRUE,
  include_pedon = TRUE,
  nullFragsAreZero = TRUE,
  stringsAsFactors = NULL,
  dsn = NULL
)

get_vegplot_from_NASIS_db(SS = TRUE, stringsAsFactors = NULL, dsn = NULL)

get_vegplot_location_from_NASIS_db(
  SS = TRUE,
  stringsAsFactors = NULL,
  dsn = NULL
)

get_vegplot_trhi_from_NASIS_db(SS = TRUE, stringsAsFactors = NULL, dsn = NULL)

get_vegplot_species_from_NASIS_db(
  SS = TRUE,
  stringsAsFactors = NULL,
  dsn = NULL
)

get_vegplot_transect_from_NASIS_db(
  SS = TRUE,
  stringsAsFactors = NULL,
  dsn = NULL
)

get_vegplot_transpecies_from_NASIS_db(
  SS = TRUE,
  stringsAsFactors = NULL,
  dsn = NULL
)

get_vegplot_transpoints_from_NASIS_db(SS = TRUE, dsn = NULL)

get_vegplot_prodquadrats_from_NASIS_db(SS = TRUE, dsn = NULL)

get_vegplot_groundsurface_from_NASIS_db(SS = TRUE, dsn = NULL)

get_vegplot_tree_si_summary_from_NASIS_db(
  SS = TRUE,
  stringsAsFactors = NULL,
  dsn = NULL
)

get_vegplot_speciesbasalarea_from_NASIS(SS = TRUE, dsn = NULL)

get_vegplot_tree_si_details_from_NASIS_db(
  SS = TRUE,
  stringsAsFactors = NULL,
  dsn = NULL
)

get_vegplot_textnote_from_NASIS_db(
  SS = TRUE,
  fixLineEndings = TRUE,
  stringsAsFactors = NULL,
  dsn = NULL
)

Arguments

Value

fetchVegdata(): A named list containing: "vegplot", "vegplotlocation", "vegplotrhi", "vegplotspecies", "vegtransect", "vegtransplantsum", 'vegsiteindexsum', "vegsiteindexdet", "vegbasalarea", and "vegplottext" tables

get_vegplot_location_from_NASIS_db(): a data.frame containing location data from the corresponding record in the site table

get_vegplot_trhi_from_NASIS_db(): a data.frame containing Rangeland Health Indicator (RHI) data from the vegplot table

get_vegplot_species_from_NASIS_db(): a data.frame containing Plot Plant Inventory data

get_vegplot_transect_from_NASIS_db(): a data.frame containing Vegetation Transect data

get_vegplot_transect_from_NASIS_db(): a data.frame containing Vegetation Transect Plant Summary data

get_vegplot_transpoints_from_NASIS_db(): a data.frame containing Vegetation Transect Point Plant Cover Details

get_vegplot_prodquadrats_from_NASIS_db(): a data.frame containing Vegetation Transect Production Quadrat data

get_vegplot_groundsurface_from_NASIS_db(): a data.frame containing summary data for line point intercept ground surface cover hits by cover type.

get_vegplot_tree_si_summary_from_NASIS_db(): a data.frame containing Vegetation Plot Tree Site Index Summary data

get_vegplot_speciesbasalarea_from_NASIS(): a data.frame containing Vegetation Plot Species Basal Area and Trees Counted data

get_vegplot_tree_si_details_from_NASIS_db(): a data.frame containing Vegetation Plot Tree Site Index Details data

get_vegplot_textnote_from_NASIS_db(): a data.frame containing Vegetation Plot text notes

Examples

vsurf <- get_vegplot_groundsurface_from_NASIS_db()

Filter KSSL Geochemical Table

Description

A function to subset KSSL "geochem" / elemental analysis result table to obtain rows/columns based on: column name, preparation code, major / trace element method.

Usage

filter_geochem(
  geochem,
  columns = NULL,
  prep_code = NULL,
  major_element_method = NULL,
  trace_element_method = NULL
)

Arguments

Value

A data.frame, subset according to the constraints specified in arguments.

Author(s)

Andrew G. Brown.

Format vector of values into a string suitable for an SQL IN statement.

Description

Concatenate a vector to SQL IN-compatible syntax: letters[1:3] becomes ('a','b','c'). Values in x are first passed through unique().

Usage

format_SQL_in_statement(x)

Arguments

Value

A character vector (unit length) containing concatenated group syntax for use in SQL IN, with unique value found in x.

Note

Only character output is supported.

Examples

format_SQL_in_statement(c(2648889L, 2648890L))

Get Logic Errors in NASIS/PedonPC Pedon Horizon

Description

Get Logic Errors in NASIS/PedonPC Pedon Horizon

Usage

getHzErrorsNASIS(strict = TRUE, SS = TRUE, dsn = NULL)

Arguments

Value

A data.frame containing problematic records with columns: 'peiid','upedonid','hzdept','hzdepb','hzname'

Get Ecological Dynamics Information Tool (EDIT) ecological sites by catalog (ESD/ESG) and MLRA

Description

Data are accessed via Ecological Dynamics Interpretive Tool (EDIT) web services: https://edit.jornada.nmsu.edu/resources/esd. geoUnit refers to MLRA codes, possibly with a leading zero and trailing "X" for two digit MLRA symbols.

Usage

get_EDIT_ecoclass_by_geoUnit(geoUnit, catalog = c("esd", "esg"))

Arguments

Value

A data.frame containing: geoUnit, id, legacyId, name. NULL if no result.

Examples

## Not run: 
   get_EDIT_ecoclass_by_geoUnit(c("018X","022A"))

## End(Not run)

Get NASIS Metadata (Domain, Column and Choice Lists)

Description

Retrieve a table containing domain and column names with choice list labels/names/sequences/values from the NASIS 7 metadata tables.

Usage

get_NASIS_metadata(dsn = NULL, include_description = FALSE)

get_NASIS_column_metadata(
  x,
  what = "ColumnPhysicalName",
  include_description = FALSE,
  dsn = NULL
)

Arguments

Details

These data are derived from the MetadataDomainDetail, MetadataDomainMaster, and MetadataTableColumn tables and help with mapping between values stored in the NASIS database and human-readable values. The human-readable values align with the values returned in public facing interfaces such as SSURGO via Soil Data Access and NASIS Web Reports. The data in these tables can also be used to create ordered factors where options for levels of a particular data element follow a logical ChoiceSequence.

If a local NASIS instance is set up, and this is the first time get_NASIS_metadata() has been called, the metadata will be obtained from the NASIS local database. Subsequent runs in the same session will use a copy of the data object NASIS.metadata cached in soilDB.env which can be accessed with get_soilDB_env()$NASIS.metadata.

For users without a local NASIS instance, a cached copy of the NASIS metadata are used (data/metadata.rda).

See ?soilDB::metadata for additional details.

Value

a data.frame containing DomainID, DomainName, DomainRanked, DisplayLabel, ChoiceSequence, ChoiceValue, ChoiceName, ChoiceLabel, ChoiceObsolete, ColumnPhysicalName, ColumnLogicalName and optionally ChoiceDescription when include_description=TRUE.

a data.frame containing selected NASIS metadata sorted first on DomainID and then on ChoiceSequence

Examples

get_NASIS_column_metadata("texcl")

Get a NASIS table key by type and table name

Description

Get a NASIS table key by type and table name

Usage

get_NASIS_table_key_by_name(
  tables,
  keycol = c("all", "fkey", "pkeyref", "pkey")
)

Arguments

Value

The key column name for the specified table name

Examples

## Not run: 
get_NASIS_table_key_by_name(c("site","phorizon_View_1","not_a_table"))

## End(Not run)#' 

Get NASIS Table Metadata (Table and Column Descriptions)

Description

Retrieve a table containing table and column names with descriptions, help text, units of measure, etc. from NASIS 7 metadata tables.

Usage

get_NASIS_table_metadata(
  table = NULL,
  column = NULL,
  what.table = "TablePhysicalName",
  what.column = "ColumnPhysicalName",
  query_string = FALSE,
  dsn = NULL
)

Arguments

Details

These data are derived from the MetadataTable and MetadataTableColumn tables and describe the expected contents of standard NASIS tables and columns.

For NASIS choice lists based on domain and column names see get_NASIS_metadata() and NASISChoiceList(). This function (get_NASIS_table_metadata()) is intended for higher-level description of the expected contents of a NASIS database instance, rather than the codes/specific values used within columns.

Value

a data.frame

See Also

get_NASIS_metadata() NASISChoiceList() uncode() code()

Examples

if (local_NASIS_defined())
 str(get_NASIS_table_metadata())

Get NASIS 7 Physical Table Names

Description

Method generalizing concepts of NASIS 7 data model to group tables by "purpose." Most of our more complex queries rely on tables from one or more purposes, so individual higher-level functions might call a function like this to identify the relevant tables from a data source.

Usage

get_NASIS_table_name_by_purpose(
  purpose = c("metadata", "lookup", "nasis", "site", "pedon", "transect", "component",
    "vegetation", "project", "techsoilservice", "area", "soilseries", "legend",
    "mapunit", "datamapunit"),
  SS = FALSE
)

Arguments

Value

character vector of table names

See Also

createStaticNASIS

Examples

## Not run: 
# get the "site" table names
get_NASIS_table_name_by_purpose("site")

# get the pedon table names
get_NASIS_table_name_by_purpose("pedon", SS = TRUE)

# metadata and lookup not affected by SS argument, but site and pedon are
get_NASIS_table_name_by_purpose(c("metadata", "lookup",
                                   "site", "pedon"), SS = TRUE)

## End(Not run)

Get Global Historical Climatology Network Daily (GHCND) data from NOAA API

Description

Obtain daily climatic summary data for a set of station IDs, years, and datatypes.

Note that typically results from the NOAA API are limited to 1000 records. However, by "chunking" up data into individual stationyeardatatypeid combinations, record results generally do not exceed 365 records for daily summaries.

In order to use this function, you must obtain an API token from this website: https://www.ncdc.noaa.gov/cdo-web/token

Usage

get_NOAA_GHCND(stations, years, datatypeids, apitoken)

Arguments

Value

A data.frame containing the GHCND data requested (limit 1000 records)

Examples

#' ## in order to use this function, you must obtain an API token from this website:
##  https://www.ncdc.noaa.gov/cdo-web/token

# get_NOAA_GHCND(c("GHCND:USC00388786", "GHCND:USC00388787"),
#                years = 2017:2020,
#                datatypeids = c("PRCP","SNOW"),
#                apitoken = "yourtokenhere")

Get NOAA station data near a given latitude and longitude

Description

Query the NOAA API to get station data (limit 1000 records) near a point. Default extent is plus or minus 0.5 degrees (bounding box) (with bbox = 1) around the specified point [lat, lng].

In order to use this function, you must obtain an API token from this website: https://www.ncdc.noaa.gov/cdo-web/token

Usage

get_NOAA_stations_nearXY(lat, lng, apitoken, bbox = 1, crs = "EPSG:4326")

Arguments

Value

data.frame containing station information for all stations within a bounding box around lat, lng.

Examples

## in order to use this function, you must obtain an API token from this website:
##  https://www.ncdc.noaa.gov/cdo-web/token

# stations <- get_NOAA_stations_nearXY(lat = 37, lng = -120,
#                                      apitoken = "yourtokenhere")

Get Official Series Description Data from JSON, HTML or TXT sources

Description

Get Official Series Description Data from JSON, HTML or TXT sources

Usage

get_OSD(
  series,
  base_url = NULL,
  result = c("json", "html", "txt"),
  fix_ocr_errors = FALSE,
  verbose = FALSE
)

get_OSD_JSON(series, base_url = NULL)

Arguments

Details

The default base_url for result="json" is to JSON files stored in a GitHub repository that is regularly updated from the official source of Series Descriptions. Using format: ⁠https://raw.githubusercontent.com/ncss-tech/SoilKnowledgeBase/main/inst/extdata/OSD/{LETTER}/{SERIES}.json⁠ for JSON. And ⁠"https://soilseriesdesc.sc.egov.usda.gov/OSD_Docs/{LETTER}/{SERIES}.html⁠ is for result="html" (official source).

fix_ocr_errors by default is turned off (FALSE). When TRUE, assume that in color data hue/value/chroma lowercase "L" ("l") is a 1, and a capital "O" is interpreted as zero. Also, in horizon designations assume lowercase "L" is a 1, and a string that starts with 0 starts with the capital letter "O".

Value

For JSON result: A data.frame with 1 row per series, and 1 column per "section" in the OSD as defined in National Soil Survey Handbook. For TXT or HTML result a list of character vectors containing OSD text with 1 element per series and one value per line.

Examples

  series <- c("Musick", "Hector", "Chewacla")
  get_OSD(series)

Get RMF data from local NASIS

Description

Prepare a list of data.frame objects with data from the "phrdxfeatures" and "phredoxfcolor" tables. These tables are related by "phrdxfiid" column, and related to horizon data via "phiid".

Usage

get_RMF_from_NASIS_db(SS = TRUE, dsn = NULL)

Arguments

Value

a list with two data.frame objects:

• RMF: contents of "phrdxfeatures" table, often >1 row per horizon

• RMF_colors: contents of "phredoxfcolor", usually >1 row per record in "phrdxfeatures"

Get mapunit ecological sites from Soil Data Access

Description

Get mapunit ecological sites from Soil Data Access

Usage

get_SDA_coecoclass(
  method = "None",
  areasymbols = NULL,
  mukeys = NULL,
  WHERE = NULL,
  query_string = FALSE,
  ecoclasstypename = c("NRCS Rangeland Site", "NRCS Forestland Site"),
  ecoclassref = "Ecological Site Description Database",
  not_rated_value = "Not assigned",
  miscellaneous_areas = TRUE,
  include_minors = TRUE,
  threshold = 0,
  dsn = NULL
)

Arguments

Details

When method="Dominant Condition" an additional field ecoclasspct_r is returned in the result with the sum of comppct_r that have the dominant condition ecoclassid. The component with the greatest comppct_r is returned for the component and coecosite level information.

Note that if there are multiple coecoclasskey per ecoclassid there may be more than one record per component.

Get Geomorphic/Surface Morphometry Data from Soil Data Access

Description

Get Geomorphic/Surface Morphometry Data from Soil Data Access or a local SSURGO data source and summarize by counts and proportions ("probabilities").

Usage

get_SDA_cosurfmorph(
  table = c("cosurfmorphgc", "cosurfmorphhpp", "cosurfmorphss", "cosurfmorphmr"),
  by = "mapunit.mukey",
  areasymbols = NULL,
  mukeys = NULL,
  WHERE = NULL,
  method = c("bygroup", "none"),
  include_minors = TRUE,
  miscellaneous_areas = FALSE,
  representative_only = TRUE,
  db = c("SSURGO", "STATSGO"),
  dsn = NULL,
  query_string = FALSE
)

Arguments

Details

Default table="cosurfmorphgc" summarizes columns geomposmntn, geomposhill, geomposflats, and geompostrce. table="cosurfmorphhpp" summarizes "hillslopeprof", table="cosurfmorphss" summarizes shapeacross and shapedown, and table="cosurfmorphmr" summarizes geomicrorelief.

Queries are a generalization of now-deprecated functions from sharpshootR package by Dylan Beaudette: geomPosMountainProbability(), geomPosHillProbability(), surfaceShapeProbability(), hillslopeProbability()

Similar summaries of SSURGO component surface morphometry data by series name can be found in fetchOSD(, extended=TRUE) or downloaded from https://github.com/ncss-tech/SoilWeb-data Full component data including surface morphometry summaries at the "site" level can be obtained with fetchSDA().

Value

a data.frame containing the grouping variable (by) and tabular summaries of counts and proportions of geomorphic records.

Author(s)

Dylan E. Beaudette, Andrew G. Brown

See Also

fetchSDA() get_SDA_pmgroupname()

Examples

## Not run: 
 # Summarize by 3D geomorphic components by component name (default `by='compname'`)
 get_SDA_cosurfmorph(WHERE = "areasymbol = 'CA630'")

 # Whole Soil Survey Area summary (using `by = 'areasymbol'`)
 get_SDA_cosurfmorph(by = 'areasymbol', WHERE = "areasymbol = 'CA630'")

 # 2D Hillslope Position summary(using `table = 'cosurfmorphhpp'`)
 get_SDA_cosurfmorph('cosurfmorphhpp', WHERE = "areasymbol = 'CA630'")

 # Surface Shape summary (using `table = 'cosurfmorphss'`)
 get_SDA_cosurfmorph('cosurfmorphss', WHERE = "areasymbol = 'CA630'")

 # Microrelief summary (using `table = 'cosurfmorphmr'`)
 get_SDA_cosurfmorph('cosurfmorphmr', WHERE = "areasymbol = 'CA630'")

## End(Not run)

Get map unit hydric soils information from Soil Data Access

Description

Assess the hydric soils composition of a map unit.

Usage

get_SDA_hydric(
  areasymbols = NULL,
  mukeys = NULL,
  WHERE = NULL,
  method = "MAPUNIT",
  include_minors = TRUE,
  miscellaneous_areas = TRUE,
  query_string = FALSE,
  dsn = NULL
)

Arguments

Details

The default classes for method="MAPUNIT" are as follows:

• 'Nonhydric' - no hydric components

• 'Hydric' - all hydric components

• 'Predominantly Hydric' - hydric component percentage is 50% or more

• 'Partially Hydric' - one or more of the major components is hydric

• 'Predominantly Nonhydric' - hydric component percentage is less than 50%

The default result will also include the following summaries of component percentages: total_comppct, hydric_majors and hydric_inclusions.

Default method "Mapunit" produces aggregate summaries of all components in the mapunit. Use "Dominant Component" and "Dominant Condition" to get the dominant component (highest percentage) or dominant hydric condition (similar conditions aggregated across components), respectively. Use "None" for no aggregation (one record per component).

Value

a data.frame

Author(s)

Jason Nemecek, Chad Ferguson, Andrew Brown

Get map unit interpretations from Soil Data Access by rule name

Description

Get map unit interpretations from Soil Data Access by rule name

Usage

get_SDA_interpretation(
  rulename,
  method = c("Dominant Component", "Dominant Condition", "Weighted Average", "None"),
  areasymbols = NULL,
  mukeys = NULL,
  WHERE = NULL,
  include_minors = TRUE,
  miscellaneous_areas = TRUE,
  query_string = FALSE,
  not_rated_value = NA_real_,
  wide_reason = FALSE,
  dsn = NULL
)

Arguments

Details

Rule Names in cointerp table

• AGR - Avocado Root Rot Hazard (CA)

• AGR - California Revised Storie Index (CA)

• AGR - Hops Site Suitability (WA)

• AGR - Map Unit Cropland Productivity (MN)

• AGR - Nitrate Leaching Potential, Nonirrigated (WA)

• AGR - No Till (TX)

• AGR - Pesticide Loss Potential-Soil Surface Runoff (NE)

• AGR - Ridge Till (TX)

• AGR - Selenium Leaching Potential (CO)

• AGR - Water Erosion Potential (NE)

• AGR - Wind Erosion Potential (TX)

• AGR - Winter Wheat Yield (MT)

• AGR-Pesticide and Nutrient Runoff Potential (ND)

• AGR-Rooting Depth (ND)

• American Wine Grape Varieties Site Desirability (Long)

• American Wine Grape Varieties Site Desirability (Medium)

• American Wine Grape Varieties Site Desirability (Very Long)

• AWM - Animal Mortality Disposal (Catastrophic) (MO)

• AWM - Irrigation Disposal of Wastewater (OH)

• AWM - Irrigation Disposal of Wastewater (VT)s

• AWM - Land Application of Municipal Biosolids, summer (OR)

• AWM - Manure and Food Processing Waste (MD)

• AWM - Manure and Food Processing Waste (OH)

• AWM - Overland Flow Process Treatment of Wastewater (VT)

• AWM - Rapid Infil Disposal of Wastewater (DE)

• AWM - Sensitive Soil Features (MN)

• AWM - Sensitive Soil Features (WI)

• BLM - Fencing

• BLM - Fire Damage Susceptibility

• BLM - Mechanical Treatment, Rolling Drum

• BLM - Rangeland Drill

• BLM - Rangeland Seeding, Colorado Plateau Ecoregion

• BLM - Rangeland Seeding, Great Basin Ecoregion

• BLM-Reclamation Suitability (MT)

• CLASS RULE - Depth to lithic bedrock (5 classes) (NPS)

• CLASS RULE - Soil Inorganic Carbon kg/m2 to 2m (NPS)

• CLASS RULE - Soil Organic Carbon kg/m2 to 2m (NPS)

• CLR-pastureland limitation (IN)

• Commodity Crop Productivity Index (Soybeans) (TN)

• CPI - Alfalfa Hay, NIRR - Palouse, Northern Rocky Mtns. (WA)

• CPI - Barley, IRR - Eastern Idaho Plateaus (ID)

• CPI - Grass Hay, IRR - Klamath Valleys and Basins (OR)

• CPI - Small Grains, IRR - Snake River Plains (ID)

• CPI - Wheat, IRR - Eastern Idaho Plateaus (ID)

• CZSS - Salinization due to Coastal Saltwater Inundation (CT)

• DHS - Catastrophic Event, Large Animal Mortality, Burial

• DHS - Catastrophic Mortality, Large Animal Disposal, Pit

• DHS - Catastrophic Mortality, Large Animal Disposal, Trench

• DHS - Potential for Radioactive Bioaccumulation

• DHS - Potential for Radioactive Sequestration

• DHS - Suitability for Composting Medium and Final Cover

• ENG - Construction Materials; Gravel Source

• ENG - Construction Materials; Gravel Source (AK)

• ENG - Construction Materials; Gravel Source (ID)

• ENG - Construction Materials; Gravel Source (OH)

• ENG - Construction Materials; Gravel Source (VT)

• ENG - Construction Materials; Gravel Source (WA)

• ENG - Construction Materials; Roadfill (OH)

• ENG - Construction Materials; Sand Source (OR)

• ENG - Construction Materials; Sand Source (WA)

• ENG - Construction Materials; Topsoil (GA)

• ENG - Construction Materials; Topsoil (MD)

• ENG - Daily Cover for Landfill

• ENG - Daily Cover for Landfill (AK)

• ENG - Disposal Field Suitability Class (NJ)

• ENG - Dwellings W/O Basements (OH)

• ENG - Dwellings with Basements (AK)

• ENG - Large Animal Disposal, Pit (CT)

• ENG - Lawn, landscape, golf fairway (CT)

• ENG - Lined Retention Systems

• ENG - Local Roads and Streets (OH)

• ENG - On-Site Waste Water Absorption Fields (MO)

• ENG - Septic Tank Absorption Fields

• ENG - Septic Tank Absorption Fields (MD)

• ENG - Septic Tank Absorption Fields (TX)

• ENG - Septic Tank, Gravity Disposal (TX)

• ENG - Sewage Lagoons

• ENG - Small Commercial Buildings (OH)

• ENG - Soil Potential Ratings of SSDS (CT)

• FOR (USFS) - Road Construction/Maintenance (Natural Surface)

• FOR - Compaction Potential (WA)

• FOR - Conservation Tree/Shrub Groups (MT)

• FOR - Damage by Fire (OH)

• FOR - General Harvest Season (VT)

• FOR - Hand Planting Suitability

• FOR - Hand Planting Suitability, MO13 (DE)

• FOR - Hand Planting Suitability, MO13 (MD)

• FOR - Log Landing Suitability

• FOR - Log Landing Suitability (ME)

• FOR - Log Landing Suitability (VT)

• FOR - Log Landing Suitability (WA)

• FOR - Mechanical Planting Suitability (CT)

• FOR - Mechanical Planting Suitability, MO13 (MD)

• FOR - Mechanical Site Preparation (Deep)

• FOR - Mechanical Site Preparation (Deep) (DE)

• FOR - Mechanical Site Preparation (Surface) (DE)

• FOR - Mechanical Site Preparation (Surface) (MI)

• FOR - Mechanical Site Preparation; Surface (ME)

• FOR - Potential Erosion Hazard, Road/Trail, Spring Thaw (AK)

• FOR - Potential Seedling Mortality (PIA)

• FOR - Potential Seedling Mortality(ME)

• FOR - Puddling Hazard

• FOR - Road Suitability (Natural Surface) (ME)

• FOR - Road Suitability (Natural Surface) (WA)

• FOR - Soil Rutting Hazard (OH)

• FOR - Soil Sustainability Forest Biomass Harvesting (CT)

• FOR - White Oak Suitability (MO)

• FOR-Biomass Harvest (WI)

• FOTG - Indiana Corn Yield Calculation (IN)

• GRL - Excavations to 24 inches for Plastic Pipelines (TX)

• GRL - Fencing, 24 inch Post Depth (MT)

• GRL - NV range seeding (Wind C = 100) (NV)

• GRL - NV range seeding (Wind C = 40) (NV)

• GRL - NV range seeding (Wind C = 60) (NV)

• GRL - NV range seeding (Wind C = 80) (NV)

• GRL - NV range seeding (Wind C >= 160) (NV)

• GRL - Rangeland Planting by Mechanical Seeding (TX)

• GRL - Rangeland Root Plowing (TX)

• Hybrid Wine Grape Varieties Site Desirability (Long)

• Low Pressure Pipe Septic System (DE)

• MIL - Bivouac Areas (DOD)

• MIL - Excavations Crew-Served Weapon Fighting Position (DOD)

• MIL - Excavations for Individual Fighting Position (DOD)

• MIL - Trafficability Veh. Type 1 50-passes wet season (DOD)

• MIL - Trafficability Veh. Type 2 50-passes wet season (DOD)

• MIL - Trafficability Veh. Type 4 1-pass wet season (DOD)

• MIL - Trafficability Veh. Type 4 50-passes wet season (DOD)

• MIL - Trafficability Veh. Type 6 50-passes wet season (DOD)

• MIL - Trafficability Veh. Type 7 50-passes wet season (DOD)

• MIL - Trafficability Veh. Type 7 dry season (DOD)

• NCCPI - Irrigated National Commodity Crop Productivity Index

• Nitrogen Loss Potential (ND)

• Potential Windthrow Hazard (TN)

• REC - Foot and ATV Trails (AK)

• REC - Playgrounds (AK)

• Reclamation Suitability (ND)

• RSK-risk assessment for manure application (OH)

• SAS - CMECS Substrate Origin

• SAS - CMECS Substrate Subclass/Group/Subgroup

• SAS - Mooring Anchor - Deadweight

• Septic System A/B Soil System (Alternate) (PA)

• Septic System CO-OP RFS III w/Spray Irrigation (PA)

• Septic System Dual Field Trench (conventional) (WV)

• Septic System Elevated Field (alternative) (WV)

• Septic System In Ground Trench (conventional) (PA)

• Septic System In Ground Trench (conventional) (WV)

• AGR - Filter Strips (TX)

• AGR - Hops Site Suitability (ID)

• AGR - Mulch Till (TX)

• AGR - Nitrate Leaching Potential, Nonirrigated (MT)

• AGR - Nitrate Leaching Potential, Nonirrigated (WV)

• AGR - No Till (VT)

• AGR - Oats Yield (MT)

• AGR - Pesticide Loss Potential-Leaching

• AGR - Pesticide Loss Potential-Leaching (NE)

• AGR - Rutting Hazard =< 10,000 Pounds per Wheel (TX)

• AGR - S. Highbush Blueberry Suitability MLRA 153 (SC)

• AGR - Wind Erosion Potential (NE)

• AGR-Available Water Capacity (ND)

• AGR-Physical Limitations (ND)

• AGR-Sodicity (ND)

• AGR-Surface Crusting (ND)

• AGR-Wind Erosion (ND)

• AWM - Irrigation Disposal of Wastewater (DE)

• AWM - Land App of Municipal Sewage Sludge (DE)

• AWM - Land App of Municipal Sewage Sludge (MD)

• AWM - Land Application of Milk (CT)

• AWM - Land Application of Municipal Biosolids, spring (OR)

• AWM - Land Application of Municipal Sewage Sludge

• AWM - Land Application of Municipal Sewage Sludge (OH)

• AWM - Land Application of Municipal Sewage Sludge (VT)

• AWM - Large Animal Disposal, Pit (MN)

• AWM - Manure and Food Processing Waste

• AWM - Manure and Food Processing Waste (VT)

• AWM - Rapid Infil Disposal of Wastewater (MD)

• AWM - Rapid Infiltration Disposal of Wastewater (VT)

• AWM - Slow Rate Process Treatment of Wastewater (VT)

• BLM - Chaining Suitability

• BLM - Fugitive Dust Resistance

• BLM - Soil Restoration Potential

• BLM - Yellow Star-thistle Invasion Susceptibility

• CLASS RULE - Depth to non-lithic bedrock (5 classes) (NPS)

• CLR-cropland limitation for corn and soybeans (IN)

• Commodity Crop Productivity Index (Corn) (WI)

• CPI - Grass Hay, NIRR - Klamath Valleys and Basins (OR)

• CPI - Potatoes Productivity Index (AK)

• CPI - Potatoes, IRR - Eastern Idaho Plateaus (ID)

• CPI - Small Grains, NIRR - Palouse Prairies (ID)

• DHS - Emergency Animal Mortality Disposal by Shallow Burial

• DHS - Rubble and Debris Disposal, Large-Scale Event

• ENG - Aquifer Assessment - 7081 (MN)

• ENG - Construction Materials - Gravel Source (MN)

• ENG - Construction Materials; Gravel Source (MI)

• ENG - Construction Materials; Gravel Source (OR)

• ENG - Construction Materials; Reclamation

• ENG - Construction Materials; Reclamation (OH)

• ENG - Construction Materials; Sand Source

• ENG - Construction Materials; Sand Source (AK)

• ENG - Construction Materials; Sand Source (ID)

• ENG - Construction Materials; Sand Source (IN)

• ENG - Construction Materials; Sand Source (OH)

• ENG - Construction Materials; Topsoil

• ENG - Construction Materials; Topsoil (WA)

• ENG - Ground-based Solar Arrays, Soil-based Anchor Systems

• ENG - Local Roads and Streets

• ENG - New Ohio Septic Rating (OH)

• ENG - Sanitary Landfill (Trench) (OH)

• ENG - Septic Tank Absorption Fields (AK)

• ENG - Septic Tank Absorption Fields (DE)

• ENG - Septic Tank Absorption Fields (NY)

• ENG - Sewage Lagoons (OH)

• ENG - Shallow Excavations (AK)

• ENG - Shallow Excavations (MI)

• ENG - Unpaved Local Roads and Streets

• FOR - Black Walnut Suitability Index (MO)

• FOR - Conservation Tree and Shrub Groups (TX)

• FOR - Construction Limitations - Haul Roads/Log Landing (OH)

• FOR - Construction Limitations For Haul Roads (MI)

• FOR - Hand Planting Suitability (ME)

• FOR - Harvest Equipment Operability (MD)

• FOR - Harvest Equipment Operability (OH)

• FOR - Harvest Equipment Operability (VT)

• FOR - Mechanical Planting Suitability

• FOR - Mechanical Planting Suitability (ME)

• FOR - Mechanical Planting Suitability, MO13 (DE)

• FOR - Potential Erosion Hazard (Off-Road/Off-Trail)

• FOR - Potential Erosion Hazard (Road/Trail) (PIA)

• FOR - Potential Seedling Mortality (VT)

• FOR - Potential Windthrow Hazard (NY)

• FOR - Potential Windthrow Hazard (VT)

• FOR - Puddling Potential (WA)

• FOR - Road Suitability (Natural Surface)

• FOR - Road Suitability (Natural Surface) (OH)

• FOR - Road Suitability (Natural Surface) (OR)

• FOR - Rutting Hazard by Season

• FOR - Shortleaf pine littleleaf disease susceptibility

• FOR - Soil Compactibility Risk

• FOR - Soil Rutting Hazard (ME)

• FOR - Windthrow Hazard

• FOR-Construction Limitations for Haul Roads/Log Landings(ME)

• FOTG - Indiana Slippage Potential (IN)

• Gravity Full Depth Septic System (DE)

• GRL - Fencing, Post Depth =<36 inches

• GRL - NV range seeding (Wind C = 50) (NV)

• GRL - Ranch Access Roads (TX)

• GRL - Rangeland Roller Chopping (TX)

• Ground Penetrating Radar Penetration

• Ground-based Solar Arrays_bedrock(ME)

• Ground-based Solar Arrays_bedrock_slope_ballast(ME)

• Hybrid Wine Grape Varieties Site Desirability (Short)

• ISDH Septic Tank Interpretation (IN)

• Land Application of Municipal Sewage Sludge (PA)

• MIL - Helicopter Landing Zones (DOD)

• MIL - Trafficability Veh. Type 2 1-pass wet season (DOD)

• MIL - Trafficability Veh. Type 5 50-passes wet season (DOD)

• MIL - Trafficability Veh. Type 5 dry season (DOD)

• MIL - Trafficability Veh. Type 7 1-pass wet season (DOD)

• NCCPI - National Commodity Crop Productivity Index (Ver 3.0)

• REC - Camp and Picnic Areas (AK)

• REC - Picnic Areas (CT)

• REC - Playgrounds (CT)

• SAS - CMECS Substrate Subclass

• Septic System Drip Irrigation (Alternate) (PA)

• Septic System Free Access Sand Filter w/Drip Irrigation (PA)

• Septic System In Ground Bed (conventional) (PA)

• Septic System Peat Based Option1 (UV & At-Grade Bed)Alt (PA)

• Septic System Peat Sys Opt3 w/Subsurface Sand Filter (PA)

• Septic System Sand Mound Bed or Trench (PA)

• Septic System Shallow Placement Pressure Dosed (Alt.) (PA)

• SOH - Aggregate Stability (ND)

• SOH - Agricultural Organic Soil Subsidence

• SOH - Dynamic Soil Properties Response to Biochar

• SOH - Organic Matter Depletion

• SOIL HEALTH ASSESSMENT (NJ)

• URB - Commercial Brick Bldg; w/Reinforced Concrete Slab (TX)

• URB - Reinforced Concrete Slab (TX)

• URB/REC - Camp Areas

• URB/REC - Camp Areas (OH)

• URB/REC - Off-Road Motorcycle Trails (OH)

• URB/REC - Paths and Trails (OH)

• URB/REC - Picnic Areas

• URB/REC - Playgrounds

• URB/REC - Playgrounds (GA)

• Vinifera Wine Grape Site Desirability (Short to Medium)

• WLF - Irr. Domestic Grasses & Legumes for Food & Cover (TX)

• WLF - Upland Coniferous Trees (TX)

• WLF - Upland Deciduous Trees (TX)

• WLF - Upland Desertic Shrubs & Trees (TX)

• WLF - Upland Native Herbaceous Plants (TX)

• WLF - Upland Shrubs & Vines (TX)

• WLF-Soil Suitability - Karner Blue Butterfly (WI)

• WMS - Drainage (IL)

• WMS - Drainage - (MI)

• WMS - Embankments, Dikes, and Levees

• WMS - Embankments, Dikes, and Levees (OH)

• WMS - Grassed Waterways - (MI)

• AGR - Air Quality; PM10 (TX)

• AGR - Air Quality; PM2_5 (TX)

• AGR - Aronia Berry Suitability (SD)

• AGR - Farmland of Statewide Importance (TX)

• AGR - Index for alfalfa hay, irrigated (NV)

• AGR - Nitrate Leaching Potential, Nonirrigated (MA)

• AGR - Rangeland Grass/Herbaceous Productivity Index (TX)

• AGR - Rutting Hazard > 10,000 Pounds per Wheel (TX)

• AGR - Water Erosion Potential (TX)

• AGR - Wine Grape Site Suitability (WA)

• AGR-Natural Fertility (ND)

• AGR-Subsurface Salinity (ND)

• AWM - Filter Group (OH)

• AWM - Irrigation Disposal of Wastewater

• AWM - Land Application of Dry and Slurry Manure (TX)

• AWM - Land Application of Municipal Biosolids, winter (OR)

• AWM - Overland Flow Process Treatment of Wastewater

• AWM - Rapid Infiltration Disposal of Wastewater

• AWM - Vegetated Treatment Area (PIA)

• AWM - Waste Field Storage Area (VT)

• BLM - Mechanical Treatment, Shredder

• BLM - Medusahead Invasion Susceptibility

• BLM - Soil Compaction Resistance

• Capping Fill Gravity Septic System (DE)

• CLASS RULE - Depth to any bedrock kind (5 classes) (NPS)

• CPI - Alfalfa Hay, IRR - Eastern Idaho Plateaus (ID)

• CPI - Alfalfa Hay, IRR - Klamath Valley and Basins (OR)

• CPI - Alfalfa Hay, IRR - Snake River Plains (ID)

• CPI - Alfalfa Hay, NIRR- Eastern Idaho Plateaus (ID)

• CPI - Grass Hay, NIRR - Palouse, Northern Rocky Mtns. (WA)

• CPI - Small Grains Productivity Index (AK)

• DHS - Catastrophic Event, Large Animal Mortality, Incinerate

• DHS - Emergency Land Disposal of Milk

• DHS - Site for Composting Facility - Subsurface

• DHS - Suitability for Clay Liner Material

• ENG - Cohesive Soil Liner (MN)

• ENG - Construction Materials - Sand Source (MN)

• ENG - Construction Materials; Gravel Source (CT)

• ENG - Construction Materials; Gravel Source (NY)

• ENG - Construction Materials; Reclamation (DE)

• ENG - Construction Materials; Roadfill

• ENG - Construction Materials; Roadfill (AK)

• ENG - Construction Materials; Sand Source (NY)

• ENG - Construction Materials; Sand Source (VT)

• ENG - Construction Materials; Topsoil (AK)

• ENG - Construction Materials; Topsoil (DE)

• ENG - Construction Materials; Topsoil (MI)

• ENG - Construction Materials; Topsoil (OR)

• ENG - Conventional On-Site Septic Systems (TN)

• ENG - Deep Infiltration Systems

• ENG - Disposal Field Gravity (DE)

• ENG - Dwellings With Basements (OH)

• ENG - Ground-based Solar Arrays, Ballast Anchor Systems

• ENG - Large Animal Disposal, Trench (CT)

• ENG - Lawn, Landscape, Golf Fairway (MI)

• ENG - Lawn, Landscape, Golf Fairway (VT)

• ENG - Sanitary Landfill (Area) (OH)

• ENG - Sanitary Landfill (Trench)

• ENG - Sanitary Landfill (Trench) (AK)

• ENG - Septage Application - Surface (MN)

• ENG - Septic Tank Absorption Fields - At-Grade (MN)

• ENG - Septic Tank Absorption Fields - Mound (MN)

• ENG - Septic Tank Leaching Chamber (TX)

• ENG - Septic Tank, Subsurface Drip Irrigation (TX)

• ENG - Shallow Excavations

• ENG - Shallow Infiltration Systems

• ENG - Small Commercial Buildings

• ENG - Soil Potential of Road Salt Applications (CT)

• ENG - Source of Caliche (TX)

• ENG - Stormwater Management / Ponds (NY)

• ENG - Unlined Retention Systems

• Farm and Garden Composting Facility - Surface

• FOR - Biomass Harvest (MA)

• FOR - Black Walnut Suitability Index (KS)

• FOR - Displacement Potential (WA)

• FOR - Drought Vulnerable Soils

• FOR - General Harvest Season (ME)

• FOR - Harvest Equipment Operability

• FOR - Mechanical Site Preparation (Deep) (MD)

• FOR - Mechanical Site Preparation (Surface)

• FOR - Mechanical Site Preparation; Deep (CT)

• FOR - Potential Erosion Hazard (Road/Trail)

• FOR - Potential Fire Damage Hazard

• FOR - Potential Seedling Mortality

• FOR - Potential Seedling Mortality (MI)

• FOR - Potential Windthrow Hazard (ME)

• FOR - Potential Windthrow Hazard (MI)

• FOR - Road Suitability (Natural Surface) (ID)

• FOR - Rutting Hazard by Month

• FOR - Windthrow Hazard (WA)

• FOTG - NLI Interp Calculation - (IN)

• Fragile Soil Index

• GRL - Juniper Encroachment Potential (NM)

• GRL - NV range seeding (Wind C = 20) (NV)

• GRL - Pasture and Hayland SG (OH)

• GRL - Rangeland Prescribed Burning (TX)

• GRL - Rangeland Soil Seed Bank Suitability (NM)

• GRL-FSG-NP-W (MT)

• GRL-SHSI Soil Health Sustainability Index (MT)

• Ground-based Solar Arrays_saturationt(ME)

• Ground-based Solar Arrays_slope(ME)

• Inland Wetlands (CT)

• IRR-restrictive features for irrigation (OH)

• MIL - Excavations for Vehicle Fighting Position (DOD)

• MIL - Trafficability Veh. Type 1 1-pass wet season (DOD)

• MIL - Trafficability Veh. Type 2 dry season (DOD)

• MIL - Trafficability Veh. Type 3 50-passes wet season (DOD)

• MIL - Trafficability Veh. Type 6 1-pass wet season (DOD)

• MIL - Trafficability Veh. Type 6 dry season (DOD)

• Muscadine Wine Grape Site Desirability (Very Long)

• NCCPI - NCCPI Cotton Submodel (II)

• Permafrost Sensitivity (AK)

• Pressure Dose Capping Fill Septic System (DE)

• REC - Camp Areas (CT)

• REC - Off-Road Motorcycle Trails (CT)

• SAS - CMECS Substrate Class

• SAS - CMECS Substrate Subclass/Group

• SAS - Eelgrass Restoration Suitability

• SAS - Land Utilization of Dredged Materials

• SAS - Northern Quahog (Hard Clam) Habitat Suitability

• Septic System At Grade Shallow Field (alternative) (WV)

• Septic System At-Grade Bed (Alternate) (PA)

• Septic System CO-OP RFS III w/Drip Irrigation (PA)

• Septic System Drip Irrigation (alternative) (WV)

• Septic System Free Access Sand Filterw/Spray Irrigation (PA)

• Septic System Peat Based Option1 w/At-Grade Bed (Alt.) (PA)

• Septic System Spray Irrigation (PA)

• Septic System Steep Slope Sand Mound (Alternate) (PA)

• Shallow Infiltration Systems

• SOH - Organic Matter Depletion Potential, Irrigated (CA)

• SOH - Soil Surface Sealing

• TROP - Plantains Productivity

• URB/REC - Camp Areas (GA)

• URB/REC - Camp Areas (MI)

• URB/REC - Golf Fairways (OH)

• URB/REC - Off-Road Motorcycle Trails

• URB/REC - Paths and Trails (MI)

• URB/REC - Playgrounds (OH)

• Vinifera Wine Grape Site Desirability (Long to Medium)

• WLF - Chufa for Turkey Forage (LA)

• WLF - Food Plots for Upland Wildlife < 2 Acres (TX)

• WLF - Freshwater Wetland Plants (TX)

• WLF - Irrigated Saline Water Wetland Plants (TX)

• WLF - Riparian Herbaceous Plants (TX)

• WLF - Riparian Shrubs, Vines, & Trees (TX)

• WLF - Saline Water Wetland Plants (TX)

• WLF - Upland Mixed Deciduous & Coniferous Trees (TX)

• WMS - Constructing Grassed Waterways (TX)

• WMS - Constructing Terraces and Diversions (OH)

• WMS - Embankments, Dikes, and Levees (VT)

• WMS - Irrigation, Sprinkler (close spaced outlet drops)

• WMS - Irrigation, Sprinkler (general)

• WMS - Pond Reservoir Area (GA)

• WMS-Subsurface Water Management, Installation (ND)

• WMS-Subsurface Water Management, Outflow Quality (ND)

• AGR - Barley Yield (MT)

• AGR - Conventional Tillage (TX)

• AGR - Grape non-irrigated (MO)

• AGR - Industrial Hemp for Fiber and Seed Production

• AGR - Nitrate Leaching Potential, Irrigated (WA)

• AGR - Pasture hayland (MO)

• AGR - Pesticide Loss Potential-Soil Surface Runoff

• AGR - Prime Farmland (TX)

• AGR - Spring Wheat Yield (MT)

• AGR-Agronomic Concerns (ND)

• AGR-Pesticide and Nutrient Leaching Potential, NIRR (ND)

• AGR-Surface Salinity (ND)

• AGR-Water Erosion Potential (ND)

• Alaska Exempt Wetland Potential (AK)

• American Wine Grape Varieties Site Desirability (Short)

• AWM - Irrigation Disposal of Wastewater (MD)

• AWM - Manure and Food Processing Waste (DE)

• AWM - Manure Stacking - Site Evaluation (TX)

• AWM - Phosphorus Management (TX)

• AWM - Slow Rate Process Treatment of Wastewater

• BLM - Pygmy Rabbit Habitat Potential

• BLM - Rangeland Tillage

• BLM - Site Degradation Susceptibility

• CA Prime Farmland (CA)

• CLASS RULE - Depth to root limiting layer (5 classes) (NPS)

• Commodity Crop Productivity Index (Corn) (TN)

• CPI - Alfalfa Hay, NIRR - Palouse, Northern Rocky Mtns. (ID)

• CPI - Barley, NIRR - Eastern Idaho Plateaus (ID)

• CPI - Grass Hay, IRR - Eastern Idaho Plateaus (ID)

• CPI - Grass Hay, NIRR - Palouse, Northern Rocky Mtns. (ID)

• CPI - Potatoes, IRR - Snake River Plains (ID)

• CPI - Small Grains, NIRR - Palouse Prairies (OR)

• CPI - Small Grains, NIRR - Palouse Prairies (WA)

• CPI - Small Grains, NIRR - Snake River Plains (ID)

• CPI - Wheat, NIRR - Eastern Idaho Plateaus (ID)

• CPI - Wild Hay, NIRR - Eastern Idaho Plateaus (ID)

• CPI - Wild Hay, NIRR - Palouse, Northern Rocky Mtns. (ID)

• CPI - Wild Hay, NIRR - Palouse, Northern Rocky Mtns. (WA)

• Deep Infiltration Systems

• DHS - Site for Composting Facility - Surface

• Elevated Sand Mound Septic System (DE)

• ENG - Animal Disposal by Composting (Catastrophic) (WV)

• ENG - Application of Municipal Sludge (TX)

• ENG - Closed-Loop Horizontal Geothermal Heat Pump (CT)

• ENG - Construction Materials; Gravel Source (IN)

• ENG - Construction Materials; Gravel Source (NE)

• ENG - Construction Materials; Reclamation (MD)

• ENG - Construction Materials; Reclamation (MI)

• ENG - Construction Materials; Roadfill (GA)

• ENG - Construction Materials; Sand Source (CT)

• ENG - Construction Materials; Sand Source (GA)

• ENG - Construction Materials; Topsoil (ID)

• ENG - Construction Materials; Topsoil (OH)

• ENG - Daily Cover for Landfill (OH)

• ENG - Disposal Field (NJ)

• ENG - Disposal Field Type Inst (NJ)

• ENG - Dwellings W/O Basements

• ENG - Dwellings With Basements

• ENG - Dwellings without Basements (AK)

• ENG - Lawn and Landscape (OH)

• ENG - Lawn, Landscape, Golf Fairway

• ENG - Local Roads and Streets (AK)

• ENG - Local Roads and Streets (GA)

• ENG - On-Site Waste Water Lagoons (MO)

• ENG - Pier Beam Building Foundations (TX)

• ENG - Sanitary Landfill (Area)

• ENG - Sanitary Landfill (Area) (AK)

• ENG - Septage Application - Incorporation or Injection (MN)

• ENG - Septic System; Disinfection, Surface Application (TX)

• ENG - Septic Tank Absorption Fields (FL)

• ENG - Septic Tank Absorption Fields (OH)

• ENG - Septic Tank Absorption Fields - Trench (MN)

• ENG - Sewage Lagoons (AK)

• ENG - Shallow Excavations (OH)

• ENG - Soil Suitability for SLAMM Marsh Migration (CT)

• ENG - Stormwater Management / Infiltration (NY)

• ENG - Stormwater Management / Wetlands (NY)

• FOR - Black Walnut Suitability (WI)

• FOR - Black Walnut Suitability (WV)

• FOR - Construction Limitations for Haul Roads/Log Landings

• FOR - Displacement Hazard

• FOR - Harvest Equipment Operability (DE)

• FOR - Harvest Equipment Operability (ME)

• FOR - Harvest Equipment Operability (MI)

• FOR - Log Landing Suitability (ID)

• FOR - Log Landing Suitability (MI)

• FOR - Log Landing Suitability (OR)

• FOR - Mechanical Planting Suitability (OH)

• FOR - Mechanical Site Preparation (Surface) (MD)

• FOR - Mechanical Site Preparation (Surface) (OH)

• FOR - Mechanical Site Preparation; Surface (CT)

• FOR - Potential Erosion Hazard (Off-Road/Off-Trail) (MI)

• FOR - Potential Erosion Hazard (Off-Road/Off-Trail) (OH)

• FOR - Potential Seedling Mortality (FL)

• FOR - Potential Seedling Mortality (OH)

• FOR - Road Suitability (Natural Surface) (VT)

• FOR - Soil Rutting Hazard

• FOTG - Indiana Soy Bean Yield Calculation (IN)

• FOTG - Indiana Wheat Yield Calculation (IN)

• FOTG - NLI report Calculation - (IN)

• GRL - Fencing, Post Depth =<24 inches

• GRL - Fencing, Post Depth Less Than 24 inches (TX)

• GRL - Fencing, Post Depth Less Than 36 inches (TX)

• GRL - NV range seeding (Wind C = 10) (NV)

• GRL - NV range seeding (Wind C = 30) (NV)

• GRL - Rangeland Chaining (TX)

• GRL - Rangeland Disking (TX)

• GRL - Rangeland Dozing/Grubbing (TX)

• GRL - Utah Juniper Encroachment Potential

• GRL - Western Juniper Encroachment Potential (OR)

• Ground-based Solar Arrays_bedrock_slope_anchor(ME)

• Ground-based Solar Arrays_saturation_flooding_Frost(ME)

• Hybrid Wine Grape Varieties Site Desirability (Medium)

• Lined Retention Systems

• MIL - Trafficability Veh. Type 1 dry season (DOD)

• MIL - Trafficability Veh. Type 3 1-pass wet season (DOD)

• MIL - Trafficability Veh. Type 3 dry season (DOD)

• MIL - Trafficability Veh. Type 4 dry season (DOD)

• MIL - Trafficability Veh. Type 5 1-pass wet season (DOD)

• NCCPI - NCCPI Corn Submodel (I)

• NCCPI - NCCPI Small Grains Submodel (II)

• NCCPI - NCCPI Soybeans Submodel (I)

• Peony Flowers Site Suitability (AK)

• Pressure Dose Full Depth Septic System (DE)

• REC - Camp Areas; Primitive (AK)

• REC - Paths and Trails (CT)

• Salinity Risk Index (ND)

• SAS - Eastern Oyster Habitat Restoration Suitability

• SAS - Mooring Anchor - Mushroom

• Septic System CO-OP RFS III w/At-Grade Bed (PA)

• Septic System Free Access Sand Filter w/At-Grade Bed (PA)

• Septic System Modified Subsurface Sand Filter (Alt.) (PA)

• Septic System Shallow In Ground Trench (conventional) (WV)

• Septic System Subsurface Sand Filter Bed (conventional) (PA)

• Septic System Subsurface Sand Filter Trench (standard) (PA)

• SOH - Limitations for Aerobic Soil Organisms

• URB - Concrete Driveways and Sidewalks (TX)

• URB - Dwellings on Concrete Slab (TX)

• URB - Lawns and Ornamental Plantings (TX)

• URB/REC - Paths and Trails

• URB/REC - Paths and Trails (GA)

• URB/REC - Playgrounds (MI)

• Vinifera Wine Grape Site Desirability (Long)

• WLF - Crawfish Aquaculture (TX)

• WLF - Desertic Herbaceous Plants (TX)

• WLF - Gopher Tortoise Burrowing Suitability

• WLF - Grain & Seed Crops for Food and Cover (TX)

• WMS - Constructing Grassed Waterways (OH)

• WMS - Irrigation, Surface (graded)

• WMS - Subsurface Drains - Installation (VT)

• WMS - Subsurface Water Management, System Performance

• WMS - Surface Drains (TX)

• WMS - Surface Irrigation Intake Family (TX)

• Septic System Low Pressure Pipe (alternative) (WV)

• Septic System Mound (alternative) (WV)

• Septic System Peat Based Option2 w/Spray Irrigation (PA)

• Septic System Steep Slope Mound (alternative) (WV)

• SOH - Concentration of Salts- Soil Surface

• SOH - Soil Susceptibility to Compaction

• Soil Habitat for Saprophyte Stage of Coccidioides

• Unlined Retention Systems

• URB - Commercial Metal Bldg; w/Reinforced Concrete Slab (TX)

• URB/REC - Picnic Areas (GA)

• URB/REC - Picnic Areas (MI)

• URB/REC - Picnic Areas (OH)

• Vinifera Wine Grape Site Desirability (Short)

• WLF - Burrowing Mammals & Reptiles (TX)

• WLF - Desert Tortoise (CA)

• WLF - Domestic Grasses & Legumes for Food and Cover (TX)

• WLF - Irrigated Grain & Seed Crops for Food & Cover (TX)

• WMS - Excavated Ponds (Aquifer-fed)

• WMS - Excavated Ponds (Aquifer-fed) (VT)

• WMS - Irrigation, General

• WMS - Irrigation, Micro (above ground)

• WMS - Irrigation, Micro (above ground) (VT)

• WMS - Irrigation, Micro (subsurface drip)

• WMS - Irrigation, Sprinkler (general) (VT)

• WMS - Pond Reservoir Area

• WMS - Pond Reservoir Area (OH)

• WMS - Subsurface Water Management, System Installation

• WMS - Constructing Terraces & Diversions (TX)

• WMS - Drainage (OH)

• WMS - Excavated Ponds (Aquifer-fed) (OH)

• WMS - Grape Production with Drip Irrigation (TX)

• WMS - Irrigation, Micro (subsurface drip) (VT)

• WMS - Irrigation, Surface (level)

• WMS - Pond Reservoir Area (MI)

• WMS - Pond Reservoir Area (VT)

• WMS - Sprinkler Irrigation (MT)

• WMS - Sprinkler Irrigation RDC (IL)

• WMS - Subsurface Drains - Performance (VT)

• WMS - Subsurface Water Management, Outflow Quality

• WMS - Surface Water Management, System

• WMS-Subsurface Water Management, Performance (ND)

Value

a data.frame

Author(s)

Jason Nemecek, Chad Ferguson, Andrew Brown

Examples

  # get two forestry interpretations for CA630
  get_SDA_interpretation(c("FOR - Potential Seedling Mortality",
                           "FOR - Road Suitability (Natural Surface)"),
                         method = "Dominant Condition",
                         areasymbols = "CA630")

Get Soil Data Access, Lab Data Mart and Web Soil Survey Usage Metrics

Description

Obtain pre-calculated tabular reports of usage, activities, areas of interest (AOI), exports, ecological sites, ratings and reports for specific areas, times and intervals.

Usage

get_SDA_metrics(query_name, query_frequency, query_year, state = NULL)

Arguments

Value

A data.frame containing query results

Author(s)

Jason Nemecek

Examples

## Not run: 
get_SDA_metrics('SDA_Usage', 'CY', 2019:2021)

## End(Not run)

Get map unit aggregate attribute information from Soil Data Access

Description

Get map unit aggregate attribute information from Soil Data Access

Usage

get_SDA_muaggatt(
  areasymbols = NULL,
  mukeys = NULL,
  WHERE = NULL,
  query_string = FALSE,
  dsn = NULL
)

Arguments

Value

a data.frame

Author(s)

Jason Nemecek, Chad Ferguson, Andrew Brown

Get map unit parent material group information from Soil Data Access

Description

Get map unit parent material group information from Soil Data Access

Usage

get_SDA_pmgroupname(
  areasymbols = NULL,
  mukeys = NULL,
  WHERE = NULL,
  method = "DOMINANT COMPONENT",
  simplify = TRUE,
  include_minors = TRUE,
  miscellaneous_areas = FALSE,
  query_string = FALSE,
  dsn = NULL
)

Arguments

Details

Default method is "Dominant Component" to get the dominant component (highest percentage). Use "Dominant Condition" or dominant parent material condition (similar conditions aggregated across components). Use "None" for no aggregation (one record per component).

Value

a data.frame

Author(s)

Jason Nemecek, Chad Ferguson, Andrew Brown

Get map unit properties from Soil Data Access

Description

Get map unit properties from Soil Data Access

Usage

get_SDA_property(
  property,
  method = c("Dominant Component (Category)", "Weighted Average", "Min/Max",
    "Dominant Component (Numeric)", "Dominant Condition", "None"),
  areasymbols = NULL,
  mukeys = NULL,
  WHERE = NULL,
  top_depth = 0,
  bottom_depth = 200,
  FUN = NULL,
  include_minors = FALSE,
  miscellaneous_areas = FALSE,
  query_string = FALSE,
  dsn = NULL
)

Arguments

Details

The property argument refers to one of the property names or columns specified in the tables below. Note that property can be specified as either a character vector of labeled properties, such as "Bulk Density 0.33 bar H2O - Rep Value", OR physical column names such as "dbthirdbar_r". To get "low" and "high" values for a particular property, replace the ⁠_r⁠ with ⁠_l⁠ or ⁠_h⁠ in the physical column name; for example property = c("dbthirdbar_l","dbthirdbar_r","dbthirdbar_h"). You can view exhaustive lists of component and component horizon level properties in the Soil Data Access "Tables and Columns Report".

Selected Component-level Properties

Selected Horizon-level Properties

Value

a data.frame with result

Author(s)

Jason Nemecek, Chad Ferguson, Andrew Brown

Examples

 # get 1/3 bar bulk density [0,25] centimeter depth weighted average from dominant component
 get_SDA_property(property = c("dbthirdbar_l","dbthirdbar_r","dbthirdbar_h"),
                  method = "Dominant Component (Numeric)",
                  areasymbols = "CA630",
                  top_depth = 0,
                  bottom_depth = 25)

Get Soil Data Viewer Attribute Information

Description

Get Soil Data Viewer Attribute Information

Usage

get_SDV_legend_elements(
  WHERE,
  alpha = 255,
  notratedcolor = rgb(1, 1, 1, 0),
  simplify = TRUE
)

Arguments

Value

A list with a data.frame element for each element of WHERE containing "attributekey", "attributename", "attributetype", "attributetablename", "attributecolumnname", "attributedescription", "nasisrulename", "label", "order", "value", "lower_value", "upper_value","red", "green", "blue" and "hex" columns.

Get Soil Inventory Resource (SRI) for USFS Region 6

Description

This function calls ECOSHARE (zip files) to get Soil Inventory Resource (SRI) data for USFS Region 6. These datasets contain both spatial and non-spatial data in the form of a File Geodatabase (GDB).

Usage

get_SRI(gdb, layers = "MapUnits", quiet = FALSE, simplify = TRUE)

Arguments

Details

Due to the fact that many Region 6 Forests do not have NRCS SSURGO surveys (at a scale of 1:24,000, these are the highest-resolution soils data generally available), Region 6 initiated a project in 2012 to bring these legacy SRI soils data into digital databases to facilitate their use in regional planning activities. The datasets available on this page are the results of that effort.

The SRI were originally compiled in 20 volumes, with the original year of publication ranging from 1969 to 1979. The Gifford-Pinchot SRI was redone following the eruption of Mt Saint Helens, and that version was published in 1992. The Olympic NF also produced two versions, the original version being published in 1969, with an update in 1982. The Colville National Forest was the only Region 6 forest that did not compile a SRI.

The data are organized into one single regional GDB, together with twenty individual forest-level GDBs. The regional database contains polygons from all twenty SRIs together with a common set of attributes for the two or three soil layers delineated in the individual mapping unit descriptions, such as texture, depth, color, rock content, etc. In general, the regional database contains physical soil attributes that could be compiled more or less completely and consistently across all forests. The individual forest-level databases contain the polygons for each individual SRI, together with various tables of management interpretations and laboratory data, together with a variety of miscellaneous tables. The information contained in these forest-level databases varies widely from forest to forest, which is why they were not merged into a regional view. Full metadata are included with each database, and scans of the original SRI volumes are provided for reference as well. A Forest Service General Technical Report that fully describes the available data is currently in preparation.

The GDB's currently available:

• Region6

• Deschutes

• Fremont

• GiffordPinchot

• Malheur

• MtBaker

• MtHood

• Ochoco

• Okanogan

• Olympic

• RogueRiver

• Siskiyou

• Siuslaw

• Umatilla

• Umpqua

• WallowaWhitman

• Wenatchee

• Willamette

• Winema

Value

An sf or data.frame object.

Note

Please use get_SRI_layers to get the layer id information needed for the layer argument. This will help with joining sf and data.frame objects.

Author(s)

Josh Erickson

See Also

get_SRI_layers()

Examples

## Not run: 

# get Deschutes SRI
sri_deschutes <- get_SRI('Deschutes')

# get multiple layers in a list

sri_deschutes_multiple <- get_SRI(gdb = 'Deschutes',
layers = c('MapUnits', 'ErosionAndHydro', 'SampleSites_MaterialsTesting'))


## End(Not run)

Get SRI Layers

Description

Get SRI Layers

Usage

get_SRI_layers(gdb)

Arguments

Value

A list of metadata about the GDB

Note

Refer to get_SRI for information on File Geodatabase (GDB) availability.

Author(s)

Josh Erickson

Examples

## Not run: 
sri_layers <- get_SRI_layers('Willamette')

## End(Not run)

Get Soil Color Data from a local NASIS Database

Description

Get, format, mix, and return color data from a NASIS database.

Usage

get_colors_from_NASIS_db(
  SS = TRUE,
  method = "dominant",
  mixColors = FALSE,
  dsn = NULL
)

Arguments

Value

A data.frame with the results.

Author(s)

Jay M. Skovlin and Dylan E. Beaudette

See Also

simplifyColorData, get_hz_data_from_NASIS_db, get_site_data_from_NASIS_db

Get Soil Color Data from a PedonPC Database

Description

Get, format, mix, and return color data from a PedonPC database.

Usage

get_colors_from_pedon_db(dsn)

Arguments

Value

A data.frame with the results.

Author(s)

Dylan E. Beaudette and Jay M. Skovlin

See Also

get_hz_data_from_pedon_db, get_site_data_from_pedon_db

Get component month data from a local NASIS Database

Description

Get component month data from a local NASIS Database.

Usage

get_comonth_from_NASIS_db(
  SS = TRUE,
  fill = FALSE,
  stringsAsFactors = NULL,
  dsn = NULL
)

Arguments

Value

A list with the results.

Author(s)

Stephen Roecker

See Also

fetchNASIS

Examples

if(local_NASIS_defined()) {
  # query text note data
  cm <- try(get_comonth_from_NASIS_db())

  # show structure of component month data
  str(cm)
}

Get component data from a local NASIS Database

Description

Get component data from a local NASIS Database

Usage

get_component_data_from_NASIS_db(
  SS = TRUE,
  nullFragsAreZero = TRUE,
  stringsAsFactors = NULL,
  dsn = NULL
)

get_component_diaghz_from_NASIS_db(SS = TRUE, dsn = NULL)

get_component_restrictions_from_NASIS_db(SS = TRUE, dsn = NULL)

get_component_correlation_data_from_NASIS_db(
  SS = TRUE,
  dropAdditional = TRUE,
  dropNotRepresentative = TRUE,
  stringsAsFactors = NULL,
  dsn = NULL
)

get_component_cogeomorph_data_from_NASIS_db(SS = TRUE, dsn = NULL)

get_component_cogeomorph_data_from_NASIS_db2(SS = TRUE, dsn = NULL)

get_component_copm_data_from_NASIS_db(
  SS = TRUE,
  stringsAsFactors = NULL,
  dsn = NULL
)

get_component_esd_data_from_NASIS_db(
  SS = TRUE,
  stringsAsFactors = NULL,
  dsn = NULL
)

get_component_otherveg_data_from_NASIS_db(SS = TRUE, dsn = NULL)

get_copedon_from_NASIS_db(SS = TRUE, dsn = NULL)

get_component_horizon_data_from_NASIS_db(
  SS = TRUE,
  fill = FALSE,
  dsn = NULL,
  nullFragsAreZero = TRUE
)

Arguments

Value

a data.frame

Author(s)

Dylan E. Beaudette, Stephen Roecker, and Jay M. Skovlin

See Also

fetchNASIS

Examples

if(local_NASIS_defined()) {
 # query text note data
 fc <- try(get_component_data_from_NASIS_db())

 # show structure of component data returned
 str(fc)
}

Get a SoilProfileCollection from a SSURGO file geodatabase

Description

Functions to load and flatten commonly used tables and from SSURGO file geodatabases, and create soil profile collection objects (SPC).

Usage

get_component_from_GDB(
  dsn = "gNATSGO_CONUS.gdb",
  WHERE = NULL,
  childs = FALSE,
  droplevels = TRUE,
  stringsAsFactors = NULL
)

get_legend_from_GDB(
  dsn = "gNATSGO_CONUS.gdb",
  WHERE = NULL,
  droplevels = TRUE,
  stringsAsFactors = NULL,
  stats = FALSE
)

get_mapunit_from_GDB(
  dsn = "gNATSGO_CONUS.gdb",
  WHERE = NULL,
  droplevels = TRUE,
  stringsAsFactors = NULL,
  stats = FALSE
)

fetchGDB(
  dsn = "gNATSGO_CONUS.gdb",
  WHERE = NULL,
  childs = FALSE,
  droplevels = TRUE,
  stringsAsFactors = NULL
)

Arguments

Details

These functions return data from SSURGO file geodatabases with the use of a simple text string that formatted as an SQL WHERE clause (e.g. WHERE = "areasymbol = 'IN001'". Any columns within the target table can be specified (except for fetchGDB() which currently can only target one table (e.g. legend, mapunit or component) at a time with the WHERE clause).

Value

A data.frame or SoilProfileCollection object.

Author(s)

Stephen Roecker

Examples

## replace `dsn` with path to your own geodatabase (SSURGO OR gNATSGO)
##
##  download CONUS gNATSGO from here:
##    https://nrcs.app.box.com/v/soils/folder/191790828371
##
# dsn <- "D:/geodata/soils/gNATSGO_CONUS.gdb"
# le <- get_legend_from_GDB(dsn = dsn, WHERE = "areasymbol LIKE '%'")
# mu <- get_mapunit_from_GDB(dsn = dsn, WHERE = "muname LIKE 'Miami%'")
# co <- get_component_from_GDB(dsn, WHERE = "compname = 'Miami'
#                              AND majcompflag = 'Yes'", childs = FALSE)
# f_in_GDB <- fetchGDB(WHERE = "areasymbol LIKE 'IN%'")

Get SSURGO/STATSGO2 Mapunit Data from Soil Data Access

Description

Functions to download and flatten commonly used tables and from Soil Data Access, and create soil profile collection objects (SPC).

Usage

get_component_from_SDA(
  WHERE = NULL,
  duplicates = FALSE,
  childs = TRUE,
  droplevels = TRUE,
  nullFragsAreZero = TRUE,
  stringsAsFactors = NULL
)

get_cointerp_from_SDA(
  WHERE = NULL,
  mrulename = NULL,
  duplicates = FALSE,
  droplevels = TRUE,
  stringsAsFactors = NULL
)

get_legend_from_SDA(WHERE = NULL, droplevels = TRUE, stringsAsFactors = NULL)

get_lmuaoverlap_from_SDA(
  WHERE = NULL,
  droplevels = TRUE,
  stringsAsFactors = NULL
)

get_mapunit_from_SDA(WHERE = NULL, droplevels = TRUE, stringsAsFactors = NULL)

get_chorizon_from_SDA(
  WHERE = NULL,
  duplicates = FALSE,
  childs = TRUE,
  nullFragsAreZero = TRUE,
  droplevels = TRUE,
  stringsAsFactors = NULL
)

fetchSDA(
  WHERE = NULL,
  duplicates = FALSE,
  childs = TRUE,
  nullFragsAreZero = TRUE,
  rmHzErrors = FALSE,
  droplevels = TRUE,
  stringsAsFactors = NULL
)

get_cosoilmoist_from_SDA(
  WHERE = NULL,
  duplicates = FALSE,
  impute = TRUE,
  stringsAsFactors = NULL
)

Arguments

Details

These functions return data from Soil Data Access with the use of a simple text string that formatted as an SQL WHERE clause (e.g. WHERE = "areasymbol = 'IN001'". All functions are SQL queries that wrap around SDAquery() and format the data for analysis.

Beware SDA includes the data for both SSURGO and STATSGO2. The areasymbol for STATSGO2 is US. For just SSURGO, include WHERE = "areareasymbol != 'US'".

If the duplicates argument is set to TRUE, duplicate components are returned. This is not necessary with data returned from NASIS, which has one unique national map unit. SDA has duplicate map national map units, one for each legend it exists in.

The value of nullFragsAreZero will have a significant impact on the rock fragment fractions returned by fetchSDA. Set nullFragsAreZero = FALSE in those cases where there are many data-gaps and NULL rock fragment values should be interpreted as NULLs. Set nullFragsAreZero = TRUE in those cases where NULL rock fragment values should be interpreted as 0.

Additional examples can be found in the Soil Data Access (SDA) Tutorial

Value

A data.frame or SoilProfileCollection object.

Author(s)

Stephen Roecker

See Also

SDA_query

Get the Component Soil Moisture Tables

Description

Read and flatten the component soil moisture month tables from a local NASIS Database.

Usage

get_cosoilmoist_from_NASIS(
  SS = TRUE,
  impute = TRUE,
  stringsAsFactors = NULL,
  dsn = NULL
)

Arguments

Details

The component soil moisture tables within NASIS house monthly data on flooding, ponding, and soil moisture status. The soil moisture status is used to specify the water table depth for components (e.g. status == "Moist").

Value

A data.frame.

Author(s)

S.M. Roecker

See Also

fetchNASIS, get_cosoilmoist_from_NASISWebReport, get_cosoilmoist_from_SDA, get_comonth_from_SDA

Examples

if(local_NASIS_defined()) {
 # load cosoilmoist (e.g. water table data)
 test <- try(get_cosoilmoist_from_NASIS())

 # inspect
 if(!inherits(test, 'try-error')) {
   head(test)
 }
}

Get Site Ecological Site History

Description

Gets the Site Ecological Site History data from local NASIS database. Used by get_extended_data_from_NASIS_db().

Usage

get_ecosite_history_from_NASIS_db(
  best = TRUE,
  SS = TRUE,
  es_classifier = NULL,
  dsn = NULL
)

Arguments

Value

a data.frame, or NULL on error

See Also

get_extended_data_from_NASIS_db()

Get accessory tables and summaries from a local NASIS Database

Description

Get accessory tables and summaries from a local NASIS Database

Usage

get_extended_data_from_NASIS_db(
  SS = TRUE,
  nullFragsAreZero = TRUE,
  stringsAsFactors = NULL,
  dsn = NULL
)

Arguments

Value

A list with the results.

Author(s)

Jay M. Skovlin and Dylan E. Beaudette

See Also

get_hz_data_from_NASIS_db, get_site_data_from_NASIS_db

Examples

if(local_NASIS_defined()) {
 # query extended data
 e <- try(get_extended_data_from_NASIS_db())

 # show contents of extended data
 str(e)
}

Get accessory tables and summaries from a local pedonPC Database

Description

Get accessory tables and summaries from a local pedonPC Database.

Usage

get_extended_data_from_pedon_db(dsn)

Arguments

Value

A list with the results.

Author(s)

Jay M. Skovlin and Dylan E. Beaudette

See Also

get_hz_data_from_pedon_db, get_site_data_from_pedon_db

Get Horizon Data from a local NASIS Database

Description

Get horizon-level data from a local NASIS database.

Usage

get_hz_data_from_NASIS_db(
  SS = TRUE,
  fill = FALSE,
  stringsAsFactors = NULL,
  dsn = NULL
)

Arguments

Value

A data.frame.

Note

NULL total rock fragment values are assumed to represent an absence of rock fragments, and set to 0.

Author(s)

Jay M. Skovlin and Dylan E. Beaudette

See Also

get_hz_data_from_NASIS_db, get_site_data_from_NASIS_db

Get Horizon Data from a PedonPC Database

Description

Get horizon-level data from a PedonPC database.

Usage

get_hz_data_from_pedon_db(dsn)

Arguments

Value

A data.frame.

Note

NULL total rock fragment values are assumed to represent an absence of rock fragments, and set to 0.

Author(s)

Dylan E. Beaudette and Jay M. Skovlin

See Also

get_colors_from_pedon_db, get_site_data_from_pedon_db

Get lab pedon layer data from a local NASIS Database

Description

Get lab pedon layer-level (horizon-level) data from a local NASIS database.

Usage

get_lablayer_data_from_NASIS_db(SS = TRUE, dsn = NULL)

Arguments

Value

A data.frame.

Note

This function queries KSSL laboratory site/horizon data from a local NASIS database from the lab layer data table.

Author(s)

Jay M. Skovlin and Dylan E. Beaudette

See Also

get_labpedon_data_from_NASIS_db

Get lab pedon data from a local NASIS Database

Description

Get lab pedon-level data from a local NASIS database.

Usage

get_labpedon_data_from_NASIS_db(SS = TRUE, dsn = NULL)

Arguments

Value

A data.frame.

Note

This function queries KSSL laboratory site/horizon data from a local NASIS database from the lab pedon data table.

Author(s)

Jay M. Skovlin and Dylan E. Beaudette

See Also

get_lablayer_data_from_NASIS_db

Get Legend, Mapunit and Legend Mapunit Area Overlap Tables

Description

Get Legend, Mapunit and Legend Mapunit Area Overlap Tables

Usage

get_mapunit_from_NASIS(
  SS = TRUE,
  repdmu = TRUE,
  droplevels = TRUE,
  stringsAsFactors = NULL,
  areatypename = c("Non-MLRA Soil Survey Area", "MLRA Soil Survey Area"),
  dsn = NULL
)

get_legend_from_NASIS(
  SS = TRUE,
  droplevels = TRUE,
  stringsAsFactors = NULL,
  areatypename = c("Non-MLRA Soil Survey Area", "MLRA Soil Survey Area"),
  dsn = NULL
)

get_lmuaoverlap_from_NASIS(
  SS = TRUE,
  droplevels = TRUE,
  stringsAsFactors = NULL,
  areatypename = c("Non-MLRA Soil Survey Area", "MLRA Soil Survey Area"),
  dsn = NULL
)

Arguments

Get pedon horizon roots data from a local NASIS Database

Description

This function returns records from the phroots table of a local NASIS database. Pedon and pedon horizon record IDs are also included for linking back to related records, typically queried via fetchNASIS().

Usage

get_phroots_from_NASIS_db(SS = TRUE, dsn = NULL)

Arguments

Value

A data.frame

Get NASIS Project Information

Description

Helper functions for accessing the NASIS Project object.

Usage

get_projectmilestone_from_NASIS(SS = TRUE, stringsAsFactors = NULL, dsn = NULL)

get_projectmapunit_from_NASIS(SS = TRUE, stringsAsFactors = NULL, dsn = NULL)

Arguments

Value

get_projectmilestone_from_NASIS(): data.frame containing project and project milestone information

get_projectmapunit_from_NASIS(): data.frame containing project and project mapunit information

Get Site Data from a local NASIS Database

Description

Get site-level data from a local NASIS database.

get_site_association_from_NASIS(): Get Associated User Site IDs for each Site.

Usage

get_site_data_from_NASIS_db(
  SS = TRUE,
  include_pedon = TRUE,
  nullFragsAreZero = TRUE,
  stringsAsFactors = NULL,
  dsn = NULL
)

get_site_association_from_NASIS(SS = TRUE, dsn = NULL)

Arguments

Details

It is possible to have multiple pedon records per site observation and multiple site observations per site, which will result in multiple records per site. See argument include_pedon=FALSE to omit joins to pedon and transect tables.

The following aggregations of child table information are performed by this function:

• Site Area Overlap for State, County and MLRA are returned for related area records, as specified in the site table, as the following column names: site_state, site_county, and site_mlra.

• Site Observation Surface Fragment data are simplified (converted to wide format) using simplifyFragmentData().

• The best Ecological Site History record is selected using get_ecosite_history_from_NASIS_db(best = TRUE).

• Site Other Vegetation Class information is aggregated by class name, using " & " as the separator when multiple classes are assigned.

• When multiple Site Bedrock entries are present, only the shallowest is returned by this function. In lieu of bedrock depth the first record in the table is returned.

Value

A data.frame

Author(s)

Jay M. Skovlin, Dylan E. Beaudette, Andrew G. Brown, Greg Schmidt

See Also

get_hz_data_from_NASIS_db(), fetchNASIS(), fetchVegdata()

Get Site Data from a PedonPC Database

Description

Get site-level data from a PedonPC database.

Usage

get_site_data_from_pedon_db(dsn)

Arguments

Value

A data.frame.

Author(s)

Dylan E. Beaudette and Jay M. Skovlin

See Also

get_hz_data_from_pedon_db, get_veg_from_AK_Site,

Get records from the Series Classification (SC) database

Description

These functions return records from the Series Classification (SC) database, either from the local NASIS database (all series) or via web report (named series only).

⁠get_competing_soilseries_from_NASIS():⁠ Get Soil Series from NASIS Matching Taxonomic Class Name

Usage

get_soilseries_from_NASIS(
  stringsAsFactors = NULL,
  dsn = NULL,
  delimiter = " over ",
  SS = FALSE
)

get_soilseries_from_NASISWebReport(soils, stringsAsFactors = NULL)

get_competing_soilseries_from_NASIS(
  x,
  what = "taxclname",
  dsn = NULL,
  SS = FALSE
)

Arguments

Value

A data.frame

Author(s)

Stephen Roecker

Get text note data from a local NASIS Database

Description

Get text note data from a local NASIS Database

Usage

get_text_notes_from_NASIS_db(SS = TRUE, fixLineEndings = TRUE, dsn = NULL)

get_mutext_from_NASIS_db(SS = TRUE, fixLineEndings = TRUE, dsn = NULL)

get_cotext_from_NASIS_db(SS = TRUE, fixLineEndings = TRUE, dsn = NULL)

Arguments

Value

A list with the results.

Author(s)

Dylan E. Beaudette and Jay M. Skovlin

See Also

get_hz_data_from_pedon_db, get_site_data_from_pedon_db

Examples

if(local_NASIS_defined()) {
 # query text note data
 t <- try(get_text_notes_from_NASIS_db())

 # show contents text note data, includes: siteobs, site, pedon, horizon level text notes data.
 str(t)

 # view text categories for site text notes
 if(!inherits(t, 'try-error')) {
  table(t$site_text$textcat)
 }
}

Get vegetation data from a local NASIS Database

Description

Get vegetation data from a local NASIS Database. Result includes two data.frames corresponding to the "Plot Plant Inventory" and "Vegetation Transect" child tables of "Vegetation Plot".

Usage

get_veg_data_from_NASIS_db(SS = TRUE, dsn = NULL)

Arguments

Value

A list of data.frame

Author(s)

Jay M. Skovlin and Dylan E. Beaudette

Examples

if(local_NASIS_defined()) {
 # query text note data
 v <- try(get_veg_from_NASIS_db())

 # show contents veg data returned
 str(v)
}

Get Vegetation Data from an AK Site Database

Description

Get Vegetation Data from an AK Site Database

Usage

get_veg_from_AK_Site(dsn)

Arguments

Value

A data.frame with vegetation data in long format, linked to site ID.

Author(s)

Dylan E. Beaudette

See Also

get_hz_data_from_pedon_db, get_site_data_from_pedon_db

Get Site and Plot-level Data from a Montana RangeDB database

Description

Get Site and Plot-level data from a Montana RangeDB database.

Usage

get_veg_from_MT_veg_db(dsn)

Arguments

Value

A data.frame.

Author(s)

Jay M. Skovlin

See Also

get_veg_species_from_MT_veg_db, get_veg_other_from_MT_veg_db

Get Vegetation Data from an NPS PLOTS Database

Description

Used to extract species, stratum, and cover vegetation data from a backend NPS PLOTS Database. Currently works for any Microsoft Access database with an .mdb file format.

Usage

get_veg_from_NPS_PLOTS_db(dsn)

Arguments

Value

A data.frame with vegetation data in a long format with linkage to NRCS soil pedon data via the site_id key field.

Note

This function currently only works on Windows.

Author(s)

Jay M. Skovlin

Get cover composition data from a Montana RangeDB database

Description

Get cover composition data from a Montana RangeDB database.

Usage

get_veg_other_from_MT_veg_db(dsn)

Arguments

Value

A data.frame.

Author(s)

Jay M. Skovlin

See Also

get_veg_from_MT_veg_db, get_veg_species_from_MT_veg_db

Get species-level Data from a Montana RangeDB database

Description

Get species-level data from a Montana RangeDB database.

Usage

get_veg_species_from_MT_veg_db(dsn)

Arguments

Value

A data.frame.

Author(s)

Jay M. Skovlin

See Also

get_veg_from_MT_veg_db, get_veg_other_from_MT_veg_db

Example SoilProfilecollection Objects Returned by fetchNASIS.

Description

Several examples of soil profile collections returned by fetchNASIS(from='pedons') as SoilProfileCollection objects.

Examples

  library(aqp)
  
  # load example dataset
  data("gopheridge")

  # what kind of object is this?
  class(gopheridge)

  # how many profiles?
  length(gopheridge)

  # there are 60 profiles, this calls for a split plot
  par(mar=c(0,0,0,0), mfrow=c(2,1))

  # plot soil colors
  aqp::plotSPC(gopheridge[1:30, ], name='hzname', color='soil_color')
  aqp::plotSPC(gopheridge[31:60, ], name='hzname', color='soil_color')

  # need a larger top margin for legend
  par(mar=c(0,0,4,0), mfrow=c(2,1))
  # generate colors based on clay content
  aqp::plotSPC(gopheridge[1:30, ], name='hzname', color='clay')
  aqp::plotSPC(gopheridge[31:60, ], name='hzname', color='clay')

  # single row and no labels
  par(mar=c(0,0,0,0), mfrow=c(1,1))
  # plot soils sorted by depth to contact
  aqp::plotSPC(gopheridge, name='', print.id=FALSE, plot.order=order(gopheridge$bedrckdepth))

  # plot first 10 profiles
  aqp::plotSPC(
    gopheridge[1:10, ],
    name = 'hzname',
    color = 'soil_color',
    label = 'upedonid',
    id.style = 'side'
  )

  # add rock fragment data to plot:
  aqp::addVolumeFraction(gopheridge[1:10, ], colname='total_frags_pct')

  # add diagnostic horizons
  aqp::addDiagnosticBracket(gopheridge[1:10, ], kind='argillic horizon', col='red', offset=-0.4)

  ## loafercreek
  data("loafercreek")
  
  # plot first 10 profiles
  aqp::plotSPC(
    loafercreek[1:10, ],
    name = 'hzname',
    color = 'soil_color',
    label = 'upedonid',
    id.style = 'side'
  )

  # add rock fragment data to plot:
  aqp::addVolumeFraction(loafercreek[1:10, ], colname='total_frags_pct')

  # add diagnostic horizons
  aqp::addDiagnosticBracket(loafercreek[1:10, ], kind='argillic horizon', col='red', offset=-0.4)

Check for presence of nasis_local ODBC data source

Description

Check for presence of a NASIS data source. This function always returns FALSE when the odbc package is not available (regardless of whether you have an ODBC data source properly set up).

Usage

local_NASIS_defined(dsn = NULL)

Arguments

Details

If dsn is specified as a character vector it is assumed to refer to a SQLite data source. The result will be TRUE or FALSE depending on the result of RSQLite::dbCanConnect().

If dsn is specified as a DBIConnection the function returns the value of DBI::dbExistsTable("MetadataDomainMaster")

Value

logical

Examples

if(local_NASIS_defined()) {
  # use fetchNASIS or some other lower-level fetch function
} else {
  message('could not find `nasis_local` ODBC data source')
}

Generate chunk labels for splitting data

Description

Generate chunk labels for splitting data

Usage

makeChunks(ids, size = 100)

Arguments

Value

A numeric vector

Examples

# split the lowercase alphabet into 2 chunks

aggregate(letters,
          by = list(makeChunks(letters, size=13)),
          FUN = paste0, collapse=",")

Make Ecological Dynamics Interpretive Tool (EDIT) web services URL

Description

Construct a URL for Ecological Dynamics Interpretive Tool (EDIT) web services (⁠https://edit.jornada.nmsu.edu/services/...⁠) to return PDF, TXT or JSON results.

Usage

make_EDIT_service_URL(
  src = c("descriptions", "downloads", "plant-community-tables", "models", "keys"),
  catalog = c("esd", "esg"),
  geoUnit = NULL,
  ecoclass = NULL,
  landuse = NULL,
  state = NULL,
  community = NULL,
  key = NULL,
  endpoint = NULL,
  querystring = NULL
)

Arguments

Details

See the following official EDIT developer resources to see which endpoints are available for Ecological Site Description (ESD) or Ecological Site Group (ESG) catalogs:

• https://edit.jornada.nmsu.edu/resources/esd

• https://edit.jornada.nmsu.edu/resources/esg

Value

A character vector containing URLs with specified parameters. This function is vectorized.

See Also

get_EDIT_ecoclass_by_geoUnit

Examples

# url for all geoUnit keys as PDF
make_EDIT_service_URL(src = "descriptions",
                      catalog = "esd",
                      geoUnit = "039X")

# url for a single key within geoUnit as PDF
make_EDIT_service_URL(src = "descriptions",
                      catalog = "esd",
                      geoUnit = "039X",
                      key = "1")

# query for "full" description in JSON
desc <-  make_EDIT_service_URL(src = "descriptions",
                               catalog = "esd",
                               geoUnit = "039X",
                               endpoint = "R039XA109AZ.json")

# query for "overview"
desc_ov <- make_EDIT_service_URL(src = "descriptions",
                                 catalog = "esd",
                                 geoUnit = "039X",
                                 ecoclass = "R039XA109AZ",
                                 endpoint = "overview.json")

# query for specific section, e.g. "water features"
desc_wf <- make_EDIT_service_URL(src = "descriptions",
                                 catalog = "esd",
                                 geoUnit = "039X",
                                 ecoclass = "R039XA109AZ",
                                 endpoint = "water-features.json")

# construct the URLs -- that is a query essentially
# then download the result with read_json

#full <- jsonlite::read_json(desc)
#overview <- jsonlite::read_json(desc_ov)
#waterfeature <- jsonlite::read_json(desc_wf)

NASIS 7 Metadata

Description

NASIS 7 Metadata from MetadataDomainDetail, MetadataDomainMaster, and MetadataTableColumn tables

Format

A data.frame with the following columns:

• DomainID - Integer. ID that uniquely identifies a domain in a data model, not just within a database.

• DomainName - Character. Domain Name.

• DomainRanked - Integer. Is domain ranked? 0 = No; 1 = Yes

• DisplayLabel - Character. Domain Display Label.

• ChoiceSequence - Integer. Order or sequence of Choices.

• ChoiceValue - Integer. Value of choice level.

• ChoiceName - Character. Name of choice level.

• ChoiceLabel - Character. Label of choice level.

• ChoiceObsolete - Integer. Is choice level obsolete? 0 = No; 1 = Yes

• ColumnPhysicalName - Character. Physical column name.

• ColumnLogicalName - Character. Logical column name.

Get Map Unit Key (mukey) grid from SoilWeb Web Coverage Service (WCS)

Description

Download chunks of the gNATSGO, gSSURGO, RSS, and STATSGO2 map unit key grid via bounding-box from the SoilWeb WCS.

Usage

mukey.wcs(
  aoi,
  db = c("gNATSGO", "gSSURGO", "RSS", "STATSGO", "PR_SSURGO", "HI_SSURGO"),
  res = 30,
  quiet = FALSE
)

Arguments

Details

aoi should be specified as one of: SpatRaster, Spatial*, RasterLayer, sf, sfc, bbox object, OR a list containing:

aoi

bounding-box specified as (xmin, ymin, xmax, ymax) e.g. c(-114.16, 47.65, -114.08, 47.68)

crs

coordinate reference system of BBOX, e.g. 'OGC:CRS84' (EPSG:4326, WGS84 Longitude/Latitude)

The WCS query is parameterized using a rectangular extent derived from the above AOI specification, after conversion to the native CRS (EPSG:5070) of the WCS grids.

Databases available from this WCS can be queried using WCS_details(wcs = 'mukey').

Value

A SpatRaster (or RasterLayer) object containing indexed map unit keys and associated raster attribute table or a try-error if request fails. By default, spatial classes from the terra package are returned. If the input object class is from the raster or sp packages a RasterLayer is returned.

Note

The gNATSGO grid includes raster soil survey map unit keys which are not in SDA.

Author(s)

D.E. Beaudette and A.G. Brown

Examples

## Not run: 
library(terra)

res <- mukey.wcs(list(aoi = c(-116.7400, 35.2904, -116.7072, 35.3026), crs = "EPSG:4326"),
                 db = 'gNATSGO', res = 30) 
  
m <- unique(values(res))

prp <- setNames(
  get_SDA_property(
    c("ph1to1h2o_r", "claytotal_r"),
    "weighted average",
    mukeys = m,
    top_depth = 0,
    bottom_depth = 25,
    include_minors = TRUE,
    miscellaneous_areas = FALSE
  )[, c("mukey", "ph1to1h2o_r", "claytotal_r")],
  c("ID",    "pH1to1_0to25", "clay_0to25")
)

levels(res) <- prp
res2 <- catalyze(res)
res2

plot(res2[['pH1to1_0to25']])

## End(Not run)

Parse contents of a web report, based on supplied arguments.

Description

Parse contents of a web report, based on supplied arguments.

Usage

parseWebReport(url, args, index = 1)

Arguments

Details

Report argument names can be inferred by inspection of the HTML source associated with any given web report.

Value

A data.frame object in the case of a single integer index, otherwise a list

Note

Most web reports are for internal use only.

Author(s)

D.E. Beaudette and S.M. Roecker

Post-process Well-Known Text from Soil Data Access

Description

This is a helper function commonly used with SDA_query to extract WKT (well-known text) representation of geometry to an sf or sp object.

Usage

processSDA_WKT(d, g = "geom", crs = 4326, p4s = NULL, as_sf = TRUE)

Arguments

Details

The SDA website can be found at https://sdmdataaccess.nrcs.usda.gov. See the SDA Tutorial for detailed examples.

The SDA website can be found at https://sdmdataaccess.nrcs.usda.gov. See the SDA Tutorial for detailed examples.

Value

An sf object or if as_sf is FALSE a ⁠Spatial*⁠ object.

Note

This function requires the sf package.

Author(s)

D.E. Beaudette, A.G. Brown

Retrieve Soil Series Extent Maps from SoilWeb

Description

This function downloads a generalized representations of a soil series extent from SoilWeb, derived from the current SSURGO snapshot. Data can be returned as vector outlines (sf object) or gridded representation of area proportion falling within 800m cells (SpatRaster object). Gridded series extent data are only available in CONUS. Vector representations are returned with a GCS/WGS84 coordinate reference system and raster representations are returned with an Albers Equal Area / NAD83 coordinate reference system (EPSG:5070).

Usage

seriesExtent(
  s,
  type = c("vector", "raster"),
  timeout = 60,
  as_Spatial = getOption("soilDB.return_Spatial", default = FALSE)
)

Arguments

Value

An R spatial object, class depending on type and as_Spatial arguments

Author(s)

D.E. Beaudette

References

https://casoilresource.lawr.ucdavis.edu/see/

Examples

## Not run: 
  
  # specify a soil series name
  s <- 'magnor'
  
  # return an sf object
  x <- seriesExtent(s, type = 'vector')
  
  # return a terra SpatRasters
  y <- seriesExtent(s, type = 'raster')
  
  library(terra)
  if (!is.null(x) && !is.null(y)) {
    x <- terra::vect(x)
    # note that CRS are different
    terra::crs(x)
    terra::crs(y)
  
    # transform vector representation to CRS of raster
    x <- terra::project(x, terra::crs(y))
  
    # graphical comparison
    par(mar = c(1, 1 , 1, 3))
    plot(y, axes = FALSE)
    plot(x, add = TRUE)
  }

## End(Not run)

Get "siblings" and "cousins" for a given soil series

Description

Look up siblings and cousins for a given soil series from the current fiscal year SSURGO snapshot via SoilWeb.

The siblings of any given soil series are defined as those soil components (major and minor) that share a parent map unit with the named series (as a major component). Component names are filtered using a snapshot of the Soil Classification database to ensure that only valid soil series names are included. Cousins are siblings of siblings. Data are sourced from SoilWeb which maintains a copy of the current SSURGO snapshot. Visualizations of soil "siblings"-related concepts can be found in the "Sibling Summary" tab of Soil Data Explorer app: https://casoilresource.lawr.ucdavis.edu/sde/.

Additional resources:

• Soil Series Query Functions

• Soil "Siblings" Tutorial

• SSSA 2019 Presentation - Mapping Soilscapes Using Soil Co-Occurrence Networks

Usage

siblings(s, only.major = FALSE, component.data = FALSE, cousins = FALSE)

Arguments

Value

A list containing:

• sib: data.frame containing siblings, major component flag, and number of co-occurrences

• sib.data: data.frame containing sibling component data (only when component.data = TRUE)

• cousins: data.frame containing cousins, major component flag, and number of co-occurrences (only when cousins = TRUE)

• cousin.data: data.frame containing cousin component data (only when ⁠cousins = TRUE, component.data = TRUE⁠)

Author(s)

D.E. Beaudette

References

O'Geen, A., Walkinshaw, M. and Beaudette, D. (2017), SoilWeb: A Multifaceted Interface to Soil Survey Information. Soil Science Society of America Journal, 81: 853-862. doi:10.2136/sssaj2016.11.0386n

See Also

OSDquery, siblings, fetchOSD

Examples

    # basic usage
    x <- siblings('zook')
    x$sib
    
    # restrict to siblings that are major components
    # e.g. the most likely siblings
    x <- siblings('zook', only.major = TRUE)
    x$sib

Simplify Coarse Fraction Data

Description

Simplify multiple coarse fraction (>2mm) records by horizon.

Usage

simplifyArtifactData(
  art,
  id.var,
  vol.var = "huartvol",
  nullFragsAreZero = nullFragsAreZero,
  ...
)

simplifyFragmentData(
  rf,
  id.var,
  vol.var = "fragvol",
  prefix = "frag",
  nullFragsAreZero = TRUE,
  msg = "rock fragment volume",
  ...
)

Arguments

Details

This function is mainly intended for processing of NASIS pedon/component data which contains multiple coarse fragment descriptions per horizon. simplifyFragmentData will "sieve out" coarse fragments into the USDA classes, split into hard and para- fragments. Likewise, simplifyArtifactData will sieve out human artifacts, and split total volume into "cohesive", "penetrable", "innocuous", and "persistent".

These functions can be applied to data sources other than NASIS by careful use of the id.var and vol.var arguments.

• rf must contain rock or other fragment volumes in the column "fragvol" (or be specified with vol.var), fragment size (mm) in columns "fragsize_l", "fragsize_r", "fragsize_h", fragment cementation class in "fraghard" and flat/non-flat in "fragshp".

• art must contain artifact volumes in the column "huartvol" (or be specified with vol.var), fragment size (mm) in columns "huartsize_l", "huartsize_r", "huartsize_h", artifact cementation class in "huarthard" and flat/non-flat in "huartshp".

Examples:

• KSSL data

Author(s)

D.E. Beaudette, A.G Brown

Simplify Color Data by ID

Description

Simplify multiple Munsell color observations associated with each horizon.

This function is mainly intended for the processing of NASIS pedon/horizon data which may or may not contain multiple colors per horizon/moisture status combination. simplifyColorData will "mix" multiple colors associated with horizons in d, according to IDs specified by id.var, using "weights" (area percentages) specified by the wt argument.

Note that this function doesn't actually simulate the mixture of pigments on a surface, rather, "mixing" is approximated via weighted average in the CIELAB colorspace.

The simplifyColorData function can be applied to data sources other than NASIS by careful use of the id.var and wt arguments. However, d must contain Munsell colors split into columns named "colorhue", "colorvalue", and "colorchroma". In addition, the moisture state ("Dry" or "Moist") must be specified in a column named "colormoistst".

Examples:

• KSSL data

• soil color mixing tutorial

Usage

simplifyColorData(d, id.var = "phiid", wt = "colorpct", bt = FALSE)

Arguments