Soil Data from the Central Sierra Nevada Region of California

Site and laboratory data from soils sampled in the central Sierra Nevada Region of California.

data(ca630)

Format

List containing:

$site : A data frame containing site information.

user_site_id: national user site id
mlra: the MLRA
county: the county
ssa: soil survey area
lon: longitude, WGS84
lat: latitude, WGS84
pedon_key: national soil profile id
user_pedon_id: local soil profile id
cntrl_depth_to_top: control section top depth (cm)
cntrl_depth_to_bot: control section bottom depth (cm)
sampled_taxon_name: soil series name

$lab : A data frame containing horizon information.

pedon_key: national soil profile id
layer_key: national horizon id
layer_sequence: horizon sequence number
hzn_top: horizon top (cm)
hzn_bot: horizon bottom (cm)
hzn_desgn: horizon name
texture_description: USDA soil texture
nh4_sum_bases: sum of bases extracted by ammonium acetate (pH 7)
ex_acid: exchangeable acidity [method ?]
CEC8.2: cation exchange capacity by sum of cations method (pH 8.2)
CEC7: cation exchange capacity by ammonium acetate (pH 7)
bs_8.2: base saturation by sum of cations method (pH 8.2)
bs_7: base saturation by ammonium acetate (pH 7)

Source

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

Details

These data were extracted from the NSSL database. ca630 is a list composed of site and lab data, each stored as data.frame objects. These data are modeled by a 1:many (site:lab) relation, with the pedon_id acting as the primary key in the site table and as the foreign key in the lab table.

Note

These data are out of date. Pending some new data + documentation. Use with caution

Examples


if (FALSE) {
library(tactile)
library(lattice)
library(Hmisc)
library(sp)

# check the data out:
data(ca630)
str(ca630)

# note that pedon_key is the link between the two tables

# make a copy of the horizon data
ca <- ca630$lab

# promote to a SoilProfileCollection class object
depths(ca) <- pedon_key ~ hzn_top + hzn_bot

# add site data, based on pedon_key
site(ca) <- ca630$site

# ID data missing coordinates: '|' is a logical OR
(missing.coords.idx <- which(is.na(ca$lat) | is.na(ca$lon)))

# remove missing coordinates by safely subsetting
if(length(missing.coords.idx) > 0)
  ca <- ca[-missing.coords.idx, ]

# register spatial data
initSpatial(ca) <- ~ lon + lat

# assign a coordinate reference system
prj(ca) <- 'EPSG:4269'

# check the result
print(ca)

# aggregate %BS 7 for all profiles into 1 cm slices
a <- slab(ca, fm= ~ bs_7)

# plot median & IQR by 1 cm slice
xyplot(
top ~ p.q50, 
data = a, 
lower=a$p.q25, 
upper=a$p.q75,
alpha=0.5,
ylim=c(160,-5), 
scales = list(alternating = 1, y = list(tick.num = 7)),
panel = panel.depth_function, 
prepanel = prepanel.depth_function,
ylab='Depth (cm)', xlab='Base Saturation at pH 7',
par.settings = tactile.theme(superpose.line = list(col = 'black', lwd = 2))
)

# aggregate %BS at pH 8.2 for all profiles by MLRA, along 1 cm slices
# note that mlra is stored in @site
a <- slab(ca, mlra ~ bs_8.2)

# keep only MLRA 18 and 22
a <- subset(a, subset=mlra %in% c('18', '22'))

# plot median & IQR by 1 cm slice, using different colors for each MLRA
xyplot(
top ~ p.q50, 
groups = factor(mlra), 
data = a,
lower=a$p.q25, 
upper=a$p.q75,
alpha=0.25,
sync.colors = TRUE,
ylim=c(160,-5), 
scales = list(alternating = 1, y = list(tick.num = 7)),
panel = panel.depth_function, 
prepanel = prepanel.depth_function,
ylab='Depth (cm)', xlab='Base Saturation at pH 7',
par.settings = tactile.theme(superpose.line = list(lwd = 2)),
auto.key = list(lines = TRUE, points = FALSE, columns = 2)
)



# Extract the 2nd horizon from all profiles as SPDF
ca.2 <- ca[, 2]

# subset profiles 1 through 10
ca.1.to.10 <- ca[1:10, ]

# basic plot method: profile plot
par(mar = c(0, 0, 3, 1))
plotSPC(ca.1.to.10, name='hzn_desgn', color = 'CEC7')
}