aqp (version 1.9.3)

ca630: Soil Data from the Central Sierra Nevada Region of California

Description

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

Usage

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

http://ssldata.nrcs.usda.gov/

Details

These data were extracted from the NSSL database. `ca630` is a list composed of site and lab data, each stored as dataframes. 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.

Examples

library(plyr)
library(lattice)
library(Hmisc)
library(maps)
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
coordinates(ca) <- ~ lon + lat

# assign a coordinate reference system
proj4string(ca) <- '+proj=longlat +datum=NAD83'

# check the result
print(ca)

# map the data (several ways to do this, here is a simple way)
map(database='county', region='california')
points(coordinates(ca), col='red', cex=0.5)

# 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, 
ylim=c(160,-5), alpha=0.5, 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=list(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=mlra , data=a, lower=a$p.q25, upper=a$p.q75, 
ylim=c(160,-5), alpha=0.5, scales=list(y=list(tick.num=7, alternating=3), x=list(alternating=1)),
panel=panel.depth_function, prepanel=prepanel.depth_function,
ylab='Depth (cm)', xlab='Base Saturation at pH 8.2', 
par.settings=list(superpose.line=list(col=c('black','blue'), lty=c(1,2), lwd=2)),
auto.key=list(columns=2, title='MLRA', points=FALSE, lines=TRUE)
)


# safely compute hz-thickness weighted mean CEC (pH 7)
# using data.frame objects
head(lab.agg.cec_7 <- ddply(ca630$lab, .(pedon_key), 
.fun=summarise, CEC_7=wtd.mean(bs_7, weights=hzn_bot-hzn_top)))

# extract a SPDF with horizon data along a slice at 25 cm
s.25 <- slice(ca, fm=25 ~ bs_7 + CEC7 + ex_acid)
spplot(s.25, zcol=c('bs_7','CEC7','ex_acid'))

# note that the ordering is preserved:
all.equal(s.25$pedon_key, profile_id(ca))

# extract a data.frame with horizon data at 10, 20, and 50 cm
s.multiple <- slice(ca, fm=c(10,20,50) ~ bs_7 + CEC7 + ex_acid)

# 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
plot(ca.1.to.10, name='hzn_desgn')