GSIF (version 0.5-5.1)

cookfarm: The Cook Agronomy Farm data set

Description

The R.J. Cook Agronomy Farm (cookfarm) is a Long-Term Agroecosystem Research Site operated by Washington State University, located near Pullman, Washington, USA. Contains spatio-temporal (3D+T) measurements of three soil properties and a number of spatial and temporal regression covariates.

Usage

data(cookfarm)

Format

The cookfarm data set contains four data frames. The readings data frame contains measurements of volumetric water content (cubic-m/cubic-m), temperature (degree C) and bulk electrical conductivity (dS/m), measured at 42 locations using 5TE sensors at five standard depths (0.3, 0.6, 0.9, 1.2, 1.5 m) for the period "2011-01-01" to "2012-12-31":

SOURCEID

factor; unique station ID

Date

date; observation day

Port*VW

numeric; volumetric water content measurements at five depths

Port*C

numeric; soil temperature measurements at five depths

Port*EC

numeric; bulk electrical conductivity measurements at five depths

The profiles data frame contains soil profile descriptions from 142 sites:

SOURCEID

factor; unique station ID

Easting

numeric; x coordinate in the local projection system

Northing

numeric; y coordinate in the local projection system

TAXNUSDA

factor; Keys to Soil Taxonomy taxon name e.g. "Caldwell"

HZDUSD

factor; horizon designation

UHDICM

numeric; upper horizon depth from the surface in cm

LHDICM

numeric; lower horizon depth from the surface in cm

BLD

bulk density in tonnes per cubic-meter

PHIHOX

numeric; pH index measured in water solution

The grids data frame contains values of regression covariates at 10 m resolution:

DEM

numeric; Digital Elevation Model

TWI

numeric; SAGA GIS Topographic Wetness Index

MUSYM

factor; soil mapping units e.g. "Thatuna silt loam"

NDRE.M

numeric; mean value of the Normalized Difference Red Edge Index (time series of 11 RapidEye images)

NDRE.sd

numeric; standard deviation of the Normalized Difference Red Edge Index (time series of 11 RapidEye images)

Cook_fall_ECa

numeric; apparent electrical conductivity image from fall

Cook_spr_ECa

numeric; apparent electrical conductivity image from spring

X2011

factor; cropping system in 2011

X2012

factor; cropping system in 2012

The weather data frame contains daily temperatures and rainfall from the nearest meteorological station:

Date

date; observation day

Precip_wrcc

numeric; observed precipitation in mm

MaxT_wrcc

numeric; observed maximum daily temperature in degree C

MinT_wrccc

numeric; observed minimum daily temperature in degree C

References

• Gasch, C.K., Hengl, T., Gr<e4>ler, B., Meyer, H., Magney, T., Brown, D.J., 2015. Spatio-temporal interpolation of soil water, temperature, and electrical conductivity in 3D+T: the Cook Agronomy Farm data set. Spatial Statistics, 14, pp.70--90.

• Gasch, C.K., D.J. Brown, E.S. Brooks, M. Yourek, M. Poggio, D.R. Cobos, C.S. Campbell, 2016? Retroactive calibration of soil moisture sensors using a two-step, soil-specific correction. Submitted to Vadose Zone Journal.

• Gasch, C.K., D.J. Brown, C.S. Campbell, D.R. Cobos, E.S. Brooks, M. Chahal, M. Poggio, 2016? A field-scale sensor network data set for monitoring and modeling the spatial and temporal variation of soil moisture in a dryland agricultural field. Submitted to Water Resources Research.

Examples

# NOT RUN {
## An example for 3D+T modelling applied to the cookfarm data set can be assesed via 
## demo(cookfarm_3DT_kriging)
## demo(cookfarm_3DT_RF)
## Please note that the demo's might take 10-15 minutes to complete.
library(rgdal)
library(sp)
library(spacetime)
library(aqp)
library(splines)
library(randomForest)
library(plyr)
library(plotKML)
data(cookfarm)

## gridded data:
grid10m <- cookfarm$grids
gridded(grid10m) <- ~x+y
proj4string(grid10m) <- CRS(cookfarm$proj4string)
spplot(grid10m["DEM"], col.regions=SAGA_pal[[1]])

## soil profiles:
profs <- cookfarm$profiles
levels(cookfarm$profiles$HZDUSD)
## Bt horizon:
sel.Bt <- grep("Bt", profs$HZDUSD, ignore.case=FALSE, fixed=FALSE)
profs$Bt <- 0
profs$Bt[sel.Bt] <- 1
depths(profs) <- SOURCEID ~ UHDICM + LHDICM
site(profs) <- ~ TAXSUSDA + Easting + Northing
coordinates(profs) <- ~Easting + Northing
proj4string(profs) <- CRS(cookfarm$proj4string)
profs.geo <- as.geosamples(profs)

## fit model for Bt horizon:
m.Bt <- GSIF::fit.gstatModel(profs.geo, Bt~DEM+TWI+MUSYM+Cook_fall_ECa
   +Cook_spr_ECa+ns(altitude, df = 4), grid10m, fit.family = binomial(logit))
plot(m.Bt)

## fit model for soil pH:
m.PHI <- fit.gstatModel(profs.geo, PHIHOX~DEM+TWI+MUSYM+Cook_fall_ECa
    +Cook_spr_ECa+ns(altitude, df = 4), grid10m)
plot(m.PHI)
# }