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.
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":
factor; unique station ID
date; observation day
numeric; volumetric water content measurements at five depths
numeric; soil temperature measurements at five depths
numeric; bulk electrical conductivity measurements at five depths
profiles data frame contains soil profile descriptions from 142 sites:
factor; unique station ID
numeric; x coordinate in the local projection system
numeric; y coordinate in the local projection system
factor; Keys to Soil Taxonomy taxon name e.g.
factor; horizon designation
numeric; upper horizon depth from the surface in cm
numeric; lower horizon depth from the surface in cm
bulk density in tonnes per cubic-meter
numeric; pH index measured in water solution
grids data frame contains values of regression covariates at 10 m resolution:
numeric; Digital Elevation Model
numeric; SAGA GIS Topographic Wetness Index
factor; soil mapping units e.g.
"Thatuna silt loam"
numeric; mean value of the Normalized Difference Red Edge Index (time series of 11 RapidEye images)
numeric; standard deviation of the Normalized Difference Red Edge Index (time series of 11 RapidEye images)
numeric; apparent electrical conductivity image from fall
numeric; apparent electrical conductivity image from spring
factor; cropping system in 2011
factor; cropping system in 2012
weather data frame contains daily temperatures and rainfall from the nearest meteorological station:
date; observation day
numeric; observed precipitation in mm
numeric; observed maximum daily temperature in degree C
numeric; observed minimum daily temperature in degree C
The farm is 37 ha, stationed in the hilly Palouse region, which receives an annual average of 550 mm of precipitation, primarily as rain and snow in November through May. Soils are deep silt loams formed on loess hills; clay silt loam horizons commonly occur at variable depths. Farming practices at Cook Farm are representative of regional dryland annual cropping systems (direct-seeded cereal grains and legume crops).
Caley Gasch, Tomislav Hengl and David J. Brown
Gasch, C.K., Hengl, T., Gräler, 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.
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## 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[]) ## 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)
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