UPDATED 2013-04-08
This functionality it now available in the soilDB and sharpshootR packages. All code on this page is now superseded by the fetchOSD() and SoilTaxonomyDendrogram()functions.
UPDATED 2012-11-07
I have been thinking about a URL-based interface to basic Official Soil Series Description (OSD) data for a while now... something that when fed a URL, would return CSV-formatted records to the calling process. These type of interfaces can later be used to support more complicated systems, such as our smartphone interface to SoilWeb. URLs can be accessed like files in R, making it possible to do something like this:
OSD Site-Level Data
read.csv(url('casoilresource.lawr.ucdavis.edu/soil_web/reflector_api/soils.php?what=osd_site_query&q_string=auburn,sobrante'))# returning:seriesname,soilorder,suborder,grtgroup,subgroup_modAUBURN,INCEPTISOLS,XEREPTS,HAPLOXEREPTS,LITHICSOBRANTE,ALFISOLS,XERALFS,HAPLOXERALFS,MOLLIC
OSD Horizon-Level Data
read.csv(url('casoilresource.lawr.ucdavis.edu/soil_web/reflector_api/soils.php?what=osd_query&q_string=auburn,sobrante'))# returning:series,layer,name,top,bottom,matrix_dry_color_hue,matrix_dry_color_value,matrix_dry_color_chroma,matrix_wet_color_hue,matrix_wet_color_value,matrix_wet_color_chroma,texture,cfragsAUBURN,1,A1,0,4,7.5YR,5,6,5YR,4,4,silt loam,AUBURN,2,A2,4,23,5YR,5,6,5YR,4,4,silt loam,AUBURN,3,Bw,23,36,5YR,5,8,5YR,4,6,silt loam,AUBURN,4,R,36,61,,,,,,,,SOBRANTE,1,A,0,13,5YR,5,4,5YR,3,4,silt loam,SOBRANTE,2,Bt1,13,28,5YR,4,6,5YR,3,6,silt loam,SOBRANTE,3,Bt2,28,61,5YR,5,6,2.5YR,3,6,light clay loam,SOBRANTE,4,Cr,61,76,,,,,,,,SOBRANTE,5,R,76,86,,,,,,,,
Simple access to basic OSD site and horizon-level data makes it possible to rapidly assemble OSD data within R. For example, multiple soil profile sketches, based on OSD data, can be quickly arranged into a figure according to their taxonomic membership. With pair-wise dissimilarities computed from categorical variables, cluster analysis reduces to an elegant sorting method and depicted as a dendrogram. Between-profile similarity can be determined graphically, with soils sharing the same taxonomic membership placed along the same terminal branches. In the example below, parent material information, soil depth class, or any number of horizon-level attributes could be combined with the site parameters for a more relevant grouping. The following code could use some clean-up and heuristics for automatically determining some of the fudge factors used to position elements within the figure. Also, it would be nice to annotate branches associated with suborder and greatgroup membership. I'll save that for next time.
Note that these data are a snapshot of the OSD database-- as of a couple years ago. Therefore, recently established soil series are missing, and changes may have been made to existing series since then. Please use these links with this in mind.
Implementation
# need theselibrary(aqp)library(cluster)library(ape)# soils of interests.list <- c('san joaquin','montpellier','grangeville','pollasky','ramona')# base URLsu.osd_site <- 'http://casoilresource.lawr.ucdavis.edu/soil_web/reflector_api/soils.php?what=osd_site_query&q_string='u.osd_hz <- 'http://casoilresource.lawr.ucdavis.edu/soil_web/reflector_api/soils.php?what=osd_query&q_string='# compile URL + requested soil seriesu.site <- paste(u.osd_site, paste(s.list, collapse=','), sep='')u.hz <- paste(u.osd_hz, paste(s.list, collapse=','), sep='')# request datas <- read.csv(url(u.site), stringsAsFactors=TRUE)h <- read.csv(url(u.hz), stringsAsFactors=FALSE)# reformatting and color conversionh$soil_color <- with(h, munsell2rgb(matrix_wet_color_hue, matrix_wet_color_value, matrix_wet_color_chroma))h <- with(h, data.frame(id=series, top, bottom, hzname, soil_color,hue=matrix_wet_color_hue, value=matrix_wet_color_value,chroma=matrix_wet_color_chroma, stringsAsFactors=FALSE))# upgrade to SoilProfileCollectiondepths(h) <- id ~ top + bottom# plot profilespar(mar=c(0,0,0,0))plot(h, name='hzname', cex.names=0.85, axis.line.offset=-4)# compute dissimilarity via subgroup classification# cluster using divisive algorithmh.hclust <- as.hclust(diana(s[, c(2,3,4,5)]))# add series name as labelsh.hclust$labels <- s$series# convert to phylo classp <- as.phylo(h.hclust)# plot profiles below corresponding leaves in the dendrogrampar(mar=c(0,0,0,0))plot(p, cex=0.8, direction='up', y.lim=c(20,0), x.lim=c(0.5, length(h)+1), show.tip.label=FALSE)# get the last plot geometrylastPP <- get("last_plot.phylo", envir = .PlotPhyloEnv)# vector of indices for plotting soil profiles below leaves of dendrogramnew_order <- sapply(1:lastPP$Ntip, function(i) which(as.integer(lastPP$xx[1:lastPP$Ntip]) == i))# plot the profiles, in the ordering defined by the dendrogram# with a couple fudge factors to make them fitplot(h, name='hzname', plot.order=new_order, max.depth=150, n.depth.ticks=6, scaling.factor=0.1, cex.names=0.75, cex.id=0.75, axis.line.offset=-4, width=0.1, y.offset=max(lastPP$yy) + 1.5, add=TRUE)# generate taxonomic labels and their positions under the dendrogramlab <- s[new_order, 'subgroup']unique.lab <- unique(lab)group.lengths <- rle(as.numeric(lab))$lengthslab.positions <- (cumsum(group.lengths) - (group.lengths / 2)) + 0.5# add labels-- note manual tweaking of y-coordinatestext(lab.positions, 2.6, unique.lab, cex=0.75, adj=0.5, font=4)
