smc {rconifers} | R Documentation |
These are species codes used for the Stand Management Cooperative (SMC) variant of the CONIFERS growth model.
The smc
data frame has 3 rows and 13 columns.
data(smc)
This data frame contains the following columns:
set.species.map
.
The dataset is similar to the smc.txt file that is distirbuted with the GUI version of CONIFERS. A species lookup table is nothing more than a data.frame with specific column names.
Jeff D. Hamann jeff.hamann@forestinformatics.com,
Martin W. Ritchie mritchie@fs.fed.us
Ritchie, M.W. 2008 . User's Guide and Help System for CONIFERS: A Simulator for Young Conifer Plantations Version 4.10. See http://www.fs.fed.us/psw/programs/ecology_of_western_forests/projects/conifers/
Vaughn, Nicholas. 2007. An individual-tree model to predict the annual growth of young stands of Douglas-fir (Pseudotsuga menziesii (Mirbel) Franco) in the Pacific northwest. M.S. Thesis, University of Washington. 91p.
calc.max.sdi
,
impute
,
plants
,
plots
project
,
rand.seed
,
rconifers
,
sample.data
,
set.species.map
,
set.variant
,
smc
,
summary.sample.data
,
swo
,
thin
library( rconifers ) ## set the variant to the SMC variant set.variant( 1 ) # load the Stand Management Cooperative species coefficients into R as a data.frame object data( smc ) # set the species map sp.map <- list(idx=smc$idx, fsp=smc$fsp, code=as.character(smc$code), em=smc$endemic.mort, msdi=smc$max.sdi, b=smc$browse.damage, m=smc$mechanical.damage, gwh=smc$genetic.worth.h, gwd=smc$genetic.worth.d) set.species.map( sp.map ) ## grow the data that was originally swo in the smc variant # load and display CONIFERS example plots data( plots ) print( plots ) # load and display CONIFERS example plants data( plants ) print( plants ) ## change all plants to Douglas-fir plants$sp.code <- "DF" # randomly remove 10 crown.width observations to test # the impute function blanks <- sample( 1:nrow( plants ), 10, replace=FALSE ) plants[blanks,]$crown.width <- NA # create the sample.data list object sample.3 <- list( plots=plots, plants=plants, age=3, x0=0.0 ) class(sample.3) <- "sample.data" # fill in missing values sample.3.imp <- impute( sample.3 ) # print the maximum stand density index for the current settings print( calc.max.sdi( sample.3.imp ) ) # print a summary of the sample print( sample.3.imp ) # now, project the sample forward for 20 years # with all of the options turned off sample.23 <- project( sample.3.imp, 20, control=list(rand.err=0,rand.seed=0,endemic.mort=0,sdi.mort=0)) ## print the projected summaries print( sample.23 ) ## plot the diagnostics from the fit a linear dbh-tht model ## before thinning opar <- par( mfcol=c(2,2 ) ) plot( lm( sample.23$plants$tht ~ sample.23$plants$dbh ) ) par( opar ) ## thin the stand to capture mortality, redistribute growth, ## and possibly generate revenue ## Proportional thin for selected tree species, does not remove shrubs sample.23.t1 <- thin( sample.23, control=list(type=1, target=50.0, target.sp="DF" ) ) print( sample.23.t1 ) ## Proportional thin across all tree species sample.23.t2 <- thin( sample.23, control=list(type=2, target=50.0 ) ) print( sample.23.t2 ) ## Thin from below, by dbh, all species sample.23.t3 <- thin( sample.23, control=list(type=3, target=50.0 ) ) print( sample.23.t3 ) ## Thin from below, by dbh for species "PM" sample.23.t4 <- thin( sample.23, control=list(type=4, target=50.0, target.sp="PM" ) ) print( sample.23.t4 ) ## plot the diagnostics from the fit a linear dbh-tht model ## after proportional thinning opar <- par( mfcol=c(2,2 ) ) plot( lm( sample.23.t2$plants$tht ~ sample.23.t2$plants$dbh ) ) par( opar ) ## print the differences, by species print( sp.sums( sample.23.t4 ) - sp.sums( sample.23 ) )