management.prob {sitree}

Final felling and thinning functions for Norwegian forest

Description

Estimates de probability of a stand to be harvested or thinning following Anton-Fernandez et al. (20012).

Usage

management.prob(tr, fl, fun.final.felling = "harv.prob",
fun.thinning = "thin.prob", common.vars, this.period, next.period, ...)

harv.prob(region, skidding.distance.100m, AgeTo5, vuprha.m3.ha,
slope.per, SI.m, SI.spp)

thin.prob(region, skidding.distance.100m, AgeTo5, vuprha.m3.ha, slope.per, SI.m, SI.spp)

Arguments

tr

A trList class object.

fl

A list describing the plot data.

fun.final.felling

Function to use to calculate final felling.

fun.thinning

Function to use to calculate thinning.

common.vars

A list with at least variables dev.class and vuprha.m3.ha.

this.period

The period for which to calculate final felling and thinning probability.

next.period

The next period to the one for which final felling and thinning probability are to be calculated.

...

Further arguments

region

A vector containing the region in Norway where every plot is situated.

skidding.distance.100m

A vector containing skidding for each plot.

AgeTo5

A vector containing number of years to development class 5 for each plot.

vuprha.m3.ha

Volume per ha in cubic meters per ha for each plot.

slope.per

Slope, in percentatge, for each plot.

SI.m

Site index (SI) in m.

SI.spp

Species for which the SI is calculated (1 = spruce, 2 = pine, 3 = deciduous).

Value

It returns a list with one element:

mng

a vector with the management to apply to each plot.

Author(s)

Clara Anton Fernandez [e-mail]

References

Antón-Fernández, C. and Astrup, R. 2012 Empirical harvest models and their use in regional business-as-usual scenarios of timber supply and carbon stock development. Scandinavian Journal of Forest Research, 27, 4, 379–392.

Examples

foo.matrix <-  matrix(0, nrow = length(tr$dbh), ncol = (5 +1))
colnames(foo.matrix) <- paste("t", 0:5, sep = "")
foo.dbh <- foo.height <- foo.matrix
foo.dbh[,1] <- tr$dbh
foo.height[,1] <- tr$height

trl <- list(
  plot.id  = tr$plot.id,
  treeid    = tr$treeid,
  dbh.mm    = foo.dbh,
  height.dm = foo.height,
  yrs.sim   = rep(0, nrow(tr)),
  tree.sp   = factor(tr$tree.sp)
)
tr.i <- trList$new(data = trl, nperiods = as.integer(5))

common.vars <-  prep.common.vars.fun(
  tr = tr.i,
  fl = fl,
  i.period       = 0,
  this.period    = "t0",
  common.vars    = "NULL",
  vars.required  = c("spp", "SBA.m2.ha", "QMD.cm", "vuprha.m3.ha", "AgeTo5"),
  period.length = 5
)
fl$management <- data.frame(matrix(NA, ncol = tr.i$nperiods,
                                   nrow = nrow(tr.i$data$dbh.mm)))
names(fl$management) <- paste0("t", 1:tr.i$nperiods)

management.prob(tr.i,
                fl,
                fun.final.felling = "harv.prob",
                fun.thinning = "thin.prob",
                common.vars = common.vars$res,
                this.period = "t0",
                next.period = "t1")

harv.prob(region = fl$region[1:3],
          skidding.distance.100m = fl$skidding.distance.100[1:3],
          AgeTo5 = c(50, 20, 15),
          vuprha.m3.ha = common.vars$res$vuprha.m3.ha[1:3],
          slope.per = fl$slope.per[1:3],
          SI.m = fl$SI.m[1:3],
          SI.spp = fl$SI.spp[1:3])

thin.prob(region = fl$region[1:3],
          skidding.distance.100m = fl$skidding.distance.100[1:3],
          AgeTo5 = c(50, 20, 15),
          vuprha.m3.ha = common.vars$res$vuprha.m3.ha[1:3],
          slope.per = fl$slope.per[1:3],
          SI.m = fl$SI.m[1:3],
          SI.spp = fl$SI.spp[1:3]
)