R/harvest_costs.R

In woodValuationDE: Wood Valuation Germany

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#### Harvest Costs ####
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#' Harvest costs per cubic meter skidded volume
#'
#' The function estimates harvest costs per cubic meter skidded wood volume
#' applying the harvest costs function of v. Bodelschwingh (2018). Consequences
#' of disturbances and calamities are implemented based on Dieter (2001),
#' Moellmann and Moehring (2017), and Fuchs et al. (2022a, 2022b). Apart from
#' Dieter (2001) and Moellmann and Moehring (2017), all functions and factors
#' are based on data from HessenForst, the public forest service of the Federal
#' State of Hesse in Germany. For further details see the \pkg{woodValuationDE}
#' \href{https://github.com/Forest-Economics-Goettingen/woodValuationDE}{README}.
#'
#' @param diameter.q Quadratic mean of the diameter at breast height (dbh) of
#'                   the harvested trees \eqn{[cm]}{[cm]}.
#' @param species Tree species, using an available \code{species.code.type}. For
#'                a list with the available species and codes call
#'                \code{\link{get_species_codes}}.
#' @param cost.level  Accessibility of the stand for logging operations
#'                    expressed as an integer of \code{1:3}, with \code{1} for
#'                    standard conditions without limitations, \code{2} for
#'                    moist sites or sites with a slope between 36 \% and 58 \%,
#'                    and \code{3} for slopes > 58 \%. The \code{cost.level}s
#'                    refer to the harvest cost model of v. Bodelschwingh
#'                    (2018).
#' @param calamity.type Defines the disturbance or calamity situation to allow
#'                      for the consideration of lower net revenues in the case
#'                      of salvage harvests. The calamity type determines the
#'                      applied consequences of disturbances/calamities,
#'                      implemented as factors for reduced revenues and higher
#'                      harvest costs. By default no calamity is assumed
#'                      \code{"none"}; \code{"calamity.dieter.2001"}
#'                      refers to a general larger calamity applying the
#'                      corrections according to Dieter (2001); five parameter
#'                      sets were implemented according to Moellmann and
#'                      Moehring (2017): \code{fire.small.moellmann.2017} refers
#'                      to damages of only some trees by fire (only conifers)
#'                      while \code{fire.large.moellmann.2017} assumes that at
#'                      least one compartment was affected, the same applies for
#'                      \code{storm.small.moellmann.2017} and
#'                      \code{storm.large.moellmann.2017} referring to damages
#'                      by storm (available for coniferous and deciduous
#'                      species), \code{insects.moellmann.2017} refers to
#'                      damages by insects; \code{"ips.fuchs.2022a"} refers to
#'                      quality losses due to infestations by the European
#'                      spruce bark beetle or \code{"ips.timely.fuchs.2022a"}
#'                      for timely salvage fellings in less advanced attack 
#'                      stages (both according to Fuchs et al. 2022a); and
#'                      \code{"stand.damage.fuchs.2022b"} to disturbances
#'                      affecting only one stand, 
#'                      \code{"regional.disturbances.fuchs.2022b"} to 
#'                      disturbances with effects on the regional wood market
#'                      and \code{"transregional.calamity.fuchs.2022b"} to
#'                      calamities affecting transregional wood markets (the
#'                      last three referring to Fuchs et al. 2022b).
#'                      User-defined types can be implemented via the
#'                      \code{calamity.factors} argument.
#' @param calamity.factors Summands \eqn{[EUR m^{-3}]}{[EUR m^(-3)]}
#'                         and factors to consider the consequences of
#'                         disturbances and calamities on wood revenues and
#'                         harvest costs. \code{"baseline"} provides a tibble
#'                         based on the references listed in
#'                         \code{calamity.type} (for details see
#'                         \href{https://github.com/Forest-Economics-Goettingen/woodValuationDE}{README}
#'                         of \pkg{woodValuationDE}). Alternatively, users can
#'                         provide a tibble with the same structure.
#' @param species.code.type Type of code in which \code{species} is given.
#'                          \code{"en"} for English species names or
#'                          \code{"nds"} for numeric species codes used in Lower
#'                          Saxony, Germany. For a list with the available
#'                          species and codes call
#'                          \code{\link{get_species_codes}}.
#' @param method argument that is currently not used, but offers the possibility
#'               to implement alternative parameters and functions in the
#'               future.
#' @return A vector with harvest costs per cubic meter skidded volume
#'         \eqn{[EUR m^{-3}]}{[EUR m^(-3)]}. The volume refers to the skidded
#'         wood volume, provided by \code{\link{vol_skidded}}.
#' @references Dieter, Matthias (2001): Land expectation values for spruce and
#'             beech calculated with Monte Carlo modelling techniques. For.
#'             Policy Econ. 2 (2), S. 157-166.
#'             \doi{10.1016/S1389-9341(01)00045-4}.
#' @references Fuchs, Jasper M.; Hittenbeck, Anika; Brandl, Susanne; Schmidt,
#'             Matthias; Paul, Carola (2022a): Adaptation Strategies for
#'             Spruce Forests - Economic Potential of Bark Beetle Management and
#'             Douglas Fir Cultivation in Future Tree Species Portfolios.
#'             Forestry 95 (2) 229-246. \doi{10.1093/forestry/cpab040}
#' @references Fuchs, Jasper M.; v. Bodelschwingh, Hilmar; Lange, Alexander;
#'             Paul, Carola; Husmann, Kai (2022b): Quantifying the
#'             consequences of disturbances on wood revenues with Impulse
#'             Response Functions. For. Policy Econ. 140, art. 102738.
#'             \doi{10.1016/j.forpol.2022.102738}.
#' @references Moellmann, Torsten B.; Moehring, Bernhard (2017): A practical way
#'             to integrate risk in forest management decisions. Ann. For. Sci.
#'             74 (4), S.75. \doi{10.1007/s13595-017-0670-x}
#' @references v. Bodelschwingh, Hilmar (2018): Oekonomische Potentiale von
#'             Waldbestaenden. Konzeption und Abschaetzung im Rahmen einer
#'             Fallstudie in hessischen Staatswaldflaechen (Economic Potentials
#'             of Forest Stands and Their Consideration in Strategic Decisions).
#'             Bad Orb: J.D. Sauerlaender`s Verlag (Schriften zur Forst- und
#'             Umweltoekonomie, 47).
#' @examples
#' harvest_costs(40,
#'               "beech")
#'
#' # species codes Lower Saxony (Germany)
#' harvest_costs(40,
#'               211,
#'               species.code.type = "nds")
#'
#' # vector input
#' harvest_costs(seq(20, 50, 5),
#'               "spruce")
#'
#' harvest_costs(40,
#'               rep(c("beech", "spruce"),
#'                   each = 3),
#'               cost.level = rep(1:3, 2))
#'
#' harvest_costs(40,
#'               rep("spruce", 6),
#'               calamity.type = c("none",
#'                                 "ips.fuchs.2022a",
#'                                 "ips.timely.fuchs.2022a",
#'                                 "stand.damage.fuchs.2022b",
#'                                 "regional.disturbance.fuchs.2022b",
#'                                 "transregional.calamity.fuchs.2022b"))
#'
#' # user-defined calamities with respective changes in harvest costs
#' harvest_costs(40,
#'               rep("spruce", 3),
#'               calamity.type = c("none",
#'                                 "my.own.calamity.1",
#'                                 "my.own.calamity.2"),
#'               calamity.factors = dplyr::tibble(
#'                 calamity.type = rep(c("none",
#'                                       "my.own.calamity.1",
#'                                       "my.own.calamity.2"),
#'                                     each = 2),
#'                 species.group = rep(c("softwood",
#'                                       "deciduous"),
#'                                     times = 3),
#'                 revenues.factor = c(1.0, 1.0,
#'                                     0.8, 0.8,
#'                                     0.2, 0.2),
#'                 cost.factor = c(1.0, 1.0,
#'                                 1.5, 1.5,
#'                                 1.0, 1.0),
#'                 cost.additional = c(0, 0,
#'                                     0, 0,
#'                                     5, 5)))

#' @import dplyr
#'
#' @export
harvest_costs <- function(
  diameter.q,
  species,
  cost.level = 1,
  calamity.type = "none",
  calamity.factors = "baseline",
  species.code.type = "en",
  method = "fuchs.orig"
) {

  if (is.character(calamity.factors)) {

    if (calamity.factors != "baseline") {

      stop("Provided value for calamity.factors unknown. See package README for further details.")

    }

    if (any(calamity.type == "calamity.dieter.2001")) {

      warning("You used 'calamity.dieter.2001': Since Dieter (2001) refers to net revenues when considering the consequences of calamities, we here also reduce the harvest costs. However, this is completely counterintuitive and only the derived net revenues are meaningful to interpret.")

    }

  } else {

    if (!(all(colnames(calamity.factors) ==
              colnames(params.wood.value$calamity.factors))) &
        !(all(is.character(calamity.factors$calamity.type))) &
        !(all(is.character(calamity.factors$species.group))) &
        !(all(is.numeric(calamity.factors$revenues.factor))) &
        !(all(is.numeric(calamity.factors$cost.factor))) &
        !(all(is.numeric(calamity.factors$cost.additional)))) {

      stop("Structure of calamity.factors does not match the required structure. See package README for further details.")

    }

    params.wood.value$calamity.factors <- calamity.factors

  }

  harvest.costs <- tibble(
    diameter.q = diameter.q,
    species = species,
    cost.level = cost.level,
    calamity.type = calamity.type
  ) %>%

    # assign the appropriate parameterized species (group)
    mutate(calamity.group = recode_species(species,
                                           species.code.type,
                                           "calamity.group"),
           species = recode_species(species,
                                    species.code.type,
                                    "bodelschwingh.costs")) %>%

    # add specific parameters
    left_join(params.wood.value$harvest.costs,
              by = c("species" = "species.code.bodelschwingh",
                     "cost.level" = "cost.level")) %>%
    left_join(params.wood.value$calamity.factors,
              by = c("calamity.type" = "calamity.type",
                     "calamity.group" = "species.group")) %>%

    mutate(
      # calculate harvest costs, considering a maximum cost level
      harvest.costs = .data$a * .data$diameter.q^.data$b + .data$c,
      harvest.costs = if_else(.data$harvest.costs < .data$max,
                              .data$harvest.costs,
                              .data$max),
      # consider consequences of calamities
      harvest.costs = .data$harvest.costs *
        .data$cost.factor +
        .data$cost.additional)

  if (any(is.na(c(harvest.costs$cost.factor,
                harvest.costs$cost.additional)))) {

    warning("For at least one species the calamity value cost.factor or cost.additional was NA.")

  }

  harvest.costs %>%
    pull(harvest.costs) %>%
    return()

}