wood_net_revenues: Net revenues for wood volumes over bark

In woodValuationDE: Wood Valuation Germany

Net revenues for wood volumes over bark

Description

The function is a wrapper for the wood valuation framework provided by woodValuationDE. It calls wood_valuation and returns only the net revenues for the user-provided wood volume over bark. The underlying functions were derived based on data from HessenForst, the public forest service of the Federal State of Hesse in Germany. For further details see the woodValuationDE README.

Usage

wood_net_revenues(
  volume,
  diameter.q,
  species,
  value.level = 2,
  cost.level = 1,
  logging.method = "combined",
  price.ref.assortment = "baseline",
  calamity.type = "none",
  calamity.factors = "baseline",
  species.code.type = "en",
  method = "fuchs.orig"
)

Arguments

volume	Wood volume [m^3], referring to volume over bark of the trees to be harvested, as usually provided by yield tables and forest simulators (German unit: Vfm).
diameter.q	Quadratic mean of the diameter at breast height (dbh) of the harvested trees [cm].
species	Tree species, using an available species.code.type. For a list with the available species and codes call get_species_codes.
value.level	Stand quality expressed as an integer of 1:3, with 1 for an extraordinarily high stand quality with high shares of wood suitable for high-valued usages such as furniture, 2 for a moderate quality, and 3 for a low quality (e.g., trees with thick branches). The value.levels refer to the applied assortment tables of Offer and Staupendahl (2018).
cost.level	Accessibility of the stand for logging operations expressed as an integer of 1:3, with 1 for standard conditions without limitations, 2 for moist sites or sites with a slope between 36 % and 58 %, and 3 for slopes > 58 %. The cost.levels refer to the harvest cost model of v. Bodelschwingh (2018).
logging.method	Logging method, with "manually" for motor-manual logging using a chain saw, "harvester" for logging with highly mechanized forest harvesters, or "combined" for a combination of the previous methods dependent on the mean diameter.
price.ref.assortment	Wood price of the reference assortments allowing to consider market fluctuations. Default is "baseline" referring to the prices from 2010 to 2015 in Hesse, Germany (for details see README of woodValuationDE or v. Bodelschwingh (2018)). Alternatively, users can provide a tibble with the same structure. The column species uses the specified species.code.type.
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 "none"; "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): fire.small.moellmann.2017 refers to damages of only some trees by fire (only conifers) while fire.large.moellmann.2017 assumes that at least one compartment was affected, the same applies for storm.small.moellmann.2017 and storm.large.moellmann.2017 referring to damages by storm (available for coniferous and deciduous species), insects.moellmann.2017 refers to damages by insects; "ips.fuchs.2022a" refers to quality losses due to infestations by the European spruce bark beetle or "ips.timely.fuchs.2022a" for timely salvage fellings in less advanced attack stages (both according to Fuchs et al. 2022a); and "stand.damage.fuchs.2022b" to disturbances affecting only one stand, "regional.disturbances.fuchs.2022b" to disturbances with effects on the regional wood market and "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 calamity.factors argument.
calamity.factors	Summands [EUR m^(-3)] and factors to consider the consequences of disturbances and calamities on wood revenues and harvest costs. "baseline" provides a tibble based on the references listed in calamity.type (for details see README of woodValuationDE). Alternatively, users can provide a tibble with the same structure.
species.code.type	Type of code in which species is given. "en" for English species names or "nds" for numeric species codes used in Lower Saxony, Germany. For a list with the available species and codes call get_species_codes.
method	argument that is currently not used, but offers the possibility to implement alternative parameters and functions in the future.

Value

A vector with the total net revenues for the entire volume over bark [EUR].

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.

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

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.

Fuchs, Jasper M.; Husmann, Kai; v. Bodelschwingh, Hilmar; Koster, Roman; Staupendahl, Kai; Offer, Armin; Moehring, Bernhard, Paul, Carola (in preparation): woodValuationDE: A consistent framework for calculating stumpage values in Germany (technical note)

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

Offer, Armin; Staupendahl, Kai (2018): Holzwerbungskosten- und Bestandessortentafeln (Wood Harvest Cost and Assortment Tables). Kassel: HessenForst (publisher).

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

wood_net_revenues(1,
                  40,
                  "beech")

# species codes Lower Saxony (Germany)
wood_net_revenues(seq(10, 70, 20),
                  40,
                  211,
                  species.code.type = "nds")

# vector input
wood_net_revenues(10,
                  seq(20, 50, 5),
                  "spruce")

wood_net_revenues(10,
                  40,
                  rep(c("beech", "spruce"),
                      each = 9),
                  value.level = rep(rep(1:3, 2),
                                    each = 3),
                  cost.level = rep(1:3, 6))

wood_net_revenues(10,
                  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 and wood revenues
wood_net_revenues(10,
                  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)))

# adapted market situation by providing alternative prices for the reference assortments
wood_net_revenues(10,
                  40,
                  c("oak", "beech", "spruce"))
wood_net_revenues(10,
                  40,
                  c("oak", "beech", "spruce"),
                  price.ref.assortment = dplyr::tibble(
                    species = c("oak", "beech", "spruce"),
                    price.ref.assortment = c(300, 80, 50)))

woodValuationDE documentation built on July 3, 2022, 5:05 p.m.