wood_revenues {woodValuationDE}

Wood revenues per cubic meter salable volume

Description

The function estimates wood revenues per cubic meter salable volume using the wood revenue model of v. Bodelschwingh (2018), which is based on the assortment tables from Offer and Staupendahl (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 woodValuationDE README.

Usage

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

Arguments

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).

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.disturbance.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 wood revenues per cubic meter [EUR m^{-3}]. The volume refers to the salable wood volume, provided by vol_salable.

References

Dieter, Matthias (2001): Land expectation values for spruce and beech calculated with Monte Carlo modelling techniques. For. Policy Econ. 2 (2), p. 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) p. 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 (2023): woodValuationDE: A consistent framework for calculating stumpage values in Germany (technical note). Allgemeine Forst- und Jagdzeitung 193 (1/2), p. 16-29. doi: 10.23765/afjz0002090 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), p. 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_revenues(40,
              "beech")

# species codes Lower Saxony (Germany)
wood_revenues(40,
              211,
              species.code.type = "nds")

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

wood_revenues(40,
              rep(c("beech", "spruce"),
                  each = 3),
              value.level = rep(1:3, 2))

wood_revenues(40,
              rep("spruce", 7),
              calamity.type = c("none",
                                "calamity.dieter.2001",
                                "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 wood revenues
wood_revenues(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_revenues(40,
              c("oak", "beech", "spruce"))
wood_revenues(40,
              c("oak", "beech", "spruce"),
              price.ref.assortment = dplyr::tibble(
                species = c("oak", "beech", "spruce"),
                price.ref.assortment = c(300, 80, 50)))