View source: R/wood_revenues.R
wood_revenues | R Documentation |
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.
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"
)
diameter.q |
Quadratic mean of the diameter at breast height (dbh) of
the harvested trees |
species |
Tree species, using an available |
value.level |
Stand quality expressed as an integer of |
logging.method |
Logging method, with |
price.ref.assortment |
Wood price of the reference assortments allowing
to consider market fluctuations. Default is
|
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
|
calamity.factors |
Summands |
species.code.type |
Type of code in which |
method |
argument that is currently not used, but offers the possibility to implement alternative parameters and functions in the future. |
A vector with wood revenues per cubic meter
[EUR m^{-3}]
. The volume refers to the salable
wood volume, provided by vol_salable
.
Dieter, Matthias (2001): Land expectation values for spruce and beech calculated with Monte Carlo modelling techniques. For. Policy Econ. 2 (2), p. 157-166. \Sexpr[results=rd]{tools:::Rd_expr_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. \Sexpr[results=rd]{tools:::Rd_expr_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. \Sexpr[results=rd]{tools:::Rd_expr_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. \Sexpr[results=rd]{tools:::Rd_expr_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).
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)))
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