View source: R/wood_valuation.R
wood_valuation | R Documentation |
The function is a wrapper for the entire procedure of wood valuation provided by woodValuationDE. It estimates the share of salable (for revenues) and skidded volume (for harvest costs), the wood revenues, and the harvest costs. Finally, it derives 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.
wood_valuation(
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"
)
volume |
Wood volume |
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 |
cost.level |
Accessibility of the stand for logging operations
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 tibble with all steps of the wood valuation (harvest quantities,
harvest costs [EUR m^{-3}]
, wood revenues
[EUR m^{-3}]
, and total net revenues
[EUR]
).
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) 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
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_valuation(1,
40,
"beech")
# species codes Lower Saxony (Germany)
wood_valuation(seq(10, 70, 20),
40,
211,
species.code.type = "nds")
# vector input
wood_valuation(10,
seq(20, 50, 5),
"spruce")
wood_valuation(10,
40,
rep(c("beech", "spruce"),
each = 9),
value.level = rep(rep(1:3, 2),
each = 3),
cost.level = rep(1:3, 6))
wood_valuation(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_valuation(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_valuation(10,
40,
c("oak", "beech", "spruce"))
wood_valuation(10,
40,
c("oak", "beech", "spruce"),
price.ref.assortment = dplyr::tibble(
species = c("oak", "beech", "spruce"),
price.ref.assortment = c(300, 80, 50)))
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.