After installing the package on your computer, the easiest (and
standard) way to access the functionality of the package is to attach it
with
3.1 Silvicultural concept definition
The fundamental requirement for running simulations with
care4cmodel is an appropriate definition of the silvicultural
concept of interest. Such definitions are objects of class
c4c_concept, and the most convienent way to generate one of
your own is to use the function c4c_concept(). The main
argument to that function is a data frame with growth and yield
information, as defined below. Many users will find it convenient to
assemble this information in a spreadsheet software like MS EXCEL, and
import it into R as a data frame. With ?c4c_concept you can
call the documentation of c4c_concept(). Using this
function includes a series of automatic checks in order to ensure that
the definition is technically correct. The package contains two
pre-defined concept definitions,
pine_thinning_from_above_1, and,
pine_no_thinning_and_clearcut_1. We use the former for
explaining the main content of such a definition. Simply type the name
of the concept to display its contents:
pine_thinning_from_above_1
#> $concept_name
#> [1] "Scots pine thinning from above"
#>
#> $units
#> time area volume mass stem_diameter
#> "year" "ha" "m3" "t" "cm"
#>
#> $growth_and_yield
#> # A tibble: 6 × 14
#> phase_no phase_name duration n_subphases vol_standing vol_remove vol_mort
#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 initial_stand 15 3 0 0 0
#> 2 2 young_growth 14 3 58.5 0 0.142
#> 3 3 immature_timber 29 6 206. 1.64 1.72
#> 4 4 mature_timber 49 10 374. 4.38 1.60
#> 5 5 final_harvest_… 19 4 446. 8.78 0.584
#> 6 6 final_harvest_… 29 6 378. 16.5 0.471
#> # ℹ 7 more variables: n_standing <dbl>, n_remove <dbl>, dbh_standing <dbl>,
#> # dbh_remove <dbl>, harvest_interval <dbl>, survival_cum <dbl>,
#> # vol_increment <dbl>
#>
#> attr(,"class")
#> [1] "c4c_concept"
Basically, such an object of class c4c_concept (as
created with the function of the same name) is a list with three
elements. The first element, concept_name is to be defined
by the user. The second element, units lists the most
important units to be used when simulating this concept. This feature is
not actively used in the current version (and is not required to be
defined by the user), i.e. all numbers connected to any silvicultural
concept are understood in these units if not explicitly stated
otherwise. However in future versions, it is planned to be more flexible
in that regard. The third element, growth_and_yield is
actually the most important. It is a data frame that defines the stand
development phases to be considered, their duration, and a set of
fundamental growth and yield variables. Typically this information comes
from observational plots, silvicultural guidelines, forest growth
simulators, yield tables, and similar sources, or a mixture of
these.
The number of stand development phases is totally up to the user; for
most applications, however, four to seven phases should be sufficient.
Each line of the data frame completely defines one such phase. The
phases are taken to be subsequent to each other in the order of the
column phase_no. The last phase is followed by the first
phase again, which typically means a final harvest followed by an
initial stand. The columns of the data frame are defined as follows
(note that, depending on your system, the contents of some columns, and
some columns themselves might be cut off in the display above):
- phase_no Integer numbers that indicate the sequence
of how the stand development phases follow after each other
- phase_name User-defined names for the stand
development phases, must be unique
- duration The (average) duration of each phase in
years
- n_subphases Integer numbers >= 1, indicating the
number of subphases to each phase to be considered internally. This is
useful and required for adjusting the “blur” when forest areas move
through a sequence of phases. I.e. when a stand development phase has a
duration of \(D\) years, and \(n\) subphases, a unit area will remain on
average \(D\) years in that phase, but
with a variance of \(\sigma^2=\frac{D^2}{n}\). In many cases,
the rule of thumb to have at least three subphases and otherwise \(\frac{D}{n}\approx 5\), will be
adequate.
- vol_standing The average standing wood volume
(m³/ha) of a stand in the phase of interest
- vol_remove The wood volume (m³/ha/a) that is
regularly removed (harvested) per year and hectare from a stand in the
phase of interest. This does not include wood that is harvested due
(partial) stand destruction by hazard events
- vol_mort The wood volume (m³/ha/a) that is
regularly dying per year and hectare in the phase of interest. Like
vol_remove, this does not include mortality due to stand destruction by
hazard events
- n_standing The average number of standing live
trees per hectare in a stand of the given phase
- n_remove The number of trees that are removed per
hectare and year due to regular harvest in the phase of interest
- dbh_standing The average mean diameter at breast
height, dbh (cm), of a stand in the phase of interest
- dbh_remove The average mean dbh (cm) of the trees
being regularly harvested in a stand of the given phase
- harvest_interval While vol_remove, and n_remove are
annual numbers, the harvest interval is the actual time between two
harvest operations in the phase of interest
- survival_cum Probability (i.e. number between 0 and
1) of a stand to survive all occuring hazard events since the
beginning of the first phase to the end of the phase of interest.
Therefore, the sequence of these numbers must be decreasing along the
sequence of phases. The sequence of probabilities given in the concept
definition is considered the baseline risk. We demonstrate below how
that baseline can be conveniently adjusted for covering other risk
scenarios
- vol_increment The average annual wood volume
incrment (m³/ha/a) in the phase of interest. Unlike the previous
variables, vol_increment is not required to be provided by the user when
generating a concept definition with
c4c_concept(). It
results directly from the phase durations, the standing volumes, the
removal, and the mortality volume
3.2 Running a simulation
The workhorse function for conducting simulation runs with
care4cmodel is simulate_single_concept(). For
simulating an example with the concept
pine_thinning_from_above_1 we use it as follows:
sim_base_out <- simulate_single_concept(
concept_def = pine_thinning_from_above_1,
init_areas = c(1000, 0, 0, 0, 0, 0),
time_span = 200,
risk_level = 3
)
The first argument concept_def is the definition of the
silvicultural concept to be used, it must be a valid object of class
c4c_concept (Section
3.1). The second argument, init_areas, is a vector that
sets the initial areas covered by the stand development phases as
defined in the silvicultural concept (in ascending order). In the
example, init_areas indicates 1000 ha in the first stand
development phase, and no areas covered by any other phase. This can be
seen as an afforestation of a 1000 ha forest area. During the
simulation, the total area (1000 ha in our example) will remain
constant, while the shares of the phases will be changing. The size of
the areas and their initial distribution is fully up to the user. Note
that the initial areas can also be specified on the level of the
internal sub-phases (Section
3.1). Call the function’s documentation with
?simulate_single_concept for this and other details. The
argument time_span is the number of years to cover with the
simulation. 200 years, as defined here, is certainly longer than
required for typical applications. With the argument
risk_level, you can adjust the occurence of stochastic
hazard events relative to the baseline risk settings made in
survival_cum in the concept definition (Section 3.1). In the example, the
setting risk_level = 3 means that the actual damage
probabilities are the same as if the events leading to the baseline risk
would happen three times in sequence. Consequently,
risk_level = 1 would use exactly the baseline risk; numbers
smaller than 1 would indicate a lower risk,
e.g. risk_level = 1/2 uses damage probabilities as if only
half of the baseline risk events happened. Setting
risk_level = 0 will simulate the development without any
stochastic hazard events. In our example, we store the output in a
variable, we call sim_base_out. Internally, the simulation
is based on functionality provided by the R package deSolve (Karline Soetaert, Thomas Petzoldt, and R. Woodrow
Setzer 2010).
3.3 Exploring the base simulation output
As its output, a simulation with
simulate_single_concept() generates an object of class
c4c_base_result. This large object comprises different
categories of information, and it has an own plot()
function for convenient result visualization as we will show below. In
essence, such an object is a named list containing several vectors,
matrices, and data frames. In the example above (Section 3.2), we stored such an
output object in the variable sim_base_out. For the sake of
clarity, we do not display the entire object here. In order to get an
overview, let us first display the names of the top level list
elements:
names(sim_base_out)
#> [1] "concept" "time_span" "detailed_init"
#> [4] "detailed_out" "init_areas" "risk_level"
#> [7] "parameters" "sim_areas" "sim_growth_and_yield"
The first six list elements, concept,
time_span, detailed_init,
detailed_out, init_areas, and
risk_level document the settings of
simulate_single_concept() that were used for the
simulation. You can access them most conveniently by their name,
e.g.
sim_base_out$init_areas
#> [1] 1000 0 0 0 0 0
The list element parameters contains interal simulation
parameters derived from the user settings; most important sub-phase wise
dwell times and detailed risk related information.
The two remaining list items sim_areas, and
sim_growth_and_yield contain actual simulation output. Both
are named lists themselves. sim_areas contains three
matrices named as follows:
names(sim_base_out$sim_areas)
#> [1] "areas" "area_inflows_regular" "area_outflows_events"
All three matrices have the same structure; each line represents a
point in time, i.e. the end of a simulation year. The first column
denotes these times in years. The other columns represent the subsequent
stand development phases from left to right. The matrix
areas simply contains each phase’s area in ha at the end of
the respective simulation year. The matrix
area_inflows_regular represents the areas in ha that flowed
into a phase from the previous phase during the respective simulation
year. Analogously, the matrix area_outflows_events contains
the areas that flowed out of a given phase into the (first subphase of
the) initial phase due to hazard events. As both of the latter matrices
represent flows during a year, they contain NA values for the initial
situation, i.e. time 0.
head(sim_base_out$sim_areas$areas)
#> time initial_stand young_growth immature_timber mature_timber
#> [1,] 0 1000.0000 0.00000 0.00000000 0.000000e+00
#> [2,] 1 933.3646 66.61132 0.02405194 1.194750e-11
#> [3,] 2 866.9807 132.68005 0.33920499 1.036965e-08
#> [4,] 3 801.3647 197.11993 1.51537479 5.018447e-07
#> [5,] 4 737.1068 258.66169 4.23155240 7.508342e-06
#> [6,] 5 674.7173 316.13968 9.14299728 5.902744e-05
#> final_harvest_phase_I final_harvest_phase_II
#> [1,] 0.000000e+00 0.000000e+00
#> [2,] 2.667036e-31 2.355412e-40
#> [3,] 1.860259e-24 1.883231e-31
#> [4,] 5.427057e-21 3.371655e-27
#> [5,] 1.395680e-18 2.739986e-24
#> [6,] 9.978331e-17 4.749860e-22
head(sim_base_out$sim_areas$area_inflows_regular)
#> time initial_stand young_growth immature_timber mature_timber
#> [1,] 0 NA NA NA NA
#> [2,] 1 1.601196e-55 66.63537 0.02405194 1.194750e-11
#> [3,] 2 1.087646e-42 66.39317 0.31516207 1.035772e-08
#> [4,] 3 4.207780e-37 65.66596 1.17651109 4.915013e-07
#> [5,] 4 2.036135e-33 64.34588 2.71796583 7.007984e-06
#> [6,] 5 1.354264e-30 62.43646 4.91347614 5.152796e-05
#> final_harvest_phase_I final_harvest_phase_II
#> [1,] NA NA
#> [2,] 2.667036e-31 2.355412e-40
#> [3,] 1.860259e-24 1.883231e-31
#> [4,] 5.425208e-21 3.371467e-27
#> [5,] 1.390282e-18 2.736636e-24
#> [6,] 9.839079e-17 4.722529e-22
head(sim_base_out$sim_areas$area_outflows_events)
#> time initial_stand young_growth immature_timber mature_timber
#> [1,] 0 NA NA NA NA
#> [2,] 1 0.17662312 0.000000000 0.000000e+00 0.000000e+00
#> [3,] 2 0.03011647 0.009276849 9.009760e-06 1.128976e-14
#> [4,] 3 0.07505072 0.049569165 3.407947e-04 2.626824e-11
#> [5,] 4 0.08116650 0.086164250 1.781222e-03 1.487123e-09
#> [6,] 5 0.02970601 0.044992483 1.979728e-03 8.860609e-09
#> final_harvest_phase_I final_harvest_phase_II
#> [1,] NA NA
#> [2,] 0.000000e+00 0.000000e+00
#> [3,] 4.118826e-34 4.773684e-43
#> [4,] 7.697644e-27 1.022242e-33
#> [5,] 2.626638e-23 2.140410e-29
#> [6,] 2.691596e-21 6.934072e-27
The other remaining list element sim_growth_and_yield is
a named list of data frames that contain growth and yield information
which was, in essence, obtained by upscaling the growth and yield
information provided in the silvicultural concept definition (Section 3.1). We obtain an
overview with:
sim_base_out$sim_growth_and_yield
#> $gyield_summary
#> # A tibble: 201 × 8
#> time vol_standing vol_rmv_cont vol_rmv_damage vol_rmv_total vol_mort
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 0 0 NA 0 0
#> 2 1 3903. 0.0393 0 0.0393 9.50
#> 3 2 7833. 0.555 0.545 1.10 19.4
#> 4 3 11846. 2.48 2.97 5.45 30.6
#> 5 4 16007. 6.92 5.41 12.3 44.0
#> 6 5 20382. 15.0 3.04 18.0 60.6
#> 7 6 25019. 27.5 3.21 30.7 81.2
#> 8 7 29871. 44.9 79.2 124. 106.
#> 9 8 35010. 67.7 98.0 166. 136.
#> 10 9 40498. 96.3 59.3 156. 170.
#> # ℹ 191 more rows
#> # ℹ 2 more variables: vol_inc_ups <dbl>, vol_inc <dbl>
#>
#> $gyield_phases
#> $gyield_phases$vol_standing
#> # A tibble: 201 × 7
#> time initial_stand young_growth immature_timber mature_timber
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 0 0 0 0
#> 2 1 0 3898. 4.96 0.00000000446
#> 3 2 0 7764. 69.9 0.00000387
#> 4 3 0 11534. 312. 0.000187
#> 5 4 0 15135. 872. 0.00280
#> 6 5 0 18498. 1884. 0.0220
#> 7 6 0 21558. 3461. 0.115
#> 8 7 0 24213. 5658. 0.451
#> 9 8 0 26477. 8531. 1.44
#> 10 9 0 28367. 12127. 3.95
#> # ℹ 191 more rows
#> # ℹ 2 more variables: final_harvest_phase_I <dbl>, final_harvest_phase_II <dbl>
#>
#> $gyield_phases$vol_rmv_cont
#> # A tibble: 201 × 7
#> time initial_stand young_growth immature_timber mature_timber
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 0 0 0 0
#> 2 1 0 0 0.0393 5.23e-11
#> 3 2 0 0 0.555 4.54e- 8
#> 4 3 0 0 2.48 2.20e- 6
#> 5 4 0 0 6.92 3.29e- 5
#> 6 5 0 0 15.0 2.58e- 4
#> 7 6 0 0 27.5 1.35e- 3
#> 8 7 0 0 44.9 5.29e- 3
#> 9 8 0 0 67.7 1.69e- 2
#> 10 9 0 0 96.3 4.62e- 2
#> # ℹ 191 more rows
#> # ℹ 2 more variables: final_harvest_phase_I <dbl>, final_harvest_phase_II <dbl>
#>
#> $gyield_phases$vol_rmv_damage
#> # A tibble: 201 × 7
#> time initial_stand young_growth immature_timber mature_timber
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 NA NA NA NA
#> 2 1 0 0 0 0
#> 3 2 0 0.543 0.00186 4.22e-12
#> 4 3 0 2.90 0.0702 9.81e- 9
#> 5 4 0 5.04 0.367 5.55e- 7
#> 6 5 0 2.63 0.408 3.31e- 6
#> 7 6 0 2.52 0.689 2.04e- 5
#> 8 7 0 55.3 23.8 1.99e- 3
#> 9 8 0 60.2 37.8 7.56e- 3
#> 10 9 0 31.8 27.5 1.17e- 2
#> # ℹ 191 more rows
#> # ℹ 2 more variables: final_harvest_phase_I <dbl>, final_harvest_phase_II <dbl>
#>
#> $gyield_phases$vol_rmv_total
#> # A tibble: 201 × 7
#> time initial_stand young_growth immature_timber mature_timber
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 0 0 0 0
#> 2 1 0 0 0.0393 5.23e-11
#> 3 2 0 0.543 0.557 4.54e- 8
#> 4 3 0 2.90 2.55 2.21e- 6
#> 5 4 0 5.04 7.29 3.34e- 5
#> 6 5 0 2.63 15.4 2.62e- 4
#> 7 6 0 2.52 28.2 1.37e- 3
#> 8 7 0 55.3 68.8 7.28e- 3
#> 9 8 0 60.2 106. 2.44e- 2
#> 10 9 0 31.8 124. 5.79e- 2
#> # ℹ 191 more rows
#> # ℹ 2 more variables: final_harvest_phase_I <dbl>, final_harvest_phase_II <dbl>
#>
#> $gyield_phases$vol_mort
#> # A tibble: 201 × 7
#> time initial_stand young_growth immature_timber mature_timber
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 0 0 0 0
#> 2 1 0 9.46 0.0414 1.91e-11
#> 3 2 0 18.8 0.584 1.65e- 8
#> 4 3 0 28.0 2.61 8.01e- 7
#> 5 4 0 36.7 7.28 1.20e- 5
#> 6 5 0 44.9 15.7 9.42e- 5
#> 7 6 0 52.3 28.9 4.93e- 4
#> 8 7 0 58.8 47.3 1.93e- 3
#> 9 8 0 64.3 71.3 6.15e- 3
#> 10 9 0 68.8 101. 1.68e- 2
#> # ℹ 191 more rows
#> # ℹ 2 more variables: final_harvest_phase_I <dbl>, final_harvest_phase_II <dbl>
#>
#> $gyield_phases$vol_inc_ups
#> # A tibble: 201 × 7
#> time initial_stand young_growth immature_timber mature_timber
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 3901. 0 0 0
#> 2 1 3641. 711. 0.220 8.91e-11
#> 3 2 3382. 1417. 3.10 7.73e- 8
#> 4 3 3126. 2105. 13.8 3.74e- 6
#> 5 4 2875. 2762. 38.6 5.60e- 5
#> 6 5 2632. 3376. 83.5 4.40e- 4
#> 7 6 2398. 3934. 153. 2.30e- 3
#> 8 7 2180. 4419. 251. 9.01e- 3
#> 9 8 1974. 4832. 378. 2.87e- 2
#> 10 9 1780. 5177. 538. 7.88e- 2
#> # ℹ 191 more rows
#> # ℹ 2 more variables: final_harvest_phase_I <dbl>, final_harvest_phase_II <dbl>
#>
#>
#> $gyield_harvest_detail
#> # A tibble: 2,412 × 8
#> # Groups: time, phase_no, phase_name, harvest_type, dbh_cm, vol_tree_m3
#> # [2,412]
#> time harvest_type phase_no phase_name dbh_cm vol_tree_m3 tree_dist_m volume
#> <dbl> <chr> <int> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 0 damage 1 initial_st… 0 0 1.2 0
#> 2 0 regular 1 initial_st… 0 0 0 0
#> 3 0 damage 2 young_grow… 5.8 0.01 1.27 0
#> 4 0 regular 2 young_grow… 0 0 0 0
#> 5 0 damage 3 immature_t… 10.3 0.05 1.55 0
#> 6 0 regular 3 immature_t… 12.9 0.09 10.7 0
#> 7 0 damage 4 mature_tim… 22.9 0.39 3.21 0
#> 8 0 regular 4 mature_tim… 25.8 0.58 11.5 0
#> 9 0 damage 5 final_harv… 30.2 0.88 4.43 0
#> 10 0 regular 5 final_harv… 41.9 1.08 15.7 0
#> # ℹ 2,402 more rows
As these data frames are mostly self explaining, let us just point
out a few details. If not stated otherwise all numbers given there
represent cubic meters wood. In the first data frame
gyield_summary, the column vol_rmv_cont stands
for wood volume that is continually removed (harvested) as planned
according to the concept definition (Section 3.1).
vol_rmv_damage in contrast, is all wood volume of the
stands that were lost due to hazard events, assuming that this wood is
harvested in its entirety. Consequently, vol_rmv_total is
the sum of both, regular and damage-induced harvest. Note that there are
two variables related to volume increment, vol_inc_ups, and
vol_inc. The former is upscaled from the concept
definition, and this the volume increment which should be solely used
for subsequent calculations. The latter results from a differently
defined post-hoc calculation and is provided for internal reasons only.
The data frames that make up the sub-list gyield_phases
give a more detailed view on the variables provided with
gyield_summary, i.e. they split them among the stand
development phases.
The last data frame, gyield_harvest_detail is of special
importance, as it contains detailed growth and yield information that is
required for calculating fuel consumption and CO2 emissions
due to harvest operations. That is, the average dbh of a tree to be
harvested, dbh_cm, the average tree volume,
vol_tree_m3, the average neighbor distance of the harvest
trees, tree_dist_m, and the volume to be harvested,
volume. Note that volume can be 0 even though
there are non-zero values for the other variables.
As mentioned in the quickstart section,
there is an own plot function for the output objects of the function
simulate_single_concept(). Its documentation is accessible
by calling ?plot.c4c_base_result. The plot shown in the
quickstart section shows the default, the development of the areas
covered by the different stand development phases over time. Due to the
CRAN policies we can only provide very few other example plots; the
reader is encouraged to try out all options listed in the documentation.
First, we visualize the standing volume:
plot(sim_base_out, variable = "vol_standing") # standing volume
Second, we plot the total harvested volume. The sharp peaks result
from wood that is removed due to hazard events.
plot(sim_base_out, variable = "vol_rmv_total") # total harvested volume
3.4 Calculating carbon related results
In order to obtain the fuel consumption, and subsequently the
CO2 emissions caused by forest operations, the package
provides the function fuel_and_co2_evaluation. The forest
operations taken into account comprise cutting, moving the timber to the
forest road (forwarding, skidding), and maintenance of the forest road
network. A typical call of fuel_and_co2_evaluation in the
context of our example is shown below. Note that in contrast to the quickstart section, we explicitly call parameters
with default values for demonstration.
# Calculate information about fuel consumption
# call documentation with ?fuel_and_co2_evaluation for detail information
carbon_out <- fuel_and_co2_evaluation(
sim_base_out, # object obtained from simulate_single_concept
road_density_m_ha = 35, # forest road density
raw_density_kg_m3 = 520, # default setting, wood density
harvest_loss = 0.1, # default, share of volume lost at harvest
bark_share = 0.12, # default, bark share of volume
mode = "standard" # default, choice is between "standard" and "nordic"
)
The only parameters, the user must provide in any case, are an object
of class c4c_base_result as obtained from
simulate_single_concept() and the forest road density
(m/ha) in the area of interest (relating to truck roads). The parameter
mode allows to choose the assumptions for the harvest
operations. Currently, there are only two options, “standard”, and
“nordic”. In both cases, it is assumed that the wood is felled and cut
by a harvester, and transported to the forest road with a forwarder.
With the option “standard”, the model uses functions estimated after
Bacescu et al. (2022) and Grigolato and Cadei (2022). With the option
“nordic”, we apply functions provided by Kärhä et
al. (2023), instead. As the cutting under the option “standard”
does only take into account harvest operations with stem diameters at
breast height of at least 15 cm, it uses the cutting function by Kärhä et al. (2023) in case of smaller lots. The
estimate for fuel consumption due to forest road maintenance is
estimated after Enache and Stampfer (2015)
with both options. For details about the other parameters call
?fuel_and_co2_evaluation.
The output of the function fuel_and_co2_evaluation() is
an object of class c4c_co2_result. It is, in essence, a
named list containing the following elements:
names(carbon_out)
#> [1] "concept" "time_span" "detailed_init" "init_areas"
#> [5] "risk_level" "parameters_co2" "co2_agg_high" "co2_agg_medium"
#> [9] "co2_by_phases"
Similar as for the base simulation results described in Section 3.3, the first six list
elements document the settings used for the calculations. The three
remaining items, co2_agg_high, co2_agg_medium,
co2_by_phases are all data frames that contain the actual
outcomes, each on a different level of aggregation. We show them below,
but note, that depending on your machine, some columns will be probably
cut off and only mentioned under more variables underneath the
data frame. The variable names should be self-explaining, note that for
all operations we provide both, the fuel consumption and the
CO2 emissions.
carbon_out$co2_agg_high
#> # A tibble: 201 × 10
#> time fuel_cutting_total_l fuel_moving_total_l fuel_road_maint_total_l
#> <dbl> <dbl> <dbl> <dbl>
#> 1 0 0 0 8750
#> 2 1 0.117 0.0174 8750
#> 3 2 7.79 0.487 8750
#> 4 3 40.4 2.42 8750
#> 5 4 78.6 5.47 8750
#> 6 5 75.3 7.98 8750
#> 7 6 112. 13.6 8750
#> 8 7 825. 55.0 8750
#> 9 8 987. 73.5 8750
#> 10 9 722. 69.0 8750
#> # ℹ 191 more rows
#> # ℹ 6 more variables: co2_cutting_total_kg <dbl>, co2_moving_total_kg <dbl>,
#> # co2_road_maint_total_kg <dbl>, co2_equiv_harvest_kg <dbl>,
#> # co2_equiv_inc_kg <dbl>, co2_equiv_standing_kg <dbl>
carbon_out$co2_agg_medium
#> # A tibble: 402 × 7
#> # Groups: time [201]
#> time harvest_type fuel_cutting_total_l fuel_moving_total_l
#> <dbl> <chr> <dbl> <dbl>
#> 1 0 damage 0 0
#> 2 0 regular 0 0
#> 3 1 damage 0 0
#> 4 1 regular 0.117 0.0174
#> 5 2 damage 6.14 0.241
#> 6 2 regular 1.65 0.246
#> 7 3 damage 33.0 1.32
#> 8 3 regular 7.37 1.10
#> 9 4 damage 58.0 2.40
#> 10 4 regular 20.6 3.07
#> # ℹ 392 more rows
#> # ℹ 3 more variables: co2_cutting_total_kg <dbl>, co2_moving_total_kg <dbl>,
#> # co2_equiv_harvest_kg <dbl>
carbon_out$co2_by_phases
#> # A tibble: 2,412 × 11
#> time harvest_type phase_no phase_name fuel_cutting_l_per_m3
#> <dbl> <chr> <int> <chr> <dbl>
#> 1 0 damage 1 initial_stand 0
#> 2 0 regular 1 initial_stand 0
#> 3 0 damage 2 young_growth 11.3
#> 4 0 regular 2 young_growth 0
#> 5 0 damage 3 immature_timber 2.79
#> 6 0 regular 3 immature_timber 2.97
#> 7 0 damage 4 mature_timber 0.813
#> 8 0 regular 4 mature_timber 0.674
#> 9 0 damage 5 final_harvest_phase_I 0.571
#> 10 0 regular 5 final_harvest_phase_I 0.531
#> # ℹ 2,402 more rows
#> # ℹ 6 more variables: fuel_cutting_total_l <dbl>, co2_cutting_total_kg <dbl>,
#> # fuel_moving_l_per_m3 <dbl>, fuel_moving_total_l <dbl>,
#> # co2_moving_total_kg <dbl>, co2_equiv_harvest_kg <dbl>
As a tool for facilitating visualization, we also provide a plot
functions for the output objects of the function
fuel_and_co2_evaluation(). Call
?plot.c4c_co2_result for a full documentation. In the quickstart section, we have provided an example
plot, where the CO2 emissions were plotted against the wood
increment in CO2 equivalents. Due to the CRAN policies, we
cannot show the full set of possible diagrams in this vignette; the user
is encouraged to check the documentation and try out the available
options. First, we show for our example the total CO2
emissions split into the components “cutting”, “moving”, and “road
maintenance:
plot(carbon_out, plot_type = "em_by_type")
Second, plotting the ratio of all CO2 emissions and the
wood increment (in CO2 equivalents). Note that, in general,
the emissions are between three and two magnitudes smaller compared to
the increment.
plot(carbon_out, plot_type = "em_inc_ratio")
