Package 'care4cmodel' reference manual

Title:	Carbon-Related Assessment of Silvicultural Concepts
Description:	A simulation model and accompanying functions that support assessing silvicultural concepts on the forest estate level with a focus on the CO2 uptake by wood growth and CO2 emissions by forest operations. For achieving this, a virtual forest estate area is split into the areas covered by typical phases of the silvicultural concept of interest. Given initial area shares of these phases, the dynamics of these areas is simulated. The typical carbon stocks and flows which are known for all phases are attributed post-hoc to the areas and upscaled to the estate level. CO2 emissions by forest operations are estimated based on the amounts and dimensions of the harvested timber. Probabilities of damage events are taken into account.
Authors:	Peter Biber [aut, cre, cph] , Stefano Grigolato [ctb] , Julia Schmucker [ctb] , Enno Uhl [ctb] , Hans Pretzsch [ctb]
Maintainer:	Peter Biber <[email protected]>
License:	GPL (>= 3)
Version:	1.0.2
Built:	2024-11-02 06:33:01 UTC
Source:	CRAN

Comprehensively Aggregate Raw Simulation Results

Description

Aggregate and prepare raw simulation output as obtained from sim_area_single_concept_with_risk in a way that makes them readable and appropriate for further processing.

Usage

aggregate_raw_sim_rslt(sim_areas_raw, concept_def)
aggregate_raw_sim_rslt(sim_areas_raw, concept_def)

Arguments

`sim_areas_raw`	Raw simulation results as obtained from `sim_area_single_concept_with_risk`
`concept_def`	The concept definition (a `c4c_concept` object) used for the simulation which generated `sim_areas_raw`

Value

A list of three matrices with named columns. Each row of these matrices represents a point in simulation time, but only integer times. The time distance from one row to the next one is one time unit, typically one year. The first column of the matrices is time, the other columns represent the stand development phases as defined in concept_def. The three list elements (matrices) are:

areas: Contains the simulated areas of each stand development phase in the area units defined in concept_def, usually ha.
area_inflows_regular: The area inflows into each stand development phase as caused by regular development, not by damage events. An entry at a given point in time represents the inflow between (and up to) this and the previous point in time. Therefore, the entries at time 0 are NA.
area_outflows_events: Area outflows of each stand development phase as caused by damage events. An entry at a given point in time represents the inflow between (and up to) this and the previous point in time. Therefore, the entries at time 0 are NA.

Examples

  # Make a simulation
  state_vars <- setup_statevars(
    pine_thinning_from_above_1, c(1000, 0, 0, 0, 0, 0)
  )
  time_span  <- 50
  parms      <- setup_parms(pine_thinning_from_above_1)
  parms$risk_mat <- setup_risk_events(
    time_span, avg_event_strength = 1, parms$risk
  )

  # Simulate
  sim_rslt_raw <- sim_area_single_concept_with_risk(
    state_vars,
    parms       = parms,
    event_times = c(0:time_span),
    time_span   = time_span
  )

  aggregate_raw_sim_rslt(sim_rslt_raw, pine_thinning_from_above_1)


# Make a simulation
  state_vars <- setup_statevars(
    pine_thinning_from_above_1, c(1000, 0, 0, 0, 0, 0)
  )
  time_span  <- 50
  parms      <- setup_parms(pine_thinning_from_above_1)
  parms$risk_mat <- setup_risk_events(
    time_span, avg_event_strength = 1, parms$risk
  )

  # Simulate
  sim_rslt_raw <- sim_area_single_concept_with_risk(
    state_vars,
    parms       = parms,
    event_times = c(0:time_span),
    time_span   = time_span
  )

  aggregate_raw_sim_rslt(sim_rslt_raw, pine_thinning_from_above_1)

Estimate Average Wood Extraction Distance From Forest Road Density

Description

Estimate Average Wood Extraction Distance From Forest Road Density

Usage

avg_extraction_distance(frd)
avg_extraction_distance(frd)

Arguments

frd

Forest road density (truck roads) in m/ha

Value

The average extraction distance from the felling spot to the nearest landing at a truck road.

Examples

  frd <- c(15, 30, 60, 100) # Forest road densities m/ha
  avg_extraction_distance(frd)

frd <- c(15, 30, 60, 100) # Forest road densities m/ha
  avg_extraction_distance(frd)

User-Friendly Construction of a c4c_concept Object

Description

For creating a c4c_concept object under normal circumstances, you should not use the constructor new_c4c_concept directly, but this function.

Usage

c4c_concept(growth_and_yield, concept_name)
c4c_concept(growth_and_yield, concept_name)

Arguments

`growth_and_yield`	data.frame with at least two rows and the columns "phase_no", "phase_name", "duration", "n_subphases", "vol_standing", "vol_remove", "vol_mort", "dbh_standing", "dbh_remove", "n_standing", "n_remove", "harvest_interval", and "survival_cum".
`concept_name`	Character, name of the concept defined

Details

Special attention needs to be paid to the definition of vol_remove in cases when the standing volume decreases from one phase to the next. If the value given for vol_remove is too low, it will result in a negative volume increment for the respective phase. This will not pass the validation called inside this function.

Value

A valid object of class c4c_concept, if it can be constructed from the input data; stops with an error otherwise. The object is basically a list. Its most important ingredient is a tibble named growth_and_yield which is a honed version of the input growth_and_yield to this function. It contains, in addition, phase wise periodical volume increments per ha (column vol_increment), which result from the given information. There is no option for user-provided volume increments in order to guarantee consistency.

Examples

  # construct dummy example (without real life relevance)
  g_and_y <- data.frame(
    phase_no         = 1:2,
    phase_name       = c("young", "older"),
    duration         = c(10, 10),
    n_subphases      = c(3, 3),
    vol_standing     = c(166, 304),
    vol_remove       = c(0, 23.6),
    vol_mort         = c(0.01, 0.11),
    n_standing       = c(3200, 970),
    n_remove         = c(0, 306),
    dbh_standing     = c(9.4, 22.3),
    dbh_remove       = c(0, 12.3),
    harvest_interval = c(0, 5),
    survival_cum     = c(0.999, 0.852)
  )

  dummy_concept <- c4c_concept(g_and_y, "dummy_concept")
  dummy_concept

# construct dummy example (without real life relevance)
  g_and_y <- data.frame(
    phase_no         = 1:2,
    phase_name       = c("young", "older"),
    duration         = c(10, 10),
    n_subphases      = c(3, 3),
    vol_standing     = c(166, 304),
    vol_remove       = c(0, 23.6),
    vol_mort         = c(0.01, 0.11),
    n_standing       = c(3200, 970),
    n_remove         = c(0, 306),
    dbh_standing     = c(9.4, 22.3),
    dbh_remove       = c(0, 12.3),
    harvest_interval = c(0, 5),
    survival_cum     = c(0.999, 0.852)
  )

  dummy_concept <- c4c_concept(g_and_y, "dummy_concept")
  dummy_concept

Fuel Consumption and CO2 Emissions for Cutting

Description

Given the output of a simulation run (i.e. an object of class c4c_base_result) as created with the function simulate_single_concept, the fuel consumption and CO2 emissions for cutting (i.e. felling, limbing, cutting the trees into logs) are calculated. Currently, this function assumes only harvester operations.

Usage

co2_eval_cutting(x, mode = c("standard", "nordic"))
co2_eval_cutting(x, mode = c("standard", "nordic"))

Arguments

x

An object of class c4c_base_result

mode

Character string to choose between "standard" (default) and "nordic". For "standard", the function fuel_cons_harvester_1 is used. Hoewever, as this function does only provide values for operations with an average harvest tree diameter of 15 cm and more, the "nordic" function fuel_cons_harvester_2 with the option thinning = TRUE is used for smaller average tree sizes.
With the choice "nordic" only the function fuel_cons_harvester_2 is used. However, if the harvested volume per ha per operation amounts to 90% and more of the standing volume per ha of the respective stand development phase, which is virutally a clearcut, the option thinning = FALSE is used. In case of damage- induced harvest, only the option thinning = TRUE is in effect in order to account for the more difficult conditions of such harvest operations.

Value

A data frame (tibble) with the columns time, harvest_type (damage or regular), phase_no, phase_name (numbers and names of the stand development phases), fuel_cutting_l_per_m3 (liters of fuel consumed per m3 of harvested wood), fuel_cutting_total_l (liters of fuel consumed in total), co2_cutting_total_kg (kg CO2 emitted).

Examples

  base_out <- simulate_single_concept(
   pine_thinning_from_above_1,
   init_areas = c(50, 100, 10, 50, 150, 600),
   time_span  = 50,
   risk_level = 3
  )

  co2_eval_cutting(base_out, "standard")
  co2_eval_cutting(base_out, "nordic")


base_out <- simulate_single_concept(
   pine_thinning_from_above_1,
   init_areas = c(50, 100, 10, 50, 150, 600),
   time_span  = 50,
   risk_level = 3
  )

  co2_eval_cutting(base_out, "standard")
  co2_eval_cutting(base_out, "nordic")

Fuel Consumption and CO2 Emissions for Moving Wood From the Felling Spot to the Forest Road

Description

Given the output of a simulation run (i.e. an object of class c4c_base_result) as created with the function simulate_single_concept, the fuel consumption and CO2 emissions for moving the wood to a truck road are calculated. Currently, this function assumes only forwarder operations.

Usage

co2_eval_moving(
  x,
  road_density,
  rel_loss = 0.1,
  mode = c("standard", "nordic")
)
co2_eval_moving(
  x,
  road_density,
  rel_loss = 0.1,
  mode = c("standard", "nordic")
)

Arguments

`x`	An object of class `c4c_base_result`
`road_density`	Forest road density (m/ha), relating to truck-accessible roads
`rel_loss`	Relative amount of the standing tree volume that is lost during harvesting (default 0.1). Note that the harvested amount is reduced with the factor 1 - rel_loss before upscaling from the fuel consumption per m³, because only the wood remaining after the harvest loss (mainly the stumps) is actually moved.
`mode`	Character string to choose between "standard" (default) and "nordic". With the option "standard", the function `fuel_cons_forwarder_1` is used, while "nordic" makes use of `fuel_cons_forwarder_2`

Value

A data frame (tibble) with the columns time, harvest_type (damage or regular), phase_no, phase_name (numbers and names of the stand development phases), fuel_moving_l_per_m3 (liters of fuel consumed per m3 of moved wood), fuel_moving_total_l (liters of fuel consumed in total), co2_moving_total_kg (kg CO2 emitted).

Examples

  base_out <- simulate_single_concept(
   pine_thinning_from_above_1,
   init_areas = c(50, 100, 10, 50, 150, 600),
   time_span  = 50,
   risk_level = 3
  )

  co2_eval_moving(base_out, road_density = 35, mode = "standard")
  co2_eval_moving(base_out, road_density = 35, mode = "nordic")

base_out <- simulate_single_concept(
   pine_thinning_from_above_1,
   init_areas = c(50, 100, 10, 50, 150, 600),
   time_span  = 50,
   risk_level = 3
  )

  co2_eval_moving(base_out, road_density = 35, mode = "standard")
  co2_eval_moving(base_out, road_density = 35, mode = "nordic")

Calculate an Exponential Decay Rate From Two Appropriate Pairs of Values

Description

Assuming an exponential decay process y = exp(-r * t), this function calculates r if the following informaion is given:
y_1 = exp(-r \* t_1), y_2 = exp(-r \* t_2)
Hereby, t_1 is the earlier, t_2 the later point in time. This implies the following conditions: t_2 > t_1, y_2 <= y_1
If these conditions are not given, the function will terminate with an error.

Usage

exp_decay_rate(t_1, t_2, y_1, y_2)
exp_decay_rate(t_1, t_2, y_1, y_2)

Arguments

`t_1`	Earlier point in time, coupled to `y_1`
`t_2`	Later point in time, coupled to `y_2`
`y_1`	Earlier value, coupled to `t_1`
`y_2`	Later value, coupled to `t_2`

Value

The exponential decay rate r, relating to the time unit of t_1 and t_2

Examples

  # Up to an age of t_1 = 30, a forest stand of interest has a survival
  # probability of 0.95. Up to an age of t_2 = 80, it has a survival
  # probability of 0.83. If we assume an exponential decay process for the
  # 50-year period, what is the exponential decay rate r?
  r <- exp_decay_rate(30, 80, 0.95, 0.83)
  print(r)

  # Check it
  0.95 * exp(-r * (80 - 30)) # 0.83

# Up to an age of t_1 = 30, a forest stand of interest has a survival
  # probability of 0.95. Up to an age of t_2 = 80, it has a survival
  # probability of 0.83. If we assume an exponential decay process for the
  # 50-year period, what is the exponential decay rate r?
  r <- exp_decay_rate(30, 80, 0.95, 0.83)
  print(r)

  # Check it
  0.95 * exp(-r * (80 - 30)) # 0.83

Overarching Evaluation of Fuel Consumption and CO2 Emissions

Description

Given the output of a simulation run generated with simulate_single_concept, i.e. an object of class c4c_base_result, a set of information related to CO2 emissions and storage is generated on different levels of aggregation.

Usage

fuel_and_co2_evaluation(
  x,
  road_density_m_ha,
  raw_density_kg_m3 = 520,
  harvest_loss = 0.1,
  bark_share = 0.12,
  mode = c("standard", "nordic")
)
fuel_and_co2_evaluation(
  x,
  road_density_m_ha,
  raw_density_kg_m3 = 520,
  harvest_loss = 0.1,
  bark_share = 0.12,
  mode = c("standard", "nordic")
)

Arguments

`x`	An object of class `c4c_base_result`
`road_density_m_ha`	The forest road density on the whole area in m/ha
`raw_density_kg_m3`	The raw wood density (kg/m³) to be used for wood volume conversions (i.e. density of air-dry wood (12% water content)). Default is 520 kg/m³ (typical for Scots pine). Internally, wood volume is converted into CO2 equivalents with `wood_to_co2`.
`harvest_loss`	Relative loss fraction of wood volume during harvest, mainly comprising the stumps (default = 0.1). Does not include bark losses.
`bark_share`	Relative wood volume share of the bark. Required, as the CO2 equivalents of harvested wood are calculated for wood volume under bark.
`mode`	Character string indicating the mode of calculating fuel consumption due to harvest operations. This relates to the functions `co2_eval_cutting` and `co2_eval_moving`, see the documentation of these functions for details.

Value

An object of class c4c_co2_result which is, in essence, a list of three result data frames (and metadata about the underlying simulation), providing information about co2 emissions, storage, and fuel consumption on different levels of aggregation.

Examples

  # Make a simulation run first
  # The resulting object base_output (class c4c_base_result) comprises
  # the simulated phase area dynamics as well as extended growth and yield
  # information
  base_output <- simulate_single_concept(
    pine_thinning_from_above_1,
    init_areas = c(1000, 0, 0, 0, 0, 0),
    time_span  = 200,
    risk_level = 3
  )

  # Generate fuel and CO2 related information
  fuel_and_co2_evaluation(
    base_output,
    road_density_m_ha = 35,
    mode = "nordic"
  )

# Make a simulation run first
  # The resulting object base_output (class c4c_base_result) comprises
  # the simulated phase area dynamics as well as extended growth and yield
  # information
  base_output <- simulate_single_concept(
    pine_thinning_from_above_1,
    init_areas = c(1000, 0, 0, 0, 0, 0),
    time_span  = 200,
    risk_level = 3
  )

  # Generate fuel and CO2 related information
  fuel_and_co2_evaluation(
    base_output,
    road_density_m_ha = 35,
    mode = "nordic"
  )

Fuel Consumption of a Forwarder per Cubic Meter Harvested Wood (Version #1)

Description

Fuel consumption per m³ harvested wood derived from the data provided by Grigolato and Cadei (2022). Includes loading, transportation, and unloading.

Usage

fuel_cons_forwarder_1(aed)
fuel_cons_forwarder_1(aed)

Arguments

aed

Average extraction distance to the nearest truck road

Value

Fuel consumption of a forwarder in liters diesel fuel per m³ wood to be handled

References

Grigolato S, Cadei A (2022). “Full-mechanized CTL production data in Scots pine forest in Poland.” https://researchdata.cab.unipd.it/659/.

Examples

  frd <- c(15, 30, 60, 100) # Forest road densities m/ha
  avg_extraction_distance(frd) |>
    fuel_cons_forwarder_1()

frd <- c(15, 30, 60, 100) # Forest road densities m/ha
  avg_extraction_distance(frd) |>
    fuel_cons_forwarder_1()

Fuel Consumption of a Forwarder per Cubic Meter Harvested Wood (Version #2)

Description

Fuel consumption per m³ harvested wood after Kärhä et al. (2023). Includes loading, transportation, and unloading.

Usage

fuel_cons_forwarder_2(aed, harvest_vol_ha, mineral_soil = TRUE)
fuel_cons_forwarder_2(aed, harvest_vol_ha, mineral_soil = TRUE)

Arguments

`aed`	Average extraction distance to the nearest truck road
`harvest_vol_ha`	Harvested merchandable wood volume over bark per ha (m³/ha)
`mineral_soil`	Logical, TRUE (default) if the operation takes place on mineral soil, FALSE if not

Value

Fuel consumption of a forwarder in liters diesel fuel per m³ wood to be handled

References

Kärhä K, Haavikko H, Kääriäinen H, Palander T, Eliasson L, Roininen K (2023). “Fossil-fuel consumption and CO2eq emissions of cut-to-length industrial roundwood logging operations in Finland.” European Journal of Forest Research, 1–17.

Examples

  frd <- c(15, 30, 60, 100) # Forest road densities m/ha
  aed <- avg_extraction_distance(frd)

  fuel_cons_forwarder_2(aed, 100, TRUE)
  fuel_cons_forwarder_2(aed, 100, FALSE)

frd <- c(15, 30, 60, 100) # Forest road densities m/ha
  aed <- avg_extraction_distance(frd)

  fuel_cons_forwarder_2(aed, 100, TRUE)
  fuel_cons_forwarder_2(aed, 100, FALSE)

Fuel Consumption of a Harvester per Cubic Meter Harvested Wood (Version #1)

Description

Fuel consumption depends on the average tree volume. For tree diameters at breast height < 15 cm the function gives back NA, because the assumed machine does not work with such small trees. Estimated after Bacescu et al. (2022).

Usage

fuel_cons_harvester_1(tree_vol, tree_dbh)
fuel_cons_harvester_1(tree_vol, tree_dbh)

Arguments

`tree_vol`	Average standing merchandable wood volume over bark (m³) per harvested tree
`tree_dbh`	Average diameter at breast height (cm) per harvested tree

Value

Fuel consumption of a harvester in liters diesel fuel per m³ harvested wood

References

Bacescu NM, Cadei A, Moskalik T, Wiśniewski M, Talbot B, Grigolato S (2022). “Efficiency Assessment of Fully Mechanized Harvesting System through the Use of Fleet Management System.” Sustainability, 14(24). ISSN 2071-1050, doi:10.3390/su142416751, https://www.mdpi.com/2071-1050/14/24/16751.

Examples

  dbh <- seq(10, 70, 10) # Vector of tree dbh in cm
  vol <- dbh ^ 2 / 1000  # Simple Volume estimate (m³) with Denzin's formula

  fuel_cons_harvester_1(vol, dbh)

dbh <- seq(10, 70, 10) # Vector of tree dbh in cm
  vol <- dbh ^ 2 / 1000  # Simple Volume estimate (m³) with Denzin's formula

  fuel_cons_harvester_1(vol, dbh)

Fuel Consumption of a Harvester per Cubic Meter Harvested Wood (Version #2)

Description

Fuel consumption of a harvester in liters diesel per m³ havested wood after Kärhä et al. (2023).

Usage

fuel_cons_harvester_2(tree_vol, harvest_vol_ha, thinning = TRUE)
fuel_cons_harvester_2(tree_vol, harvest_vol_ha, thinning = TRUE)

Arguments

`tree_vol`	Average standing merchandable wood volume over bark (m³) per harvested tree
`harvest_vol_ha`	Harvested merchandable wood volume over bark per ha (m³/ha)
`thinning`	Logical, TRUE (default) if the harvest is a thinning, or another kind of felling operation (FALSE)

Value

Fuel consumption of a harvester in liters diesel fuel per m³ harvested wood

References

Examples

  tree_vol    <- c(0.03,  0.10,  1.00,  2.00,  5.00)
  harvest_vol <- c(5.00, 10.00, 50.00, 25.00, 10.00)

  fuel_cons_harvester_2(tree_vol, harvest_vol, TRUE)
  fuel_cons_harvester_2(tree_vol, harvest_vol, FALSE)

tree_vol    <- c(0.03,  0.10,  1.00,  2.00,  5.00)
  harvest_vol <- c(5.00, 10.00, 50.00, 25.00, 10.00)

  fuel_cons_harvester_2(tree_vol, harvest_vol, TRUE)
  fuel_cons_harvester_2(tree_vol, harvest_vol, FALSE)

Annual Fuel Consumption for Truck Road Network Maintenance

Description

Estimate the annual diesel fuel consumption per ha for maintaining an existing truck road network in the forest. Estimate based on Enache and Stampfer (2015).

Usage

fuel_cons_road_maintenance(frd)
fuel_cons_road_maintenance(frd)

Arguments

frd

Forest road density in m/ha

Value

Diesel fuel consumption for truck road network maintenance in l/ha/year

References

Enache A, Stampfer K (2015). “Machine utilization rates, energy requirements and greenhouse gas emissions of forest road construction and maintenance in Romanian mountain forests.” Journal of Green Engineering, 4(4), 325–350.

Examples

  frd <- c(15, 30, 60, 100)
  fuel_cons_road_maintenance(frd)

frd <- c(15, 30, 60, 100)
  fuel_cons_road_maintenance(frd)

Convert Fuel Consumption into CO2 Emission

Description

Simple conversion assuming a factor of 2.61 kg CO2 / l diesel fuel

Usage

fuel_to_co2(fuel_cons_ltrs, fuel_type = c("diesel"))
fuel_to_co2(fuel_cons_ltrs, fuel_type = c("diesel"))

Arguments

`fuel_cons_ltrs`	Fuel amount consumed by a harvester in liters.
`fuel_type`	Fuel type (character string), required to find the correct conversion factor. Currently, only "diesel" is accepted.

Value

The emitted amount of CO2 in kg coming form burning fuel_cons_ltrs

Examples

  dbh  <- seq(20, 70, 10) # Vector of tree dbh in cm
  vol  <- dbh ^ 2 / 1000  # Simple Volume estimate (m³) with Denzin's formula

  fuel_cons_harvester_1(vol, dbh) |>
    fuel_to_co2()

dbh  <- seq(20, 70, 10) # Vector of tree dbh in cm
  vol  <- dbh ^ 2 / 1000  # Simple Volume estimate (m³) with Denzin's formula

  fuel_cons_harvester_1(vol, dbh) |>
    fuel_to_co2()

Extensive Growth and Yield Evaluation

Description

Provide an extensive compilation of growth and yield related results.

Usage

growth_and_yield_evaluation(sim_agg, concept_def, detailed_out = FALSE)
growth_and_yield_evaluation(sim_agg, concept_def, detailed_out = FALSE)

Arguments

`sim_agg`	Aggregated simulated area dynamics, more precisely, the output of `aggregate_raw_sim_rslt`
`concept_def`	Concept definition matching `sim_agg`, a `c4c_concept` object
`detailed_out`	Boolean, if TRUE, also pre-evaluation output (calculated by the internal function growth_and_yield_pre_eval) will be part of the result list (default = FALSE)

Details

The result object, a list, contains the following elements:

gyield_summary: A tibble that contains phase overarching growth and yield information. In this tibble, each row is a point in time. The columns (in addition to time) are: vol_standing, the standing volume on the total area of interest; vol_rmv_cont,the continuous removals that take place, as long as an area is in a given phase; vol_rmv_trans, the volume removals occurring at phase transitions from phases with more to such with less standing volume; vol_rmv_damage, the volume losses due to damage events; vol_rmv_harvest, regular harvest volume, the sum of vol_rmv_cont and vol_rmv_trans; vol_rmv_total, all removed volume, the sum of vol_rmv_harvest and vol_rmv_damage; vol_mort, the mortality volume (normal, not event-triggered); vol_inc, the volume increment on the whole area resulting from the vol_standing, vol_rmv_total, and vol_mort.
gyield_phases: A list of tibbles, one for each variable as contained in gyield_summary (except the volume increment) which makes no sense in a phase-wise context), but in phase-wise resolution. In each tibble, each row is a point in time, the columns represent the stand development phases.
gyield_pre (in case the user has chosen detailed_out = TRUE): A tibble, where each row is a point in time. The columns (in addition to time) are: vol_standing, the standing volume on the total area of interest; vol_rmv_cont,the continuous removals that take place, as long as an area is in a given phase; vol_rmv_trans, the volume removals occurring at phase transitions from phases with more to such with less standing volume;vol_rmv_damage, the volume losses due to damage events; vol_rmv_harvest, regular harvest volume, the sum of vol_rmv_cont and vol_rmv_trans; vol_rmv_total, all removed volume, the sum of vol_rmv_harvest and vol_rmv_damage; vol_mort, the mortality volume (normal, not event-triggered); vol_inc, the volume increment on the whole area resulting from the vol_standing, vol_rmv_total, and vol_mort.

Value

A list with two elements (see also details),

gyield_summary: A tibble that contains phase overarching growth and yield information.
gyield_phases: A list of tibbles, each one representing one of the growth and yield variables also found in gyield_summary, but here on the level of the single stand development phases.

If the user has selected detailed_out = TRUE, there will also be another list element, gyield_pre, which contains the interim information from which gyield_summary and gyield_phases are calculated.

Examples

  # Run a simulation and store the aggregated outcome
  state_vars <- setup_statevars(
    pine_thinning_from_above_1, c(1000, 0, 0, 0, 0, 0)
  )
  time_span <- 200
  parms     <- setup_parms(pine_thinning_from_above_1)
  parms$risk_mat <- setup_risk_events(
    time_span, avg_event_strength = 1, parms$risk
  )

  sim_areas_agg <- sim_area_single_concept_with_risk(
    state_vars,
    parms       = parms,
    event_times = c(0:time_span),
    time_span   = time_span
  ) |>
    aggregate_raw_sim_rslt(pine_thinning_from_above_1)

  # Growth and yield evaluation
  growth_and_yield_evaluation(sim_areas_agg, pine_thinning_from_above_1)

# Run a simulation and store the aggregated outcome
  state_vars <- setup_statevars(
    pine_thinning_from_above_1, c(1000, 0, 0, 0, 0, 0)
  )
  time_span <- 200
  parms     <- setup_parms(pine_thinning_from_above_1)
  parms$risk_mat <- setup_risk_events(
    time_span, avg_event_strength = 1, parms$risk
  )

  sim_areas_agg <- sim_area_single_concept_with_risk(
    state_vars,
    parms       = parms,
    event_times = c(0:time_span),
    time_span   = time_span
  ) |>
    aggregate_raw_sim_rslt(pine_thinning_from_above_1)

  # Growth and yield evaluation
  growth_and_yield_evaluation(sim_areas_agg, pine_thinning_from_above_1)

Check if an Object is of Class c4c_concept

Description

Check if an Object is of Class c4c_concept

Usage

is_c4c_concept(x)
is_c4c_concept(x)

Arguments

`x`	object to check

Value

TRUE, if x has class c4c_concept, FALSE if not

Examples

  data(pine_thinning_from_above_1)
  x <- unclass(pine_thinning_from_above_1)

  is_c4c_concept(pine_thinning_from_above_1)
  is_c4c_concept(x)

data(pine_thinning_from_above_1)
  x <- unclass(pine_thinning_from_above_1)

  is_c4c_concept(pine_thinning_from_above_1)
  is_c4c_concept(x)

Constructor for a c4c_concept Object

Description

Constructor for a c4c_concept Object

Usage

new_c4c_concept(x = list())
new_c4c_concept(x = list())

Arguments

`x`	a list object

Value

Returns an object of class c4c_concept

Examples

  # remove the c4c_class attribute for the example's sake
  x <- unclass(pine_thinning_from_above_1)

  x <- new_c4c_concept(x)

# remove the c4c_class attribute for the example's sake
  x <- unclass(pine_thinning_from_above_1)

  x <- new_c4c_concept(x)

pine_no_thinning_and_clearcut_1

Description

Generated from simulation runs to mimic a concept of treating a Scots pine stand with no thinnings and a short final harvest phase with the model SILVA.

Usage

pine_no_thinning_and_clearcut_1
pine_no_thinning_and_clearcut_1

Format

An object of class c4c_concept of length 3.

pine_thinning_from_above_1

Description

Generated from simulation runs to mimic a concept similar to the Scots pine management of the Bavarian State Forest with the model SILVA.

Usage

pine_thinning_from_above_1
pine_thinning_from_above_1

Format

An object of class c4c_concept of length 3.

Plot Function for c4c_base_result Objects

Description

Plot Function for c4c_base_result Objects

Usage

## S3 method for class 'c4c_base_result'
plot(
  x,
  variable = c("area", "vol_standing", "vol_inc_ups", "vol_rmv_total", "vol_rmv_cont",
    "vol_rmv_damage", "vol_mort", "hrvst_det_reg", "hrvst_det_dam"),
  ...
)
## S3 method for class 'c4c_base_result'
plot(
  x,
  variable = c("area", "vol_standing", "vol_inc_ups", "vol_rmv_total", "vol_rmv_cont",
    "vol_rmv_damage", "vol_mort", "hrvst_det_reg", "hrvst_det_dam"),
  ...
)

Arguments

x

Object of class c4c_base_result

variable

Character string, specifies the variable to be plotted. The options are:

"area": The areas covered by the different stand development phases (SDPH) over time
"vol_standing": The standing volume by SDPH over time
"vol_inc_ups": The volume increment by SDPH (upscaled from the concept definition) over time
"vol_rmv_total": The total removed volume (comprising regular and damage-induced harvest) by SDPH over time
"vol_rmv_cont": The volume removed due to regular harvest by SDPH over time
"vol_rmv_damage": The volume removed due to damage-induced harvest by SDPH over time
"vol_mort": The volume losses due to mortality by SDPH over time
"hrvst_det_reg": The regular harvest volume over time by tree size classes (mean harvest diameter)
"hrvst_det_dam": The damage-induced harvest volume over time by tree size classes (mean harvest diameter)

...

Other parameters, currently not used

Value

A ggplot object

Examples


  sim_base_out <- simulate_single_concept(
    pine_thinning_from_above_1,
    init_areas = c(1000, 0, 0, 0, 0, 0),
    time_span  = 200,
    risk_level = 3
  )

  # Make a plot
  plot(sim_base_out, variable = "area")
  # Also try the following options for the parameter "variable":
  # "vol_standing", "vol_inc_ups", "vol_rmv_total", "vol_rmv_cont",
  # "vol_rmv_damage", "vol_mort", "hrvst_det_reg", "hrvst_det_dam"

sim_base_out <- simulate_single_concept(
    pine_thinning_from_above_1,
    init_areas = c(1000, 0, 0, 0, 0, 0),
    time_span  = 200,
    risk_level = 3
  )

  # Make a plot
  plot(sim_base_out, variable = "area")
  # Also try the following options for the parameter "variable":
  # "vol_standing", "vol_inc_ups", "vol_rmv_total", "vol_rmv_cont",
  # "vol_rmv_damage", "vol_mort", "hrvst_det_reg", "hrvst_det_dam"

Plot Function for c4c_co2_result Objects

Description

Plot Function for c4c_co2_result Objects

Usage

## S3 method for class 'c4c_co2_result'
plot(
  x,
  plot_type = c("em_by_type", "fl_by_type", "em_by_phase", "fl_by_phase", "em_vs_inc",
    "em_vs_hrv", "em_inc_ratio"),
  ...
)
## S3 method for class 'c4c_co2_result'
plot(
  x,
  plot_type = c("em_by_type", "fl_by_type", "em_by_phase", "fl_by_phase", "em_vs_inc",
    "em_vs_hrv", "em_inc_ratio"),
  ...
)

Arguments

x

Object of class c4c_base_result

plot_type

Character string, specifies the kind of diagram to be plotted. The options are:

"em_by_type": The CO2 emissions by operation type (cutting, moving, forest road maintenance) over time
"fl_by_type": The fuel consumption by operation type (cutting, moving, forest road maintenance) over time
"em_by_phase": The CO2 emissions by stand development phase over time, not including forest road maintenance
"fl_by_phase": The fuel consumption by stand development phase over time, not including forest road maintenance
"em_vs_inc": The CO2 emissions plotted against the wood increment (in CO2 equivalents)
"em_vs_hrv": The CO2 emissions plotted against the harvest (in CO2 equivalents)
"em_inc_ratio": The ratio of all CO2 emissions and the wood increment (in CO2 equivalents) over time

...

Other parameters; currently not used

Value

A ggplot object

Examples


  sim_co2_out <- simulate_single_concept(
    pine_thinning_from_above_1,
    init_areas = c(1000, 0, 0, 0, 0, 0),
    time_span  = 200,
    risk_level = 3
  ) |>
    fuel_and_co2_evaluation(road_density_m_ha = 35, mode = "nordic")

  # Make a plot
  plot(sim_co2_out,  plot_type = "em_by_type")
  # Also try the plot types "fl_by_type", "em_by_phase", "fl_by_phase",
  # "em_vs_inc", "em_vs_hrv", "em_inc_ratio"

sim_co2_out <- simulate_single_concept(
    pine_thinning_from_above_1,
    init_areas = c(1000, 0, 0, 0, 0, 0),
    time_span  = 200,
    risk_level = 3
  ) |>
    fuel_and_co2_evaluation(road_density_m_ha = 35, mode = "nordic")

  # Make a plot
  plot(sim_co2_out,  plot_type = "em_by_type")
  # Also try the plot types "fl_by_type", "em_by_phase", "fl_by_phase",
  # "em_vs_inc", "em_vs_hrv", "em_inc_ratio"

Parameter Setup

Description

Given a c4c_concept concept definition, a list of parameter elements is handed back. This information is required for simulations and subsequent evaluations.

Usage

setup_parms(concept_def)
setup_parms(concept_def)

Arguments

concept_def

Concept definition as a c4c_concept object

Details

The element risk as part of the output describe as 'normal' risk as assumed for the silvicultural concept defined in concept_def. This can be adjusted with the parameter avg_event_strength of the function setup_risk_events, which has to be called in any case after the parameter setup.

Value

A list with three elements. The first, dwell_time, is a vector of dwell times for each subphase area, i.e. it indicates the average time a unit area is dwelling in this subphase (assuming an exponential distribution over time). The second element, risk, is a vector of the same length and order. It represents, for each subphase area, the average relative loss rate per year. It is derived from the cumulative survival probabilities (survival_com) given in the data frame growth_and_yield which is part of the concept definition (concept_def). The third element, phase_indexes, is a tibble which contains, for each stand development phase in concept_def, a vector of indexes which can be used to easier access the phase wise information in the different kinds of simulation outputs.

Examples

  parms <- pine_thinning_from_above_1 |> setup_parms()
  parms

parms <- pine_thinning_from_above_1 |> setup_parms()
  parms

setup_risk_events

Description

Low-level function for setting up a risk matrix for a simulation run. Available for users who want to build simulation runs out of single elements. Regular users are recommended to use the function simulate_single_concept for running a simulation with one single command (where this function is internally used).

Usage

setup_risk_events(time_span, avg_event_strength = 1, area_risks)
setup_risk_events(time_span, avg_event_strength = 1, area_risks)

Arguments

`time_span`	Simulation time span to be covered (integer)
`avg_event_strength`	Number which indicates the average strength of a damage event in the simulation. Default is 1 which means that the survival probabilities as defined in the silvicultural concept of interest are applied exactly as they are. A value of 2 would mean that one damage event would have the same effect as would two subsequent events with normal strength. A value of 0 would trigger no damage events at all.
`area_risks`	Vector of subphase-wise baseline damage risks, contained in the list made with `setup_parms` under the name `risk`.

Details

The function uses exponentially distributed random numbers (with expectation = 1) for simulating the strenghth of damaging events. Such kind of distribution where small events are much more frequent than strong ones is a realistic assumption for forest damages. Such a random number is drawn for each simulation point in time. The actual damage strength (i.e. relative area loss) for a given subphase is then calculated as follows:
rel_area_loss = 1 - ((1 - x) ^ avg_event_strength) ^ event_strength,
where

x: The baseline area loss risk of a given stand development subphase as resulting from the silvicultural concept definition of interest
avg_event_strength: The user defined overall average event strenghth
event_strength: Exponentially distributed random number with expectation 1, indicating the damage event strength in a given year

Value

A matrix where each row is a point in simulation time, and each column represents a subphase of the silvicultural concept of interest (in increasing order). Each matrix element describes the relative area loss that will happen at a given time to a given subphase.

Examples

  parms <- setup_parms(pine_no_thinning_and_clearcut_1)
  setup_risk_events(time_span = 200,
                    avg_event_strength = 3,
                    area_risks = parms$risk)

parms <- setup_parms(pine_no_thinning_and_clearcut_1)
  setup_risk_events(time_span = 200,
                    avg_event_strength = 3,
                    area_risks = parms$risk)

State Variable Setup and Initialization Given a `c4c_concept` concept definition, and a matching vector of initial areas the state variables required for simulating the concept are set up and initialized.

Description

The state variables to be created are the areas attributed to the single stand development phases defined in the concept definition of interest. More precisely, each subphase has an area which is a state variable. When initializing, the intial areas can be given for each phase in init_areas, or for each subphase. In the former case the initial phase areas are equally divided among the respective subphases.
In order to allow post-hoc reconstruction of the area flows, the function also creates the cumulative inflows and outflows to each subphase area as state variables and initializes them with 0.

Usage

setup_statevars(concept_def, init_areas, detailed = FALSE)
setup_statevars(concept_def, init_areas, detailed = FALSE)

Arguments

`concept_def`	The concept definition of interest as a `c4c_object`
`init_areas`	A vector providing the initial areas for the stand development (sub) phases defined in `concept_def` in the same order. In case `init_areas` relates to the phases, `detailed` must be `FALSE`. If it relates directly to the subphases, `detailed` must be `TRUE`. In the former case, the areas given for a phase are equally divided among its subphases. The areas must be given in the same area unit as named in `concept_def`, usually ha.
`detailed`	Logical, `FALSE` (default) indicates that `init_areas` is given for each stand development phase (and will be equally distributed among the subphases). `TRUE` indicates that `init_areas` is directly given for each subphase.

Value

A vector which is actually a sequence of three different blocks. This format is required for simulations with deSolve. Each block has as many elements as the total number of subpbases defined in concept_def. Each element refers to each subphase in the order of the phase sequence. The first block, contains the initial areas attributed to all subphases in the order of the phase sequence. The second and the third block will track the cumulative in- and outflows of each area during the simulation. They are initialized with 0.

Examples

  # Initialize with phase wise initial areas
  init_areas <- c(1000, 400, 250, 125, 125, 100)
  state_vars <- pine_thinning_from_above_1 |> setup_statevars(init_areas)
  state_vars

  # Initialize with subphase wise initial areas
  # Assume, we are afforesting 1000 ha, so all area has to be initially in
  # the first subphase of the first stand development phase
  n_sub <- sum(pine_thinning_from_above_1$growth_and_yield$n_subphases)
  init_areas    <- rep(0, n_sub)
  init_areas[1] <- 1000
  state_vars    <- setup_statevars(pine_thinning_from_above_1,
                                   init_areas,
                                   detailed = TRUE)
  state_vars

# Initialize with phase wise initial areas
  init_areas <- c(1000, 400, 250, 125, 125, 100)
  state_vars <- pine_thinning_from_above_1 |> setup_statevars(init_areas)
  state_vars

  # Initialize with subphase wise initial areas
  # Assume, we are afforesting 1000 ha, so all area has to be initially in
  # the first subphase of the first stand development phase
  n_sub <- sum(pine_thinning_from_above_1$growth_and_yield$n_subphases)
  init_areas    <- rep(0, n_sub)
  init_areas[1] <- 1000
  state_vars    <- setup_statevars(pine_thinning_from_above_1,
                                   init_areas,
                                   detailed = TRUE)
  state_vars

sim_area_single_concept_with_risk

Description

Low-level simulation function for area-phase dynamics, available for users who want to compose simulations out of the single steps. Regular users are recommended to use the function simulate_single_concept.

Usage

sim_area_single_concept_with_risk(
  state_vars,
  parms,
  event_times,
  time_span = 100L,
  time_frac = 4L,
  integ_method = "lsoda"
)
sim_area_single_concept_with_risk(
  state_vars,
  parms,
  event_times,
  time_span = 100L,
  time_frac = 4L,
  integ_method = "lsoda"
)

Arguments

`state_vars`	list of state variable(s) (vectors), all initialized
`parms`	list of parameter(s) (vectors)
`event_times`	vector of integers specifying the points in time when damage events can happen. Usually all numbers between (and including) zero and the endpoint of the simulation (i.e. `c(0, time_span)`).
`time_span`	simulation time span, integer, in the chosen time unit (typically years)
`time_frac`	integer >= 1, defines the time step to be used for numerical integration (time step = 1 / time_frac), i.e. one time unit will be split into time_frac substeps. Too small values of `time_frac` may cause the model showing chaotic dynamics, too large values increase computation time without meaningful gains in precision. The default value of 4 is mostly a good choice; in doubt, increase `time_frac` until the results do not change meaningfully anymore (risk events should be turned off during that procedure).
`integ_method`	integration method, passed to `ode`

Value

an object of class deSolve

Examples

  # Work with the example data pine_thinning_from_above_1
  # Initialize state variables (areas per stand development phase)
  state_vars <- setup_statevars(pine_thinning_from_above_1,
                                c(1000, 0, 0, 0, 0, 0))

  # Set time frame
  time_span          <- 200

  # Initialize parameters
  parms     <- setup_parms(pine_thinning_from_above_1)

  # Build risk matrix and add it to parms
  parms$risk_mat <- setup_risk_events(
    time_span, avg_event_strength = 1, parms$risk
  )

  # Simulate
  sim_area_single_concept_with_risk(
    state_vars,
    parms       = parms,
    event_times = c(0:time_span),
    time_span   = time_span
  )

# Work with the example data pine_thinning_from_above_1
  # Initialize state variables (areas per stand development phase)
  state_vars <- setup_statevars(pine_thinning_from_above_1,
                                c(1000, 0, 0, 0, 0, 0))

  # Set time frame
  time_span          <- 200

  # Initialize parameters
  parms     <- setup_parms(pine_thinning_from_above_1)

  # Build risk matrix and add it to parms
  parms$risk_mat <- setup_risk_events(
    time_span, avg_event_strength = 1, parms$risk
  )

  # Simulate
  sim_area_single_concept_with_risk(
    state_vars,
    parms       = parms,
    event_times = c(0:time_span),
    time_span   = time_span
  )

Run a Simulation for a Single Silvicultural Concept

Description

Top level function for running a simulation and obtaining all fundamental results, i.e. the simulated area dynamics and all growth and yield related outcomes.

Usage

simulate_single_concept(
  concept_def,
  init_areas,
  time_span,
  risk_level = 1,
  detailed_init = FALSE,
  detailed_out = FALSE,
  ...
)
simulate_single_concept(
  concept_def,
  init_areas,
  time_span,
  risk_level = 1,
  detailed_init = FALSE,
  detailed_out = FALSE,
  ...
)

Arguments

`concept_def`	Silvicultural Concept definition to be used in the simulation; a `c4c_concept` object
`init_areas`	The initial areas for each stand development phase defined in `concept_def` (if `detailed_init == FALSE`, default) or the initial areas for each stand development subphase as defined in `concept_def` (if `detailed_init == FALSE`)
`time_span`	Time span to be covered by the simulation
`risk_level`	Risk level relative to the standard risk level as defined in `concept_def`. The default is 1 which means, the standard risk level will be applied. If `risk_level == 0`, no damaging events will happen; e.g. `risk_level == 2` will increase damage probabilities as if the standard level risk events would occur two times.
`detailed_init`	Logical; is `init_areas` provided for each stand development phase (default, FALSE), or for each subphase (TRUE)?
`detailed_out`	Logical; should the output also include growth and yield pre-evaluation results (which are a very detailed interim evaluation output that is usally only required for internal efficiency)? The default is FALSE.
`...`	Additional arguments to `sim_area_single_concept_with_risk`

Details

The output of this function is an object of class c4c_base_result. There is no other way to generate such an object, therefore there is neither a constructor nor a validator available to the user.

Value

An object of class c4c_base_result which is actually a named list containing all information that was used to define and set up a simulation, as well as all fundamental simulation results, i.e. the simulated area dynamics, and all growth and yield related results.

Examples

  simulate_single_concept(
    pine_thinning_from_above_1,
    init_areas = c(1000, 0, 0, 0, 0, 0),
    time_span  = 200,
    risk_level = 3
  )

simulate_single_concept(
    pine_thinning_from_above_1,
    init_areas = c(1000, 0, 0, 0, 0, 0),
    time_span  = 200,
    risk_level = 3
  )

Weibull-Based Estimates of Stand Survival

Description

Estimates the probability of a forest stand to survive a period t after its establishment, based on the Weibull-Method published by Staupendahl (2011) Forstarchiv 82, 10-19.

Usage

survival_weibull(t, alpha, s_100)
survival_weibull(t, alpha, s_100)

Arguments

`t`	Time in years after stand establishment
`alpha`	Shape parameter. According to Staupendahl, Values < 1 indicate a high risk at young ages, alpha = 1 indicates an indifference of the risk to t (exponential distribution), values > 1 indicate high risk at old ages, where 1 < alpha < 2 means a degressively increasing, alpha = 2 a constantly increasing, and alpha > 2 a progressively increasing risk.
`s_100`	Survival probability up to t = 100

Details

The parameter s_100 represents the survival probability after t = 100 years, and alpha is the shape parameter, indicating the risk profile of the stand (type) of interest.

Value

The probability to survive up to t = 100

Examples

  # Calculations for Common oak, European beech, Norway spruce, Douglas fir,
  # and Scots pine with parameters after Staupendahl and Zucchini (AFJZ 2011)
  t <- seq(0, 120, 5)
  survival_weibull(t, alpha = 2.75, s_100 = 0.971) # oak
  survival_weibull(t, alpha = 1.76, s_100 = 0.967) # beech
  survival_weibull(t, alpha = 2.78, s_100 = 0.726) # spruce
  survival_weibull(t, alpha = 3.11, s_100 = 0.916) # Douglas
  survival_weibull(t, alpha = 2.45, s_100 = 0.923) # pine


# Calculations for Common oak, European beech, Norway spruce, Douglas fir,
  # and Scots pine with parameters after Staupendahl and Zucchini (AFJZ 2011)
  t <- seq(0, 120, 5)
  survival_weibull(t, alpha = 2.75, s_100 = 0.971) # oak
  survival_weibull(t, alpha = 1.76, s_100 = 0.967) # beech
  survival_weibull(t, alpha = 2.78, s_100 = 0.726) # spruce
  survival_weibull(t, alpha = 3.11, s_100 = 0.916) # Douglas
  survival_weibull(t, alpha = 2.45, s_100 = 0.923) # pine

Validator for a c4c_concept Object

Description

Validator for a c4c_concept Object

Usage

validate_c4c_concept(x)
validate_c4c_concept(x)

Arguments

`x`	an object of class c4c_concept to be validated

Value

Returns the input object if it passes validation, stops with an error otherwise

Examples

  pine_thinning_from_above_1 |> validate_c4c_concept()
  pine_no_thinning_and_clearcut_1 |> validate_c4c_concept()

pine_thinning_from_above_1 |> validate_c4c_concept()
  pine_no_thinning_and_clearcut_1 |> validate_c4c_concept()

Convert Wood Volume to CO2 Equivalents

Description

Convert Wood Volume to CO2 Equivalents

Usage

wood_to_co2(volume_m3, raw_density_kg_m3, water_perc = 12)
wood_to_co2(volume_m3, raw_density_kg_m3, water_perc = 12)

Arguments

`volume_m3`	Wood volume to be converted in cubic meters
`raw_density_kg_m3`	The raw density of the wood in kg/m³
`water_perc`	The water content of the wood at raw density in percent (usually defined as 12 %, i.e. air-dry)

Value

The CO2 equivalent of the input wood volume in kg

Examples

  # Conversion of 1 m³ wood with typical values for important tree species
  wood_to_co2(1, raw_density_kg_m3 = 520) # Scots pine
  wood_to_co2(1, raw_density_kg_m3 = 470) # Norway spruce
  wood_to_co2(1, raw_density_kg_m3 = 600) # European larch
  wood_to_co2(1, raw_density_kg_m3 = 700) # sessile/pedunculate oak
  wood_to_co2(1, raw_density_kg_m3 = 730) # European beech


# Conversion of 1 m³ wood with typical values for important tree species
  wood_to_co2(1, raw_density_kg_m3 = 520) # Scots pine
  wood_to_co2(1, raw_density_kg_m3 = 470) # Norway spruce
  wood_to_co2(1, raw_density_kg_m3 = 600) # European larch
  wood_to_co2(1, raw_density_kg_m3 = 700) # sessile/pedunculate oak
  wood_to_co2(1, raw_density_kg_m3 = 730) # European beech

Package 'care4cmodel'

Help Index

Comprehensively Aggregate Raw Simulation Results

Description

Usage

Arguments

Value

Examples

Estimate Average Wood Extraction Distance From Forest Road Density

Description

Usage

Arguments

Value

Examples

User-Friendly Construction of a c4c_concept Object

Description

Usage

Arguments

Details

Value

Examples

Fuel Consumption and CO2 Emissions for Cutting

Description

Usage

Arguments

Value

Examples

Fuel Consumption and CO2 Emissions for Moving Wood From the Felling Spot to the Forest Road

Description

Usage

Arguments

Value

Examples

Calculate an Exponential Decay Rate From Two Appropriate Pairs of Values

Description

Usage

Arguments

Value

Examples

Overarching Evaluation of Fuel Consumption and CO2 Emissions

Description

Usage

Arguments

Value

Examples

Fuel Consumption of a Forwarder per Cubic Meter Harvested Wood (Version #1)

Description

Usage

Arguments

Value

References

Examples

Fuel Consumption of a Forwarder per Cubic Meter Harvested Wood (Version #2)

Description

Usage

Arguments

Value

References

Examples

Fuel Consumption of a Harvester per Cubic Meter Harvested Wood (Version #1)

Description

Usage

Arguments

Value

References

Examples

Fuel Consumption of a Harvester per Cubic Meter Harvested Wood (Version #2)

Description

Usage

Arguments

Value

References

Examples

Annual Fuel Consumption for Truck Road Network Maintenance

Description

Usage

Arguments

Value

References

Examples

State Variable Setup and Initialization Given a `c4c_concept` concept definition, and a matching vector of initial areas the state variables required for simulating the concept are set up and initialized.