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Automatically Predict Biomass Using the Best Available Model

silv_predict_biomass_auto.Rd

Evaluates vectors of tree species, diameters, and heights, and automatically selects the best available allometric model based on a specified priority. If tree height is not provided, is NA, or is 0, the function automatically falls back to the eq_biomass_montero_2005 model.

Usage

silv_predict_biomass_auto(
  species,
  diameter,
  height = NULL,
  component = "tree",
  ntrees = NULL,
  rcd = NULL,
  bp = NULL,
  priority = c("ruiz-peinado-2011", "ruiz-peinado-2012", "montero-2005",
    "dieguez-aranda-2009", "manrique-2017", "menendez-2022", "cudjoe-2024"),
  quiet = FALSE
)

Arguments

species

A character vector specifying the scientific names of the tree species.

diameter

A numeric vector of tree diameters at breast height (in cm).

height

An optional numeric vector of tree heights (in m). Defaults to NULL.

component

A character string specifying the tree component for biomass calculation (e.g., "tree", "stem", "branches"). Defaults to "tree".

ntrees

An optional numeric vector indicating the number of trees in each class. Defaults to NULL (equivalent to 1 tree per entry).

rcd

An optional numeric vector of root collar diameters (in cm). Required for young plantation equations (eq_biomass_menendez_2022).

bp

An optional numeric vector of biomass packing values (in m3). Required for some species in young plantation equations.

priority

A character vector specifying the priority order for model selection. Defaults to c("ruiz-peinado-2011", "ruiz-peinado-2012", "montero-2005", "dieguez-aranda-2009", "manrique-2017", "menendez-2022", "cudjoe-2024").

quiet

A logical value. If TRUE, suppresses any informational messages and warnings.

Value

A data.frame containing two columns:

  • biomass: Numeric vector of predicted biomass values (in kg).

  • biomass_model: Character vector specifying the model ID used.

Examples

# 1. Vector-based calculation: automatic model selection for mixed species
species_vec <- c("Pinus pinaster", "Quercus petraea")
d_vec <- c(20, 25)
h_vec <- c(12, 14)
auto_results <- silv_predict_biomass_auto(species_vec, d_vec, h_vec)
#> Warning: No compatible model was found in priority for species: "Quercus petraea" with
#> component "tree".
#> ! Cite this model using <https://doi.org/10.5424/fs/2011201-11643>
#> ℹ Diameter is assumed to in centimeters, and height is assumed to be in meters
print(auto_results)
#>    biomass     biomass_model
#> 1 110.7419 ruiz-peinado-2011
#> 2       NA              <NA>

# 2. Vector-based calculation: fallback to Montero 2005 when height is missing
fallback_results <- silv_predict_biomass_auto(species_vec, d_vec, height = NULL)
#> Warning: No compatible model was found in priority for species: "Quercus petraea" with
#> component "tree".
#> ! Cite this model using <https://gregoriomontero.wordpress.com/wp-content/uploads/2016/09/2005-01-monografc3ada-forestal-13-m-produccic3b3n-de-biomasa-y-fijacic3b3n-de-co2-por-los-bosques-espac3b1oles.pdf>
#> ℹ Diameter is assumed to in centimeters, and height is assumed to be in meters
print(fallback_results)
#>    biomass biomass_model
#> 1 89.13531  montero-2005
#> 2       NA          <NA>

# 3. Dataset-based tutorial: apply to a mixed-species forest inventory data frame
inventory <- data.frame(
  tree_id  = 1:3,
  species  = c("Pinus pinaster", "Quercus petraea", "Pinus sylvestris"),
  dbh_cm   = c(22.5, 18.0, 31.2),
  height_m = c(15.0, 11.5, 18.0)
)

# Run auto-selection and bind the results directly
biomass_data <- silv_predict_biomass_auto(
  species  = inventory$species,
  diameter = inventory$dbh_cm,
  height   = inventory$height_m
)
#> Warning: No compatible model was found in priority for species: "Quercus petraea" with
#> component "tree".
#> ! Cite this model using <https://doi.org/10.5424/fs/2011201-11643>
#> ℹ Diameter is assumed to in centimeters, and height is assumed to be in meters

inventory_with_biomass <- cbind(inventory, biomass_data)
print(inventory_with_biomass)
#>   tree_id          species dbh_cm height_m  biomass     biomass_model
#> 1       1   Pinus pinaster   22.5     15.0 159.2250 ruiz-peinado-2011
#> 2       2  Quercus petraea   18.0     11.5       NA              <NA>
#> 3       3 Pinus sylvestris   31.2     18.0 460.3449 ruiz-peinado-2011