Ung2009 Growth and Yield Model — Ung2009 • CanadaForestAllometry

This function implements the models described in Ung et al. (2009). Two models (Nat1 and Nat2) are available:

Nat1: A multi-equation system predicting tree height (H), basal area (BA), and volume (V) based on stand age, growth degree days (GDD), precipitation (PREC), and species-specific parameters.
Nat2: A single equation that focuses on predicting stand volume (V) using age, GDD, and PREC.

Usage

Ung2009(species, age, GDD, PREC, model = c("both", "Nat1", "Nat2"))

Arguments

species: Character. A valid species name as defined in the parameter table of the model.
age: Numeric vector. The age(s) of the stand in years. Must be greater than 0.
GDD: Numeric. Growth degree days.
PREC: Numeric. Precipitation.
model: Character. Which model(s) to use. One of "Nat1", "Nat2", or "both". Defaults to "both".

Value

A tibble containing:

age
H (if Nat1 is used): Predicted tree height (m).
BA (if Nat1 is used): Predicted basal area (m²/ha).
V (if Nat1 is used): Predicted volume (m³/ha).
V2 (if Nat2 is used): Predicted volume (m³/ha) based on the Nat2 model.

Details

In the Nat1 model, height and basal area are each modeled using age, GDD, and PREC in a Chapman-Richards formulation, and volume is derived via an allometric equation. In contrast, Nat2 is more streamlined, modeling volume directly as a function of these same variables but without explicit height and basal area components.

References

Ung, C.-H., Bernier, P.Y., Guo, X.J., Lambert, M.-C. (2009). "A simple growth and yield model for assessing changes in standing volume across Canada’s forests." The Forestry Chronicle, 85, 57–64. doi:10.5558/tfc85057-1 .

Examples

# Example 1: Use both models (default), returning columns: age, H, BA, V, V2
res_both <- Ung2009(
  species = "ABIE.BAL",
  age = 1:150,
  GDD = 1500,
  PREC = 800
)
res_both
#> # A tibble: 150 × 5
#>      age             H       BA        V       V2
#>    <int>         <dbl>    <dbl>    <dbl>    <dbl>
#>  1     1 0.00000000972 3.79e-30 6.49e-37 7.47e-37
#>  2     2 0.000439      1.66e-14 1.72e-17 1.92e-17
#>  3     3 0.0156        2.72e- 9 5.13e-11 5.67e-11
#>  4     4 0.0932        1.10e- 6 8.85e- 8 9.74e- 8
#>  5     5 0.272         4.03e- 5 7.75e- 6 8.50e- 6
#>  6     6 0.556         4.44e- 4 1.53e- 4 1.67e- 4
#>  7     7 0.926         2.47e- 3 1.29e- 3 1.41e- 3
#>  8     8 1.36          8.93e- 3 6.35e- 3 6.94e- 3
#>  9     9 1.83          2.43e- 2 2.20e- 2 2.40e- 2
#> 10    10 2.32          5.41e- 2 5.94e- 2 6.48e- 2
#> # ℹ 140 more rows

# Example 2: Use only the Nat1 model, returning columns: age, H, BA, V
res_nat1 <- Ung2009(
  species = "ABIE.BAL",
  age = 1:150,
  GDD = 1500,
  PREC = 800,
  model = "Nat1"
)
res_nat1
#> # A tibble: 150 × 4
#>      age             H       BA        V
#>    <int>         <dbl>    <dbl>    <dbl>
#>  1     1 0.00000000972 3.79e-30 6.49e-37
#>  2     2 0.000439      1.66e-14 1.72e-17
#>  3     3 0.0156        2.72e- 9 5.13e-11
#>  4     4 0.0932        1.10e- 6 8.85e- 8
#>  5     5 0.272         4.03e- 5 7.75e- 6
#>  6     6 0.556         4.44e- 4 1.53e- 4
#>  7     7 0.926         2.47e- 3 1.29e- 3
#>  8     8 1.36          8.93e- 3 6.35e- 3
#>  9     9 1.83          2.43e- 2 2.20e- 2
#> 10    10 2.32          5.41e- 2 5.94e- 2
#> # ℹ 140 more rows

# Example 3: Use only the Nat2 model, returning columns: age, V2
res_nat2 <- Ung2009(
  species = "ABIE.BAL",
  age = 1:150,
  GDD = 1500,
  PREC = 800,
  model = "Nat2"
)
res_nat2
#> # A tibble: 150 × 2
#>      age       V2
#>    <int>    <dbl>
#>  1     1 7.47e-37
#>  2     2 1.92e-17
#>  3     3 5.67e-11
#>  4     4 9.74e- 8
#>  5     5 8.50e- 6
#>  6     6 1.67e- 4
#>  7     7 1.41e- 3
#>  8     8 6.94e- 3
#>  9     9 2.40e- 2
#> 10    10 6.48e- 2
#> # ℹ 140 more rows