Soderberg 1986 composite preset demo#

This notebook demonstrates the Soderberg1986Pipeline, which keeps the regeneration + young-stand + mortality + valuation workflow from the Elfving preset but uses Soderberg (1986) for mature-tree DBH growth.

It also reports qmd_cm and hq_m in each projection step.

[1]:

import matplotlib.pyplot as plt

from pyforestry.base.helpers.primitives import SiteBase
from pyforestry.sweden.simulation.presets import (
    Soderberg1986PipelineConfig,
    build_soderberg_1986_pipeline,
)
from pyforestry.sweden.site import Sweden, SwedishSite
from pyforestry.sweden.siteindex.sis.generated_site_category_trees import (
    predict_site_categories_county_tree,
)
from pyforestry.sweden.siteindex.sis.hagglund_lundmark_1977 import Hagglund_Lundmark_1977_SIS

[2]:

class SwedishSiteDemo(SwedishSite):
    """Concrete wrapper for notebooks (implements SiteBase abstract method)."""

    def compute_attributes(self) -> None:
        SwedishSite.__post_init__(self)

    def __post_init__(self) -> None:
        SiteBase.__post_init__(self)

[3]:

requested_sis = 20.0
site_category_species = "Pinus sylvestris"
county = Sweden.County.KOPPARBERG_OVRIGA

predicted_site_categories = predict_site_categories_county_tree(
    sis_hagglund_1979=requested_sis,
    species=site_category_species,
    Direktlan=county,
)

site = SwedishSiteDemo(
    latitude=60.5,
    longitude=15.0,
    altitude=150.0,
    field_layer=predicted_site_categories["field_layer"],
    bottom_layer=predicted_site_categories["bottom_layer"],
    soil_texture=predicted_site_categories["soil_texture"],
    soil_moisture=predicted_site_categories["soil_moisture"],
    soil_depth=predicted_site_categories["soil_depth"],
    soil_water=predicted_site_categories["soil_water"],
    ditched=predicted_site_categories["ditched"],
)

achieved_sis = Hagglund_Lundmark_1977_SIS(
    species=site_category_species,
    latitude=site.latitude,
    altitude=site.altitude or 0.0,
    soil_moisture=site.soil_moisture,
    ground_layer=site.bottom_layer or Sweden.BottomLayer.FRESH_MOSS,
    vegetation=site.field_layer,
    soil_texture=site.soil_texture or Sweden.SoilTextureTill.SANDY,
    climate_code=site.climate_zone or Sweden.ClimateZone.K1,
    lateral_water=site.soil_water or Sweden.SoilWater.SELDOM_NEVER,
    soil_depth=site.soil_depth or Sweden.SoilDepth.DEEP,
    incline_percent=site.incline_percent or 0.0,
    aspect=site.aspect or 0.0,
    nfi_adjustments=True,
    dlan=site.county or county,
    ditched=bool(site.ditched),
    peat=False,
    gotland=False,
    coast=(site.distance_to_coast or 9999.0) < 50.0,
    limes_norrlandicus=bool(site.n_of_limes_norrlandicus),
)

sis_closeness = {
    "requested_sis": requested_sis,
    "achieved_sis": float(achieved_sis),
    "sis_abs_error": abs(float(achieved_sis) - requested_sis),
    "sis_rel_error_pct": 100.0 * abs(float(achieved_sis) - requested_sis) / requested_sis,
}

display({"sis_closeness": sis_closeness, "predicted_site_categories": predicted_site_categories})

config = Soderberg1986PipelineConfig(
    sample_trees=120,
    random_seed=2026,
    deterministic=True,
    dt_years=5.0,
)
preset = build_soderberg_1986_pipeline(config)
preset

{'sis_closeness': {'requested_sis': 20.0,
  'achieved_sis': 20.698654683125625,
  'sis_abs_error': 0.6986546831256248,
  'sis_rel_error_pct': 3.493273415628124},
 'predicted_site_categories': {'field_layer': <SwedenFieldLayer.LICHEN_FREQUENT: Vegetation(code=17, swedish_name='Lavrik', english_name='Lichen, frequent occurrence', index=-0.5)>,
  'bottom_layer': <SwedenBottomLayer.BOGMOSS_TYPE: BottomLayerType(code=4, english_name='Bogmoss type (Sphagnum)', swedish_name='Vitmosstyp')>,
  'soil_texture': <SwedenSoilTextureTill.SANDY_MOIG: SoilTextureCategory(code=4, swedish_name='Sandig-moig morän', english_name='Sandy-silty till', short_name='Medium sand')>,
  'soil_moisture': <SwedenSoilMoisture.MESIC: SoilMoistureData(code=2, swedish_description='frisk', english_description='Mesic (subsoil water depth = 1-2 m)')>,
  'soil_depth': <SwedenSoilDepth.DEEP: SoilDepthCat(code=1, swedish_description='Mäktigt >70 cm. Inga synliga hällar', english_description='Deep >70cm. No visible stone outcrops.')>,
  'soil_water': <SwedenSoilWater.SHORTER_PERIODS: SoilWaterCat(code=2, swedish_description='kortare perioder', english_description='Shorter periods')>,
  'ditched': False}}

[3]:

<pyforestry.sweden.simulation.presets.soderberg_1986_pipeline.Soderberg1986Pipeline at 0x7f5b50713ef0>

[4]:

results = preset.run_projection(site=site, n_steps=30)
results

/home/runner/work/pyforestry/pyforestry/src/pyforestry/sweden/simulation/mortality/engine.py:452: UserWarning: Soderberg self-thinning annual probability exceeded [0, 1]; clamped.
  probabilities, adjusted, factors, diag = calibrate_soderberg(

[4]:
step_index age_years years_elapsed stems_per_ha mean_dbh_cm mean_height_m qmd_cm hq_m basal_area_m2_ha standing_volume_m3sk_per_ha ... birch_ba_share handover_dbh_cm handover_mean_height_m handover_smoothing_width_m phase_over_weight young_damage_index_mean young_damage_mortality_stems_removed_per_ha mortality_fraction_mean mortality_stems_removed_per_ha young_stand_potential_q
0 0.0 12.0 0.0 6244.836223 1.016164 1.715820 1.684151 3.064966 1.391148 4.292985 ... 0.307493 10.0 7.0 1.0 0.023479 0.000000 0.000000 0.000000 0.000000 77.405649
1 1.0 17.0 5.0 5887.431819 3.373596 2.804763 4.696728 4.211589 10.200150 33.834751 ... 0.458221 10.0 7.0 1.0 0.019741 0.334118 357.404404 0.000000 0.000000 77.405649
2 2.0 22.0 10.0 5499.208242 4.615034 3.919477 6.362217 6.038806 17.482637 71.296407 ... 0.414063 10.0 7.0 1.0 0.058055 0.309568 288.794491 0.032829 99.429086 77.405649
3 3.0 27.0 15.0 5084.802155 5.556470 4.934087 7.677256 7.882106 23.538348 114.949419 ... 0.399718 10.0 7.0 1.0 0.148796 0.290809 257.924056 0.055494 156.482031 77.405649
4 4.0 32.0 20.0 4705.641512 6.203203 5.389893 8.578298 9.114058 27.196372 143.155275 ... 0.390548 10.0 7.0 1.0 0.194099 0.341270 202.933136 0.066187 176.227507 77.405649
5 5.0 37.0 25.0 4307.224024 6.768155 5.962535 9.285682 9.803689 29.168558 165.199010 ... 0.393291 10.0 7.0 1.0 0.251268 0.463665 225.047761 0.071493 173.369728 77.405649
6 6.0 42.0 30.0 3936.774729 7.287962 6.469048 9.919011 10.499224 30.420559 182.264489 ... 0.395706 10.0 7.0 1.0 0.377420 0.477029 206.776141 0.073884 163.673154 77.405649
7 7.0 47.0 35.0 3596.678361 7.783706 6.930908 10.506292 11.094284 31.181026 195.363627 ... 0.397434 10.0 7.0 1.0 0.505314 0.491090 188.403721 0.074936 151.692646 77.405649
8 8.0 52.0 40.0 3287.329031 8.266876 7.361355 11.063079 11.611386 31.599855 205.332663 ... 0.398452 10.0 7.0 1.0 0.621156 0.505849 170.305820 0.074843 139.043510 77.405649
9 9.0 57.0 45.0 3007.777677 8.734517 7.759863 11.585687 12.061089 31.708758 212.142586 ... 0.397483 10.0 7.0 1.0 0.717468 0.521320 152.786107 0.073910 126.765247 77.405649
10 10.0 62.0 50.0 2756.631382 9.190474 8.134081 12.082087 12.460832 31.604762 216.604213 ... 0.394939 10.0 7.0 1.0 0.791263 0.537515 136.051462 0.072522 115.094832 77.405649
11 11.0 67.0 55.0 2652.905302 9.249516 8.137408 12.331702 12.987322 31.685288 222.284691 ... 0.389116 10.0 7.0 1.0 0.999081 0.000000 0.000000 0.070903 103.726081 77.405649
12 12.0 72.0 60.0 2560.638803 9.366369 8.167811 12.662473 13.509515 32.245956 232.089992 ... 0.376996 10.0 7.0 1.0 0.999391 0.000000 0.000000 0.068372 92.266498 77.405649
13 13.0 77.0 65.0 2477.607550 9.460003 8.171638 12.968338 15.968278 32.725860 241.007641 ... 0.365634 10.0 7.0 1.0 0.999618 0.000000 0.000000 0.066229 83.031253 77.405649
14 14.0 82.0 70.0 2402.953597 9.533547 8.153773 13.250945 16.228496 33.138205 249.104273 ... 0.354941 10.0 7.0 1.0 0.999751 0.000000 0.000000 0.063229 74.653952 77.405649
15 15.0 87.0 75.0 2335.714465 9.589946 8.119309 13.512401 16.454990 33.494590 256.485740 ... 0.344834 10.0 7.0 1.0 0.999832 0.000000 0.000000 0.060289 67.239132 77.405649
16 16.0 92.0 80.0 2274.954678 9.631853 8.081602 13.755009 16.681488 33.805269 263.563665 ... 0.335236 10.0 7.0 1.0 0.999883 0.000000 0.000000 0.057442 60.759787 77.405649
17 17.0 97.0 85.0 2219.794718 9.662476 8.032080 13.982430 16.894542 34.085368 270.124524 ... 0.326027 10.0 7.0 1.0 0.999917 0.000000 0.000000 0.054923 55.159960 77.405649
18 18.0 102.0 90.0 2169.520331 9.683232 7.972650 14.195932 17.100926 34.338510 276.199986 ... 0.317182 10.0 7.0 1.0 0.999941 0.000000 0.000000 0.052596 50.274387 77.405649
19 19.0 107.0 95.0 2123.456024 9.695306 7.904718 14.396855 17.277268 34.567535 281.819989 ... 0.308681 10.0 7.0 1.0 0.999956 0.000000 0.000000 0.050546 46.064308 77.405649
20 20.0 112.0 100.0 2081.056392 9.699681 7.829509 14.586294 17.462713 34.774721 287.010290 ... 0.300503 10.0 7.0 1.0 0.999967 0.000000 0.000000 0.048803 42.399632 77.405649
21 21.0 117.0 105.0 2041.927036 9.697173 7.748225 14.764918 17.630408 34.961673 291.788321 ... 0.292634 10.0 7.0 1.0 0.999975 0.000000 0.000000 0.047131 39.129356 77.405649
22 22.0 122.0 110.0 2005.762358 9.688628 7.662071 14.933355 17.804587 35.130485 296.176691 ... 0.285055 10.0 7.0 1.0 0.999980 0.000000 0.000000 0.045411 36.164678 77.405649
23 23.0 127.0 115.0 1972.152857 9.674679 7.571604 15.092675 17.954178 35.282790 300.201347 ... 0.277749 10.0 7.0 1.0 0.999984 0.000000 0.000000 0.043931 33.609501 77.405649
24 24.0 132.0 120.0 1940.873517 9.655954 7.477711 15.243372 18.119017 35.420054 303.880869 ... 0.270700 10.0 7.0 1.0 0.999987 0.000000 0.000000 0.042511 31.279340 77.405649
25 25.0 137.0 125.0 1911.657218 9.632968 7.380909 15.386188 18.259857 35.543645 307.236808 ... 0.263896 10.0 7.0 1.0 0.999989 0.000000 0.000000 0.041067 29.216299 77.405649
26 26.0 142.0 130.0 1884.274155 9.606164 7.281642 15.521797 18.396174 35.654798 310.289668 ... 0.257323 10.0 7.0 1.0 0.999991 0.000000 0.000000 0.039904 27.383063 77.405649
27 27.0 147.0 135.0 1858.544493 9.575948 7.180361 15.650734 18.530563 35.754627 313.057725 ... 0.250968 10.0 7.0 1.0 0.999992 0.000000 0.000000 0.038923 25.729662 77.405649
28 28.0 152.0 140.0 1834.326827 9.542704 7.077524 15.773418 18.660129 35.844145 315.557303 ... 0.244820 10.0 7.0 1.0 0.999993 0.000000 0.000000 0.037936 24.217667 77.405649
29 29.0 157.0 145.0 1811.485309 9.506761 6.973489 15.890283 18.800153 35.924271 317.804481 ... 0.238868 10.0 7.0 1.0 0.999994 0.000000 0.000000 0.037077 22.841518 77.405649
30 30.0 162.0 150.0 1789.904832 9.468421 6.868587 16.001703 18.923901 35.995834 319.813995 ... 0.233103 10.0 7.0 1.0 0.999995 0.000000 0.000000 0.036296 21.580476 77.405649

31 rows × 28 columns

[5]:

results[[
    "step_index",
    "age_years",
    "stems_per_ha",
    "qmd_cm",
    "hq_m",
    "mean_dbh_cm",
    "mean_height_m",
    "basal_area_m2_ha",
    "standing_volume_m3sk_per_ha",
    "standing_value_sek_per_ha",
]]

[5]:
step_index age_years stems_per_ha qmd_cm hq_m mean_dbh_cm mean_height_m basal_area_m2_ha standing_volume_m3sk_per_ha standing_value_sek_per_ha
0 0.0 12.0 6244.836223 1.684151 3.064966 1.016164 1.715820 1.391148 4.292985 0.000000
1 1.0 17.0 5887.431819 4.696728 4.211589 3.373596 2.804763 10.200150 33.834751 1372.304092
2 2.0 22.0 5499.208242 6.362217 6.038806 4.615034 3.919477 17.482637 71.296407 12692.054898
3 3.0 27.0 5084.802155 7.677256 7.882106 5.556470 4.934087 23.538348 114.949419 20775.844321
4 4.0 32.0 4705.641512 8.578298 9.114058 6.203203 5.389893 27.196372 143.155275 26233.018062
5 5.0 37.0 4307.224024 9.285682 9.803689 6.768155 5.962535 29.168558 165.199010 31530.603910
6 6.0 42.0 3936.774729 9.919011 10.499224 7.287962 6.469048 30.420559 182.264489 36919.740421
7 7.0 47.0 3596.678361 10.506292 11.094284 7.783706 6.930908 31.181026 195.363627 42254.780825
8 8.0 52.0 3287.329031 11.063079 11.611386 8.266876 7.361355 31.599855 205.332663 43805.791507
9 9.0 57.0 3007.777677 11.585687 12.061089 8.734517 7.759863 31.708758 212.142586 45463.319060
10 10.0 62.0 2756.631382 12.082087 12.460832 9.190474 8.134081 31.604762 216.604213 46586.542860
11 11.0 67.0 2652.905302 12.331702 12.987322 9.249516 8.137408 31.685288 222.284691 48403.461380
12 12.0 72.0 2560.638803 12.662473 13.509515 9.366369 8.167811 32.245956 232.089992 52287.919616
13 13.0 77.0 2477.607550 12.968338 15.968278 9.460003 8.171638 32.725860 241.007641 54674.936502
14 14.0 82.0 2402.953597 13.250945 16.228496 9.533547 8.153773 33.138205 249.104273 56690.340129
15 15.0 87.0 2335.714465 13.512401 16.454990 9.589946 8.119309 33.494590 256.485740 59506.741526
16 16.0 92.0 2274.954678 13.755009 16.681488 9.631853 8.081602 33.805269 263.563665 60291.683122
17 17.0 97.0 2219.794718 13.982430 16.894542 9.662476 8.032080 34.085368 270.124524 60494.028088
18 18.0 102.0 2169.520331 14.195932 17.100926 9.683232 7.972650 34.338510 276.199986 64209.124434
19 19.0 107.0 2123.456024 14.396855 17.277268 9.695306 7.904718 34.567535 281.819989 68665.114837
20 20.0 112.0 2081.056392 14.586294 17.462713 9.699681 7.829509 34.774721 287.010290 69350.104008
21 21.0 117.0 2041.927036 14.764918 17.630408 9.697173 7.748225 34.961673 291.788321 75133.721674
22 22.0 122.0 2005.762358 14.933355 17.804587 9.688628 7.662071 35.130485 296.176691 78589.827449
23 23.0 127.0 1972.152857 15.092675 17.954178 9.674679 7.571604 35.282790 300.201347 80552.319427
24 24.0 132.0 1940.873517 15.243372 18.119017 9.655954 7.477711 35.420054 303.880869 81733.687137
25 25.0 137.0 1911.657218 15.386188 18.259857 9.632968 7.380909 35.543645 307.236808 83746.281239
26 26.0 142.0 1884.274155 15.521797 18.396174 9.606164 7.281642 35.654798 310.289668 87480.294625
27 27.0 147.0 1858.544493 15.650734 18.530563 9.575948 7.180361 35.754627 313.057725 88485.621544
28 28.0 152.0 1834.326827 15.773418 18.660129 9.542704 7.077524 35.844145 315.557303 90143.062042
29 29.0 157.0 1811.485309 15.890283 18.800153 9.506761 6.973489 35.924271 317.804481 92416.914963
30 30.0 162.0 1789.904832 16.001703 18.923901 9.468421 6.868587 35.995834 319.813995 96641.183128 
[6]:

fig, axes = plt.subplots(2, 2, figsize=(11, 7), sharex=True)
axes = axes.flatten()

axes[0].plot(results["age_years"], results["qmd_cm"], marker="o")
axes[0].set_ylabel("QMD (cm)")

axes[1].plot(results["age_years"], results["hq_m"], marker="o")
axes[1].set_ylabel("Hq (m)")

axes[2].plot(results["age_years"], results["standing_volume_m3sk_per_ha"], marker="o")
axes[2].set_ylabel("Standing volume (m3/ha)")
axes[2].set_xlabel("Stand age (years)")

axes[3].plot(results["age_years"], results["standing_value_sek_per_ha"], marker="o")
axes[3].set_ylabel("Standing value (SEK/ha)")
axes[3].set_xlabel("Stand age (years)")

plt.tight_layout()
../_images/notebooks_soderberg_1986_preset_6_0.png

Notes#

  • Mature-tree growth in this preset comes from Soderberg1986Model.

  • Unlike Elfving 2010, there is no stand-level basal-area correction stage in the mature-growth model.

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