pyforestry.sweden.systems.eko1985 package

Submodules

pyforestry.sweden.systems.eko1985.cohorts module

Species cohort classes and factory for the Eko 1985 stand model.

Each cohort subclass implements the species-specific volume, mortality, and basal-area increment equations from Ekö (1985).

Source:

Ekö, P.-M. (1985). En produktionsmodell för skog i Sverige, baserad på bestånd från riksskogstaxeringens provytor = A growth simulator for Swedish forests, based on data from the national forest survey. Sveriges lantbruksuniversitet, institutionen för skogsskötsel, Rapport nr 16, Umeå.

class pyforestry.sweden.systems.eko1985.cohorts.BeechEngineCohort(ba, stems, age, stand, site, species: TreeName | str | None = None)

Bases: EngineStandPart

Beech cohort ported from the legacy EkoBeech formulas.

MORT_INDEX: int | None = 3

species row into the mortality coefficient tables (set by each subclass)

get_bai5(ba_quotient_chronic_mortality=0.0, ba_quotient_acute_mortality=0.0)

Compute beech basal-area increment over five years.

get_volume(ba=None, qmd=None, age=None, stems=None, hk=None)

Compute beech volume for the given stand state.

class pyforestry.sweden.systems.eko1985.cohorts.BirchEngineCohort(ba, stems, age, stand, site, species: TreeName | str | None = None)

Bases: EngineStandPart

Birch cohort ported from the legacy EkoBirch formulas.

MORT_INDEX: int | None = 2

species row into the mortality coefficient tables (set by each subclass)

get_bai5(ba_quotient_chronic_mortality=0.0, ba_quotient_acute_mortality=0.0)

Compute birch basal-area increment over five years.

get_volume(ba=None, qmd=None, age=None, stems=None, hk=None)

Compute birch volume for the given stand state.

Ekö 1985 Tabell 10c publishes two volume functions for birch – område Nord+Mellan and område Syd (per §8.2.1 only North and Central are pooled; South is fitted separately). Both are transcribed faithfully and, by default, the canonical dissertation function for the cohort’s region is used (unlike the övrigt löv Syd function, birch’s Syd function stays physical).

The recovered ProdMod2 program instead applies the Nord+Mellan function to every region for birch: its volume-coefficient table is region-identical at INDEX_BIRCH, exactly as for övrigt löv (INDEX_OTHER). Setting broadleaf_volume_prodmod on the site reproduces that – the Nord+Mellan function is then used in the South too. See BroadleafEngineCohort.get_volume() for the full rationale and sources.

class pyforestry.sweden.systems.eko1985.cohorts.BroadleafEngineCohort(ba, stems, age, stand, site, species: TreeName | str | None = None)

Bases: EngineStandPart

Implementation for the grouped ‘other broadleaf’ cohort.

MORT_INDEX: int | None = 5

species row into the mortality coefficient tables (set by each subclass)

get_bai5(ba_quotient_chronic_mortality=0.0, ba_quotient_acute_mortality=0.0)

Compute broadleaf basal-area increment over five years.

get_volume(ba=None, qmd=None, age=None, stems=None, hk=None)

Compute ‘other broadleaf’ (övrigt löv) volume for the given stand state.

Ekö 1985 Tabell 10f publishes two volume functions for övrigt löv – one for område Nord+Mellan and one for område Syd (per §8.2.1 only North and Central are pooled; South is fitted separately). Both are transcribed faithfully here, and by default the canonical dissertation function for the cohort’s region is used.

Caveat on the Syd function: it carries a TG (total stand basal area, all species) term, +0.859600e-01 * TG (Tabell 10f Syd, std. error 24 %), entering as exp(0.859600e-01 * TG). Övrigt löv volume is thereby modelled to climb steeply with total stand density, so a small broadleaf cohort inside a dense mixed stand extrapolates far outside the (sparse-broadleaf) data Ekö fitted: e.g. TG = 35.5 m2/ha alone scales the estimate by e**3.05 ~= 21x, a ~140 m form height at BA 7. The coefficient is a verified transcription of the scan (0.859600E-01, sharing the E-01 exponent of its GALL1/ln(ALT) neighbours) – not a typo on our side; whether Ekö’s printed E-01 should read E-02 cannot be confirmed, as the recovered ProdMod2 program never implements the Syd function to cross-check.

ProdMod2 instead applies the Nord+Mellan function to every region for övrigt löv: its volume-coefficient table is byte-identical across localisation 0/1/2 at INDEX_OTHER (SpeciesData/Growth/ get_species_growth_coefficients.cpp, consumed by SpeciesData_485edc.cpp), while the same decompiler keeps the per-region spruce/pine rows distinct, so the collapse is deliberate. Setting broadleaf_volume_prodmod on the site (Eko1985SiteContext.broadleaf_volume_prodmod / EkoStandSite.broadleaf_volume_prodmod) reproduces that behaviour – the Nord+Mellan function is then used in the South too, giving physical form heights. The default (False) keeps the canonical dissertation Syd function. The same flag likewise routes birch (Tabell 10c) through its Nord+Mellan function, which ProdMod2 pools identically; see BirchEngineCohort.get_volume. (Beech and oak have a single volume function each, so they are unaffected.)

class pyforestry.sweden.systems.eko1985.cohorts.OakEngineCohort(ba, stems, age, stand, site, species: TreeName | str | None = None)

Bases: EngineStandPart

Oak cohort ported from the legacy EkoOak formulas.

MORT_INDEX: int | None = 4

species row into the mortality coefficient tables (set by each subclass)

get_bai5(ba_quotient_chronic_mortality=0.0, ba_quotient_acute_mortality=0.0)

Compute oak basal-area increment over five years.

get_volume(ba=None, qmd=None, age=None, stems=None, hk=None)

Compute oak volume for the given stand state.

class pyforestry.sweden.systems.eko1985.cohorts.PineEngineCohort(ba, stems, age, stand, site, species: TreeName | str | None = None)

Bases: EngineStandPart

Pine cohort ported from the legacy EkoPine formulas.

MORT_INDEX: int | None = 0

species row into the mortality coefficient tables (set by each subclass)

get_bai5(ba_quotient_chronic_mortality=0.0, ba_quotient_acute_mortality=0.0)

Compute pine basal-area increment over five years.

get_volume(ba=None, qmd=None, age=None, stems=None, hk=None)

Compute pine volume for the given stand state.

class pyforestry.sweden.systems.eko1985.cohorts.SpruceEngineCohort(ba, stems, age, stand, site, species: TreeName | str | None = None)

Bases: EngineStandPart

Spruce cohort ported from the legacy EkoSpruce formulas.

MORT_INDEX: int | None = 1

species row into the mortality coefficient tables (set by each subclass)

get_bai5(ba_quotient_chronic_mortality=0.0, ba_quotient_acute_mortality=0.0)

Compute spruce basal-area increment over five years.

get_volume(ba=None, qmd=None, age=None, stems=None, hk=None)

Compute spruce volume for the given stand state.

pyforestry.sweden.systems.eko1985.engine module

Eko 1985 engine expressed on the canonical simulation runtime.

Ekö 1985 growth couples every cohort to every other through competition (HK) and cross-species mortality, so the five-year update is intrinsically a whole-stand operation computed from one pre-growth snapshot. Mortality is part of that growth equation, not a separate phase.

This used to be expressed on a per-part runtime: cohorts wrapped in StandPart views inside a StandComposite, stepped by a StageRuntime that called the growth stage once per part. Since the step is whole-stand, N-1 of those calls had to be made inert by an _armed latch, and thinning bypassed the runtime entirely. The wrapper cost three types and bought nothing: EngineStand already holds its cohorts in self.parts, so it now iterates them directly.

class pyforestry.sweden.systems.eko1985.engine.EngineStand(parts: list[EngineStandPart], site: EkoStandSite)

Bases: object

The Ekö 1985 stand: a list of species cohorts stepped together.

static get_mai(volume: float, total_age: float) → float

Return mean annual increment from volume and age.

static get_qmd(ba: float, stems: float) → float

Return quadratic mean diameter (cm) from basal area and stems.

grow(years: float = 5.0, apply_mortality: bool = True)

Advance the stand by years in one simultaneous whole-stand step.

grow5(apply_mortality: bool = True)

Five-year convenience wrapper around grow().

thin(removals: Mapping) → None

Remove the requested basal area from each cohort at constant QMD.

class pyforestry.sweden.systems.eko1985.engine.EngineStandPart(ba: float, stems: float, age: float, species: TreeName | str, site: EkoStandSite)

Bases: object

Minimal cohort base for the Eko 1985 engine (carried as a StandPart view).

MORT_INDEX: int | None = None

species row into the mortality coefficient tables (set by each subclass)

property basal_area: float

Expose basal area (m2/ha) under the StandPart metric attribute name.

get_bai5(ba_quotient_chronic_mortality=0.0, ba_quotient_acute_mortality=0.0)

Compute basal-area increment over five years.

get_mortality(increment: int = 5)

Return the five-year natural-mortality fractions (chronic, acute).

Transcribed from the ProdMod2 program (HUGIN natural-avgang functions; Bengtsson 1979, cf. Haegglund 1981a, via Eko 1985): both fractions are (increment/100) * polynomial clamped at zero. The chronic term uses basal area and a capped stem count; the acute term uses an age class derived from the largest breast-height age in the stand (only spruce is age-class sensitive). Localisation is binary: North vs Central/South.

The second and fourth return values are the mean diameters of the removed trees, kept for interface compatibility; the growth stage ignores them.

get_volume(ba, qmd, age, stems, hk)

Compute volume from basal area, QMD, age, stems, and HK.

register_stand(stand: EngineStand) → None

Attach the cohort to its parent stand.

pyforestry.sweden.systems.eko1985.model module

Refactored Eko 1985 stand interface built on pyforestry primitives.

This module ports the original formulas inline while exposing:

• primitive types such as StandBasalArea, QuadraticMeanDiameter, and SiteIndexValue,

• site handling that uses SwedishSite metadata when available,

• site-index derivations routed through Hägglund (1970), Carbonnier (1975), and the Leijon (1979) translation helpers.

class pyforestry.sweden.systems.eko1985.model.Eko1985Cohort(species: TreeName, basal_area: StandBasalArea, stems: Stems, age: AgeMeasurement, volume: StandVolume | None = None, hk: float | None = None, ba_other_species: float | None = None, qmd_other_species: QuadraticMeanDiameter | None = None, bai5: float | None = None, volume_increment: StandVolume | float | None = None, gross_volume_increment: StandVolume | float | None = None)

Bases: object

State for a single species cohort using primitive containers.

ba_other_species: float | None = None

bai5: float | None = None

classmethod from_values(species: TreeName | str, basal_area_m2_per_ha: float, stems_per_ha: float, age_years: float | int | AgeMeasurement) → Eko1985Cohort

Build a cohort from primitive numeric inputs.

The site index used by the species’ Ekö 1985 functions is a site property – H100 pine (dm) for pine, H100 spruce (dm) for every other species – taken from the site context, not from the cohort.

gross_volume_increment: StandVolume | float | None = None

hk: float | None = None

qmd_other_species: QuadraticMeanDiameter | None = None

update_from_engine(part: EngineStandPart) → None

Refresh cohort state from an in-file engine part.

volume: StandVolume | None = None

volume_increment: StandVolume | float | None = None

class pyforestry.sweden.systems.eko1985.model.Eko1985Model(site_context: Eko1985SiteContext | None = None, *, default_age: float | None = None)

Bases: GrowthModel

Simulation API adapter for the refactored Eko 1985 stand model.

available_actions() → dict[str, ActionSpec]

Expose thinning actions compatible with aggregate inventory mode.

build_context(stand: Stand, *, site_context: Eko1985SiteContext | None = None, cohort_ages: Mapping[TreeName | str, float] | None = None, default_age: float | None = None, **kwargs: Any) → SimulationContext

Build an aggregate simulation context with site + cohort age metadata.

property component_id: str

Stable identifier for the Eko 1985 stand model.

requirements() → Requirements

Declare aggregate inventory requirements.

property source: SourceReference

Bibliographic provenance for the Eko 1985 stand model.

update_step(ctx: SimulationContext, dt: float) → None

Advance cohorts using the Eko 1985 growth engine.

class pyforestry.sweden.systems.eko1985.model.Eko1985Stand(cohorts: Sequence[Eko1985Cohort], site: Eko1985SiteContext)

Bases: object

pyforestry-primitive friendly wrapper over the refactored Eko 1985 model.

grow(years: float = 5.0, apply_mortality: bool = True) → Mapping[str, list[dict[str, Any]]]

Advance the stand, then return a summary snapshot.

grow5(apply_mortality: bool = True) → Mapping[str, list[dict[str, Any]]]

Five-year convenience wrapper.

snapshot() → Mapping[str, dict]

Return primitive-rich snapshot of the current stand state.

stand_totals() → dict

Return stand-level totals as primitives.

thin(removals: Mapping[TreeName | str, float]) → None

Apply a constant-QMD thinning using the refactored rules.

Keys may be TreeName instances, scientific-name strings, or legacy labels.

volume_for(cohort: Eko1985Cohort, *, basal_area: float | None = None, qmd: float | None = None, age: float | None = None, stems: float | None = None, hk: float | None = None) → StandVolume

Compute volume for a cohort with optional overrides.

pyforestry.sweden.systems.eko1985.site_context module

Extracted site/context helpers for the Ekö 1985 model adapter.

Source:

Ekö, P.-M. (1985). En produktionsmodell för skog i Sverige, baserad på bestånd från riksskogstaxeringens provytor = A growth simulator for Swedish forests, based on data from the national forest survey. Sveriges lantbruksuniversitet, institutionen för skogsskötsel, Rapport nr 16, Umeå.

class pyforestry.sweden.systems.eko1985.site_context.DominantHeightObservation(height_m: float, age: AgeMeasurement)

Bases: object

Observed dominant height at a given age.

classmethod from_values(height_m: float, age: AgeMeasurement | float | int) → DominantHeightObservation

Create an observation from numeric inputs.

class pyforestry.sweden.systems.eko1985.site_context.Eko1985SiteContext(latitude: float | None = None, longitude: float | None = None, altitude: float | None = None, vegetation: SwedenFieldLayer | int | None = None, soil_moisture: SwedenSoilMoisture | int | None = None, fertilised: bool = False, thinned_5y: bool = False, thinned: bool = False, tax77: bool = False, broadleaf_volume_prodmod: bool = False, broadleaf_growth_prodmod: bool = False, spruce_site_index: SiteIndexValue | float | None = None, pine_site_index: SiteIndexValue | float | None = None, spruce_height_obs: DominantHeightObservation | None = None, pine_height_obs: DominantHeightObservation | None = None, pine_regeneration: HagglundPineRegeneration = HagglundPineRegeneration.UNKNOWN, beech_height_obs: DominantHeightObservation | None = None, carbonnier_model: CarbonnierHeightModel | None = <factory>, swedish_site: SwedishSite | None = None)

Bases: object

Site inputs resolved to refactored EkoStandSite using pyforestry primitives.

altitude: float | None = None

beech_height_obs: DominantHeightObservation | None = None

broadleaf_growth_prodmod: bool = False

key the birch/beech/oak/’other’ basal-area-increment SI-class (and beech’s linear SI term) off the stand’s spruce site index (ProdMod2 reads species_values[1] for every broadleaf), and clamp the BAI basal-area/stem inputs to ProdMod2’s diameter limits. False (default) keeps the canonical Ekö 1985 reading – each cohort’s own SI, no input clamping. See EngineStandPart._bai_class_si_dm / _bai_diameter_caps.

Type:

Use ProdMod2’s broadleaf-growth behaviour

broadleaf_volume_prodmod: bool = False

Use ProdMod2’s broadleaf-volume behaviour (the Nord+Mellan function in every region) instead of the canonical Ekö 1985 region-specific Syd functions for birch (Tabell 10c) and ‘other broadleaf’ (Tabell 10f) in the South – ProdMod2’s volume table is region-identical for both. See BroadleafEngineCohort.get_volume.

fertilised: bool = False

pine_height_obs: DominantHeightObservation | None = None

pine_site_index: SiteIndexValue | float | None = None

resolve_site_indices() → tuple[SiteIndexValue | None, SiteIndexValue | None, SiteIndexValue | None]

Return (spruce, pine, beech) site indices at H100.

resolved_region() → str

Resolve model region from coordinates via SwedishSite.

soil_moisture: SwedenSoilMoisture | int | None = None

soil_moisture_code() → int | None

Return soil moisture code compatible with the legacy model.

spruce_height_obs: DominantHeightObservation | None = None

spruce_site_index: SiteIndexValue | float | None = None

swedish_site: SwedishSite | None = None

tax77: bool = False

thinned: bool = False

thinned_5y: bool = False

to_site() → EkoStandSite

Create a refactored EkoStandSite with derived site indices.

vegetation: SwedenFieldLayer | int | None = None

vegetation_code() → int | None

Return vegetation code compatible with the legacy model.

class pyforestry.sweden.systems.eko1985.site_context.EkoStandSite(latitude: float | None = None, altitude: float | None = None, vegetation: int | SwedenFieldLayer | None = None, soil_moisture: int | SwedenSoilMoisture | None = None, H100_Spruce: float | None = None, region: str | RegionSE | None = None, H100_Pine: float | None = None, fertilised: bool = False, thinned_5y: bool = False, thinned: bool = False, tax77: bool = False, broadleaf_volume_prodmod: bool = False, broadleaf_growth_prodmod: bool = False)

Bases: object

Replicates legacy EkoStandSite but uses translate helpers directly.

class pyforestry.sweden.systems.eko1985.site_context.RegionSE(*values)

Bases: Enum

Swedish region categories used by the Eko 1985 model.

MELLERSTA = 'Central'

NORRA = 'North'

SODRA = 'South'

SÖDRA = 'South'

pyforestry.sweden.systems.eko1985.site_context.qmd_cm(ba_m2_per_ha: float, stems_per_ha: float) → float

Quadratic mean diameter (cm) given basal area and stem count.

Module contents

Formula-level extracted helpers for the Eko 1985 model family.