> ## Documentation Index
> Fetch the complete documentation index at: https://pyfia.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# growth
# `pyfia.estimation.estimators.growth`
Growth estimation for FIA data using GRM methodology.
Implements FIA's Growth-Removal-Mortality methodology for calculating
annual tree growth using TREE\_GRM\_COMPONENT, TREE\_GRM\_MIDPT, and
TREE\_GRM\_BEGIN tables following EVALIDator approach.
## Functions
### `growth`
```python theme={null}
growth(db: str | FIA, grp_by: str | list[str] | None = None, by_species: bool = False, by_size_class: bool = False, size_class_type: str = 'standard', land_type: str = 'forest', tree_type: str = 'gs', measure: str = 'volume', tree_domain: str | None = None, area_domain: str | None = None, totals: bool = True, variance: bool = False, most_recent: bool = False) -> pl.DataFrame
```
Estimate annual tree growth from FIA data using GRM methodology.
Calculates annual growth of tree volume, biomass, or tree count using
FIA's Growth-Removal-Mortality (GRM) tables following EVALIDator
methodology.
**Args:**
* `db`: Database connection or path to FIA database.
* `grp_by`: Column name(s) to group results by.
* `by_species`: If True, group results by species code (SPCD).
* `by_size_class`: If True, group results by diameter size classes.
* `size_class_type`: Type of size class grouping to use (only applies when by\_size\_class=True):
* "standard": FIA numeric ranges (1.0-4.9, 5.0-9.9, etc.)
* "descriptive": Text labels (Saplings, Small, Medium, Large)
* "market": Timber market categories (Pulpwood, Chip-n-Saw, Sawtimber)
* `land_type`: Land type to include in estimation.
* `tree_type`: Tree type to include.
* `measure`: What to measure in the growth estimation.
* `tree_domain`: SQL-like filter expression for tree-level filtering.
* `area_domain`: SQL-like filter expression for area/condition-level filtering.
* `totals`: If True, include population-level total estimates.
* `variance`: If True, calculate and include variance and standard error estimates.
* `most_recent`: If True, automatically filter to the most recent evaluation.
**Returns:**
* Growth estimates with columns:
* GROWTH\_ACRE: Annual growth per acre
* GROWTH\_TOTAL: Total annual growth (if totals=True)
* GROWTH\_ACRE\_SE: Standard error of per-acre estimate (if variance=True)
* Additional grouping columns if specified
**Examples:**
Basic volume growth on forestland:
> > > results = growth(db, measure="volume", land\_type="forest")
> > > Growth by species (tree count):
> > > results = growth(db, by\_species=True, measure="count")
## Classes
### `GrowthEstimator`
Growth estimator for FIA data using GRM methodology.
Estimates annual tree growth in terms of volume, biomass, or trees per acre
using the TREE\_GRM\_COMPONENT, TREE\_GRM\_MIDPT, and TREE\_GRM\_BEGIN tables.
Follows EVALIDator methodology with component-based calculations.
**Methods:**
#### `component_type`
```python theme={null}
component_type(self) -> Literal['growth', 'mortality', 'removals']
```
Return 'growth' as the GRM component type.
#### `load_data`
```python theme={null}
load_data(self) -> pl.LazyFrame | None
```
Load and join tables following EVALIDator SQL join sequence.
Growth requires complex join pattern with BEGINEND cross-join.
Cannot use the simple GRM loading pattern.
#### `apply_filters`
```python theme={null}
apply_filters(self, data: pl.LazyFrame) -> pl.LazyFrame
```
Apply growth-specific filters.
CRITICAL: Include ALL components for BEGINEND cross-join methodology.
Do NOT filter by COND\_STATUS\_CD - GRM columns already handle land basis.
#### `calculate_values`
```python theme={null}
calculate_values(self, data: pl.LazyFrame) -> pl.LazyFrame
```
Calculate growth values using BEGINEND ONEORTWO methodology.
Implements EVALIDator's ONEORTWO logic:
* ONEORTWO=2: Add ending volumes (positive contribution)
* ONEORTWO=1: Subtract beginning volumes (negative contribution)
Sum across ONEORTWO rows gives NET growth = ending - beginning
#### `aggregate_results`
```python theme={null}
aggregate_results(self, data: pl.LazyFrame | None) -> AggregationResult
```
Aggregate growth with two-stage aggregation.
**Returns:**
* Bundle containing results, plot\_tree\_data, and group\_cols for
explicit variance calculation.
#### `calculate_variance`
```python theme={null}
calculate_variance(self, agg_result: AggregationResult) -> pl.DataFrame
```
Calculate variance for growth estimates using ratio-of-means formula.
Implements Bechtold & Patterson (2005) stratified variance calculation.
**Args:**
* `agg_result`: Bundle containing results, plot\_tree\_data, and group\_cols from
aggregate\_results().
**Returns:**
* Results with variance columns added.
#### `format_output`
```python theme={null}
format_output(self, results: pl.DataFrame) -> pl.DataFrame
```
Format growth estimation output.