diff --git a/.gitignore b/.gitignore
index 8e211bb8..70d379dd 100755
--- a/.gitignore
+++ b/.gitignore
@@ -165,6 +165,12 @@ data/
 # Documentation build
 site/
 data/
+# Exception: package-bundled NSVB coefficient tables (vendored from GTR-WO-104 Supp1)
+!src/pyfia/carbon/nsvb/data/
+!src/pyfia/carbon/nsvb/data/**
+# Exception: NGHGI report-target CSVs (frozen EPA Chapter 6 published numbers)
+!src/pyfia/carbon/data/
+!src/pyfia/carbon/data/nghgi_*.csv
 
 # JOSS paper (kept separate from repo)
 paper.md
diff --git a/CHANGELOG.md b/CHANGELOG.md
index c6887dc2..6a11cf2f 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -5,6 +5,36 @@ All notable changes to pyFIA will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [Unreleased]
+
+### Added
+- **`live_tree()` function** — NSVB live tree carbon estimation:
+  - Recomputes above-ground biomass from scratch using the NSVB framework (Westfall et al. 2023, GTR-WO-104)
+  - Species-specific S10a carbon fractions (0.40–0.55) replace the flat 0.47 multiplier used by `biomass()`
+  - 3-level coefficient lookup precedence: Bailey DIVISION, species-level, Jenkins fallback
+  - Cull adjustment using Harmon et al. (2011) DECAYCD=3 density proportions
+  - `pool='ag'|'bg'|'total'` — AG via NSVB, BG bridges to FIADB `TREE.CARBON_BG`
+  - Validated against FIADB `TREE.CARBON_AG` on Georgia EVALID 132401 (130,952 trees): median per-tree relative error 0.085%
+- **`standing_dead()` function** — NSVB standing dead carbon estimation:
+  - Same NSVB biomass pipeline as `live_tree()`, plus decay-class reductions from `REF_TREE_DECAY_PROP`
+  - `DENSITY_PROP` x wood, `BARK_LOSS_PROP` x bark, `BRANCH_LOSS_PROP` x branch by hardwood/softwood x DECAYCD
+  - S10b dead-tree carbon fractions by hardwood/softwood x DECAYCD
+  - No `TREE.CULL` adjustment for dead trees (per FIADB Appendix K)
+  - `pool='ag'|'bg'|'total'` — same pool semantics as `live_tree()`
+  - Broken-top corrections: crown-proportion adjustment (Appendix K) + paraboloid volume-ratio for trees with `ACTUALHT < HT`
+  - Vendored Table S11 (`REF_TREE_STND_DEAD_CR_PROP`) for mean intact crown ratios by ecoregion province
+  - Validated against FIADB on Georgia EVALID 132401 (6,870 trees): median 10.9% per-tree relative error
+- **`pyfia.carbon` subpackage** — NSVB equation library, coefficient loaders, carbon fractions:
+  - `pyfia.carbon.nsvb.equations` — Models 1, 2, 4, 5, harmonization, vectorized pipelines
+  - `pyfia.carbon.nsvb.coefficients` — S1a–S8b coefficient tables, Bailey DIVISION lookup
+  - `pyfia.carbon.nsvb.carbon_fractions` — S10a (live), S10b (dead), `REF_TREE_DECAY_PROP` loaders
+  - Vendored coefficient CSVs from GTR-WO-104 supplementary archive
+- **NSVB validation gate** — `tests/validation/test_live_tree_nsvb.py` and `test_standing_dead_nsvb.py`:
+  - Per-tree parity tests against FIADB `CARBON_AG` on real Georgia inventory data
+  - Layered diagnostics: carbon rel-error, biomass ratio, FIADB-implied carbon fraction
+  - Ratchet thresholds that detect regressions and reward improvements
+  - EVALID-scoped to the current annual evaluation (avoids legacy CRM data contamination)
+
 ## [1.2.0] - 2025-01-18
 
 ### Added
diff --git a/CLAUDE.md b/CLAUDE.md
index 482bfd6d..4824bcbb 100755
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -70,6 +70,7 @@ pyfia/
 ├── src/pyfia/           # Library source code
 │   ├── core/            # Database, backends, and settings
 │   ├── estimation/      # Statistical estimation
+│   ├── carbon/          # Carbon estimation (all 6 IPCC/NGHGI pools + total_ecosystem)
 │   ├── filtering/       # Domain filtering
 │   ├── downloader/      # FIA data download from DataMart
 │   ├── evalidator/      # EVALIDator API client for validation
diff --git a/README.md b/README.md
index 083031b8..4fc2a613 100755
--- a/README.md
+++ b/README.md
@@ -33,7 +33,7 @@ pip install pyfia
 ```
 
 ```python
-from pyfia import FIA, biomass, tpa, volume, area
+from pyfia import FIA, tpa, volume, area, total_ecosystem
 
 with FIA("path/to/FIA_database.duckdb") as db:
     db.clip_by_state(37)  # North Carolina
@@ -41,9 +41,11 @@ with FIA("path/to/FIA_database.duckdb") as db:
 
     # Core estimates
     trees = tpa(db, tree_domain="STATUSCD == 1")
-    carbon = biomass(db, by_species=True)
     timber = volume(db, land_type="timber")
     forest = area(db, land_type="forest")
+
+    # Total ecosystem carbon (all 6 IPCC/NGHGI pools)
+    carbon = total_ecosystem(db)
 ```
 
 ## Core Functions
@@ -54,6 +56,14 @@ with FIA("path/to/FIA_database.duckdb") as db:
 | `biomass()` | Above/belowground biomass | `biomass(db, by_species=True)` |
 | `volume()` | Merchantable volume (ft³) | `volume(db, land_type="timber")` |
 | `area()` | Forest land area | `area(db, grp_by="FORTYPCD")` |
+| `live_tree()` | NSVB live tree carbon | `live_tree(db, pool="ag")` |
+| `standing_dead()` | NSVB standing dead carbon | `standing_dead(db, pool="ag")` |
+| `understory()` | Understory vegetation carbon | `understory(db, pool="total")` |
+| `downed_dead()` | Downed dead wood carbon | `downed_dead(db)` |
+| `litter()` | Litter carbon | `litter(db)` |
+| `soil_organic()` | Soil organic carbon | `soil_organic(db)` |
+| `total_ecosystem()` | Total ecosystem carbon (all 6 pools) | `total_ecosystem(db)` |
+| `stock_change()` | Carbon stock change between periods | `stock_change(db, pool="all")` |
 | `site_index()` | Site productivity index | `site_index(db, grp_by="COUNTYCD")` |
 | `mortality()` | Annual mortality rates | `mortality(db)` |
 | `growth()` | Net growth estimation | `growth(db)` |
diff --git a/docs/DEVELOPMENT.md b/docs/DEVELOPMENT.md
index 7cc7827d..f43fd54a 100644
--- a/docs/DEVELOPMENT.md
+++ b/docs/DEVELOPMENT.md
@@ -47,6 +47,17 @@ pyfia/
 │   ├── fia.py         # Main FIA database class
 │   ├── data_reader.py # Efficient data loading
 │   └── backends/      # DuckDB, SQLite, MotherDuck backends
+├── carbon/             # NSVB carbon estimation
+│   ├── live_tree.py   # Live tree carbon (NSVB AG + FIADB BG bridge)
+│   ├── standing_dead.py # Standing dead carbon (NSVB + decay reductions)
+│   ├── understory.py  # Understory vegetation carbon (condition-level)
+│   ├── downed_dead.py # Downed dead wood carbon (condition-level)
+│   ├── litter.py      # Litter carbon (condition-level)
+│   ├── soil_organic.py # Soil organic carbon (condition-level)
+│   ├── total_ecosystem.py # Sum of all 6 pools
+│   ├── stock_change.py # Carbon stock change between inventory periods
+│   ├── data/          # Non-NSVB coefficient tables (Birdsey/Smith & Heath)
+│   └── nsvb/          # NSVB equation library, coefficients, carbon fractions
 ├── estimation/         # Statistical estimation
 │   ├── base.py        # BaseEstimator with Template Method pattern
 │   ├── grm.py         # GRM data loading and adjustment
@@ -85,7 +96,7 @@ pyfia/
 - Key methods: `clip_by_evalid()`, `clip_by_state()`, `clip_most_recent()`
 
 **Estimation Functions**
-- Simple API: `area()`, `biomass()`, `volume()`, `tpa()`, `mortality()`, `growth()`, `removals()`, `area_change()`, `site_index()`, `tree_metrics()`, `carbon_pools()`
+- Simple API: `area()`, `biomass()`, `volume()`, `tpa()`, `live_tree()`, `standing_dead()`, `understory()`, `downed_dead()`, `litter()`, `soil_organic()`, `total_ecosystem()`, `stock_change()`, `mortality()`, `growth()`, `removals()`, `area_change()`, `site_index()`, `tree_metrics()`
 - All support domain filtering, grouping, variance calculations
 - BaseEstimator uses Template Method for consistent workflow
 
diff --git a/docs/api/index.md b/docs/api/index.md
index dc79ee2e..996dadf0 100644
--- a/docs/api/index.md
+++ b/docs/api/index.md
@@ -25,6 +25,8 @@ result = estimator(db, **options)  # Returns pl.DataFrame
 | [`volume()`](volume.md) | Estimate tree volume |
 | [`tpa()`](tpa.md) | Trees per acre and basal area |
 | [`biomass()`](biomass.md) | Tree biomass and carbon |
+| [`live_tree()`](live_tree.md) | NSVB live tree carbon |
+| [`standing_dead()`](standing_dead.md) | NSVB standing dead carbon |
 | [`site_index()`](site_index.md) | Area-weighted mean site index |
 | [`mortality()`](mortality.md) | Annual tree mortality |
 | [`growth()`](growth.md) | Annual tree growth |
diff --git a/docs/api/live_tree.md b/docs/api/live_tree.md
new file mode 100644
index 00000000..acb14481
--- /dev/null
+++ b/docs/api/live_tree.md
@@ -0,0 +1,119 @@
+# Live Tree Carbon Estimation
+
+Estimate live tree carbon using the NSVB biomass framework with species-specific carbon fractions.
+
+## Overview
+
+The `live_tree()` function recomputes above-ground live tree biomass from scratch using the National Scale Volume and Biomass (NSVB) framework of Westfall et al. (2023, GTR-WO-104) and converts to carbon via species-specific S10a carbon fractions. This produces carbon estimates that align with the EPA NGHGI LULUCF live tree pool and match FIADB's pre-computed `CARBON_AG` column for NSVB-era inventories (September 2023 onward).
+
+```python
+import pyfia
+
+db = pyfia.FIA("georgia.duckdb")
+db.clip_by_state("GA")
+db.clip_most_recent(eval_type="VOL")
+
+# Above-ground live tree carbon
+result = pyfia.live_tree(db, pool="ag")
+
+# Total carbon (AG + BG bridge)
+total = pyfia.live_tree(db, pool="total")
+```
+
+## Function Reference
+
+::: pyfia.live_tree
+    options:
+      show_root_heading: true
+      show_source: true
+
+## Carbon Pools
+
+| Pool | Description | Method |
+|------|-------------|--------|
+| `"ag"` | Above-ground (default) | NSVB pipeline: stem wood + bark + branches, harmonized to total AGB, then multiplied by species-specific S10a carbon fractions |
+| `"bg"` | Below-ground (coarse roots) | Bridge to FIADB `TREE.CARBON_BG` (Phase 1 shortcut; native NSVB root model planned) |
+| `"total"` | AG + BG | NSVB above-ground + FIADB below-ground bridge |
+
+## How It Differs from `biomass()`
+
+| | `live_tree()` | `biomass()` |
+|---|---|---|
+| **Biomass source** | Recomputed from scratch via NSVB equations | Reads FIADB pre-computed `DRYBIO_*` columns |
+| **Carbon fraction** | Species-specific S10a (0.40-0.55) | Flat 0.47 multiplier |
+| **Coefficient lookup** | 3-level precedence (DIVISION, species, Jenkins) | N/A (pre-computed) |
+| **Cull adjustment** | NSVB cull formula with DECAYCD=3 density prop | Built into FIADB values |
+| **Transparency** | Full recompute, auditable | Black-box FIADB values |
+
+For NSVB-era inventories (2024+), both should agree closely. `live_tree()` is the preferred path for carbon accounting work that needs methodological transparency.
+
+## Technical Notes
+
+The NSVB pipeline predicts five biomass components per tree:
+
+1. Stem inside-bark wood volume (S1a) x wood density x 62.4 = gross wood weight
+2. Stem bark biomass (S6a)
+3. Branch biomass (S7a)
+4. Total above-ground biomass (S8a) - directly predicted
+
+The component sum is harmonized proportionally to the directly-predicted total AGB. Cull-reduced wood uses the Harmon et al. (2011) DECAYCD=3 density proportion (0.54 hardwood, 0.92 softwood). Carbon = harmonized AGB x species-specific S10a fraction.
+
+The optional `PLOTGEOM.ECOSUBCD` join activates Level 2 of the NSVB coefficient precedence (SPCD + Bailey DIVISION), closing a ~3% growing-stock biomass bias present in the species-level-only fallback. When `PLOTGEOM` is missing from older databases, the estimator falls back gracefully with a one-shot log warning.
+
+## Examples
+
+### Above-Ground Carbon Per Acre
+
+```python
+result = pyfia.live_tree(db, pool="ag")
+print(f"Carbon: {result['CARBON_ACRE'][0]:.2f} tons/acre")
+```
+
+### Carbon by Species
+
+```python
+result = pyfia.live_tree(db, pool="ag", by_species=True)
+result = pyfia.join_species_names(result, db)
+print(result.sort("CARBON_ACRE", descending=True).head(10))
+```
+
+### Carbon by Ownership Group
+
+```python
+result = pyfia.live_tree(
+    db,
+    pool="total",
+    grp_by="OWNGRPCD",
+    totals=True,
+    variance=True,
+)
+# OWNGRPCD: 10=National Forest, 20=Other Federal,
+#           30=State/Local, 40=Private
+print(result)
+```
+
+### Large Tree Carbon by Forest Type
+
+```python
+result = pyfia.live_tree(
+    db,
+    pool="ag",
+    grp_by="FORTYPCD",
+    tree_domain="DIA >= 20.0",
+)
+result = pyfia.join_forest_type_names(result, db)
+print(result)
+```
+
+### Carbon on Timberland with Standard Errors
+
+```python
+result = pyfia.live_tree(
+    db,
+    pool="ag",
+    land_type="timber",
+    variance=True,
+)
+print(f"Carbon: {result['CARBON_ACRE'][0]:.2f} +/- "
+      f"{result['CARBON_ACRE_SE'][0]:.2f} tons/acre")
+```
diff --git a/docs/api/standing_dead.md b/docs/api/standing_dead.md
new file mode 100644
index 00000000..290ca637
--- /dev/null
+++ b/docs/api/standing_dead.md
@@ -0,0 +1,152 @@
+# Standing Dead Carbon Estimation
+
+Estimate standing dead tree carbon using the NSVB biomass framework with decay-class reductions and dead-tree carbon fractions.
+
+## Overview
+
+The `standing_dead()` function recomputes above-ground standing dead tree biomass from scratch using the National Scale Volume and Biomass (NSVB) framework and applies the FIADB `REF_TREE_DECAY_PROP` density and structural-loss reductions by decay class. The reduced biomass is converted to carbon via S10b dead-tree carbon fractions (hardwood/softwood x DECAYCD). This produces carbon estimates that align with the EPA NGHGI LULUCF standing dead pool.
+
+```python
+import pyfia
+
+db = pyfia.FIA("georgia.duckdb")
+db.clip_by_state("GA")
+db.clip_most_recent(eval_type="VOL")
+
+# Above-ground standing dead carbon
+result = pyfia.standing_dead(db, pool="ag")
+
+# Standing dead carbon by decay class
+by_decay = pyfia.standing_dead(db, pool="ag", grp_by="DECAYCD")
+```
+
+## Function Reference
+
+::: pyfia.standing_dead
+    options:
+      show_root_heading: true
+      show_source: true
+
+## Carbon Pools
+
+| Pool | Description | Method |
+|------|-------------|--------|
+| `"ag"` | Above-ground (default) | NSVB pipeline with REF_TREE_DECAY_PROP reductions + S10b dead carbon fractions |
+| `"bg"` | Below-ground (coarse roots) | Bridge to FIADB `TREE.CARBON_BG` (same as live tree) |
+| `"total"` | AG + BG | NSVB dead above-ground + FIADB below-ground bridge |
+
+## Decay-Class Reductions
+
+Standing dead trees lose mass through decomposition. The FIADB `REF_TREE_DECAY_PROP` table provides three multiplicative reduction factors applied to each gross NSVB component by hardwood/softwood classification and decay class (1-5):
+
+| Factor | Applied to | Description |
+|--------|-----------|-------------|
+| `DENSITY_PROP` | Stem wood | Fraction of wood biomass remaining after density loss |
+| `BARK_LOSS_PROP` | Stem bark | Fraction of bark biomass remaining |
+| `BRANCH_LOSS_PROP` | Branches | Fraction of branch biomass remaining |
+
+### Reduction Factors by Decay Class
+
+| Decay Class | Description | HW Density | HW Bark | HW Branch | SW Density | SW Bark | SW Branch |
+|:-----------:|-------------|:----------:|:-------:|:---------:|:----------:|:-------:|:---------:|
+| 1 | All limbs present, intact | 0.99 | 1.00 | 1.00 | 0.97 | 1.00 | 1.00 |
+| 2 | Few limbs, no fine branches | 0.80 | 0.80 | 0.50 | 1.00 | 0.80 | 0.50 |
+| 3 | Limb stubs only, top broken | 0.54 | 0.50 | 0.10 | 0.92 | 0.50 | 0.10 |
+| 4 | Few stubs, top broken | 0.43 | 0.20 | 0.00 | 0.55 | 0.20 | 0.00 |
+| 5 | No limbs, <20% bark | 0.43 | 0.00 | 0.00 | 0.55 | 0.00 | 0.00 |
+
+Per FIADB User Guide v9.1 Appendix K, `TREE.CULL` is **not** applied to standing dead tree biomass. The decay reductions above are the only mass adjustments.
+
+## Dead Carbon Fractions (S10b)
+
+Unlike live trees (which use per-species S10a fractions), dead tree carbon fractions come from S10b and vary only by hardwood/softwood and decay class:
+
+| Decay Class | Hardwood | Softwood |
+|:-----------:|:--------:|:--------:|
+| 1 | 0.470 | 0.501 |
+| 2 | 0.473 | 0.504 |
+| 3 | 0.481 | 0.506 |
+| 4 | 0.480 | 0.520 |
+| 5 | 0.472 | 0.527 |
+
+## Population Filter
+
+The standing-dead population is automatically filtered as:
+
+- `STATUSCD = 2` (dead tree)
+- `STANDING_DEAD_CD = 1` (standing, not downed)
+- `DECAYCD IS NOT NULL` (required for the decay-proportion lookup)
+- `DIA >= 1.0` (NSVB floor)
+
+Trees with `STANDING_DEAD_CD = 0` (downed dead) belong to the down dead wood pool and are excluded.
+
+## Broken-Top Corrections
+
+Approximately 75% of standing dead trees have broken tops (`ACTUALHT < HT`). The pipeline applies two adjustments for these trees per FIADB User Guide v9.1 Appendix K:
+
+| Adjustment | Component | Formula |
+|-----------|-----------|---------|
+| Crown proportion | Branch biomass | `Broken_crn_prop = max(0, (ACTUALHT - (1 - CRprop_HT) * HT) / (CRprop_HT * HT))` |
+| Volume ratio | Wood & bark | `(ACTUALHT / HT) ^ (2/3)` — paraboloid taper approximation |
+
+The mean intact crown ratio (`CR_MEAN`) is looked up from Table S11 (`REF_TREE_STND_DEAD_CR_PROP`) by Bailey ecoregion province and hardwood/softwood classification. When the province is unknown, the UNDEFINED fallback (softwood: 46.8%, hardwood: 38.0%) is used.
+
+The volume-ratio adjustment uses a paraboloid taper exponent (2/3) rather than FIADB's Model 6 (Schumacher-Hall) volume-ratio model, which is not implemented. This approximation accounts for the fact that the wider lower stem contains a disproportionately large fraction of total stem volume.
+
+## Technical Notes
+
+## Examples
+
+### Standing Dead Carbon Per Acre
+
+```python
+result = pyfia.standing_dead(db, pool="ag")
+print(f"SD Carbon: {result['CARBON_ACRE'][0]:.2f} tons/acre")
+```
+
+### Carbon by Decay Class
+
+```python
+result = pyfia.standing_dead(
+    db,
+    pool="ag",
+    grp_by="DECAYCD",
+)
+for row in result.iter_rows(named=True):
+    print(f"Decay {row['DECAYCD']}: {row['CARBON_ACRE']:.3f} tons/acre")
+```
+
+### Carbon by Species
+
+```python
+result = pyfia.standing_dead(db, pool="ag", by_species=True)
+result = pyfia.join_species_names(result, db)
+print(result.sort("CARBON_ACRE", descending=True).head(10))
+```
+
+### Large Snag Carbon by Ownership
+
+```python
+result = pyfia.standing_dead(
+    db,
+    pool="ag",
+    grp_by="OWNGRPCD",
+    tree_domain="DIA >= 20.0",
+    totals=True,
+)
+print(result)
+```
+
+### Total Standing Dead Carbon with Variance
+
+```python
+result = pyfia.standing_dead(
+    db,
+    pool="total",
+    land_type="forest",
+    variance=True,
+    totals=True,
+)
+print(f"SD Carbon: {result['CARBON_TOTAL'][0]:,.0f} +/- "
+      f"{result['CARBON_TOTAL_SE'][0]:,.0f} tons")
+```
diff --git a/docs/getting-started.md b/docs/getting-started.md
index e188f820..19a605b3 100644
--- a/docs/getting-started.md
+++ b/docs/getting-started.md
@@ -124,6 +124,24 @@ volume_with_names = pyfia.join_species_names(volume, db)
 print(volume_with_names)
 ```
 
+### 5. Carbon Estimation (NSVB)
+
+For NGHGI-aligned carbon accounting, use `live_tree()` and `standing_dead()` — these recompute biomass from scratch using the NSVB framework with species-specific carbon fractions:
+
+```python
+import pyfia
+
+# Above-ground live tree carbon
+live_c = pyfia.live_tree(db, pool="ag")
+print(f"Live AG carbon: {live_c['CARBON_ACRE'][0]:.2f} tons/acre")
+
+# Standing dead carbon by decay class
+sd_c = pyfia.standing_dead(db, pool="ag", grp_by="DECAYCD")
+print(sd_c)
+```
+
+See [live_tree()](api/live_tree.md) and [standing_dead()](api/standing_dead.md) for details.
+
 ## Complete Example
 
 ```python
diff --git a/docs/index.md b/docs/index.md
index 58aba996..724d96eb 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -106,6 +106,8 @@ with FIA(db_path) as db:
 | [`volume()`](api/volume.md) | Estimate standing tree volume |
 | [`tpa()`](api/tpa.md) | Calculate trees per acre and basal area |
 | [`biomass()`](api/biomass.md) | Estimate tree biomass and carbon |
+| [`live_tree()`](api/live_tree.md) | NSVB live tree carbon (species-specific fractions) |
+| [`standing_dead()`](api/standing_dead.md) | NSVB standing dead carbon (decay-class reductions) |
 | [`site_index()`](api/site_index.md) | Estimate area-weighted mean site index |
 | [`mortality()`](api/mortality.md) | Calculate annual mortality rates |
 | [`growth()`](api/growth.md) | Estimate annual growth |
diff --git a/mkdocs.yml b/mkdocs.yml
index 150bc693..18196246 100755
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -115,6 +115,8 @@ nav:
       - Volume: api/volume.md
       - Trees Per Acre: api/tpa.md
       - Biomass: api/biomass.md
+      - Live Tree Carbon: api/live_tree.md
+      - Standing Dead Carbon: api/standing_dead.md
       - Site Index: api/site_index.md
       - Mortality: api/mortality.md
       - Growth: api/growth.md
diff --git a/pyproject.toml b/pyproject.toml
index 0aa62a7a..b71799c7 100755
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -103,6 +103,10 @@ package-dir = {"" = "src"}
 where = ["src"]
 include = ["pyfia*"]
 
+[tool.setuptools.package-data]
+# Vendored NSVB coefficient tables (GTR-WO-104 Supp1) — load via importlib.resources
+"pyfia.carbon.nsvb.data" = ["*.csv", "*.md"]
+
 [tool.ruff]
 line-length = 88
 target-version = "py311"
diff --git a/scripts/nghgi/__init__.py b/scripts/nghgi/__init__.py
new file mode 100644
index 00000000..286c5fb5
--- /dev/null
+++ b/scripts/nghgi/__init__.py
@@ -0,0 +1,18 @@
+"""NGHGI report reproduction utilities (not part of the pyfia public API).
+
+These modules live under ``scripts/`` rather than ``pyfia/`` because they
+exist to reproduce EPA Chapter 6 / Annex 3.13 forest carbon tables, not
+to provide new estimation capability. The pyfia carbon estimators
+themselves stay in ``pyfia.carbon``.
+
+Stages
+------
+- ``stage_a``                — reproduce EPA Table 6-10 forest carbon stocks
+- ``stage_b``                — state-level flux vs EPA Annex 3.13 Table A-208
+- ``multi_year``             — multi-year stock comparison 2019-2023
+- ``dead_wood_diagnostic``   — isolate the NSVB-vs-FIADB standing-dead gap
+- ``_compile``               — pool aggregation helper used by the above
+
+All scripts read state DuckDB files from a directory configured via
+``--db-dir`` or the ``PYFIA_FIADB_DIR`` environment variable.
+"""
diff --git a/scripts/nghgi/_compile.py b/scripts/nghgi/_compile.py
new file mode 100644
index 00000000..72290a96
--- /dev/null
+++ b/scripts/nghgi/_compile.py
@@ -0,0 +1,383 @@
+"""
+NGHGI pool aggregation — EPA Chapter 6 LULUCF.
+
+The EPA NGHGI report (Inventory of U.S. GHG Emissions and Sinks) reports forest
+ecosystem carbon under six pools whose decomposition does NOT line up 1:1 with
+pyFIA's pool estimators:
+
+    EPA pool                    pyFIA module(s)
+    --------                    ---------------
+    Aboveground Biomass         live_tree(pool='ag') + understory(pool='ag')
+    Belowground Biomass         live_tree(pool='bg') + understory(pool='bg')
+    Dead Wood                   standing_dead(pool='total') + downed_dead
+    Litter                      litter
+    Soil (Mineral)              soil_organic        [Domke 2017 mineral soil model]
+    Soil (Organic)              not reproducible    [Histosols, IPCC defaults]
+
+This module is a *report-reproduction helper*. It belongs under ``scripts/``,
+not in the pyfia public API, because it bakes in EPA Chapter 6 column
+semantics and lives only to validate pyfia's carbon estimators against the
+published NGHGI tables.
+
+Units
+-----
+pyFIA pool estimators return short tons.  EPA Table 6-10 reports MMT C
+(million metric tons of carbon).  Conversion: 1 short ton = 0.907185 metric
+tons.
+
+References
+----------
+- USEPA (2024). Chapter 6 LULUCF, Tables 6-9 and 6-10.
+- Westfall et al. (2023) GTR-WO-104 (NSVB).
+- Domke et al. (2013, 2016, 2017) for downed dead, litter, soil.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Iterable
+
+import polars as pl
+
+from pyfia.core import FIA
+
+SHORT_TONS_PER_METRIC_TON = 1.0 / 0.907185
+METRIC_TONS_PER_SHORT_TON = 0.907185
+MMT_C_PER_SHORT_TON = METRIC_TONS_PER_SHORT_TON / 1_000_000
+
+_DATA_DIR = Path(__file__).parent / "data"
+
+
+EPA_POOLS = (
+    "AGB",
+    "BGB",
+    "DEAD_WOOD",
+    "LITTER",
+    "SOIL_MINERAL",
+    "SOIL_ORGANIC",
+    "FOREST_ECOSYSTEM",
+)
+
+
+def _short_tons_to_mmt_c(short_tons: float) -> float:
+    """Convert short tons of carbon to million metric tons (MMT C)."""
+    return short_tons * MMT_C_PER_SHORT_TON
+
+
+def compile_state_stocks(
+    db: str | FIA,
+    *,
+    evalid: int | None = None,
+    state_label: str | None = None,
+    mode: str = "fiadb",
+) -> pl.DataFrame:
+    """
+    Compute EPA-pool-aggregated carbon stocks for one state's evaluation.
+
+    Runs six carbon pool estimators against the supplied EVALID, then
+    re-aggregates them into the six EPA NGHGI pools (plus Forest Ecosystem
+    total) used in EPA Chapter 6 Table 6-10.
+
+    Parameters
+    ----------
+    db : str or FIA
+        Path or open FIA instance.  If a path is given, the connection is
+        opened, used, and closed by this function.
+    evalid : int, optional
+        EVALID to clip to.  If None, the caller is expected to have already
+        clipped the FIA instance.
+    state_label : str, optional
+        Free-form label propagated into the output (e.g. "Georgia",
+        state postal code, FIPS).  Useful when stacking multiple states.
+    mode : {'fiadb', 'nsvb'}, default 'fiadb'
+        Tree-level carbon source.  ``'fiadb'`` reads FIADB-stored
+        ``TREE.CARBON_AG`` and ``TREE.CARBON_BG`` via
+        ``pyfia.estimation.estimators.carbon_pool`` — this is the path
+        EPA's report ultimately consumes and is the correct choice for
+        report reproduction (especially for pre-2023 EVALIDs which were
+        compiled before the NSVB framework transition). ``'nsvb'`` uses
+        pyFIA's NSVB-recomputation pool estimators
+        (``pyfia.carbon.live_tree`` / ``standing_dead``); use this only
+        for methodology comparison or post-2023 inventories.
+
+    Returns
+    -------
+    pl.DataFrame
+        One row per EPA pool (AGB, BGB, DEAD_WOOD, LITTER, SOIL_MINERAL,
+        FOREST_ECOSYSTEM).  Columns:
+
+        - STATE: state_label (if provided)
+        - EPA_POOL: EPA pool name
+        - STOCK_SHORT_TONS: population total in short tons of C
+        - STOCK_MMT_C: same total converted to million metric tons C
+        - STOCK_PER_ACRE_ST: short tons per acre
+        - N_PLOTS: plot count from the underlying estimator(s)
+        - SOURCE: which estimator(s) contributed
+
+        SOIL_ORGANIC is NOT included — it is not reproducible from FIADB
+        attributes alone (EPA uses IPCC defaults applied to Histosol plots).
+        Callers comparing to EPA totals must add it separately from
+        ``nghgi_2024_table_6_10.csv``.
+
+    Notes
+    -----
+    Forest-remaining-forest only is approximated via ``land_type='forest'``
+    in each estimator.  This includes both forest-remaining-forest and
+    land-converted-to-forest in EPA's CRT taxonomy.  EPA Section 6.2
+    excludes the latter; reproducing that split is Phase 2 work.
+    """
+    from pyfia.carbon.downed_dead import downed_dead
+    from pyfia.carbon.litter import litter
+    from pyfia.carbon.soil_organic import soil_organic
+    from pyfia.carbon.understory import understory
+    from pyfia.estimation.estimators.carbon_pools import carbon_pool
+
+    mode = mode.lower()
+    if mode not in ("fiadb", "nsvb"):
+        raise ValueError(f"mode must be 'fiadb' or 'nsvb', got {mode!r}")
+
+    if isinstance(db, str):
+        owns = True
+        fia = FIA(db)
+        fia.__enter__()
+    else:
+        owns = False
+        fia = db
+
+    try:
+        if evalid is not None:
+            fia.clip_by_evalid(evalid)
+
+        common = dict(land_type="forest", totals=True, variance=False)
+
+        if mode == "fiadb":
+            live_ag = carbon_pool(fia, pool="ag", tree_type="live", **common)
+            live_bg = carbon_pool(fia, pool="bg", tree_type="live", **common)
+            std_dead = carbon_pool(fia, pool="total", tree_type="dead", **common)
+            tree_source = "FIADB CARBON_AG/BG via carbon_pool"
+        else:  # mode == "nsvb"
+            from pyfia.carbon.live_tree import live_tree
+            from pyfia.carbon.standing_dead import standing_dead
+
+            live_ag = live_tree(fia, pool="ag", **common)
+            live_bg = live_tree(fia, pool="bg", **common)
+            std_dead = standing_dead(fia, pool="total", **common)
+            tree_source = "NSVB recomputation"
+
+        und_ag = understory(fia, pool="ag", **common)
+        und_bg = understory(fia, pool="bg", **common)
+        dwn_dead = downed_dead(fia, **common)
+        lit = litter(fia, **common)
+        soil = soil_organic(fia, **common)
+
+        def _stock(df: pl.DataFrame, label: str) -> tuple[float, float, int]:
+            """Extract (total, per-acre, n_plots) from a pool result.
+
+            Empty results from a pool estimator are reported, not silently
+            coerced to zero — this prevents an upstream filter that removes
+            all plots from masquerading as a true zero stock.
+            """
+            if df is None or len(df) == 0:
+                raise ValueError(
+                    f"Pool estimator {label!r} returned an empty result "
+                    "(no plots after stratification). The EPA pool rollup "
+                    "would be misleading; fix the filter or EVALID."
+                )
+            total = (
+                float(df["CARBON_TOTAL"][0]) if "CARBON_TOTAL" in df.columns else 0.0
+            )
+            acre = float(df["CARBON_ACRE"][0]) if "CARBON_ACRE" in df.columns else 0.0
+            n = int(df["N_PLOTS"][0]) if "N_PLOTS" in df.columns else 0
+            return total, acre, n
+
+        lt_ag_t, lt_ag_a, n_lt = _stock(live_ag, "live_tree(ag)")
+        lt_bg_t, lt_bg_a, _ = _stock(live_bg, "live_tree(bg)")
+        sd_t, sd_a, n_sd = _stock(std_dead, "standing_dead")
+        u_ag_t, u_ag_a, n_u = _stock(und_ag, "understory(ag)")
+        u_bg_t, u_bg_a, _ = _stock(und_bg, "understory(bg)")
+        dd_t, dd_a, n_dd = _stock(dwn_dead, "downed_dead")
+        li_t, li_a, n_li = _stock(lit, "litter")
+        sm_t, sm_a, n_sm = _stock(soil, "soil_organic")
+
+        agb_t = lt_ag_t + u_ag_t
+        bgb_t = lt_bg_t + u_bg_t
+        dw_t = sd_t + dd_t
+        eco_t = agb_t + bgb_t + dw_t + li_t + sm_t
+
+        agb_a = lt_ag_a + u_ag_a
+        bgb_a = lt_bg_a + u_bg_a
+        dw_a = sd_a + dd_a
+        eco_a = agb_a + bgb_a + dw_a + li_a + sm_a
+
+        rows = [
+            (
+                "AGB",
+                agb_t,
+                agb_a,
+                max(n_lt, n_u),
+                f"live[{tree_source}](ag) + understory(ag)",
+            ),
+            (
+                "BGB",
+                bgb_t,
+                bgb_a,
+                max(n_lt, n_u),
+                f"live[{tree_source}](bg) + understory(bg)",
+            ),
+            (
+                "DEAD_WOOD",
+                dw_t,
+                dw_a,
+                max(n_sd, n_dd),
+                f"standing_dead[{tree_source}](total) + downed_dead",
+            ),
+            ("LITTER", li_t, li_a, n_li, "litter"),
+            ("SOIL_MINERAL", sm_t, sm_a, n_sm, "soil_organic [Domke 2017 mineral]"),
+            (
+                "FOREST_ECOSYSTEM",
+                eco_t,
+                eco_a,
+                max(n_lt, n_sm),
+                "AGB+BGB+DEAD_WOOD+LITTER+SOIL_MINERAL",
+            ),
+        ]
+
+        df = pl.DataFrame(
+            {
+                "EPA_POOL": [r[0] for r in rows],
+                "STOCK_SHORT_TONS": [r[1] for r in rows],
+                "STOCK_MMT_C": [_short_tons_to_mmt_c(r[1]) for r in rows],
+                "STOCK_PER_ACRE_ST": [r[2] for r in rows],
+                "N_PLOTS": [r[3] for r in rows],
+                "SOURCE": [r[4] for r in rows],
+            }
+        )
+
+        if state_label is not None:
+            df = df.with_columns(pl.lit(state_label).alias("STATE"))
+            df = df.select(["STATE", *[c for c in df.columns if c != "STATE"]])
+
+        return df
+
+    finally:
+        if owns:
+            fia.__exit__(None, None, None)
+
+
+def compile_conus_stocks(
+    state_evalids: Iterable[tuple[str, str, int]],
+) -> pl.DataFrame:
+    """
+    Compile per-state EPA-pool stocks across multiple state databases.
+
+    Iterates over state databases, runs :func:`compile_state_stocks` on
+    each, and concatenates results.  Caller is responsible for picking the
+    correct EVALID per state (e.g. most recent annual VOL evaluation
+    covering the report's reference year).
+
+    Parameters
+    ----------
+    state_evalids : iterable of (state_label, db_path, evalid)
+        One tuple per state.  ``state_label`` is propagated into the
+        ``STATE`` column.
+
+    Returns
+    -------
+    pl.DataFrame
+        Long-format frame with columns ``STATE``, ``EPA_POOL``,
+        ``STOCK_SHORT_TONS``, ``STOCK_MMT_C``, ``STOCK_PER_ACRE_ST``,
+        ``N_PLOTS``, ``SOURCE``.  Use ``group_by('EPA_POOL').agg(...)``
+        downstream to roll up to CONUS totals.
+    """
+    frames: list[pl.DataFrame] = []
+    for state_label, db_path, evalid in state_evalids:
+        df = compile_state_stocks(db_path, evalid=evalid, state_label=state_label)
+        frames.append(df)
+    if not frames:
+        return pl.DataFrame()
+    return pl.concat(frames, how="vertical_relaxed")
+
+
+def load_published_targets(year: int = 2022) -> pl.DataFrame:
+    """
+    Load EPA Chapter 6 Table 6-10 published carbon stocks for a given year.
+
+    Returns the published per-pool MMT C target values for comparison with
+    pyFIA-computed totals.  Available years: 1990, 2005, 2019, 2020, 2021,
+    2022, 2023.
+
+    Parameters
+    ----------
+    year : int
+        Reporting year matching a column in EPA Table 6-10.
+
+    Returns
+    -------
+    pl.DataFrame
+        Columns: EPA_POOL, YEAR, STOCK_MMT_C, NOTE.
+    """
+    df = pl.read_csv(_DATA_DIR / "nghgi_2024_table_6_10.csv")
+    df = df.rename(
+        {
+            "epa_pool": "EPA_POOL",
+            "year": "YEAR",
+            "stock_mmt_c": "STOCK_MMT_C",
+            "note": "NOTE",
+        }
+    )
+    return df.filter(pl.col("YEAR") == year)
+
+
+def load_state_flux_targets() -> pl.DataFrame:
+    """Load EPA Annex 3.13 Table A-208 state-level flux (2022)."""
+    return pl.read_csv(_DATA_DIR / "nghgi_2024_table_a_208_state_flux_2022.csv")
+
+
+def compare_to_published(
+    pyfia_stocks: pl.DataFrame,
+    *,
+    year: int = 2022,
+) -> pl.DataFrame:
+    """
+    Side-by-side compare a pyFIA stocks rollup to the EPA published targets.
+
+    Parameters
+    ----------
+    pyfia_stocks : pl.DataFrame
+        Output of :func:`compile_state_stocks` (single state) or a CONUS
+        rollup with one row per ``EPA_POOL`` and a ``STOCK_MMT_C`` column.
+    year : int
+        EPA reporting year to compare against.
+
+    Returns
+    -------
+    pl.DataFrame
+        Columns: EPA_POOL, PYFIA_MMT_C, EPA_MMT_C, ABS_DIFF_MMT_C,
+        PCT_DIFF.  Useful for quick eyeballing of where the rollup
+        agrees vs diverges.
+    """
+    if "STATE" in pyfia_stocks.columns:
+        rollup = (
+            pyfia_stocks.group_by("EPA_POOL")
+            .agg(pl.col("STOCK_MMT_C").sum())
+            .rename({"STOCK_MMT_C": "PYFIA_MMT_C"})
+        )
+    else:
+        rollup = pyfia_stocks.select(["EPA_POOL", "STOCK_MMT_C"]).rename(
+            {"STOCK_MMT_C": "PYFIA_MMT_C"}
+        )
+
+    targets = (
+        load_published_targets(year=year)
+        .select(["EPA_POOL", "STOCK_MMT_C"])
+        .rename({"STOCK_MMT_C": "EPA_MMT_C"})
+    )
+
+    merged = rollup.join(targets, on="EPA_POOL", how="full", coalesce=True)
+    merged = merged.with_columns(
+        (pl.col("PYFIA_MMT_C") - pl.col("EPA_MMT_C")).alias("ABS_DIFF_MMT_C"),
+        (
+            100.0 * (pl.col("PYFIA_MMT_C") - pl.col("EPA_MMT_C")) / pl.col("EPA_MMT_C")
+        ).alias("PCT_DIFF"),
+    )
+    return merged.sort("EPA_POOL")
diff --git a/scripts/nghgi/_paths.py b/scripts/nghgi/_paths.py
new file mode 100644
index 00000000..38b0930b
--- /dev/null
+++ b/scripts/nghgi/_paths.py
@@ -0,0 +1,35 @@
+"""Shared path resolution for NGHGI reproduction scripts."""
+
+from __future__ import annotations
+
+import os
+from pathlib import Path
+
+
+def resolve_db_dir(cli_value: str | os.PathLike | None) -> Path:
+    """Resolve the state-DB directory from CLI, env, or default.
+
+    Precedence:
+      1. ``cli_value`` (``--db-dir`` argument), if not None
+      2. ``PYFIA_FIADB_DIR`` environment variable, if set
+      3. Default: ``./data/fiadb`` relative to the current working directory
+
+    Raises ``FileNotFoundError`` with a clear message if the resolved path
+    does not exist.
+    """
+    if cli_value is not None:
+        path = Path(cli_value).expanduser().resolve()
+        source = "--db-dir"
+    elif os.environ.get("PYFIA_FIADB_DIR"):
+        path = Path(os.environ["PYFIA_FIADB_DIR"]).expanduser().resolve()
+        source = "$PYFIA_FIADB_DIR"
+    else:
+        path = (Path.cwd() / "data" / "fiadb").resolve()
+        source = "default (./data/fiadb)"
+
+    if not path.is_dir():
+        raise FileNotFoundError(
+            f"FIA state-DB directory not found: {path} (resolved from {source}). "
+            "Pass --db-dir <path> or set PYFIA_FIADB_DIR to override."
+        )
+    return path
diff --git a/scripts/nghgi/data/nghgi_2024_table_6_10.csv b/scripts/nghgi/data/nghgi_2024_table_6_10.csv
new file mode 100644
index 00000000..b259c3d0
--- /dev/null
+++ b/scripts/nghgi/data/nghgi_2024_table_6_10.csv
@@ -0,0 +1,57 @@
+epa_pool,year,stock_mmt_c,note
+Forest_Area_kha,1990,283500,Forest land area in 1000 hectares; CONUS+AK+HI+territories
+Forest_Area_kha,2005,282521,
+Forest_Area_kha,2019,281137,
+Forest_Area_kha,2020,281779,
+Forest_Area_kha,2021,281780,
+Forest_Area_kha,2022,281752,
+Forest_Area_kha,2023,281725,
+AGB,1990,12739,Aboveground Biomass: live tree AG + understory AG
+AGB,2005,15122,
+AGB,2019,17199,
+AGB,2020,17340,
+AGB,2021,17483,
+AGB,2022,17622,
+AGB,2023,17757,
+BGB,1990,2255,Belowground Biomass: live tree BG + understory BG (10%)
+BGB,2005,2718,
+BGB,2019,3124,
+BGB,2020,3151,
+BGB,2021,3179,
+BGB,2022,3207,
+BGB,2023,3233,
+DEAD_WOOD,1990,1977,Dead Wood: standing dead + downed dead
+DEAD_WOOD,2005,2521,
+DEAD_WOOD,2019,3038,
+DEAD_WOOD,2020,3074,
+DEAD_WOOD,2021,3111,
+DEAD_WOOD,2022,3148,
+DEAD_WOOD,2023,3184,
+LITTER,1990,3789,Litter and duff (Domke 2016 model)
+LITTER,2005,3794,
+LITTER,2019,3775,
+LITTER,2020,3767,
+LITTER,2021,3768,
+LITTER,2022,3768,
+LITTER,2023,3761,
+SOIL_MINERAL,1990,28407,Mineral soil to 1m depth (Domke 2017 model)
+SOIL_MINERAL,2005,28401,
+SOIL_MINERAL,2019,28400,
+SOIL_MINERAL,2020,28400,
+SOIL_MINERAL,2021,28401,
+SOIL_MINERAL,2022,28401,
+SOIL_MINERAL,2023,28401,
+SOIL_ORGANIC,1990,5976,"Histosols/organic soils; not reproducible from FIADB attributes alone (IPCC defaults)"
+SOIL_ORGANIC,2005,5981,
+SOIL_ORGANIC,2019,5983,
+SOIL_ORGANIC,2020,5983,
+SOIL_ORGANIC,2021,5983,
+SOIL_ORGANIC,2022,5983,
+SOIL_ORGANIC,2023,5983,
+FOREST_ECOSYSTEM,1990,55142,Sum of AGB+BGB+Dead Wood+Litter+Soil(Mineral)+Soil(Organic)
+FOREST_ECOSYSTEM,2005,58536,
+FOREST_ECOSYSTEM,2019,61519,
+FOREST_ECOSYSTEM,2020,61717,
+FOREST_ECOSYSTEM,2021,61926,
+FOREST_ECOSYSTEM,2022,62130,
+FOREST_ECOSYSTEM,2023,62320,
diff --git a/scripts/nghgi/data/nghgi_2024_table_a_208_state_flux_2022.csv b/scripts/nghgi/data/nghgi_2024_table_a_208_state_flux_2022.csv
new file mode 100644
index 00000000..25e871ac
--- /dev/null
+++ b/scripts/nghgi/data/nghgi_2024_table_a_208_state_flux_2022.csv
@@ -0,0 +1,57 @@
+state,state_name,flux_mmt_c_2022,lower_bound_mmt_c,lower_bound_pct,upper_bound_mmt_c,upper_bound_pct
+AL,Alabama,-13.5,-15.3,-14,-11.6,14
+AK,Alaska,7.1,0.0,-101,14.2,101
+AZ,Arizona,0.6,0.2,-70,1.0,70
+AR,Arkansas,-9.3,-10.9,-17,-7.8,17
+CA,California,-8.0,-16.0,-100,0.0,100
+CO,Colorado,2.3,-6.3,-375,10.8,375
+CT,Connecticut,-0.8,-1.1,-35,-0.5,35
+DE,Delaware,-0.0,-0.1,-105,0.0,105
+FL,Florida,-3.4,-4.0,-20,-2.7,20
+GA,Georgia,-8.8,-9.3,-6,-8.4,6
+HI,Hawaii,0.9,-5.3,-490,3.5,490
+ID,Idaho,-0.1,-3.7,-2570,3.4,2570
+IL,Illinois,-2.0,-3.0,-51,-1.0,51
+IN,Indiana,-3.0,-4.6,-53,-1.4,53
+IA,Iowa,-1.0,-1.3,-32,-0.7,32
+KS,Kansas,-0.8,-1.2,-53,-0.4,53
+KY,Kentucky,-6.2,-7.8,-25,-4.6,25
+LA,Louisiana,-7.0,-7.5,-7,-6.5,7
+ME,Maine,-5.1,-8.1,-59,-2.1,59
+MD,Maryland,-1.4,-1.9,-39,-0.8,39
+MA,Massachusetts,-1.4,-1.7,-27,-1.0,27
+MI,Michigan,-5.9,-9.5,-60,-2.4,60
+MN,Minnesota,-5.3,-7.6,-43,-3.0,43
+MS,Mississippi,-15.9,-18.7,-18,-13.0,18
+MO,Missouri,-4.8,-7.4,-54,-2.2,54
+MT,Montana,1.4,-6.6,-588,9.4,588
+NE,Nebraska,-0.3,-0.3,-19,-0.2,19
+NV,Nevada,-0.1,-0.3,-460,0.2,460
+NH,New Hampshire,-1.7,-2.3,-36,-1.1,36
+NJ,New Jersey,-0.9,-1.0,-10,-0.8,10
+NM,New Mexico,0.3,-1.7,-713,2.2,713
+NY,New York,-8.9,-11.2,-26,-6.6,26
+NC,North Carolina,-8.9,-10.1,-14,-7.6,14
+ND,North Dakota,-0.1,-0.2,-159,0.0,159
+OH,Ohio,-3.9,-6.0,-53,-1.8,53
+OK,Oklahoma,-2.8,-3.4,-22,-2.2,22
+OR,Oregon,-10.7,-12.8,-20,-8.6,20
+PA,Pennsylvania,-6.2,-10.6,-72,-1.7,72
+RI,Rhode Island,-0.1,-0.3,-115,0.0,115
+SC,South Carolina,-4.3,-4.8,-13,-3.7,13
+SD,South Dakota,0.3,0.0,-107,0.6,107
+TN,Tennessee,-7.8,-9.3,-19,-6.3,19
+TX,Texas,-10.7,-11.2,-5,-10.2,5
+UT,Utah,0.4,-1.0,-337,1.8,337
+VT,Vermont,-1.7,-2.4,-43,-0.9,43
+VA,Virginia,-9.8,-12.5,-28,-7.1,28
+WA,Washington,-4.6,-9.2,-100,0.0,100
+WV,West Virginia,-7.3,-9.1,-24,-5.6,24
+WI,Wisconsin,-6.5,-6.9,-7,-6.0,7
+WY,Wyoming,0.2,-0.4,-319,0.8,319
+AS,American Samoa,-0.0,-0.2,-656,0.1,656
+GU,Guam,-0.0,-0.3,-584,0.2,584
+MP,Northern Mariana Islands,-0.0,-0.2,-673,0.2,673
+PR,Puerto Rico,-0.3,-2.8,-780,2.2,780
+VI,US Virgin Islands,-0.0,-0.2,-787,0.1,787
+TOTAL,Total,-189.3,-209.9,-11,-168.8,11
diff --git a/scripts/nghgi/dead_wood_diagnostic.py b/scripts/nghgi/dead_wood_diagnostic.py
new file mode 100644
index 00000000..d8665bbc
--- /dev/null
+++ b/scripts/nghgi/dead_wood_diagnostic.py
@@ -0,0 +1,205 @@
+"""
+Dead Wood diagnostic — isolate whether the +38% over-count vs EPA Chapter 6
+comes from standing dead, downed dead, or both.
+
+Compares three estimates per state, all routed through pyfia's identical
+stratification pipeline (EVALID, land_type='forest', BaseEstimator path) so
+the only variable is the per-tree/per-condition carbon source:
+
+  A) `carbon.standing_dead(pool='total')` — NSVB recomputation per tree
+     (Westfall 2023) with REF_TREE_DECAY_PROP reductions and Appendix K
+     broken-top corrections. AG via NSVB; BG via bridge to TREE.CARBON_BG.
+
+  B) `estimation.carbon_pool(pool='total', tree_type='dead')` — uses
+     FIADB-stored TREE.CARBON_AG + TREE.CARBON_BG where STATUSCD=2.
+     This is the path EPA's report ultimately consumes.
+
+  C) `carbon.downed_dead` — uses pre-computed COND.CARBON_DOWN_DEAD
+     (Domke 2013 model). Shared between pyfia and EPA, no recomputation.
+
+If A >> B, the standing-dead NSVB recomputation is the over-count source.
+If A ≈ B, the issue is elsewhere (downed_dead or pool definition).
+
+Usage:
+    uv run python scripts/nghgi/dead_wood_diagnostic.py
+    uv run python scripts/nghgi/dead_wood_diagnostic.py --states GA,OR,TX,WA
+    uv run python scripts/nghgi/dead_wood_diagnostic.py --db-dir /path/to/fiadb
+
+Database directory resolution (in order):
+    1. --db-dir CLI argument
+    2. $PYFIA_FIADB_DIR environment variable
+    3. ./data/fiadb (relative to current working directory)
+"""
+
+from __future__ import annotations
+
+import argparse
+import sys
+import time
+from pathlib import Path
+
+import polars as pl
+
+sys.path.insert(0, str(Path(__file__).parent))
+
+from _paths import resolve_db_dir  # noqa: E402
+
+from pyfia import FIA  # noqa: E402
+from pyfia.carbon.downed_dead import downed_dead  # noqa: E402
+from pyfia.carbon.standing_dead import standing_dead  # noqa: E402
+from pyfia.estimation.estimators.carbon_pools import carbon_pool  # noqa: E402
+
+DEFAULT_STATES = ["GA", "OR", "TX", "WA"]
+
+MMT_C_PER_SHORT_TON = 0.907185 / 1_000_000
+
+
+def _to_mmt(short_tons: float) -> float:
+    return short_tons * MMT_C_PER_SHORT_TON
+
+
+def _select_2022_evalid(db: FIA) -> int:
+    evals = db.query(
+        """
+        SELECT EVALID, END_INVYR
+        FROM POP_EVAL
+        WHERE EVAL_DESCR LIKE '%CURRENT AREA, CURRENT VOLUME%'
+        ORDER BY END_INVYR DESC
+        """
+    )
+    match = evals.filter(pl.col("END_INVYR") == 2022)
+    return int(match["EVALID"][0]) if not match.is_empty() else int(evals["EVALID"][0])
+
+
+def diagnose_state(state: str, *, db_dir: Path) -> dict:
+    db_path = db_dir / f"{state}.duckdb"
+    t0 = time.perf_counter()
+
+    with FIA(str(db_path)) as db:
+        evalid = _select_2022_evalid(db)
+        db.clip_by_evalid(evalid)
+
+        sd_nsvb = standing_dead(db, pool="total", land_type="forest", totals=True)
+        a_total_st = float(sd_nsvb["CARBON_TOTAL"][0]) if len(sd_nsvb) else 0.0
+        a_n_plots = int(sd_nsvb["N_PLOTS"][0]) if len(sd_nsvb) else 0
+        a_n_trees = int(sd_nsvb["N_TREES"][0]) if "N_TREES" in sd_nsvb.columns else 0
+
+        sd_fiadb = carbon_pool(
+            db, pool="total", tree_type="dead", land_type="forest", totals=True
+        )
+        b_total_st = float(sd_fiadb["CARBON_TOTAL"][0]) if len(sd_fiadb) else 0.0
+        b_n_plots = int(sd_fiadb["N_PLOTS"][0]) if "N_PLOTS" in sd_fiadb.columns else 0
+        b_n_trees = int(sd_fiadb["N_TREES"][0]) if "N_TREES" in sd_fiadb.columns else 0
+
+        dd = downed_dead(db, land_type="forest", totals=True)
+        c_total_st = float(dd["CARBON_TOTAL"][0]) if len(dd) else 0.0
+
+    return {
+        "state": state,
+        "evalid": evalid,
+        "elapsed_s": time.perf_counter() - t0,
+        "A_standing_dead_nsvb_st": a_total_st,
+        "A_standing_dead_nsvb_mmt": _to_mmt(a_total_st),
+        "A_n_plots": a_n_plots,
+        "A_n_trees": a_n_trees,
+        "B_standing_dead_fiadb_st": b_total_st,
+        "B_standing_dead_fiadb_mmt": _to_mmt(b_total_st),
+        "B_n_plots": b_n_plots,
+        "B_n_trees": b_n_trees,
+        "C_downed_dead_mmt": _to_mmt(c_total_st),
+        "A_minus_B_mmt": _to_mmt(a_total_st - b_total_st),
+        "A_over_B_pct": (
+            100.0 * (a_total_st - b_total_st) / b_total_st
+            if b_total_st
+            else float("nan")
+        ),
+    }
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "--states",
+        default=",".join(DEFAULT_STATES),
+        help=f"Comma-separated postal codes (default: {','.join(DEFAULT_STATES)})",
+    )
+    parser.add_argument(
+        "--db-dir",
+        type=str,
+        default=None,
+        help="Directory containing per-state DuckDB files (overrides $PYFIA_FIADB_DIR).",
+    )
+    args = parser.parse_args()
+
+    try:
+        db_dir = resolve_db_dir(args.db_dir)
+    except FileNotFoundError as e:
+        print(f"ERROR: {e}", file=sys.stderr)
+        return 2
+
+    states = [s.strip().upper() for s in args.states.split(",") if s.strip()]
+
+    print(f"\n=== Dead Wood diagnostic — {len(states)} states ===")
+    print("    A = standing_dead (NSVB recomputation, pyfia new path)")
+    print("    B = carbon_pool(pool=total, tree_type=dead) (FIADB CARBON_AG+BG)")
+    print("    C = downed_dead (Domke 2013, shared with EPA)")
+    print(f"    State DB directory: {db_dir}\n")
+
+    rows = []
+    for st in states:
+        r = diagnose_state(st, db_dir=db_dir)
+        rows.append(r)
+        print(
+            f"  {st}: EVALID={r['evalid']} "
+            f"A={r['A_standing_dead_nsvb_mmt']:.2f} "
+            f"B={r['B_standing_dead_fiadb_mmt']:.2f} "
+            f"A−B={r['A_minus_B_mmt']:+.2f} MMT C "
+            f"({r['A_over_B_pct']:+.1f}%)  "
+            f"plotsA/B={r['A_n_plots']}/{r['B_n_plots']}  "
+            f"treesA/B={r['A_n_trees']}/{r['B_n_trees']}  "
+            f"({r['elapsed_s']:.1f}s)"
+        )
+
+    df = pl.DataFrame(rows)
+
+    print("\n=== Per-state results (MMT C) ===")
+    summary = df.select(
+        [
+            "state",
+            "A_standing_dead_nsvb_mmt",
+            "B_standing_dead_fiadb_mmt",
+            "A_minus_B_mmt",
+            "A_over_B_pct",
+            "C_downed_dead_mmt",
+        ]
+    )
+    with pl.Config(tbl_rows=20, tbl_cols=10, fmt_str_lengths=40, tbl_width_chars=140):
+        print(summary)
+
+    a_sum = df["A_standing_dead_nsvb_mmt"].sum()
+    b_sum = df["B_standing_dead_fiadb_mmt"].sum()
+    c_sum = df["C_downed_dead_mmt"].sum()
+    print(f"\n=== Across {len(states)} states ===")
+    print(f"  A (NSVB recompute) standing dead total: {a_sum:.1f} MMT C")
+    print(f"  B (FIADB CARBON_AG+BG) standing dead total: {b_sum:.1f} MMT C")
+    print(
+        f"  Standing dead delta (A − B): {a_sum - b_sum:+.1f} MMT C "
+        f"({100.0 * (a_sum - b_sum) / b_sum:+.1f}%)"
+    )
+    print(f"  C (downed dead, shared method) total: {c_sum:.1f} MMT C")
+    print()
+    print("Interpretation:")
+    if abs(100.0 * (a_sum - b_sum) / b_sum) > 5:
+        print("  → standing_dead NSVB recomputation diverges materially from")
+        print("    FIADB-stored CARBON_AG+CARBON_BG. EPA's report is built on the")
+        print("    FIADB-stored values; reproducing the report requires using path B,")
+        print("    not pyfia's new NSVB recomputation.")
+    else:
+        print("  → standing_dead path is consistent with FIADB-stored values.")
+        print("    The +38% Dead Wood gap must come from downed_dead or pool")
+        print("    definition. Check downed_dead validation status.")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/scripts/nghgi/multi_year.py b/scripts/nghgi/multi_year.py
new file mode 100644
index 00000000..8b5290e2
--- /dev/null
+++ b/scripts/nghgi/multi_year.py
@@ -0,0 +1,308 @@
+"""
+NGHGI multi-year validation — confirm Stage A accuracy across years
+2019-2023 (the inventory years for which EPA Table 6-10 publishes
+distinct stock values; 1990 and 2005 are modeled by EPA via age-class
+projection and not directly reproducible from FIADB).
+
+For each target year, picks each CONUS-48 state's CURRENT AREA, CURRENT
+VOLUME EVALID with the closest END_INVYR ≤ target year, runs the
+EPA-pool-aggregated stock compilation in FIADB-fidelity mode, sums to
+CONUS, and compares to the EPA Table 6-10 published value for that year.
+
+Usage:
+    uv run python scripts/nghgi/multi_year.py
+    uv run python scripts/nghgi/multi_year.py --years 2020,2022
+    uv run python scripts/nghgi/multi_year.py --db-dir /path/to/fiadb
+
+Database directory resolution (in order):
+    1. --db-dir CLI argument
+    2. $PYFIA_FIADB_DIR environment variable
+    3. ./data/fiadb (relative to current working directory)
+"""
+
+from __future__ import annotations
+
+import argparse
+import sys
+import time
+from pathlib import Path
+
+import polars as pl
+
+sys.path.insert(0, str(Path(__file__).parent))
+
+from _compile import compile_state_stocks, load_published_targets  # noqa: E402
+from _paths import resolve_db_dir  # noqa: E402
+
+from pyfia import FIA  # noqa: E402
+
+DEFAULT_YEARS = [2019, 2020, 2021, 2022, 2023]
+
+CONUS_48 = [
+    "AL",
+    "AR",
+    "AZ",
+    "CA",
+    "CO",
+    "CT",
+    "DE",
+    "FL",
+    "GA",
+    "IA",
+    "ID",
+    "IL",
+    "IN",
+    "KS",
+    "KY",
+    "LA",
+    "MA",
+    "MD",
+    "ME",
+    "MI",
+    "MN",
+    "MO",
+    "MS",
+    "MT",
+    "NC",
+    "ND",
+    "NE",
+    "NH",
+    "NJ",
+    "NM",
+    "NV",
+    "NY",
+    "OH",
+    "OK",
+    "OR",
+    "PA",
+    "RI",
+    "SC",
+    "SD",
+    "TN",
+    "TX",
+    "UT",
+    "VA",
+    "VT",
+    "WA",
+    "WI",
+    "WV",
+    "WY",
+]
+
+
+def _select_evalid_for_year(db: FIA, target_year: int) -> tuple[int, int] | None:
+    """Return (evalid, end_invyr) for the closest annual VOL EVALID with
+    END_INVYR ≤ target_year. Returns None if no such EVALID exists."""
+    evals = db.query(
+        """
+        SELECT EVALID, END_INVYR
+        FROM POP_EVAL
+        WHERE EVAL_DESCR LIKE '%CURRENT AREA, CURRENT VOLUME%'
+        ORDER BY END_INVYR DESC
+        """
+    )
+    if evals.is_empty():
+        return None
+    le_target = evals.filter(pl.col("END_INVYR") <= target_year)
+    if le_target.is_empty():
+        row = evals.tail(1)
+    else:
+        row = le_target.head(1)
+    return int(row["EVALID"][0]), int(row["END_INVYR"][0])
+
+
+def run_state_year(
+    state: str, year: int, *, db_dir: Path
+) -> tuple[pl.DataFrame, dict] | None:
+    db_path = db_dir / f"{state}.duckdb"
+    if not db_path.exists():
+        return None
+    t0 = time.perf_counter()
+    with FIA(str(db_path)) as db:
+        sel = _select_evalid_for_year(db, year)
+        if sel is None:
+            return None
+        evalid, end_yr = sel
+        db.clip_by_evalid(evalid)
+        df = compile_state_stocks(db, state_label=state, mode="fiadb")
+    df = df.with_columns(
+        pl.lit(evalid).alias("EVALID"),
+        pl.lit(end_yr).alias("END_INVYR"),
+        pl.lit(year).alias("TARGET_YEAR"),
+    )
+    return df, {
+        "state": state,
+        "year": year,
+        "evalid": evalid,
+        "end_invyr": end_yr,
+        "elapsed_s": time.perf_counter() - t0,
+    }
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--years", default=",".join(str(y) for y in DEFAULT_YEARS))
+    parser.add_argument("--states", default=",".join(CONUS_48))
+    parser.add_argument(
+        "--db-dir",
+        type=str,
+        default=None,
+        help="Directory containing per-state DuckDB files (overrides $PYFIA_FIADB_DIR).",
+    )
+    args = parser.parse_args()
+
+    try:
+        db_dir = resolve_db_dir(args.db_dir)
+    except FileNotFoundError as e:
+        print(f"ERROR: {e}", file=sys.stderr)
+        return 2
+
+    years = [int(y) for y in args.years.split(",") if y.strip()]
+    states = [s.strip().upper() for s in args.states.split(",") if s.strip()]
+
+    print("\n=== NGHGI multi-year validation ===")
+    print(f"    States: {len(states)} (CONUS-48 default)")
+    print(f"    Years: {years}")
+    print(f"    State DB directory: {db_dir}\n")
+
+    all_rows: list[pl.DataFrame] = []
+    skipped: list[tuple[str, int]] = []
+    end_yr_summary: dict[int, dict[int, int]] = {y: {} for y in years}
+
+    for year in years:
+        print(f"[Year {year}]")
+        t_year = time.perf_counter()
+        n_ok = 0
+        for state in states:
+            res = run_state_year(state, year, db_dir=db_dir)
+            if res is None:
+                skipped.append((state, year))
+                continue
+            df, meta = res
+            all_rows.append(df)
+            end_yr_summary[year].setdefault(meta["end_invyr"], 0)
+            end_yr_summary[year][meta["end_invyr"]] += 1
+            n_ok += 1
+        print(
+            f"  {n_ok}/{len(states)} states ok in {time.perf_counter() - t_year:.1f}s. "
+            f"END_INVYR distribution: "
+            f"{dict(sorted(end_yr_summary[year].items(), reverse=True))}"
+        )
+
+    if not all_rows:
+        print("No successful runs — aborting.")
+        return 1
+
+    combined = pl.concat(all_rows, how="vertical_relaxed")
+
+    rollup = (
+        combined.group_by(["TARGET_YEAR", "EPA_POOL"])
+        .agg(pl.col("STOCK_MMT_C").sum().alias("PYFIA_MMT_C"))
+        .sort(["TARGET_YEAR", "EPA_POOL"])
+    )
+
+    epa_all = pl.concat(
+        [
+            load_published_targets(year=y)
+            .select(["EPA_POOL", "YEAR", "STOCK_MMT_C"])
+            .rename({"STOCK_MMT_C": "EPA_MMT_C", "YEAR": "TARGET_YEAR"})
+            for y in years
+        ],
+        how="vertical_relaxed",
+    )
+
+    cmp = rollup.join(epa_all, on=["TARGET_YEAR", "EPA_POOL"], how="left")
+    cmp = cmp.with_columns(
+        (pl.col("PYFIA_MMT_C") - pl.col("EPA_MMT_C")).alias("ABS_DIFF"),
+        (
+            100.0 * (pl.col("PYFIA_MMT_C") - pl.col("EPA_MMT_C")) / pl.col("EPA_MMT_C")
+        ).alias("PCT_DIFF"),
+    )
+
+    # Synthesize a "Forest Ecosystem + EPA Soil Organic" row per year.
+    eco_rows = []
+    for y in years:
+        py_eco = float(
+            cmp.filter(
+                (pl.col("TARGET_YEAR") == y)
+                & (pl.col("EPA_POOL") == "FOREST_ECOSYSTEM")
+            )["PYFIA_MMT_C"][0]
+        )
+        epa_eco = float(
+            cmp.filter(
+                (pl.col("TARGET_YEAR") == y)
+                & (pl.col("EPA_POOL") == "FOREST_ECOSYSTEM")
+            )["EPA_MMT_C"][0]
+        )
+        epa_so = float(
+            load_published_targets(y).filter(pl.col("EPA_POOL") == "SOIL_ORGANIC")[
+                "STOCK_MMT_C"
+            ][0]
+        )
+        py_eco_full = py_eco + epa_so
+        eco_rows.append(
+            {
+                "TARGET_YEAR": y,
+                "EPA_POOL": "FOREST_ECO_PLUS_SO",
+                "PYFIA_MMT_C": py_eco_full,
+                "EPA_MMT_C": epa_eco,
+                "ABS_DIFF": py_eco_full - epa_eco,
+                "PCT_DIFF": 100.0 * (py_eco_full - epa_eco) / epa_eco,
+            }
+        )
+    cmp_full = pl.concat([cmp, pl.DataFrame(eco_rows)], how="vertical_relaxed").sort(
+        ["TARGET_YEAR", "EPA_POOL"]
+    )
+
+    print("\n=== Per-year, per-pool comparison (CONUS-48 vs EPA, MMT C) ===")
+    with pl.Config(tbl_rows=80, tbl_cols=10, fmt_str_lengths=30, tbl_width_chars=140):
+        print(cmp_full)
+
+    print(
+        "\n=== Headline: Forest Ecosystem (CONUS-48 + EPA Soil Organic) vs EPA Total ==="
+    )
+    headline = cmp_full.filter(pl.col("EPA_POOL") == "FOREST_ECO_PLUS_SO").select(
+        ["TARGET_YEAR", "PYFIA_MMT_C", "EPA_MMT_C", "ABS_DIFF", "PCT_DIFF"]
+    )
+    with pl.Config(tbl_rows=20, tbl_cols=10, fmt_str_lengths=30, tbl_width_chars=120):
+        print(headline)
+
+    print("\n=== Combined Dead Organic Matter (Dead Wood + Litter) vs EPA ===")
+    dom_rows = []
+    for y in years:
+        py_dom = float(
+            cmp.filter(
+                (pl.col("TARGET_YEAR") == y)
+                & (pl.col("EPA_POOL").is_in(["DEAD_WOOD", "LITTER"]))
+            )["PYFIA_MMT_C"].sum()
+        )
+        epa_dom = float(
+            cmp.filter(
+                (pl.col("TARGET_YEAR") == y)
+                & (pl.col("EPA_POOL").is_in(["DEAD_WOOD", "LITTER"]))
+            )["EPA_MMT_C"].sum()
+        )
+        dom_rows.append(
+            {
+                "TARGET_YEAR": y,
+                "PYFIA_DOM": py_dom,
+                "EPA_DOM": epa_dom,
+                "ABS_DIFF": py_dom - epa_dom,
+                "PCT_DIFF": 100.0 * (py_dom - epa_dom) / epa_dom,
+            }
+        )
+    with pl.Config(tbl_rows=20, tbl_cols=10, fmt_str_lengths=30, tbl_width_chars=120):
+        print(pl.DataFrame(dom_rows))
+
+    if skipped:
+        print(f"\nSkipped (no eligible EVALID): {len(skipped)} state-years")
+        for st, yr in skipped[:10]:
+            print(f"  {st} {yr}")
+        if len(skipped) > 10:
+            print(f"  ... and {len(skipped) - 10} more")
+
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/scripts/nghgi/stage_a.py b/scripts/nghgi/stage_a.py
new file mode 100644
index 00000000..47f7ad1e
--- /dev/null
+++ b/scripts/nghgi/stage_a.py
@@ -0,0 +1,277 @@
+"""
+NGHGI Stage A — reproduce EPA Chapter 6 Table 6-10 forest ecosystem
+carbon stocks across CONUS using pyfia.
+
+Iterates the 48 conterminous US state DuckDB files (and optionally Alaska),
+runs the EPA-pool-aggregated stock compilation per state, sums to CONUS, and
+prints a side-by-side comparison vs the published EPA Table 6-10.
+
+Usage:
+    uv run python scripts/nghgi/stage_a.py
+    uv run python scripts/nghgi/stage_a.py --include-ak
+    uv run python scripts/nghgi/stage_a.py --states GA,FL,SC
+    uv run python scripts/nghgi/stage_a.py --db-dir /path/to/fiadb
+
+Database directory resolution (in order):
+    1. --db-dir CLI argument
+    2. $PYFIA_FIADB_DIR environment variable
+    3. ./data/fiadb (relative to current working directory)
+
+The output is intentionally "honest about what we're reproducing": each
+state's selected EVALID and END_INVYR is logged so methodological
+discrepancies vs the published 2022 target (which used the September 2023
+FIADB snapshot) are visible.
+"""
+
+from __future__ import annotations
+
+import argparse
+import sys
+import time
+from pathlib import Path
+
+import polars as pl
+
+# Allow `from _compile import ...` regardless of CWD: scripts in
+# ``scripts/nghgi/`` add their own directory to sys.path automatically when
+# invoked directly, but we add it explicitly for clarity.
+sys.path.insert(0, str(Path(__file__).parent))
+
+from _compile import compare_to_published, compile_state_stocks  # noqa: E402
+from _paths import resolve_db_dir  # noqa: E402
+
+from pyfia import FIA  # noqa: E402
+
+# 48 conterminous US states. EPA's Section 6.2 reproduction target uses
+# stock-difference here. AK uses a mix (coastal SD, interior gain-loss);
+# HI / AS / GU / MP / PR / VI use gain-loss only and are out of scope for
+# this reproduction.
+CONUS_48 = [
+    "AL",
+    "AR",
+    "AZ",
+    "CA",
+    "CO",
+    "CT",
+    "DE",
+    "FL",
+    "GA",
+    "IA",
+    "ID",
+    "IL",
+    "IN",
+    "KS",
+    "KY",
+    "LA",
+    "MA",
+    "MD",
+    "ME",
+    "MI",
+    "MN",
+    "MO",
+    "MS",
+    "MT",
+    "NC",
+    "ND",
+    "NE",
+    "NH",
+    "NJ",
+    "NM",
+    "NV",
+    "NY",
+    "OH",
+    "OK",
+    "OR",
+    "PA",
+    "RI",
+    "SC",
+    "SD",
+    "TN",
+    "TX",
+    "UT",
+    "VA",
+    "VT",
+    "WA",
+    "WI",
+    "WV",
+    "WY",
+]
+
+
+def _select_evalid_for_state(db: FIA, prefer_end_year: int | None = None) -> int:
+    """Pick the most recent annual VOL EVALID for the open state DB.
+
+    If ``prefer_end_year`` is given, return the EVALID whose END_INVYR
+    matches if present, else fall back to most-recent.
+    """
+    evals = db.query(
+        """
+        SELECT EVALID, END_INVYR, EVAL_DESCR
+        FROM POP_EVAL
+        WHERE EVAL_DESCR LIKE '%CURRENT AREA, CURRENT VOLUME%'
+        ORDER BY END_INVYR DESC
+        """
+    )
+    if evals.is_empty():
+        raise RuntimeError("No CURRENT AREA, CURRENT VOLUME EVALIDs found")
+    if prefer_end_year is not None:
+        match = evals.filter(pl.col("END_INVYR") == prefer_end_year)
+        if not match.is_empty():
+            return int(match["EVALID"][0])
+    return int(evals["EVALID"][0])
+
+
+def run_state(
+    state: str,
+    *,
+    db_dir: Path,
+    prefer_end_year: int | None = None,
+) -> tuple[pl.DataFrame, dict]:
+    """Compile EPA-pool stocks for one state. Returns (df, meta)."""
+    db_path = db_dir / f"{state}.duckdb"
+    if not db_path.exists():
+        raise FileNotFoundError(db_path)
+
+    t0 = time.perf_counter()
+    with FIA(str(db_path)) as db:
+        evalid = _select_evalid_for_state(db, prefer_end_year=prefer_end_year)
+        db.clip_by_evalid(evalid)
+        end_yr = int(
+            db.query(f"SELECT END_INVYR FROM POP_EVAL WHERE EVALID = {evalid}")[
+                "END_INVYR"
+            ][0]
+        )
+        df = compile_state_stocks(db, state_label=state)
+    df = df.with_columns(
+        pl.lit(evalid).alias("EVALID"),
+        pl.lit(int(end_yr)).alias("END_INVYR"),
+    )
+    meta = {
+        "state": state,
+        "evalid": evalid,
+        "end_invyr": int(end_yr),
+        "elapsed_s": time.perf_counter() - t0,
+    }
+    return df, meta
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "--states",
+        type=str,
+        default=None,
+        help="Comma-separated state postal codes to run. Default: all CONUS-48.",
+    )
+    parser.add_argument(
+        "--include-ak",
+        action="store_true",
+        help="Also include Alaska (mixes stock-difference and gain-loss methods in EPA report).",
+    )
+    parser.add_argument(
+        "--prefer-end-year",
+        type=int,
+        default=2022,
+        help="Prefer EVALIDs with this END_INVYR if available (default 2022 to match EPA Chapter 6 reporting year).",
+    )
+    parser.add_argument(
+        "--target-year",
+        type=int,
+        default=2022,
+        help="EPA Table 6-10 column to compare against (default 2022).",
+    )
+    parser.add_argument(
+        "--db-dir",
+        type=str,
+        default=None,
+        help="Directory containing per-state DuckDB files (overrides $PYFIA_FIADB_DIR).",
+    )
+    args = parser.parse_args()
+
+    try:
+        db_dir = resolve_db_dir(args.db_dir)
+    except FileNotFoundError as e:
+        print(f"ERROR: {e}", file=sys.stderr)
+        return 2
+
+    if args.states:
+        states = [s.strip().upper() for s in args.states.split(",") if s.strip()]
+    else:
+        states = list(CONUS_48)
+        if args.include_ak:
+            states.append("AK")
+
+    print(f"\n=== NGHGI Stage A reproduction — {len(states)} states ===")
+    print(f"    Target: EPA Chapter 6 Table 6-10, year {args.target_year}")
+    print(f"    Prefer EVALID with END_INVYR={args.prefer_end_year} per state")
+    print(f"    State DB directory: {db_dir}\n")
+
+    all_frames: list[pl.DataFrame] = []
+    metas: list[dict] = []
+    failures: list[tuple[str, str]] = []
+
+    for i, st in enumerate(states, 1):
+        try:
+            df, meta = run_state(
+                st, db_dir=db_dir, prefer_end_year=args.prefer_end_year
+            )
+            all_frames.append(df)
+            metas.append(meta)
+            print(
+                f"  [{i:>2}/{len(states)}] {st}: EVALID={meta['evalid']} "
+                f"end_yr={meta['end_invyr']} ({meta['elapsed_s']:.1f}s)"
+            )
+        except Exception as e:
+            print(f"  [{i:>2}/{len(states)}] {st}: FAILED — {type(e).__name__}: {e}")
+            failures.append((st, str(e)))
+
+    if not all_frames:
+        print("\nNo successful state runs — aborting.")
+        return 1
+
+    print(
+        f"\nSucceeded: {len(all_frames)}/{len(states)} states. "
+        f"Failed: {len(failures)}.\n"
+    )
+
+    long_df = pl.concat(all_frames, how="vertical_relaxed")
+
+    # Per-state pivot for inspection
+    per_state = long_df.pivot(values="STOCK_MMT_C", index="STATE", on="EPA_POOL").sort(
+        "STATE"
+    )
+    print("=== Per-state stocks (MMT C) ===")
+    with pl.Config(tbl_rows=60, tbl_cols=10, fmt_str_lengths=80):
+        print(per_state)
+
+    # CONUS rollup
+    print("\n=== CONUS rollup vs EPA Chapter 6 Table 6-10 ===")
+    cmp = compare_to_published(long_df, year=args.target_year)
+    with pl.Config(tbl_rows=12, tbl_cols=10, fmt_str_lengths=40):
+        print(cmp)
+
+    print(
+        "\nNotes:\n"
+        "  - SOIL_ORGANIC (Histosols) is NOT reproduced from FIADB; EPA uses\n"
+        "    IPCC defaults for that pool. Compare the SOIL_ORGANIC row\n"
+        "    against the constant published target only.\n"
+        "  - EPA's Table 6-10 stock columns include AK + HI + territories.\n"
+        "    This run is CONUS-48"
+        + (" + AK" if "AK" in states else "")
+        + ". Expect a gap from non-CONUS forest land.\n"
+        "  - HWP (Harvested Wood) and Drained Organic Soils are out of scope.\n"
+        "  - Pool decomposition: EPA AGB = pyfia live_tree(AG) + understory(AG);\n"
+        "    EPA Dead Wood = standing_dead + downed_dead. Verify mapping in\n"
+        "    scripts/nghgi/_compile.py if numbers look off.\n"
+    )
+
+    if failures:
+        print("\nFailures:")
+        for st, err in failures:
+            print(f"  {st}: {err}")
+
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/scripts/nghgi/stage_b.py b/scripts/nghgi/stage_b.py
new file mode 100644
index 00000000..72097332
--- /dev/null
+++ b/scripts/nghgi/stage_b.py
@@ -0,0 +1,318 @@
+"""
+NGHGI Stage B — state-level flux validation against EPA Annex 3.13 Table A-208.
+
+Annex 3.13 publishes state-level *flux* (annual carbon stock change) for
+all forest pools combined, 2022 only — no state-level stocks table exists.
+This script runs pyfia's ``stock_change`` (condition-level: understory,
+downed dead wood, litter, soil organic) per CONUS-48 state and compares
+to the EPA published total per state.
+
+Two important caveats:
+
+1. pyfia's ``stock_change`` covers **only condition-level pools** —
+   live tree and standing dead flux (the dominant component, ~70-80%
+   of national flux) are deferred to a tree-level GRM decomposition.
+   The per-state residual ``EPA − pyfia_condition`` is the expected
+   tree-level component.
+
+2. EPA Table A-208's "Stock Change" sign convention:
+   negative = net carbon uptake (sink).  pyfia's stock_change column
+   ``CARBON_TOTAL`` is ΔC = C(t₂) − C(t₁), so positive = accumulation
+   (also a sink).  We negate pyfia values to align with EPA's flux
+   convention (negative = sink).
+
+Usage:
+    uv run python scripts/nghgi/stage_b.py
+    uv run python scripts/nghgi/stage_b.py --states GA,SC,FL
+    uv run python scripts/nghgi/stage_b.py --db-dir /path/to/fiadb
+
+Database directory resolution (in order):
+    1. --db-dir CLI argument
+    2. $PYFIA_FIADB_DIR environment variable
+    3. ./data/fiadb (relative to current working directory)
+"""
+
+from __future__ import annotations
+
+import argparse
+import sys
+import time
+from pathlib import Path
+
+import polars as pl
+
+sys.path.insert(0, str(Path(__file__).parent))
+
+from _compile import load_state_flux_targets  # noqa: E402
+from _paths import resolve_db_dir  # noqa: E402
+
+from pyfia import FIA  # noqa: E402
+from pyfia.carbon.stock_change import stock_change  # noqa: E402
+
+CONUS_48 = [
+    "AL",
+    "AR",
+    "AZ",
+    "CA",
+    "CO",
+    "CT",
+    "DE",
+    "FL",
+    "GA",
+    "IA",
+    "ID",
+    "IL",
+    "IN",
+    "KS",
+    "KY",
+    "LA",
+    "MA",
+    "MD",
+    "ME",
+    "MI",
+    "MN",
+    "MO",
+    "MS",
+    "MT",
+    "NC",
+    "ND",
+    "NE",
+    "NH",
+    "NJ",
+    "NM",
+    "NV",
+    "NY",
+    "OH",
+    "OK",
+    "OR",
+    "PA",
+    "RI",
+    "SC",
+    "SD",
+    "TN",
+    "TX",
+    "UT",
+    "VA",
+    "VT",
+    "WA",
+    "WI",
+    "WV",
+    "WY",
+]
+
+MMT_C_PER_SHORT_TON = 0.907185 / 1_000_000
+
+
+def _select_2022_evalid(db: FIA) -> int | None:
+    evals = db.query(
+        """
+        SELECT EVALID, END_INVYR
+        FROM POP_EVAL
+        WHERE EVAL_DESCR LIKE '%CURRENT AREA, CURRENT VOLUME%'
+        ORDER BY END_INVYR DESC
+        """
+    )
+    if evals.is_empty():
+        return None
+    le = evals.filter(pl.col("END_INVYR") <= 2022)
+    return int(le["EVALID"][0]) if not le.is_empty() else int(evals["EVALID"][0])
+
+
+def run_state(state: str, *, db_dir: Path) -> dict | None:
+    db_path = db_dir / f"{state}.duckdb"
+    if not db_path.exists():
+        return None
+    t0 = time.perf_counter()
+    with FIA(str(db_path)) as db:
+        evalid = _select_2022_evalid(db)
+        if evalid is None:
+            return None
+        db.clip_by_evalid(evalid)
+        try:
+            sc = stock_change(db, pool="all")
+        except Exception as e:
+            return {
+                "state": state,
+                "evalid": evalid,
+                "error": f"{type(e).__name__}: {e}",
+                "elapsed_s": time.perf_counter() - t0,
+            }
+
+    if sc is None or len(sc) == 0:
+        return {
+            "state": state,
+            "evalid": evalid,
+            "pyfia_condition_flux_mmt_c": 0.0,
+            "n_remeasured_plots": 0,
+            "elapsed_s": time.perf_counter() - t0,
+        }
+
+    total_st = (
+        float(sc["CARBON_CHANGE_TOTAL"].sum())
+        if "CARBON_CHANGE_TOTAL" in sc.columns
+        else 0.0
+    )
+    n_plots = int(sc["N_PLOTS"][0]) if "N_PLOTS" in sc.columns and len(sc) > 0 else 0
+
+    # stock_change already annualizes by REMPER. Convert to MMT C and
+    # negate to match EPA's sign convention (negative = net uptake).
+    pyfia_mmt = -total_st * MMT_C_PER_SHORT_TON
+
+    return {
+        "state": state,
+        "evalid": evalid,
+        "pyfia_condition_flux_mmt_c": pyfia_mmt,
+        "n_remeasured_plots": n_plots,
+        "elapsed_s": time.perf_counter() - t0,
+    }
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--states", default=",".join(CONUS_48))
+    parser.add_argument(
+        "--db-dir",
+        type=str,
+        default=None,
+        help="Directory containing per-state DuckDB files (overrides $PYFIA_FIADB_DIR).",
+    )
+    args = parser.parse_args()
+
+    try:
+        db_dir = resolve_db_dir(args.db_dir)
+    except FileNotFoundError as e:
+        print(f"ERROR: {e}", file=sys.stderr)
+        return 2
+
+    states = [s.strip().upper() for s in args.states.split(",") if s.strip()]
+
+    print("\n=== NGHGI Stage B — state-level flux validation ===")
+    print("    pyfia: condition-level stock_change (4 pools)")
+    print("    EPA:   Table A-208 all-pools state flux, 2022")
+    print("    Expected residual: ~70-80% (= tree-level flux, not yet reproduced)")
+    print(f"    State DB directory: {db_dir}\n")
+
+    rows: list[dict] = []
+    failures: list[tuple[str, str]] = []
+    for i, st in enumerate(states, 1):
+        r = run_state(st, db_dir=db_dir)
+        if r is None:
+            failures.append((st, "no DB or no EVALID"))
+            continue
+        if "error" in r:
+            failures.append((st, r["error"]))
+            print(f"  [{i:>2}/{len(states)}] {st}: FAILED — {r['error']}")
+            continue
+        rows.append(r)
+        print(
+            f"  [{i:>2}/{len(states)}] {st}: EVALID={r['evalid']} "
+            f"pyfia_cond={r['pyfia_condition_flux_mmt_c']:+.2f} MMT C/yr "
+            f"({r['n_remeasured_plots']} plots, {r['elapsed_s']:.1f}s)"
+        )
+
+    if not rows:
+        print("\nNo successful runs. Failures:")
+        for st, err in failures:
+            print(f"  {st}: {err}")
+        return 1
+
+    pyfia_df = pl.DataFrame(rows).rename({"state": "STATE"})
+
+    epa_df = load_state_flux_targets().rename(
+        {
+            "state": "STATE",
+            "flux_mmt_c_2022": "EPA_FLUX_MMT_C",
+            "lower_bound_mmt_c": "EPA_LOWER",
+            "upper_bound_mmt_c": "EPA_UPPER",
+        }
+    )
+
+    cmp = pyfia_df.join(epa_df, on="STATE", how="left").select(
+        [
+            "STATE",
+            "evalid",
+            "n_remeasured_plots",
+            "pyfia_condition_flux_mmt_c",
+            "EPA_FLUX_MMT_C",
+            "EPA_LOWER",
+            "EPA_UPPER",
+        ]
+    )
+
+    cmp = cmp.with_columns(
+        (pl.col("EPA_FLUX_MMT_C") - pl.col("pyfia_condition_flux_mmt_c")).alias(
+            "RESIDUAL_TREE_LEVEL_MMT_C"
+        )
+    )
+
+    out = (
+        cmp.rename(
+            {
+                "evalid": "EVALID",
+                "n_remeasured_plots": "N_REMEAS",
+                "pyfia_condition_flux_mmt_c": "PYFIA_COND",
+            }
+        )
+        .select(
+            [
+                "STATE",
+                "EVALID",
+                "N_REMEAS",
+                "PYFIA_COND",
+                "EPA_FLUX_MMT_C",
+                "EPA_LOWER",
+                "EPA_UPPER",
+                "RESIDUAL_TREE_LEVEL_MMT_C",
+            ]
+        )
+        .sort("EPA_FLUX_MMT_C")
+    )
+
+    print("\n=== Per-state flux comparison (2022, MMT C/yr) ===")
+    print("    PYFIA_COND = pyfia condition-level (4 pools) flux")
+    print("    EPA_FLUX = EPA Table A-208 all-pools flux")
+    print("    RESIDUAL = EPA - pyfia_cond = expected tree-level flux")
+    print("    Sign convention: negative = net uptake (sink)\n")
+    with pl.Config(tbl_rows=60, tbl_cols=10, fmt_str_lengths=20, tbl_width_chars=160):
+        print(out)
+
+    print("\n=== CONUS-48 rollup ===")
+    py_total = float(out["PYFIA_COND"].sum())
+    epa_total = float(out["EPA_FLUX_MMT_C"].sum())
+    res_total = float(out["RESIDUAL_TREE_LEVEL_MMT_C"].sum())
+    print(f"  pyfia condition flux total:  {py_total:+.1f} MMT C/yr")
+    print(f"  EPA all-pools flux total:    {epa_total:+.1f} MMT C/yr")
+    print(f"  Residual (= tree-level):     {res_total:+.1f} MMT C/yr")
+    pct_cond = 100.0 * py_total / epa_total if epa_total else float("nan")
+    pct_tree = 100.0 * res_total / epa_total if epa_total else float("nan")
+    print(f"  Condition pools account for: {pct_cond:.1f}% of EPA total")
+    print(f"  Tree pools account for:      {pct_tree:.1f}% of EPA total")
+
+    print("\n  Note: EPA Table A-208 total reported in Annex 3.13: -189.3 MMT C/yr")
+    print(f"  Our CONUS-48 EPA sum:        {epa_total:+.1f} MMT C/yr")
+    print("  (delta from -189.3 reflects HI/AK/territories in EPA total)\n")
+
+    if failures:
+        print(f"Failures ({len(failures)}):")
+        for st, err in failures:
+            print(f"  {st}: {err}")
+
+    in_band = out.filter(
+        (pl.col("PYFIA_COND") >= pl.col("EPA_LOWER"))
+        & (pl.col("PYFIA_COND") <= pl.col("EPA_UPPER"))
+    )
+    print(
+        f"\nStates where pyfia condition flux falls within EPA all-pools "
+        f"uncertainty band: {len(in_band)}/{len(out)}"
+    )
+    print(
+        "(expected to be low — pyfia condition flux is only part of EPA's "
+        "all-pools flux; it would only fall in the band when tree-level "
+        "flux is small or EPA uncertainty is huge)"
+    )
+
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/src/pyfia/__init__.py b/src/pyfia/__init__.py
index 6670b3c3..ade88331 100755
--- a/src/pyfia/__init__.py
+++ b/src/pyfia/__init__.py
@@ -1,14 +1,8 @@
 """
 PyFIA - A Python library for USDA Forest Inventory and Analysis (FIA) data analysis.
 
-A high-performance Python library for analyzing USDA Forest Inventory and Analysis (FIA) data
-using modern data science tools like Polars and DuckDB.
-
-Part of the FIA Python Ecosystem:
-- PyFIA: Survey/plot data analysis (https://github.com/mihiarc/pyfia)
-- GridFIA: Spatial raster analysis (https://github.com/mihiarc/gridfia)
-- PyFVS: Growth/yield simulation (https://github.com/mihiarc/pyfvs)
-- AskFIA: AI conversational interface (https://github.com/mihiarc/askfia)
+A high-performance Python library for analyzing USDA Forest Inventory and
+Analysis (FIA) data using modern data science tools like Polars and DuckDB.
 """
 
 from __future__ import annotations
@@ -17,6 +11,15 @@
 __author__ = "Chris Mihiar"
 
 # Core exports - Main functionality
+# Estimation functions - High-level API
+from pyfia.carbon.downed_dead import downed_dead
+from pyfia.carbon.litter import litter
+from pyfia.carbon.live_tree import live_tree
+from pyfia.carbon.soil_organic import soil_organic
+from pyfia.carbon.standing_dead import standing_dead
+from pyfia.carbon.stock_change import stock_change
+from pyfia.carbon.total_ecosystem import total_ecosystem
+from pyfia.carbon.understory import understory
 from pyfia.core.data_reader import FIADataReader
 from pyfia.core.exceptions import (
     ConfigurationError,
@@ -50,8 +53,6 @@
     clear_cache,
     download,
 )
-
-# Estimation functions - High-level API
 from pyfia.estimation.estimators.area import area
 from pyfia.estimation.estimators.area_change import area_change
 from pyfia.estimation.estimators.biomass import biomass
@@ -113,6 +114,14 @@
     "area",
     "area_change",
     "biomass",
+    "downed_dead",
+    "litter",
+    "live_tree",
+    "soil_organic",
+    "standing_dead",
+    "stock_change",
+    "total_ecosystem",
+    "understory",
     "volume",
     "tpa",
     "mortality",
diff --git a/src/pyfia/carbon/__init__.py b/src/pyfia/carbon/__init__.py
new file mode 100644
index 00000000..bede7de9
--- /dev/null
+++ b/src/pyfia/carbon/__init__.py
@@ -0,0 +1,180 @@
+"""
+Forest carbon estimation for pyFIA — all six IPCC/NGHGI reporting pools.
+
+This subpackage implements forest carbon stock estimation across the IPCC and
+NGHGI reporting pools.  Tree-level pools (live tree, standing dead) use the
+National Scale Volume and Biomass framework (NSVB; Westfall et al. 2023,
+GTR-WO-104) with species-specific carbon fractions.  Condition-level pools
+(understory, downed dead wood, litter, soil organic carbon) read pre-computed
+attributes from the FIADB COND table.  :func:`total_ecosystem` sums all six
+pools into a single estimate.
+
+Pools implemented
+=================
+
+Live tree
+---------
+:func:`live_tree` — above-ground live tree carbon via the vectorized NSVB
+pipeline (Models 1/2/4/5, 3-level coefficient lookup precedence: Bailey
+DIVISION → species-level → Jenkins fallback), with species-specific S10a
+carbon fractions. Cull adjustment per Appendix K. BG bridges to FIADB
+``TREE.CARBON_BG``.
+
+Validated against FIADB ``TREE.CARBON_AG`` on Georgia EVALID 132401
+(130,952 trees): median per-tree relative error 0.085%.
+
+Standing dead
+-------------
+:func:`standing_dead` — standing dead tree carbon via the same NSVB pipeline
+with ``REF_TREE_DECAY_PROP`` decay reductions (DENSITY_PROP × wood,
+BARK_LOSS_PROP × bark, BRANCH_LOSS_PROP × branch) and S10b dead carbon
+fractions. No ``TREE.CULL`` adjustment for dead trees (per Appendix K).
+
+Broken-top corrections (``ACTUALHT < HT``) apply the Appendix K
+crown-proportion adjustment to branch biomass and a paraboloid taper
+volume-ratio approximation to wood/bark, using mean intact crown ratios
+from Table S11 (``REF_TREE_STND_DEAD_CR_PROP``).
+
+Validated against FIADB on Georgia EVALID 132401 (6,870 trees): median
+per-tree relative error 10.89%.
+
+Understory vegetation
+---------------------
+:func:`understory` — understory vegetation carbon (seedlings + woody
+shrubs < 2.54 cm DBH) via the Smith & Heath (2008) model, reading
+pre-computed ``COND.CARBON_UNDERSTORY_AG`` and ``COND.CARBON_UNDERSTORY_BG``
+from the FIADB.  The model descends from the Birdsey (1996) ratios by
+forest type group and region; the AG/BG split is 90/10 (Smith et al. 2006).
+
+This is a **condition-level** estimator (no TREE table); it inherits from
+``BaseEstimator`` directly rather than ``CarbonEstimatorBase``.
+
+Validated against manual SQL replication on Georgia EVALID 132301:
+population totals match exactly.
+
+Downed dead wood
+-----------------
+:func:`downed_dead` — downed dead wood (coarse woody debris) carbon via
+the Domke et al. (2013) model, reading pre-computed
+``COND.CARBON_DOWN_DEAD`` from the FIADB.  No AG/BG split.
+
+This is a **condition-level** estimator (no TREE table); it inherits from
+``BaseEstimator`` directly rather than ``CarbonEstimatorBase``.
+
+Validated against manual SQL replication on Georgia EVALID 132301:
+population totals match exactly.
+
+Litter
+------
+:func:`litter` — litter and duff carbon via the Domke et al. (2016)
+model, reading pre-computed ``COND.CARBON_LITTER`` from the FIADB.
+No AG/BG split.
+
+This is a **condition-level** estimator (no TREE table); it inherits from
+``BaseEstimator`` directly rather than ``CarbonEstimatorBase``.
+
+Validated against manual SQL replication on Georgia EVALID 132301:
+population totals match exactly.
+
+Soil organic carbon
+-------------------
+:func:`soil_organic` — soil organic carbon (mineral soil to 1 m depth)
+via the Domke et al. (2017) model, reading pre-computed
+``COND.CARBON_SOIL_ORG`` from the FIADB.  No AG/BG split.
+
+This is a **condition-level** estimator (no TREE table); it inherits from
+``BaseEstimator`` directly rather than ``CarbonEstimatorBase``.
+
+Validated against manual SQL replication on Georgia EVALID 132301:
+population totals match exactly.
+
+Total ecosystem
+---------------
+:func:`total_ecosystem` — convenience function that estimates all six pools
+independently and sums per-acre and population totals.  The result contains
+one row per pool plus a ``TOTAL_ECOSYSTEM`` summary row.
+
+Validated against sum of individual pool calls on Georgia EVALID 132301:
+exact match (0.00 difference).
+
+Stock change (condition-level)
+------------------------------
+:func:`stock_change` — carbon stock-change accounting for condition-level
+pools.  Computes ``C(t₂) − C(t₁)`` per remeasured condition, annualized
+by REMPER, aggregated via the two-stage post-stratified pipeline using
+t₂'s stratification.  Follows the ``AreaChangeEstimator`` pattern: t₂ from
+EVALID-scoped pipeline, t₁ from full COND table via ``PREV_PLT_CN``.
+
+Supports: understory, downed dead, litter, soil organic (``pool='all'``).
+Tree-level stock change (live tree, standing dead via GRM decomposition) deferred.
+
+Validated against manual SQL replication on Georgia EVALID 132301:
+population totals match exactly for all three non-understory pools.
+
+Pools deferred
+==============
+- Tree-level stock change (live tree, standing dead via GRM decomposition)
+- Native NSVB belowground coarse-root model (replaces the BG bridge)
+
+Architectural rules
+===================
+1. **Public API is functions, not classes.** ``live_tree(db, ...)``,
+   ``standing_dead(db, ...)``. Match the pyfia convention.
+
+2. **Vectorize coefficient lookups via polars joins.** The scalar
+   ``predict_tree_biomass`` is a test oracle only. The production path
+   is ``compute_nsvb_biomass`` / ``compute_nsvb_dead_biomass``.
+
+3. **Inherit from ``CarbonEstimatorBase`` (tree-level) or ``BaseEstimator``
+   (condition-level).** New pool estimators must follow the template-method
+   pattern (``load_data → apply_filters → calculate_values →
+   aggregate_results → calculate_variance → format_output``).
+
+4. **Match the ``mortality()`` docstring quality.**
+
+5. **Bridge BG carbon to FIADB ``CARBON_BG`` for now.** The bridge is
+   acknowledged tech debt; a native NSVB root model will replace it.
+
+References
+----------
+- Westfall, J.A. et al. (2023). GTR-WO-104. DOI: 10.2737/WO-GTR-104
+- Woodall, C.W. et al. (2015). GTR-NRS-154 (FCAF methodology blueprint).
+- Harmon, M.E. et al. (2011). GTR-WO-104 Table 1 (dead-tree density).
+- Smith, J.E. et al. (2006). GTR-NE-343 (understory yield tables).
+- Smith, J.E.; Heath, L.S. (2008). GTR-NRS-13 (FIADB carbon attributes).
+- Birdsey, R.A. (1996). Forests and Global Change Vol. 2 (understory ratios).
+- Domke, G.M. et al. (2013). Forest Ecol. Manage. 292, 50-57 (downed dead).
+- Domke, G.M. et al. (2016). Sci. Total Environ. 557-558, 469-478 (litter).
+- Domke, G.M. et al. (2017). Ecol. Appl. 27(4), 1223-1235 (soil organic C).
+- Bechtold, W.A. & Patterson, P.L. (2005). GTR-SRS-80, Ch. 4 (change estimation).
+- USEPA (2024). NGHGI Annex 3.13.
+"""
+
+from __future__ import annotations
+
+from pyfia.carbon.downed_dead import DownedDeadEstimator, downed_dead
+from pyfia.carbon.litter import LitterEstimator, litter
+from pyfia.carbon.live_tree import LiveTreeEstimator, live_tree
+from pyfia.carbon.soil_organic import SoilOrganicEstimator, soil_organic
+from pyfia.carbon.standing_dead import StandingDeadEstimator, standing_dead
+from pyfia.carbon.stock_change import CarbonStockChangeEstimator, stock_change
+from pyfia.carbon.total_ecosystem import total_ecosystem
+from pyfia.carbon.understory import UnderstoryEstimator, understory
+
+__all__ = [
+    "downed_dead",
+    "litter",
+    "live_tree",
+    "soil_organic",
+    "standing_dead",
+    "stock_change",
+    "total_ecosystem",
+    "understory",
+    "CarbonStockChangeEstimator",
+    "DownedDeadEstimator",
+    "LitterEstimator",
+    "LiveTreeEstimator",
+    "SoilOrganicEstimator",
+    "StandingDeadEstimator",
+    "UnderstoryEstimator",
+]
diff --git a/src/pyfia/carbon/_estimator_base.py b/src/pyfia/carbon/_estimator_base.py
new file mode 100644
index 00000000..0fecf946
--- /dev/null
+++ b/src/pyfia/carbon/_estimator_base.py
@@ -0,0 +1,270 @@
+"""
+Shared base class for NSVB carbon pool estimators.
+
+Extracts the ~200 lines of duplicated infrastructure that LiveTreeEstimator
+and StandingDeadEstimator share verbatim: reference-table loading
+(_load_ref_species, _load_plotgeom), the two-stage aggregation pipeline,
+variance calculation, and output formatting. Pool-specific logic
+(calculate_values, apply_filters, get_tree_columns) stays in the subclasses.
+"""
+
+from __future__ import annotations
+
+import logging
+
+import polars as pl
+
+from ..core import FIA
+from ..estimation.base import AggregationResult, BaseEstimator
+from ..estimation.columns import get_cond_columns as _get_cond_columns
+from ..estimation.constants import LBS_TO_SHORT_TONS
+from ..estimation.tree_expansion import apply_tree_adjustment_factors
+from ..estimation.utils import (
+    validate_aggregation_result,
+    validate_required_columns,
+)
+from .nsvb.coefficients import ecosubcd_to_division_expr
+
+logger = logging.getLogger(__name__)
+
+
+class CarbonEstimatorBase(BaseEstimator):
+    """Shared infrastructure for NSVB carbon pool estimators.
+
+    Subclasses must implement :meth:`get_tree_columns` and
+    :meth:`calculate_values`. Everything else — table requirements,
+    condition columns, reference-table loading, aggregation, variance,
+    and output formatting — is identical across pools.
+
+    Class attributes that subclasses should set:
+
+    - ``_estimator_label``: human-readable name for validation messages
+      (e.g., ``"live_tree"``, ``"standing_dead"``).
+    """
+
+    _estimator_label: str = "carbon"
+
+    def __init__(self, db: str | FIA, config: dict) -> None:
+        super().__init__(db, config)
+        self._plotgeom_cache: pl.DataFrame | None = None
+
+    # ------------------------------------------------------------------
+    # Table / column requirements
+    # ------------------------------------------------------------------
+
+    def get_required_tables(self) -> list[str]:
+        return ["TREE", "COND", "PLOT", "POP_PLOT_STRATUM_ASSGN", "POP_STRATUM"]
+
+    def get_cond_columns(self) -> list[str]:
+        return _get_cond_columns(
+            land_type=self.config.get("land_type", "forest"),
+            grp_by=self.config.get("grp_by"),
+            include_prop_basis=False,
+        )
+
+    # ------------------------------------------------------------------
+    # Reference-table helpers
+    # ------------------------------------------------------------------
+
+    def _load_ref_species(self) -> pl.DataFrame:
+        """Load REF_SPECIES columns needed by the NSVB pipeline.
+
+        Returns ``(SPCD Int64, JENKINS_SPGRPCD Int64, WDSG Float64)``.
+        Cached on the instance for the duration of one estimator run.
+        """
+        if self._ref_species_cache is not None:
+            return self._ref_species_cache
+        df = self.db._reader.read_table(
+            "REF_SPECIES",
+            columns=["SPCD", "JENKINS_SPGRPCD", "WOOD_SPGR_GREENVOL_DRYWT"],
+        )
+        if hasattr(df, "collect"):
+            df = df.collect()
+        df = df.with_columns(
+            [
+                pl.col("SPCD").cast(pl.Int64),
+                pl.col("JENKINS_SPGRPCD").cast(pl.Int64),
+                pl.col("WOOD_SPGR_GREENVOL_DRYWT").cast(pl.Float64).alias("WDSG"),
+            ]
+        ).select(["SPCD", "JENKINS_SPGRPCD", "WDSG"])
+        self._ref_species_cache = df
+        return df
+
+    def _load_plotgeom(self) -> pl.DataFrame | None:
+        """Load ``PLOTGEOM.ECOSUBCD`` for the DIVISION / ECOPROV lookups.
+
+        Returns ``(PLT_CN, ECOSUBCD)`` or ``None`` when the table is
+        missing.  Negative result cached as an empty DataFrame sentinel.
+        """
+        if self._plotgeom_cache is not None:
+            return self._plotgeom_cache if self._plotgeom_cache.height > 0 else None
+
+        try:
+            df = self.db._reader.read_table(
+                "PLOTGEOM",
+                columns=["CN", "ECOSUBCD"],
+            )
+        except Exception as exc:  # noqa: BLE001
+            logger.warning(
+                "PLOTGEOM not available (%s) — DIVISION lookup disabled, "
+                "falling back to species-level + Jenkins coefficient "
+                "precedence (~3%% high biomass bias on growing-stock trees).",
+                exc,
+            )
+            self._plotgeom_cache = pl.DataFrame()
+            return None
+
+        if hasattr(df, "collect"):
+            df = df.collect()
+        df = df.select(
+            [
+                pl.col("CN").alias("PLT_CN"),
+                pl.col("ECOSUBCD"),
+            ]
+        ).unique(subset=["PLT_CN"])
+        self._plotgeom_cache = df
+        return df
+
+    # ------------------------------------------------------------------
+    # Shared helpers used inside calculate_values
+    # ------------------------------------------------------------------
+
+    def _join_ref_species(self, data: pl.LazyFrame) -> pl.LazyFrame:
+        """Normalize SPCD dtype and join REF_SPECIES for WDSG + JENKINS_SPGRPCD."""
+        data = data.with_columns(pl.col("SPCD").cast(pl.Int64))
+        ref_species = self._load_ref_species()
+        return data.join(ref_species.lazy(), on="SPCD", how="left")
+
+    def _join_plotgeom_division(self, data: pl.LazyFrame) -> pl.LazyFrame:
+        """Join PLOTGEOM and derive DIVISION from ECOSUBCD.
+
+        Adds both ``ECOSUBCD`` and ``DIVISION`` columns when PLOTGEOM is
+        available; otherwise returns the frame unchanged.
+        """
+        plotgeom = self._load_plotgeom()
+        if plotgeom is None:
+            return data
+        data = data.join(plotgeom.lazy(), on="PLT_CN", how="left")
+        data = data.with_columns(
+            ecosubcd_to_division_expr("ECOSUBCD").alias("DIVISION")
+        )
+        return data
+
+    def _apply_bg_bridge(self, data: pl.LazyFrame, pool: str) -> pl.LazyFrame:
+        """Add ``_CARBON_BG_LB`` column from the FIADB BG bridge.
+
+        For ``pool in ('bg', 'total')``, reads ``TREE.CARBON_BG`` directly
+        (in lb per tree).  For ``pool='ag'``, zeroes out the BG
+        contribution.  BG bridge is in pounds per tree — the same units as
+        ``_CARBON_AG_LB``.
+        """
+        if pool in ("bg", "total"):
+            # TREE.CARBON_BG is in pounds per tree (same as _CARBON_AG_LB).
+            # This BG bridge will be replaced by a native NSVB root model.
+            return data.with_columns(
+                pl.col("CARBON_BG")
+                .cast(pl.Float64)
+                .fill_null(0.0)
+                .alias("_CARBON_BG_LB")
+            )
+        return data.with_columns(pl.lit(0.0).alias("_CARBON_BG_LB"))
+
+    def _compute_carbon_acre(self, data: pl.LazyFrame) -> pl.LazyFrame:
+        """Sum AG + BG, convert lb → short tons, multiply by TPA_UNADJ."""
+        return data.with_columns(
+            (
+                (pl.col("_CARBON_AG_LB") + pl.col("_CARBON_BG_LB"))
+                * pl.col("TPA_UNADJ").cast(pl.Float64)
+                * LBS_TO_SHORT_TONS
+            ).alias("CARBON_ACRE"),
+        )
+
+    # ------------------------------------------------------------------
+    # Aggregation / variance / formatting (identical across pools)
+    # ------------------------------------------------------------------
+
+    def aggregate_results(self, data: pl.LazyFrame | None) -> AggregationResult:
+        if data is None:
+            return AggregationResult(
+                results=pl.DataFrame(),
+                plot_tree_data=pl.DataFrame(),
+                group_cols=[],
+            )
+
+        validate_required_columns(
+            data, ["PLT_CN", "CARBON_ACRE"], f"{self._estimator_label} carbon data"
+        )
+
+        strat_data = self._get_stratification_data()
+        data_with_strat = data.join(strat_data, on="PLT_CN", how="inner")
+
+        data_with_strat = apply_tree_adjustment_factors(
+            data_with_strat,
+            size_col="DIA",
+            macro_breakpoint_col="MACRO_BREAKPOINT_DIA",
+        )
+
+        data_with_strat = data_with_strat.with_columns(
+            (pl.col("CARBON_ACRE") * pl.col("ADJ_FACTOR")).alias("CARBON_ADJ")
+        )
+
+        group_cols = self._setup_grouping()
+
+        plot_tree_data, data_with_strat = self._preserve_plot_tree_data(
+            data_with_strat,
+            metric_cols=["CARBON_ADJ"],
+            group_cols=group_cols,
+        )
+
+        results = self._apply_two_stage_aggregation(
+            data_with_strat=data_with_strat,
+            metric_mappings={"CARBON_ADJ": "CONDITION_CARBON"},
+            group_cols=group_cols,
+            use_grm_adjustment=False,
+        )
+
+        if not self.config.get("totals", True):
+            if "CARBON_TOTAL" in results.columns:
+                results = results.drop("CARBON_TOTAL")
+
+        return AggregationResult(
+            results=results,
+            plot_tree_data=plot_tree_data,
+            group_cols=group_cols,
+        )
+
+    def calculate_variance(self, agg_result: AggregationResult) -> pl.DataFrame:
+        validate_aggregation_result(agg_result, self._estimator_label)
+        metric_configs = [
+            {
+                "adjusted_col": "CARBON_ADJ",
+                "acre_se_col": "CARBON_ACRE_SE",
+                "total_se_col": "CARBON_TOTAL_SE",
+            },
+        ]
+        return self._calculate_variance_for_metrics(agg_result, metric_configs)
+
+    def format_output(self, results: pl.DataFrame) -> pl.DataFrame:
+        year = self._extract_evaluation_year()
+        results = results.with_columns(pl.lit(year).alias("YEAR"))
+
+        pool = self.config.get("pool", "ag").upper()
+        results = results.with_columns(pl.lit(pool).alias("POOL"))
+
+        col_order = [
+            "YEAR",
+            "POOL",
+            "CARBON_ACRE",
+            "CARBON_TOTAL",
+            "CARBON_ACRE_SE",
+            "CARBON_TOTAL_SE",
+            "N_PLOTS",
+            "N_TREES",
+        ]
+
+        for col in results.columns:
+            if col not in col_order:
+                col_order.insert(1, col)
+
+        final_cols = [col for col in col_order if col in results.columns]
+        return results.select(final_cols)
diff --git a/src/pyfia/carbon/downed_dead.py b/src/pyfia/carbon/downed_dead.py
new file mode 100644
index 00000000..b2f23a85
--- /dev/null
+++ b/src/pyfia/carbon/downed_dead.py
@@ -0,0 +1,417 @@
+"""
+Downed dead wood carbon estimation from FIADB condition-level attributes.
+
+Downed dead wood — coarse woody debris (CWD) lying on the forest floor
+— is estimated in the FIADB using the
+Domke et al. (2013) model, which relates CWD carbon density to geographic
+area, forest type group, and stand age class.
+
+This estimator reads ``COND.CARBON_DOWN_DEAD`` (short tons per acre),
+then runs it through pyFIA's post-stratified aggregation pipeline to
+produce per-acre and population estimates that match EVALIDator.
+
+Downed dead wood has no above-ground / below-ground split; the single
+``CARBON_DOWN_DEAD`` column represents the total pool.
+
+National magnitude: ~2,700 Tg C total downed dead wood, roughly 3 % of
+total forest ecosystem carbon (GTR-NRS-154, Table 2).
+
+Public API: :func:`downed_dead`.  See its docstring for parameters,
+examples, and the pool semantics.
+
+References
+----------
+- Domke, G.M.; Woodall, C.W.; Smith, J.E. (2013). Accounting for density
+  reductions in dead wood decay classes. Forest Ecology and Management,
+  292, 50-57.
+- Woodall, C.W. et al. (2015). GTR-NRS-154 (FCAF methodology blueprint).
+- USEPA (2024). NGHGI Annex 3.13.
+"""
+
+from __future__ import annotations
+
+import logging
+
+import polars as pl
+
+from ..core import FIA
+from ..estimation.base import AggregationResult, BaseEstimator
+from ..estimation.columns import get_cond_columns as _get_cond_columns
+from ..estimation.utils import (
+    ensure_evalid_set,
+    ensure_fia_instance,
+    validate_aggregation_result,
+    validate_required_columns,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class DownedDeadEstimator(BaseEstimator):
+    """Downed dead wood carbon estimator.
+
+    Reads pre-computed ``COND.CARBON_DOWN_DEAD`` from the FIADB and
+    aggregates via the standard post-stratified estimation pipeline.
+
+    This is a **condition-level** estimator — there is no tree-level data.
+    The TREE table is not loaded; data comes from ``COND x PLOT`` only.
+    The adjustment factor is ``ADJ_FACTOR_SUBP`` (subplot-level),
+    matching FIADB/EVALIDator conventions for condition-level carbon
+    attributes.
+    """
+
+    _estimator_label = "DownedDead"
+
+    # ------------------------------------------------------------------
+    # Table / column requirements
+    # ------------------------------------------------------------------
+
+    def get_required_tables(self) -> list[str]:
+        return ["COND", "PLOT", "POP_PLOT_STRATUM_ASSGN", "POP_STRATUM"]
+
+    def get_tree_columns(self) -> list[str]:
+        return []
+
+    def get_cond_columns(self) -> list[str]:
+        cols = _get_cond_columns(
+            land_type=self.config.get("land_type", "forest"),
+            grp_by=self.config.get("grp_by"),
+            include_prop_basis=False,
+        )
+        if "CARBON_DOWN_DEAD" not in cols:
+            cols.append("CARBON_DOWN_DEAD")
+        return cols
+
+    # ------------------------------------------------------------------
+    # Override apply_filters for condition-level data
+    # ------------------------------------------------------------------
+
+    def apply_filters(self, data: pl.LazyFrame) -> pl.LazyFrame:
+        """Apply condition/area filters only (no tree-type filtering)."""
+        from ..filtering import apply_area_filters, get_land_domain_indicator
+
+        columns = data.collect_schema().names()
+
+        if self.config.get("area_domain"):
+            data = apply_area_filters(data, area_domain=self.config["area_domain"])
+
+        land_type = self.config.get("land_type", "forest")
+        if land_type and land_type != "all" and "COND_STATUS_CD" in columns:
+            data = data.filter(get_land_domain_indicator(land_type))
+
+        return data
+
+    # ------------------------------------------------------------------
+    # Core estimation logic
+    # ------------------------------------------------------------------
+
+    def calculate_values(self, data: pl.LazyFrame) -> pl.LazyFrame:
+        """Alias CARBON_DOWN_DEAD to CARBON_ACRE."""
+        cond_carbon = pl.col("CARBON_DOWN_DEAD").cast(pl.Float64).fill_null(0.0)
+        return data.with_columns(cond_carbon.alias("CARBON_ACRE"))
+
+    # ------------------------------------------------------------------
+    # Aggregation — condition-level with ADJ_FACTOR_SUBP
+    # ------------------------------------------------------------------
+
+    def aggregate_results(self, data: pl.LazyFrame | None) -> AggregationResult:
+        if data is None:
+            return AggregationResult(
+                results=pl.DataFrame(),
+                plot_tree_data=pl.DataFrame(),
+                group_cols=[],
+            )
+
+        validate_required_columns(
+            data, ["PLT_CN", "CARBON_ACRE"], "downed dead carbon data"
+        )
+
+        strat_data = self._get_stratification_data()
+        data_with_strat = data.join(strat_data, on="PLT_CN", how="inner")
+
+        # Condition-level attribute: CARBON_ACRE is a density (tons/acre).
+        # The two-stage pipeline denominator is sum(CONDPROP_UNADJ * EXPNS),
+        # so the numerator must include CONDPROP_UNADJ to give the
+        # condition's contribution proportional to its area on the plot.
+        # ADJ_FACTOR_SUBP corrects for nonresponse at the subplot level.
+        data_with_strat = data_with_strat.with_columns(
+            pl.col("ADJ_FACTOR_SUBP").cast(pl.Float64).alias("ADJ_FACTOR")
+        )
+
+        data_with_strat = data_with_strat.with_columns(
+            (
+                pl.col("CARBON_ACRE")
+                * pl.col("CONDPROP_UNADJ").cast(pl.Float64)
+                * pl.col("ADJ_FACTOR")
+            ).alias("CARBON_ADJ")
+        )
+
+        group_cols = self._setup_grouping()
+
+        plot_tree_data, data_with_strat = self._preserve_plot_tree_data(
+            data_with_strat,
+            metric_cols=["CARBON_ADJ"],
+            group_cols=group_cols,
+        )
+
+        results = self._apply_two_stage_aggregation(
+            data_with_strat=data_with_strat,
+            metric_mappings={"CARBON_ADJ": "CONDITION_CARBON"},
+            group_cols=group_cols,
+            use_grm_adjustment=False,
+        )
+
+        if not self.config.get("totals", True):
+            if "CARBON_TOTAL" in results.columns:
+                results = results.drop("CARBON_TOTAL")
+
+        return AggregationResult(
+            results=results,
+            plot_tree_data=plot_tree_data,
+            group_cols=group_cols,
+        )
+
+    # ------------------------------------------------------------------
+    # Variance and output formatting
+    # ------------------------------------------------------------------
+
+    def calculate_variance(self, agg_result: AggregationResult) -> pl.DataFrame:
+        validate_aggregation_result(agg_result, self._estimator_label)
+        metric_configs = [
+            {
+                "adjusted_col": "CARBON_ADJ",
+                "acre_se_col": "CARBON_ACRE_SE",
+                "total_se_col": "CARBON_TOTAL_SE",
+            },
+        ]
+        return self._calculate_variance_for_metrics(agg_result, metric_configs)
+
+    def format_output(self, results: pl.DataFrame) -> pl.DataFrame:
+        year = self._extract_evaluation_year()
+        results = results.with_columns(pl.lit(year).alias("YEAR"))
+        results = results.with_columns(pl.lit("TOTAL").alias("POOL"))
+
+        col_order = [
+            "YEAR",
+            "POOL",
+            "CARBON_ACRE",
+            "CARBON_TOTAL",
+            "CARBON_ACRE_SE",
+            "CARBON_TOTAL_SE",
+            "N_PLOTS",
+            "N_TREES",
+        ]
+
+        for col in results.columns:
+            if col not in col_order:
+                col_order.insert(1, col)
+
+        final_cols = [col for col in col_order if col in results.columns]
+        return results.select(final_cols)
+
+
+# ======================================================================
+# Public API
+# ======================================================================
+
+
+def downed_dead(
+    db: str | FIA,
+    pool: str = "total",
+    grp_by: str | list[str] | None = None,
+    land_type: str = "forest",
+    area_domain: str | None = None,
+    plot_domain: str | None = None,
+    totals: bool = True,
+    variance: bool = False,
+    most_recent: bool = False,
+) -> pl.DataFrame:
+    """
+    Estimate downed dead wood carbon from FIA data.
+
+    Downed dead wood (coarse woody debris) encompasses all dead wood
+    lying on the forest floor with a diameter >= 7.62 cm (3 in) at the
+    point of intersection with a sampling transect.  Carbon density is
+    estimated from the Domke et al. (2013) model, parameterised on
+    geographic area, forest type group, and stand age.  The FIADB
+    pre-computes condition-level carbon density and stores it in
+    ``COND.CARBON_DOWN_DEAD``.
+
+    Nationally, downed dead wood carbon totals ~2,700 Tg C, representing
+    ~3 % of total forest ecosystem carbon (GTR-NRS-154, Table 2).
+    Per-acre values are typically 1-5 short tons/acre depending on forest
+    type and region.
+
+    Parameters
+    ----------
+    db : str | FIA
+        Database connection or path to FIA database. Can be either a path
+        string to a DuckDB/SQLite file or an existing FIA connection object.
+    pool : {'total'}, default 'total'
+        Carbon pool to estimate.  Downed dead wood has no above-ground /
+        below-ground split — only ``'total'`` is accepted.
+    grp_by : str or list of str, optional
+        Column name(s) to group results by. Can be any column from the
+        FIA COND or PLOT tables. Common grouping columns include:
+
+        - 'FORTYPCD': Forest type code
+        - 'FORTYPGRPCD': Forest type group code
+        - 'OWNGRPCD': Ownership group
+        - 'STATECD': State FIPS code
+        - 'COUNTYCD': County code
+
+        For complete column descriptions, see USDA FIA Database User Guide.
+    land_type : {'forest', 'timber', 'all'}, default 'forest'
+        Land type to include in estimation:
+
+        - 'forest': All forestland
+        - 'timber': Productive timberland only (unreserved, productive)
+        - 'all': All land conditions
+    area_domain : str, optional
+        SQL-like filter expression for area/condition-level filtering.
+        Example: ``"OWNGRPCD == 40 AND FORTYPCD == 161"``.
+    plot_domain : str, optional
+        SQL-like filter expression for plot-level filtering.
+    totals : bool, default True
+        If True, include population-level total estimates in addition to
+        per-acre values.
+    variance : bool, default False
+        If True, calculate and include variance and standard error
+        estimates following Bechtold & Patterson (2005).
+    most_recent : bool, default False
+        If True, automatically filter to the most recent EXPVOL evaluation
+        for each state in the database before estimation.
+
+    Returns
+    -------
+    pl.DataFrame
+        Downed dead wood carbon estimates with the following columns:
+
+        - **YEAR** : int
+            Evaluation reference year from EVALID.
+        - **POOL** : str
+            Pool identifier — always ``'TOTAL'``.
+        - **CARBON_ACRE** : float
+            Carbon per acre in short tons.
+        - **CARBON_TOTAL** : float (if ``totals=True``)
+            Total carbon in short tons expanded to population level.
+        - **CARBON_ACRE_SE** : float (if ``variance=True``)
+            Standard error of the per-acre estimate.
+        - **CARBON_TOTAL_SE** : float (if ``variance=True`` and ``totals=True``)
+            Standard error of the population total.
+        - **N_PLOTS** : int
+            Number of FIA plots included in the estimation.
+        - **N_TREES** : int
+            Number of tree records (used for distribution weighting).
+        - **[grouping columns]** : various
+            Any columns specified in ``grp_by``.
+
+    See Also
+    --------
+    live_tree : Estimate live tree carbon using the NSVB framework.
+    standing_dead : Estimate standing dead tree carbon.
+    understory : Estimate understory vegetation carbon.
+    litter : Estimate litter carbon.
+    soil_organic : Estimate soil organic carbon.
+    pyfia.carbon : Overview of all carbon pool estimators.
+
+    Notes
+    -----
+    **Methodology**
+
+    Downed dead wood is not directly measured on standard FIA plots in a
+    manner that produces per-condition densities.  Instead, the FIADB
+    pre-computes carbon density per condition using the Domke et al.
+    (2013) model, which accounts for density reductions across decay
+    classes and uses regional CWD volume equations.
+
+    This estimator reads those pre-computed values directly from the COND
+    table and runs them through the standard post-stratified aggregation
+    pipeline, ensuring exact agreement with EVALIDator estimates.
+
+    **No AG/BG split**
+
+    Unlike understory vegetation, downed dead wood has no above-ground /
+    below-ground partitioning.  The ``CARBON_DOWN_DEAD`` column represents
+    the entire pool.
+
+    Examples
+    --------
+    Total downed dead wood carbon per acre on forestland:
+
+    >>> results = downed_dead(db, pool="total")
+    >>> print(f"Carbon: {results['CARBON_ACRE'][0]:.3f} tons/acre")
+
+    Downed dead wood carbon by forest type group:
+
+    >>> results = downed_dead(db, pool="total", grp_by="FORTYPGRPCD")
+
+    Downed dead wood carbon on timberland with standard errors:
+
+    >>> results = downed_dead(
+    ...     db,
+    ...     land_type="timber",
+    ...     variance=True,
+    ... )
+
+    References
+    ----------
+    .. [1] Domke, G.M.; Woodall, C.W.; Smith, J.E. (2013). Accounting for
+       density reductions in dead wood decay classes. Forest Ecology and
+       Management, 292, 50-57.
+    .. [2] Woodall, C.W. et al. (2015). The current and future role of
+       forest carbon in the United States. Gen. Tech. Rep. NRS-154.
+    .. [3] USEPA (2024). Inventory of U.S. Greenhouse Gas Emissions and
+       Sinks, Chapter 6 and Annex 3.13.
+    """
+    from ..validation import (
+        validate_boolean,
+        validate_domain_expression,
+        validate_grp_by,
+        validate_land_type,
+    )
+
+    # ----- Validate pool -----
+    pool = pool.lower()
+    if pool != "total":
+        raise ValueError(
+            f"Invalid pool '{pool}' for downed dead wood. "
+            f"Only 'total' is supported — downed dead wood has no AG/BG split."
+        )
+
+    # ----- Validate standard inputs -----
+    land_type = validate_land_type(land_type)
+    grp_by = validate_grp_by(grp_by)
+    area_domain = validate_domain_expression(area_domain, "area_domain")
+    plot_domain = validate_domain_expression(plot_domain, "plot_domain")
+    totals = validate_boolean(totals, "totals")
+    variance = validate_boolean(variance, "variance")
+    most_recent = validate_boolean(most_recent, "most_recent")
+
+    # ----- Resolve db + EVALID -----
+    db, owns_db = ensure_fia_instance(db)
+    if most_recent and db.evalid is None:
+        db.clip_most_recent(eval_type="VOL")
+    else:
+        ensure_evalid_set(db, eval_type="VOL", estimator_name="downed_dead")
+
+    # ----- Build config and run estimator -----
+    config = {
+        "pool": pool,
+        "grp_by": grp_by,
+        "by_species": False,
+        "by_size_class": False,
+        "land_type": land_type,
+        "area_domain": area_domain,
+        "plot_domain": plot_domain,
+        "totals": totals,
+        "variance": variance,
+        "most_recent": most_recent,
+    }
+
+    try:
+        estimator = DownedDeadEstimator(db, config)
+        return estimator.estimate()
+    finally:
+        if owns_db and hasattr(db, "close"):
+            db.close()
diff --git a/src/pyfia/carbon/litter.py b/src/pyfia/carbon/litter.py
new file mode 100644
index 00000000..55a70681
--- /dev/null
+++ b/src/pyfia/carbon/litter.py
@@ -0,0 +1,414 @@
+"""
+Litter carbon estimation from FIADB condition-level attributes.
+
+Litter — organic material on the forest floor including decomposing
+leaves, needles, fine woody debris, and humus (duff) — is estimated in
+the FIADB using the Domke et al. (2016) model,
+which relates litter carbon density to geographic area, forest type
+group, and stand age class.
+
+This estimator reads ``COND.CARBON_LITTER`` (short tons per acre), then
+runs it through pyFIA's post-stratified aggregation pipeline to produce
+per-acre and population estimates that match EVALIDator.
+
+Litter has no above-ground / below-ground split; the single
+``CARBON_LITTER`` column represents the total pool.
+
+National magnitude: ~5,200 Tg C total litter, roughly 6 % of total
+forest ecosystem carbon (GTR-NRS-154, Table 2).
+
+Public API: :func:`litter`.  See its docstring for parameters,
+examples, and the pool semantics.
+
+References
+----------
+- Domke, G.M.; Perry, C.H.; Walters, B.F.; et al. (2016). Estimating
+  litter carbon stocks on forest land in the United States. Science of
+  the Total Environment, 557-558, 469-478.
+- Woodall, C.W. et al. (2015). GTR-NRS-154 (FCAF methodology blueprint).
+- USEPA (2024). NGHGI Annex 3.13.
+"""
+
+from __future__ import annotations
+
+import logging
+
+import polars as pl
+
+from ..core import FIA
+from ..estimation.base import AggregationResult, BaseEstimator
+from ..estimation.columns import get_cond_columns as _get_cond_columns
+from ..estimation.utils import (
+    ensure_evalid_set,
+    ensure_fia_instance,
+    validate_aggregation_result,
+    validate_required_columns,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class LitterEstimator(BaseEstimator):
+    """Litter carbon estimator.
+
+    Reads pre-computed ``COND.CARBON_LITTER`` from the FIADB and
+    aggregates via the standard post-stratified estimation pipeline.
+
+    This is a **condition-level** estimator — there is no tree-level data.
+    The TREE table is not loaded; data comes from ``COND x PLOT`` only.
+    The adjustment factor is ``ADJ_FACTOR_SUBP`` (subplot-level),
+    matching FIADB/EVALIDator conventions for condition-level carbon
+    attributes.
+    """
+
+    _estimator_label = "Litter"
+
+    # ------------------------------------------------------------------
+    # Table / column requirements
+    # ------------------------------------------------------------------
+
+    def get_required_tables(self) -> list[str]:
+        return ["COND", "PLOT", "POP_PLOT_STRATUM_ASSGN", "POP_STRATUM"]
+
+    def get_tree_columns(self) -> list[str]:
+        return []
+
+    def get_cond_columns(self) -> list[str]:
+        cols = _get_cond_columns(
+            land_type=self.config.get("land_type", "forest"),
+            grp_by=self.config.get("grp_by"),
+            include_prop_basis=False,
+        )
+        if "CARBON_LITTER" not in cols:
+            cols.append("CARBON_LITTER")
+        return cols
+
+    # ------------------------------------------------------------------
+    # Override apply_filters for condition-level data
+    # ------------------------------------------------------------------
+
+    def apply_filters(self, data: pl.LazyFrame) -> pl.LazyFrame:
+        """Apply condition/area filters only (no tree-type filtering)."""
+        from ..filtering import apply_area_filters, get_land_domain_indicator
+
+        columns = data.collect_schema().names()
+
+        if self.config.get("area_domain"):
+            data = apply_area_filters(data, area_domain=self.config["area_domain"])
+
+        land_type = self.config.get("land_type", "forest")
+        if land_type and land_type != "all" and "COND_STATUS_CD" in columns:
+            data = data.filter(get_land_domain_indicator(land_type))
+
+        return data
+
+    # ------------------------------------------------------------------
+    # Core estimation logic
+    # ------------------------------------------------------------------
+
+    def calculate_values(self, data: pl.LazyFrame) -> pl.LazyFrame:
+        """Alias CARBON_LITTER to CARBON_ACRE."""
+        cond_carbon = pl.col("CARBON_LITTER").cast(pl.Float64).fill_null(0.0)
+        return data.with_columns(cond_carbon.alias("CARBON_ACRE"))
+
+    # ------------------------------------------------------------------
+    # Aggregation — condition-level with ADJ_FACTOR_SUBP
+    # ------------------------------------------------------------------
+
+    def aggregate_results(self, data: pl.LazyFrame | None) -> AggregationResult:
+        if data is None:
+            return AggregationResult(
+                results=pl.DataFrame(),
+                plot_tree_data=pl.DataFrame(),
+                group_cols=[],
+            )
+
+        validate_required_columns(data, ["PLT_CN", "CARBON_ACRE"], "litter carbon data")
+
+        strat_data = self._get_stratification_data()
+        data_with_strat = data.join(strat_data, on="PLT_CN", how="inner")
+
+        # Condition-level attribute: CARBON_ACRE is a density (tons/acre).
+        # The two-stage pipeline denominator is sum(CONDPROP_UNADJ * EXPNS),
+        # so the numerator must include CONDPROP_UNADJ to give the
+        # condition's contribution proportional to its area on the plot.
+        # ADJ_FACTOR_SUBP corrects for nonresponse at the subplot level.
+        data_with_strat = data_with_strat.with_columns(
+            pl.col("ADJ_FACTOR_SUBP").cast(pl.Float64).alias("ADJ_FACTOR")
+        )
+
+        data_with_strat = data_with_strat.with_columns(
+            (
+                pl.col("CARBON_ACRE")
+                * pl.col("CONDPROP_UNADJ").cast(pl.Float64)
+                * pl.col("ADJ_FACTOR")
+            ).alias("CARBON_ADJ")
+        )
+
+        group_cols = self._setup_grouping()
+
+        plot_tree_data, data_with_strat = self._preserve_plot_tree_data(
+            data_with_strat,
+            metric_cols=["CARBON_ADJ"],
+            group_cols=group_cols,
+        )
+
+        results = self._apply_two_stage_aggregation(
+            data_with_strat=data_with_strat,
+            metric_mappings={"CARBON_ADJ": "CONDITION_CARBON"},
+            group_cols=group_cols,
+            use_grm_adjustment=False,
+        )
+
+        if not self.config.get("totals", True):
+            if "CARBON_TOTAL" in results.columns:
+                results = results.drop("CARBON_TOTAL")
+
+        return AggregationResult(
+            results=results,
+            plot_tree_data=plot_tree_data,
+            group_cols=group_cols,
+        )
+
+    # ------------------------------------------------------------------
+    # Variance and output formatting
+    # ------------------------------------------------------------------
+
+    def calculate_variance(self, agg_result: AggregationResult) -> pl.DataFrame:
+        validate_aggregation_result(agg_result, self._estimator_label)
+        metric_configs = [
+            {
+                "adjusted_col": "CARBON_ADJ",
+                "acre_se_col": "CARBON_ACRE_SE",
+                "total_se_col": "CARBON_TOTAL_SE",
+            },
+        ]
+        return self._calculate_variance_for_metrics(agg_result, metric_configs)
+
+    def format_output(self, results: pl.DataFrame) -> pl.DataFrame:
+        year = self._extract_evaluation_year()
+        results = results.with_columns(pl.lit(year).alias("YEAR"))
+        results = results.with_columns(pl.lit("TOTAL").alias("POOL"))
+
+        col_order = [
+            "YEAR",
+            "POOL",
+            "CARBON_ACRE",
+            "CARBON_TOTAL",
+            "CARBON_ACRE_SE",
+            "CARBON_TOTAL_SE",
+            "N_PLOTS",
+            "N_TREES",
+        ]
+
+        for col in results.columns:
+            if col not in col_order:
+                col_order.insert(1, col)
+
+        final_cols = [col for col in col_order if col in results.columns]
+        return results.select(final_cols)
+
+
+# ======================================================================
+# Public API
+# ======================================================================
+
+
+def litter(
+    db: str | FIA,
+    pool: str = "total",
+    grp_by: str | list[str] | None = None,
+    land_type: str = "forest",
+    area_domain: str | None = None,
+    plot_domain: str | None = None,
+    totals: bool = True,
+    variance: bool = False,
+    most_recent: bool = False,
+) -> pl.DataFrame:
+    """
+    Estimate litter carbon from FIA data.
+
+    Litter encompasses all organic material on the forest floor including
+    decomposing leaves, needles, fine woody debris (< 7.62 cm / 3 in
+    diameter), and humus (duff).  Carbon density is estimated from the
+    Domke et al. (2016) model, parameterised on geographic area, forest
+    type group, and stand age.  The FIADB pre-computes condition-level
+    carbon density and stores it in ``COND.CARBON_LITTER``.
+
+    Nationally, litter carbon totals ~5,200 Tg C, representing ~6 % of
+    total forest ecosystem carbon (GTR-NRS-154, Table 2).  Per-acre
+    values are typically 3-10 short tons/acre depending on forest type
+    and region.
+
+    Parameters
+    ----------
+    db : str | FIA
+        Database connection or path to FIA database. Can be either a path
+        string to a DuckDB/SQLite file or an existing FIA connection object.
+    pool : {'total'}, default 'total'
+        Carbon pool to estimate.  Litter has no above-ground /
+        below-ground split — only ``'total'`` is accepted.
+    grp_by : str or list of str, optional
+        Column name(s) to group results by. Can be any column from the
+        FIA COND or PLOT tables. Common grouping columns include:
+
+        - 'FORTYPCD': Forest type code
+        - 'FORTYPGRPCD': Forest type group code
+        - 'OWNGRPCD': Ownership group
+        - 'STATECD': State FIPS code
+        - 'COUNTYCD': County code
+
+        For complete column descriptions, see USDA FIA Database User Guide.
+    land_type : {'forest', 'timber', 'all'}, default 'forest'
+        Land type to include in estimation:
+
+        - 'forest': All forestland
+        - 'timber': Productive timberland only (unreserved, productive)
+        - 'all': All land conditions
+    area_domain : str, optional
+        SQL-like filter expression for area/condition-level filtering.
+        Example: ``"OWNGRPCD == 40 AND FORTYPCD == 161"``.
+    plot_domain : str, optional
+        SQL-like filter expression for plot-level filtering.
+    totals : bool, default True
+        If True, include population-level total estimates in addition to
+        per-acre values.
+    variance : bool, default False
+        If True, calculate and include variance and standard error
+        estimates following Bechtold & Patterson (2005).
+    most_recent : bool, default False
+        If True, automatically filter to the most recent EXPVOL evaluation
+        for each state in the database before estimation.
+
+    Returns
+    -------
+    pl.DataFrame
+        Litter carbon estimates with the following columns:
+
+        - **YEAR** : int
+            Evaluation reference year from EVALID.
+        - **POOL** : str
+            Pool identifier — always ``'TOTAL'``.
+        - **CARBON_ACRE** : float
+            Carbon per acre in short tons.
+        - **CARBON_TOTAL** : float (if ``totals=True``)
+            Total carbon in short tons expanded to population level.
+        - **CARBON_ACRE_SE** : float (if ``variance=True``)
+            Standard error of the per-acre estimate.
+        - **CARBON_TOTAL_SE** : float (if ``variance=True`` and ``totals=True``)
+            Standard error of the population total.
+        - **N_PLOTS** : int
+            Number of FIA plots included in the estimation.
+        - **N_TREES** : int
+            Number of tree records (used for distribution weighting).
+        - **[grouping columns]** : various
+            Any columns specified in ``grp_by``.
+
+    See Also
+    --------
+    live_tree : Estimate live tree carbon using the NSVB framework.
+    standing_dead : Estimate standing dead tree carbon.
+    understory : Estimate understory vegetation carbon.
+    downed_dead : Estimate downed dead wood carbon.
+    soil_organic : Estimate soil organic carbon.
+    pyfia.carbon : Overview of all carbon pool estimators.
+
+    Notes
+    -----
+    **Methodology**
+
+    Litter is not directly measured on standard FIA plots in a manner
+    that produces per-condition densities.  Instead, the FIADB
+    pre-computes carbon density per condition using the Domke et al.
+    (2016) model, which uses empirical relationships between litter
+    carbon stocks and forest type, stand age, and geographic region.
+
+    This estimator reads those pre-computed values directly from the COND
+    table and runs them through the standard post-stratified aggregation
+    pipeline, ensuring exact agreement with EVALIDator estimates.
+
+    **No AG/BG split**
+
+    Litter has no above-ground / below-ground partitioning.  The
+    ``CARBON_LITTER`` column represents the entire pool.
+
+    Examples
+    --------
+    Total litter carbon per acre on forestland:
+
+    >>> results = litter(db, pool="total")
+    >>> print(f"Carbon: {results['CARBON_ACRE'][0]:.3f} tons/acre")
+
+    Litter carbon by forest type group:
+
+    >>> results = litter(db, pool="total", grp_by="FORTYPGRPCD")
+
+    Litter carbon on timberland with standard errors:
+
+    >>> results = litter(
+    ...     db,
+    ...     land_type="timber",
+    ...     variance=True,
+    ... )
+
+    References
+    ----------
+    .. [1] Domke, G.M.; Perry, C.H.; Walters, B.F.; et al. (2016).
+       Estimating litter carbon stocks on forest land in the United
+       States. Science of the Total Environment, 557-558, 469-478.
+    .. [2] Woodall, C.W. et al. (2015). The current and future role of
+       forest carbon in the United States. Gen. Tech. Rep. NRS-154.
+    .. [3] USEPA (2024). Inventory of U.S. Greenhouse Gas Emissions and
+       Sinks, Chapter 6 and Annex 3.13.
+    """
+    from ..validation import (
+        validate_boolean,
+        validate_domain_expression,
+        validate_grp_by,
+        validate_land_type,
+    )
+
+    # ----- Validate pool -----
+    pool = pool.lower()
+    if pool != "total":
+        raise ValueError(
+            f"Invalid pool '{pool}' for litter. "
+            f"Only 'total' is supported — litter has no AG/BG split."
+        )
+
+    # ----- Validate standard inputs -----
+    land_type = validate_land_type(land_type)
+    grp_by = validate_grp_by(grp_by)
+    area_domain = validate_domain_expression(area_domain, "area_domain")
+    plot_domain = validate_domain_expression(plot_domain, "plot_domain")
+    totals = validate_boolean(totals, "totals")
+    variance = validate_boolean(variance, "variance")
+    most_recent = validate_boolean(most_recent, "most_recent")
+
+    # ----- Resolve db + EVALID -----
+    db, owns_db = ensure_fia_instance(db)
+    if most_recent and db.evalid is None:
+        db.clip_most_recent(eval_type="VOL")
+    else:
+        ensure_evalid_set(db, eval_type="VOL", estimator_name="litter")
+
+    # ----- Build config and run estimator -----
+    config = {
+        "pool": pool,
+        "grp_by": grp_by,
+        "by_species": False,
+        "by_size_class": False,
+        "land_type": land_type,
+        "area_domain": area_domain,
+        "plot_domain": plot_domain,
+        "totals": totals,
+        "variance": variance,
+        "most_recent": most_recent,
+    }
+
+    try:
+        estimator = LitterEstimator(db, config)
+        return estimator.estimate()
+    finally:
+        if owns_db and hasattr(db, "close"):
+            db.close()
diff --git a/src/pyfia/carbon/live_tree.py b/src/pyfia/carbon/live_tree.py
new file mode 100644
index 00000000..df3fc5d4
--- /dev/null
+++ b/src/pyfia/carbon/live_tree.py
@@ -0,0 +1,372 @@
+"""
+Live tree carbon estimation using the NSVB biomass framework.
+
+Recomputes above-ground live tree biomass tree-by-tree via the vectorized
+NSVB pipeline in :mod:`pyfia.carbon.nsvb.equations`, converts to carbon
+using species-specific S10a live-tree carbon fractions, and expands to
+per-acre and population estimates via pyfia's post-stratified estimator.
+
+Belowground (BG) carbon is currently bridged directly to the FIADB
+``TREE.CARBON_BG`` column; a native NSVB coarse-root model is deferred.
+The BG bridge is acknowledged as architectural tech debt at
+``pyfia/carbon/__init__.py``.
+
+Public API: :func:`live_tree`. See its docstring for parameters,
+examples, and the pool semantics.
+"""
+
+from __future__ import annotations
+
+import logging
+
+import polars as pl
+
+from ..core import FIA
+from ..estimation.columns import get_tree_columns as _get_tree_columns
+from ..estimation.utils import (
+    ensure_evalid_set,
+    ensure_fia_instance,
+)
+from ._estimator_base import CarbonEstimatorBase
+from .nsvb.carbon_fractions import (
+    _compute_default_live_carbon_fraction,
+    load_carbon_fractions_live_df,
+)
+from .nsvb.equations import compute_nsvb_biomass
+
+logger = logging.getLogger(__name__)
+
+
+class LiveTreeEstimator(CarbonEstimatorBase):
+    """Live tree carbon estimator using the NSVB biomass framework.
+
+    Inherits shared infrastructure (reference-table loading, aggregation,
+    variance, formatting) from :class:`CarbonEstimatorBase`. The only
+    estimator-specific logic is :meth:`calculate_values` (NSVB pipeline +
+    S10a carbon fractions) and the tree column list.
+    """
+
+    _estimator_label = "LiveTree"
+
+    def get_tree_columns(self) -> list[str]:
+        estimator_cols = ["SPCD", "DIA", "HT", "CULL", "CARBON_BG"]
+        return _get_tree_columns(
+            estimator_cols=estimator_cols,
+            grp_by=self.config.get("grp_by"),
+        )
+
+    def calculate_values(self, data: pl.LazyFrame) -> pl.LazyFrame:
+        """Run the NSVB pipeline and produce per-acre carbon columns.
+
+        Steps: join REF_SPECIES → join PLOTGEOM/DIVISION → filter sub-inch
+        trees → NSVB biomass → S10a carbon fractions → BG bridge →
+        CARBON_ACRE.
+        """
+        pool = self.config.get("pool", "ag").lower()
+
+        # Join REF_SPECIES and PLOTGEOM/DIVISION
+        data = self._join_ref_species(data)
+        data = self._join_plotgeom_division(data)
+
+        # Filter sub-inch trees (NSVB not parameterized below 1.0")
+        data = data.filter(pl.col("DIA") >= 1.0)
+
+        # NSVB biomass + S10a carbon conversion
+        if pool in ("ag", "total"):
+            data = compute_nsvb_biomass(data)
+            default_frac = _compute_default_live_carbon_fraction()
+            cf_df = load_carbon_fractions_live_df()
+            data = data.join(cf_df.lazy(), on="SPCD", how="left")
+            data = data.with_columns(
+                pl.col("CARBON_FRAC_LIVE")
+                .fill_null(default_frac)
+                .alias("CARBON_FRAC_LIVE"),
+            )
+            data = data.with_columns(
+                (pl.col("agb") * pl.col("CARBON_FRAC_LIVE")).alias("_CARBON_AG_LB")
+            )
+        else:  # pool == "bg"
+            data = data.with_columns(pl.lit(0.0).alias("_CARBON_AG_LB"))
+
+        # BG bridge + CARBON_ACRE
+        data = self._apply_bg_bridge(data, pool)
+        data = self._compute_carbon_acre(data)
+
+        return data
+
+
+def live_tree(
+    db: str | FIA,
+    pool: str = "ag",
+    grp_by: str | list[str] | None = None,
+    by_species: bool = False,
+    by_size_class: bool = False,
+    land_type: str = "forest",
+    tree_domain: str | None = None,
+    area_domain: str | None = None,
+    plot_domain: str | None = None,
+    totals: bool = True,
+    variance: bool = False,
+    most_recent: bool = False,
+) -> pl.DataFrame:
+    """
+    Estimate live tree carbon from FIA data using the NSVB framework.
+
+    Recomputes above-ground live tree biomass from scratch using the
+    National Scale Volume and Biomass (NSVB) framework of Westfall et al.
+    (2023, GTR-WO-104) — the same framework USDA FIA uses to populate the
+    FIADB ``CARBON_AG`` column for inventories from September 2023 onward.
+    Species-specific live carbon fractions from Table S10a (GTR-WO-104)
+    replace the flat ~0.47 multiplier used by ``pyfia.biomass()``, producing
+    carbon estimates that align with the EPA NGHGI LULUCF live tree pool.
+
+    Belowground carbon is bridged directly to the FIADB pre-computed
+    ``TREE.CARBON_BG`` column; a native NSVB coarse-root model is
+    deferred.
+
+    Parameters
+    ----------
+    db : str | FIA
+        Database connection or path to FIA database. Can be either a path
+        string to a DuckDB/SQLite file or an existing FIA connection object.
+    pool : {'ag', 'bg', 'total'}, default 'ag'
+        Live tree carbon pool to estimate:
+
+        - 'ag': Above-ground live tree carbon via the NSVB pipeline —
+          stem wood + stem bark + branches, harmonized to the directly-
+          predicted total AGB and converted to carbon via species-specific
+          S10a fractions. Foliage is excluded (not part of AGB in NSVB).
+        - 'bg': Below-ground live tree carbon (coarse roots) via a bridge
+          to FIADB ``TREE.CARBON_BG``. A native NSVB root model is deferred.
+        - 'total': ``'ag' + 'bg'`` (NSVB AG + FIADB BG bridge).
+    grp_by : str or list of str, optional
+        Column name(s) to group results by. Can be any column from the
+        FIA tables used in the estimation (PLOT, COND, TREE). Common
+        grouping columns include:
+
+        - 'FORTYPCD': Forest type code
+        - 'OWNGRPCD': Ownership group (10=National Forest, 20=Other Federal,
+          30=State/Local, 40=Private)
+        - 'STATECD': State FIPS code
+        - 'COUNTYCD': County code
+        - 'INVYR': Inventory year
+        - 'STDAGE': Stand age class
+        - 'SITECLCD': Site productivity class
+
+        For complete column descriptions, see USDA FIA Database User Guide.
+    by_species : bool, default False
+        If True, group results by species code (SPCD). Convenience parameter
+        equivalent to adding 'SPCD' to ``grp_by``.
+    by_size_class : bool, default False
+        If True, group results by diameter size classes (1.0-4.9", 5.0-9.9",
+        10.0-19.9", 20.0-29.9", 30.0+ in).
+    land_type : {'forest', 'timber', 'all'}, default 'forest'
+        Land type to include in estimation:
+
+        - 'forest': All forestland
+        - 'timber': Productive timberland only (unreserved, productive)
+        - 'all': All land conditions
+    tree_domain : str, optional
+        SQL-like filter expression for tree-level filtering. Example:
+        ``"DIA >= 10.0 AND SPCD == 131"``. Applied on top of the live-tree
+        filter (``STATUSCD == 1``), which is always on for this function.
+    area_domain : str, optional
+        SQL-like filter expression for area/condition-level filtering.
+        Example: ``"OWNGRPCD == 40 AND FORTYPCD == 161"``.
+    plot_domain : str, optional
+        SQL-like filter expression for plot-level filtering.
+    totals : bool, default True
+        If True, include population-level total estimates in addition to
+        per-acre values.
+    variance : bool, default False
+        If True, calculate and include variance and standard error
+        estimates following Bechtold & Patterson (2005).
+    most_recent : bool, default False
+        If True, automatically filter to the most recent EXPVOL evaluation
+        for each state in the database before estimation.
+
+    Returns
+    -------
+    pl.DataFrame
+        Live tree carbon estimates with the following columns:
+
+        - **YEAR** : int
+            Evaluation reference year from EVALID.
+        - **POOL** : str
+            Pool identifier — one of ``'AG'``, ``'BG'``, ``'TOTAL'``.
+        - **CARBON_ACRE** : float
+            Carbon per acre in short tons.
+        - **CARBON_TOTAL** : float (if ``totals=True``)
+            Total carbon in short tons expanded to population level.
+        - **CARBON_ACRE_SE** : float (if ``variance=True``)
+            Standard error of the per-acre estimate.
+        - **CARBON_TOTAL_SE** : float (if ``variance=True`` and ``totals=True``)
+            Standard error of the population total.
+        - **N_PLOTS** : int
+            Number of FIA plots included in the estimation.
+        - **N_TREES** : int
+            Number of individual tree records.
+        - **[grouping columns]** : various
+            Any columns specified in ``grp_by`` or via ``by_species`` /
+            ``by_size_class``.
+
+    See Also
+    --------
+    biomass : Estimate tree biomass (dry weight) using FIA's pre-computed DRYBIO columns.
+    pyfia.estimation.estimators.carbon.carbon : Legacy carbon estimator that reads the
+        FIADB ``CARBON_AG`` / ``CARBON_BG`` columns directly. ``live_tree`` is the
+        NSVB-native alternative; both should agree at the tree level for NSVB-era
+        inventories (Sep 2023 and later).
+    pyfia.carbon.nsvb.equations.compute_nsvb_biomass : The vectorized NSVB biomass
+        pipeline this function wraps.
+
+    Notes
+    -----
+    **NSVB Pipeline**
+
+    For each live tree the function predicts:
+
+    1. Stem inside-bark wood volume (S1a)
+    2. Stem bark volume (S2a)
+    3. Stem bark biomass (S6a)
+    4. Branch biomass (S7a)
+    5. Total AGB (S8a), predicted directly from D and H
+
+    The first four are summed and harmonized proportionally to the
+    directly-predicted total AGB (which becomes the truth), yielding
+    ``w_wood + w_bark + w_branch == agb`` by construction. Cull-reduced
+    wood weight uses the Harmon et al. (2011) ``DECAYCD=3`` density
+    proportions (0.54 hardwood, 0.92 softwood). The hardwood/softwood
+    split is the ``SPCD < 300`` rule, which is consistent with the
+    NSVB Model 2 ``k`` constant selection and correctly classifies
+    SPCD=10 (fir spp.) as softwood despite S10a's misclassification.
+
+    Carbon = AGB × species-specific S10a fraction. Species missing from
+    S10a fall back to the S10a arithmetic mean (~0.4741), with a
+    warn-once log entry.
+
+    **Belowground Bridge**
+
+    The current implementation does not include the Heath et al. (2009)
+    coarse-root model. When ``pool in ('bg', 'total')``, the function reads
+    FIADB ``TREE.CARBON_BG`` directly and adds it to the estimate. A native
+    NSVB BG model is deferred.
+
+    **EVALID Handling**
+
+    If no EVALID is set on the database and ``most_recent=True``, the
+    function auto-selects the most recent EXPVOL evaluation. For explicit
+    control, call ``db.clip_by_evalid(...)`` before calling
+    ``live_tree``.
+
+    Examples
+    --------
+    Above-ground live tree carbon per acre on forestland:
+
+    >>> results = live_tree(db, pool="ag")
+    >>> print(f"Carbon: {results['CARBON_ACRE'][0]:.1f} tons/acre")
+
+    Total live tree carbon (AG + BG bridge) by ownership group:
+
+    >>> results = live_tree(db, pool="total", grp_by="OWNGRPCD")
+    >>> for row in results.iter_rows(named=True):
+    ...     print(f"OWNGRPCD {row['OWNGRPCD']}: {row['CARBON_ACRE']:.2f} tons/acre")
+
+    Above-ground carbon by species on timberland with standard errors:
+
+    >>> results = live_tree(
+    ...     db,
+    ...     pool="ag",
+    ...     by_species=True,
+    ...     land_type="timber",
+    ...     variance=True,
+    ... )
+
+    Large live tree carbon (≥ 20" DBH) by forest type:
+
+    >>> results = live_tree(
+    ...     db,
+    ...     pool="ag",
+    ...     grp_by="FORTYPCD",
+    ...     tree_domain="DIA >= 20.0",
+    ...     totals=True,
+    ... )
+    """
+    from ..validation import (
+        validate_boolean,
+        validate_domain_expression,
+        validate_grp_by,
+        validate_land_type,
+    )
+
+    # ----- Validate pool -----
+    pool = pool.lower()
+    valid_pools = {"ag", "bg", "total"}
+    if pool not in valid_pools:
+        raise ValueError(
+            f"Invalid pool '{pool}'. Must be one of: {sorted(valid_pools)}"
+        )
+
+    # ----- Validate standard estimator inputs -----
+    land_type = validate_land_type(land_type)
+    grp_by = validate_grp_by(grp_by)
+    tree_domain = validate_domain_expression(tree_domain, "tree_domain")
+    area_domain = validate_domain_expression(area_domain, "area_domain")
+    plot_domain = validate_domain_expression(plot_domain, "plot_domain")
+    by_species = validate_boolean(by_species, "by_species")
+    by_size_class = validate_boolean(by_size_class, "by_size_class")
+    totals = validate_boolean(totals, "totals")
+    variance = validate_boolean(variance, "variance")
+    most_recent = validate_boolean(most_recent, "most_recent")
+
+    # ----- Resolve db + EVALID -----
+    db, owns_db = ensure_fia_instance(db)
+    # Live tree carbon uses EXPVOL evaluations (same as biomass).
+    if most_recent and db.evalid is None:
+        db.clip_most_recent(eval_type="VOL")
+    else:
+        ensure_evalid_set(db, eval_type="VOL", estimator_name="live_tree")
+
+    # ----- Build config and run estimator -----
+    config = {
+        "pool": pool,
+        "grp_by": grp_by,
+        "by_species": by_species,
+        "by_size_class": by_size_class,
+        "land_type": land_type,
+        "tree_type": "live",
+        "tree_domain": tree_domain,
+        "area_domain": area_domain,
+        "plot_domain": plot_domain,
+        "totals": totals,
+        "variance": variance,
+        "most_recent": most_recent,
+    }
+
+    try:
+        estimator = LiveTreeEstimator(db, config)
+        if pool == "total":
+            # The cross-era warning is best-effort: if we can't determine
+            # the inventory year (EVALID parse failures, missing POP_EVAL,
+            # type coercion problems), skip the warning rather than fail
+            # the whole estimation.
+            try:
+                year = estimator._extract_evaluation_year()
+                if int(year) < 2024:
+                    logger.warning(
+                        "live_tree(pool='total'): selected EVALID year (%d) "
+                        "pre-dates the NSVB framework transition "
+                        "(September 2023). The BG bridge reads FIADB "
+                        "TREE.CARBON_BG directly, which for pre-NSVB "
+                        "inventories was computed via legacy Jenkins-based "
+                        "allometry — combining it with NSVB-recomputed AG "
+                        "may produce cross-era inconsistencies. Use "
+                        "pool='ag' if you need NSVB-only consistency.",
+                        int(year),
+                    )
+            except (ValueError, TypeError, AttributeError, IndexError, KeyError) as exc:
+                logger.debug("Skipping live_tree year warning: %s", exc)
+        return estimator.estimate()
+    finally:
+        if owns_db and hasattr(db, "close"):
+            db.close()
diff --git a/src/pyfia/carbon/nsvb/__init__.py b/src/pyfia/carbon/nsvb/__init__.py
new file mode 100644
index 00000000..238a70ac
--- /dev/null
+++ b/src/pyfia/carbon/nsvb/__init__.py
@@ -0,0 +1,77 @@
+"""
+NSVB (Westfall et al. 2023, GTR-WO-104) equation library and coefficient loaders.
+
+This subpackage implements the National Scale Volume and Biomass framework
+from scratch in pure Python, validated against the worked examples in the
+GTR-WO-104 source PDF. The reverse-engineering approach is intentional — see
+``pyfia_carbon_tech_spec.md`` section 1.2 for the rationale.
+
+Phase 1 implements Models 1, 2, 4, and 5 (the model forms used by the live
+tree biomass pipeline). Models 3 and 6 are deferred to a later phase.
+"""
+
+from pyfia.carbon.nsvb.carbon_fractions import (
+    DEFAULT_LIVE_CARBON_FRACTION,
+    get_carbon_fraction_dead,
+    get_carbon_fraction_live,
+    load_carbon_fractions_dead,
+    load_carbon_fractions_dead_df,
+    load_carbon_fractions_live,
+    load_carbon_fractions_live_df,
+    load_dead_decay_proportions_df,
+)
+from pyfia.carbon.nsvb.coefficients import (
+    CoefficientTables,
+    VectorizedLookupTables,
+    build_division_lookup,
+    build_jenkins_lookup,
+    build_species_level_lookup,
+    ecosubcd_to_division,
+    get_vectorized_lookup_tables,
+    load_nsvb_coefficients,
+    lookup_coefficients,
+)
+from pyfia.carbon.nsvb.equations import (
+    Coefficients,
+    TreeBiomassResult,
+    compute_nsvb_biomass,
+    compute_nsvb_dead_biomass,
+    harmonize_components,
+    model_1,
+    model_2,
+    model_4,
+    model_5_jenkins,
+    nsvb_biomass_expr,
+    predict_tree_biomass,
+)
+
+__all__ = [
+    "Coefficients",
+    "CoefficientTables",
+    "DEFAULT_LIVE_CARBON_FRACTION",
+    "TreeBiomassResult",
+    "VectorizedLookupTables",
+    "build_division_lookup",
+    "build_jenkins_lookup",
+    "build_species_level_lookup",
+    "compute_nsvb_biomass",
+    "compute_nsvb_dead_biomass",
+    "ecosubcd_to_division",
+    "get_carbon_fraction_dead",
+    "get_carbon_fraction_live",
+    "get_vectorized_lookup_tables",
+    "harmonize_components",
+    "load_carbon_fractions_dead",
+    "load_carbon_fractions_dead_df",
+    "load_carbon_fractions_live",
+    "load_carbon_fractions_live_df",
+    "load_dead_decay_proportions_df",
+    "load_nsvb_coefficients",
+    "lookup_coefficients",
+    "model_1",
+    "model_2",
+    "model_4",
+    "model_5_jenkins",
+    "nsvb_biomass_expr",
+    "predict_tree_biomass",
+]
diff --git a/src/pyfia/carbon/nsvb/carbon_fractions.py b/src/pyfia/carbon/nsvb/carbon_fractions.py
new file mode 100644
index 00000000..7705aa66
--- /dev/null
+++ b/src/pyfia/carbon/nsvb/carbon_fractions.py
@@ -0,0 +1,318 @@
+"""
+Species-specific carbon fraction lookups for NSVB live and dead trees.
+
+Implements the species-level carbon fraction lookup from Tables S10a (live) and
+S10b (dead, by hardwood/softwood × DECAYCD) of GTR-WO-104. Live tree carbon
+fractions average ~47.4% across species but vary from roughly 40% to 55%
+depending on species; the flat 0.47 multiplier used by the existing pyFIA
+``biomass()`` estimator is replaced here with a species-specific lookup.
+
+S10a (live) powers ``pyfia.carbon.live_tree``. S10b (dead) and the related
+``REF_TREE_DECAY_PROP`` density / bark / branch loss proportions power
+``pyfia.carbon.standing_dead`` (Phase 2). Both pipelines hit the same
+hardwood/softwood × DECAYCD lookup, so the loaders are colocated here.
+"""
+
+from __future__ import annotations
+
+import functools
+import logging
+from importlib import resources
+from typing import Any
+
+import polars as pl
+
+logger = logging.getLogger(__name__)
+
+# Track which unknown SPCDs we've already warned about, to avoid log spam when
+# processing 1M-tree state inventories.
+_warned_unknown_spcds: set[int] = set()
+
+
+@functools.cache
+def _compute_default_live_carbon_fraction() -> float:
+    """Compute the S10a arithmetic-mean live carbon fraction.
+
+    Backs the ``DEFAULT_LIVE_CARBON_FRACTION`` module attribute via PEP 562
+    ``__getattr__``. Calculated lazily (on first access) so that importing
+    this module does no file I/O, and cached so subsequent accesses are O(1).
+
+    The reason this is computed rather than hardcoded: the S10a CSV is
+    vendored under ``pyfia.carbon.nsvb.data``, and a future re-vendor could
+    perturb the population mean. A hardcoded constant would silently drift;
+    a lazy computation always reflects the current CSV.
+    """
+    table = load_carbon_fractions_live()
+    return sum(table.values()) / len(table)
+
+
+def __getattr__(name: str) -> Any:
+    """PEP 562 lazy module attribute resolver.
+
+    Enables ``from pyfia.carbon.nsvb.carbon_fractions import DEFAULT_LIVE_CARBON_FRACTION``
+    to work as if the constant were defined at the top of the module, while
+    actually computing the value lazily from the S10a table on first access.
+    After the first access the value is cached in ``globals()`` so lookups
+    become standard attribute access.
+    """
+    if name == "DEFAULT_LIVE_CARBON_FRACTION":
+        value = _compute_default_live_carbon_fraction()
+        globals()[name] = value
+        return value
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+
+
+@functools.lru_cache(maxsize=1)
+def load_carbon_fractions_live() -> dict[int, float]:
+    """Load Table S10a live tree carbon fractions as a SPCD-keyed dict.
+
+    The vendored CSV stores values as percent (e.g., ``48.04`` for 48.04%);
+    this loader divides by 100 so callers receive a fraction in the
+    [0.40, 0.55] range. Cached at process level via ``@lru_cache``.
+
+    This is the scalar reference loader used by ``get_carbon_fraction_live``
+    and by the PR 1 test oracle. The vectorized live-tree estimator uses
+    :func:`load_carbon_fractions_live_df` instead, which returns a Polars
+    DataFrame ready for joining to a trees frame on ``SPCD``.
+
+    Returns
+    -------
+    dict[int, float]
+        Map from FIA species code to live carbon fraction (decimal, not percent).
+    """
+    data_pkg = resources.files("pyfia.carbon.nsvb.data")
+    with resources.as_file(data_pkg / "carbon_fraction_live.csv") as path:
+        df = pl.read_csv(path)
+    return {
+        int(row["SPCD"]): float(row["fia_wood_c"]) / 100.0
+        for row in df.iter_rows(named=True)
+    }
+
+
+@functools.lru_cache(maxsize=1)
+def load_carbon_fractions_live_df() -> pl.DataFrame:
+    """Load Table S10a live tree carbon fractions as a join-ready DataFrame.
+
+    Returns a 2-column DataFrame ``(SPCD Int64, CARBON_FRAC_LIVE Float64)``
+    suitable for a left join against a trees LazyFrame in the vectorized
+    live-tree estimator. The ``CARBON_FRAC_LIVE`` column is already
+    converted from percent to decimal (e.g., 48.04 → 0.4804), matching
+    the units returned by :func:`load_carbon_fractions_live`.
+
+    Trees whose SPCD has no row in S10a should be filled with
+    :data:`DEFAULT_LIVE_CARBON_FRACTION` after the join using
+    ``pl.col("CARBON_FRAC_LIVE").fill_null(DEFAULT_LIVE_CARBON_FRACTION)``.
+
+    Cached at process level via ``@lru_cache``.
+
+    Returns
+    -------
+    pl.DataFrame
+        Columns ``(SPCD, CARBON_FRAC_LIVE)`` with one row per FIA species
+        code present in S10a (~2,676 rows).
+    """
+    data_pkg = resources.files("pyfia.carbon.nsvb.data")
+    with resources.as_file(data_pkg / "carbon_fraction_live.csv") as path:
+        df = pl.read_csv(path, schema_overrides={"SPCD": pl.Int64})
+    return df.select(
+        [
+            pl.col("SPCD"),
+            (pl.col("fia_wood_c").cast(pl.Float64) / 100.0).alias("CARBON_FRAC_LIVE"),
+        ]
+    )
+
+
+def get_carbon_fraction_live(spcd: int, fallback: float | None = None) -> float:
+    """Look up the live carbon fraction for a given species code.
+
+    Parameters
+    ----------
+    spcd : int
+        FIA species code from ``TREE.SPCD``.
+    fallback : float, optional
+        Carbon fraction to return if ``spcd`` has no row in S10a. Defaults
+        to the S10a arithmetic mean (computed lazily from the vendored table,
+        approximately 0.4741). The first time a given unknown SPCD is
+        encountered, a warning is logged.
+
+    Returns
+    -------
+    float
+        Live carbon fraction in decimal form (e.g., 0.4804 for SPCD=10).
+    """
+    if fallback is None:
+        fallback = _compute_default_live_carbon_fraction()
+    table = load_carbon_fractions_live()
+    if spcd in table:
+        return table[spcd]
+    if spcd not in _warned_unknown_spcds:
+        _warned_unknown_spcds.add(spcd)
+        logger.warning(
+            "SPCD=%d not found in S10a live carbon fractions; falling back to "
+            "S10a mean (%.4f). Future occurrences for this SPCD will not log.",
+            spcd,
+            fallback,
+        )
+    return fallback
+
+
+@functools.lru_cache(maxsize=1)
+def load_carbon_fractions_dead() -> dict[tuple[str, int], float]:
+    """Load Table S10b dead tree carbon fractions, keyed by (hw_sw, DECAYCD).
+
+    Returns
+    -------
+    dict[tuple[str, int], float]
+        Map from ``("hardwood" | "softwood", decay_class_1_to_5)`` to carbon
+        fraction (decimal). Source CSV stores values as percent; this loader
+        divides by 100.
+    """
+    data_pkg = resources.files("pyfia.carbon.nsvb.data")
+    with resources.as_file(data_pkg / "carbon_fraction_dead.csv") as path:
+        df = pl.read_csv(path)
+    out: dict[tuple[str, int], float] = {}
+    for row in df.iter_rows(named=True):
+        hw_sw = row["S/H"].strip().lower()
+        decay = int(row["Decay code"])
+        frac = float(row["C fraction"]) / 100.0
+        out[(hw_sw, decay)] = frac
+    return out
+
+
+@functools.lru_cache(maxsize=1)
+def load_carbon_fractions_dead_df() -> pl.DataFrame:
+    """Load Table S10b dead tree carbon fractions as a join-ready DataFrame.
+
+    Returns a 3-column DataFrame ``(hw_sw Utf8, DECAYCD Int64,
+    CARBON_FRAC_DEAD Float64)`` suitable for a left join against a trees
+    LazyFrame in the vectorized standing-dead estimator. The
+    ``CARBON_FRAC_DEAD`` column is converted from percent to decimal
+    (e.g., 47.0 → 0.4700) at load time, matching the units returned by
+    :func:`load_carbon_fractions_dead`.
+
+    Cached at process level via ``@lru_cache``.
+
+    Returns
+    -------
+    pl.DataFrame
+        Columns ``(hw_sw, DECAYCD, CARBON_FRAC_DEAD)`` with one row per
+        ``(hardwood|softwood) × decay class 1-5`` combination (10 rows total).
+    """
+    data_pkg = resources.files("pyfia.carbon.nsvb.data")
+    with resources.as_file(data_pkg / "carbon_fraction_dead.csv") as path:
+        df = pl.read_csv(path)
+    return df.select(
+        [
+            pl.col("S/H").str.strip_chars().str.to_lowercase().alias("hw_sw"),
+            pl.col("Decay code").cast(pl.Int64).alias("DECAYCD"),
+            (pl.col("C fraction").cast(pl.Float64) / 100.0).alias("CARBON_FRAC_DEAD"),
+        ]
+    )
+
+
+def get_carbon_fraction_dead(hw_sw: str, decaycd: int) -> float:
+    """Look up the dead tree carbon fraction by hardwood/softwood and decay class.
+
+    Parameters
+    ----------
+    hw_sw : str
+        ``"hardwood"`` or ``"softwood"``.
+    decaycd : int
+        FIA decay class (1-5).
+
+    Returns
+    -------
+    float
+        Dead carbon fraction in decimal form.
+
+    Raises
+    ------
+    KeyError
+        If the (hw_sw, decaycd) pair is not in S10b.
+    """
+    table = load_carbon_fractions_dead()
+    return table[(hw_sw.lower(), decaycd)]
+
+
+@functools.lru_cache(maxsize=1)
+def load_dead_cr_prop_df() -> pl.DataFrame:
+    """Load Table S11 mean intact crown ratios for standing dead trees.
+
+    The vendored ``dead_cr_prop.csv`` mirrors the FIADB
+    ``REF_TREE_STND_DEAD_CR_PROP`` table — the mean compacted crown ratio
+    for intact standing dead trees by Bailey ecoregion province (ECOPROV)
+    and hardwood/softwood classification. Used by the Phase 2.5 broken-top
+    correction in :func:`pyfia.carbon.nsvb.equations.compute_nsvb_dead_biomass`
+    to estimate how much branch biomass is missing above the break point.
+
+    Source: GTR-WO-104 Table S11 (Westfall et al. 2023).
+
+    Returns
+    -------
+    pl.DataFrame
+        Columns ``(ECOPROV Utf8, hw_sw Utf8, CR_MEAN Float64)`` with one
+        row per province × hardwood/softwood combination (~88 rows including
+        the UNDEFINED fallback). ``CR_MEAN`` is in percent (e.g., 43.9
+        means 43.9% crown ratio). Ready for a left join on
+        ``["ECOPROV", "hw_sw"]``.
+    """
+    data_pkg = resources.files("pyfia.carbon.nsvb.data")
+    with resources.as_file(data_pkg / "dead_cr_prop.csv") as path:
+        df = pl.read_csv(
+            path,
+            schema_overrides={
+                "ECOPROV": pl.Utf8,
+                "hw_sw": pl.Utf8,
+                "CR_MEAN": pl.Float64,
+            },
+        )
+    return df
+
+
+@functools.lru_cache(maxsize=1)
+def load_dead_decay_proportions_df() -> pl.DataFrame:
+    """Load the FIADB ``REF_TREE_DECAY_PROP`` density / loss proportions.
+
+    The vendored ``dead_decay_proportions.csv`` file mirrors the canonical
+    FIADB ``REF_TREE_DECAY_PROP`` table (FIADB User Guide v9.1 §11.36) and
+    matches the consolidated NSVB hardwood/softwood × DECAYCD values published
+    in GTR-WO-104 Table 1 (Westfall et al. 2023). It supplies the three
+    multiplicative reduction factors that ``pyfia.carbon.standing_dead``
+    applies to the gross NSVB component biomass:
+
+    - ``DENSITY_PROP`` — fraction of stem wood biomass remaining after decay
+    - ``BARK_LOSS_PROP`` — fraction of stem bark biomass remaining after decay
+      (despite the ``LOSS`` suffix in the FIADB column name, the value is the
+      *remaining* proportion, not the *lost* proportion — this matches the
+      FIADB User Guide §11.36 description and the verbatim Appendix K formula
+      ``BARK_LOSS_PROP * Bark``)
+    - ``BRANCH_LOSS_PROP`` — fraction of branch biomass remaining after decay
+
+    Returns
+    -------
+    pl.DataFrame
+        Columns ``(hw_sw, DECAYCD, DENSITY_PROP, BARK_LOSS_PROP,
+        BRANCH_LOSS_PROP)`` with one row per ``(hardwood|softwood) × decay
+        class 1-5`` combination (10 rows total). Ready for a left join on
+        ``["hw_sw", "DECAYCD"]``.
+
+    Notes
+    -----
+    The five values per row are constants — they do not vary by species
+    within the hardwood/softwood class. This is the FIADB-canonical
+    parameterization; finer per-species DRFs from Harmon et al. (2011)
+    Table 7 are not used by FIADB and are not vendored here.
+    """
+    data_pkg = resources.files("pyfia.carbon.nsvb.data")
+    with resources.as_file(data_pkg / "dead_decay_proportions.csv") as path:
+        df = pl.read_csv(
+            path,
+            schema_overrides={
+                "hw_sw": pl.Utf8,
+                "DECAYCD": pl.Int64,
+                "DENSITY_PROP": pl.Float64,
+                "BARK_LOSS_PROP": pl.Float64,
+                "BRANCH_LOSS_PROP": pl.Float64,
+            },
+        )
+    return df
diff --git a/src/pyfia/carbon/nsvb/coefficients.py b/src/pyfia/carbon/nsvb/coefficients.py
new file mode 100644
index 00000000..76895578
--- /dev/null
+++ b/src/pyfia/carbon/nsvb/coefficients.py
@@ -0,0 +1,523 @@
+"""
+NSVB coefficient table loaders and lookup precedence.
+
+Loads the vendored CSVs from ``pyfia.carbon.nsvb.data`` via ``importlib.resources``
+(wheel-safe) and provides per-tree coefficient resolution following the NSVB
+lookup precedence documented in the GTR-WO-104 worked examples.
+
+Lookup precedence (per ``gtr_wo104_westfall2023.md:684``):
+
+1. ``SPCD + DIVISION + STDORGCD`` exact match
+2. ``SPCD + DIVISION`` (STDORGCD null)
+3. ``SPCD`` only (DIVISION and STDORGCD both null) — the species-level fallback
+4. ``JENKINS_SPGRPCD`` fallback (Model 5, with WDSG multiplication required)
+
+The CSVs already include species-level fallback rows; we do not need to synthesize
+them. Phase 1.5 added the ``ECOSUBCD → Bailey DIVISION`` mapping via
+:func:`ecosubcd_to_division`, activating Level 2 of the lookup precedence.
+Level 1 (STDORGCD) is still unused (~10 rows across all 5 tables).
+"""
+
+from __future__ import annotations
+
+import functools
+from dataclasses import dataclass
+from importlib import resources
+
+import polars as pl
+
+# Filename map for the vendored CSVs (relative to ``pyfia.carbon.nsvb.data``).
+_CSV_FILES = {
+    "volib_spcd": "volib_spcd.csv",
+    "volib_jenkins": "volib_jenkins.csv",
+    "volbk_spcd": "volbk_spcd.csv",
+    "volbk_jenkins": "volbk_jenkins.csv",
+    "bark_biomass_spcd": "bark_biomass_spcd.csv",
+    "bark_biomass_jenkins": "bark_biomass_jenkins.csv",
+    "branch_biomass_spcd": "branch_biomass_spcd.csv",
+    "branch_biomass_jenkins": "branch_biomass_jenkins.csv",
+    "total_biomass_spcd": "total_biomass_spcd.csv",
+    "total_biomass_jenkins": "total_biomass_jenkins.csv",
+}
+
+
+@dataclass(frozen=True)
+class CoefficientTables:
+    """Bundle of all NSVB coefficient tables loaded as Polars DataFrames.
+
+    Each ``*_spcd`` table has columns
+    ``SPCD, DIVISION, STDORGCD, model, a, a1, b, b1, c, c1`` and each
+    ``*_jenkins`` table has columns ``JENKINS_SPGRPCD, model, a, b, c``
+    (or close to it — the schemas vary slightly across the S*b tables).
+    """
+
+    volib_spcd: pl.DataFrame
+    volib_jenkins: pl.DataFrame
+    volbk_spcd: pl.DataFrame
+    volbk_jenkins: pl.DataFrame
+    bark_biomass_spcd: pl.DataFrame
+    bark_biomass_jenkins: pl.DataFrame
+    branch_biomass_spcd: pl.DataFrame
+    branch_biomass_jenkins: pl.DataFrame
+    total_biomass_spcd: pl.DataFrame
+    total_biomass_jenkins: pl.DataFrame
+
+
+@dataclass(frozen=True)
+class VectorizedLookupTables:
+    """Bundle of join-ready NSVB coefficient lookup tables for the vectorized path.
+
+    Each component is represented by three parallel lookups for Levels 2–4 of
+    the NSVB precedence:
+
+    - ``*_div``: DIVISION-specific rows (DIVISION non-null + STDORGCD null) with
+      the columns ``(SPCD, DIVISION, model, a, b, b1, c)`` — joined on the
+      ``(SPCD, DIVISION)`` composite key (Level 2).
+    - ``*_spcd``: species-level rows (DIVISION null + STDORGCD null) with the
+      columns ``(SPCD, model, a, b, b1, c)`` — ready for a left join on
+      ``SPCD`` (Level 3).
+    - ``*_jen``: Jenkins-group fallback rows with the columns
+      ``(JENKINS_SPGRPCD, model, a, b, b1, c)`` — ready for a left join on
+      ``JENKINS_SPGRPCD`` (Level 4). ``b1`` is synthesized as ``0.0`` because
+      the Jenkins tables only carry ``(a, b, c)`` and Model 5 (the only form
+      Jenkins rows dispatch to) does not use ``b1``.
+
+    The vectorized orchestrator runs all three joins per component and then
+    coalesces DIVISION first, species-level second, Jenkins third, replicating
+    the NSVB lookup precedence (Level 2 → Level 3 → Level 4) without any
+    Python-level loops. Level 1 (SPCD + DIVISION + STDORGCD) is still dead
+    code in Phase 1.5 — only ~10 rows across all 5 tables — and is deferred
+    until the validation gate justifies it.
+    """
+
+    volib_spcd: pl.DataFrame
+    volib_jen: pl.DataFrame
+    volib_div: pl.DataFrame
+    volbk_spcd: pl.DataFrame
+    volbk_jen: pl.DataFrame
+    volbk_div: pl.DataFrame
+    bark_bio_spcd: pl.DataFrame
+    bark_bio_jen: pl.DataFrame
+    bark_bio_div: pl.DataFrame
+    branch_bio_spcd: pl.DataFrame
+    branch_bio_jen: pl.DataFrame
+    branch_bio_div: pl.DataFrame
+    total_agb_spcd: pl.DataFrame
+    total_agb_jen: pl.DataFrame
+    total_agb_div: pl.DataFrame
+
+
+# Common coefficient columns used by the vectorized path. Matches the inputs
+# consumed by ``nsvb_biomass_expr``.
+_VECTORIZED_COEF_COLS = ("model", "a", "b", "b1", "c")
+
+
+def build_species_level_lookup(table_spcd: pl.DataFrame) -> pl.DataFrame:
+    """Prepare a ``*_spcd`` coefficient table for vectorized SPCD joins.
+
+    Filters to Phase 1's species-level rows (``DIVISION`` is null AND
+    ``STDORGCD`` is null), selects the common coefficient columns used by
+    the vectorized biomass expression, and returns a DataFrame keyed on
+    ``SPCD``. The DIVISION-specific rows (Levels 1-2 of the NSVB lookup
+    precedence) are deliberately dropped because Phase 1 has no
+    ``PLOT.ECOSUBCD → DIVISION`` mapping.
+
+    Also drops Model 2 / Model 4 rows with a null ``b1`` as a defensive
+    measure. In the downstream ``_join_and_eval_component`` coalesce, a
+    null ``b1`` on the species-level side would silently fall back to the
+    Jenkins ``b1=0.0`` synthetic value and corrupt the Model 2/4 math
+    row-wise. The current vendored CSVs have no such rows (all null
+    ``b1`` entries are on Model 1 rows, which do not consume ``b1``),
+    so this filter is a no-op today. It is a regression guard against
+    future CSV re-vendor drift: a rogue null ``b1`` on a Model 2/4 row
+    will be dropped here and the SPCD will fall through to Jenkins as a
+    whole, rather than silently producing wrong per-row math.
+
+    Parameters
+    ----------
+    table_spcd : pl.DataFrame
+        One of the 5 ``*_spcd`` coefficient tables from
+        :class:`CoefficientTables`.
+
+    Returns
+    -------
+    pl.DataFrame
+        Columns ``(SPCD, model, a, b, b1, c)``. One row per SPCD that has a
+        species-level entry in the source table.
+    """
+    return table_spcd.filter(
+        pl.col("DIVISION").is_null()
+        & pl.col("STDORGCD").is_null()
+        & ~(pl.col("model").is_in([2, 4]) & pl.col("b1").is_null())
+    ).select(["SPCD", *_VECTORIZED_COEF_COLS])
+
+
+def build_jenkins_lookup(table_jenkins: pl.DataFrame) -> pl.DataFrame:
+    """Prepare a ``*_jenkins`` coefficient table for vectorized joins.
+
+    Selects the common coefficient columns and synthesizes a ``b1`` column
+    set to ``0.0`` — Jenkins tables only carry ``(a, b, c)`` because Model 5
+    (the only form Jenkins rows ever dispatch to) has no ``b1`` parameter.
+    The synthesized column keeps the schema identical to the species-level
+    lookup so the downstream orchestrator can coalesce across both tables
+    uniformly.
+
+    Parameters
+    ----------
+    table_jenkins : pl.DataFrame
+        One of the 5 ``*_jenkins`` coefficient tables from
+        :class:`CoefficientTables`.
+
+    Returns
+    -------
+    pl.DataFrame
+        Columns ``(JENKINS_SPGRPCD, model, a, b, b1, c)``.
+    """
+    return table_jenkins.select(
+        [
+            pl.col("JENKINS_SPGRPCD"),
+            pl.col("model"),
+            pl.col("a"),
+            pl.col("b"),
+            pl.lit(0.0, dtype=pl.Float64).alias("b1"),
+            pl.col("c"),
+        ]
+    )
+
+
+def build_division_lookup(table_spcd: pl.DataFrame) -> pl.DataFrame:
+    """Prepare a ``*_spcd`` coefficient table for vectorized ``(SPCD, DIVISION)``
+    joins (Level 2 of the NSVB lookup precedence).
+
+    Filters to rows where ``DIVISION`` is non-null AND ``STDORGCD`` is null
+    (the DIVISION-specific Bailey ecoprovince refinements that Phase 1 of
+    the carbon estimator skipped). Returns a DataFrame keyed on the
+    composite ``(SPCD, DIVISION)``; the vectorized orchestrator joins this
+    first, then falls through to the species-level lookup (Level 3) and
+    the Jenkins fallback (Level 4) via a 3-way coalesce.
+
+    Level 1 of the NSVB precedence (``SPCD + DIVISION + STDORGCD``) is still
+    deliberately excluded — across all 5 coefficient tables it is only ~10
+    rows and would require threading ``COND.STDORGCD`` through the pipeline.
+    Revisit once the DIVISION closure has been measured.
+
+    Applies the same defensive null-``b1`` filter as
+    :func:`build_species_level_lookup`: a Model 2 / Model 4 row with a null
+    ``b1`` would silently mix rows in the downstream coalesce, so such rows
+    are dropped from the lookup.
+
+    Parameters
+    ----------
+    table_spcd : pl.DataFrame
+        One of the 5 ``*_spcd`` coefficient tables from
+        :class:`CoefficientTables`.
+
+    Returns
+    -------
+    pl.DataFrame
+        Columns ``(SPCD, DIVISION, model, a, b, b1, c)``. One row per
+        unique ``(SPCD, DIVISION)`` pair with a Level-2 entry.
+    """
+    return table_spcd.filter(
+        pl.col("DIVISION").is_not_null()
+        & pl.col("STDORGCD").is_null()
+        & ~(pl.col("model").is_in([2, 4]) & pl.col("b1").is_null())
+    ).select(["SPCD", "DIVISION", *_VECTORIZED_COEF_COLS])
+
+
+def ecosubcd_to_division(ecosubcd: str | None) -> str | None:
+    """Extract the Bailey DIVISION code from a ``PLOTGEOM.ECOSUBCD`` value.
+
+    ``ECOSUBCD`` is a 5-7 character Bailey ecoprovince subsection code
+    (e.g., ``"231Ae"`` for the Southeastern Mixed Forest section 231A,
+    subsection 231Ae, or ``"M231Aa"`` for the Ouachita Mixed Forest
+    mountain variant). The Bailey hierarchy is:
+
+    ::
+
+        Domain → Division → Province → Section → Subsection
+        200    → 230      → 231      → 231A    → 231Ae
+
+    This function walks one level up the hierarchy from Subsection to
+    Division by:
+
+    - extracting the 3-digit Province code (first 3 chars after any ``M``)
+    - replacing its last digit with ``"0"`` to obtain the Division
+    - preserving the ``"M"`` prefix for mountain Divisions
+
+    Examples
+    --------
+    >>> ecosubcd_to_division("231Ae")
+    '230'
+    >>> ecosubcd_to_division("232Bh")
+    '230'
+    >>> ecosubcd_to_division("M231Aa")
+    'M230'
+    >>> ecosubcd_to_division("220Eb")
+    '220'
+    >>> ecosubcd_to_division(None) is None
+    True
+    >>> ecosubcd_to_division("") is None
+    True
+    >>> ecosubcd_to_division("XYZ") is None
+    True
+
+    Parameters
+    ----------
+    ecosubcd : str or None
+        The ECOSUBCD value from ``PLOTGEOM.ECOSUBCD`` or ``PLOT.ECOSUBCD``
+        (the column exists on both tables — pyfia's DataMart CSV downloads
+        pull it reliably from ``PLOTGEOM``).
+
+    Returns
+    -------
+    str or None
+        The Bailey DIVISION code matching the ``DIVISION`` column in the
+        NSVB coefficient tables (e.g., ``"230"``, ``"M230"``, ``"240"``),
+        or ``None`` if the input is null, empty, or malformed.
+    """
+    if ecosubcd is None:
+        return None
+    s = ecosubcd.strip().upper()
+    if not s:
+        return None
+    m_prefix = ""
+    if s.startswith("M"):
+        m_prefix = "M"
+        s = s[1:]
+    if len(s) < 3 or not s[:2].isdigit() or not s[2].isdigit():
+        return None
+    return m_prefix + s[:2] + "0"
+
+
+def ecosubcd_to_division_expr(col: str = "ECOSUBCD") -> pl.Expr:
+    """Vectorized Polars expression equivalent of :func:`ecosubcd_to_division`.
+
+    Replaces ``pl.col(col).map_elements(ecosubcd_to_division)`` with pure
+    Polars string operations — no per-row Python dispatch. Logic:
+
+    1. Uppercase + strip the input.
+    2. If it starts with ``"M"``, take ``"M" + chars[1:3] + "0"``.
+    3. Otherwise take ``chars[0:2] + "0"``.
+    4. Null / empty / malformed inputs produce null.
+
+    Returns
+    -------
+    pl.Expr
+        Expression producing the Bailey DIVISION code (Utf8, nullable).
+    """
+    raw = pl.col(col).str.strip_chars().str.to_uppercase()
+    has_m = raw.str.starts_with("M")
+    # Characters after any M prefix
+    digits_m = raw.str.slice(1)  # "M231Aa" → "231Aa"
+    digits_no_m = raw  # "231Ae" → "231Ae"
+
+    # Build division: first 2 digits + "0", with M prefix preserved
+    div_m = pl.lit("M") + digits_m.str.slice(0, 2) + pl.lit("0")
+    div_no_m = digits_no_m.str.slice(0, 2) + pl.lit("0")
+
+    # Validate: the 3rd character (after M if present) must be a digit,
+    # and minimum 3 chars after prefix.  Use a regex match for the prefix
+    # digits to detect malformed values.
+    valid_m = digits_m.str.contains(r"^\d{3}")
+    valid_no_m = digits_no_m.str.contains(r"^\d{3}")
+
+    return (
+        pl.when(raw.is_null() | (raw == ""))
+        .then(pl.lit(None, dtype=pl.Utf8))
+        .when(has_m & valid_m)
+        .then(div_m)
+        .when(~has_m & valid_no_m)
+        .then(div_no_m)
+        .otherwise(pl.lit(None, dtype=pl.Utf8))
+    )
+
+
+@functools.lru_cache(maxsize=1)
+def get_vectorized_lookup_tables() -> VectorizedLookupTables:
+    """Return the full bundle of join-ready NSVB lookup tables.
+
+    Calls :func:`build_species_level_lookup`, :func:`build_jenkins_lookup`,
+    and :func:`build_division_lookup` on each of the 5 component table
+    pairs from :func:`load_nsvb_coefficients` and caches the result at
+    process level. This is the single entry point the vectorized live-tree
+    biomass orchestrator uses.
+
+    The bundle is small (~1200 rows across all 15 DataFrames) and is shared
+    across every ``LiveTreeEstimator`` invocation in the process.
+    """
+    raw = load_nsvb_coefficients()
+    return VectorizedLookupTables(
+        volib_spcd=build_species_level_lookup(raw.volib_spcd),
+        volib_jen=build_jenkins_lookup(raw.volib_jenkins),
+        volib_div=build_division_lookup(raw.volib_spcd),
+        volbk_spcd=build_species_level_lookup(raw.volbk_spcd),
+        volbk_jen=build_jenkins_lookup(raw.volbk_jenkins),
+        volbk_div=build_division_lookup(raw.volbk_spcd),
+        bark_bio_spcd=build_species_level_lookup(raw.bark_biomass_spcd),
+        bark_bio_jen=build_jenkins_lookup(raw.bark_biomass_jenkins),
+        bark_bio_div=build_division_lookup(raw.bark_biomass_spcd),
+        branch_bio_spcd=build_species_level_lookup(raw.branch_biomass_spcd),
+        branch_bio_jen=build_jenkins_lookup(raw.branch_biomass_jenkins),
+        branch_bio_div=build_division_lookup(raw.branch_biomass_spcd),
+        total_agb_spcd=build_species_level_lookup(raw.total_biomass_spcd),
+        total_agb_jen=build_jenkins_lookup(raw.total_biomass_jenkins),
+        total_agb_div=build_division_lookup(raw.total_biomass_spcd),
+    )
+
+
+# Explicit dtypes for the SPCD-keyed tables. DIVISION is a Bailey ecoprovince
+# code (e.g., "M240", "240A") — always Utf8. STDORGCD is a nullable integer
+# (1 or 2), stored as Int64. Passing these explicitly avoids relying on schema
+# inference, which previously only worked because letters happened to appear
+# in the first 10k rows of every table and would silently break if a future
+# re-vendor reordered the rows.
+_SPCD_DTYPES = {
+    "SPCD": pl.Int64,
+    "DIVISION": pl.Utf8,
+    "STDORGCD": pl.Int64,
+    "model": pl.Int64,
+    "a": pl.Float64,
+    "a1": pl.Float64,
+    "b": pl.Float64,
+    "b1": pl.Float64,
+    "c": pl.Float64,
+    "c1": pl.Float64,
+}
+
+# Jenkins-keyed tables have a narrower schema and no DIVISION/STDORGCD.
+_JENKINS_DTYPES = {
+    "JENKINS_SPGRPCD": pl.Int64,
+    "model": pl.Int64,
+    "a": pl.Float64,
+    "b": pl.Float64,
+    "c": pl.Float64,
+}
+
+
+@functools.lru_cache(maxsize=1)
+def load_nsvb_coefficients() -> CoefficientTables:
+    """Load all NSVB coefficient tables from the vendored CSV bundle.
+
+    Cached at process level via ``@lru_cache``: subsequent calls return the same
+    ``CoefficientTables`` instance in O(1). Uses ``importlib.resources.files``
+    so it works in dev installs, installed wheels, zip-imported wheels, and
+    PyOxidizer-style frozen builds without ``__file__`` path tricks.
+
+    Schemas are passed explicitly via ``schema_overrides`` so DIVISION is always
+    loaded as ``Utf8`` and STDORGCD as ``Int64`` regardless of the first-row
+    content of the CSV. This is what lets the upcoming ``ECOSUBCD → DIVISION``
+    lookup key-join cleanly against the coefficient tables.
+    """
+    data_pkg = resources.files("pyfia.carbon.nsvb.data")
+    loaded: dict[str, pl.DataFrame] = {}
+    for key, filename in _CSV_FILES.items():
+        schema = _JENKINS_DTYPES if key.endswith("_jenkins") else _SPCD_DTYPES
+        with resources.as_file(data_pkg / filename) as path:
+            df = pl.read_csv(path, schema_overrides=schema)
+        loaded[key] = df
+    return CoefficientTables(**loaded)
+
+
+def _row_to_dict(row: pl.DataFrame, source: str) -> dict:
+    """Convert a single-row Polars DataFrame to a coefficient dict.
+
+    Returns the row's columns as a plain Python dict, with NaN/null values
+    coerced to 0.0 for numeric fields and the lookup ``source`` tag attached.
+    """
+    raw = row.to_dicts()[0]
+    out: dict = {"source": source}
+    for key in ("model", "a", "a1", "b", "b1", "c", "c1"):
+        val = raw.get(key)
+        if val is None:
+            out[key] = 0.0
+        else:
+            out[key] = float(val)
+    out["model"] = int(out["model"])
+    return out
+
+
+def lookup_coefficients(
+    table_spcd: pl.DataFrame,
+    table_jenkins: pl.DataFrame,
+    spcd: int,
+    jenkins_spgrpcd: int | None = None,
+    division: str | None = None,
+    stdorgcd: int | None = None,
+) -> dict:
+    """Resolve a coefficient row for one tree following NSVB lookup precedence.
+
+    The Phase 1 implementation walks precedence levels 1-4 in order. Phase 1
+    typically only hits levels 3 and 4: the SPCD-only species-level row, or
+    the Jenkins fallback for unsupported species. The DIVISION-specific levels
+    1 and 2 are wired but unused until pyFIA gains a ``PLOT.ECOSUBCD →
+    DIVISION`` mapping.
+
+    Parameters
+    ----------
+    table_spcd : pl.DataFrame
+        The ``S*a`` species-keyed table (e.g., ``volib_spcd``).
+    table_jenkins : pl.DataFrame
+        The ``S*b`` Jenkins-keyed fallback table.
+    spcd : int
+        FIA species code from ``TREE.SPCD``.
+    jenkins_spgrpcd : int, optional
+        Jenkins species group code from ``REF_SPECIES.JENKINS_SPGRPCD``.
+        Required if the species is not in the SPCD table (level 4 fallback).
+    division : str, optional
+        Bailey ecoprovince division code (e.g., ``"M240"``). Phase 1 always
+        passes ``None`` here.
+    stdorgcd : int, optional
+        Stand origin code from ``COND.STDORGCD``. Phase 1 always passes ``None``.
+
+    Returns
+    -------
+    dict
+        Coefficient dict with keys ``model, a, a1, b, b1, c, c1, source``.
+        ``source`` is one of ``"spcd_division_stdorg"``, ``"spcd_division"``,
+        ``"spcd"``, or ``"jenkins"``.
+
+    Raises
+    ------
+    KeyError
+        If neither the SPCD nor any Jenkins fallback can be resolved.
+    """
+    # TODO(PR 2): Scalar reference implementation. The vectorized
+    # LiveTreeEstimator must replace per-tree calls with a polars join on
+    # SPCD against the coefficient tables, not call this function in a loop.
+    # See `pyfia/carbon/__init__.py` "Architectural rules" rule 2.
+    df = table_spcd.filter(pl.col("SPCD") == spcd)
+
+    # Level 1: SPCD + DIVISION + STDORGCD exact match
+    if division is not None and stdorgcd is not None:
+        match = df.filter(
+            (pl.col("DIVISION") == division) & (pl.col("STDORGCD") == stdorgcd)
+        )
+        if match.height > 0:
+            return _row_to_dict(match.head(1), "spcd_division_stdorg")
+
+    # Level 2: SPCD + DIVISION (STDORGCD null)
+    if division is not None:
+        match = df.filter(
+            (pl.col("DIVISION") == division) & pl.col("STDORGCD").is_null()
+        )
+        if match.height > 0:
+            return _row_to_dict(match.head(1), "spcd_division")
+
+    # Level 3: SPCD only (DIVISION and STDORGCD both null) — the species-level row
+    match = df.filter(pl.col("DIVISION").is_null() & pl.col("STDORGCD").is_null())
+    if match.height > 0:
+        return _row_to_dict(match.head(1), "spcd")
+
+    # Level 4: Jenkins fallback. Note that S*b tables have different schemas
+    # (no DIVISION/STDORGCD columns) and are keyed on JENKINS_SPGRPCD.
+    if jenkins_spgrpcd is not None:
+        jdf = table_jenkins.filter(pl.col("JENKINS_SPGRPCD") == jenkins_spgrpcd)
+        if jdf.height > 0:
+            return _row_to_dict(jdf.head(1), "jenkins")
+
+    raise KeyError(
+        f"No NSVB coefficients for SPCD={spcd}, JENKINS_SPGRPCD={jenkins_spgrpcd}, "
+        f"DIVISION={division}, STDORGCD={stdorgcd}. Check the species coverage "
+        "in src/pyfia/carbon/nsvb/data/."
+    )
diff --git a/src/pyfia/carbon/nsvb/data/README.md b/src/pyfia/carbon/nsvb/data/README.md
new file mode 100644
index 00000000..8c24f0ee
--- /dev/null
+++ b/src/pyfia/carbon/nsvb/data/README.md
@@ -0,0 +1,144 @@
+# NSVB Coefficient Tables (vendored)
+
+This directory contains coefficient and carbon-fraction tables from the National Scale
+Volume and Biomass (NSVB) framework documented in:
+
+> Westfall, J.A. et al. (2023). *A National-Scale Tree Volume, Biomass, and Carbon
+> Modeling System for the United States.* Gen. Tech. Rep. WO-104. Washington, DC:
+> U.S. Department of Agriculture, Forest Service, Washington Office.
+> DOI: 10.2737/WO-GTR-104
+> Supplementary archive: 10.2737/WO-GTR-104-Supp1
+
+These CSVs are loaded by `pyfia.carbon.nsvb.coefficients` and
+`pyfia.carbon.nsvb.carbon_fractions` via `importlib.resources` and ship inside the wheel.
+
+## File map
+
+| File | Source table (Supp1) | Rows | Purpose |
+|---|---|---|---|
+| `volib_spcd.csv` | S1a | 406 | Stem inside-bark wood volume coefficients by SPCD/DIVISION/STDORGCD |
+| `volib_jenkins.csv` | S1b | 9 | Same, Jenkins-group fallback |
+| `volbk_spcd.csv` | S2a | 339 | Stem bark volume coefficients |
+| `volbk_jenkins.csv` | S2b | 9 | Jenkins fallback |
+| `bark_biomass_spcd.csv` | S6a | 206 | Stem bark dry biomass |
+| `bark_biomass_jenkins.csv` | S6b | 9 | Jenkins fallback |
+| `branch_biomass_spcd.csv` | S7a | 175 | Branch dry biomass |
+| `branch_biomass_jenkins.csv` | S7b | 9 | Jenkins fallback |
+| `total_biomass_spcd.csv` | S8a | 173 | Total above-ground biomass (used in harmonization) |
+| `total_biomass_jenkins.csv` | S8b | 9 | Jenkins fallback |
+| `carbon_fraction_live.csv` | S10a (trimmed) | 2676 | Live tree carbon fractions by SPCD |
+| `carbon_fraction_dead.csv` | S10b | 10 | Dead tree carbon fractions by hw/sw × DECAYCD |
+| `dead_decay_proportions.csv` | REF_TREE_DECAY_PROP / Table 1 | 10 | Standing dead density/bark/branch loss proportions by hw/sw × DECAYCD |
+| `dead_cr_prop.csv` | S11 / REF_TREE_STND_DEAD_CR_PROP | 86 | Mean intact crown ratio by Bailey ecoregion province × hw/sw (broken-top corrections) |
+
+## Coefficient table schema (S1a–S8b)
+
+Columns: `SPCD, DIVISION, STDORGCD, model, a, a1, b, b1, c, c1`
+
+- `SPCD` — FIA species code
+- `DIVISION` — Bailey ecoprovince division code (e.g., `M240`, `210`). May be empty for the
+  species-level fallback row.
+- `STDORGCD` — Stand origin code. Usually empty.
+- `model` — Which NSVB model form to use (1, 2, 4, or 5). See `equations.py`.
+- `a, a1, b, b1, c, c1` — Model parameters. Which are populated depends on `model`.
+
+**Lookup precedence** (per NSVB worked example, `gtr_wo104_westfall2023.md:684`):
+1. SPCD + DIVISION + STDORGCD exact match
+2. SPCD + DIVISION (STDORGCD null)
+3. SPCD only (DIVISION and STDORGCD both null) — the species-level fallback
+4. JENKINS_SPGRPCD fallback (Model 5 with WDSG multiplication)
+
+The CSVs already include species-level fallback rows; the loader does not need to
+synthesize them.
+
+## Carbon fraction tables
+
+### `carbon_fraction_live.csv` (trimmed from S10a)
+
+Columns: `SPCD, hw_sw, fia_wood_c`
+
+- `SPCD` — FIA species code
+- `hw_sw` — `"hardwood"` or `"softwood"`. Renamed from S10a's `division` column
+  (which uses `angiosperm`/`gymnosperm` — botanically equivalent but semantically
+  collides with the ecological `DIVISION` column in S1a–S8b). The values were also
+  remapped: `angiosperm` → `hardwood`, `gymnosperm` → `softwood`.
+- `fia_wood_c` — Carbon as percent (e.g., `48.04`). The loader divides by 100 at
+  load time to produce a fraction in `[0.40, 0.55]`.
+
+### `carbon_fraction_dead.csv` (S10b)
+
+Columns: `Decay code, S/H, C fraction`
+
+10 rows: hardwood/softwood × decay class 1–5. Loaded by `carbon_fractions.py` and
+used by the Phase 2 standing dead estimator for the biomass → carbon conversion.
+
+### `dead_decay_proportions.csv` (REF_TREE_DECAY_PROP / GTR-WO-104 Table 1)
+
+Columns: `hw_sw, DECAYCD, DENSITY_PROP, BARK_LOSS_PROP, BRANCH_LOSS_PROP`
+
+10 rows: hardwood/softwood × decay class 1–5. Mirrors the FIADB
+`REF_TREE_DECAY_PROP` table and the consolidated NSVB hardwood/softwood × DECAYCD
+values from GTR-WO-104 Table 1 (Westfall et al. 2023). Loaded by `carbon_fractions.py`
+and used by `equations.compute_nsvb_dead_biomass` to apply decay reductions to gross
+NSVB component biomass (stem wood × DENSITY_PROP, bark × BARK_LOSS_PROP, branch ×
+BRANCH_LOSS_PROP). Despite the `LOSS` suffix in the FIADB column names, the values
+are the *remaining* proportions (not the *lost* proportions).
+
+### `dead_cr_prop.csv` (Table S11 / REF_TREE_STND_DEAD_CR_PROP)
+
+Columns: `ECOPROV, hw_sw, CR_MEAN`
+
+86 rows: Bailey ecoregion province × hardwood/softwood, plus two UNDEFINED
+fallback rows. Vendored from GTR-WO-104 Table S11. The `ECOPROV` column
+contains 3-digit Bailey province codes (e.g., `231`, `M242`) — note these
+are **province** codes, not the `DIVISION` codes used by the NSVB coefficient
+tables (which replace the last digit with `0`). `CR_MEAN` is the mean
+compacted crown ratio in percent (e.g., `43.9` means 43.9%). Loaded by
+`carbon_fractions.load_dead_cr_prop_df()` and used by
+`equations.compute_nsvb_dead_biomass` for the Appendix K broken-top
+crown-proportion correction on standing dead trees with `ACTUALHT < HT`.
+
+## Vendoring procedure (for maintainers)
+
+When WO-104 is revised, re-vendor as follows:
+
+1. Fetch the supplementary archive from <https://doi.org/10.2737/WO-GTR-104-Supp1>.
+2. Copy `Table S{1a,1b,2a,2b,6a,6b,7a,7b,8a,8b,10a,10b}*.csv` from the archive into
+   this directory, renaming each to its short form (see file map above).
+3. Trim `carbon_fraction_live.csv` to keep only `SPCD, hw_sw, fia_wood_c`, and
+   remap `angiosperm`/`gymnosperm` → `hardwood`/`softwood`. Reference one-liner:
+
+   ```python
+   import csv
+   mapping = {'angiosperm': 'hardwood', 'gymnosperm': 'softwood'}
+   with open('Table S10a_fia_wood_c_frac_live.csv.csv') as fin, \
+        open('carbon_fraction_live.csv', 'w', newline='') as fout:
+       reader = csv.DictReader(fin)
+       writer = csv.DictWriter(fout, fieldnames=['SPCD', 'hw_sw', 'fia_wood_c'])
+       writer.writeheader()
+       for row in reader:
+           writer.writerow({
+               'SPCD': row['SPCD'],
+               'hw_sw': mapping.get(row['division'].strip(), row['division'].strip()),
+               'fia_wood_c': row['fia.wood.c'],
+           })
+   ```
+
+4. Run `uv run pytest tests/unit/test_nsvb_*.py tests/unit/test_carbon_fractions.py`
+   to verify no schema regressions.
+5. Bump the patch version in `pyproject.toml` and commit with a message referencing
+   the Supp1 revision date.
+
+## Known issues in source data
+
+- **SPCD 10 (`fir spp.`)** is labeled `angiosperm` in S10a but should be `gymnosperm`
+  (Abies is a softwood genus). This is a data error in the source CSV; we preserve
+  it as-is rather than silently fixing it. SPCD 11 (Pacific silver fir, Abies amabilis)
+  is correctly labeled `gymnosperm`.
+- The CSV coefficients are stored at ~9 significant figures. The WO-104 worked
+  examples cite coefficients to 12+ significant figures. As a result, end-to-end
+  pipeline tests using CSV-loaded coefficients are accurate to ~6 decimal places,
+  not the 9+ decimal places shown in the prose. The `tests/unit/test_nsvb_equations.py`
+  regression tests use hand-coded high-precision coefficients from the worked
+  example prose, separate from the CSV path, so the equation math itself is verified
+  to 9 decimal places.
diff --git a/src/pyfia/carbon/nsvb/data/bark_biomass_jenkins.csv b/src/pyfia/carbon/nsvb/data/bark_biomass_jenkins.csv
new file mode 100644
index 00000000..85ebf414
--- /dev/null
+++ b/src/pyfia/carbon/nsvb/data/bark_biomass_jenkins.csv
@@ -0,0 +1,10 @@
+JENKINS_SPGRPCD,model,a,b,c
+1,1,0.001056271,1.75979085,1.595518427
+2,1,0.024642529,1.646231709,0.998562497
+3,1,0.037089719,2.860515142,0.142562661
+4,1,0.031937896,1.664554704,0.881859685
+5,1,0.097302469,1.616028784,0.578632787
+6,1,0.167920201,1.877056641,0.439608792
+7,1,0.120087385,1.990554372,0.41355154
+8,1,0.060205448,1.933727566,0.590397069
+9,1,0.036743204,1.630276513,0.962121833
diff --git a/src/pyfia/carbon/nsvb/data/bark_biomass_spcd.csv b/src/pyfia/carbon/nsvb/data/bark_biomass_spcd.csv
new file mode 100644
index 00000000..44f4f516
--- /dev/null
+++ b/src/pyfia/carbon/nsvb/data/bark_biomass_spcd.csv
@@ -0,0 +1,207 @@
+SPCD,DIVISION,STDORGCD,model,a,b,b1,c
+11,,,1,0.048055045,2.112855427,,0.543316117
+12,130,,2,0.027530352,2.165991411,2.242656509,0.639554183
+12,210,,2,0.004554801,1.618961857,1.705068319,1.381801534
+12,,,2,0.020335672,2.040082238,2.119684476,0.780832477
+15,M260,,1,0.000104701,0.869227967,,2.907020845
+15,,,1,0.011441127,2.168321806,,0.949708668
+16,,,1,0.048055045,2.032598212,,0.543316117
+17,,,1,0.048055045,2.125583418,,0.543316117
+19,M330,,2,0.022436719,2.00445589,1.742779656,0.775790533
+19,,,2,0.020827837,1.84907566,1.899617911,0.863463343
+20,,,1,0.048055045,2.259631473,,0.543316117
+68,,,1,0.059594973,1.562441301,,0.602911936
+71,130,,1,0.048319892,1.560752017,,0.739990007
+71,210,,1,0.081220229,1.909673882,,0.419221616
+71,,,1,0.074211558,1.729204892,,0.538671194
+73,,,1,0.059594973,1.881231523,,0.602911936
+90,,,1,0.052650851,1.669699254,,0.671493473
+93,,,1,0.058071391,1.433523972,,0.819746866
+94,130,,1,0.036605874,1.726868832,,0.744349517
+94,210,,1,0.034629031,1.589629422,,0.787146323
+94,M130,,1,0.099609876,1.934312453,,0.424070637
+94,M210,,1,0.030369934,1.556865877,,0.888769054
+94,,,1,0.034795222,1.649734858,,0.810728368
+95,130,,1,0.038118181,1.679771775,,0.796496664
+95,210,,1,0.05016543,1.592127236,,0.717866724
+95,M130,,1,0.079779291,1.803275465,,0.584944341
+95,,,1,0.051116053,1.72358547,,0.703195012
+97,,,1,0.058014505,1.91780615,,0.547480628
+98,M240,,1,0.049553302,1.463704472,,0.759819073
+98,,,1,0.049553302,1.463704472,,0.759819073
+105,130,,2,0.03706776,1.76533371,1.446274944,0.670566959
+105,210,,2,0.058683145,1.448466441,1.824891694,0.720389028
+105,,,2,0.048147163,1.6935246,1.543556927,0.642405307
+107,230,,1,0.530178789,1.808252468,,-0.016375423
+107,,,1,0.530178789,1.808252468,,-0.016375423
+108,M130,,1,0.039437496,1.758115853,,0.683384567
+108,M210,,1,0.073194929,1.733598811,,0.54749036
+108,M330,,1,0.04916401,1.564770043,,0.692947924
+108,,,1,0.061204576,1.52979646,,0.683116029
+110,230,,1,0.020450554,1.789271239,,0.928946762
+110,M230,,1,0.551138099,1.983882846,,0.006365659
+110,,,1,0.034039673,1.874929814,,0.754498102
+111,230,0,1,0.046930237,0.819023691,,1.393983197
+111,,0,1,0.052649522,0.836104011,,1.35741823
+111,230,1,4,0.197373655,1.450674694,-0.050385585,0.538874889
+111,,1,4,0.197373655,1.450674694,-0.050385585,0.538874889
+115,230,,1,0.069590888,1.694923675,,0.618372743
+115,,,1,0.069590888,1.694923675,,0.618372743
+121,230,,1,0.061210129,1.596483262,,0.817962656
+121,,,1,0.061210129,1.596483262,,0.817962656
+122,M310,,1,0.077785157,1.675881164,,0.712880315
+122,,,1,0.07732639,1.761223104,,0.66295371
+123,M220,,1,0.032703997,1.903092443,,0.698871535
+123,,,1,0.030322951,1.828656086,,0.751189292
+125,130,,2,0.014681172,1.707481851,1.549684489,0.924695694
+125,210,,2,0.043657756,1.745977544,1.861107773,0.619728947
+125,,,2,0.030527229,1.68789083,1.758110982,0.747370748
+126,M220,,1,0.067742191,2.067725566,,0.498583263
+126,,,1,0.04600067,1.943277897,,0.661031637
+128,230,,1,0.078850422,1.75060765,,0.610216932
+128,,,1,0.078850422,1.75060765,,0.610216932
+129,210,,2,0.01873811,1.816986181,1.722985549,0.914618822
+129,M220,,2,0.027010815,1.530603739,1.815264037,0.953058057
+129,,,2,0.027582546,1.698093152,1.784740212,0.879141183
+131,230,0,4,0.035614081,1.384818216,-0.034932785,0.887335694
+131,,0,4,0.036927899,1.386630276,-0.035296634,0.876614885
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diff --git a/src/pyfia/carbon/nsvb/data/branch_biomass_jenkins.csv b/src/pyfia/carbon/nsvb/data/branch_biomass_jenkins.csv
new file mode 100644
index 00000000..9619069a
--- /dev/null
+++ b/src/pyfia/carbon/nsvb/data/branch_biomass_jenkins.csv
@@ -0,0 +1,10 @@
+JENKINS_SPGRPCD,model,a,b,c
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+2,5,14.02057768,2.279310614,-0.624678091
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+8,5,0.79860485,2.969162133,-0.301902411
+9,5,0.198994402,2.681943186,0.288130955
diff --git a/src/pyfia/carbon/nsvb/data/branch_biomass_spcd.csv b/src/pyfia/carbon/nsvb/data/branch_biomass_spcd.csv
new file mode 100644
index 00000000..f969cb84
--- /dev/null
+++ b/src/pyfia/carbon/nsvb/data/branch_biomass_spcd.csv
@@ -0,0 +1,176 @@
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diff --git a/src/pyfia/carbon/nsvb/data/carbon_fraction_dead.csv b/src/pyfia/carbon/nsvb/data/carbon_fraction_dead.csv
new file mode 100644
index 00000000..e36a3b1c
--- /dev/null
+++ b/src/pyfia/carbon/nsvb/data/carbon_fraction_dead.csv
@@ -0,0 +1,11 @@
+Decay code,S/H,C fraction
+1,Hardwood,47
+2,Hardwood,47.3
+3,Hardwood,48.1
+4,Hardwood,48
+5,Hardwood,47.2
+1,Softwood,50.1
+2,Softwood,50.4
+3,Softwood,50.6
+4,Softwood,52
+5,Softwood,52.7
diff --git a/src/pyfia/carbon/nsvb/data/carbon_fraction_live.csv b/src/pyfia/carbon/nsvb/data/carbon_fraction_live.csv
new file mode 100644
index 00000000..33e53bee
--- /dev/null
+++ b/src/pyfia/carbon/nsvb/data/carbon_fraction_live.csv
@@ -0,0 +1,2677 @@
+SPCD,hw_sw,fia_wood_c
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+8821,hardwood,45.6
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+8824,hardwood,47.655
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+8827,hardwood,48.160645
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+8932,hardwood,47.48
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+8935,hardwood,47.48
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+8942,hardwood,47.3725
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+8944,hardwood,43.8
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diff --git a/src/pyfia/carbon/nsvb/data/dead_cr_prop.csv b/src/pyfia/carbon/nsvb/data/dead_cr_prop.csv
new file mode 100644
index 00000000..7158dd98
--- /dev/null
+++ b/src/pyfia/carbon/nsvb/data/dead_cr_prop.csv
@@ -0,0 +1,87 @@
+ECOPROV,hw_sw,CR_MEAN
+132,softwood,43.9
+132,hardwood,41.6
+133,softwood,49.6
+133,hardwood,41.5
+211,softwood,42.6
+211,hardwood,39.2
+212,softwood,48.0
+212,hardwood,38.6
+221,softwood,42.2
+221,hardwood,38.5
+222,softwood,44.5
+222,hardwood,38.8
+223,softwood,42.8
+223,hardwood,38.5
+231,softwood,34.8
+231,hardwood,37.4
+232,softwood,33.1
+232,hardwood,35.8
+234,softwood,40.6
+234,hardwood,38.9
+242,softwood,48.2
+242,hardwood,35.6
+251,softwood,62.1
+251,hardwood,38.5
+255,softwood,43.1
+255,hardwood,36.6
+261,softwood,38.9
+261,hardwood,40.2
+262,softwood,28.8
+262,hardwood,40.0
+263,softwood,40.3
+263,hardwood,38.1
+313,softwood,65.7
+313,hardwood,46.9
+315,softwood,74.2
+315,hardwood,43.6
+321,softwood,63.1
+321,hardwood,63.8
+322,softwood,64.8
+322,hardwood,56.5
+331,softwood,56.3
+331,hardwood,41.2
+332,softwood,56.4
+332,hardwood,38.9
+341,softwood,68.5
+341,hardwood,39.9
+342,softwood,65.6
+342,hardwood,40.2
+411,softwood,29.5
+411,hardwood,28.1
+M132,softwood,45.9
+M132,hardwood,42.4
+M133,softwood,55.8
+M133,hardwood,44.9
+M134,softwood,58.7
+M134,hardwood,52.4
+M211,softwood,41.2
+M211,hardwood,39.0
+M221,softwood,42.2
+M221,hardwood,37.6
+M223,softwood,44.3
+M223,hardwood,40.3
+M231,softwood,36.4
+M231,hardwood,41.4
+M241,softwood,42.7
+M241,hardwood,44.0
+M242,softwood,50.4
+M242,hardwood,37.8
+M261,softwood,48.6
+M261,hardwood,40.7
+M262,softwood,61.7
+M262,hardwood,41.1
+M313,softwood,56.7
+M313,hardwood,53.3
+M331,softwood,56.1
+M331,hardwood,37.2
+M332,softwood,53.6
+M332,hardwood,43.5
+M333,softwood,48.8
+M333,hardwood,38.9
+M334,softwood,49.8
+M334,hardwood,47.9
+M341,softwood,66.6
+M341,hardwood,40.9
+UNDEFINED,softwood,46.8
+UNDEFINED,hardwood,38.0
diff --git a/src/pyfia/carbon/nsvb/data/dead_decay_proportions.csv b/src/pyfia/carbon/nsvb/data/dead_decay_proportions.csv
new file mode 100644
index 00000000..17d66cc5
--- /dev/null
+++ b/src/pyfia/carbon/nsvb/data/dead_decay_proportions.csv
@@ -0,0 +1,11 @@
+hw_sw,DECAYCD,DENSITY_PROP,BARK_LOSS_PROP,BRANCH_LOSS_PROP
+hardwood,1,0.99,1.0,1.0
+hardwood,2,0.80,0.8,0.5
+hardwood,3,0.54,0.5,0.1
+hardwood,4,0.43,0.2,0.0
+hardwood,5,0.43,0.0,0.0
+softwood,1,0.97,1.0,1.0
+softwood,2,1.00,0.8,0.5
+softwood,3,0.92,0.5,0.1
+softwood,4,0.55,0.2,0.0
+softwood,5,0.55,0.0,0.0
diff --git a/src/pyfia/carbon/nsvb/data/total_biomass_jenkins.csv b/src/pyfia/carbon/nsvb/data/total_biomass_jenkins.csv
new file mode 100644
index 00000000..e8d5e04f
--- /dev/null
+++ b/src/pyfia/carbon/nsvb/data/total_biomass_jenkins.csv
@@ -0,0 +1,10 @@
+JENKINS_SPGRPCD,model,a,b,c
+1,5,0.523742886,1.92197857,0.78279546
+2,5,0.38826478,1.726525224,0.984599835
+3,5,0.772534536,2.184545296,0.575832011
+4,5,0.35304343,2.204853568,0.728083165
+5,5,0.947256394,1.904257586,0.723009833
+6,5,1.650798031,2.107712564,0.48072183
+7,5,0.332263991,2.060786768,0.850173608
+8,5,0.433906441,2.115626102,0.735074518
+9,5,0.303328094,2.01600356,0.903672396
diff --git a/src/pyfia/carbon/nsvb/data/total_biomass_spcd.csv b/src/pyfia/carbon/nsvb/data/total_biomass_spcd.csv
new file mode 100644
index 00000000..d6e076ea
--- /dev/null
+++ b/src/pyfia/carbon/nsvb/data/total_biomass_spcd.csv
@@ -0,0 +1,174 @@
+SPCD,DIVISION,STDORGCD,model,a,a1,b,b1,c,c1
+11,,,1,0.349568157,,2.036595585,,0.610614134,
+12,210,,2,0.328925371,,2.068305047,2.173714588,0.600489623,
+12,,,2,0.328136654,,2.068856431,2.178341624,0.600282409,
+15,M260,,1,0.014607388,,1.512436368,,1.71211905,
+15,,,1,0.073497618,,1.998948783,,1.008150026,
+16,,,1,0.349568157,,2.068859342,,0.610614134,
+17,,,1,0.349568157,,2.083812739,,0.610614134,
+19,,,2,0.443491134,,2.038793267,2.314536598,0.520420476,
+20,,,1,0.349568157,,2.070000431,,0.610614134,
+68,,,1,0.301398904,,2.034237148,,0.695059531,
+71,210,,1,0.664116474,,2.010379452,,0.50920278,
+71,,,1,0.882434652,,2.025368218,,0.435750124,
+73,,,1,0.301398904,,1.991589532,,0.695059531,
+93,M330,,1,0.213931593,,1.67741863,,0.941537364,
+93,,,1,0.286625447,,1.737783775,,0.837560422,
+94,210,,1,0.039235479,,2.226011879,,1.144335464,
+94,,,1,0.21805211,,2.489339298,,0.520617556,
+95,210,,1,0.313619663,,2.122370911,,0.62071126,
+95,,,1,0.313619663,,2.122370911,,0.62071126,
+97,,,1,0.371615725,,2.043904648,,0.646763187,
+98,,,1,0.371615725,,1.970403305,,0.646763187,
+105,210,,1,0.495643869,,2.109006809,,0.508476798,
+105,,,1,0.495643869,,2.109006809,,0.508476798,
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+111,230,0,2,0.177868614,,2.180014222,1.976884888,0.78319536,
+111,,0,2,0.177868614,,2.180014222,1.976884888,0.78319536,
+111,230,1,3,0.358815475,1.849604207,0.149984321,,0.841295541,0.332484731
+111,,1,3,0.358815475,1.849604207,0.149984321,,0.841295541,0.332484731
+116,,,1,0.148830682,,2.057563893,,0.810673827,
+121,230,,1,0.112404906,,2.075821407,,0.926658282,
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+125,210,,2,0.163474811,,2.033616764,2.140153969,0.782544189,
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+129,210,,1,0.272341852,,1.978761046,,0.706249069,
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+131,230,0,2,0.045928912,,1.956540143,1.955393942,1.154203033,
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+222,230,,1,0.110459157,,1.538421675,,1.171293113,
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+318,210,,1,0.197488976,,1.931805898,,0.92308401,
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+375,210,,1,0.921838183,,2.20808746,,0.365204162,
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+740,230,,1,0.099825153,,2.032553584,,0.898045231,
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+762,210,,1,0.0923161,,1.819131811,,1.13745364,
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+802,210,,2,0.185615748,,1.909923379,1.867881975,0.937170791,
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diff --git a/src/pyfia/carbon/nsvb/data/volbk_jenkins.csv b/src/pyfia/carbon/nsvb/data/volbk_jenkins.csv
new file mode 100644
index 00000000..9abb71cd
--- /dev/null
+++ b/src/pyfia/carbon/nsvb/data/volbk_jenkins.csv
@@ -0,0 +1,10 @@
+JENKINS_SPGRPCD,model,a,b,c
+1,1,0.000628047,1.695696318,1.061090008
+2,1,0.003452267,2.305064228,0.353845624
+3,1,0.008445325,2.442093833,0.020466657
+4,1,0.003742405,2.047884489,0.476931052
+5,1,0.003579986,1.870401387,0.454720079
+6,1,0.006055511,2.323948217,0.168927852
+7,1,0.001806756,2.094420478,0.523012456
+8,1,0.001879521,1.721074102,0.825002196
+9,1,0.003935233,1.728309956,0.626634668
diff --git a/src/pyfia/carbon/nsvb/data/volbk_spcd.csv b/src/pyfia/carbon/nsvb/data/volbk_spcd.csv
new file mode 100644
index 00000000..afde65d3
--- /dev/null
+++ b/src/pyfia/carbon/nsvb/data/volbk_spcd.csv
@@ -0,0 +1,340 @@
+SPCD,DIVISION,STDORGCD,model,a,a1,b,b1,c,c1
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diff --git a/src/pyfia/carbon/nsvb/data/volib_jenkins.csv b/src/pyfia/carbon/nsvb/data/volib_jenkins.csv
new file mode 100644
index 00000000..765e03ab
--- /dev/null
+++ b/src/pyfia/carbon/nsvb/data/volib_jenkins.csv
@@ -0,0 +1,10 @@
+JENKINS_SPGRPCD,model,a,b,c
+1,1,0.006419335,1.808424036,0.857982524
+2,1,0.002661495,1.701389437,1.118450094
+3,1,0.002186946,1.774662786,1.138638416
+4,1,0.002966164,1.892635521,1.004043997
+5,1,0.002589658,1.720103984,1.133217595
+6,1,0.003638299,1.797344245,1.002439989
+7,1,0.004762398,2.000019654,0.824681735
+8,1,0.002340041,1.894587354,1.03509406
+9,1,0.002653263,1.8973528,1.015463475
diff --git a/src/pyfia/carbon/nsvb/data/volib_spcd.csv b/src/pyfia/carbon/nsvb/data/volib_spcd.csv
new file mode 100644
index 00000000..6dff9873
--- /dev/null
+++ b/src/pyfia/carbon/nsvb/data/volib_spcd.csv
@@ -0,0 +1,407 @@
+SPCD,DIVISION,STDORGCD,model,a,a1,b,b1,c,c1
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+827,230,,2,0.002510178,,1.870602163,1.825369835,1.041912932,
+827,,,2,0.002510178,,1.870602163,1.825369835,1.041912932,
+828,230,,1,0.002492455,,1.91102546,,1.010625345,
+828,,,1,0.002492455,,1.91102546,,1.010625345,
+830,,,1,0.00286459,,1.968248331,,0.964612913,
+831,230,,1,0.002308302,,1.84342235,,1.060906206,
+831,,,1,0.002329714,,1.84192663,,1.060155664,
+832,220,,1,0.001340168,,1.904576977,,1.172515505,
+832,230,,1,0.001557419,,1.873893241,,1.133291605,
+832,M220,,1,0.002081683,,1.960328445,,1.013901564,
+832,,,1,0.001714369,,1.936348905,,1.076702143,
+833,210,,1,0.002322315,,1.799849142,,1.117365216,
+833,220,,1,0.001573268,,1.823458182,,1.190545046,
+833,230,,1,0.001472015,,1.838192568,,1.17871924,
+833,M220,,1,0.002134823,,1.905313358,,1.048458986,
+833,,,1,0.002362152,,1.874299976,,1.055751972,
+834,,,1,0.00286459,,1.949227114,,0.964612913,
+835,220,,1,0.001673819,,1.936343771,,1.111522047,
+835,230,,1,0.002373136,,1.920734316,,1.01402981,
+835,,,1,0.003305717,,1.988355446,,0.896401484,
+837,210,,2,0.001518878,,2.113998971,1.649991362,1.046010305,
+837,220,,2,0.000437947,,2.428124901,1.79498111,1.160762625,
+837,230,,2,0.00155351,,1.889213723,2.042730169,1.115496949,
+837,250,,2,0.000919117,,2.706014496,1.694757756,0.846614554,
+837,M210,,2,0.002954279,,2.08159177,1.928845656,0.887282921,
+837,M220,,2,0.00126221,,2.046186215,1.895886476,1.0848866,
+837,,,2,0.00151444,,2.22320961,1.883670123,0.959477251,
+838,230,,1,0.002131697,,1.767292517,,1.120625347,
+838,,,1,0.002131697,,1.767292517,,1.120625347,
+901,M220,,1,0.001660506,,1.818805739,,1.117347086,
+901,,,1,0.001693706,,1.817515456,,1.113882311,
+920,,,1,0.00247111,,1.817154576,,1.031521892,
+922,,,1,0.00247111,,1.860927908,,1.031521892,
+931,,,1,0.002952358,,1.931093987,,0.96875716,
+950,210,,4,0.003226314,,2.302737543,0.017167865,0.793085798,
+950,M220,,4,0.002008879,,2.001563697,0.004256522,1.025581517,
+950,,,4,0.003505685,,2.274426602,0.01484614,0.780472682,
+951,210,,2,0.001934393,,2.153803577,1.932362544,0.963175885,
+951,220,,2,0.001186781,,1.933040653,1.927964512,1.188299017,
+951,,,2,0.001590209,,2.081672706,1.932067041,1.045752102,
+970,230,,1,0.001524725,,1.798591052,,1.190571411,
+970,,,1,0.001844723,,1.826132667,,1.129759189,
+971,,,1,0.002952358,,1.913957722,,0.96875716,
+972,210,,2,0.001288116,,1.816246182,1.834838985,1.217959936,
+972,,,2,0.001506761,,1.879128146,1.790820226,1.148150698,
+975,,,1,0.002952358,,1.946558607,,0.96875716,
+999,230,,1,0.004080563,,1.865598495,,0.894005944,
+999,M220,,1,0.001871464,,1.892577254,,1.081824247,
+999,,,1,0.002017556,,1.864705353,,1.084696923,
diff --git a/src/pyfia/carbon/nsvb/equations.py b/src/pyfia/carbon/nsvb/equations.py
new file mode 100644
index 00000000..d1bf9009
--- /dev/null
+++ b/src/pyfia/carbon/nsvb/equations.py
@@ -0,0 +1,1285 @@
+"""
+NSVB allometric equation forms (Westfall et al. 2023, GTR-WO-104).
+
+Pure-math equation library for the National Scale Volume and Biomass framework.
+No I/O, no Polars dependency — just floats in and floats out. Coefficient lookup
+lives in coefficients.py; carbon fractions live in carbon_fractions.py.
+
+The four model forms used in Phase 1 (live tree biomass) are:
+
+- **Model 1**: ``y = a * D^b * H^c`` — power form, the most common.
+- **Model 2**: ``y = a * k^(b-b1) * D^b1 * H^c`` — with ``k = 9`` for softwoods
+  (SPCD < 300) and ``k = 11`` for hardwoods (SPCD >= 300). The constants 9 and 11
+  match the sawlog top-diameter cutoffs used elsewhere in NSVB. Verified against
+  the Douglas-fir (SPCD=202) wood-volume worked example and back-solved against
+  the red maple (SPCD=316) bark-volume worked example.
+- **Model 4**: ``y = a * D^b * H^c * exp(-b1 * D)`` — power form modulated by an
+  exponential of D. The b1 parameter is typically a small negative number, so
+  the multiplicative factor is slightly greater than 1 across the practical D
+  range. Verified against the red maple S8a (total AGB) worked example.
+- **Model 5 (Jenkins fallback)**: ``y = a * D^b * H^c * WDSG`` — used when a
+  species lacks SPCD-specific coefficients and falls back to its Jenkins species
+  group. Wood density (WDSG) comes from FIADB ``REF_SPECIES.WOOD_SPGR_GREENVOL_DRYWT``.
+
+**Model 3 and Model 6 are NOT implemented in Phase 1**. Model 3 is a three-parameter
+form whose exact shape is picture-omitted in the source PDF and unverified.
+Model 6 is the iterative Schumacher-Hall volume-ratio model used for merchantable
+subdivision (DRYBIO_BOLE / DRYBIO_TOP / DRYBIO_STUMP); total above-ground biomass
+and total carbon do not require it. Both will arrive in a later phase if downstream
+work needs them.
+
+The orchestrator :func:`predict_tree_biomass` runs the full per-tree pipeline:
+predict component biomasses (wood, bark, branches), predict directly the total AGB,
+then harmonize the components so they sum exactly to the predicted total. The
+harmonization is documented in ``gtr_wo104_westfall2023.md`` lines 600-612 (live tree
+worked example) and lines 866-898 (cull-adjusted variant for the red maple example).
+
+References
+----------
+- Westfall, J.A. et al. (2023). GTR-WO-104. DOI: 10.2737/WO-GTR-104
+- Worked examples: ``gtr_wo104_westfall2023.md`` lines 394-680 (Douglas-fir,
+  no cull) and lines 682-960 (red maple, with 3% cull).
+"""
+
+from __future__ import annotations
+
+import math
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Literal
+
+import polars as pl
+
+if TYPE_CHECKING:
+    from pyfia.carbon.nsvb.coefficients import VectorizedLookupTables
+
+# Sawlog top-diameter cutoffs that double as the Model 2 ``k`` constant.
+# Per worked example line 462: softwood top diameter is 7-9 inches and hardwood is
+# 9-11 inches, depending on species. The Model 2 base constant matches the upper
+# bound of these cutoffs.
+_K_SOFTWOOD = 9.0
+_K_HARDWOOD = 11.0
+_HARDWOOD_SPCD_THRESHOLD = 300
+
+
+def _model_k(spcd: int) -> float:
+    """Return the Model 2 base constant ``k`` for a given species code.
+
+    Softwoods (SPCD < 300) use k=9; hardwoods (SPCD >= 300) use k=11. The split
+    point matches the FIA hardwood/softwood classification used throughout NSVB.
+    """
+    return _K_HARDWOOD if spcd >= _HARDWOOD_SPCD_THRESHOLD else _K_SOFTWOOD
+
+
+def model_1(d: float, h: float, a: float, b: float, c: float) -> float:
+    """NSVB Model 1: ``y = a * D^b * H^c``.
+
+    The most common NSVB form, used for stem wood volume (S1a), stem bark volume
+    (S2a), stem bark biomass (S6a), branch biomass (S7a), total AGB (S8a), and
+    foliage biomass (S9a) for the majority of FIA species.
+
+    Parameters
+    ----------
+    d : float
+        Diameter at breast height (inches).
+    h : float
+        Total tree height (feet).
+    a, b, c : float
+        Model 1 coefficients from the relevant ``S*a`` table.
+
+    Returns
+    -------
+    float
+        Predicted quantity in the units of the source table (cubic feet for
+        volume tables, pounds for biomass tables).
+    """
+    return float(a * (d**b) * (h**c))
+
+
+def model_2(
+    d: float, h: float, a: float, b: float, b1: float, c: float, k: float
+) -> float:
+    """NSVB Model 2: ``y = a * k^(b - b1) * D^b1 * H^c``.
+
+    Power form with a species-class base constant ``k``. Used for stem wood
+    volume and stem bark volume on a subset of species. The ``k`` constant
+    is 9 for softwoods (SPCD < 300) and 11 for hardwoods (SPCD >= 300); use
+    :func:`_model_k` (or pass it explicitly) when calling from the orchestrator.
+
+    Parameters
+    ----------
+    d : float
+        Diameter at breast height (inches).
+    h : float
+        Total tree height (feet).
+    a, b, b1, c : float
+        Model 2 coefficients from the relevant ``S*a`` table.
+    k : float
+        Species-class base constant — typically 9.0 (softwood) or 11.0 (hardwood).
+
+    Returns
+    -------
+    float
+        Predicted quantity in source-table units.
+    """
+    return float(a * (k ** (b - b1)) * (d**b1) * (h**c))
+
+
+def model_4(d: float, h: float, a: float, b: float, b1: float, c: float) -> float:
+    """NSVB Model 4: ``y = a * D^b * H^c * exp(-b1 * D)``.
+
+    Power form modulated by an exponential of D. Used for total AGB on a small
+    set of species (e.g., red maple SPCD=316). The b1 coefficient is typically
+    small (often slightly negative), so the exp factor is close to 1.
+
+    Parameters
+    ----------
+    d : float
+        Diameter at breast height (inches).
+    h : float
+        Total tree height (feet).
+    a, b, b1, c : float
+        Model 4 coefficients from the relevant ``S*a`` table.
+
+    Returns
+    -------
+    float
+        Predicted quantity in source-table units.
+    """
+    return float(a * (d**b) * (h**c) * math.exp(-b1 * d))
+
+
+def model_5_jenkins(
+    d: float, h: float, a: float, b: float, c: float, wdsg: float
+) -> float:
+    """NSVB Model 5 (Jenkins-group fallback): ``y = a * D^b * H^c * WDSG``.
+
+    Used when a species lacks SPCD-specific coefficients in S1a-S8a and falls
+    back to its Jenkins species group from S1b-S8b. Wood density (WDSG) is
+    sourced from FIADB ``REF_SPECIES.WOOD_SPGR_GREENVOL_DRYWT``.
+
+    Parameters
+    ----------
+    d : float
+        Diameter at breast height (inches).
+    h : float
+        Total tree height (feet).
+    a, b, c : float
+        Model 5 coefficients from the relevant ``S*b`` Jenkins table.
+    wdsg : float
+        Wood specific gravity (green volume, dry weight basis) for the species.
+
+    Returns
+    -------
+    float
+        Predicted quantity in source-table units.
+    """
+    return float(a * (d**b) * (h**c) * wdsg)
+
+
+def harmonize_components(
+    agb_predicted: float,
+    w_wood: float,
+    w_bark: float,
+    w_branch: float,
+) -> tuple[float, float, float]:
+    """Proportionally redistribute component weights to sum to the predicted AGB.
+
+    NSVB makes two independent predictions of total above-ground biomass: one
+    by summing the component predictions (wood + bark + branch) and one by
+    directly predicting AGB from D and H via S8a/S8b. The two estimates rarely
+    agree exactly. Westfall et al. (2023) chose the directly-predicted AGB as
+    the truth and rescale the component sum proportionally so the harmonized
+    components sum exactly to ``agb_predicted`` while preserving their relative
+    ratios.
+
+    Per worked example lines 600-612 (Douglas-fir, no cull) and 886-898 (red
+    maple, with cull). For trees with cull or decay, the caller should pass
+    the *cull-reduced* component weights and the *cull-reduced* predicted AGB
+    (``agb_predicted = AGB_predicted * AGBReduce``); see
+    :func:`predict_tree_biomass` for the full pipeline.
+
+    Parameters
+    ----------
+    agb_predicted : float
+        Directly-predicted total above-ground biomass (lb).
+    w_wood, w_bark, w_branch : float
+        Component biomass predictions (lb).
+
+    Returns
+    -------
+    tuple[float, float, float]
+        Harmonized (wood, bark, branch) weights such that
+        ``wood_h + bark_h + branch_h == agb_predicted`` to numerical precision.
+        If the component sum is zero or negative, returns
+        ``(agb_predicted, 0.0, 0.0)`` as a degenerate fallback.
+    """
+    component_sum = w_wood + w_bark + w_branch
+    if component_sum <= 0:
+        return (agb_predicted, 0.0, 0.0)
+    wood_h = agb_predicted * (w_wood / component_sum)
+    bark_h = agb_predicted * (w_bark / component_sum)
+    branch_h = agb_predicted * (w_branch / component_sum)
+    return wood_h, bark_h, branch_h
+
+
+# DECAYCD=3 wood density proportions used to discount cull wood weight, per
+# worked example line 550 and Harmon et al. (2011) Table 1. Cull wood is
+# typically partially rotten, so its weight is reduced (but not removed entirely).
+_CULL_DENS_PROP = {"hardwood": 0.54, "softwood": 0.92}
+
+
+@dataclass(frozen=True)
+class Coefficients:
+    """A bundle of coefficients for one tree's NSVB pipeline.
+
+    Each component table (volib, volbk, bark biomass, branch biomass, total
+    AGB) supplies its own coefficient row, looked up via
+    :func:`pyfia.carbon.nsvb.coefficients.lookup_coefficients`. The
+    ``volib`` and ``volbk`` entries can be Model 1 or Model 2; ``bark_bio``
+    and ``branch_bio`` are Model 1 in the data we've inspected;
+    ``total_agb`` can be Model 1 or Model 4.
+
+    Each entry is a dict with keys ``model, a, a1, b, b1, c, c1, source``.
+    ``source`` is the lookup-precedence outcome (``spcd_division_stdorg``,
+    ``spcd_division``, ``spcd``, or ``jenkins``).
+    """
+
+    volib: dict
+    volbk: dict
+    bark_bio: dict
+    branch_bio: dict
+    total_agb: dict
+
+
+@dataclass(frozen=True)
+class TreeBiomassResult:
+    """Per-tree NSVB biomass output (component-harmonized).
+
+    All weights are in pounds. ``agb`` equals ``w_wood + w_bark + w_branch``
+    by construction (harmonization invariant).
+    """
+
+    w_wood: float
+    w_bark: float
+    w_branch: float
+    agb: float
+    v_wood_ib: float  # for adjusted wood density downstream (Phase 2+)
+    v_bark: float
+
+
+def _eval_component(coef: dict, d: float, h: float, spcd: int, wdsg: float) -> float:
+    """Dispatch a single component prediction to the right model form."""
+    model = int(coef["model"])
+    if model == 1:
+        return model_1(d, h, coef["a"], coef["b"], coef["c"])
+    if model == 2:
+        return model_2(
+            d, h, coef["a"], coef["b"], coef["b1"], coef["c"], _model_k(spcd)
+        )
+    if model == 4:
+        return model_4(d, h, coef["a"], coef["b"], coef["b1"], coef["c"])
+    if model == 5:
+        return model_5_jenkins(d, h, coef["a"], coef["b"], coef["c"], wdsg)
+    raise ValueError(
+        f"NSVB model {model} not supported in Phase 1 — only models 1, 2, 4, 5 are implemented."
+    )
+
+
+def predict_tree_biomass(
+    spcd: int,
+    dia: float,
+    ht: float,
+    coefficients: Coefficients,
+    wdsg: float,
+    hw_sw: Literal["hardwood", "softwood"],
+    cull: float = 0.0,
+) -> TreeBiomassResult:
+    """Run the full NSVB per-tree biomass pipeline for one live tree.
+
+    Pipeline (matches the Douglas-fir example at ``gtr_wo104_westfall2023.md:394``
+    and the red maple example at ``:682``):
+
+    1. Predict total stem inside-bark wood volume from S1a/S1b.
+    2. Predict total stem bark volume from S2a/S2b.
+    3. Convert wood volume to gross weight: ``W_w = V_w * WDSG * 62.4``.
+    4. Apply cull deduction to wood weight: ``W_w_red = V_w * (1 - CULL/100 * (1 - DensProp)) * WDSG * 62.4``
+       where ``DensProp`` is 0.54 for hardwoods and 0.92 for softwoods (the
+       DECAYCD=3 wood density proportion from Harmon et al. 2011, used as the
+       standard cull-density assumption per worked example line 550).
+    5. Predict stem bark biomass from S6a/S6b.
+    6. Predict branch biomass from S7a.
+    7. Predict total AGB directly from S8a/S8b.
+    8. Compute the cull-reduction factor:
+       ``AGBReduce = (W_w_red + W_b + W_br) / (W_w + W_b + W_br)``.
+    9. Reduce predicted AGB: ``AGB_predicted_red = AGB_predicted * AGBReduce``.
+    10. Harmonize components against ``AGB_predicted_red`` so they sum exactly
+        to it (proportional redistribution).
+
+    Note: Phase 1 assumes live trees with intact tops, so bark and branch
+    weights have no broken-top deductions. Standing dead trees and broken-top
+    deductions arrive in Phase 2.
+
+    Foliage is **not** part of AGB and is not computed here.
+
+    Belowground (coarse root) biomass is **not** computed here. Phase 1 reads
+    FIADB ``CARBON_BG`` directly via the live-tree estimator's BG bridge.
+
+    Parameters
+    ----------
+    spcd : int
+        FIA species code. Used to select the Model 2 ``k`` constant.
+    dia : float
+        Diameter at breast height (inches). Must be >= 1.0.
+    ht : float
+        Total tree height (feet).
+    coefficients : Coefficients
+        Bundle of coefficient rows for the five tables (volib, volbk,
+        bark_bio, branch_bio, total_agb), looked up upstream by
+        :func:`pyfia.carbon.nsvb.coefficients.lookup_coefficients`.
+    wdsg : float
+        Wood specific gravity (green volume, dry weight) from FIADB
+        ``REF_SPECIES.WOOD_SPGR_GREENVOL_DRYWT``.
+    hw_sw : {"hardwood", "softwood"}
+        Hardwood/softwood classification for the cull density proportion.
+        Case-insensitive at runtime (``"Hardwood"`` and ``"HARDWOOD"`` are
+        accepted and normalized). Callers should derive this from
+        ``"softwood" if spcd < 300 else "hardwood"`` to stay consistent with
+        :func:`_model_k`, which uses the same SPCD threshold to select the
+        Model 2 base constant. This rule also resolves the SPCD=10 ("fir spp.")
+        edge case — S10a misclassifies SPCD=10 as hardwood, but the SPCD<300
+        rule correctly classifies it as softwood.
+    cull : float, default 0.0
+        Cull percentage from FIADB ``TREE.CULL`` (0-100). Defaults to 0 for
+        live trees with no cull.
+
+    Returns
+    -------
+    TreeBiomassResult
+        Harmonized component weights and total AGB in pounds, plus the gross
+        wood and bark volumes (cubic feet) for downstream adjusted-density
+        calculations.
+
+    Raises
+    ------
+    ValueError
+        If ``dia < 1.0`` (NSVB is not parameterized below the FIA minimum
+        tally diameter of 1.0 inch, and some Model 1 forms would produce
+        complex-number results for d<1 with fractional b).
+        If ``hw_sw`` is not one of ``"hardwood"``/``"softwood"`` (after
+        case-insensitive normalization).
+        If a coefficient row specifies a Model 3 or Model 6 (not implemented
+        in Phase 1).
+    """
+    # TODO(PR 2): Scalar reference implementation. PR 2's LiveTreeEstimator
+    # must implement this pipeline as polars expressions on a LazyFrame
+    # joined to the coefficient tables on SPCD, not by calling this function
+    # per tree. See `pyfia/carbon/__init__.py` "Architectural rules" rule 2.
+
+    # Boundary validation — give clear errors instead of letting the math
+    # functions raise cryptic complex-number TypeErrors or KeyErrors.
+    if dia < 1.0:
+        raise ValueError(
+            f"dia must be >= 1.0 inches (FIA minimum tally diameter); got {dia}. "
+            "NSVB Models 1-5 are not parameterized below 1.0 and can produce "
+            "complex-number results for fractional b exponents."
+        )
+    hw_sw_norm = hw_sw.lower()
+    if hw_sw_norm not in ("hardwood", "softwood"):
+        raise ValueError(
+            f"hw_sw must be 'hardwood' or 'softwood' (case-insensitive); got {hw_sw!r}"
+        )
+
+    # Step 1: Total stem inside-bark wood volume (cubic feet)
+    v_wood_ib = _eval_component(coefficients.volib, dia, ht, spcd, wdsg)
+
+    # Step 2: Total stem bark volume (cubic feet)
+    v_bark = _eval_component(coefficients.volbk, dia, ht, spcd, wdsg)
+
+    # Step 3-4: Convert wood volume to weight, with cull-reduced variant
+    dens_prop = _CULL_DENS_PROP[hw_sw_norm]
+    w_wood_gross = v_wood_ib * wdsg * 62.4
+    w_wood_red = v_wood_ib * (1.0 - cull / 100.0 * (1.0 - dens_prop)) * wdsg * 62.4
+
+    # Step 5: Stem bark biomass (live, no broken-top deduction)
+    w_bark = _eval_component(coefficients.bark_bio, dia, ht, spcd, wdsg)
+
+    # Step 6: Branch biomass (live, no broken-top deduction)
+    w_branch = _eval_component(coefficients.branch_bio, dia, ht, spcd, wdsg)
+
+    # Step 7: Directly-predicted total AGB
+    agb_predicted = _eval_component(coefficients.total_agb, dia, ht, spcd, wdsg)
+
+    # Step 8: Cull-reduction factor (NB: only wood is cull-reduced for live trees;
+    # bark and branch use their gross values in the denominator per worked example
+    # lines 870-880).
+    component_gross_sum = w_wood_gross + w_bark + w_branch
+    component_red_sum = w_wood_red + w_bark + w_branch
+    if component_gross_sum <= 0:
+        agb_reduce = 0.0
+    else:
+        agb_reduce = component_red_sum / component_gross_sum
+
+    # Step 9: Reduce predicted AGB
+    agb_predicted_red = agb_predicted * agb_reduce
+
+    # Step 10: Harmonize the cull-reduced components against the reduced predicted AGB
+    w_wood_h, w_bark_h, w_branch_h = harmonize_components(
+        agb_predicted_red, w_wood_red, w_bark, w_branch
+    )
+
+    return TreeBiomassResult(
+        w_wood=w_wood_h,
+        w_bark=w_bark_h,
+        w_branch=w_branch_h,
+        agb=w_wood_h + w_bark_h + w_branch_h,
+        v_wood_ib=v_wood_ib,
+        v_bark=v_bark,
+    )
+
+
+# ---------------------------------------------------------------------------
+# Vectorized NSVB pipeline (PR 2) — the production data path.
+# ---------------------------------------------------------------------------
+#
+# The functions above (``predict_tree_biomass`` and ``_eval_component``) are
+# scalar reference implementations retained as test oracles. The functions
+# below replicate the same math as polars expressions over a LazyFrame,
+# enabling FIA-scale (1M+ trees) evaluation via coefficient-table joins
+# instead of per-tree Python dispatch. See ``pyfia/carbon/__init__.py``
+# "Architectural rules" rule 2.
+
+
+def nsvb_biomass_expr(
+    *,
+    model: pl.Expr,
+    a: pl.Expr,
+    b: pl.Expr,
+    b1: pl.Expr,
+    c: pl.Expr,
+    d: pl.Expr,
+    h: pl.Expr,
+    spcd: pl.Expr,
+    wdsg: pl.Expr,
+) -> pl.Expr:
+    """Build a polars expression that dispatches NSVB Models 1/2/4/5.
+
+    The scalar equivalent of this function is :func:`_eval_component`.
+    It returns a single expression suitable for use inside
+    ``LazyFrame.with_columns`` that produces the predicted component value
+    (volume or biomass, in source-table units) for every row, with the
+    model form selected per-row from the ``model`` column.
+
+    Models 3 and 6 are not implemented in Phase 1; rows dispatching to
+    those return ``None`` which will surface as a null downstream. The
+    orchestrator :func:`compute_nsvb_biomass` asserts that no nulls appear
+    in the output columns, so an unsupported model becomes a loud failure.
+
+    The Model 2 base constant ``k`` is ``11`` for hardwoods (SPCD >= 300)
+    and ``9`` for softwoods, matching :func:`_model_k` used by the scalar
+    path. This also resolves the SPCD=10 misclassification that S10a
+    carries — see ``pyfia/carbon/__init__.py`` for the architectural
+    discussion.
+
+    Parameters
+    ----------
+    model, a, b, b1, c : pl.Expr
+        Coefficient column expressions (typically the output of a coalesce
+        across species-level + Jenkins lookup joins, or ``pl.col(...)``
+        references to pre-joined coefficient columns). All numeric
+        coefficient columns must be ``Float64``; ``model`` must be an
+        integer dtype.
+    d, h : pl.Expr
+        Diameter at breast height (inches) and total height (feet) column
+        expressions. Must be ``Float64``.
+    spcd : pl.Expr
+        Species code column expression used to select the Model 2 ``k``
+        constant via the ``SPCD < 300`` rule.
+    wdsg : pl.Expr
+        Wood specific gravity column expression (``REF_SPECIES.WOOD_SPGR_GREENVOL_DRYWT``).
+        Used only by Model 5.
+
+    Returns
+    -------
+    pl.Expr
+        An expression that evaluates to the predicted quantity (volume or
+        biomass) in source-table units (cubic feet or pounds). Call
+        ``.alias("v_wood_ib")`` (or similar) to name the output column.
+    """
+    k = (
+        pl.when(spcd >= _HARDWOOD_SPCD_THRESHOLD)
+        .then(_K_HARDWOOD)
+        .otherwise(_K_SOFTWOOD)
+    )
+    return (
+        pl.when(model == 1)
+        .then(a * d.pow(b) * h.pow(c))
+        .when(model == 2)
+        .then(a * k.pow(b - b1) * d.pow(b1) * h.pow(c))
+        .when(model == 4)
+        .then(a * d.pow(b) * h.pow(c) * (-b1 * d).exp())
+        .when(model == 5)
+        .then(a * d.pow(b) * h.pow(c) * wdsg)
+        .otherwise(None)
+    )
+
+
+def _join_and_eval_component(
+    trees: pl.LazyFrame,
+    spcd_table: pl.DataFrame,
+    jen_table: pl.DataFrame,
+    div_table: pl.DataFrame,
+    out_col: str,
+    *,
+    has_division: bool,
+) -> pl.LazyFrame:
+    """Join a LazyFrame to the (division, species-level, Jenkins) coefficient
+    triple for one component and evaluate the NSVB biomass expression.
+
+    Implementation of NSVB lookup precedence Levels 2 → 3 → 4 as polars
+    joins:
+
+    1. If ``has_division`` is True: left-join ``div_table`` on
+       ``(SPCD, DIVISION)`` (Level 2).
+    2. Left-join ``spcd_table`` on ``SPCD`` (species-level fallback, Level 3).
+    3. Left-join ``jen_table`` on ``JENKINS_SPGRPCD`` (Jenkins fallback,
+       Level 4).
+    4. Coalesce the three coefficient rows in precedence order:
+       division → species-level → Jenkins.
+    5. Evaluate :func:`nsvb_biomass_expr` to produce ``out_col``.
+    6. Drop the temporary coefficient columns.
+
+    When ``has_division`` is False (the trees frame has no ``DIVISION``
+    column — backward-compatible path for synthetic tests and for callers
+    that haven't yet wired the ``PLOTGEOM.ECOSUBCD`` join), the division
+    join is skipped entirely and the behavior matches the old 2-way
+    coalesce exactly.
+
+    Parameters
+    ----------
+    trees : pl.LazyFrame
+        Input frame with at least ``SPCD``, ``DIA``, ``HT``, ``WDSG``,
+        ``JENKINS_SPGRPCD`` columns. If ``has_division`` is True, must also
+        have a ``DIVISION`` column (Utf8/String, nullable).
+    spcd_table : pl.DataFrame
+        Species-level lookup from
+        :func:`pyfia.carbon.nsvb.coefficients.build_species_level_lookup`
+        with columns ``(SPCD, model, a, b, b1, c)``.
+    jen_table : pl.DataFrame
+        Jenkins fallback from
+        :func:`pyfia.carbon.nsvb.coefficients.build_jenkins_lookup` with
+        columns ``(JENKINS_SPGRPCD, model, a, b, b1, c)``.
+    div_table : pl.DataFrame
+        DIVISION-specific lookup from
+        :func:`pyfia.carbon.nsvb.coefficients.build_division_lookup` with
+        columns ``(SPCD, DIVISION, model, a, b, b1, c)``. Only consulted
+        when ``has_division`` is True.
+    out_col : str
+        Name for the output column (e.g., ``"v_wood_ib"``).
+    has_division : bool
+        Whether the caller has populated a ``DIVISION`` column on the trees
+        frame. When False, skip the division join entirely.
+
+    Returns
+    -------
+    pl.LazyFrame
+        The input frame with ``out_col`` appended. Temporary coefficient
+        columns are dropped before return.
+    """
+    # Rename coefficient columns on each side so they don't collide with the
+    # trees frame or with each other.
+    spcd_lf = spcd_table.lazy().rename(
+        {
+            "model": "_model_s",
+            "a": "_a_s",
+            "b": "_b_s",
+            "b1": "_b1_s",
+            "c": "_c_s",
+        }
+    )
+    jen_lf = jen_table.lazy().rename(
+        {
+            "model": "_model_j",
+            "a": "_a_j",
+            "b": "_b_j",
+            "b1": "_b1_j",
+            "c": "_c_j",
+        }
+    )
+
+    if has_division:
+        div_lf = div_table.lazy().rename(
+            {
+                "model": "_model_d",
+                "a": "_a_d",
+                "b": "_b_d",
+                "b1": "_b1_d",
+                "c": "_c_d",
+            }
+        )
+        trees = trees.join(div_lf, on=["SPCD", "DIVISION"], how="left")
+
+    trees = trees.join(spcd_lf, on="SPCD", how="left")
+    trees = trees.join(jen_lf, on="JENKINS_SPGRPCD", how="left")
+
+    # Coalesce: division (Level 2) → species-level (Level 3) → Jenkins (Level 4).
+    # When has_division=False, the _*_d columns don't exist and we fall back
+    # to the 2-way coalesce matching the original implementation.
+    if has_division:
+        model_expr = pl.coalesce(
+            pl.col("_model_d"), pl.col("_model_s"), pl.col("_model_j")
+        )
+        a_expr = pl.coalesce(pl.col("_a_d"), pl.col("_a_s"), pl.col("_a_j"))
+        b_expr = pl.coalesce(pl.col("_b_d"), pl.col("_b_s"), pl.col("_b_j"))
+        b1_expr = pl.coalesce(pl.col("_b1_d"), pl.col("_b1_s"), pl.col("_b1_j"))
+        c_expr = pl.coalesce(pl.col("_c_d"), pl.col("_c_s"), pl.col("_c_j"))
+    else:
+        model_expr = pl.coalesce(pl.col("_model_s"), pl.col("_model_j"))
+        a_expr = pl.coalesce(pl.col("_a_s"), pl.col("_a_j"))
+        b_expr = pl.coalesce(pl.col("_b_s"), pl.col("_b_j"))
+        b1_expr = pl.coalesce(pl.col("_b1_s"), pl.col("_b1_j"))
+        c_expr = pl.coalesce(pl.col("_c_s"), pl.col("_c_j"))
+
+    trees = trees.with_columns(
+        nsvb_biomass_expr(
+            model=model_expr,
+            a=a_expr,
+            b=b_expr,
+            b1=b1_expr,
+            c=c_expr,
+            d=pl.col("DIA").cast(pl.Float64),
+            h=pl.col("HT").cast(pl.Float64),
+            spcd=pl.col("SPCD"),
+            wdsg=pl.col("WDSG").cast(pl.Float64),
+        ).alias(out_col)
+    )
+
+    drop_cols = [
+        "_model_s",
+        "_a_s",
+        "_b_s",
+        "_b1_s",
+        "_c_s",
+        "_model_j",
+        "_a_j",
+        "_b_j",
+        "_b1_j",
+        "_c_j",
+    ]
+    if has_division:
+        drop_cols.extend(["_model_d", "_a_d", "_b_d", "_b1_d", "_c_d"])
+    return trees.drop(drop_cols)
+
+
+def compute_nsvb_biomass(
+    trees: pl.LazyFrame,
+    lookup: VectorizedLookupTables | None = None,
+) -> pl.LazyFrame:
+    """Vectorized NSVB live-tree biomass pipeline (the production data path).
+
+    Executes the same 10-step pipeline as :func:`predict_tree_biomass`
+    (5 component predictions → cull adjustment → harmonization) as a
+    sequence of polars joins and expressions over a LazyFrame, with no
+    per-tree Python dispatch.
+
+    The hardwood/softwood classification used for the cull density
+    proportion and the Model 2 ``k`` constant is derived from the
+    ``SPCD < 300`` rule to stay consistent with :func:`_model_k` and to
+    sidestep the S10a misclassification of SPCD=10 — see
+    ``pyfia/carbon/__init__.py`` "Items deferred from PR 1 review".
+
+    Parameters
+    ----------
+    trees : pl.LazyFrame
+        Input frame with at least the following columns:
+
+        - ``SPCD`` (Int): FIA species code
+        - ``DIA`` (Float): diameter at breast height (inches, must be >= 1.0)
+        - ``HT`` (Float): total tree height (feet)
+        - ``CULL`` (Float, nullable): cull percentage (0-100). Null is
+          treated as 0.
+        - ``WDSG`` (Float): wood specific gravity
+          (``REF_SPECIES.WOOD_SPGR_GREENVOL_DRYWT``)
+        - ``JENKINS_SPGRPCD`` (Int): Jenkins species group for the Level 4
+          fallback (``REF_SPECIES.JENKINS_SPGRPCD``)
+
+        Optional:
+
+        - ``DIVISION`` (Utf8, nullable): Bailey ecoprovince DIVISION code
+          (e.g., ``"230"``, ``"M230"``, computed from ``PLOT.ECOSUBCD`` via
+          :func:`pyfia.carbon.nsvb.coefficients.ecosubcd_to_division`).
+          When present, the orchestrator activates Level 2 of the NSVB
+          lookup precedence: the ``(SPCD, DIVISION)`` lookup runs first,
+          falling through to species-level (Level 3) and Jenkins (Level 4)
+          per-row via coalesce. When absent, only Levels 3 + 4 are used
+          (backward-compatible with callers that haven't wired the
+          PLOTGEOM join).
+
+        Additional columns (grouping variables, plot identifiers, etc.)
+        pass through untouched.
+    lookup : VectorizedLookupTables, optional
+        Pre-built coefficient lookup bundle from
+        :func:`pyfia.carbon.nsvb.coefficients.get_vectorized_lookup_tables`.
+        If omitted, fetches the cached process-level bundle.
+
+    Returns
+    -------
+    pl.LazyFrame
+        The input frame with six new columns appended:
+
+        - ``v_wood_ib`` (Float, cu ft): total stem inside-bark wood volume
+        - ``v_bark`` (Float, cu ft): total stem bark volume
+        - ``w_wood`` (Float, lb): harmonized stem wood biomass
+        - ``w_bark`` (Float, lb): harmonized stem bark biomass
+        - ``w_branch`` (Float, lb): harmonized branch biomass
+        - ``agb`` (Float, lb): harmonized total above-ground biomass
+          (equals ``w_wood + w_bark + w_branch`` by construction)
+
+        All other input columns pass through.
+    """
+    if lookup is None:
+        from pyfia.carbon.nsvb.coefficients import get_vectorized_lookup_tables
+
+        lookup = get_vectorized_lookup_tables()
+
+    # Probe the schema once so the 5 per-component joins don't each re-collect
+    # it. DIVISION column detection activates the Level 2 lookup path.
+    has_division = "DIVISION" in trees.collect_schema().names()
+
+    # Step 1 — Total stem inside-bark wood volume (cubic feet).
+    trees = _join_and_eval_component(
+        trees,
+        lookup.volib_spcd,
+        lookup.volib_jen,
+        lookup.volib_div,
+        "v_wood_ib",
+        has_division=has_division,
+    )
+
+    # Step 2 — Total stem bark volume (cubic feet).
+    trees = _join_and_eval_component(
+        trees,
+        lookup.volbk_spcd,
+        lookup.volbk_jen,
+        lookup.volbk_div,
+        "v_bark",
+        has_division=has_division,
+    )
+
+    # Step 5 — Stem bark biomass (lb).
+    trees = _join_and_eval_component(
+        trees,
+        lookup.bark_bio_spcd,
+        lookup.bark_bio_jen,
+        lookup.bark_bio_div,
+        "_w_bark_pre",
+        has_division=has_division,
+    )
+
+    # Step 6 — Branch biomass (lb).
+    trees = _join_and_eval_component(
+        trees,
+        lookup.branch_bio_spcd,
+        lookup.branch_bio_jen,
+        lookup.branch_bio_div,
+        "_w_branch_pre",
+        has_division=has_division,
+    )
+
+    # Step 7 — Directly-predicted total AGB (lb).
+    trees = _join_and_eval_component(
+        trees,
+        lookup.total_agb_spcd,
+        lookup.total_agb_jen,
+        lookup.total_agb_div,
+        "_agb_predicted",
+        has_division=has_division,
+    )
+
+    # Step 3-4 — Convert wood volume to weight, with a cull-reduced variant.
+    # DECAYCD=3 wood density proportion: 0.92 softwood, 0.54 hardwood. The
+    # split on SPCD<300 is consistent with _model_k and sidesteps the SPCD=10
+    # S10a misclassification (SPCD=10 is softwood under this rule).
+    trees = trees.with_columns(
+        [
+            pl.col("CULL").fill_null(0.0).cast(pl.Float64).alias("_cull"),
+            pl.when(pl.col("SPCD") >= _HARDWOOD_SPCD_THRESHOLD)
+            .then(pl.lit(_CULL_DENS_PROP["hardwood"]))
+            .otherwise(pl.lit(_CULL_DENS_PROP["softwood"]))
+            .alias("_dens_prop"),
+        ]
+    )
+    trees = trees.with_columns(
+        [
+            (pl.col("v_wood_ib") * pl.col("WDSG").cast(pl.Float64) * 62.4).alias(
+                "_w_wood_gross"
+            ),
+            (
+                pl.col("v_wood_ib")
+                * (1.0 - pl.col("_cull") / 100.0 * (1.0 - pl.col("_dens_prop")))
+                * pl.col("WDSG").cast(pl.Float64)
+                * 62.4
+            ).alias("_w_wood_red"),
+        ]
+    )
+
+    # Step 8 — Cull-reduction factor. Matches the scalar path: bark/branch use
+    # their gross values in both the numerator and denominator (only wood is
+    # cull-reduced for live trees per worked example lines 870-880).
+    trees = trees.with_columns(
+        [
+            (
+                pl.col("_w_wood_gross")
+                + pl.col("_w_bark_pre")
+                + pl.col("_w_branch_pre")
+            ).alias("_comp_gross_sum"),
+            (
+                pl.col("_w_wood_red") + pl.col("_w_bark_pre") + pl.col("_w_branch_pre")
+            ).alias("_comp_red_sum"),
+        ]
+    )
+    trees = trees.with_columns(
+        [
+            pl.when(pl.col("_comp_gross_sum") <= 0)
+            .then(pl.lit(0.0))
+            .otherwise(pl.col("_comp_red_sum") / pl.col("_comp_gross_sum"))
+            .alias("_agb_reduce"),
+        ]
+    )
+
+    # Step 9 — Reduce the directly-predicted AGB by the cull factor.
+    trees = trees.with_columns(
+        [(pl.col("_agb_predicted") * pl.col("_agb_reduce")).alias("_agb_pred_red")]
+    )
+
+    # Step 10 — Harmonize (wood, bark, branch) so they sum to _agb_pred_red
+    # while preserving the relative component ratios. Matches the scalar
+    # harmonize_components exactly, including the degenerate
+    # (component_sum <= 0) fallback: all AGB in wood, zeros elsewhere.
+    trees = trees.with_columns(
+        [
+            pl.when(pl.col("_comp_red_sum") > 0)
+            .then(
+                pl.col("_agb_pred_red")
+                * pl.col("_w_wood_red")
+                / pl.col("_comp_red_sum")
+            )
+            .otherwise(pl.col("_agb_pred_red"))
+            .alias("w_wood"),
+            pl.when(pl.col("_comp_red_sum") > 0)
+            .then(
+                pl.col("_agb_pred_red")
+                * pl.col("_w_bark_pre")
+                / pl.col("_comp_red_sum")
+            )
+            .otherwise(pl.lit(0.0))
+            .alias("w_bark"),
+            pl.when(pl.col("_comp_red_sum") > 0)
+            .then(
+                pl.col("_agb_pred_red")
+                * pl.col("_w_branch_pre")
+                / pl.col("_comp_red_sum")
+            )
+            .otherwise(pl.lit(0.0))
+            .alias("w_branch"),
+        ]
+    )
+    trees = trees.with_columns(
+        [(pl.col("w_wood") + pl.col("w_bark") + pl.col("w_branch")).alias("agb")]
+    )
+
+    # Drop all temporary columns so the caller sees only the public outputs.
+    return trees.drop(
+        [
+            "_cull",
+            "_dens_prop",
+            "_w_wood_gross",
+            "_w_wood_red",
+            "_w_bark_pre",
+            "_w_branch_pre",
+            "_agb_predicted",
+            "_comp_gross_sum",
+            "_comp_red_sum",
+            "_agb_reduce",
+            "_agb_pred_red",
+        ]
+    )
+
+
+def compute_nsvb_dead_biomass(
+    trees: pl.LazyFrame,
+    decay_props: pl.DataFrame,
+    lookup: VectorizedLookupTables | None = None,
+    cr_prop_table: pl.DataFrame | None = None,
+) -> pl.LazyFrame:
+    """Vectorized NSVB standing-dead biomass pipeline.
+
+    Mirrors :func:`compute_nsvb_biomass` but applies the FIADB
+    ``REF_TREE_DECAY_PROP`` reductions (``DENSITY_PROP``, ``BARK_LOSS_PROP``,
+    ``BRANCH_LOSS_PROP``) by hardwood/softwood × ``DECAYCD`` *instead* of
+    the live-tree ``CULL`` reduction. Per FIADB User Guide v9.1 Appendix K
+    "Cull" subsection: "For dead tree biomass, no adjustments for TREE.CULL
+    or other types of cull are made." So ``TREE.CULL`` is intentionally
+    ignored on this path even when populated.
+
+    **Broken-top corrections** (Phase 2.5): when the trees frame has an
+    ``ACTUALHT`` column and ``cr_prop_table`` is provided, trees with
+    ``ACTUALHT < HT`` receive two adjustments before the decay reductions:
+
+    - *Branch biomass* is multiplied by ``Broken_crn_prop``, the fraction
+      of the intact crown remaining below the break, computed from the
+      mean intact crown ratio in ``REF_TREE_STND_DEAD_CR_PROP`` (Table S11)
+      keyed on Bailey ecoregion province × hardwood/softwood.
+    - *Wood and bark* are reduced by ``ACTUALHT / HT``, a linear
+      approximation of the stem volume ratio below the break. This
+      replaces the Model 6 (Schumacher-Hall) volume-ratio computation
+      that FIADB uses for sub-stem partitioning, which is not implemented.
+
+    Parameters
+    ----------
+    trees : pl.LazyFrame
+        Input frame with at least: ``SPCD``, ``DIA``, ``HT``, ``DECAYCD``,
+        ``WDSG``, ``JENKINS_SPGRPCD``.
+
+        Optional:
+
+        - ``DIVISION`` (Utf8, nullable): activates Level 2 NSVB lookup.
+        - ``ACTUALHT`` (Float, nullable): actual measured height for
+          broken-top trees. When present and ``cr_prop_table`` is not None,
+          trees with ``ACTUALHT < HT`` receive broken-top corrections.
+        - ``ECOSUBCD`` (Utf8, nullable): used to derive Bailey ecoregion
+          province for the ``REF_TREE_STND_DEAD_CR_PROP`` lookup. When
+          absent, all trees fall back to the UNDEFINED mean crown ratio.
+    decay_props : pl.DataFrame
+        FIADB ``REF_TREE_DECAY_PROP`` lookup with columns ``(hw_sw,
+        DECAYCD, DENSITY_PROP, BARK_LOSS_PROP, BRANCH_LOSS_PROP)``.
+    lookup : VectorizedLookupTables, optional
+        Pre-built coefficient lookup bundle. If omitted, uses the cached
+        process-level bundle.
+    cr_prop_table : pl.DataFrame, optional
+        Table S11 mean crown ratios from
+        :func:`pyfia.carbon.nsvb.carbon_fractions.load_dead_cr_prop_df`.
+        Columns ``(ECOPROV, hw_sw, CR_MEAN)``. When None, broken-top
+        corrections are skipped (Phase 2 baseline behavior).
+
+    Returns
+    -------
+    pl.LazyFrame
+        The input frame with new columns: ``v_wood_ib``, ``v_bark``,
+        ``w_wood``, ``w_bark``, ``w_branch``, ``agb``. For broken-top
+        trees, volumes and biomass reflect the remaining stem/crown below
+        the break point.
+    """
+    if lookup is None:
+        from pyfia.carbon.nsvb.coefficients import get_vectorized_lookup_tables
+
+        lookup = get_vectorized_lookup_tables()
+
+    has_division = "DIVISION" in trees.collect_schema().names()
+
+    # Steps 1-2 — Stem wood and stem bark volumes (cu ft).
+    trees = _join_and_eval_component(
+        trees,
+        lookup.volib_spcd,
+        lookup.volib_jen,
+        lookup.volib_div,
+        "v_wood_ib",
+        has_division=has_division,
+    )
+    trees = _join_and_eval_component(
+        trees,
+        lookup.volbk_spcd,
+        lookup.volbk_jen,
+        lookup.volbk_div,
+        "v_bark",
+        has_division=has_division,
+    )
+
+    # Steps 4-5 — Bark and branch biomass (lb), gross intact predictions.
+    trees = _join_and_eval_component(
+        trees,
+        lookup.bark_bio_spcd,
+        lookup.bark_bio_jen,
+        lookup.bark_bio_div,
+        "_w_bark_pre",
+        has_division=has_division,
+    )
+    trees = _join_and_eval_component(
+        trees,
+        lookup.branch_bio_spcd,
+        lookup.branch_bio_jen,
+        lookup.branch_bio_div,
+        "_w_branch_pre",
+        has_division=has_division,
+    )
+
+    # Step 6 — Directly-predicted total AGB (lb), intact.
+    trees = _join_and_eval_component(
+        trees,
+        lookup.total_agb_spcd,
+        lookup.total_agb_jen,
+        lookup.total_agb_div,
+        "_agb_predicted",
+        has_division=has_division,
+    )
+
+    # Step 3 — Convert wood volume to gross weight.
+    trees = trees.with_columns(
+        [
+            (pl.col("v_wood_ib") * pl.col("WDSG").cast(pl.Float64) * 62.4).alias(
+                "_w_wood_gross"
+            ),
+        ]
+    )
+
+    # ----- Broken-top corrections (Phase 2.5) -----
+    # For trees with ACTUALHT < HT, reduce branch biomass by the crown
+    # proportion remaining below the break, and reduce wood/bark by a
+    # linear volume ratio ACTUALHT/HT.  Corrections applied BEFORE the
+    # decay reductions so both adjustments compound.  The intact gross sum
+    # is saved BEFORE the correction so AGBReduce = broken_decayed / intact
+    # captures both the broken-top and decay reductions against the intact
+    # AGB prediction.
+    schema_names = trees.collect_schema().names()
+    has_actualht = "ACTUALHT" in schema_names
+    _has_broken_top_correction = False
+    if has_actualht and cr_prop_table is not None:
+        has_ecosubcd = "ECOSUBCD" in schema_names
+
+        # Derive Bailey ecoregion province from ECOSUBCD.  Province is the
+        # 3-digit numeric prefix (with optional M) — NOT the same as
+        # DIVISION, which replaces the last digit with 0.
+        if has_ecosubcd:
+            ecoprov_expr = (
+                pl.when(pl.col("ECOSUBCD").is_null() | (pl.col("ECOSUBCD") == ""))
+                .then(pl.lit("UNDEFINED"))
+                .when(pl.col("ECOSUBCD").str.to_uppercase().str.starts_with("M"))
+                .then(
+                    pl.lit("M") + pl.col("ECOSUBCD").str.to_uppercase().str.slice(1, 3)
+                )
+                .otherwise(pl.col("ECOSUBCD").str.to_uppercase().str.slice(0, 3))
+            )
+        else:
+            ecoprov_expr = pl.lit("UNDEFINED")
+
+        hw_sw_expr = (
+            pl.when(pl.col("SPCD") >= _HARDWOOD_SPCD_THRESHOLD)
+            .then(pl.lit("hardwood"))
+            .otherwise(pl.lit("softwood"))
+        )
+
+        _has_broken_top_correction = True
+
+        # Save intact gross component sum BEFORE applying corrections.
+        # AGBReduce will use this as the denominator so that it captures
+        # both the broken-top and decay reductions against the intact
+        # AGB prediction from S8a/S8b.
+        trees = trees.with_columns(
+            (
+                pl.col("_w_wood_gross")
+                + pl.col("_w_bark_pre")
+                + pl.col("_w_branch_pre")
+            ).alias("_comp_gross_sum_intact")
+        )
+
+        trees = trees.with_columns(
+            [
+                ecoprov_expr.alias("_ecoprov"),
+                hw_sw_expr.alias("_bt_hw_sw"),
+            ]
+        )
+
+        # Join CR_MEAN from Table S11 on (ECOPROV, hw_sw).
+        cr_lf = cr_prop_table.lazy().rename(
+            {"ECOPROV": "_ecoprov", "hw_sw": "_bt_hw_sw", "CR_MEAN": "_cr_mean"}
+        )
+        trees = trees.join(cr_lf, on=["_ecoprov", "_bt_hw_sw"], how="left")
+
+        # Fall back to UNDEFINED CR_MEAN for unmatched provinces.
+        undef = cr_prop_table.filter(pl.col("ECOPROV") == "UNDEFINED")
+        undef_sw = float(undef.filter(pl.col("hw_sw") == "softwood")["CR_MEAN"][0])
+        undef_hw = float(undef.filter(pl.col("hw_sw") == "hardwood")["CR_MEAN"][0])
+        trees = trees.with_columns(
+            pl.col("_cr_mean")
+            .fill_null(
+                pl.when(pl.col("SPCD") >= _HARDWOOD_SPCD_THRESHOLD)
+                .then(pl.lit(undef_hw))
+                .otherwise(pl.lit(undef_sw))
+            )
+            .alias("_cr_mean")
+        )
+
+        # Identify broken-top trees: ACTUALHT < HT and ACTUALHT not null.
+        actualht = pl.col("ACTUALHT").cast(pl.Float64, strict=False)
+        ht = pl.col("HT").cast(pl.Float64)
+        is_broken = actualht.is_not_null() & (actualht < ht) & (actualht > 0)
+
+        # Appendix K crown-proportion formula:
+        #   CRprop_HT = (HT - ACTUALHT * (1 - CR/100)) / HT
+        #   Broken_crn_prop = max(0, (ACTUALHT - (1-CRprop_HT)*HT) / (CRprop_HT*HT))
+        cr_frac = pl.col("_cr_mean") / 100.0
+        crprop_ht = (ht - actualht * (1.0 - cr_frac)) / ht
+
+        # Guard against zero/negative CRprop_HT (degenerate cases where
+        # ACTUALHT ≈ 0 or CR_MEAN ≈ 100).
+        broken_crn = ((actualht - (1.0 - crprop_ht) * ht) / (crprop_ht * ht)).clip(
+            lower_bound=0.0
+        )
+
+        # Volume ratio: paraboloid taper approximation of stem volume below
+        # break.  Real stems have a shape between a paraboloid (exponent 2/3)
+        # and a cone (exponent 1).  The 2/3 exponent captures the fact that
+        # the wider lower stem contains a disproportionately large fraction of
+        # total stem volume, preventing the over-correction a linear ratio
+        # produces.  This replaces the Schumacher-Hall Model 6 volume-ratio
+        # calculation that FIADB uses for sub-stem partitioning.
+        vol_ratio = (actualht / ht).pow(2.0 / 3.0)
+
+        trees = trees.with_columns(
+            [
+                pl.when(is_broken).then(broken_crn).otherwise(1.0).alias("_crn_prop"),
+                pl.when(is_broken).then(vol_ratio).otherwise(1.0).alias("_vol_ratio"),
+            ]
+        )
+
+        # Apply broken-top reductions to gross components.
+        trees = trees.with_columns(
+            [
+                (pl.col("_w_wood_gross") * pl.col("_vol_ratio")).alias("_w_wood_gross"),
+                (pl.col("_w_bark_pre") * pl.col("_vol_ratio")).alias("_w_bark_pre"),
+                (pl.col("_w_branch_pre") * pl.col("_crn_prop")).alias("_w_branch_pre"),
+                (pl.col("v_wood_ib") * pl.col("_vol_ratio")).alias("v_wood_ib"),
+                (pl.col("v_bark") * pl.col("_vol_ratio")).alias("v_bark"),
+            ]
+        )
+
+        trees = trees.drop(
+            ["_ecoprov", "_bt_hw_sw", "_cr_mean", "_crn_prop", "_vol_ratio"]
+        )
+
+    # Step 7 — Join the FIADB REF_TREE_DECAY_PROP table on (hw_sw, DECAYCD).
+    # The hw_sw column is derived from the SPCD<300 rule to stay consistent
+    # with _model_k and the live-tree pipeline (sidesteps the SPCD=10 S10a
+    # misclassification).
+    decay_lf = decay_props.lazy().rename(
+        {
+            "hw_sw": "_hw_sw",
+            "DECAYCD": "_decay_join",
+            "DENSITY_PROP": "_density_prop",
+            "BARK_LOSS_PROP": "_bark_loss_prop",
+            "BRANCH_LOSS_PROP": "_branch_loss_prop",
+        }
+    )
+    trees = trees.with_columns(
+        [
+            pl.when(pl.col("SPCD") >= _HARDWOOD_SPCD_THRESHOLD)
+            .then(pl.lit("hardwood"))
+            .otherwise(pl.lit("softwood"))
+            .alias("_hw_sw"),
+            pl.col("DECAYCD").cast(pl.Int64).alias("_decay_join"),
+        ]
+    )
+    trees = trees.join(decay_lf, on=["_hw_sw", "_decay_join"], how="left")
+
+    # Step 8 — Apply per-component decay reductions.
+    trees = trees.with_columns(
+        [
+            (pl.col("_w_wood_gross") * pl.col("_density_prop")).alias("_w_wood_dead"),
+            (pl.col("_w_bark_pre") * pl.col("_bark_loss_prop")).alias("_w_bark_dead"),
+            (pl.col("_w_branch_pre") * pl.col("_branch_loss_prop")).alias(
+                "_w_branch_dead"
+            ),
+        ]
+    )
+
+    # Step 9 — AGB reduction factor and reduced predicted AGB.
+    # When broken-top corrections were applied, the denominator must use
+    # the intact gross sum (saved before the corrections) so that
+    # AGBReduce = broken_decayed / intact captures both the broken-top
+    # reduction and the decay reduction against the intact AGB prediction.
+    trees = trees.with_columns(
+        [
+            (
+                pl.col("_w_wood_dead")
+                + pl.col("_w_bark_dead")
+                + pl.col("_w_branch_dead")
+            ).alias("_comp_dead_sum"),
+        ]
+    )
+    if _has_broken_top_correction:
+        gross_denom = pl.col("_comp_gross_sum_intact")
+    else:
+        trees = trees.with_columns(
+            (
+                pl.col("_w_wood_gross")
+                + pl.col("_w_bark_pre")
+                + pl.col("_w_branch_pre")
+            ).alias("_comp_gross_sum"),
+        )
+        gross_denom = pl.col("_comp_gross_sum")
+    trees = trees.with_columns(
+        pl.when(gross_denom <= 0)
+        .then(pl.lit(0.0))
+        .otherwise(pl.col("_comp_dead_sum") / gross_denom)
+        .alias("_agb_reduce"),
+    )
+    trees = trees.with_columns(
+        (pl.col("_agb_predicted") * pl.col("_agb_reduce")).alias("_agb_pred_dead")
+    )
+
+    # Step 10 — Harmonize dead components against the reduced predicted AGB.
+    # Same proportional redistribution as the live path; see
+    # ``harmonize_components`` for the scalar reference.
+    trees = trees.with_columns(
+        [
+            pl.when(pl.col("_comp_dead_sum") > 0)
+            .then(
+                pl.col("_agb_pred_dead")
+                * pl.col("_w_wood_dead")
+                / pl.col("_comp_dead_sum")
+            )
+            .otherwise(pl.col("_agb_pred_dead"))
+            .alias("w_wood"),
+            pl.when(pl.col("_comp_dead_sum") > 0)
+            .then(
+                pl.col("_agb_pred_dead")
+                * pl.col("_w_bark_dead")
+                / pl.col("_comp_dead_sum")
+            )
+            .otherwise(pl.lit(0.0))
+            .alias("w_bark"),
+            pl.when(pl.col("_comp_dead_sum") > 0)
+            .then(
+                pl.col("_agb_pred_dead")
+                * pl.col("_w_branch_dead")
+                / pl.col("_comp_dead_sum")
+            )
+            .otherwise(pl.lit(0.0))
+            .alias("w_branch"),
+        ]
+    )
+    trees = trees.with_columns(
+        [(pl.col("w_wood") + pl.col("w_bark") + pl.col("w_branch")).alias("agb")]
+    )
+
+    drop_cols = [
+        "_hw_sw",
+        "_decay_join",
+        "_density_prop",
+        "_bark_loss_prop",
+        "_branch_loss_prop",
+        "_w_wood_gross",
+        "_w_bark_pre",
+        "_w_branch_pre",
+        "_agb_predicted",
+        "_w_wood_dead",
+        "_w_bark_dead",
+        "_w_branch_dead",
+        "_comp_dead_sum",
+        "_agb_reduce",
+        "_agb_pred_dead",
+    ]
+    if _has_broken_top_correction:
+        drop_cols.append("_comp_gross_sum_intact")
+    else:
+        drop_cols.append("_comp_gross_sum")
+    return trees.drop(drop_cols)
diff --git a/src/pyfia/carbon/soil_organic.py b/src/pyfia/carbon/soil_organic.py
new file mode 100644
index 00000000..99e2cdc6
--- /dev/null
+++ b/src/pyfia/carbon/soil_organic.py
@@ -0,0 +1,417 @@
+"""
+Soil organic carbon estimation from FIADB condition-level attributes.
+
+Soil organic carbon (SOC) — the organic carbon stored in mineral soil to
+a depth of 1 metre — is estimated in the FIADB using the Domke et al.
+(2017) model, which relates SOC density
+to soil taxonomic order, clay content, and climate variables.
+
+This estimator reads ``COND.CARBON_SOIL_ORG`` (short tons per acre),
+then runs it through pyFIA's post-stratified aggregation pipeline to
+produce per-acre and population estimates that match EVALIDator.
+
+Soil organic carbon has no above-ground / below-ground split; the single
+``CARBON_SOIL_ORG`` column represents the total pool.
+
+National magnitude: ~20,400 Tg C total SOC on forestland, roughly 52 %
+of total forest ecosystem carbon and the single largest pool
+(GTR-NRS-154, Table 2).
+
+Public API: :func:`soil_organic`.  See its docstring for parameters,
+examples, and the pool semantics.
+
+References
+----------
+- Domke, G.M.; Perry, C.H.; Walters, B.F.; et al. (2017). Toward
+  inventory-based estimates of soil organic carbon in forests of the
+  United States. Ecological Applications, 27(4), 1223-1235.
+- Woodall, C.W. et al. (2015). GTR-NRS-154 (FCAF methodology blueprint).
+- USEPA (2024). NGHGI Annex 3.13.
+"""
+
+from __future__ import annotations
+
+import logging
+
+import polars as pl
+
+from ..core import FIA
+from ..estimation.base import AggregationResult, BaseEstimator
+from ..estimation.columns import get_cond_columns as _get_cond_columns
+from ..estimation.utils import (
+    ensure_evalid_set,
+    ensure_fia_instance,
+    validate_aggregation_result,
+    validate_required_columns,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class SoilOrganicEstimator(BaseEstimator):
+    """Soil organic carbon estimator.
+
+    Reads pre-computed ``COND.CARBON_SOIL_ORG`` from the FIADB and
+    aggregates via the standard post-stratified estimation pipeline.
+
+    This is a **condition-level** estimator — there is no tree-level data.
+    The TREE table is not loaded; data comes from ``COND x PLOT`` only.
+    The adjustment factor is ``ADJ_FACTOR_SUBP`` (subplot-level),
+    matching FIADB/EVALIDator conventions for condition-level carbon
+    attributes.
+    """
+
+    _estimator_label = "SoilOrganic"
+
+    # ------------------------------------------------------------------
+    # Table / column requirements
+    # ------------------------------------------------------------------
+
+    def get_required_tables(self) -> list[str]:
+        return ["COND", "PLOT", "POP_PLOT_STRATUM_ASSGN", "POP_STRATUM"]
+
+    def get_tree_columns(self) -> list[str]:
+        return []
+
+    def get_cond_columns(self) -> list[str]:
+        cols = _get_cond_columns(
+            land_type=self.config.get("land_type", "forest"),
+            grp_by=self.config.get("grp_by"),
+            include_prop_basis=False,
+        )
+        if "CARBON_SOIL_ORG" not in cols:
+            cols.append("CARBON_SOIL_ORG")
+        return cols
+
+    # ------------------------------------------------------------------
+    # Override apply_filters for condition-level data
+    # ------------------------------------------------------------------
+
+    def apply_filters(self, data: pl.LazyFrame) -> pl.LazyFrame:
+        """Apply condition/area filters only (no tree-type filtering)."""
+        from ..filtering import apply_area_filters, get_land_domain_indicator
+
+        columns = data.collect_schema().names()
+
+        if self.config.get("area_domain"):
+            data = apply_area_filters(data, area_domain=self.config["area_domain"])
+
+        land_type = self.config.get("land_type", "forest")
+        if land_type and land_type != "all" and "COND_STATUS_CD" in columns:
+            data = data.filter(get_land_domain_indicator(land_type))
+
+        return data
+
+    # ------------------------------------------------------------------
+    # Core estimation logic
+    # ------------------------------------------------------------------
+
+    def calculate_values(self, data: pl.LazyFrame) -> pl.LazyFrame:
+        """Alias CARBON_SOIL_ORG to CARBON_ACRE."""
+        cond_carbon = pl.col("CARBON_SOIL_ORG").cast(pl.Float64).fill_null(0.0)
+        return data.with_columns(cond_carbon.alias("CARBON_ACRE"))
+
+    # ------------------------------------------------------------------
+    # Aggregation — condition-level with ADJ_FACTOR_SUBP
+    # ------------------------------------------------------------------
+
+    def aggregate_results(self, data: pl.LazyFrame | None) -> AggregationResult:
+        if data is None:
+            return AggregationResult(
+                results=pl.DataFrame(),
+                plot_tree_data=pl.DataFrame(),
+                group_cols=[],
+            )
+
+        validate_required_columns(
+            data, ["PLT_CN", "CARBON_ACRE"], "soil organic carbon data"
+        )
+
+        strat_data = self._get_stratification_data()
+        data_with_strat = data.join(strat_data, on="PLT_CN", how="inner")
+
+        # Condition-level attribute: CARBON_ACRE is a density (tons/acre).
+        # The two-stage pipeline denominator is sum(CONDPROP_UNADJ * EXPNS),
+        # so the numerator must include CONDPROP_UNADJ to give the
+        # condition's contribution proportional to its area on the plot.
+        # ADJ_FACTOR_SUBP corrects for nonresponse at the subplot level.
+        data_with_strat = data_with_strat.with_columns(
+            pl.col("ADJ_FACTOR_SUBP").cast(pl.Float64).alias("ADJ_FACTOR")
+        )
+
+        data_with_strat = data_with_strat.with_columns(
+            (
+                pl.col("CARBON_ACRE")
+                * pl.col("CONDPROP_UNADJ").cast(pl.Float64)
+                * pl.col("ADJ_FACTOR")
+            ).alias("CARBON_ADJ")
+        )
+
+        group_cols = self._setup_grouping()
+
+        plot_tree_data, data_with_strat = self._preserve_plot_tree_data(
+            data_with_strat,
+            metric_cols=["CARBON_ADJ"],
+            group_cols=group_cols,
+        )
+
+        results = self._apply_two_stage_aggregation(
+            data_with_strat=data_with_strat,
+            metric_mappings={"CARBON_ADJ": "CONDITION_CARBON"},
+            group_cols=group_cols,
+            use_grm_adjustment=False,
+        )
+
+        if not self.config.get("totals", True):
+            if "CARBON_TOTAL" in results.columns:
+                results = results.drop("CARBON_TOTAL")
+
+        return AggregationResult(
+            results=results,
+            plot_tree_data=plot_tree_data,
+            group_cols=group_cols,
+        )
+
+    # ------------------------------------------------------------------
+    # Variance and output formatting
+    # ------------------------------------------------------------------
+
+    def calculate_variance(self, agg_result: AggregationResult) -> pl.DataFrame:
+        validate_aggregation_result(agg_result, self._estimator_label)
+        metric_configs = [
+            {
+                "adjusted_col": "CARBON_ADJ",
+                "acre_se_col": "CARBON_ACRE_SE",
+                "total_se_col": "CARBON_TOTAL_SE",
+            },
+        ]
+        return self._calculate_variance_for_metrics(agg_result, metric_configs)
+
+    def format_output(self, results: pl.DataFrame) -> pl.DataFrame:
+        year = self._extract_evaluation_year()
+        results = results.with_columns(pl.lit(year).alias("YEAR"))
+        results = results.with_columns(pl.lit("TOTAL").alias("POOL"))
+
+        col_order = [
+            "YEAR",
+            "POOL",
+            "CARBON_ACRE",
+            "CARBON_TOTAL",
+            "CARBON_ACRE_SE",
+            "CARBON_TOTAL_SE",
+            "N_PLOTS",
+            "N_TREES",
+        ]
+
+        for col in results.columns:
+            if col not in col_order:
+                col_order.insert(1, col)
+
+        final_cols = [col for col in col_order if col in results.columns]
+        return results.select(final_cols)
+
+
+# ======================================================================
+# Public API
+# ======================================================================
+
+
+def soil_organic(
+    db: str | FIA,
+    pool: str = "total",
+    grp_by: str | list[str] | None = None,
+    land_type: str = "forest",
+    area_domain: str | None = None,
+    plot_domain: str | None = None,
+    totals: bool = True,
+    variance: bool = False,
+    most_recent: bool = False,
+) -> pl.DataFrame:
+    """
+    Estimate soil organic carbon from FIA data.
+
+    Soil organic carbon (SOC) encompasses all organic carbon stored in
+    mineral soil to a depth of 1 metre on forestland.  Carbon density is
+    estimated from the Domke et al. (2017) model, parameterised on soil
+    taxonomic order, clay content, and climate variables.  The FIADB
+    pre-computes condition-level carbon density and stores it in
+    ``COND.CARBON_SOIL_ORG``.
+
+    Nationally, SOC totals ~20,400 Tg C, representing ~52 % of total
+    forest ecosystem carbon — the single largest pool (GTR-NRS-154,
+    Table 2).  Per-acre values are typically 15-40 short tons/acre
+    depending on soil type, climate, and region.
+
+    Parameters
+    ----------
+    db : str | FIA
+        Database connection or path to FIA database. Can be either a path
+        string to a DuckDB/SQLite file or an existing FIA connection object.
+    pool : {'total'}, default 'total'
+        Carbon pool to estimate.  Soil organic carbon has no above-ground /
+        below-ground split — only ``'total'`` is accepted.
+    grp_by : str or list of str, optional
+        Column name(s) to group results by. Can be any column from the
+        FIA COND or PLOT tables. Common grouping columns include:
+
+        - 'FORTYPCD': Forest type code
+        - 'FORTYPGRPCD': Forest type group code
+        - 'OWNGRPCD': Ownership group
+        - 'STATECD': State FIPS code
+        - 'COUNTYCD': County code
+
+        For complete column descriptions, see USDA FIA Database User Guide.
+    land_type : {'forest', 'timber', 'all'}, default 'forest'
+        Land type to include in estimation:
+
+        - 'forest': All forestland
+        - 'timber': Productive timberland only (unreserved, productive)
+        - 'all': All land conditions
+    area_domain : str, optional
+        SQL-like filter expression for area/condition-level filtering.
+        Example: ``"OWNGRPCD == 40 AND FORTYPCD == 161"``.
+    plot_domain : str, optional
+        SQL-like filter expression for plot-level filtering.
+    totals : bool, default True
+        If True, include population-level total estimates in addition to
+        per-acre values.
+    variance : bool, default False
+        If True, calculate and include variance and standard error
+        estimates following Bechtold & Patterson (2005).
+    most_recent : bool, default False
+        If True, automatically filter to the most recent EXPVOL evaluation
+        for each state in the database before estimation.
+
+    Returns
+    -------
+    pl.DataFrame
+        Soil organic carbon estimates with the following columns:
+
+        - **YEAR** : int
+            Evaluation reference year from EVALID.
+        - **POOL** : str
+            Pool identifier — always ``'TOTAL'``.
+        - **CARBON_ACRE** : float
+            Carbon per acre in short tons.
+        - **CARBON_TOTAL** : float (if ``totals=True``)
+            Total carbon in short tons expanded to population level.
+        - **CARBON_ACRE_SE** : float (if ``variance=True``)
+            Standard error of the per-acre estimate.
+        - **CARBON_TOTAL_SE** : float (if ``variance=True`` and ``totals=True``)
+            Standard error of the population total.
+        - **N_PLOTS** : int
+            Number of FIA plots included in the estimation.
+        - **N_TREES** : int
+            Number of tree records (used for distribution weighting).
+        - **[grouping columns]** : various
+            Any columns specified in ``grp_by``.
+
+    See Also
+    --------
+    live_tree : Estimate live tree carbon using the NSVB framework.
+    standing_dead : Estimate standing dead tree carbon.
+    understory : Estimate understory vegetation carbon.
+    downed_dead : Estimate downed dead wood carbon.
+    litter : Estimate litter carbon.
+    pyfia.carbon : Overview of all carbon pool estimators.
+
+    Notes
+    -----
+    **Methodology**
+
+    Soil organic carbon is not directly measured on FIA plots.  Instead,
+    the FIADB pre-computes carbon density per condition using the Domke
+    et al. (2017) model, which combines gridded soil survey data
+    (gNATSGO/SSURGO) with FIA plot locations to assign SOC densities
+    based on soil taxonomic order, clay content, and climate variables.
+
+    This estimator reads those pre-computed values directly from the COND
+    table and runs them through the standard post-stratified aggregation
+    pipeline, ensuring exact agreement with EVALIDator estimates.
+
+    **No AG/BG split**
+
+    Soil organic carbon has no above-ground / below-ground partitioning.
+    The ``CARBON_SOIL_ORG`` column represents the entire pool (mineral
+    soil to 1 m depth).
+
+    Examples
+    --------
+    Total soil organic carbon per acre on forestland:
+
+    >>> results = soil_organic(db, pool="total")
+    >>> print(f"Carbon: {results['CARBON_ACRE'][0]:.3f} tons/acre")
+
+    Soil organic carbon by forest type group:
+
+    >>> results = soil_organic(db, pool="total", grp_by="FORTYPGRPCD")
+
+    Soil organic carbon on timberland with standard errors:
+
+    >>> results = soil_organic(
+    ...     db,
+    ...     land_type="timber",
+    ...     variance=True,
+    ... )
+
+    References
+    ----------
+    .. [1] Domke, G.M.; Perry, C.H.; Walters, B.F.; et al. (2017).
+       Toward inventory-based estimates of soil organic carbon in forests
+       of the United States. Ecological Applications, 27(4), 1223-1235.
+    .. [2] Woodall, C.W. et al. (2015). The current and future role of
+       forest carbon in the United States. Gen. Tech. Rep. NRS-154.
+    .. [3] USEPA (2024). Inventory of U.S. Greenhouse Gas Emissions and
+       Sinks, Chapter 6 and Annex 3.13.
+    """
+    from ..validation import (
+        validate_boolean,
+        validate_domain_expression,
+        validate_grp_by,
+        validate_land_type,
+    )
+
+    # ----- Validate pool -----
+    pool = pool.lower()
+    if pool != "total":
+        raise ValueError(
+            f"Invalid pool '{pool}' for soil organic carbon. "
+            f"Only 'total' is supported — soil organic carbon has no AG/BG split."
+        )
+
+    # ----- Validate standard inputs -----
+    land_type = validate_land_type(land_type)
+    grp_by = validate_grp_by(grp_by)
+    area_domain = validate_domain_expression(area_domain, "area_domain")
+    plot_domain = validate_domain_expression(plot_domain, "plot_domain")
+    totals = validate_boolean(totals, "totals")
+    variance = validate_boolean(variance, "variance")
+    most_recent = validate_boolean(most_recent, "most_recent")
+
+    # ----- Resolve db + EVALID -----
+    db, owns_db = ensure_fia_instance(db)
+    if most_recent and db.evalid is None:
+        db.clip_most_recent(eval_type="VOL")
+    else:
+        ensure_evalid_set(db, eval_type="VOL", estimator_name="soil_organic")
+
+    # ----- Build config and run estimator -----
+    config = {
+        "pool": pool,
+        "grp_by": grp_by,
+        "by_species": False,
+        "by_size_class": False,
+        "land_type": land_type,
+        "area_domain": area_domain,
+        "plot_domain": plot_domain,
+        "totals": totals,
+        "variance": variance,
+        "most_recent": most_recent,
+    }
+
+    try:
+        estimator = SoilOrganicEstimator(db, config)
+        return estimator.estimate()
+    finally:
+        if owns_db and hasattr(db, "close"):
+            db.close()
diff --git a/src/pyfia/carbon/standing_dead.py b/src/pyfia/carbon/standing_dead.py
new file mode 100644
index 00000000..be0fe962
--- /dev/null
+++ b/src/pyfia/carbon/standing_dead.py
@@ -0,0 +1,323 @@
+"""
+Standing dead tree carbon estimation using the NSVB biomass framework.
+
+Recomputes above-ground standing dead tree biomass tree-by-tree via the
+vectorized NSVB pipeline in :mod:`pyfia.carbon.nsvb.equations`, applies
+the FIADB ``REF_TREE_DECAY_PROP`` density and loss reductions
+(``DENSITY_PROP`` × wood, ``BARK_LOSS_PROP`` × bark, ``BRANCH_LOSS_PROP`` ×
+branch) keyed by hardwood/softwood × ``DECAYCD``, and converts the reduced
+biomass to carbon via species-class S10b dead-tree carbon fractions.
+
+Broken-top corrections (``ACTUALHT < HT``) apply the Appendix K
+crown-proportion adjustment to branch biomass and a volume-ratio adjustment
+to wood/bark biomass, using the mean intact crown ratio from Table S11
+(``REF_TREE_STND_DEAD_CR_PROP``) keyed by Bailey ecoregion province ×
+hardwood/softwood.
+
+Belowground (BG) carbon for standing dead trees is bridged directly to the
+FIADB ``TREE.CARBON_BG`` column, mirroring the live-tree estimator's
+BG bridge. A native NSVB coarse-root model for dead trees is deferred.
+
+Public API: :func:`standing_dead`. See its docstring for parameters,
+examples, and the pool semantics.
+"""
+
+from __future__ import annotations
+
+import logging
+
+import polars as pl
+
+from ..core import FIA
+from ..estimation.columns import get_tree_columns as _get_tree_columns
+from ..estimation.utils import (
+    ensure_evalid_set,
+    ensure_fia_instance,
+)
+from ._estimator_base import CarbonEstimatorBase
+from .nsvb.carbon_fractions import (
+    load_carbon_fractions_dead_df,
+    load_dead_cr_prop_df,
+    load_dead_decay_proportions_df,
+)
+from .nsvb.equations import compute_nsvb_dead_biomass
+
+logger = logging.getLogger(__name__)
+
+
+class StandingDeadEstimator(CarbonEstimatorBase):
+    """Standing dead tree carbon estimator using the NSVB biomass framework.
+
+    Inherits shared infrastructure from :class:`CarbonEstimatorBase`. The
+    standing-dead-specific logic is in :meth:`apply_filters` (the
+    ``STANDING_DEAD_CD = 1 + DECAYCD IS NOT NULL`` requirements) and
+    :meth:`calculate_values` (dead biomass pipeline + S10b fractions +
+    broken-top corrections).
+    """
+
+    _estimator_label = "StandingDead"
+
+    def get_tree_columns(self) -> list[str]:
+        estimator_cols = [
+            "SPCD",
+            "DIA",
+            "HT",
+            "ACTUALHT",
+            "DECAYCD",
+            "STANDING_DEAD_CD",
+            "CARBON_BG",
+        ]
+        return _get_tree_columns(
+            estimator_cols=estimator_cols,
+            grp_by=self.config.get("grp_by"),
+        )
+
+    def apply_filters(self, data: pl.LazyFrame) -> pl.LazyFrame:
+        """Apply standard filters plus standing-dead requirements.
+
+        On top of ``STATUSCD = 2`` (from ``tree_type = 'dead'``):
+        ``STANDING_DEAD_CD = 1``, ``DECAYCD IS NOT NULL``, ``DIA >= 1.0``.
+        """
+        data = super().apply_filters(data)
+
+        columns = data.collect_schema().names()
+        if "STANDING_DEAD_CD" in columns:
+            data = data.filter(
+                pl.col("STANDING_DEAD_CD").cast(pl.Utf8, strict=False) == "1"
+            )
+        if "DECAYCD" in columns:
+            data = data.filter(
+                pl.col("DECAYCD").cast(pl.Int64, strict=False).is_not_null()
+            )
+        if "DIA" in columns:
+            data = data.filter(pl.col("DIA") >= 1.0)
+        return data
+
+    def calculate_values(self, data: pl.LazyFrame) -> pl.LazyFrame:
+        """Run the NSVB dead pipeline with broken-top corrections.
+
+        Steps: join REF_SPECIES → join PLOTGEOM/DIVISION → cast DECAYCD →
+        NSVB dead biomass (with broken-top corrections) → S10b carbon
+        fractions → BG bridge → CARBON_ACRE.
+        """
+        pool = self.config.get("pool", "ag").lower()
+
+        # Join REF_SPECIES and PLOTGEOM/DIVISION
+        data = self._join_ref_species(data)
+        data = self._join_plotgeom_division(data)
+
+        # Cast DECAYCD from Utf8 to Int64 for the decay-prop join
+        data = data.with_columns(pl.col("DECAYCD").cast(pl.Int64, strict=False))
+
+        # NSVB dead biomass pipeline (with broken-top corrections when
+        # ACTUALHT is available and the CR prop table loads)
+        if pool in ("ag", "total"):
+            decay_props = load_dead_decay_proportions_df()
+            cr_prop_table = load_dead_cr_prop_df()
+            data = compute_nsvb_dead_biomass(
+                data, decay_props, cr_prop_table=cr_prop_table
+            )
+
+            # S10b dead carbon fractions joined on (hw_sw, DECAYCD)
+            cf_df = load_carbon_fractions_dead_df()
+            data = data.with_columns(
+                pl.when(pl.col("SPCD") >= 300)
+                .then(pl.lit("hardwood"))
+                .otherwise(pl.lit("softwood"))
+                .alias("_hw_sw_cf"),
+            )
+            data = data.join(
+                cf_df.rename({"hw_sw": "_hw_sw_cf"}).lazy(),
+                on=["_hw_sw_cf", "DECAYCD"],
+                how="left",
+            )
+            data = data.with_columns(
+                (pl.col("agb") * pl.col("CARBON_FRAC_DEAD")).alias("_CARBON_AG_LB")
+            )
+            data = data.drop(["_hw_sw_cf"])
+        else:  # pool == "bg"
+            data = data.with_columns(pl.lit(0.0).alias("_CARBON_AG_LB"))
+
+        # BG bridge + CARBON_ACRE
+        data = self._apply_bg_bridge(data, pool)
+        data = self._compute_carbon_acre(data)
+
+        return data
+
+
+def standing_dead(
+    db: str | FIA,
+    pool: str = "ag",
+    grp_by: str | list[str] | None = None,
+    by_species: bool = False,
+    by_size_class: bool = False,
+    land_type: str = "forest",
+    tree_domain: str | None = None,
+    area_domain: str | None = None,
+    plot_domain: str | None = None,
+    totals: bool = True,
+    variance: bool = False,
+    most_recent: bool = False,
+) -> pl.DataFrame:
+    """
+    Estimate standing dead tree carbon from FIA data using the NSVB framework.
+
+    Recomputes above-ground standing dead tree biomass from scratch using
+    the National Scale Volume and Biomass (NSVB) framework of Westfall et
+    al. (2023, GTR-WO-104) and applies the FIADB ``REF_TREE_DECAY_PROP``
+    decay reductions (DENSITY_PROP × wood, BARK_LOSS_PROP × bark,
+    BRANCH_LOSS_PROP × branch) keyed by hardwood/softwood × DECAYCD. The
+    reduced biomass is then converted to carbon via species-class S10b
+    dead-tree carbon fractions from GTR-WO-104, replacing the flat ~0.47
+    multiplier and producing carbon estimates that align with the EPA
+    NGHGI LULUCF standing dead pool.
+
+    Broken-top corrections apply the Appendix K crown-proportion
+    adjustment to branch biomass and a volume-ratio adjustment to wood/bark
+    for trees with ``ACTUALHT < HT``, using the mean intact crown ratio
+    from Table S11 (``REF_TREE_STND_DEAD_CR_PROP``) keyed by Bailey
+    ecoregion province × hardwood/softwood.
+
+    Belowground carbon for standing dead trees is bridged directly to the
+    FIADB pre-computed ``TREE.CARBON_BG`` column; a native NSVB coarse-root
+    model for dead trees is deferred.
+
+    The standing-dead population is filtered as
+    ``STATUSCD = 2 AND STANDING_DEAD_CD = 1 AND DECAYCD IS NOT NULL``,
+    which matches the trees FIADB itself populates ``CARBON_AG`` for.
+    Trees with ``STANDING_DEAD_CD = 0`` (downed dead) belong to the down
+    dead wood pool and are excluded.
+
+    Parameters
+    ----------
+    db : str | FIA
+        Database connection or path to FIA database. Can be either a path
+        string to a DuckDB/SQLite file or an existing FIA connection object.
+    pool : {'ag', 'bg', 'total'}, default 'ag'
+        Standing dead carbon pool to estimate:
+
+        - 'ag': Above-ground standing dead carbon via the NSVB pipeline +
+          REF_TREE_DECAY_PROP reductions + broken-top corrections + S10b
+          dead carbon fractions.
+        - 'bg': Below-ground standing dead carbon (coarse roots) via the
+          bridge to FIADB ``TREE.CARBON_BG``.
+        - 'total': ``'ag' + 'bg'`` (NSVB AG + FIADB BG bridge).
+    grp_by : str or list of str, optional
+        Column name(s) to group results by.
+    by_species : bool, default False
+        If True, group results by species code (SPCD).
+    by_size_class : bool, default False
+        If True, group results by diameter size classes.
+    land_type : {'forest', 'timber', 'all'}, default 'forest'
+        Land type to include in estimation.
+    tree_domain : str, optional
+        SQL-like filter expression for tree-level filtering.
+    area_domain : str, optional
+        SQL-like filter expression for area/condition-level filtering.
+    plot_domain : str, optional
+        SQL-like filter expression for plot-level filtering.
+    totals : bool, default True
+        If True, include population-level total estimates.
+    variance : bool, default False
+        If True, calculate variance and standard error estimates.
+    most_recent : bool, default False
+        If True, auto-filter to the most recent EXPVOL evaluation.
+
+    Returns
+    -------
+    pl.DataFrame
+        Standing dead carbon estimates with columns: YEAR, POOL,
+        CARBON_ACRE, CARBON_TOTAL (if totals), CARBON_ACRE_SE (if
+        variance), CARBON_TOTAL_SE (if variance and totals), N_PLOTS,
+        N_TREES, plus any grouping columns.
+
+    See Also
+    --------
+    live_tree : Estimate live tree carbon via the NSVB framework.
+
+    Examples
+    --------
+    Above-ground standing dead carbon per acre on forestland:
+
+    >>> results = standing_dead(db, pool="ag")
+    >>> print(f"SD Carbon: {results['CARBON_ACRE'][0]:.1f} tons/acre")
+
+    Standing dead carbon by decay class on timberland:
+
+    >>> results = standing_dead(
+    ...     db,
+    ...     pool="ag",
+    ...     grp_by="DECAYCD",
+    ...     land_type="timber",
+    ... )
+    """
+    from ..validation import (
+        validate_boolean,
+        validate_domain_expression,
+        validate_grp_by,
+        validate_land_type,
+    )
+
+    pool = pool.lower()
+    valid_pools = {"ag", "bg", "total"}
+    if pool not in valid_pools:
+        raise ValueError(
+            f"Invalid pool '{pool}'. Must be one of: {sorted(valid_pools)}"
+        )
+
+    land_type = validate_land_type(land_type)
+    grp_by = validate_grp_by(grp_by)
+    tree_domain = validate_domain_expression(tree_domain, "tree_domain")
+    area_domain = validate_domain_expression(area_domain, "area_domain")
+    plot_domain = validate_domain_expression(plot_domain, "plot_domain")
+    by_species = validate_boolean(by_species, "by_species")
+    by_size_class = validate_boolean(by_size_class, "by_size_class")
+    totals = validate_boolean(totals, "totals")
+    variance = validate_boolean(variance, "variance")
+    most_recent = validate_boolean(most_recent, "most_recent")
+
+    db, owns_db = ensure_fia_instance(db)
+    if most_recent and db.evalid is None:
+        db.clip_most_recent(eval_type="VOL")
+    else:
+        ensure_evalid_set(db, eval_type="VOL", estimator_name="standing_dead")
+
+    config = {
+        "pool": pool,
+        "grp_by": grp_by,
+        "by_species": by_species,
+        "by_size_class": by_size_class,
+        "land_type": land_type,
+        "tree_type": "dead",
+        "tree_domain": tree_domain,
+        "area_domain": area_domain,
+        "plot_domain": plot_domain,
+        "totals": totals,
+        "variance": variance,
+        "most_recent": most_recent,
+    }
+
+    try:
+        estimator = StandingDeadEstimator(db, config)
+        if pool == "total":
+            # Best-effort cross-era warning; see live_tree.py for rationale.
+            try:
+                year = estimator._extract_evaluation_year()
+                if int(year) < 2024:
+                    logger.warning(
+                        "standing_dead(pool='total'): selected EVALID year "
+                        "(%d) pre-dates the NSVB framework transition "
+                        "(September 2023). The BG bridge reads FIADB "
+                        "TREE.CARBON_BG directly, which for pre-NSVB "
+                        "inventories was computed via legacy CRM-based "
+                        "allometry — combining it with NSVB-recomputed AG "
+                        "may produce cross-era inconsistencies. Use "
+                        "pool='ag' if you need NSVB-only consistency.",
+                        int(year),
+                    )
+            except (ValueError, TypeError, AttributeError, IndexError, KeyError) as exc:
+                logger.debug("Skipping standing_dead year warning: %s", exc)
+        return estimator.estimate()
+    finally:
+        if owns_db and hasattr(db, "close"):
+            db.close()
diff --git a/src/pyfia/carbon/stock_change.py b/src/pyfia/carbon/stock_change.py
new file mode 100644
index 00000000..3957c87c
--- /dev/null
+++ b/src/pyfia/carbon/stock_change.py
@@ -0,0 +1,568 @@
+"""
+Carbon stock-change estimation for condition-level FIADB carbon pools.
+
+Computes the change in carbon stocks between two inventory periods for
+the four condition-level pools: understory vegetation, downed dead wood,
+litter, and soil organic carbon.  Stock change is calculated as
+``C(t₂) − C(t₁)`` per remeasured condition, optionally annualized by
+the remeasurement period (REMPER), then aggregated via the standard
+post-stratified estimation pipeline using t₂'s stratification.
+
+This module follows the ``AreaChangeEstimator`` pattern: t₂ data comes
+from the EVALID-scoped pipeline; t₁ data is loaded from the full
+(unfiltered) COND table and linked via ``PREV_PLT_CN + CONDID``.
+
+Tree-level stock change (live tree, standing dead) requires GRM fate
+decomposition and is not yet implemented.
+
+Public API: :func:`stock_change`.
+
+References
+----------
+- Bechtold, W.A. & Patterson, P.L. (2005). GTR-SRS-80, Chapter 4:
+  Change Estimation.
+- Woodall, C.W. et al. (2015). GTR-NRS-154 (FCAF methodology).
+"""
+
+from __future__ import annotations
+
+import logging
+
+import polars as pl
+
+from ..core import FIA
+from ..estimation.base import AggregationResult, BaseEstimator
+from ..estimation.columns import get_cond_columns as _get_cond_columns
+from ..estimation.utils import (
+    ensure_evalid_set,
+    ensure_fia_instance,
+    validate_aggregation_result,
+    validate_required_columns,
+)
+
+logger = logging.getLogger(__name__)
+
+# Pool name → COND column(s) for the carbon density attribute.
+# Understory has AG + BG; the other three have a single total column.
+_POOL_COLUMNS: dict[str, list[str]] = {
+    "understory": ["CARBON_UNDERSTORY_AG", "CARBON_UNDERSTORY_BG"],
+    "downed_dead": ["CARBON_DOWN_DEAD"],
+    "litter": ["CARBON_LITTER"],
+    "soil_organic": ["CARBON_SOIL_ORG"],
+}
+
+_VALID_POOLS = set(_POOL_COLUMNS.keys())
+
+
+class CarbonStockChangeEstimator(BaseEstimator):
+    """Condition-level carbon stock-change estimator.
+
+    Computes ``C(t₂) − C(t₁)`` per remeasured condition for one
+    condition-level carbon pool, then aggregates via the standard
+    post-stratified two-stage pipeline.
+
+    Follows the ``AreaChangeEstimator`` pattern:
+
+    * t₂ data is loaded through the standard EVALID-scoped pipeline.
+    * t₁ data is loaded from the **full** COND table (unfiltered by
+      EVALID) via ``db._reader.read_table()``, then joined to t₂ via
+      ``PREV_PLT_CN + CONDID``.
+    * Stratification (``POP_PLOT_STRATUM_ASSGN``, ``POP_STRATUM``) is
+      always from t₂'s EVALID.
+    """
+
+    _estimator_label = "CarbonStockChange"
+
+    # ------------------------------------------------------------------
+    # Table / column requirements
+    # ------------------------------------------------------------------
+
+    def get_required_tables(self) -> list[str]:
+        return ["COND", "PLOT", "POP_PLOT_STRATUM_ASSGN", "POP_STRATUM"]
+
+    def get_tree_columns(self) -> list[str]:
+        return []
+
+    def get_cond_columns(self) -> list[str]:
+        cols = _get_cond_columns(
+            land_type=self.config.get("land_type", "forest"),
+            grp_by=self.config.get("grp_by"),
+            include_prop_basis=False,
+        )
+        # Add the carbon column(s) for this pool
+        pool = self.config["pool"]
+        for c in _POOL_COLUMNS[pool]:
+            if c not in cols:
+                cols.append(c)
+        return cols
+
+    # ------------------------------------------------------------------
+    # load_data — the key override (area_change.py pattern)
+    # ------------------------------------------------------------------
+
+    def load_data(self) -> pl.LazyFrame | None:
+        """Load t₂ and t₁ condition data and link via PREV_PLT_CN.
+
+        Join sequence:
+        1. COND (t₂) — EVALID-scoped via standard pipeline
+        2. PLOT (t₂) — EVALID-scoped; filter to PREV_PLT_CN IS NOT NULL
+        3. COND (t₁) — **full table** loaded via ``db._reader.read_table``
+        4. Stratification data — from t₂'s EVALID
+        """
+        pool = self.config["pool"]
+        carbon_cols = _POOL_COLUMNS[pool]
+
+        # --- Load EVALID-scoped t2 tables ---
+        for table in self.get_required_tables():
+            if table not in self.db.tables:
+                self.db.load_table(table)
+
+        cond = self.db.tables["COND"]
+        if not isinstance(cond, pl.LazyFrame):
+            cond = cond.lazy()
+
+        # Select the columns we need from COND (t2)
+        cond_cols = self.get_cond_columns()
+        available = cond.collect_schema().names()
+        cond_select = [c for c in cond_cols if c in available]
+        cond_t2 = cond.select(cond_select)
+
+        # --- Load PLOT for REMPER and PREV_PLT_CN ---
+        plot = self.db.tables["PLOT"]
+        if not isinstance(plot, pl.LazyFrame):
+            plot = plot.lazy()
+
+        plot_cols = ["CN", "STATECD", "INVYR", "REMPER", "PREV_PLT_CN"]
+        plot_available = plot.collect_schema().names()
+        plot_cols = [c for c in plot_cols if c in plot_available]
+        plot = plot.select(plot_cols)
+
+        # Join t2 COND with PLOT
+        data = cond_t2.join(
+            plot,
+            left_on="PLT_CN",
+            right_on="CN",
+            how="inner",
+        )
+
+        # Filter to remeasured plots only
+        data = data.filter(
+            pl.col("PREV_PLT_CN").is_not_null()
+            & pl.col("REMPER").is_not_null()
+            & (pl.col("REMPER") > 0)
+        )
+
+        # --- Load FULL COND table (unfiltered) for t1 ---
+        # IMPORTANT: PREV_PLT_CN references plots from previous inventory
+        # cycles that are outside the current EVALID scope.
+        t1_cols = ["PLT_CN", "CONDID"] + carbon_cols
+        cond_prev = self.db._reader.read_table(
+            "COND",
+            columns=t1_cols,
+            lazy=True,
+        )
+
+        # Add a sentinel column to detect successful joins (vs NULL carbon)
+        cond_prev = cond_prev.with_columns(pl.lit(True).alias("_t1_matched"))
+
+        # Rename t1 carbon columns with t1_ prefix
+        t1_rename: dict[str, str] = {"PLT_CN": "t1_PLT_CN", "CONDID": "t1_CONDID"}
+        for c in carbon_cols:
+            t1_rename[c] = f"t1_{c}"
+        cond_prev = cond_prev.rename(t1_rename)
+
+        # Rename t2 carbon columns with t2_ prefix for clarity
+        t2_rename: dict[str, str] = {}
+        for c in carbon_cols:
+            if c in data.collect_schema().names():
+                t2_rename[c] = f"t2_{c}"
+        if t2_rename:
+            data = data.rename(t2_rename)
+
+        # Join t1 conditions via PREV_PLT_CN + matching CONDID.
+        # CONDID is assigned per condition per plot visit; most conditions
+        # retain the same CONDID across remeasurements (~97% in Georgia).
+        # The more precise PREVCOND mapping lives in SUBP_COND_CHNG_MTRX
+        # (subplot-level), which is overkill for condition-level carbon.
+        data = data.join(
+            cond_prev,
+            left_on=["PREV_PLT_CN", "CONDID"],
+            right_on=["t1_PLT_CN", "t1_CONDID"],
+            how="left",
+        )
+
+        # Filter to conditions where t1 match was found (LEFT JOIN hit).
+        # The _t1_matched sentinel distinguishes "no match" (NULL) from
+        # "match but NULL carbon value" (which fill_null handles later).
+        data = data.filter(pl.col("_t1_matched").is_not_null())
+        data = data.drop("_t1_matched")
+
+        # --- Join stratification data (from t2's EVALID) ---
+        strat_data = self._get_stratification_data()
+        data = data.join(strat_data, on="PLT_CN", how="inner")
+
+        return data
+
+    # ------------------------------------------------------------------
+    # apply_filters — condition/area filters on t2 data
+    # ------------------------------------------------------------------
+
+    def apply_filters(self, data: pl.LazyFrame) -> pl.LazyFrame:
+        """Apply condition/area filters only (no tree-type filtering)."""
+        from ..filtering import apply_area_filters, get_land_domain_indicator
+
+        columns = data.collect_schema().names()
+
+        if self.config.get("area_domain"):
+            data = apply_area_filters(data, area_domain=self.config["area_domain"])
+
+        land_type = self.config.get("land_type", "forest")
+        if land_type and land_type != "all" and "COND_STATUS_CD" in columns:
+            data = data.filter(get_land_domain_indicator(land_type))
+
+        return data
+
+    # ------------------------------------------------------------------
+    # calculate_values — compute delta per condition
+    # ------------------------------------------------------------------
+
+    def calculate_values(self, data: pl.LazyFrame) -> pl.LazyFrame:
+        """Compute ``C(t₂) − C(t₁)`` and optionally annualize by REMPER."""
+        pool = self.config["pool"]
+        carbon_cols = _POOL_COLUMNS[pool]
+        annualize = self.config.get("annualize", True)
+
+        # Exclude conditions where ALL carbon columns are NULL at both
+        # t1 and t2 — these have no carbon data and would produce spurious
+        # zero deltas.  Conditions with NULL on one side only are kept
+        # (fill_null(0.0) handles the asymmetric case correctly).
+        all_null_expr = pl.lit(True)
+        for c in carbon_cols:
+            all_null_expr = (
+                all_null_expr
+                & pl.col(f"t2_{c}").is_null()
+                & pl.col(f"t1_{c}").is_null()
+            )
+        data = data.filter(~all_null_expr)
+
+        # Sum all carbon columns for this pool (understory has AG+BG)
+        t2_expr = pl.lit(0.0)
+        t1_expr = pl.lit(0.0)
+        for c in carbon_cols:
+            t2_expr = t2_expr + pl.col(f"t2_{c}").cast(pl.Float64).fill_null(0.0)
+            t1_expr = t1_expr + pl.col(f"t1_{c}").cast(pl.Float64).fill_null(0.0)
+
+        delta = t2_expr - t1_expr
+
+        if annualize:
+            delta = delta / pl.col("REMPER").cast(pl.Float64)
+
+        return data.with_columns(delta.alias("CARBON_CHANGE_ACRE"))
+
+    # ------------------------------------------------------------------
+    # aggregate_results — condition-level with ADJ_FACTOR_SUBP
+    # ------------------------------------------------------------------
+
+    def aggregate_results(self, data: pl.LazyFrame | None) -> AggregationResult:
+        if data is None:
+            return AggregationResult(
+                results=pl.DataFrame(),
+                plot_tree_data=pl.DataFrame(),
+                group_cols=[],
+            )
+
+        validate_required_columns(
+            data, ["PLT_CN", "CARBON_CHANGE_ACRE"], "carbon stock-change data"
+        )
+
+        # Condition-level attribute: CARBON_CHANGE_ACRE is a density
+        # (tons/acre/year). The two-stage pipeline denominator is
+        # sum(CONDPROP_UNADJ * EXPNS), so the numerator must include
+        # CONDPROP_UNADJ to give the condition's contribution proportional
+        # to its area on the plot. ADJ_FACTOR_SUBP corrects for nonresponse.
+        data = data.with_columns(
+            pl.col("ADJ_FACTOR_SUBP").cast(pl.Float64).alias("ADJ_FACTOR")
+        )
+
+        data = data.with_columns(
+            (
+                pl.col("CARBON_CHANGE_ACRE")
+                * pl.col("CONDPROP_UNADJ").cast(pl.Float64)
+                * pl.col("ADJ_FACTOR")
+            ).alias("CARBON_CHANGE_ADJ")
+        )
+
+        group_cols = self._setup_grouping()
+
+        plot_tree_data, data = self._preserve_plot_tree_data(
+            data,
+            metric_cols=["CARBON_CHANGE_ADJ"],
+            group_cols=group_cols,
+        )
+
+        results = self._apply_two_stage_aggregation(
+            data_with_strat=data,
+            metric_mappings={"CARBON_CHANGE_ADJ": "CONDITION_CARBON_CHANGE"},
+            group_cols=group_cols,
+            use_grm_adjustment=False,
+        )
+
+        if not self.config.get("totals", True):
+            if "CARBON_CHANGE_TOTAL" in results.columns:
+                results = results.drop("CARBON_CHANGE_TOTAL")
+
+        return AggregationResult(
+            results=results,
+            plot_tree_data=plot_tree_data,
+            group_cols=group_cols,
+        )
+
+    # ------------------------------------------------------------------
+    # Variance and output formatting
+    # ------------------------------------------------------------------
+
+    def calculate_variance(self, agg_result: AggregationResult) -> pl.DataFrame:
+        validate_aggregation_result(agg_result, self._estimator_label)
+        metric_configs = [
+            {
+                "adjusted_col": "CARBON_CHANGE_ADJ",
+                "acre_se_col": "CARBON_CHANGE_ACRE_SE",
+                "total_se_col": "CARBON_CHANGE_TOTAL_SE",
+            },
+        ]
+        return self._calculate_variance_for_metrics(agg_result, metric_configs)
+
+    def format_output(self, results: pl.DataFrame) -> pl.DataFrame:
+        year = self._extract_evaluation_year()
+        results = results.with_columns(pl.lit(year).alias("YEAR"))
+
+        pool = self.config["pool"].upper()
+        results = results.with_columns(pl.lit(pool).alias("POOL"))
+
+        col_order = [
+            "YEAR",
+            "POOL",
+            "CARBON_CHANGE_ACRE",
+            "CARBON_CHANGE_TOTAL",
+            "CARBON_CHANGE_ACRE_SE",
+            "CARBON_CHANGE_TOTAL_SE",
+            "N_PLOTS",
+            "N_TREES",
+        ]
+
+        for col in results.columns:
+            if col not in col_order:
+                col_order.insert(2, col)
+
+        final_cols = [col for col in col_order if col in results.columns]
+        return results.select(final_cols)
+
+
+# ======================================================================
+# Public API
+# ======================================================================
+
+
+def stock_change(
+    db: str | FIA,
+    pool: str | list[str] = "all",
+    grp_by: str | list[str] | None = None,
+    land_type: str = "forest",
+    area_domain: str | None = None,
+    annualize: bool = True,
+    totals: bool = True,
+    variance: bool = False,
+    most_recent: bool = False,
+) -> pl.DataFrame:
+    """
+    Estimate carbon stock change between two inventory periods.
+
+    Computes the change in carbon stocks for condition-level pools
+    (understory, downed dead wood, litter, soil organic carbon) between
+    the current evaluation and the previous measurement on remeasured
+    plots.  The t₂ evaluation is the EVALID set on the database; t₁ is
+    found automatically via ``PLOT.PREV_PLT_CN``.
+
+    Stock change is calculated as ``C(t₂) − C(t₁)`` per remeasured
+    condition, optionally annualized by dividing by the remeasurement
+    period (REMPER).  Positive values indicate carbon accumulation
+    (sequestration); negative values indicate carbon loss.
+
+    Parameters
+    ----------
+    db : str | FIA
+        Database connection or path to FIA database.
+    pool : str or list of str, default 'all'
+        Carbon pool(s) to estimate stock change for.  Accepts:
+
+        - ``'understory'``: Understory vegetation (AG + BG)
+        - ``'downed_dead'``: Downed dead wood
+        - ``'litter'``: Litter and duff
+        - ``'soil_organic'``: Soil organic carbon
+        - ``'all'``: All four condition-level pools (default)
+
+        Tree-level pools (``'live_tree'``, ``'standing_dead'``) are not
+        yet supported and will raise ``ValueError``.
+    grp_by : str or list of str, optional
+        Column name(s) to group results by.
+    land_type : {'forest', 'timber', 'all'}, default 'forest'
+        Land type to include in estimation.
+    area_domain : str, optional
+        SQL-like filter expression for condition-level filtering.
+    annualize : bool, default True
+        If True, divide stock change by the remeasurement period
+        (REMPER) to produce annual change rates (tons/acre/year).
+        If False, report total change over the remeasurement period.
+    totals : bool, default True
+        If True, include population-level total estimates.
+    variance : bool, default False
+        If True, calculate standard error estimates.
+    most_recent : bool, default False
+        If True, automatically filter to the most recent evaluation.
+
+    Returns
+    -------
+    pl.DataFrame
+        Stock-change estimates with columns:
+
+        - **YEAR** : int — Evaluation reference year (t₂).
+        - **POOL** : str — Pool identifier.
+        - **CARBON_CHANGE_ACRE** : float — Change per acre (tons/acre
+          or tons/acre/year if annualized).
+        - **CARBON_CHANGE_TOTAL** : float — Population total change
+          (if ``totals=True``).
+        - **CARBON_CHANGE_ACRE_SE** / **CARBON_CHANGE_TOTAL_SE** :
+          float — Standard errors (if ``variance=True``).
+        - **N_PLOTS** : int — Number of remeasured plots.
+
+    See Also
+    --------
+    downed_dead : Estimate downed dead wood carbon stocks.
+    litter : Estimate litter carbon stocks.
+    soil_organic : Estimate soil organic carbon stocks.
+    understory : Estimate understory vegetation carbon stocks.
+    total_ecosystem : Estimate total ecosystem carbon stocks.
+
+    Notes
+    -----
+    **Methodology**
+
+    Stock change follows Bechtold & Patterson (2005), Chapter 4.  For
+    each remeasured condition, the change in carbon density is computed
+    from pre-computed COND attributes.  The delta is annualized by
+    REMPER (typically 5–7 years), adjusted by ``CONDPROP_UNADJ ×
+    ADJ_FACTOR_SUBP``, and aggregated via the two-stage post-stratified
+    pipeline using t₂'s stratification.
+
+    Only remeasured plots (``PREV_PLT_CN IS NOT NULL AND REMPER > 0``)
+    contribute.  The t₁ conditions are loaded from the full COND table
+    (unfiltered by EVALID) and linked via ``PREV_PLT_CN + CONDID``.
+
+    **Tree-level pools**
+
+    Live tree and standing dead stock change require GRM fate
+    decomposition and are not yet implemented.
+
+    Examples
+    --------
+    Annual stock change for all condition-level pools:
+
+    >>> results = stock_change(db)
+
+    Downed dead wood stock change by ownership:
+
+    >>> results = stock_change(db, pool="downed_dead", grp_by="OWNGRPCD")
+
+    Non-annualized litter change with standard errors:
+
+    >>> results = stock_change(db, pool="litter", annualize=False, variance=True)
+
+    References
+    ----------
+    .. [1] Bechtold, W.A. & Patterson, P.L. (2005). The Enhanced Forest
+       Inventory and Analysis Program. GTR-SRS-80, Chapter 4.
+    .. [2] Woodall, C.W. et al. (2015). GTR-NRS-154.
+    """
+    from ..validation import (
+        validate_boolean,
+        validate_domain_expression,
+        validate_grp_by,
+        validate_land_type,
+    )
+
+    # ----- Validate pool -----
+    tree_pools = {"live_tree", "standing_dead"}
+    if isinstance(pool, str):
+        pool_lower = pool.lower()
+        if pool_lower in tree_pools:
+            raise ValueError(
+                f"Tree-level stock change for '{pool}' is not yet implemented. "
+                f"Supported pools: {sorted(_VALID_POOLS)} or 'all'."
+            )
+        if pool_lower == "all":
+            pools = sorted(_VALID_POOLS)
+        elif pool_lower in _VALID_POOLS:
+            pools = [pool_lower]
+        else:
+            raise ValueError(
+                f"Invalid pool '{pool}'. "
+                f"Must be one of: {sorted(_VALID_POOLS | {'all'})}"
+            )
+    elif isinstance(pool, list):
+        pools = []
+        for p in pool:
+            p_lower = p.lower()
+            if p_lower in tree_pools:
+                raise ValueError(
+                    f"Tree-level stock change for '{p}' is not yet implemented."
+                )
+            if p_lower not in _VALID_POOLS:
+                raise ValueError(
+                    f"Invalid pool '{p}'. Must be one of: {sorted(_VALID_POOLS)}"
+                )
+            pools.append(p_lower)
+    else:
+        raise TypeError(f"pool must be str or list[str], got {type(pool).__name__}")
+
+    # ----- Validate standard inputs -----
+    land_type = validate_land_type(land_type)
+    grp_by = validate_grp_by(grp_by)
+    area_domain = validate_domain_expression(area_domain, "area_domain")
+    annualize = validate_boolean(annualize, "annualize")
+    totals = validate_boolean(totals, "totals")
+    variance = validate_boolean(variance, "variance")
+    most_recent = validate_boolean(most_recent, "most_recent")
+
+    # ----- Resolve db + EVALID -----
+    db, owns_db = ensure_fia_instance(db)
+    if most_recent and db.evalid is None:
+        db.clip_most_recent(eval_type="VOL")
+    else:
+        ensure_evalid_set(db, eval_type="VOL", estimator_name="stock_change")
+
+    try:
+        results = []
+        for p in pools:
+            config = {
+                "pool": p,
+                "grp_by": grp_by,
+                "by_species": False,
+                "by_size_class": False,
+                "land_type": land_type,
+                "area_domain": area_domain,
+                "annualize": annualize,
+                "totals": totals,
+                "variance": variance,
+            }
+            estimator = CarbonStockChangeEstimator(db, config)
+            results.append(estimator.estimate())
+
+        if len(results) == 1:
+            return results[0]
+
+        # Stack multi-pool results
+        return pl.concat(results, how="diagonal_relaxed")
+
+    finally:
+        if owns_db and hasattr(db, "close"):
+            db.close()
diff --git a/src/pyfia/carbon/total_ecosystem.py b/src/pyfia/carbon/total_ecosystem.py
new file mode 100644
index 00000000..c2b4f56f
--- /dev/null
+++ b/src/pyfia/carbon/total_ecosystem.py
@@ -0,0 +1,249 @@
+"""
+Total ecosystem carbon estimation — sum of all six IPCC/NGHGI pools.
+
+Combines live tree, standing dead, understory vegetation, downed dead
+wood, litter, and soil organic carbon into a single total ecosystem
+estimate.  Each pool is estimated independently via its own estimator,
+then the per-acre and population totals are summed.
+
+The summed total can be validated against EVALIDator snum=103 ("Total
+all pools") and snum=97 ("Total forest carbon").
+
+Public API: :func:`total_ecosystem`.
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import Callable
+
+import polars as pl
+
+from ..core import FIA
+from ..estimation.utils import ensure_evalid_set, ensure_fia_instance
+
+logger = logging.getLogger(__name__)
+
+
+def total_ecosystem(
+    db: str | FIA,
+    grp_by: str | list[str] | None = None,
+    land_type: str = "forest",
+    area_domain: str | None = None,
+    plot_domain: str | None = None,
+    totals: bool = True,
+    variance: bool = False,
+    most_recent: bool = False,
+) -> pl.DataFrame:
+    """
+    Estimate total ecosystem carbon across all six IPCC/NGHGI pools.
+
+    Sums per-acre and population total estimates from:
+
+    1. Live tree (AG + BG)
+    2. Standing dead (AG + BG)
+    3. Understory vegetation (AG + BG)
+    4. Downed dead wood
+    5. Litter
+    6. Soil organic carbon
+
+    Each pool is estimated independently, then results are stacked and
+    summed across pools.  This produces the total forest ecosystem carbon
+    stock comparable to EVALIDator snum=103 ("Total all pools").
+
+    Parameters
+    ----------
+    db : str | FIA
+        Database connection or path to FIA database.
+    grp_by : str or list of str, optional
+        Column name(s) to group results by (e.g., 'FORTYPCD', 'OWNGRPCD').
+        Forwarded to every pool estimator; the ``TOTAL_ECOSYSTEM`` summary
+        row is summed per group.
+    land_type : {'forest', 'timber', 'all'}, default 'forest'
+        Land type to include in estimation.
+    area_domain : str, optional
+        SQL-like filter expression for area/condition-level filtering.
+    plot_domain : str, optional
+        SQL-like filter expression for plot-level filtering.
+    totals : bool, default True
+        If True, include population-level total estimates.
+    variance : bool, default False
+        If True, calculate and include standard error estimates for the
+        individual pool rows.  The ``TOTAL_ECOSYSTEM`` summary row does
+        NOT receive a combined SE — see Notes below.
+    most_recent : bool, default False
+        If True, automatically filter to the most recent evaluation.
+
+    Returns
+    -------
+    pl.DataFrame
+        One row per pool plus a ``TOTAL_ECOSYSTEM`` row (one per group, if
+        ``grp_by`` is set).  Columns: POOL, CARBON_ACRE, CARBON_TOTAL
+        (if totals), and SE columns (if variance, on pool rows only).
+
+    Notes
+    -----
+    Standard error is intentionally NOT computed for the ``TOTAL_ECOSYSTEM``
+    row.  The naive ``sqrt(sum(SE_pool^2))`` assumes independence between
+    pools, which is incorrect: pool estimates share plot- and stratum-level
+    sampling variance through the common post-stratification.  Reporting a
+    combined SE without modelling that covariance would overstate
+    precision.  Callers needing a combined SE should compute one explicitly
+    against their own assumptions, or use ``stock_change`` for paired
+    plot-level inference.
+
+    See Also
+    --------
+    live_tree : Live tree carbon.
+    standing_dead : Standing dead tree carbon.
+    understory : Understory vegetation carbon.
+    downed_dead : Downed dead wood carbon.
+    litter : Litter carbon.
+    soil_organic : Soil organic carbon.
+
+    Examples
+    --------
+    >>> results = total_ecosystem(db)
+    >>> total_row = results.filter(pl.col("POOL") == "TOTAL_ECOSYSTEM")
+    >>> print(f"Total: {total_row['CARBON_ACRE'][0]:.2f} tons/acre")
+
+    >>> grouped = total_ecosystem(db, grp_by="FORTYPCD")
+    >>> totals = grouped.filter(pl.col("POOL") == "TOTAL_ECOSYSTEM")
+    """
+    from ..validation import (
+        validate_boolean,
+        validate_domain_expression,
+        validate_grp_by,
+        validate_land_type,
+    )
+    from .downed_dead import downed_dead
+    from .litter import litter
+    from .live_tree import live_tree
+    from .soil_organic import soil_organic
+    from .standing_dead import standing_dead
+    from .understory import understory
+
+    # ----- Validate inputs -----
+    land_type = validate_land_type(land_type)
+    area_domain = validate_domain_expression(area_domain, "area_domain")
+    plot_domain = validate_domain_expression(plot_domain, "plot_domain")
+    totals = validate_boolean(totals, "totals")
+    variance = validate_boolean(variance, "variance")
+    most_recent = validate_boolean(most_recent, "most_recent")
+    grp_by_norm = validate_grp_by(grp_by)
+    # validate_grp_by returns None for empty input; normalize to list form
+    grp_cols: list[str] = (
+        [grp_by_norm]
+        if isinstance(grp_by_norm, str)
+        else list(grp_by_norm)
+        if grp_by_norm
+        else []
+    )
+
+    # ----- Resolve db + EVALID -----
+    db, owns_db = ensure_fia_instance(db)
+    if most_recent and db.evalid is None:
+        db.clip_most_recent(eval_type="VOL")
+    else:
+        ensure_evalid_set(db, eval_type="VOL", estimator_name="total_ecosystem")
+
+    common_kwargs: dict = {
+        "grp_by": grp_by_norm,
+        "land_type": land_type,
+        "area_domain": area_domain,
+        "plot_domain": plot_domain,
+        "totals": totals,
+        "variance": variance,
+    }
+
+    try:
+        pool_specs: list[tuple[str, Callable[..., pl.DataFrame]]] = [
+            ("LIVE_TREE", live_tree),
+            ("STANDING_DEAD", standing_dead),
+            ("UNDERSTORY", understory),
+            ("DOWNED_DEAD", downed_dead),
+            ("LITTER", litter),
+            ("SOIL_ORGANIC", soil_organic),
+        ]
+
+        pool_results: list[pl.DataFrame] = []
+        for label, fn in pool_specs:
+            frame = fn(db, pool="total", **common_kwargs)
+            frame = frame.with_columns(pl.lit(label).alias("POOL"))
+            pool_results.append(frame)
+
+        # Guard against empty results (e.g. filters eliminate all data).
+        non_empty = [r for r in pool_results if len(r) > 0]
+        if not non_empty:
+            return pl.DataFrame({"POOL": ["TOTAL_ECOSYSTEM"], "CARBON_ACRE": [0.0]})
+
+        sum_cols = ["CARBON_ACRE"]
+        if totals:
+            sum_cols.append("CARBON_TOTAL")
+
+        # Build the TOTAL_ECOSYSTEM row(s) by summing across pools.
+        # Stack only the columns we need so disparate per-pool extras
+        # (N_TREES, by_species columns, etc.) don't interfere with the sum.
+        keep_cols = grp_cols + [
+            c for c in sum_cols if all(c in r.columns for r in non_empty)
+        ]
+        if not keep_cols or keep_cols == grp_cols:
+            # No sum columns present — fall back to fallback row.
+            return pl.DataFrame({"POOL": ["TOTAL_ECOSYSTEM"], "CARBON_ACRE": [0.0]})
+
+        stacked = pl.concat(
+            [r.select(keep_cols) for r in non_empty],
+            how="vertical_relaxed",
+        )
+
+        if grp_cols:
+            total_df = stacked.group_by(grp_cols).agg(
+                [pl.col(c).sum().alias(c) for c in sum_cols if c in stacked.columns]
+            )
+        else:
+            total_df = stacked.select(
+                [pl.col(c).sum().alias(c) for c in sum_cols if c in stacked.columns]
+            )
+
+        total_df = total_df.with_columns(pl.lit("TOTAL_ECOSYSTEM").alias("POOL"))
+
+        # Propagate YEAR from first non-empty pool, when present.
+        if "YEAR" in non_empty[0].columns and "YEAR" not in grp_cols:
+            year_val = non_empty[0]["YEAR"][0]
+            total_df = total_df.with_columns(pl.lit(year_val).alias("YEAR"))
+
+        # Normalize columns across all pool results and total
+        all_frames = pool_results + [total_df]
+        all_cols: set[str] = set()
+        for frame in all_frames:
+            all_cols.update(frame.columns)
+
+        normalized = []
+        for frame in all_frames:
+            for col in all_cols:
+                if col not in frame.columns:
+                    frame = frame.with_columns(pl.lit(None).alias(col))
+            normalized.append(frame)
+
+        result = pl.concat(normalized, how="diagonal_relaxed")
+
+        # Order columns: grp_by columns first, then standard layout.
+        col_order = grp_cols + [
+            "YEAR",
+            "POOL",
+            "CARBON_ACRE",
+            "CARBON_TOTAL",
+            "CARBON_ACRE_SE",
+            "CARBON_TOTAL_SE",
+            "N_PLOTS",
+            "N_TREES",
+        ]
+        final_cols = [c for c in col_order if c in result.columns]
+        for c in result.columns:
+            if c not in final_cols:
+                final_cols.append(c)
+        return result.select(final_cols)
+
+    finally:
+        if owns_db and hasattr(db, "close"):
+            db.close()
diff --git a/src/pyfia/carbon/understory.py b/src/pyfia/carbon/understory.py
new file mode 100644
index 00000000..2cadf99c
--- /dev/null
+++ b/src/pyfia/carbon/understory.py
@@ -0,0 +1,468 @@
+"""
+Understory vegetation carbon estimation from FIADB condition-level attributes.
+
+Understory vegetation — all biomass of undergrowth plants in a forest,
+including woody shrubs and trees < 2.54 cm DBH — is
+not directly measured on FIA plots.  Instead, the FIADB pre-computes
+condition-level carbon density using the Smith & Heath (2008) model, which
+is parameterised on geographic area, forest type, and (for most stand
+types) live tree carbon density.
+
+This estimator reads ``COND.CARBON_UNDERSTORY_AG`` and
+``COND.CARBON_UNDERSTORY_BG`` (short tons per acre), then runs them
+through pyFIA's post-stratified aggregation pipeline to produce per-acre
+and population estimates that match EVALIDator.
+
+The Smith & Heath model descends from the Birdsey (1996) ratios published
+in *Forests and Global Change Vol. 2* (Sampson & Hair eds.) and refined in
+GTR-NE-343 (Smith et al. 2006, Appendix A).  The AG/BG split follows
+Smith et al. (2006): 10 % of total understory carbon is belowground.
+
+National magnitude: ~735 Tg C total understory, ~0.8 % of forest
+ecosystem carbon (GTR-NRS-154, Table 2).
+
+Public API: :func:`understory`.  See its docstring for parameters,
+examples, and the pool semantics.
+
+References
+----------
+- Birdsey, R.A. (1996). In *Forests and Global Change Vol. 2*, Sampson &
+  Hair eds., American Forests. (Operational ratios.)
+- Smith, J.E.; Heath, L.S.; Skog, K.E.; Birdsey, R.A. (2006). GTR-NE-343.
+  DOI: 10.2737/NE-GTR-343.  (Appendix A carbon yield tables.)
+- Smith, J.E.; Heath, L.S. (2008). GTR-NRS-13.  (FIADB implementation.)
+- USEPA (2024). NGHGI Annex 3.13.  (Current operational methodology.)
+"""
+
+from __future__ import annotations
+
+import logging
+
+import polars as pl
+
+from ..core import FIA
+from ..estimation.base import AggregationResult, BaseEstimator
+from ..estimation.columns import get_cond_columns as _get_cond_columns
+from ..estimation.utils import (
+    ensure_evalid_set,
+    ensure_fia_instance,
+    validate_aggregation_result,
+    validate_required_columns,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class UnderstoryEstimator(BaseEstimator):
+    """Understory vegetation carbon estimator.
+
+    Reads pre-computed ``COND.CARBON_UNDERSTORY_AG`` and
+    ``COND.CARBON_UNDERSTORY_BG`` from the FIADB and aggregates via the
+    standard post-stratified estimation pipeline.
+
+    Unlike :class:`LiveTreeEstimator` and :class:`StandingDeadEstimator`,
+    this is a **condition-level** estimator — there is no tree-level data.
+    The TREE table is not loaded; data comes from ``COND × PLOT`` only.
+    The adjustment factor is ``ADJ_FACTOR_SUBP`` (subplot-level),
+    matching FIADB/EVALIDator conventions for condition-level carbon
+    attributes.
+    """
+
+    _estimator_label = "Understory"
+
+    # ------------------------------------------------------------------
+    # Table / column requirements
+    # ------------------------------------------------------------------
+
+    def get_required_tables(self) -> list[str]:
+        # Condition-level estimator — no TREE table needed.
+        return ["COND", "PLOT", "POP_PLOT_STRATUM_ASSGN", "POP_STRATUM"]
+
+    def get_tree_columns(self) -> list[str]:
+        return []
+
+    def get_cond_columns(self) -> list[str]:
+        cols = _get_cond_columns(
+            land_type=self.config.get("land_type", "forest"),
+            grp_by=self.config.get("grp_by"),
+            include_prop_basis=False,
+        )
+        for c in ("CARBON_UNDERSTORY_AG", "CARBON_UNDERSTORY_BG"):
+            if c not in cols:
+                cols.append(c)
+        return cols
+
+    # ------------------------------------------------------------------
+    # Override apply_filters for condition-level data
+    # ------------------------------------------------------------------
+
+    def apply_filters(self, data: pl.LazyFrame) -> pl.LazyFrame:
+        """Apply condition/area filters only (no tree-type filtering)."""
+        from ..filtering import apply_area_filters, get_land_domain_indicator
+
+        columns = data.collect_schema().names()
+
+        if self.config.get("area_domain"):
+            data = apply_area_filters(data, area_domain=self.config["area_domain"])
+
+        land_type = self.config.get("land_type", "forest")
+        if land_type and land_type != "all" and "COND_STATUS_CD" in columns:
+            data = data.filter(get_land_domain_indicator(land_type))
+
+        return data
+
+    # ------------------------------------------------------------------
+    # Core estimation logic
+    # ------------------------------------------------------------------
+
+    def calculate_values(self, data: pl.LazyFrame) -> pl.LazyFrame:
+        """Select the understory carbon column and set CARBON_ACRE.
+
+        The FIADB columns are already in short tons per acre per
+        condition — just pick the right pool and alias to CARBON_ACRE.
+        """
+        pool = self.config.get("pool", "ag").lower()
+
+        ag_col = pl.col("CARBON_UNDERSTORY_AG").cast(pl.Float64).fill_null(0.0)
+        bg_col = pl.col("CARBON_UNDERSTORY_BG").cast(pl.Float64).fill_null(0.0)
+
+        if pool == "ag":
+            cond_carbon = ag_col
+        elif pool == "bg":
+            cond_carbon = bg_col
+        else:  # total
+            cond_carbon = ag_col + bg_col
+
+        return data.with_columns(cond_carbon.alias("CARBON_ACRE"))
+
+    # ------------------------------------------------------------------
+    # Aggregation — condition-level with ADJ_FACTOR_SUBP
+    # ------------------------------------------------------------------
+
+    def aggregate_results(self, data: pl.LazyFrame | None) -> AggregationResult:
+        if data is None:
+            return AggregationResult(
+                results=pl.DataFrame(),
+                plot_tree_data=pl.DataFrame(),
+                group_cols=[],
+            )
+
+        validate_required_columns(
+            data, ["PLT_CN", "CARBON_ACRE"], "understory carbon data"
+        )
+
+        strat_data = self._get_stratification_data()
+        data_with_strat = data.join(strat_data, on="PLT_CN", how="inner")
+
+        # Condition-level attribute: CARBON_ACRE is a density (tons/acre).
+        # The two-stage pipeline denominator is sum(CONDPROP_UNADJ * EXPNS),
+        # so the numerator must include CONDPROP_UNADJ to give the
+        # condition's contribution proportional to its area on the plot.
+        # ADJ_FACTOR_SUBP corrects for nonresponse at the subplot level.
+        data_with_strat = data_with_strat.with_columns(
+            pl.col("ADJ_FACTOR_SUBP").cast(pl.Float64).alias("ADJ_FACTOR")
+        )
+
+        data_with_strat = data_with_strat.with_columns(
+            (
+                pl.col("CARBON_ACRE")
+                * pl.col("CONDPROP_UNADJ").cast(pl.Float64)
+                * pl.col("ADJ_FACTOR")
+            ).alias("CARBON_ADJ")
+        )
+
+        group_cols = self._setup_grouping()
+
+        plot_tree_data, data_with_strat = self._preserve_plot_tree_data(
+            data_with_strat,
+            metric_cols=["CARBON_ADJ"],
+            group_cols=group_cols,
+        )
+
+        results = self._apply_two_stage_aggregation(
+            data_with_strat=data_with_strat,
+            metric_mappings={"CARBON_ADJ": "CONDITION_CARBON"},
+            group_cols=group_cols,
+            use_grm_adjustment=False,
+        )
+
+        if not self.config.get("totals", True):
+            if "CARBON_TOTAL" in results.columns:
+                results = results.drop("CARBON_TOTAL")
+
+        return AggregationResult(
+            results=results,
+            plot_tree_data=plot_tree_data,
+            group_cols=group_cols,
+        )
+
+    # ------------------------------------------------------------------
+    # Variance and output formatting
+    # ------------------------------------------------------------------
+
+    def calculate_variance(self, agg_result: AggregationResult) -> pl.DataFrame:
+        validate_aggregation_result(agg_result, self._estimator_label)
+        metric_configs = [
+            {
+                "adjusted_col": "CARBON_ADJ",
+                "acre_se_col": "CARBON_ACRE_SE",
+                "total_se_col": "CARBON_TOTAL_SE",
+            },
+        ]
+        return self._calculate_variance_for_metrics(agg_result, metric_configs)
+
+    def format_output(self, results: pl.DataFrame) -> pl.DataFrame:
+        year = self._extract_evaluation_year()
+        results = results.with_columns(pl.lit(year).alias("YEAR"))
+
+        pool = self.config.get("pool", "ag").upper()
+        results = results.with_columns(pl.lit(pool).alias("POOL"))
+
+        col_order = [
+            "YEAR",
+            "POOL",
+            "CARBON_ACRE",
+            "CARBON_TOTAL",
+            "CARBON_ACRE_SE",
+            "CARBON_TOTAL_SE",
+            "N_PLOTS",
+            "N_TREES",
+        ]
+
+        for col in results.columns:
+            if col not in col_order:
+                col_order.insert(1, col)
+
+        final_cols = [col for col in col_order if col in results.columns]
+        return results.select(final_cols)
+
+
+# ======================================================================
+# Public API
+# ======================================================================
+
+
+def understory(
+    db: str | FIA,
+    pool: str = "ag",
+    grp_by: str | list[str] | None = None,
+    land_type: str = "forest",
+    area_domain: str | None = None,
+    plot_domain: str | None = None,
+    totals: bool = True,
+    variance: bool = False,
+    most_recent: bool = False,
+) -> pl.DataFrame:
+    """
+    Estimate understory vegetation carbon from FIA data.
+
+    Understory vegetation encompasses all biomass of undergrowth plants
+    in a forest, including woody shrubs and trees less than 2.54 cm DBH.
+    Carbon density is estimated from the Smith & Heath (2008) model,
+    parameterised on geographic area, forest type, and live tree carbon
+    density.  The model descends from the Birdsey (1996) ratios and is
+    the same model USDA FIA uses to populate the FIADB
+    ``COND.CARBON_UNDERSTORY_AG`` and ``COND.CARBON_UNDERSTORY_BG``
+    columns used in the EPA NGHGI.
+
+    Nationally, understory carbon totals ~735 Tg C, representing ~0.8 %
+    of total forest ecosystem carbon (GTR-NRS-154, Table 2).  Per-acre
+    values are typically 0.5–2.5 short tons/acre depending on forest type
+    and region.
+
+    Parameters
+    ----------
+    db : str | FIA
+        Database connection or path to FIA database. Can be either a path
+        string to a DuckDB/SQLite file or an existing FIA connection object.
+    pool : {'ag', 'bg', 'total'}, default 'ag'
+        Understory carbon pool to estimate:
+
+        - 'ag': Above-ground understory carbon (seedlings + woody shrubs
+          above-ground portions).  Approximately 90 % of total understory
+          carbon.
+        - 'bg': Below-ground understory carbon (fine roots of understory
+          vegetation).  Approximately 10 % of total understory carbon
+          (Smith et al. 2006).
+        - 'total': ``'ag' + 'bg'``.
+    grp_by : str or list of str, optional
+        Column name(s) to group results by. Can be any column from the
+        FIA COND or PLOT tables. Common grouping columns include:
+
+        - 'FORTYPCD': Forest type code
+        - 'FORTYPGRPCD': Forest type group code
+        - 'OWNGRPCD': Ownership group
+        - 'STATECD': State FIPS code
+        - 'COUNTYCD': County code
+
+        For complete column descriptions, see USDA FIA Database User Guide.
+    land_type : {'forest', 'timber', 'all'}, default 'forest'
+        Land type to include in estimation:
+
+        - 'forest': All forestland
+        - 'timber': Productive timberland only (unreserved, productive)
+        - 'all': All land conditions
+    area_domain : str, optional
+        SQL-like filter expression for area/condition-level filtering.
+        Example: ``"OWNGRPCD == 40 AND FORTYPCD == 161"``.
+    plot_domain : str, optional
+        SQL-like filter expression for plot-level filtering.
+    totals : bool, default True
+        If True, include population-level total estimates in addition to
+        per-acre values.
+    variance : bool, default False
+        If True, calculate and include variance and standard error
+        estimates following Bechtold & Patterson (2005).
+    most_recent : bool, default False
+        If True, automatically filter to the most recent EXPVOL evaluation
+        for each state in the database before estimation.
+
+    Returns
+    -------
+    pl.DataFrame
+        Understory carbon estimates with the following columns:
+
+        - **YEAR** : int
+            Evaluation reference year from EVALID.
+        - **POOL** : str
+            Pool identifier — one of ``'AG'``, ``'BG'``, ``'TOTAL'``.
+        - **CARBON_ACRE** : float
+            Carbon per acre in short tons.
+        - **CARBON_TOTAL** : float (if ``totals=True``)
+            Total carbon in short tons expanded to population level.
+        - **CARBON_ACRE_SE** : float (if ``variance=True``)
+            Standard error of the per-acre estimate.
+        - **CARBON_TOTAL_SE** : float (if ``variance=True`` and ``totals=True``)
+            Standard error of the population total.
+        - **N_PLOTS** : int
+            Number of FIA plots included in the estimation.
+        - **N_TREES** : int
+            Number of tree records (used for distribution weighting).
+        - **[grouping columns]** : various
+            Any columns specified in ``grp_by``.
+
+    See Also
+    --------
+    live_tree : Estimate live tree carbon using the NSVB framework.
+    standing_dead : Estimate standing dead tree carbon.
+    pyfia.carbon : Overview of all carbon pool estimators.
+
+    Notes
+    -----
+    **Methodology**
+
+    Understory vegetation is not directly measured on FIA plots.  The
+    FIADB pre-computes carbon density per condition using a model based on
+    geographic area, forest type, and (for most stand types) live tree
+    carbon density (Smith & Heath 2008; GTR-NRS-13).  For nonstocked and
+    pinyon-juniper stands, the model uses geographic area and forest type
+    alone.
+
+    This estimator reads those pre-computed values directly from the COND
+    table and runs them through the standard post-stratified aggregation
+    pipeline, ensuring exact agreement with EVALIDator estimates.
+
+    The underlying model traces back to Birdsey (1996), which established
+    understory-to-overstory carbon ratios by forest type group and region.
+    The ratios typically range from 0.5–3 % of live tree carbon for mature
+    stands, with higher ratios in young stands and in Southern pine types.
+
+    **AG/BG Split**
+
+    10 % of total understory carbon is assigned to belowground (Smith et
+    al. 2006).  This is a fixed proportion applied uniformly across all
+    forest types.
+
+    **EVALID Handling**
+
+    If no EVALID is set on the database and ``most_recent=True``, the
+    function auto-selects the most recent EXPVOL evaluation.
+
+    Examples
+    --------
+    Above-ground understory carbon per acre on forestland:
+
+    >>> results = understory(db, pool="ag")
+    >>> print(f"Carbon: {results['CARBON_ACRE'][0]:.3f} tons/acre")
+
+    Total understory carbon (AG + BG) by forest type group:
+
+    >>> results = understory(db, pool="total", grp_by="FORTYPGRPCD")
+    >>> for row in results.iter_rows(named=True):
+    ...     print(f"FTGRP {row['FORTYPGRPCD']}: {row['CARBON_ACRE']:.3f} tons/acre")
+
+    Understory carbon on timberland with standard errors:
+
+    >>> results = understory(
+    ...     db,
+    ...     pool="total",
+    ...     land_type="timber",
+    ...     variance=True,
+    ... )
+
+    References
+    ----------
+    .. [1] Birdsey, R.A. (1996). Carbon storage for major forest types and
+       regions in the coterminous United States. In: Sampson, R.N.; Hair, D.,
+       eds. *Forests and Global Change, Volume 2*. American Forests,
+       Washington, DC. pp. 1-26.
+    .. [2] Smith, J.E.; Heath, L.S.; Skog, K.E.; Birdsey, R.A. (2006).
+       Methods for calculating forest ecosystem and harvested carbon with
+       standard estimates for forest types of the United States.
+       Gen. Tech. Rep. NE-343. Newtown Square, PA: USDA Forest Service.
+    .. [3] Smith, J.E.; Heath, L.S. (2008). Carbon stocks and stock changes
+       in U.S. forests. In: USDA Forest Service GTR-NRS-13.
+    .. [4] USEPA (2024). Inventory of U.S. Greenhouse Gas Emissions and
+       Sinks, Chapter 6 and Annex 3.13.
+    """
+    from ..validation import (
+        validate_boolean,
+        validate_domain_expression,
+        validate_grp_by,
+        validate_land_type,
+    )
+
+    # ----- Validate pool -----
+    pool = pool.lower()
+    valid_pools = {"ag", "bg", "total"}
+    if pool not in valid_pools:
+        raise ValueError(
+            f"Invalid pool '{pool}'. Must be one of: {sorted(valid_pools)}"
+        )
+
+    # ----- Validate standard inputs -----
+    land_type = validate_land_type(land_type)
+    grp_by = validate_grp_by(grp_by)
+    area_domain = validate_domain_expression(area_domain, "area_domain")
+    plot_domain = validate_domain_expression(plot_domain, "plot_domain")
+    totals = validate_boolean(totals, "totals")
+    variance = validate_boolean(variance, "variance")
+    most_recent = validate_boolean(most_recent, "most_recent")
+
+    # ----- Resolve db + EVALID -----
+    db, owns_db = ensure_fia_instance(db)
+    if most_recent and db.evalid is None:
+        db.clip_most_recent(eval_type="VOL")
+    else:
+        ensure_evalid_set(db, eval_type="VOL", estimator_name="understory")
+
+    # ----- Build config and run estimator -----
+    config = {
+        "pool": pool,
+        "grp_by": grp_by,
+        "by_species": False,
+        "by_size_class": False,
+        "land_type": land_type,
+        "area_domain": area_domain,
+        "plot_domain": plot_domain,
+        "totals": totals,
+        "variance": variance,
+        "most_recent": most_recent,
+    }
+
+    try:
+        estimator = UnderstoryEstimator(db, config)
+        return estimator.estimate()
+    finally:
+        if owns_db and hasattr(db, "close"):
+            db.close()
diff --git a/src/pyfia/core/fia.py b/src/pyfia/core/fia.py
index c96a1fa4..385e3b42 100755
--- a/src/pyfia/core/fia.py
+++ b/src/pyfia/core/fia.py
@@ -30,7 +30,6 @@
 logger = logging.getLogger(__name__)
 
 
-
 class FIA:
     """
     Main FIA database class for working with Forest Inventory and Analysis data.
@@ -1304,6 +1303,86 @@ def area_change(self, **kwargs) -> pl.DataFrame:
 
         return area_change(self, **kwargs)
 
+    def live_tree(self, **kwargs) -> pl.DataFrame:
+        """
+        Estimate live tree carbon using the NSVB framework.
+
+        See live_tree() function for full parameter documentation.
+        """
+        from pyfia.carbon import live_tree
+
+        return live_tree(self, **kwargs)
+
+    def standing_dead(self, **kwargs) -> pl.DataFrame:
+        """
+        Estimate standing dead tree carbon using the NSVB framework.
+
+        See standing_dead() function for full parameter documentation.
+        """
+        from pyfia.carbon import standing_dead
+
+        return standing_dead(self, **kwargs)
+
+    def understory(self, **kwargs) -> pl.DataFrame:
+        """
+        Estimate understory vegetation carbon.
+
+        See understory() function for full parameter documentation.
+        """
+        from pyfia.carbon import understory
+
+        return understory(self, **kwargs)
+
+    def downed_dead(self, **kwargs) -> pl.DataFrame:
+        """
+        Estimate downed dead wood carbon.
+
+        See downed_dead() function for full parameter documentation.
+        """
+        from pyfia.carbon import downed_dead
+
+        return downed_dead(self, **kwargs)
+
+    def litter(self, **kwargs) -> pl.DataFrame:
+        """
+        Estimate litter carbon.
+
+        See litter() function for full parameter documentation.
+        """
+        from pyfia.carbon import litter
+
+        return litter(self, **kwargs)
+
+    def soil_organic(self, **kwargs) -> pl.DataFrame:
+        """
+        Estimate soil organic carbon.
+
+        See soil_organic() function for full parameter documentation.
+        """
+        from pyfia.carbon import soil_organic
+
+        return soil_organic(self, **kwargs)
+
+    def total_ecosystem(self, **kwargs) -> pl.DataFrame:
+        """
+        Estimate total ecosystem carbon across all six pools.
+
+        See total_ecosystem() function for full parameter documentation.
+        """
+        from pyfia.carbon.total_ecosystem import total_ecosystem
+
+        return total_ecosystem(self, **kwargs)
+
+    def stock_change(self, **kwargs) -> pl.DataFrame:
+        """
+        Estimate carbon stock change between inventory periods.
+
+        See stock_change() function for full parameter documentation.
+        """
+        from pyfia.carbon.stock_change import stock_change
+
+        return stock_change(self, **kwargs)
+
 
 class MotherDuckFIA(FIA):
     """
diff --git a/src/pyfia/downloader/tables.py b/src/pyfia/downloader/tables.py
index 4c4a465f..9fd106e5 100644
--- a/src/pyfia/downloader/tables.py
+++ b/src/pyfia/downloader/tables.py
@@ -13,12 +13,16 @@
 from __future__ import annotations
 
 # Common tables required for pyFIA estimation functions
-# These match the rFIA "common=TRUE" default tables
+# These match the rFIA "common=TRUE" default tables, plus pyfia-specific
+# additions that the estimators need.
 COMMON_TABLES: list[str] = [
     "COND",  # Condition data
     "COND_DWM_CALC",  # Down woody material calculations
     "INVASIVE_SUBPLOT_SPP",  # Invasive species subplot data
     "PLOT",  # Plot-level data
+    "PLOTGEOM",  # Plot geometry & ECOSUBCD (Bailey ecoprovince, used by
+    # pyfia.carbon.live_tree for the Phase 1.5+ DIVISION coefficient
+    # lookup; not in rFIA common tables).
     "POP_ESTN_UNIT",  # Population estimation units
     "POP_EVAL",  # Population evaluations
     "POP_EVAL_GRP",  # Population evaluation groups
diff --git a/tests/conftest.py b/tests/conftest.py
index 05321b35..bb50e777 100755
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -75,7 +75,11 @@ def georgia_db(georgia_db_path):
     )
     db = FIA(db_path)
     yield db
-    db.close()
+    # FIA class cleanup is handled by FIADataReader; only MotherDuckFIA
+    # exposes an explicit close(). Guard so session teardown doesn't raise
+    # AttributeError on the common DuckDB file path.
+    if hasattr(db, "close"):
+        db.close()
 
 
 @pytest.fixture
@@ -86,7 +90,11 @@ def fia_db(georgia_db_path):
     )
     db = FIA(db_path)
     yield db
-    db.close()
+    # FIA class cleanup is handled by FIADataReader; only MotherDuckFIA
+    # exposes an explicit close(). Guard so session teardown doesn't raise
+    # AttributeError on the common DuckDB file path.
+    if hasattr(db, "close"):
+        db.close()
 
 
 @pytest.fixture
diff --git a/tests/property/test_nsvb_properties.py b/tests/property/test_nsvb_properties.py
new file mode 100644
index 00000000..18aac602
--- /dev/null
+++ b/tests/property/test_nsvb_properties.py
@@ -0,0 +1,316 @@
+"""
+Property-based tests for the NSVB equation library.
+
+Uses Hypothesis to generate random valid inputs (DBH, height, coefficient
+shapes, etc.) and verifies invariants that must hold for all valid inputs:
+
+1. **Harmonization additivity**: ``wood_h + bark_h + branch_h == agb_predicted``
+   to floating-point precision, for any positive component values.
+2. **Carbon fraction bounds**: every value returned by
+   ``get_carbon_fraction_live`` is in [0.30, 0.65] (the actual S10a range).
+3. **Model 1 monotonicity in D**: for fixed H, ``model_1`` is monotonically
+   increasing in D when b > 0 (which it always is in the NSVB tables).
+4. **Model 1 monotonicity in H**: for fixed D, ``model_1`` is monotonically
+   increasing in H when c > 0 (the common case).
+5. **Model 4 reduces to Model 1 when b1=0**: an algebraic invariant that
+   pins the exp factor to 1 in the degenerate case.
+6. **Pipeline produces positive AGB for any valid tree**: smoke test that
+   ``predict_tree_biomass`` doesn't return negative or NaN values for the
+   Douglas-fir coefficient bundle across the full DBH range [1, 60].
+"""
+
+from __future__ import annotations
+
+import math
+
+from hypothesis import assume, given, settings
+from hypothesis import strategies as st
+
+from pyfia.carbon.nsvb.carbon_fractions import (
+    get_carbon_fraction_live,
+    load_carbon_fractions_live,
+)
+from pyfia.carbon.nsvb.equations import (
+    Coefficients,
+    harmonize_components,
+    model_1,
+    model_4,
+    predict_tree_biomass,
+)
+
+# Reuse the Douglas-fir coefficients from the equations test as a stable
+# coefficient bundle for pipeline property tests.
+_DOUGFIR_COEFS = Coefficients(
+    volib={
+        "model": 2,
+        "a": 0.001929099661,
+        "a1": 0.0,
+        "b": 2.162413104203,
+        "b1": 1.690400253097,
+        "c": 0.985444005253,
+        "c1": 0.0,
+        "source": "test",
+    },
+    volbk={
+        "model": 1,
+        "a": 0.000031886237,
+        "a1": 0.0,
+        "b": 1.21260513951,
+        "b1": 0.0,
+        "c": 1.978577263767,
+        "c1": 0.0,
+        "source": "test",
+    },
+    bark_bio={
+        "model": 1,
+        "a": 0.009106538193,
+        "a1": 0.0,
+        "b": 1.437894424586,
+        "b1": 0.0,
+        "c": 1.336514272981,
+        "c1": 0.0,
+        "source": "test",
+    },
+    branch_bio={
+        "model": 1,
+        "a": 9.521330809106,
+        "a1": 0.0,
+        "b": 1.762316117442,
+        "b1": 0.0,
+        "c": -0.40574259177,
+        "c1": 0.0,
+        "source": "test",
+    },
+    total_agb={
+        "model": 1,
+        "a": 0.135206506787,
+        "a1": 0.0,
+        "b": 1.713527048035,
+        "b1": 0.0,
+        "c": 1.047613377046,
+        "c1": 0.0,
+        "source": "test",
+    },
+)
+
+
+# ---------------------------------------------------------------------------
+# Harmonization invariant
+# ---------------------------------------------------------------------------
+
+
+@given(
+    agb=st.floats(min_value=0.1, max_value=1e6, allow_nan=False, allow_infinity=False),
+    w_wood=st.floats(
+        min_value=0.01, max_value=1e6, allow_nan=False, allow_infinity=False
+    ),
+    w_bark=st.floats(
+        min_value=0.01, max_value=1e6, allow_nan=False, allow_infinity=False
+    ),
+    w_branch=st.floats(
+        min_value=0.01, max_value=1e6, allow_nan=False, allow_infinity=False
+    ),
+)
+@settings(max_examples=200)
+def test_harmonization_additivity(agb, w_wood, w_bark, w_branch):
+    """Harmonized components must sum exactly to the predicted AGB.
+
+    This is the load-bearing invariant for any downstream code that needs
+    additive decomposition (e.g., merchantable subdivisions in Phase 2+).
+    """
+    wood_h, bark_h, branch_h = harmonize_components(agb, w_wood, w_bark, w_branch)
+    total = wood_h + bark_h + branch_h
+    assert math.isclose(total, agb, rel_tol=1e-12)
+
+
+@given(
+    agb=st.floats(min_value=1.0, max_value=1e5),
+    w_wood=st.floats(min_value=1.0, max_value=1e5),
+    w_bark=st.floats(min_value=1.0, max_value=1e5),
+    w_branch=st.floats(min_value=1.0, max_value=1e5),
+)
+@settings(max_examples=200)
+def test_harmonization_preserves_relative_ratios(agb, w_wood, w_bark, w_branch):
+    """The harmonized components preserve the relative ratios of the inputs.
+
+    If raw wood:bark = 4:2, harmonized wood:bark must also be 4:2.
+    """
+    wood_h, bark_h, _ = harmonize_components(agb, w_wood, w_bark, w_branch)
+    raw_ratio = w_wood / w_bark
+    harmonized_ratio = wood_h / bark_h
+    assert math.isclose(raw_ratio, harmonized_ratio, rel_tol=1e-12)
+
+
+# ---------------------------------------------------------------------------
+# Carbon fraction bounds
+# ---------------------------------------------------------------------------
+
+
+def test_all_carbon_fractions_in_decimal_range():
+    """Every value in S10a must be in the decimal range, not percent.
+
+    This is a smoke test of the percent→decimal normalization. We use a
+    direct iteration rather than hypothesis because we want to check
+    every species, not a random sample.
+    """
+    table = load_carbon_fractions_live()
+    for spcd, frac in table.items():
+        assert 0.30 <= frac <= 0.65, f"SPCD={spcd} carbon fraction {frac} out of range"
+
+
+@given(spcd=st.sampled_from(sorted(load_carbon_fractions_live().keys())))
+@settings(max_examples=200)
+def test_get_carbon_fraction_live_returns_known_value(spcd):
+    """For any SPCD known to S10a, the lookup returns the table value
+    (not the fallback)."""
+    table = load_carbon_fractions_live()
+    result = get_carbon_fraction_live(spcd)
+    assert result == table[spcd]
+
+
+# ---------------------------------------------------------------------------
+# Model 1 monotonicity
+# ---------------------------------------------------------------------------
+
+
+@given(
+    d_lo=st.floats(min_value=1.0, max_value=30.0),
+    d_hi=st.floats(min_value=1.0, max_value=30.0),
+    h=st.floats(min_value=10.0, max_value=200.0),
+    a=st.floats(min_value=1e-6, max_value=10.0),
+    b=st.floats(min_value=0.5, max_value=3.0),
+    c=st.floats(min_value=0.5, max_value=2.0),
+)
+@settings(max_examples=200)
+def test_model_1_monotonic_in_d(d_lo, d_hi, h, a, b, c):
+    """For fixed H and positive coefficients, Model 1 is increasing in D.
+
+    Models with b > 0 (the universal case in the NSVB tables) must satisfy
+    ``y(D_lo, H) < y(D_hi, H)`` whenever ``D_lo < D_hi``.
+
+    Note on the 1e-6 gap: the strict-inequality assertion holds mathematically
+    but fails at floating-point ULP-level differences (e.g., d_lo=1.0 and
+    d_hi=1.0000000000000002 map to indistinguishable f64 outputs). We only
+    test the property when the two diameters differ by at least 1e-6 inches,
+    which is well below any meaningful tree-measurement precision.
+    """
+    assume(abs(d_hi - d_lo) > 1e-6)
+    lo, hi = sorted([d_lo, d_hi])
+    y_lo = model_1(lo, h, a, b, c)
+    y_hi = model_1(hi, h, a, b, c)
+    assert y_lo < y_hi
+
+
+@given(
+    d=st.floats(min_value=1.0, max_value=30.0),
+    h_lo=st.floats(min_value=10.0, max_value=200.0),
+    h_hi=st.floats(min_value=10.0, max_value=200.0),
+    a=st.floats(min_value=1e-6, max_value=10.0),
+    b=st.floats(min_value=0.5, max_value=3.0),
+    c=st.floats(min_value=0.5, max_value=2.0),
+)
+@settings(max_examples=200)
+def test_model_1_monotonic_in_h_when_c_positive(d, h_lo, h_hi, a, b, c):
+    """For fixed D and positive c, Model 1 is increasing in H.
+
+    Note that c can be negative for some components (e.g., branch biomass
+    has c≈-0.41 — branch mass declines with height for fixed DBH). This test
+    only covers the positive-c case; the negative-c case is the dual.
+
+    Uses a 1e-6 ft minimum gap between heights for the same f64-ULP reason
+    documented in ``test_model_1_monotonic_in_d``.
+    """
+    assume(abs(h_hi - h_lo) > 1e-6)
+    lo, hi = sorted([h_lo, h_hi])
+    y_lo = model_1(d, lo, a, b, c)
+    y_hi = model_1(d, hi, a, b, c)
+    assert y_lo < y_hi
+
+
+# ---------------------------------------------------------------------------
+# Model 4 algebraic invariant
+# ---------------------------------------------------------------------------
+
+
+@given(
+    d=st.floats(min_value=1.0, max_value=30.0),
+    h=st.floats(min_value=10.0, max_value=200.0),
+    a=st.floats(min_value=1e-6, max_value=10.0),
+    b=st.floats(min_value=0.5, max_value=3.0),
+    c=st.floats(min_value=-1.0, max_value=2.0),
+)
+@settings(max_examples=200)
+def test_model_4_reduces_to_model_1_when_b1_zero(d, h, a, b, c):
+    """When b1 == 0, exp(0) == 1 and Model 4 collapses to Model 1."""
+    m1 = model_1(d, h, a, b, c)
+    m4 = model_4(d, h, a, b, b1=0.0, c=c)
+    assert math.isclose(m1, m4, rel_tol=1e-12)
+
+
+# ---------------------------------------------------------------------------
+# Pipeline robustness
+# ---------------------------------------------------------------------------
+
+
+@given(
+    dia=st.floats(min_value=1.0, max_value=60.0),
+    ht=st.floats(min_value=10.0, max_value=200.0),
+)
+@settings(max_examples=100)
+def test_pipeline_returns_positive_agb_for_valid_inputs(dia, ht):
+    """Pipeline must return positive, finite AGB across the practical
+    DBH and height range for the Douglas-fir coefficient bundle."""
+    result = predict_tree_biomass(
+        spcd=202,
+        dia=dia,
+        ht=ht,
+        coefficients=_DOUGFIR_COEFS,
+        wdsg=0.45,
+        hw_sw="softwood",
+        cull=0.0,
+    )
+    assert result.agb > 0
+    assert math.isfinite(result.agb)
+    # Component sum must equal AGB to floating-point precision
+    assert math.isclose(
+        result.w_wood + result.w_bark + result.w_branch,
+        result.agb,
+        rel_tol=1e-12,
+    )
+
+
+@given(
+    dia=st.floats(min_value=5.0, max_value=40.0),
+    ht=st.floats(min_value=20.0, max_value=150.0),
+    cull=st.floats(min_value=0.0, max_value=50.0),
+)
+@settings(max_examples=100)
+def test_cull_reduces_agb(dia, ht, cull):
+    """Increasing cull percentage must monotonically reduce the harmonized AGB.
+
+    For live trees, cull only deducts from wood weight (not bark or branch),
+    so AGBReduce < 1 whenever CULL > 0.
+    """
+    no_cull = predict_tree_biomass(
+        spcd=202,
+        dia=dia,
+        ht=ht,
+        coefficients=_DOUGFIR_COEFS,
+        wdsg=0.45,
+        hw_sw="softwood",
+        cull=0.0,
+    )
+    with_cull = predict_tree_biomass(
+        spcd=202,
+        dia=dia,
+        ht=ht,
+        coefficients=_DOUGFIR_COEFS,
+        wdsg=0.45,
+        hw_sw="softwood",
+        cull=cull,
+    )
+    if cull == 0.0:
+        assert math.isclose(no_cull.agb, with_cull.agb, rel_tol=1e-12)
+    else:
+        # Cull must reduce or hold AGB equal (it never increases it)
+        assert with_cull.agb <= no_cull.agb
diff --git a/tests/unit/test_carbon_fractions.py b/tests/unit/test_carbon_fractions.py
new file mode 100644
index 00000000..ab9b3b42
--- /dev/null
+++ b/tests/unit/test_carbon_fractions.py
@@ -0,0 +1,216 @@
+"""
+Unit tests for the NSVB carbon fraction lookup module.
+
+Verifies:
+
+1. S10a CSV loads correctly with the trimmed schema (SPCD, hw_sw, fia_wood_c)
+2. Percent → decimal normalization (the source CSV stores e.g. 48.04, not 0.4804)
+3. SPCD=202 (Douglas-fir) and SPCD=316 (red maple) lookups match worked-example values
+4. Unknown SPCD falls back to the national mean and emits exactly one warning
+5. S10b dead carbon fractions load with the (hw_sw, decaycd) keying
+"""
+
+from __future__ import annotations
+
+import logging
+
+import polars as pl
+
+from pyfia.carbon.nsvb import carbon_fractions
+from pyfia.carbon.nsvb.carbon_fractions import (
+    DEFAULT_LIVE_CARBON_FRACTION,
+    get_carbon_fraction_dead,
+    get_carbon_fraction_live,
+    load_carbon_fractions_dead,
+    load_carbon_fractions_live,
+    load_carbon_fractions_live_df,
+)
+
+
+class TestLoadCarbonFractionsLive:
+    """S10a loader: ``carbon_fraction_live.csv`` → dict[SPCD] -> float."""
+
+    def test_returns_dict(self):
+        result = load_carbon_fractions_live()
+        assert isinstance(result, dict)
+
+    def test_has_2676_species(self):
+        """The trimmed S10a has 2676 species rows."""
+        result = load_carbon_fractions_live()
+        assert len(result) == 2676
+
+    def test_lru_cache(self):
+        first = load_carbon_fractions_live()
+        second = load_carbon_fractions_live()
+        assert first is second
+
+    def test_values_in_decimal_form(self):
+        """All values must be in decimal form, not percent.
+
+        Bounds are [0.30, 0.65] — wide enough to capture the actual S10a
+        range (min 0.365, max 0.609 across the 2676 species) but tight
+        enough to detect a percent-vs-decimal unit error (which would put
+        values at 30-65 instead of 0.30-0.65). The spec section 3.1.2
+        cited "~0.474 mean" but the per-species range is wider than I
+        initially assumed — measured directly from the vendored CSV.
+        """
+        result = load_carbon_fractions_live()
+        for spcd, frac in result.items():
+            assert 0.30 <= frac <= 0.65, f"SPCD={spcd} has out-of-range fraction {frac}"
+
+    def test_mean_close_to_population_mean(self):
+        """The S10a population mean is ~0.474 per spec section 3.1.2."""
+        result = load_carbon_fractions_live()
+        mean = sum(result.values()) / len(result)
+        assert 0.46 <= mean <= 0.49, (
+            f"Population mean {mean:.4f} outside expected range"
+        )
+
+
+class TestGetCarbonFractionLive:
+    """Per-species lookup with fallback."""
+
+    def test_douglas_fir_matches_worked_example(self):
+        """SPCD=202 should be ~0.5156 per S10a (worked example line 680)."""
+        result = get_carbon_fraction_live(202)
+        # Expected: 51.55958333 / 100 = 0.5155958333
+        assert abs(result - 0.5155958333) < 1e-6
+
+    def test_red_maple_matches_s10a(self):
+        """SPCD=316 red maple should be ~0.4857 per S10a."""
+        result = get_carbon_fraction_live(316)
+        # Expected: 48.57333333 / 100 = 0.4857333333
+        assert abs(result - 0.4857333333) < 1e-6
+
+    def test_unknown_spcd_returns_fallback(self):
+        """SPCD with no S10a entry returns the national-mean default."""
+        result = get_carbon_fraction_live(99999)
+        assert result == DEFAULT_LIVE_CARBON_FRACTION
+
+    def test_unknown_spcd_warns_only_once(self, caplog):
+        """First lookup of an unknown SPCD logs a warning; subsequent
+        lookups for the same SPCD do not (to avoid log spam)."""
+        # Reset the warned-set so this test is reproducible
+        carbon_fractions._warned_unknown_spcds.clear()
+
+        with caplog.at_level(
+            logging.WARNING, logger="pyfia.carbon.nsvb.carbon_fractions"
+        ):
+            get_carbon_fraction_live(99998)
+            get_carbon_fraction_live(99998)
+            get_carbon_fraction_live(99998)
+
+        warning_count = sum(1 for r in caplog.records if "99998" in r.message)
+        assert warning_count == 1
+
+    def test_default_fallback_value(self):
+        """The default fallback is the S10a arithmetic mean.
+
+        Computed lazily from the vendored S10a table on first access (PEP 562
+        ``__getattr__``). The value tracks the CSV so a future re-vendor
+        automatically updates the constant — this is the regression guard
+        against the hardcoded 0.4716 value that had drifted from the actual
+        table mean (~0.4741).
+        """
+        # Matches GTR-WO-104 "live ~47.4% mean" notation.
+        assert 0.47 < DEFAULT_LIVE_CARBON_FRACTION < 0.48
+        # Must exactly equal the arithmetic mean of the loaded dict.
+        table = load_carbon_fractions_live()
+        expected = sum(table.values()) / len(table)
+        assert DEFAULT_LIVE_CARBON_FRACTION == expected
+
+    def test_custom_fallback(self):
+        """Caller can override the fallback value."""
+        result = get_carbon_fraction_live(99997, fallback=0.50)
+        assert result == 0.50
+
+
+class TestLoadCarbonFractionsLiveDf:
+    """S10a polars loader: the join-ready DataFrame used by the vectorized path."""
+
+    def test_returns_polars_dataframe(self):
+        result = load_carbon_fractions_live_df()
+        assert isinstance(result, pl.DataFrame)
+
+    def test_columns(self):
+        """Schema is exactly (SPCD Int64, CARBON_FRAC_LIVE Float64)."""
+        result = load_carbon_fractions_live_df()
+        assert result.columns == ["SPCD", "CARBON_FRAC_LIVE"]
+        assert result.schema["SPCD"] == pl.Int64
+        assert result.schema["CARBON_FRAC_LIVE"] == pl.Float64
+
+    def test_matches_dict_loader(self):
+        """The DataFrame loader must agree with the dict loader row-for-row.
+
+        Both loaders consume the same vendored CSV, so every SPCD in one
+        must appear in the other with the same float value. This catches
+        drift if someone modifies one loader without the other.
+        """
+        df = load_carbon_fractions_live_df()
+        table = load_carbon_fractions_live()
+        assert df.height == len(table)
+        for row in df.iter_rows(named=True):
+            assert row["SPCD"] in table
+            assert abs(row["CARBON_FRAC_LIVE"] - table[row["SPCD"]]) < 1e-12
+
+    def test_percent_to_decimal_conversion(self):
+        """Values are in decimal form (e.g., 0.4804), not percent (48.04)."""
+        df = load_carbon_fractions_live_df()
+        # All fractions should be in the [0.3, 0.7] range
+        assert df["CARBON_FRAC_LIVE"].min() > 0.3
+        assert df["CARBON_FRAC_LIVE"].max() < 0.7
+
+    def test_lru_cache_returns_same_instance(self):
+        first = load_carbon_fractions_live_df()
+        second = load_carbon_fractions_live_df()
+        assert first is second
+
+    def test_join_ready(self):
+        """The loader output is suitable for a left join to a trees frame."""
+        df = load_carbon_fractions_live_df()
+        trees = pl.DataFrame({"SPCD": [202, 316, 99999]})
+        joined = trees.join(df, on="SPCD", how="left")
+        # Douglas-fir and red maple should have non-null CARBON_FRAC_LIVE
+        assert joined.filter(pl.col("SPCD") == 202)["CARBON_FRAC_LIVE"][0] is not None
+        assert joined.filter(pl.col("SPCD") == 316)["CARBON_FRAC_LIVE"][0] is not None
+        # Unknown SPCD should have null (caller fills with DEFAULT)
+        assert joined.filter(pl.col("SPCD") == 99999)["CARBON_FRAC_LIVE"][0] is None
+
+
+class TestLoadCarbonFractionsDead:
+    """S10b loader: dead tree carbon fractions by hw/sw × decay class."""
+
+    def test_returns_dict(self):
+        result = load_carbon_fractions_dead()
+        assert isinstance(result, dict)
+
+    def test_ten_rows(self):
+        """S10b has 10 entries: 2 hw/sw × 5 decay classes."""
+        result = load_carbon_fractions_dead()
+        assert len(result) == 10
+
+    def test_lookup_hardwood_decay_3(self):
+        """Verify the hardwood/decay-3 entry exists and is reasonable."""
+        result = get_carbon_fraction_dead("hardwood", 3)
+        # Expected: ~0.473 (47.3%) per S10b
+        assert 0.46 <= result <= 0.49
+
+    def test_lookup_softwood_decay_5(self):
+        """Verify the softwood/decay-5 entry exists."""
+        result = get_carbon_fraction_dead("softwood", 5)
+        assert 0.50 <= result <= 0.55
+
+    def test_case_insensitive_hw_sw(self):
+        """``Hardwood`` and ``hardwood`` should both work."""
+        a = get_carbon_fraction_dead("Hardwood", 1)
+        b = get_carbon_fraction_dead("hardwood", 1)
+        assert a == b
+
+    def test_all_decay_classes_present(self):
+        """Every decay class 1-5 must exist for both hardwood and softwood."""
+        for hw_sw in ("hardwood", "softwood"):
+            for decay in range(1, 6):
+                result = get_carbon_fraction_dead(hw_sw, decay)
+                assert 0.40 <= result <= 0.55, (
+                    f"({hw_sw}, decay={decay}) -> {result} out of range"
+                )
diff --git a/tests/unit/test_downed_dead_estimator.py b/tests/unit/test_downed_dead_estimator.py
new file mode 100644
index 00000000..97a103e7
--- /dev/null
+++ b/tests/unit/test_downed_dead_estimator.py
@@ -0,0 +1,143 @@
+"""Unit tests for DownedDeadEstimator class.
+
+Tests the DownedDeadEstimator methods in isolation using mock data.
+No database connection required.
+"""
+
+import polars as pl
+import pytest
+
+from pyfia.carbon.downed_dead import DownedDeadEstimator, downed_dead
+
+
+class MockDB:
+    """Mock database for testing estimator methods in isolation."""
+
+    def __init__(self):
+        self.db_path = "/fake/path"
+        self.tables = {}
+        self.evalid = None
+        self.evalids = None
+        self._state_filter = None
+
+
+class TestGetRequiredTables:
+    """Tests for get_required_tables method."""
+
+    def test_returns_condition_level_tables(self):
+        config = {"pool": "total", "land_type": "forest"}
+        estimator = DownedDeadEstimator(MockDB(), config)
+        tables = estimator.get_required_tables()
+
+        assert "COND" in tables
+        assert "PLOT" in tables
+        assert "POP_PLOT_STRATUM_ASSGN" in tables
+        assert "POP_STRATUM" in tables
+
+    def test_tree_table_not_required(self):
+        config = {"pool": "total", "land_type": "forest"}
+        estimator = DownedDeadEstimator(MockDB(), config)
+        tables = estimator.get_required_tables()
+        assert "TREE" not in tables
+
+
+class TestGetTreeColumns:
+    def test_returns_empty_list(self):
+        config = {"pool": "total"}
+        estimator = DownedDeadEstimator(MockDB(), config)
+        cols = estimator.get_tree_columns()
+        assert cols == []
+
+
+class TestGetCondColumns:
+    def test_includes_carbon_down_dead_column(self):
+        config = {"land_type": "forest"}
+        estimator = DownedDeadEstimator(MockDB(), config)
+        cols = estimator.get_cond_columns()
+
+        assert "CARBON_DOWN_DEAD" in cols
+        assert "PLT_CN" in cols
+        assert "CONDID" in cols
+        assert "CONDPROP_UNADJ" in cols
+        assert "COND_STATUS_CD" in cols
+
+
+class TestCalculateValues:
+    """Tests for calculate_values method."""
+
+    @pytest.fixture
+    def mock_db(self):
+        return MockDB()
+
+    def test_total_pool(self, mock_db):
+        config = {"pool": "total"}
+        estimator = DownedDeadEstimator(mock_db, config)
+
+        data = pl.DataFrame(
+            {"CARBON_DOWN_DEAD": [2.5, 1.3]}
+        ).lazy()
+
+        result = estimator.calculate_values(data).collect()
+        assert abs(result["CARBON_ACRE"][0] - 2.5) < 1e-10
+        assert abs(result["CARBON_ACRE"][1] - 1.3) < 1e-10
+
+    def test_null_values_treated_as_zero(self, mock_db):
+        config = {"pool": "total"}
+        estimator = DownedDeadEstimator(mock_db, config)
+
+        data = pl.DataFrame(
+            {"CARBON_DOWN_DEAD": [None, 1.0]}
+        ).lazy()
+
+        result = estimator.calculate_values(data).collect()
+        assert result["CARBON_ACRE"][0] == 0.0
+        assert abs(result["CARBON_ACRE"][1] - 1.0) < 1e-10
+
+    def test_all_null_gives_zero(self, mock_db):
+        config = {"pool": "total"}
+        estimator = DownedDeadEstimator(mock_db, config)
+
+        data = pl.DataFrame(
+            {"CARBON_DOWN_DEAD": [None]}
+        ).lazy()
+
+        result = estimator.calculate_values(data).collect()
+        assert result["CARBON_ACRE"][0] == 0.0
+
+    def test_empty_dataframe(self, mock_db):
+        config = {"pool": "total"}
+        estimator = DownedDeadEstimator(mock_db, config)
+
+        data = pl.DataFrame(
+            {"CARBON_DOWN_DEAD": pl.Series([], dtype=pl.Float64)}
+        ).lazy()
+
+        result = estimator.calculate_values(data).collect()
+        assert len(result) == 0
+
+
+class TestPoolValidation:
+    def test_invalid_pool_raises_error(self):
+        with pytest.raises(ValueError, match="Invalid pool"):
+            downed_dead(MockDB(), pool="ag")
+
+    def test_bg_pool_raises_error(self):
+        with pytest.raises(ValueError, match="no AG/BG split"):
+            downed_dead(MockDB(), pool="bg")
+
+    def test_total_pool_accepted(self):
+        # Should not raise ValueError on pool validation
+        # (will fail on DB access later)
+        with pytest.raises(Exception):
+            downed_dead(MockDB(), pool="total")
+
+    def test_case_insensitive_total(self):
+        with pytest.raises(Exception):
+            downed_dead(MockDB(), pool="TOTAL")
+
+
+class TestEstimatorLabel:
+    def test_label_is_downed_dead(self):
+        config = {"pool": "total"}
+        estimator = DownedDeadEstimator(MockDB(), config)
+        assert estimator._estimator_label == "DownedDead"
diff --git a/tests/unit/test_litter_estimator.py b/tests/unit/test_litter_estimator.py
new file mode 100644
index 00000000..16bfb2f7
--- /dev/null
+++ b/tests/unit/test_litter_estimator.py
@@ -0,0 +1,141 @@
+"""Unit tests for LitterEstimator class.
+
+Tests the LitterEstimator methods in isolation using mock data.
+No database connection required.
+"""
+
+import polars as pl
+import pytest
+
+from pyfia.carbon.litter import LitterEstimator, litter
+
+
+class MockDB:
+    """Mock database for testing estimator methods in isolation."""
+
+    def __init__(self):
+        self.db_path = "/fake/path"
+        self.tables = {}
+        self.evalid = None
+        self.evalids = None
+        self._state_filter = None
+
+
+class TestGetRequiredTables:
+    """Tests for get_required_tables method."""
+
+    def test_returns_condition_level_tables(self):
+        config = {"pool": "total", "land_type": "forest"}
+        estimator = LitterEstimator(MockDB(), config)
+        tables = estimator.get_required_tables()
+
+        assert "COND" in tables
+        assert "PLOT" in tables
+        assert "POP_PLOT_STRATUM_ASSGN" in tables
+        assert "POP_STRATUM" in tables
+
+    def test_tree_table_not_required(self):
+        config = {"pool": "total", "land_type": "forest"}
+        estimator = LitterEstimator(MockDB(), config)
+        tables = estimator.get_required_tables()
+        assert "TREE" not in tables
+
+
+class TestGetTreeColumns:
+    def test_returns_empty_list(self):
+        config = {"pool": "total"}
+        estimator = LitterEstimator(MockDB(), config)
+        cols = estimator.get_tree_columns()
+        assert cols == []
+
+
+class TestGetCondColumns:
+    def test_includes_carbon_litter_column(self):
+        config = {"land_type": "forest"}
+        estimator = LitterEstimator(MockDB(), config)
+        cols = estimator.get_cond_columns()
+
+        assert "CARBON_LITTER" in cols
+        assert "PLT_CN" in cols
+        assert "CONDID" in cols
+        assert "CONDPROP_UNADJ" in cols
+        assert "COND_STATUS_CD" in cols
+
+
+class TestCalculateValues:
+    """Tests for calculate_values method."""
+
+    @pytest.fixture
+    def mock_db(self):
+        return MockDB()
+
+    def test_total_pool(self, mock_db):
+        config = {"pool": "total"}
+        estimator = LitterEstimator(mock_db, config)
+
+        data = pl.DataFrame(
+            {"CARBON_LITTER": [5.2, 3.8]}
+        ).lazy()
+
+        result = estimator.calculate_values(data).collect()
+        assert abs(result["CARBON_ACRE"][0] - 5.2) < 1e-10
+        assert abs(result["CARBON_ACRE"][1] - 3.8) < 1e-10
+
+    def test_null_values_treated_as_zero(self, mock_db):
+        config = {"pool": "total"}
+        estimator = LitterEstimator(mock_db, config)
+
+        data = pl.DataFrame(
+            {"CARBON_LITTER": [None, 4.0]}
+        ).lazy()
+
+        result = estimator.calculate_values(data).collect()
+        assert result["CARBON_ACRE"][0] == 0.0
+        assert abs(result["CARBON_ACRE"][1] - 4.0) < 1e-10
+
+    def test_all_null_gives_zero(self, mock_db):
+        config = {"pool": "total"}
+        estimator = LitterEstimator(mock_db, config)
+
+        data = pl.DataFrame(
+            {"CARBON_LITTER": [None]}
+        ).lazy()
+
+        result = estimator.calculate_values(data).collect()
+        assert result["CARBON_ACRE"][0] == 0.0
+
+    def test_empty_dataframe(self, mock_db):
+        config = {"pool": "total"}
+        estimator = LitterEstimator(mock_db, config)
+
+        data = pl.DataFrame(
+            {"CARBON_LITTER": pl.Series([], dtype=pl.Float64)}
+        ).lazy()
+
+        result = estimator.calculate_values(data).collect()
+        assert len(result) == 0
+
+
+class TestPoolValidation:
+    def test_invalid_pool_raises_error(self):
+        with pytest.raises(ValueError, match="Invalid pool"):
+            litter(MockDB(), pool="ag")
+
+    def test_bg_pool_raises_error(self):
+        with pytest.raises(ValueError, match="no AG/BG split"):
+            litter(MockDB(), pool="bg")
+
+    def test_total_pool_accepted(self):
+        with pytest.raises(Exception):
+            litter(MockDB(), pool="total")
+
+    def test_case_insensitive_total(self):
+        with pytest.raises(Exception):
+            litter(MockDB(), pool="TOTAL")
+
+
+class TestEstimatorLabel:
+    def test_label_is_litter(self):
+        config = {"pool": "total"}
+        estimator = LitterEstimator(MockDB(), config)
+        assert estimator._estimator_label == "Litter"
diff --git a/tests/unit/test_live_tree_estimator.py b/tests/unit/test_live_tree_estimator.py
new file mode 100644
index 00000000..2db84e2b
--- /dev/null
+++ b/tests/unit/test_live_tree_estimator.py
@@ -0,0 +1,250 @@
+"""Unit tests for the LiveTreeEstimator class and live_tree() public function.
+
+Two layers of coverage:
+
+1. **Isolated unit tests** (the bulk of this file) — exercise config wiring,
+   column selection, pool dispatch, and input validation against a MockDB
+   with no real FIA database. These run in CI unconditionally.
+
+2. **Database smoke test** (``test_live_tree_smoke_ag_pool``) — exercises
+   the full template-method pipeline against the Georgia test database
+   if available (via the ``georgia_db`` fixture from ``tests/conftest.py``).
+   Skipped automatically when no database is present.
+
+The equivalence between the vectorized NSVB pipeline and the scalar oracle
+is tested in ``tests/unit/test_nsvb_vectorized.py`` — this file does not
+re-check the math, only the estimator plumbing.
+"""
+
+from __future__ import annotations
+
+import pytest
+
+from pyfia.carbon.live_tree import LiveTreeEstimator, live_tree
+
+
+class MockDB:
+    """Mock database for testing estimator methods in isolation.
+
+    Matches the pattern used in ``test_carbon_pools_estimator.py``. The
+    attributes below are the ones ``BaseEstimator.__init__`` touches, plus
+    ``_reader`` as a stub so :meth:`LiveTreeEstimator._load_ref_species`
+    can be monkey-patched in tests that need it.
+    """
+
+    def __init__(self):
+        self.db_path = "/fake/path"
+        self.tables = {}
+        self.evalid = None
+        self.evalids = None
+        self._state_filter = None
+        self._reader = None
+
+
+# ---------------------------------------------------------------------------
+# Isolated estimator config tests
+# ---------------------------------------------------------------------------
+
+
+class TestGetRequiredTables:
+    def test_returns_five_core_tables(self):
+        """REF_SPECIES is loaded separately in calculate_values, not via the loader."""
+        estimator = LiveTreeEstimator(MockDB(), {"pool": "ag", "land_type": "forest"})
+        tables = estimator.get_required_tables()
+        assert "TREE" in tables
+        assert "COND" in tables
+        assert "PLOT" in tables
+        assert "POP_PLOT_STRATUM_ASSGN" in tables
+        assert "POP_STRATUM" in tables
+        assert len(tables) == 5
+
+    def test_tables_consistent_across_pools(self):
+        for pool in ("ag", "bg", "total"):
+            estimator = LiveTreeEstimator(MockDB(), {"pool": pool})
+            tables = estimator.get_required_tables()
+            assert set(tables) == {
+                "TREE",
+                "COND",
+                "PLOT",
+                "POP_PLOT_STRATUM_ASSGN",
+                "POP_STRATUM",
+            }
+
+
+class TestGetTreeColumns:
+    def test_nsvb_and_bg_bridge_columns(self):
+        """All tree columns the NSVB pipeline + BG bridge need are requested."""
+        estimator = LiveTreeEstimator(MockDB(), {"pool": "total"})
+        cols = estimator.get_tree_columns()
+        assert "SPCD" in cols
+        assert "DIA" in cols
+        assert "HT" in cols
+        assert "CULL" in cols
+        assert "CARBON_BG" in cols
+        assert "TPA_UNADJ" in cols
+        # Standard framework columns added by _get_tree_columns()
+        assert "CN" in cols
+        assert "PLT_CN" in cols
+        assert "CONDID" in cols
+        assert "STATUSCD" in cols  # live filter
+        # ACTUALHT is not loaded in Phase 1 — intact-top assumption
+        assert "ACTUALHT" not in cols
+
+    def test_tree_columns_include_grouping(self):
+        """grp_by columns are added to the tree column list."""
+        estimator = LiveTreeEstimator(MockDB(), {"pool": "ag", "grp_by": "SPGRPCD"})
+        cols = estimator.get_tree_columns()
+        assert "SPGRPCD" in cols
+
+
+class TestLiveTreeFunctionValidation:
+    """Input validation for the public ``live_tree()`` function."""
+
+    def test_invalid_pool_raises(self):
+        with pytest.raises(ValueError, match="Invalid pool"):
+            live_tree(MockDB(), pool="xyz")
+
+    def test_pool_case_insensitive(self, monkeypatch):
+        """pool='AG', 'Ag', 'ag' are all accepted.
+
+        Uses monkeypatch to prevent the estimator from touching a real DB —
+        we only want to verify that validation accepts the input.
+        """
+        # Short-circuit ensure_fia_instance + ensure_evalid_set so we don't
+        # actually run an estimation. We just need the validation path to
+        # pass without raising for the case variants.
+        # importlib is used because ``pyfia.carbon.__init__`` re-exports the
+        # ``live_tree`` function, shadowing the submodule name under normal
+        # attribute access.
+        import importlib
+
+        live_tree_module = importlib.import_module("pyfia.carbon.live_tree")
+
+        def fake_ensure_fia_instance(db):
+            return (MockDB(), False)
+
+        def fake_ensure_evalid_set(*args, **kwargs):
+            pass
+
+        class ShortCircuitError(Exception):
+            pass
+
+        def fake_estimate(self):
+            raise ShortCircuitError()
+
+        monkeypatch.setattr(
+            live_tree_module, "ensure_fia_instance", fake_ensure_fia_instance
+        )
+        monkeypatch.setattr(
+            live_tree_module, "ensure_evalid_set", fake_ensure_evalid_set
+        )
+        monkeypatch.setattr(LiveTreeEstimator, "estimate", fake_estimate)
+
+        for variant in ("ag", "AG", "Ag", "bg", "BG", "total", "TOTAL"):
+            with pytest.raises(ShortCircuitError):
+                live_tree(MockDB(), pool=variant)
+
+    def test_total_is_valid_pool(self, monkeypatch):
+        """'total' is a valid pool value (AG + BG bridge)."""
+        # Validation path should accept 'total' without raising.
+        import importlib
+
+        live_tree_module = importlib.import_module("pyfia.carbon.live_tree")
+
+        def fake_ensure_fia_instance(db):
+            return (MockDB(), False)
+
+        def fake_ensure_evalid_set(*args, **kwargs):
+            pass
+
+        class ShortCircuitError(Exception):
+            pass
+
+        def fake_estimate(self):
+            raise ShortCircuitError()
+
+        monkeypatch.setattr(
+            live_tree_module, "ensure_fia_instance", fake_ensure_fia_instance
+        )
+        monkeypatch.setattr(
+            live_tree_module, "ensure_evalid_set", fake_ensure_evalid_set
+        )
+        monkeypatch.setattr(LiveTreeEstimator, "estimate", fake_estimate)
+
+        with pytest.raises(ShortCircuitError):
+            live_tree(MockDB(), pool="total")
+
+
+# ---------------------------------------------------------------------------
+# Full end-to-end smoke test (skipped if no FIA database present)
+# ---------------------------------------------------------------------------
+
+
+class TestLiveTreeEndToEnd:
+    """End-to-end smoke test using the Georgia test database fixture.
+
+    Skipped automatically when no database is available — the
+    ``georgia_db_path`` fixture in ``tests/conftest.py`` calls
+    ``pytest.skip(...)`` if it can't find a DuckDB file or a MotherDuck token.
+    """
+
+    def test_live_tree_ag_pool_runs_to_completion(self, georgia_db):
+        """``live_tree(db, pool='ag', most_recent=True)`` returns a non-empty,
+        positively-valued DataFrame with the expected column layout."""
+        result = live_tree(georgia_db, pool="ag", most_recent=True)
+
+        assert result.height > 0, "live_tree returned an empty DataFrame"
+
+        # Canonical column layout
+        assert "YEAR" in result.columns
+        assert "POOL" in result.columns
+        assert "CARBON_ACRE" in result.columns
+        assert "CARBON_TOTAL" in result.columns
+        assert "N_PLOTS" in result.columns
+        assert "N_TREES" in result.columns
+
+        # POOL column tags the estimate
+        assert result["POOL"][0] == "AG"
+
+        # Per-acre carbon should be positive and within a plausible range
+        # (forestland typically runs 10-50 short tons/acre of live tree AG carbon)
+        carbon_acre = result["CARBON_ACRE"][0]
+        assert carbon_acre > 0, f"Non-positive CARBON_ACRE: {carbon_acre}"
+        assert carbon_acre < 500, f"Implausibly large CARBON_ACRE: {carbon_acre}"
+
+    def test_live_tree_bg_pool_uses_fiadb_bridge(self, georgia_db):
+        """``pool='bg'`` returns belowground carbon via the FIADB bridge."""
+        result = live_tree(georgia_db, pool="bg", most_recent=True)
+
+        assert result.height > 0
+        assert result["POOL"][0] == "BG"
+        carbon_acre = result["CARBON_ACRE"][0]
+        assert carbon_acre > 0
+        # BG is typically 15-25% of AG, so should be smaller in absolute terms.
+        assert carbon_acre < 100
+
+    def test_live_tree_total_is_sum_of_components(self, georgia_db):
+        """``pool='total'`` ≈ ``pool='ag'`` + ``pool='bg'`` at the per-acre level.
+
+        Not strictly additive to floating-point precision because of
+        ratio-of-means aggregation, but should agree to within a fraction
+        of a percent.
+        """
+        ag = live_tree(georgia_db, pool="ag", most_recent=True)
+        bg = live_tree(georgia_db, pool="bg", most_recent=True)
+        total = live_tree(georgia_db, pool="total", most_recent=True)
+
+        ag_acre = ag["CARBON_ACRE"][0]
+        bg_acre = bg["CARBON_ACRE"][0]
+        total_acre = total["CARBON_ACRE"][0]
+
+        # Relative difference should be <1%
+        assert abs(total_acre - (ag_acre + bg_acre)) / total_acre < 0.01, (
+            f"total={total_acre}, ag+bg={ag_acre + bg_acre}"
+        )
+
+    def test_live_tree_by_species_groups(self, georgia_db):
+        """``by_species=True`` produces multiple rows, one per species."""
+        result = live_tree(georgia_db, pool="ag", by_species=True, most_recent=True)
+        assert result.height > 1
+        assert "SPCD" in result.columns
diff --git a/tests/unit/test_nsvb_coefficients.py b/tests/unit/test_nsvb_coefficients.py
new file mode 100644
index 00000000..499239aa
--- /dev/null
+++ b/tests/unit/test_nsvb_coefficients.py
@@ -0,0 +1,519 @@
+"""
+Unit tests for NSVB coefficient table loaders and lookup precedence.
+
+Verifies:
+
+1. CSV bundle loads via ``importlib.resources`` (wheel-safe)
+2. All 10 SPCD/Jenkins tables are present with reasonable row counts
+3. The ``@lru_cache`` returns the same instance on repeat calls
+4. ``lookup_coefficients`` walks the precedence levels in the documented order
+5. Species-level fallback works for SPCDs that lack DIVISION-specific rows
+6. Jenkins fallback works for the level-4 case
+7. KeyError is raised for SPCDs with no coverage at any level
+"""
+
+from __future__ import annotations
+
+import polars as pl
+import pytest
+
+from pyfia.carbon.nsvb.coefficients import (
+    CoefficientTables,
+    VectorizedLookupTables,
+    build_division_lookup,
+    build_jenkins_lookup,
+    build_species_level_lookup,
+    ecosubcd_to_division,
+    get_vectorized_lookup_tables,
+    load_nsvb_coefficients,
+    lookup_coefficients,
+)
+
+
+class TestLoadNSVBCoefficients:
+    """CSV bundle loading via importlib.resources."""
+
+    def test_returns_coefficient_tables_dataclass(self):
+        coefs = load_nsvb_coefficients()
+        assert isinstance(coefs, CoefficientTables)
+
+    def test_lru_cache_returns_same_instance(self):
+        """Repeat calls hit the cache and return the same object."""
+        first = load_nsvb_coefficients()
+        second = load_nsvb_coefficients()
+        assert first is second
+
+    def test_all_ten_tables_present(self):
+        coefs = load_nsvb_coefficients()
+        assert isinstance(coefs.volib_spcd, pl.DataFrame)
+        assert isinstance(coefs.volib_jenkins, pl.DataFrame)
+        assert isinstance(coefs.volbk_spcd, pl.DataFrame)
+        assert isinstance(coefs.volbk_jenkins, pl.DataFrame)
+        assert isinstance(coefs.bark_biomass_spcd, pl.DataFrame)
+        assert isinstance(coefs.bark_biomass_jenkins, pl.DataFrame)
+        assert isinstance(coefs.branch_biomass_spcd, pl.DataFrame)
+        assert isinstance(coefs.branch_biomass_jenkins, pl.DataFrame)
+        assert isinstance(coefs.total_biomass_spcd, pl.DataFrame)
+        assert isinstance(coefs.total_biomass_jenkins, pl.DataFrame)
+
+    def test_table_row_counts(self):
+        """Verify the vendored tables have the expected number of rows.
+
+        These counts come directly from the WO-104 supplementary archive
+        and serve as a regression check that we haven't accidentally
+        replaced or corrupted a CSV during vendoring.
+        """
+        coefs = load_nsvb_coefficients()
+        assert coefs.volib_spcd.height == 406
+        assert coefs.volib_jenkins.height == 9
+        assert coefs.volbk_spcd.height == 339
+        assert coefs.volbk_jenkins.height == 9
+        assert coefs.bark_biomass_spcd.height == 206
+        assert coefs.bark_biomass_jenkins.height == 9
+        assert coefs.branch_biomass_spcd.height == 175
+        assert coefs.branch_biomass_jenkins.height == 9
+        assert coefs.total_biomass_spcd.height == 173
+        assert coefs.total_biomass_jenkins.height == 9
+
+    def test_spcd_table_schema(self):
+        """All ``*_spcd`` tables share the same column structure."""
+        coefs = load_nsvb_coefficients()
+        expected = {
+            "SPCD",
+            "DIVISION",
+            "STDORGCD",
+            "model",
+            "a",
+            "a1",
+            "b",
+            "b1",
+            "c",
+            "c1",
+        }
+        for tbl in (
+            coefs.volib_spcd,
+            coefs.volbk_spcd,
+            coefs.total_biomass_spcd,
+        ):
+            cols = set(tbl.columns)
+            assert expected.issubset(cols), f"Missing columns: {expected - cols}"
+
+    def test_spcd_table_explicit_dtypes(self):
+        """All ``*_spcd`` tables are loaded with explicit schemas.
+
+        Regression test for the fix that replaced ``infer_schema_length=10_000``
+        with ``schema_overrides``. DIVISION must be ``Utf8`` (ecoprovince codes
+        like "M240") and STDORGCD must be ``Int64`` — the upcoming vectorized
+        lookup joins rely on these dtypes being stable regardless of which
+        rows happen to be at the top of the CSV on a future re-vendor.
+
+        Note: the bark/branch biomass tables have a narrower schema
+        (``a, b, b1, c`` only, no ``a1`` / ``c1``) than volume/total biomass.
+        """
+        coefs = load_nsvb_coefficients()
+        for tbl in (
+            coefs.volib_spcd,
+            coefs.volbk_spcd,
+            coefs.bark_biomass_spcd,
+            coefs.branch_biomass_spcd,
+            coefs.total_biomass_spcd,
+        ):
+            assert tbl.schema["SPCD"] == pl.Int64
+            assert tbl.schema["DIVISION"] == pl.Utf8
+            assert tbl.schema["STDORGCD"] == pl.Int64
+            assert tbl.schema["model"] == pl.Int64
+            # Common numeric columns present in every table
+            for col in ("a", "b", "b1", "c"):
+                assert tbl.schema[col] == pl.Float64, (
+                    f"{col} in {tbl.columns}: expected Float64, got {tbl.schema[col]}"
+                )
+            # a1 / c1 only exist in volib_spcd, volbk_spcd, total_biomass_spcd
+            for col in ("a1", "c1"):
+                if col in tbl.columns:
+                    assert tbl.schema[col] == pl.Float64
+
+    def test_jenkins_table_explicit_dtypes(self):
+        """All ``*_jenkins`` tables are loaded with explicit schemas."""
+        coefs = load_nsvb_coefficients()
+        for tbl in (
+            coefs.volib_jenkins,
+            coefs.volbk_jenkins,
+            coefs.bark_biomass_jenkins,
+            coefs.branch_biomass_jenkins,
+            coefs.total_biomass_jenkins,
+        ):
+            assert tbl.schema["JENKINS_SPGRPCD"] == pl.Int64
+            assert tbl.schema["model"] == pl.Int64
+            assert tbl.schema["a"] == pl.Float64
+            assert tbl.schema["b"] == pl.Float64
+            assert tbl.schema["c"] == pl.Float64
+
+
+class TestLookupCoefficients:
+    """NSVB coefficient lookup precedence (4 levels)."""
+
+    def test_spcd_only_fallback_douglas_fir(self):
+        """SPCD=202 species-level row exists in S1a (volib).
+
+        With ``division=None``, lookup should hit Level 3 and return the
+        species-level row.
+        """
+        coefs = load_nsvb_coefficients()
+        result = lookup_coefficients(
+            coefs.volib_spcd,
+            coefs.volib_jenkins,
+            spcd=202,
+            jenkins_spgrpcd=10,  # arbitrary, won't be used
+        )
+        assert result["source"] == "spcd"
+        assert result["model"] == 2  # Douglas-fir uses Model 2 for stem wood vol
+        # Coefficients should be non-zero — exact values depend on the species-level row
+        assert result["a"] > 0
+        assert result["b"] > 0
+
+    def test_spcd_only_fallback_red_maple(self):
+        """SPCD=316 species-level row exists in S1a (volib).
+
+        Red maple uses Model 1 for stem wood volume at the species level.
+        """
+        coefs = load_nsvb_coefficients()
+        result = lookup_coefficients(
+            coefs.volib_spcd,
+            coefs.volib_jenkins,
+            spcd=316,
+            jenkins_spgrpcd=8,  # arbitrary
+        )
+        assert result["source"] == "spcd"
+        assert result["model"] == 1
+
+    def test_division_lookup_when_division_provided(self):
+        """When DIVISION is provided AND a row exists, prefer Level 2."""
+        coefs = load_nsvb_coefficients()
+        result = lookup_coefficients(
+            coefs.volib_spcd,
+            coefs.volib_jenkins,
+            spcd=202,
+            division="240",  # Marine division — Douglas-fir has a row here
+            jenkins_spgrpcd=10,
+        )
+        assert result["source"] == "spcd_division"
+        assert result["model"] == 2
+
+    def test_division_lookup_falls_back_to_species_when_missing(self):
+        """When DIVISION is provided BUT no row exists, fall through to Level 3.
+
+        Per the red maple worked example: SPCD=316 has no DIVISION=M210 row
+        in S1a, so lookup falls back to the species-level row.
+        """
+        coefs = load_nsvb_coefficients()
+        result = lookup_coefficients(
+            coefs.volib_spcd,
+            coefs.volib_jenkins,
+            spcd=316,
+            division="M210",  # No row exists for 316/M210 — should fall back
+            jenkins_spgrpcd=8,
+        )
+        assert result["source"] == "spcd"
+        assert result["model"] == 1  # red maple species-level uses Model 1
+
+    def test_total_biomass_red_maple_uses_model_4(self):
+        """Critical regression: SPCD=316 in S8a uses Model 4, not Model 1.
+
+        This pins the Model 4 wiring — if someone accidentally changes the
+        equation dispatch, this test will catch it.
+        """
+        coefs = load_nsvb_coefficients()
+        result = lookup_coefficients(
+            coefs.total_biomass_spcd,
+            coefs.total_biomass_jenkins,
+            spcd=316,
+            jenkins_spgrpcd=8,
+        )
+        assert result["source"] == "spcd"
+        assert result["model"] == 4
+        # Model 4 uses b1; the species-level row should have a non-zero b1
+        assert result["b1"] != 0.0
+
+    def test_keyerror_for_unsupported_spcd(self):
+        """SPCD with no coverage at any level should raise KeyError."""
+        coefs = load_nsvb_coefficients()
+        with pytest.raises(KeyError, match="No NSVB coefficients"):
+            lookup_coefficients(
+                coefs.volib_spcd,
+                coefs.volib_jenkins,
+                spcd=99999,  # impossible SPCD
+                jenkins_spgrpcd=None,
+            )
+
+    def test_jenkins_fallback_when_jenkins_table_keyed(self):
+        """Level 4 Jenkins fallback path.
+
+        Note: the Jenkins tables in the vendored CSVs use ``JENKINS_SPGRPCD``
+        as the key column. We verify the fallback returns a Jenkins-tagged
+        result when a non-existent SPCD is paired with a valid Jenkins group.
+        """
+        coefs = load_nsvb_coefficients()
+        # Pick a Jenkins group that exists in the table
+        jenkins_groups = coefs.volib_jenkins["JENKINS_SPGRPCD"].to_list()
+        valid_jenkins = jenkins_groups[0]
+        result = lookup_coefficients(
+            coefs.volib_spcd,
+            coefs.volib_jenkins,
+            spcd=99999,  # not in S1a
+            jenkins_spgrpcd=valid_jenkins,
+        )
+        assert result["source"] == "jenkins"
+
+
+class TestBuildSpeciesLevelLookup:
+    """Vectorized path helper: species-level row filter for *_spcd tables."""
+
+    def test_selected_columns(self):
+        """Returns only the columns the vectorized biomass expression needs."""
+        coefs = load_nsvb_coefficients()
+        result = build_species_level_lookup(coefs.volib_spcd)
+        assert result.columns == ["SPCD", "model", "a", "b", "b1", "c"]
+
+    def test_keeps_only_species_level_rows(self):
+        """Rows where DIVISION or STDORGCD is non-null are filtered out.
+
+        Phase 1 has no ECOSUBCD → DIVISION mapping, so DIVISION-specific
+        rows (Levels 1-2 of the NSVB precedence) are dead code and must be
+        dropped before the vectorized join runs.
+        """
+        coefs = load_nsvb_coefficients()
+        result = build_species_level_lookup(coefs.volib_spcd)
+        # Original table has 406 rows; species-level is a strict subset
+        assert result.height < coefs.volib_spcd.height
+        assert result.height > 0
+
+    def test_douglas_fir_row_present(self):
+        """SPCD=202 Douglas-fir has a species-level row with Model 2 for volib."""
+        coefs = load_nsvb_coefficients()
+        result = build_species_level_lookup(coefs.volib_spcd)
+        dougfir = result.filter(pl.col("SPCD") == 202)
+        assert dougfir.height == 1
+        assert dougfir["model"][0] == 2
+
+    def test_spcd_unique_in_output(self):
+        """Each SPCD appears at most once after filtering — single row per species."""
+        coefs = load_nsvb_coefficients()
+        for tbl in (
+            coefs.volib_spcd,
+            coefs.volbk_spcd,
+            coefs.bark_biomass_spcd,
+            coefs.branch_biomass_spcd,
+            coefs.total_biomass_spcd,
+        ):
+            result = build_species_level_lookup(tbl)
+            assert result["SPCD"].is_unique().all()
+
+    def test_works_on_narrow_biomass_tables(self):
+        """bark_biomass_spcd / branch_biomass_spcd have no a1/c1 but still
+        expose (a, b, b1, c) — the builder must handle both schemas."""
+        coefs = load_nsvb_coefficients()
+        for tbl in (coefs.bark_biomass_spcd, coefs.branch_biomass_spcd):
+            result = build_species_level_lookup(tbl)
+            assert result.columns == ["SPCD", "model", "a", "b", "b1", "c"]
+            assert result.height > 0
+
+
+class TestBuildJenkinsLookup:
+    """Vectorized path helper: Jenkins fallback table for *_jenkins tables."""
+
+    def test_selected_columns(self):
+        """Returns uniform columns including synthesized b1=0."""
+        coefs = load_nsvb_coefficients()
+        result = build_jenkins_lookup(coefs.volib_jenkins)
+        assert result.columns == [
+            "JENKINS_SPGRPCD",
+            "model",
+            "a",
+            "b",
+            "b1",
+            "c",
+        ]
+
+    def test_b1_is_always_zero(self):
+        """Jenkins tables have no b1 column; the synthesized value is 0.0.
+
+        Model 5 (the only form Jenkins rows dispatch to) does not use b1,
+        so any value is correct; 0.0 keeps the coalesce in the orchestrator
+        from emitting a null when the downstream expression reads b1.
+        """
+        coefs = load_nsvb_coefficients()
+        result = build_jenkins_lookup(coefs.volib_jenkins)
+        assert (result["b1"] == 0.0).all()
+        assert result.schema["b1"] == pl.Float64
+
+    def test_all_jenkins_rows_preserved(self):
+        """No filtering — Jenkins tables are used as-is (all 9 groups)."""
+        coefs = load_nsvb_coefficients()
+        result = build_jenkins_lookup(coefs.volib_jenkins)
+        assert result.height == coefs.volib_jenkins.height
+        assert result.height == 9
+
+
+class TestGetVectorizedLookupTables:
+    """Cached bundle of all 5 component lookup pairs."""
+
+    def test_returns_vectorized_bundle(self):
+        bundle = get_vectorized_lookup_tables()
+        assert isinstance(bundle, VectorizedLookupTables)
+
+    def test_lru_cache_returns_same_instance(self):
+        first = get_vectorized_lookup_tables()
+        second = get_vectorized_lookup_tables()
+        assert first is second
+
+    def test_all_ten_tables_present_and_ready_to_join(self):
+        """Each table has the vectorized-path column layout."""
+        bundle = get_vectorized_lookup_tables()
+
+        spcd_expected = ["SPCD", "model", "a", "b", "b1", "c"]
+        jen_expected = ["JENKINS_SPGRPCD", "model", "a", "b", "b1", "c"]
+
+        for name in (
+            "volib_spcd",
+            "volbk_spcd",
+            "bark_bio_spcd",
+            "branch_bio_spcd",
+            "total_agb_spcd",
+        ):
+            tbl = getattr(bundle, name)
+            assert tbl.columns == spcd_expected, f"{name} has wrong columns"
+            assert tbl.height > 0
+
+        for name in (
+            "volib_jen",
+            "volbk_jen",
+            "bark_bio_jen",
+            "branch_bio_jen",
+            "total_agb_jen",
+        ):
+            tbl = getattr(bundle, name)
+            assert tbl.columns == jen_expected, f"{name} has wrong columns"
+            assert tbl.height == 9  # 9 Jenkins species groups
+
+    def test_douglas_fir_volib_model_2(self):
+        """Regression sentinel: SPCD=202 volib row is Model 2 (stem wood k=9)."""
+        bundle = get_vectorized_lookup_tables()
+        row = bundle.volib_spcd.filter(pl.col("SPCD") == 202).to_dicts()[0]
+        assert row["model"] == 2
+
+    def test_red_maple_total_agb_model_4(self):
+        """Regression sentinel: SPCD=316 total_agb row is Model 4."""
+        bundle = get_vectorized_lookup_tables()
+        row = bundle.total_agb_spcd.filter(pl.col("SPCD") == 316).to_dicts()[0]
+        assert row["model"] == 4
+
+    def test_division_lookups_present(self):
+        """Each component now has a DIVISION-keyed lookup (Level 2)."""
+        bundle = get_vectorized_lookup_tables()
+        div_expected = ["SPCD", "DIVISION", "model", "a", "b", "b1", "c"]
+        for name in (
+            "volib_div",
+            "volbk_div",
+            "bark_bio_div",
+            "branch_bio_div",
+            "total_agb_div",
+        ):
+            tbl = getattr(bundle, name)
+            assert tbl.columns == div_expected, f"{name} has wrong columns"
+            # Every division row has a non-null DIVISION code
+            assert tbl["DIVISION"].null_count() == 0
+            # Every division row has a non-null SPCD
+            assert tbl["SPCD"].null_count() == 0
+
+    def test_division_230_has_georgia_species(self):
+        """DIVISION 230 (Subtropical, covering Georgia) has species-level rows
+        in the S8a (total_agb) table, including loblolly pine (SPCD=131)."""
+        bundle = get_vectorized_lookup_tables()
+        div_230 = bundle.total_agb_div.filter(pl.col("DIVISION") == "230")
+        spcds = set(div_230["SPCD"].to_list())
+        # Loblolly pine (131), slash pine (111), and longleaf pine (121) are
+        # the dominant southern pines in Georgia — if the Phase 1.5 fix is
+        # to land, these need a DIVISION=230 row.
+        assert 131 in spcds or 121 in spcds or 111 in spcds, (
+            f"no southern pines in DIVISION 230 S8a rows: {sorted(spcds)}"
+        )
+
+
+class TestBuildDivisionLookup:
+    """Vectorized path helper: DIVISION-keyed row filter for *_spcd tables."""
+
+    def test_selected_columns(self):
+        """Returns a (SPCD, DIVISION, model, a, b, b1, c) lookup."""
+        coefs = load_nsvb_coefficients()
+        result = build_division_lookup(coefs.volib_spcd)
+        assert result.columns == ["SPCD", "DIVISION", "model", "a", "b", "b1", "c"]
+
+    def test_only_division_rows(self):
+        """Rows where DIVISION is null are filtered out."""
+        coefs = load_nsvb_coefficients()
+        result = build_division_lookup(coefs.volib_spcd)
+        assert result["DIVISION"].null_count() == 0
+        # Volib_spcd has 256 DIVISION rows in the raw table; the defensive
+        # Model 2/4 null-b1 filter may drop a few, but we should still have
+        # most of them.
+        assert result.height > 200
+
+    def test_stdorgcd_null_only(self):
+        """Phase 1.5 skips Level 1 (STDORGCD-specific rows)."""
+        coefs = load_nsvb_coefficients()
+        raw_div = coefs.volib_spcd.filter(pl.col("DIVISION").is_not_null())
+        lookup_div = build_division_lookup(coefs.volib_spcd)
+        # lookup should have <= raw (Level 1 rows dropped by STDORGCD.is_null())
+        assert lookup_div.height <= raw_div.height
+
+    def test_composite_key_uniqueness(self):
+        """Each (SPCD, DIVISION) appears at most once after filtering."""
+        coefs = load_nsvb_coefficients()
+        result = build_division_lookup(coefs.volib_spcd)
+        pairs = result.select(["SPCD", "DIVISION"])
+        assert pairs.n_unique() == pairs.height
+
+
+class TestEcosubcdToDivision:
+    """Bailey ECOSUBCD → DIVISION crosswalk."""
+
+    def test_southeastern_mixed_forest(self):
+        """SE Mixed Forest provinces 231, 232 → Division 230 (Subtropical)."""
+        assert ecosubcd_to_division("231Ae") == "230"
+        assert ecosubcd_to_division("231Aa") == "230"
+        assert ecosubcd_to_division("232Bh") == "230"
+        assert ecosubcd_to_division("232Ba") == "230"
+
+    def test_mountain_prefix(self):
+        """Mountain prefix M is preserved: M231Aa → M230."""
+        assert ecosubcd_to_division("M231Aa") == "M230"
+        assert ecosubcd_to_division("M261Ea") == "M260"
+        assert ecosubcd_to_division("M220Ad") == "M220"
+
+    def test_non_subtropical_divisions(self):
+        """Sanity-check other domains."""
+        assert ecosubcd_to_division("211Aa") == "210"  # Warm Continental
+        assert ecosubcd_to_division("221Aa") == "220"  # Hot Continental
+        assert ecosubcd_to_division("242Bb") == "240"  # Marine
+
+    def test_null_and_empty_inputs(self):
+        """Null, empty, or whitespace input → None."""
+        assert ecosubcd_to_division(None) is None
+        assert ecosubcd_to_division("") is None
+        assert ecosubcd_to_division("   ") is None
+
+    def test_malformed_input(self):
+        """Non-digit or too-short prefix → None."""
+        assert ecosubcd_to_division("XYZ") is None
+        assert ecosubcd_to_division("M") is None
+        assert ecosubcd_to_division("12") is None
+        assert ecosubcd_to_division("A23") is None
+
+    def test_case_insensitive(self):
+        """Lowercase input is normalized to uppercase (M prefix preserved)."""
+        assert ecosubcd_to_division("m231aa") == "M230"
+        assert ecosubcd_to_division("231ae") == "230"
+
+    def test_whitespace_stripped(self):
+        """Leading/trailing whitespace is stripped."""
+        assert ecosubcd_to_division("  231Ae  ") == "230"
diff --git a/tests/unit/test_nsvb_equations.py b/tests/unit/test_nsvb_equations.py
new file mode 100644
index 00000000..7b2853df
--- /dev/null
+++ b/tests/unit/test_nsvb_equations.py
@@ -0,0 +1,801 @@
+"""
+Unit tests for the NSVB equation library.
+
+These tests verify the pure-math equation forms (Models 1, 2, 4, 5) and the
+``predict_tree_biomass`` orchestrator against the worked numerical examples in
+the GTR-WO-104 source PDF (transcribed in
+``references_md/tier1_fcaf/gtr_wo104_westfall2023.md`` lines 394-960).
+
+Two worked examples are reproduced:
+
+1. **Douglas-fir** (SPCD=202, D=20", H=110', no cull, DIVISION=240) — exercises
+   Model 2 (stem wood vol, k=9 softwood) and Model 1 (everything else).
+   Expected total AGB: 3154.5539926725 lb. Carbon: 1626.4748946459 lb.
+
+2. **Red maple** (SPCD=316, D=11.1", H=38', CULL=3, DIVISION=M210→species-level
+   fallback) — exercises Model 1 (volib), Model 2 (volbk, k=11 hardwood),
+   Model 4 (total AGB with exp(-b1*D) factor), and the cull-reduction path.
+   Expected total AGB after cull-adjusted harmonization: 528.135964525863 lb.
+
+Coefficients are hand-coded from the worked-example prose to high precision
+(12+ significant figures). The CSV-loaded coefficients in
+``src/pyfia/carbon/nsvb/data/`` are stored at ~9 significant figures and would
+introduce rounding error at the 7th-8th decimal — the regression tests need
+the prose precision to verify the equation math itself, separate from CSV
+fidelity.
+"""
+
+from __future__ import annotations
+
+import math
+
+import pytest
+
+from pyfia.carbon.nsvb.coefficients import (
+    load_nsvb_coefficients,
+    lookup_coefficients,
+)
+from pyfia.carbon.nsvb.equations import (
+    Coefficients,
+    _model_k,
+    harmonize_components,
+    model_1,
+    model_2,
+    model_4,
+    model_5_jenkins,
+    predict_tree_biomass,
+)
+
+# ---------------------------------------------------------------------------
+# Hand-coded high-precision coefficients from the WO-104 worked examples
+# ---------------------------------------------------------------------------
+
+
+# Douglas-fir SPCD=202, DIVISION=240, no cull (worked example lines 394-680)
+DOUGFIR_INPUTS = {
+    "spcd": 202,
+    "dia": 20.0,
+    "ht": 110.0,
+    "wdsg": 0.45,
+    "hw_sw": "softwood",
+    "cull": 0.0,
+}
+
+DOUGFIR_COEFS = Coefficients(
+    volib={
+        "model": 2,
+        "a": 0.001929099661,
+        "a1": 0.0,
+        "b": 2.162413104203,
+        "b1": 1.690400253097,
+        "c": 0.985444005253,
+        "c1": 0.0,
+        "source": "test",
+    },
+    volbk={
+        "model": 1,
+        "a": 0.000031886237,
+        "a1": 0.0,
+        "b": 1.21260513951,
+        "b1": 0.0,
+        "c": 1.978577263767,
+        "c1": 0.0,
+        "source": "test",
+    },
+    bark_bio={
+        "model": 1,
+        "a": 0.009106538193,
+        "a1": 0.0,
+        "b": 1.437894424586,
+        "b1": 0.0,
+        "c": 1.336514272981,
+        "c1": 0.0,
+        "source": "test",
+    },
+    branch_bio={
+        "model": 1,
+        "a": 9.521330809106,
+        "a1": 0.0,
+        "b": 1.762316117442,
+        "b1": 0.0,
+        "c": -0.40574259177,
+        "c1": 0.0,
+        "source": "test",
+    },
+    total_agb={
+        "model": 1,
+        "a": 0.135206506787,
+        "a1": 0.0,
+        "b": 1.713527048035,
+        "b1": 0.0,
+        "c": 1.047613377046,
+        "c1": 0.0,
+        "source": "test",
+    },
+)
+
+DOUGFIR_EXPECTED = {
+    "v_wood_ib": 88.452275544288,
+    "v_bark": 13.191436232306,
+    "w_wood_gross": 2483.739897283610,
+    "w_bark_gross": 361.782496100100,
+    "w_branch_gross": 277.487756904646,
+    "agb_predicted": 3154.5539926725,
+    "agb_component": 3123.010150288360,
+    "wood_harmonized": 2508.826815376370,
+    "bark_harmonized": 365.436666110811,
+    "branch_harmonized": 280.290511185328,
+    "carbon_total": 1626.474894645920,
+    "carbon_fraction": 0.515595833333,
+}
+
+# Red maple SPCD=316, species-level fallback, CULL=3 (worked example lines 682-960)
+REDMAPLE_INPUTS = {
+    "spcd": 316,
+    "dia": 11.1,
+    "ht": 38.0,
+    "wdsg": 0.49,
+    "hw_sw": "hardwood",
+    "cull": 3.0,
+}
+
+REDMAPLE_COEFS = Coefficients(
+    volib={
+        "model": 1,
+        "a": 0.001983918881,
+        "a1": 0.0,
+        "b": 1.810559393287,
+        "b1": 0.0,
+        "c": 1.129417635145,
+        "c1": 0.0,
+        "source": "test",
+    },
+    volbk={
+        "model": 2,
+        "a": 0.003743084443,
+        "a1": 0.0,
+        "b": 2.226890355309,
+        "b1": 1.685993125661,
+        "c": 0.275066356213,
+        "c1": 0.0,
+        "source": "test",
+    },
+    bark_bio={
+        "model": 1,
+        "a": 0.061595466174,
+        "a1": 0.0,
+        "b": 1.818642599217,
+        "b1": 0.0,
+        "c": 0.654020672095,
+        "c1": 0.0,
+        "source": "test",
+    },
+    branch_bio={
+        "model": 1,
+        "a": 0.011144618401,
+        "a1": 0.0,
+        "b": 3.269520661293,
+        "b1": 0.0,
+        "c": 0.421304343724,
+        "c1": 0.0,
+        "source": "test",
+    },
+    total_agb={
+        "model": 4,
+        "a": 0.31573027567,
+        "a1": 0.0,
+        "b": 1.853839844372,
+        "b1": -0.024745684975,
+        "c": 0.740557378679,
+        "c1": 0.0,
+        "source": "test",
+    },
+)
+
+REDMAPLE_EXPECTED = {
+    "v_wood_ib": 9.427112777611,
+    "v_bark": 2.155106401987,
+    "w_wood_gross": 288.243400288234,
+    "w_wood_red": 284.265641364256,
+    "w_bark": 52.945466015848,
+    "w_branch": 135.001927997271,
+    "agb_predicted": 532.584798820042,
+    "agb_component_red": 472.213035377375,
+    "agb_reduce": 0.991646711840,
+    "agb_predicted_red": 528.135964525863,
+    "wood_harmonized": 317.930462388645,
+    "bark_harmonized": 59.215656211618,
+    "branch_harmonized": 150.989845925600,
+}
+
+# Tolerance: 1e-9 relative error matches the worked-example precision
+TOL = 1e-9
+
+
+def _close(actual: float, expected: float, tol: float = TOL) -> bool:
+    if expected == 0:
+        return abs(actual) < tol
+    return abs(actual - expected) / abs(expected) < tol
+
+
+# ---------------------------------------------------------------------------
+# Model 1 — power form
+# ---------------------------------------------------------------------------
+
+
+class TestModel1:
+    """Pure power form: ``y = a * D^b * H^c``."""
+
+    def test_douglas_fir_stem_bark_volume(self):
+        """S2a Model 1, line 424 of worked example.
+
+        Vtot_bk_Gross = 0.000031886237 * 20^1.21260513951 * 110^1.978577263767
+                      = 13.191436232306
+        """
+        result = model_1(20.0, 110.0, 0.000031886237, 1.21260513951, 1.978577263767)
+        assert _close(result, 13.191436232306)
+
+    def test_douglas_fir_stem_bark_biomass(self):
+        """S6a Model 1, line 560 of worked example.
+
+        Wtot_bk = 0.009106538193 * 20^1.437894424586 * 110^1.336514272981
+                = 361.782496100100
+        """
+        result = model_1(20.0, 110.0, 0.009106538193, 1.437894424586, 1.336514272981)
+        assert _close(result, 361.782496100100)
+
+    def test_douglas_fir_branch_biomass(self):
+        """S7a Model 1, line 570 of worked example.
+
+        Wbranch = 9.521330809106 * 20^1.762316117442 * 110^-0.40574259177
+                = 277.487756904646
+
+        Note negative exponent on H — branch biomass declines with height
+        for trees of fixed diameter (taller trees have less branch mass).
+        """
+        result = model_1(20.0, 110.0, 9.521330809106, 1.762316117442, -0.40574259177)
+        assert _close(result, 277.487756904646)
+
+    def test_douglas_fir_total_agb(self):
+        """S8a Model 1, line 578 of worked example.
+
+        AGB_Predicted = 0.135206506787 * 20^1.713527048035 * 110^1.047613377046
+                      = 3154.5539926725
+        """
+        result = model_1(20.0, 110.0, 0.135206506787, 1.713527048035, 1.047613377046)
+        assert _close(result, 3154.5539926725)
+
+
+# ---------------------------------------------------------------------------
+# Model 2 — power form with k constant
+# ---------------------------------------------------------------------------
+
+
+class TestModel2:
+    """Power form with species-class base constant k.
+
+    Verifies the k=9 (softwood) and k=11 (hardwood) discovery from the
+    worked examples.
+    """
+
+    def test_softwood_k_constant(self):
+        """SPCD < 300 → k = 9.0."""
+        assert _model_k(202) == 9.0
+        assert _model_k(1) == 9.0
+        assert _model_k(299) == 9.0
+
+    def test_hardwood_k_constant(self):
+        """SPCD >= 300 → k = 11.0."""
+        assert _model_k(316) == 11.0
+        assert _model_k(300) == 11.0
+        assert _model_k(998) == 11.0
+
+    def test_douglas_fir_stem_wood_volume(self):
+        """S1a Model 2, line 418 of worked example. SPCD=202 (softwood, k=9).
+
+        Vtot_ib_Gross = 0.001929099661 * 9^(2.162413104203 - 1.690400253097)
+                        * 20^1.690400253097 * 110^0.985444005253
+                      = 88.452275544288
+        """
+        result = model_2(
+            d=20.0,
+            h=110.0,
+            a=0.001929099661,
+            b=2.162413104203,
+            b1=1.690400253097,
+            c=0.985444005253,
+            k=9.0,
+        )
+        assert _close(result, 88.452275544288)
+
+    def test_red_maple_stem_bark_volume(self):
+        """S2a Model 2, line 698 of worked example. SPCD=316 (hardwood, k=11).
+
+        This is the test that pinned down k=11 for hardwoods. With k=9 the
+        result would be 1.94, not 2.16.
+        """
+        result = model_2(
+            d=11.1,
+            h=38.0,
+            a=0.003743084443,
+            b=2.226890355309,
+            b1=1.685993125661,
+            c=0.275066356213,
+            k=11.0,
+        )
+        assert _close(result, 2.155106401987)
+
+
+# ---------------------------------------------------------------------------
+# Model 4 — exp-modulated power form
+# ---------------------------------------------------------------------------
+
+
+class TestModel4:
+    """Form: ``y = a * D^b * H^c * exp(-b1 * D)``.
+
+    Discovered from the red maple S8a worked example (line 860). The
+    sidecar's documentation of Model 4 was incomplete.
+    """
+
+    def test_red_maple_total_agb(self):
+        """S8a Model 4, line 862 of worked example.
+
+        AGB_Predicted = 0.31573027567 * 11.1^1.853839844372 * 38^0.740557378679
+                        * exp(-(-0.024745684975 * 11.1))
+                      = 532.584798820042
+
+        The negative b1 means the exp factor is > 1.
+        """
+        result = model_4(
+            d=11.1,
+            h=38.0,
+            a=0.31573027567,
+            b=1.853839844372,
+            b1=-0.024745684975,
+            c=0.740557378679,
+        )
+        assert _close(result, 532.584798820042)
+
+    def test_model_4_reduces_to_model_1_when_b1_zero(self):
+        """When b1=0, exp(0)=1, so Model 4 should equal Model 1."""
+        m1 = model_1(15.0, 80.0, 0.1, 1.5, 1.2)
+        m4 = model_4(15.0, 80.0, 0.1, 1.5, 0.0, 1.2)
+        assert _close(m4, m1)
+
+
+# ---------------------------------------------------------------------------
+# Model 5 — Jenkins fallback
+# ---------------------------------------------------------------------------
+
+
+class TestModel5Jenkins:
+    """Form: ``y = a * D^b * H^c * WDSG``."""
+
+    def test_jenkins_form_multiplies_by_wdsg(self):
+        """The only difference from Model 1 is the WDSG multiplication."""
+        m1 = model_1(15.0, 80.0, 0.1, 1.5, 1.2)
+        m5 = model_5_jenkins(15.0, 80.0, 0.1, 1.5, 1.2, wdsg=0.55)
+        assert _close(m5, m1 * 0.55)
+
+
+# ---------------------------------------------------------------------------
+# Harmonization
+# ---------------------------------------------------------------------------
+
+
+class TestHarmonization:
+    """Component proportional redistribution to sum exactly to predicted AGB."""
+
+    def test_douglas_fir_harmonization(self):
+        """No-cull harmonization, line 602-612 of worked example.
+
+        AGB_predicted = 3154.5539926725
+        Components (gross, no cull): 2483.739897283610, 361.782496100100, 277.487756904646
+        Component sum:               3123.010150288360
+        Expected harmonized: 2508.826815376370, 365.436666110811, 280.290511185328
+        """
+        wood_h, bark_h, branch_h = harmonize_components(
+            agb_predicted=3154.5539926725,
+            w_wood=2483.739897283610,
+            w_bark=361.782496100100,
+            w_branch=277.487756904646,
+        )
+        assert _close(wood_h, 2508.826815376370)
+        assert _close(bark_h, 365.436666110811)
+        assert _close(branch_h, 280.290511185328)
+
+    def test_harmonization_invariant(self):
+        """wood_h + bark_h + branch_h must equal agb_predicted exactly."""
+        wood_h, bark_h, branch_h = harmonize_components(
+            agb_predicted=1000.0,
+            w_wood=400.0,
+            w_bark=200.0,
+            w_branch=300.0,
+        )
+        assert math.isclose(wood_h + bark_h + branch_h, 1000.0, rel_tol=1e-15)
+
+    def test_harmonization_handles_zero_components(self):
+        """Degenerate case: all components zero. Should return predicted in wood slot."""
+        wood_h, bark_h, branch_h = harmonize_components(
+            agb_predicted=500.0,
+            w_wood=0.0,
+            w_bark=0.0,
+            w_branch=0.0,
+        )
+        assert wood_h == 500.0
+        assert bark_h == 0.0
+        assert branch_h == 0.0
+
+    def test_harmonization_preserves_relative_ratios(self):
+        """If components are 4:2:3 in raw form, harmonized should also be 4:2:3."""
+        wood_h, bark_h, branch_h = harmonize_components(
+            agb_predicted=900.0,
+            w_wood=400.0,
+            w_bark=200.0,
+            w_branch=300.0,
+        )
+        assert math.isclose(wood_h / bark_h, 400.0 / 200.0, rel_tol=1e-12)
+        assert math.isclose(bark_h / branch_h, 200.0 / 300.0, rel_tol=1e-12)
+
+
+# ---------------------------------------------------------------------------
+# Full pipeline orchestrator
+# ---------------------------------------------------------------------------
+
+
+class TestPredictTreeBiomass:
+    """End-to-end NSVB pipeline regression vs the worked examples."""
+
+    def test_douglas_fir_full_pipeline(self):
+        """No-cull case (CULL=0). AGBReduce=1.0, simple harmonization."""
+        result = predict_tree_biomass(
+            coefficients=DOUGFIR_COEFS,
+            **DOUGFIR_INPUTS,
+        )
+        assert _close(result.v_wood_ib, DOUGFIR_EXPECTED["v_wood_ib"])
+        assert _close(result.v_bark, DOUGFIR_EXPECTED["v_bark"])
+        assert _close(result.w_wood, DOUGFIR_EXPECTED["wood_harmonized"])
+        assert _close(result.w_bark, DOUGFIR_EXPECTED["bark_harmonized"])
+        assert _close(result.w_branch, DOUGFIR_EXPECTED["branch_harmonized"])
+        assert _close(result.agb, DOUGFIR_EXPECTED["agb_predicted"])
+
+    def test_red_maple_full_pipeline_with_cull(self):
+        """With-cull case (CULL=3). AGBReduce<1, cull-adjusted harmonization."""
+        result = predict_tree_biomass(
+            coefficients=REDMAPLE_COEFS,
+            **REDMAPLE_INPUTS,
+        )
+        assert _close(result.v_wood_ib, REDMAPLE_EXPECTED["v_wood_ib"])
+        assert _close(result.v_bark, REDMAPLE_EXPECTED["v_bark"])
+        assert _close(result.w_wood, REDMAPLE_EXPECTED["wood_harmonized"])
+        assert _close(result.w_bark, REDMAPLE_EXPECTED["bark_harmonized"])
+        assert _close(result.w_branch, REDMAPLE_EXPECTED["branch_harmonized"])
+        assert _close(result.agb, REDMAPLE_EXPECTED["agb_predicted_red"])
+
+    def test_pipeline_harmonization_invariant(self):
+        """Component sum equals total AGB to floating-point precision."""
+        result = predict_tree_biomass(
+            coefficients=DOUGFIR_COEFS,
+            **DOUGFIR_INPUTS,
+        )
+        assert math.isclose(
+            result.w_wood + result.w_bark + result.w_branch,
+            result.agb,
+            rel_tol=1e-12,
+        )
+
+    def test_pipeline_carbon_conversion(self):
+        """Multiplying AGB by the species carbon fraction reproduces the
+        worked-example carbon value (line 680)."""
+        result = predict_tree_biomass(
+            coefficients=DOUGFIR_COEFS,
+            **DOUGFIR_INPUTS,
+        )
+        carbon = result.agb * DOUGFIR_EXPECTED["carbon_fraction"]
+        assert _close(carbon, DOUGFIR_EXPECTED["carbon_total"])
+
+    def test_unsupported_model_raises(self):
+        """Model 3 and Model 6 are not implemented in Phase 1."""
+        bad_coefs = Coefficients(
+            volib={
+                "model": 3,  # Model 3 not implemented
+                "a": 0.001,
+                "a1": 0.0,
+                "b": 1.5,
+                "b1": 0.0,
+                "c": 1.0,
+                "c1": 0.0,
+                "source": "test",
+            },
+            volbk=DOUGFIR_COEFS.volbk,
+            bark_bio=DOUGFIR_COEFS.bark_bio,
+            branch_bio=DOUGFIR_COEFS.branch_bio,
+            total_agb=DOUGFIR_COEFS.total_agb,
+        )
+        with pytest.raises(ValueError, match="model 3 not supported"):
+            predict_tree_biomass(
+                coefficients=bad_coefs,
+                **DOUGFIR_INPUTS,
+            )
+
+    def test_small_tree_uses_same_pipeline(self):
+        """Per WO-104 line 1624, saplings (DBH < 5) use the same pipeline.
+
+        We don't have a worked example to compare against, but we can verify
+        that the pipeline runs without raising and returns a positive AGB
+        for a 2.5" sapling.
+        """
+        result = predict_tree_biomass(
+            spcd=202,
+            dia=2.5,  # sapling, well below 5"
+            ht=20.0,
+            coefficients=DOUGFIR_COEFS,
+            wdsg=0.45,
+            hw_sw="softwood",
+            cull=0.0,
+        )
+        assert result.agb > 0
+        # Sapling AGB should be much smaller than the 20" mature tree AGB
+        assert result.agb < 100  # 20" tree was 3154 lb; 2.5" sapling should be << 100
+
+    def test_hw_sw_casing_normalized(self):
+        """hw_sw is accepted case-insensitively.
+
+        Addresses PR 1 review item: previously ``"Hardwood"`` raised KeyError
+        inside ``_CULL_DENS_PROP[hw_sw]``. Now normalized to lowercase at the
+        function boundary so callers don't have to think about casing.
+        """
+        # "Softwood" (title case) should produce the same result as "softwood"
+        base = predict_tree_biomass(coefficients=DOUGFIR_COEFS, **DOUGFIR_INPUTS)
+        title_inputs = dict(DOUGFIR_INPUTS)
+        title_inputs["hw_sw"] = "Softwood"
+        titled = predict_tree_biomass(coefficients=DOUGFIR_COEFS, **title_inputs)
+        assert titled.agb == base.agb
+
+        upper_inputs = dict(DOUGFIR_INPUTS)
+        upper_inputs["hw_sw"] = "SOFTWOOD"
+        upper = predict_tree_biomass(coefficients=DOUGFIR_COEFS, **upper_inputs)
+        assert upper.agb == base.agb
+
+    def test_invalid_hw_sw_raises_clear_error(self):
+        """An invalid hw_sw string raises ValueError with a clear message."""
+        bad_inputs = dict(DOUGFIR_INPUTS)
+        bad_inputs["hw_sw"] = "conifer"
+        with pytest.raises(ValueError, match="hw_sw must be"):
+            predict_tree_biomass(coefficients=DOUGFIR_COEFS, **bad_inputs)
+
+    def test_dia_below_one_raises_clear_error(self):
+        """dia < 1.0 raises ValueError with a clear message.
+
+        Addresses PR 1 review item: previously dia<1 could produce complex
+        numbers or cryptic TypeErrors inside the Model 1 power form.
+        """
+        small_inputs = dict(DOUGFIR_INPUTS)
+        small_inputs["dia"] = 0.5
+        with pytest.raises(ValueError, match="dia must be >= 1.0"):
+            predict_tree_biomass(coefficients=DOUGFIR_COEFS, **small_inputs)
+
+
+# ---------------------------------------------------------------------------
+# CSV → pipeline regression sentinels (the production data path)
+# ---------------------------------------------------------------------------
+
+# Species-level (DIVISION=null) expected pipeline outputs for the worked-example
+# trees, computed once from the vendored CSVs. These constants are sentinels
+# for the 5 component CSVs — any change to volib_spcd, volbk_spcd,
+# bark_biomass_spcd, branch_biomass_spcd, or total_biomass_spcd that affects
+# SPCD=202 or SPCD=316 will perturb one of these values and break the test.
+#
+# Locked at f64-tight precision (math.isclose rel_tol=1e-9). The CSV stores
+# coefficients at ~9 significant figures, but the pipeline output is purely
+# deterministic given the CSV — same CSV in, same float out. If a future
+# re-vendor changes the CSV intentionally, the failure shows the exact diff
+# and the maintainer updates the constants here.
+#
+# Note: harmonization rescales (w_wood, w_bark, w_branch) proportionally to
+# hit the directly-predicted total AGB, so individual component CSV
+# corruption only shows up via these per-component checks — the AGB total
+# alone cannot detect it because harmonization compensates. v_wood_ib and
+# v_bark are the only signals for volib_spcd and volbk_spcd corruption since
+# volbk feeds nothing in the Phase 1 AGB path (its value is stored for
+# Phase 2+ adjusted-density use).
+DOUGFIR_CSV_EXPECTED = {
+    "agb": 3151.7844814148,
+    "w_wood": 2442.4991973909,
+    "w_bark": 387.9115217065,
+    "w_branch": 321.3737623174,
+    "v_wood_ib": 83.6844822743,  # locks volib_spcd.csv (SPCD=202 species-level row)
+    "v_bark": 18.1718836702,  # locks volbk_spcd.csv (SPCD=202 species-level row)
+}
+
+REDMAPLE_CSV_EXPECTED = {
+    "agb": 528.1359652182,
+    "w_wood": 317.9304736164,
+    "w_bark": 59.2156546044,
+    "w_branch": 150.9898369973,
+    "v_wood_ib": 9.4271133316,  # locks volib_spcd.csv (SPCD=316 species-level row)
+    "v_bark": 2.1551061437,  # locks volbk_spcd.csv (SPCD=316 species-level row)
+}
+
+
+def _bundle_via_csv(spcd: int, jenkins_spgrpcd: int) -> Coefficients:
+    """Build a ``Coefficients`` bundle by walking the real CSV lookup path.
+
+    This is exactly what PR 2's ``LiveTreeEstimator`` will reach for at
+    runtime (modulo the planned vectorization). Reusing it here ensures the
+    regression sentinels exercise the same code path the production estimator
+    will hit, not a hand-coded shortcut.
+    """
+    tables = load_nsvb_coefficients()
+    return Coefficients(
+        volib=lookup_coefficients(
+            tables.volib_spcd,
+            tables.volib_jenkins,
+            spcd=spcd,
+            jenkins_spgrpcd=jenkins_spgrpcd,
+        ),
+        volbk=lookup_coefficients(
+            tables.volbk_spcd,
+            tables.volbk_jenkins,
+            spcd=spcd,
+            jenkins_spgrpcd=jenkins_spgrpcd,
+        ),
+        bark_bio=lookup_coefficients(
+            tables.bark_biomass_spcd,
+            tables.bark_biomass_jenkins,
+            spcd=spcd,
+            jenkins_spgrpcd=jenkins_spgrpcd,
+        ),
+        branch_bio=lookup_coefficients(
+            tables.branch_biomass_spcd,
+            tables.branch_biomass_jenkins,
+            spcd=spcd,
+            jenkins_spgrpcd=jenkins_spgrpcd,
+        ),
+        total_agb=lookup_coefficients(
+            tables.total_biomass_spcd,
+            tables.total_biomass_jenkins,
+            spcd=spcd,
+            jenkins_spgrpcd=jenkins_spgrpcd,
+        ),
+    )
+
+
+class TestPipelineViaCSV:
+    """End-to-end NSVB pipeline regression vs the worked examples using the
+    actual CSV-loaded coefficients (the production data path).
+
+    Unlike :class:`TestPredictTreeBiomass` above — which uses hand-coded
+    high-precision coefficients to verify the equation form — these tests
+    walk the full ``lookup_coefficients`` → ``predict_tree_biomass`` path so
+    a CSV corruption, schema drift, or species-level row recalibration in
+    *any* of the five vendored coefficient tables will fail the suite.
+
+    Each test makes assertions at two layers:
+
+    1. **Worked-example agreement** (``agb`` vs WO-104, ~0.5% tolerance) —
+       documents the per-PR contract that Phase 1 reproduces the GTR-WO-104
+       worked-example AGB to within 0.5% even with the deferred ECOSUBCD
+       lookup. Currently measured: Douglas-fir 0.09%, red maple 0.00%.
+
+    2. **CSV regression sentinels** (``v_wood_ib``, ``v_bark``, ``w_wood``,
+       ``w_bark``, ``w_branch``, ``agb`` vs the species-level CSV expected
+       constants, f64-tight ``rel_tol=1e-9``) — locks each of the five
+       vendored coefficient CSVs against unintended modification. Without
+       per-component locks, the harmonization step washes out corruption to
+       four of the five tables (it proportionally rescales components to hit
+       the directly-predicted total AGB), so a single AGB assertion would
+       only catch ``total_biomass_spcd.csv`` regressions.
+
+    See ``pyfia/carbon/__init__.py`` "Items deferred from PR 1 review" for
+    why the species-level fallback is used in Phase 1.
+    """
+
+    def test_douglas_fir_pipeline_via_csv(self):
+        """Worked example expected AGB: 3154.55 lb (DIVISION=240).
+
+        With Phase 1's species-level fallback the CSV pipeline lands at
+        3151.78 lb — within 0.09% of the worked example. AGB asserted at
+        0.5% tolerance against WO-104; full result vector locked at
+        f64-tight tolerance against the species-level CSV expected values.
+
+        Also asserts every component of the bundle was resolved at the
+        ``"spcd"`` precedence level (not Jenkins fallback) — catches
+        mis-routing through Level 4 if the SPCD-keyed tables get truncated.
+        """
+        bundle = _bundle_via_csv(spcd=202, jenkins_spgrpcd=10)
+        # Every component must hit the species-level row, not the Jenkins fallback
+        assert bundle.volib["source"] == "spcd"
+        assert bundle.volbk["source"] == "spcd"
+        assert bundle.bark_bio["source"] == "spcd"
+        assert bundle.branch_bio["source"] == "spcd"
+        assert bundle.total_agb["source"] == "spcd"
+
+        result = predict_tree_biomass(coefficients=bundle, **DOUGFIR_INPUTS)
+
+        # Layer 1: worked-example agreement (the PR contract)
+        expected_worked = DOUGFIR_EXPECTED["agb_predicted"]
+        rel_err = abs(result.agb - expected_worked) / expected_worked
+        assert rel_err < 0.005, (
+            f"CSV pipeline AGB={result.agb:.4f} differs from worked-example "
+            f"{expected_worked:.4f} by {rel_err * 100:.3f}% (>0.5% tolerance)"
+        )
+
+        # Layer 2: per-CSV regression sentinels (locks all 5 component tables)
+        assert math.isclose(result.agb, DOUGFIR_CSV_EXPECTED["agb"], rel_tol=1e-9), (
+            f"agb={result.agb} drifted from CSV-expected {DOUGFIR_CSV_EXPECTED['agb']}"
+        )
+        assert math.isclose(
+            result.w_wood, DOUGFIR_CSV_EXPECTED["w_wood"], rel_tol=1e-9
+        ), (
+            f"w_wood={result.w_wood} drifted — bark_biomass_spcd or "
+            f"branch_biomass_spcd CSV may have changed"
+        )
+        assert math.isclose(
+            result.w_bark, DOUGFIR_CSV_EXPECTED["w_bark"], rel_tol=1e-9
+        ), f"w_bark={result.w_bark} drifted — bark_biomass_spcd CSV may have changed"
+        assert math.isclose(
+            result.w_branch, DOUGFIR_CSV_EXPECTED["w_branch"], rel_tol=1e-9
+        ), (
+            f"w_branch={result.w_branch} drifted — branch_biomass_spcd CSV "
+            f"may have changed"
+        )
+        assert math.isclose(
+            result.v_wood_ib, DOUGFIR_CSV_EXPECTED["v_wood_ib"], rel_tol=1e-9
+        ), f"v_wood_ib={result.v_wood_ib} drifted — volib_spcd CSV may have changed"
+        assert math.isclose(
+            result.v_bark, DOUGFIR_CSV_EXPECTED["v_bark"], rel_tol=1e-9
+        ), f"v_bark={result.v_bark} drifted — volbk_spcd CSV may have changed"
+
+    def test_red_maple_pipeline_via_csv(self):
+        """Worked example expected AGB (cull-reduced): 528.14 lb.
+
+        The red maple worked example uses the species-level fallback for
+        both volib (Model 1) and total_agb (Model 4), so this test exercises
+        the cull-reduction path AND the Model 4 dispatch via the real CSV
+        lookup — not just the equation form. Full result vector is locked
+        against the species-level CSV expected constants at f64-tight
+        precision.
+        """
+        bundle = _bundle_via_csv(spcd=316, jenkins_spgrpcd=8)
+        assert bundle.volib["source"] == "spcd"
+        assert bundle.volbk["source"] == "spcd"
+        assert bundle.bark_bio["source"] == "spcd"
+        assert bundle.branch_bio["source"] == "spcd"
+        assert bundle.total_agb["source"] == "spcd"
+        # Critical regression: total_agb for SPCD=316 must dispatch to Model 4
+        assert bundle.total_agb["model"] == 4
+
+        result = predict_tree_biomass(coefficients=bundle, **REDMAPLE_INPUTS)
+
+        # Layer 1: worked-example agreement (the PR contract)
+        expected_worked = REDMAPLE_EXPECTED["agb_predicted_red"]
+        rel_err = abs(result.agb - expected_worked) / expected_worked
+        assert rel_err < 0.005, (
+            f"CSV pipeline AGB={result.agb:.4f} differs from worked-example "
+            f"{expected_worked:.4f} by {rel_err * 100:.3f}% (>0.5% tolerance)"
+        )
+
+        # Layer 2: per-CSV regression sentinels (locks all 5 component tables)
+        assert math.isclose(result.agb, REDMAPLE_CSV_EXPECTED["agb"], rel_tol=1e-9), (
+            f"agb={result.agb} drifted from CSV-expected {REDMAPLE_CSV_EXPECTED['agb']}"
+        )
+        assert math.isclose(
+            result.w_wood, REDMAPLE_CSV_EXPECTED["w_wood"], rel_tol=1e-9
+        ), f"w_wood={result.w_wood} drifted from CSV-expected"
+        assert math.isclose(
+            result.w_bark, REDMAPLE_CSV_EXPECTED["w_bark"], rel_tol=1e-9
+        ), f"w_bark={result.w_bark} drifted — bark_biomass_spcd CSV may have changed"
+        assert math.isclose(
+            result.w_branch, REDMAPLE_CSV_EXPECTED["w_branch"], rel_tol=1e-9
+        ), (
+            f"w_branch={result.w_branch} drifted — branch_biomass_spcd CSV "
+            f"may have changed"
+        )
+        assert math.isclose(
+            result.v_wood_ib, REDMAPLE_CSV_EXPECTED["v_wood_ib"], rel_tol=1e-9
+        ), f"v_wood_ib={result.v_wood_ib} drifted — volib_spcd CSV may have changed"
+        assert math.isclose(
+            result.v_bark, REDMAPLE_CSV_EXPECTED["v_bark"], rel_tol=1e-9
+        ), f"v_bark={result.v_bark} drifted — volbk_spcd CSV may have changed"
diff --git a/tests/unit/test_nsvb_vectorized.py b/tests/unit/test_nsvb_vectorized.py
new file mode 100644
index 00000000..2f7ccded
--- /dev/null
+++ b/tests/unit/test_nsvb_vectorized.py
@@ -0,0 +1,525 @@
+"""
+Equivalence tests for the vectorized NSVB biomass pipeline.
+
+The scalar reference pipeline (``predict_tree_biomass`` + ``lookup_coefficients``)
+is locked against the GTR-WO-104 worked examples in ``test_nsvb_equations.py``.
+The vectorized pipeline in ``pyfia.carbon.nsvb.equations.compute_nsvb_biomass``
+must produce identical (f64-tight) outputs for every tree, because it is
+intentionally a polars-expression rewrite of the same math.
+
+This module is the cross-check: for a diverse synthetic tree dataset covering
+Models 1, 2, and 4, hardwoods and softwoods, SPCDs that hit the species-level
+lookup and SPCDs that fall through to the Jenkins fallback, and trees with
+and without cull, each tree's vectorized output must equal its scalar oracle
+output at ``rel_tol=1e-9`` (six columns: ``v_wood_ib``, ``v_bark``,
+``w_wood``, ``w_bark``, ``w_branch``, ``agb``).
+
+Any divergence breaks the test with exact per-tree diagnostics, which is the
+fastest way to track down a bug introduced into the vectorized pipeline —
+finer-grained than comparing aggregated per-acre estimates.
+
+See ``pyfia/carbon/__init__.py`` "Architectural rules" rule 2 for why the
+scalar path stays around as an oracle even though the vectorized path is
+the production data path.
+"""
+
+from __future__ import annotations
+
+import math
+import random
+
+import polars as pl
+import pytest
+
+from pyfia.carbon.nsvb.coefficients import (
+    load_nsvb_coefficients,
+    lookup_coefficients,
+)
+from pyfia.carbon.nsvb.equations import (
+    Coefficients,
+    compute_nsvb_biomass,
+    predict_tree_biomass,
+)
+
+# ---------------------------------------------------------------------------
+# Synthetic tree generator
+# ---------------------------------------------------------------------------
+
+# SPCDs with species-level rows in ALL 5 coefficient tables. Picked to span
+# the Model 1, 2, 4 dispatches and both hardwood/softwood branches.
+#
+# (SPCD, WDSG_proxy, JENKINS_SPGRPCD_proxy, hw_sw):
+#   SPCD=110 shortleaf pine     (softwood, Model 1/2 for volib)
+#   SPCD=202 Douglas-fir        (softwood, Model 2 for volib — worked example 1)
+#   SPCD=121 longleaf pine      (softwood, species-level)
+#   SPCD=131 loblolly pine      (softwood, species-level)
+#   SPCD=316 red maple          (hardwood, Model 1 volib, Model 4 total_agb — worked example 2)
+#   SPCD=802 white oak          (hardwood, species-level)
+#   SPCD=833 chestnut oak       (hardwood, species-level)
+#   SPCD=621 yellow-poplar      (hardwood, species-level)
+#
+# The Jenkins fallback is exercised by injecting a few trees with an SPCD that
+# isn't in the species-level tables (e.g., 9999 → falls through to Jenkins).
+_SPECIES_LEVEL_SPCDS: list[tuple[int, float, int]] = [
+    # (SPCD, WDSG, JENKINS_SPGRPCD) — WDSG and JENKINS are arbitrary but must
+    # be consistent between the vectorized and scalar runs.
+    (110, 0.46, 4),  # shortleaf pine
+    (202, 0.45, 3),  # Douglas-fir
+    (121, 0.54, 4),  # longleaf pine
+    (131, 0.47, 4),  # loblolly pine
+    (316, 0.49, 8),  # red maple
+    (802, 0.60, 6),  # white oak
+    (833, 0.57, 6),  # chestnut oak
+    (621, 0.40, 8),  # yellow-poplar
+]
+
+# At least one Jenkins-fallback SPCD so the (Level 4) path is exercised.
+# 9999 has no row in any *_spcd table, so the lookup cascades to the
+# Jenkins group provided. Group 3 is "soft maple/birch" — arbitrary.
+_JENKINS_FALLBACK_SPCDS: list[tuple[int, float, int]] = [
+    (9999, 0.50, 3),
+]
+
+
+def _build_synthetic_trees(n: int, seed: int = 42) -> pl.DataFrame:
+    """Build a synthetic tree DataFrame for equivalence testing.
+
+    Each tree gets random DIA (1.0-30.0 in), HT (5-130 ft), CULL (0 or 0-20),
+    sampled uniformly, with SPCD drawn from ``_SPECIES_LEVEL_SPCDS`` plus a
+    small fraction of Jenkins-fallback SPCDs. WDSG and JENKINS_SPGRPCD are
+    carried along unchanged so both the vectorized and scalar evaluators
+    see identical inputs.
+
+    Also carries a pre-computed ``hw_sw`` column derived from SPCD<300 —
+    this matches the rule used by both ``_model_k`` and the vectorized
+    orchestrator, keeping the scalar oracle call consistent with the
+    vectorized path.
+    """
+    rng = random.Random(seed)
+    all_species = _SPECIES_LEVEL_SPCDS + _JENKINS_FALLBACK_SPCDS
+
+    rows: list[dict] = []
+    for _ in range(n):
+        spcd, wdsg, jen = rng.choice(all_species)
+        dia = rng.uniform(1.0, 30.0)
+        ht = rng.uniform(5.0, 130.0)
+        # 30% of trees get nonzero cull (0-20%), 10% get NULL cull, rest 0.
+        r = rng.random()
+        if r < 0.1:
+            cull = None  # test null handling
+        elif r < 0.4:
+            cull = rng.uniform(0.0, 20.0)
+        else:
+            cull = 0.0
+        rows.append(
+            {
+                "SPCD": spcd,
+                "DIA": dia,
+                "HT": ht,
+                "CULL": cull,
+                "WDSG": wdsg,
+                "JENKINS_SPGRPCD": jen,
+            }
+        )
+
+    return pl.DataFrame(
+        rows,
+        schema={
+            "SPCD": pl.Int64,
+            "DIA": pl.Float64,
+            "HT": pl.Float64,
+            "CULL": pl.Float64,
+            "WDSG": pl.Float64,
+            "JENKINS_SPGRPCD": pl.Int64,
+        },
+    )
+
+
+# ---------------------------------------------------------------------------
+# Scalar oracle helpers
+# ---------------------------------------------------------------------------
+
+
+def _bundle_via_csv(spcd: int, jenkins_spgrpcd: int) -> Coefficients:
+    """Build a scalar ``Coefficients`` bundle by walking the real CSV lookup.
+
+    Mirrors the helper of the same name in ``test_nsvb_equations.py``. Duplicated
+    here so this file is self-contained and doesn't depend on test-module import
+    order.
+    """
+    tables = load_nsvb_coefficients()
+    return Coefficients(
+        volib=lookup_coefficients(
+            tables.volib_spcd,
+            tables.volib_jenkins,
+            spcd=spcd,
+            jenkins_spgrpcd=jenkins_spgrpcd,
+        ),
+        volbk=lookup_coefficients(
+            tables.volbk_spcd,
+            tables.volbk_jenkins,
+            spcd=spcd,
+            jenkins_spgrpcd=jenkins_spgrpcd,
+        ),
+        bark_bio=lookup_coefficients(
+            tables.bark_biomass_spcd,
+            tables.bark_biomass_jenkins,
+            spcd=spcd,
+            jenkins_spgrpcd=jenkins_spgrpcd,
+        ),
+        branch_bio=lookup_coefficients(
+            tables.branch_biomass_spcd,
+            tables.branch_biomass_jenkins,
+            spcd=spcd,
+            jenkins_spgrpcd=jenkins_spgrpcd,
+        ),
+        total_agb=lookup_coefficients(
+            tables.total_biomass_spcd,
+            tables.total_biomass_jenkins,
+            spcd=spcd,
+            jenkins_spgrpcd=jenkins_spgrpcd,
+        ),
+    )
+
+
+def _scalar_per_tree(trees: pl.DataFrame) -> pl.DataFrame:
+    """Run the scalar oracle pipeline tree-by-tree and return a DataFrame.
+
+    Matches the vectorized output schema: ``v_wood_ib``, ``v_bark``,
+    ``w_wood``, ``w_bark``, ``w_branch``, ``agb``.
+    """
+    out = []
+    for row in trees.iter_rows(named=True):
+        bundle = _bundle_via_csv(row["SPCD"], row["JENKINS_SPGRPCD"])
+        hw_sw = "hardwood" if row["SPCD"] >= 300 else "softwood"
+        cull = row["CULL"] if row["CULL"] is not None else 0.0
+        result = predict_tree_biomass(
+            spcd=row["SPCD"],
+            dia=row["DIA"],
+            ht=row["HT"],
+            coefficients=bundle,
+            wdsg=row["WDSG"],
+            hw_sw=hw_sw,
+            cull=cull,
+        )
+        out.append(
+            {
+                "SPCD": row["SPCD"],
+                "v_wood_ib": result.v_wood_ib,
+                "v_bark": result.v_bark,
+                "w_wood": result.w_wood,
+                "w_bark": result.w_bark,
+                "w_branch": result.w_branch,
+                "agb": result.agb,
+            }
+        )
+    return pl.DataFrame(out)
+
+
+# ---------------------------------------------------------------------------
+# Equivalence tests
+# ---------------------------------------------------------------------------
+
+
+class TestVectorizedMatchesScalarOracle:
+    """The vectorized pipeline must match the scalar oracle tree-for-tree."""
+
+    @pytest.mark.parametrize("n_trees", [50, 500])
+    def test_equivalence_on_synthetic_trees(self, n_trees):
+        """For a diverse random tree set, vectorized == scalar at f64 precision.
+
+        Covers Models 1, 2, 4 (via SPCDs that dispatch to each), hardwood
+        and softwood paths, trees with and without cull, nullable cull
+        values, and at least one Jenkins-fallback SPCD per batch.
+
+        ``rel_tol=1e-9`` is tighter than the existing ``TestPipelineViaCSV``
+        sentinels and is achievable because polars and the scalar math use
+        the same IEEE-754 operations on the same coefficient floats — there
+        is no semantic reordering.
+        """
+        trees = _build_synthetic_trees(n=n_trees, seed=2026)
+
+        vec = compute_nsvb_biomass(trees.lazy()).collect()
+        scalar = _scalar_per_tree(trees)
+
+        # Row counts must match (nothing is filtered out)
+        assert vec.height == scalar.height == n_trees
+
+        # Element-wise equivalence on all six output columns.
+        # We preserve row order on both sides (neither pipeline reorders),
+        # so positional comparison is valid.
+        for col in ("v_wood_ib", "v_bark", "w_wood", "w_bark", "w_branch", "agb"):
+            vec_vals = vec[col].to_list()
+            sc_vals = scalar[col].to_list()
+            for i, (v, s) in enumerate(zip(vec_vals, sc_vals)):
+                assert math.isclose(v, s, rel_tol=1e-9), (
+                    f"{col} divergence at row {i}: vectorized={v} scalar={s} "
+                    f"(SPCD={trees['SPCD'][i]}, DIA={trees['DIA'][i]}, "
+                    f"HT={trees['HT'][i]}, CULL={trees['CULL'][i]})"
+                )
+
+    def test_douglas_fir_worked_example_exact(self):
+        """Worked example 1: Douglas-fir intact tree matches scalar sentinel.
+
+        This is the same tree locked in ``TestPipelineViaCSV`` for the scalar
+        path. The vectorized path must hit the same f64-tight sentinel values.
+        """
+        trees = pl.DataFrame(
+            {
+                "SPCD": [202],
+                "DIA": [20.0],
+                "HT": [110.0],
+                "CULL": [0.0],
+                "WDSG": [0.45],
+                "JENKINS_SPGRPCD": [10],
+            }
+        )
+        result = compute_nsvb_biomass(trees.lazy()).collect()
+        # Sentinels from DOUGFIR_CSV_EXPECTED in test_nsvb_equations.py
+        assert math.isclose(result["agb"][0], 3151.7844814148, rel_tol=1e-9)
+        assert math.isclose(result["w_wood"][0], 2442.4991973909, rel_tol=1e-9)
+        assert math.isclose(result["w_bark"][0], 387.9115217065, rel_tol=1e-9)
+        assert math.isclose(result["w_branch"][0], 321.3737623174, rel_tol=1e-9)
+        assert math.isclose(result["v_wood_ib"][0], 83.6844822743, rel_tol=1e-9)
+        assert math.isclose(result["v_bark"][0], 18.1718836702, rel_tol=1e-9)
+
+    def test_red_maple_worked_example_exact(self):
+        """Worked example 2: red maple with 3% cull, exercises Model 4 + cull path.
+
+        Same tree as in the scalar ``TestPipelineViaCSV`` regression sentinels.
+        Exercises the full cull-reduction + harmonization arithmetic under
+        the vectorized path.
+        """
+        trees = pl.DataFrame(
+            {
+                "SPCD": [316],
+                "DIA": [11.1],
+                "HT": [38.0],
+                "CULL": [3.0],
+                "WDSG": [0.49],
+                "JENKINS_SPGRPCD": [8],
+            }
+        )
+        result = compute_nsvb_biomass(trees.lazy()).collect()
+        # Sentinels from REDMAPLE_CSV_EXPECTED in test_nsvb_equations.py
+        assert math.isclose(result["agb"][0], 528.1359652182, rel_tol=1e-9)
+        assert math.isclose(result["w_wood"][0], 317.9304736164, rel_tol=1e-9)
+        assert math.isclose(result["w_bark"][0], 59.2156546044, rel_tol=1e-9)
+        assert math.isclose(result["w_branch"][0], 150.9898369973, rel_tol=1e-9)
+        assert math.isclose(result["v_wood_ib"][0], 9.4271133316, rel_tol=1e-9)
+        assert math.isclose(result["v_bark"][0], 2.1551061437, rel_tol=1e-9)
+
+    def test_agb_equals_component_sum(self):
+        """Harmonization invariant: agb == w_wood + w_bark + w_branch per tree."""
+        trees = _build_synthetic_trees(n=100, seed=99)
+        result = compute_nsvb_biomass(trees.lazy()).collect()
+        summed = result["w_wood"] + result["w_bark"] + result["w_branch"]
+        for i, (agb, s) in enumerate(zip(result["agb"].to_list(), summed.to_list())):
+            assert math.isclose(agb, s, rel_tol=1e-12), (
+                f"row {i}: agb={agb} != sum of components={s}"
+            )
+
+    def test_cull_nulls_treated_as_zero(self):
+        """A null CULL is equivalent to CULL=0.
+
+        The vectorized path calls ``.fill_null(0.0)`` on CULL; asserting that
+        here prevents silent behavior change if the null-handling is removed.
+        """
+        trees = pl.DataFrame(
+            {
+                "SPCD": [202, 202],
+                "DIA": [20.0, 20.0],
+                "HT": [110.0, 110.0],
+                "CULL": [None, 0.0],
+                "WDSG": [0.45, 0.45],
+                "JENKINS_SPGRPCD": [3, 3],
+            },
+            schema={
+                "SPCD": pl.Int64,
+                "DIA": pl.Float64,
+                "HT": pl.Float64,
+                "CULL": pl.Float64,
+                "WDSG": pl.Float64,
+                "JENKINS_SPGRPCD": pl.Int64,
+            },
+        )
+        result = compute_nsvb_biomass(trees.lazy()).collect()
+        # Both rows should produce identical outputs
+        assert result["agb"][0] == result["agb"][1]
+        assert result["w_wood"][0] == result["w_wood"][1]
+
+    def test_jenkins_fallback_tree(self):
+        """A tree with an SPCD absent from all *_spcd tables uses Jenkins fallback.
+
+        The vectorized orchestrator's species-level join will return null
+        coefficients for SPCD=9999, and the coalesce with the Jenkins join
+        must fill them in from the JENKINS_SPGRPCD match. If this path is
+        broken, the output will be null, and the final AGB will also be null.
+        """
+        trees = pl.DataFrame(
+            {
+                "SPCD": [9999],  # no species-level row anywhere
+                "DIA": [15.0],
+                "HT": [60.0],
+                "CULL": [0.0],
+                "WDSG": [0.5],
+                "JENKINS_SPGRPCD": [3],  # soft maple/birch group
+            }
+        )
+        result = compute_nsvb_biomass(trees.lazy()).collect()
+        # Must not be null — Jenkins fallback resolved all 5 components
+        assert result["agb"][0] is not None
+        assert result["agb"][0] > 0
+        # Match the scalar oracle exactly
+        scalar = _scalar_per_tree(trees)
+        assert math.isclose(result["agb"][0], scalar["agb"][0], rel_tol=1e-9)
+
+
+class TestDivisionLookupPath:
+    """Level 2 (DIVISION-specific) lookup path.
+
+    The synthetic-tree equivalence suite above covers only Levels 3-4
+    because the scalar oracle ``predict_tree_biomass`` (via its
+    ``lookup_coefficients`` backend) always passes ``division=None``.
+    These tests exercise the new DIVISION coalesce path directly.
+    """
+
+    def test_division_lookup_produces_non_null_agb(self):
+        """A tree with a DIVISION that has a matching S1a/S6a/S7a/S8a row
+        should still produce a valid AGB (proving the 3-way coalesce
+        doesn't silently emit nulls when DIVISION is set)."""
+        # SPCD=131 (loblolly pine, the dominant Georgia species) in
+        # DIVISION 230 (Subtropical).
+        trees = pl.DataFrame(
+            {
+                "SPCD": [131],
+                "DIA": [15.0],
+                "HT": [80.0],
+                "CULL": [0.0],
+                "WDSG": [0.47],
+                "JENKINS_SPGRPCD": [4],
+                "DIVISION": ["230"],
+            }
+        )
+        result = compute_nsvb_biomass(trees.lazy()).collect()
+        assert result["agb"][0] is not None
+        assert result["agb"][0] > 0
+        # Harmonization invariant still holds
+        assert math.isclose(
+            result["agb"][0],
+            result["w_wood"][0] + result["w_bark"][0] + result["w_branch"][0],
+            rel_tol=1e-12,
+        )
+
+    def test_division_null_falls_back_to_species_level(self):
+        """A tree with DIVISION=null should produce the same result as a
+        tree without the DIVISION column at all — falling back to the
+        species-level lookup via coalesce."""
+        trees_with_null = pl.DataFrame(
+            {
+                "SPCD": [131],
+                "DIA": [15.0],
+                "HT": [80.0],
+                "CULL": [0.0],
+                "WDSG": [0.47],
+                "JENKINS_SPGRPCD": [4],
+                "DIVISION": [None],
+            },
+            schema={
+                "SPCD": pl.Int64,
+                "DIA": pl.Float64,
+                "HT": pl.Float64,
+                "CULL": pl.Float64,
+                "WDSG": pl.Float64,
+                "JENKINS_SPGRPCD": pl.Int64,
+                "DIVISION": pl.Utf8,
+            },
+        )
+        trees_without = trees_with_null.drop("DIVISION")
+
+        with_null = compute_nsvb_biomass(trees_with_null.lazy()).collect()
+        without = compute_nsvb_biomass(trees_without.lazy()).collect()
+
+        for col in ("v_wood_ib", "v_bark", "w_wood", "w_bark", "w_branch", "agb"):
+            assert math.isclose(with_null[col][0], without[col][0], rel_tol=1e-12), (
+                f"{col}: with_null={with_null[col][0]} != without={without[col][0]}"
+            )
+
+    def test_division_changes_result_for_species_with_division_row(self):
+        """Positive control: for a species with a DIVISION-specific row,
+        specifying the DIVISION should change the output vs the
+        species-level fallback. Proves the DIVISION lookup actually takes
+        effect rather than silently falling through to species-level.
+
+        Uses SPCD=316 (red maple) in DIVISION=230 — verified via a direct
+        diff of ``bundle.total_agb_div`` vs ``bundle.total_agb_spcd`` that
+        this SPCD has a DIVISION=230 S8a row with coefficients differing
+        from the species-level row by a material amount (total_diff
+        ~0.36 across a, b, c).
+        """
+        base_data = {
+            "SPCD": [316],
+            "DIA": [11.1],
+            "HT": [38.0],
+            "CULL": [0.0],
+            "WDSG": [0.49],
+            "JENKINS_SPGRPCD": [8],
+        }
+        trees_species_level = pl.DataFrame(base_data)
+        trees_division = pl.DataFrame({**base_data, "DIVISION": ["230"]})
+
+        species_result = compute_nsvb_biomass(trees_species_level.lazy()).collect()
+        division_result = compute_nsvb_biomass(trees_division.lazy()).collect()
+
+        # At least one of the 5 components should differ materially
+        # (confirming the DIVISION lookup produced a different coefficient).
+        # If they all match exactly, the DIVISION row doesn't exist or the
+        # coalesce is silently bypassing it.
+        differs = any(
+            not math.isclose(
+                species_result[col][0], division_result[col][0], rel_tol=1e-6
+            )
+            for col in ("v_wood_ib", "v_bark", "w_wood", "w_bark", "w_branch", "agb")
+        )
+        assert differs, (
+            "species-level and DIVISION=230 results are identical — "
+            "SPCD=316 should have a differing DIVISION=230 row or the "
+            "DIVISION coalesce is not taking effect"
+        )
+
+        # Also assert that the directly-predicted total AGB actually
+        # responds to DIVISION (not just a noise-level component change).
+        assert not math.isclose(
+            species_result["agb"][0], division_result["agb"][0], rel_tol=1e-3
+        ), (
+            f"AGB essentially unchanged by DIVISION lookup: "
+            f"species={species_result['agb'][0]:.4f}, "
+            f"division={division_result['agb'][0]:.4f}"
+        )
+
+    def test_division_with_no_matching_row_falls_through(self):
+        """A tree with DIVISION set to a value that doesn't have a matching
+        row should fall through to species-level, matching a tree without
+        DIVISION set at all."""
+        trees_bogus = pl.DataFrame(
+            {
+                "SPCD": [131],
+                "DIA": [15.0],
+                "HT": [80.0],
+                "CULL": [0.0],
+                "WDSG": [0.47],
+                "JENKINS_SPGRPCD": [4],
+                "DIVISION": ["NOPE"],
+            }
+        )
+        trees_no_div = trees_bogus.drop("DIVISION")
+
+        bogus_result = compute_nsvb_biomass(trees_bogus.lazy()).collect()
+        no_div_result = compute_nsvb_biomass(trees_no_div.lazy()).collect()
+
+        for col in ("v_wood_ib", "v_bark", "w_wood", "w_bark", "w_branch", "agb"):
+            assert math.isclose(
+                bogus_result[col][0], no_div_result[col][0], rel_tol=1e-12
+            ), (
+                f"{col}: bogus_div={bogus_result[col][0]} != no_div={no_div_result[col][0]}"
+            )
diff --git a/tests/unit/test_soil_organic_estimator.py b/tests/unit/test_soil_organic_estimator.py
new file mode 100644
index 00000000..0912e32e
--- /dev/null
+++ b/tests/unit/test_soil_organic_estimator.py
@@ -0,0 +1,141 @@
+"""Unit tests for SoilOrganicEstimator class.
+
+Tests the SoilOrganicEstimator methods in isolation using mock data.
+No database connection required.
+"""
+
+import polars as pl
+import pytest
+
+from pyfia.carbon.soil_organic import SoilOrganicEstimator, soil_organic
+
+
+class MockDB:
+    """Mock database for testing estimator methods in isolation."""
+
+    def __init__(self):
+        self.db_path = "/fake/path"
+        self.tables = {}
+        self.evalid = None
+        self.evalids = None
+        self._state_filter = None
+
+
+class TestGetRequiredTables:
+    """Tests for get_required_tables method."""
+
+    def test_returns_condition_level_tables(self):
+        config = {"pool": "total", "land_type": "forest"}
+        estimator = SoilOrganicEstimator(MockDB(), config)
+        tables = estimator.get_required_tables()
+
+        assert "COND" in tables
+        assert "PLOT" in tables
+        assert "POP_PLOT_STRATUM_ASSGN" in tables
+        assert "POP_STRATUM" in tables
+
+    def test_tree_table_not_required(self):
+        config = {"pool": "total", "land_type": "forest"}
+        estimator = SoilOrganicEstimator(MockDB(), config)
+        tables = estimator.get_required_tables()
+        assert "TREE" not in tables
+
+
+class TestGetTreeColumns:
+    def test_returns_empty_list(self):
+        config = {"pool": "total"}
+        estimator = SoilOrganicEstimator(MockDB(), config)
+        cols = estimator.get_tree_columns()
+        assert cols == []
+
+
+class TestGetCondColumns:
+    def test_includes_carbon_soil_org_column(self):
+        config = {"land_type": "forest"}
+        estimator = SoilOrganicEstimator(MockDB(), config)
+        cols = estimator.get_cond_columns()
+
+        assert "CARBON_SOIL_ORG" in cols
+        assert "PLT_CN" in cols
+        assert "CONDID" in cols
+        assert "CONDPROP_UNADJ" in cols
+        assert "COND_STATUS_CD" in cols
+
+
+class TestCalculateValues:
+    """Tests for calculate_values method."""
+
+    @pytest.fixture
+    def mock_db(self):
+        return MockDB()
+
+    def test_total_pool(self, mock_db):
+        config = {"pool": "total"}
+        estimator = SoilOrganicEstimator(mock_db, config)
+
+        data = pl.DataFrame(
+            {"CARBON_SOIL_ORG": [25.4, 18.7]}
+        ).lazy()
+
+        result = estimator.calculate_values(data).collect()
+        assert abs(result["CARBON_ACRE"][0] - 25.4) < 1e-10
+        assert abs(result["CARBON_ACRE"][1] - 18.7) < 1e-10
+
+    def test_null_values_treated_as_zero(self, mock_db):
+        config = {"pool": "total"}
+        estimator = SoilOrganicEstimator(mock_db, config)
+
+        data = pl.DataFrame(
+            {"CARBON_SOIL_ORG": [None, 20.0]}
+        ).lazy()
+
+        result = estimator.calculate_values(data).collect()
+        assert result["CARBON_ACRE"][0] == 0.0
+        assert abs(result["CARBON_ACRE"][1] - 20.0) < 1e-10
+
+    def test_all_null_gives_zero(self, mock_db):
+        config = {"pool": "total"}
+        estimator = SoilOrganicEstimator(mock_db, config)
+
+        data = pl.DataFrame(
+            {"CARBON_SOIL_ORG": [None]}
+        ).lazy()
+
+        result = estimator.calculate_values(data).collect()
+        assert result["CARBON_ACRE"][0] == 0.0
+
+    def test_empty_dataframe(self, mock_db):
+        config = {"pool": "total"}
+        estimator = SoilOrganicEstimator(mock_db, config)
+
+        data = pl.DataFrame(
+            {"CARBON_SOIL_ORG": pl.Series([], dtype=pl.Float64)}
+        ).lazy()
+
+        result = estimator.calculate_values(data).collect()
+        assert len(result) == 0
+
+
+class TestPoolValidation:
+    def test_invalid_pool_raises_error(self):
+        with pytest.raises(ValueError, match="Invalid pool"):
+            soil_organic(MockDB(), pool="ag")
+
+    def test_bg_pool_raises_error(self):
+        with pytest.raises(ValueError, match="no AG/BG split"):
+            soil_organic(MockDB(), pool="bg")
+
+    def test_total_pool_accepted(self):
+        with pytest.raises(Exception):
+            soil_organic(MockDB(), pool="total")
+
+    def test_case_insensitive_total(self):
+        with pytest.raises(Exception):
+            soil_organic(MockDB(), pool="TOTAL")
+
+
+class TestEstimatorLabel:
+    def test_label_is_soil_organic(self):
+        config = {"pool": "total"}
+        estimator = SoilOrganicEstimator(MockDB(), config)
+        assert estimator._estimator_label == "SoilOrganic"
diff --git a/tests/unit/test_stock_change.py b/tests/unit/test_stock_change.py
new file mode 100644
index 00000000..18f46867
--- /dev/null
+++ b/tests/unit/test_stock_change.py
@@ -0,0 +1,216 @@
+"""Unit tests for CarbonStockChangeEstimator and stock_change function.
+
+Tests the stock-change logic in isolation using mock data.
+No database connection required.
+"""
+
+import polars as pl
+import pytest
+
+from pyfia.carbon.stock_change import (
+    CarbonStockChangeEstimator,
+    _POOL_COLUMNS,
+    _VALID_POOLS,
+    stock_change,
+)
+
+
+class MockDB:
+    """Mock database for testing estimator methods in isolation."""
+
+    def __init__(self):
+        self.db_path = "/fake/path"
+        self.tables = {}
+        self.evalid = None
+        self.evalids = None
+        self._state_filter = None
+
+
+class TestGetRequiredTables:
+    def test_returns_condition_level_tables(self):
+        config = {"pool": "downed_dead", "land_type": "forest"}
+        estimator = CarbonStockChangeEstimator(MockDB(), config)
+        tables = estimator.get_required_tables()
+
+        assert "COND" in tables
+        assert "PLOT" in tables
+        assert "POP_PLOT_STRATUM_ASSGN" in tables
+        assert "POP_STRATUM" in tables
+
+    def test_tree_table_not_required(self):
+        config = {"pool": "litter", "land_type": "forest"}
+        estimator = CarbonStockChangeEstimator(MockDB(), config)
+        assert "TREE" not in estimator.get_required_tables()
+
+
+class TestGetTreeColumns:
+    def test_returns_empty_list(self):
+        config = {"pool": "soil_organic"}
+        estimator = CarbonStockChangeEstimator(MockDB(), config)
+        assert estimator.get_tree_columns() == []
+
+
+class TestGetCondColumns:
+    def test_includes_carbon_column_for_pool(self):
+        for pool, expected_cols in _POOL_COLUMNS.items():
+            config = {"pool": pool, "land_type": "forest"}
+            estimator = CarbonStockChangeEstimator(MockDB(), config)
+            cols = estimator.get_cond_columns()
+            for ec in expected_cols:
+                assert ec in cols, f"{ec} missing for pool {pool}"
+
+
+class TestCalculateValues:
+    """Tests for the delta calculation logic."""
+
+    @pytest.fixture
+    def mock_db(self):
+        return MockDB()
+
+    def test_basic_delta(self, mock_db):
+        config = {"pool": "downed_dead", "annualize": False}
+        estimator = CarbonStockChangeEstimator(mock_db, config)
+
+        data = pl.DataFrame(
+            {
+                "t2_CARBON_DOWN_DEAD": [5.0, 3.0],
+                "t1_CARBON_DOWN_DEAD": [4.0, 4.0],
+                "REMPER": [5.0, 5.0],
+            }
+        ).lazy()
+
+        result = estimator.calculate_values(data).collect()
+        assert abs(result["CARBON_CHANGE_ACRE"][0] - 1.0) < 1e-10
+        assert abs(result["CARBON_CHANGE_ACRE"][1] - (-1.0)) < 1e-10
+
+    def test_annualized_delta(self, mock_db):
+        config = {"pool": "litter", "annualize": True}
+        estimator = CarbonStockChangeEstimator(mock_db, config)
+
+        data = pl.DataFrame(
+            {
+                "t2_CARBON_LITTER": [10.0],
+                "t1_CARBON_LITTER": [5.0],
+                "REMPER": [5.0],
+            }
+        ).lazy()
+
+        result = estimator.calculate_values(data).collect()
+        # (10 - 5) / 5 = 1.0
+        assert abs(result["CARBON_CHANGE_ACRE"][0] - 1.0) < 1e-10
+
+    def test_understory_sums_ag_bg(self, mock_db):
+        config = {"pool": "understory", "annualize": False}
+        estimator = CarbonStockChangeEstimator(mock_db, config)
+
+        data = pl.DataFrame(
+            {
+                "t2_CARBON_UNDERSTORY_AG": [1.2],
+                "t2_CARBON_UNDERSTORY_BG": [0.3],
+                "t1_CARBON_UNDERSTORY_AG": [1.0],
+                "t1_CARBON_UNDERSTORY_BG": [0.2],
+                "REMPER": [5.0],
+            }
+        ).lazy()
+
+        result = estimator.calculate_values(data).collect()
+        # (1.2+0.3) - (1.0+0.2) = 0.3
+        assert abs(result["CARBON_CHANGE_ACRE"][0] - 0.3) < 1e-10
+
+    def test_null_t2_treated_as_zero(self, mock_db):
+        config = {"pool": "soil_organic", "annualize": False}
+        estimator = CarbonStockChangeEstimator(mock_db, config)
+
+        data = pl.DataFrame(
+            {
+                "t2_CARBON_SOIL_ORG": [None],
+                "t1_CARBON_SOIL_ORG": [20.0],
+                "REMPER": [5.0],
+            }
+        ).lazy()
+
+        result = estimator.calculate_values(data).collect()
+        assert abs(result["CARBON_CHANGE_ACRE"][0] - (-20.0)) < 1e-10
+
+    def test_null_t1_treated_as_zero(self, mock_db):
+        config = {"pool": "soil_organic", "annualize": False}
+        estimator = CarbonStockChangeEstimator(mock_db, config)
+
+        data = pl.DataFrame(
+            {
+                "t2_CARBON_SOIL_ORG": [25.0],
+                "t1_CARBON_SOIL_ORG": [None],
+                "REMPER": [5.0],
+            }
+        ).lazy()
+
+        result = estimator.calculate_values(data).collect()
+        assert abs(result["CARBON_CHANGE_ACRE"][0] - 25.0) < 1e-10
+
+    def test_negative_change(self, mock_db):
+        """Carbon loss produces negative values."""
+        config = {"pool": "downed_dead", "annualize": True}
+        estimator = CarbonStockChangeEstimator(mock_db, config)
+
+        data = pl.DataFrame(
+            {
+                "t2_CARBON_DOWN_DEAD": [2.0],
+                "t1_CARBON_DOWN_DEAD": [5.0],
+                "REMPER": [6.0],
+            }
+        ).lazy()
+
+        result = estimator.calculate_values(data).collect()
+        # (2 - 5) / 6 = -0.5
+        assert abs(result["CARBON_CHANGE_ACRE"][0] - (-0.5)) < 1e-10
+
+
+class TestPoolValidation:
+    def test_live_tree_raises_error(self):
+        with pytest.raises(ValueError, match="not yet implemented"):
+            stock_change(MockDB(), pool="live_tree")
+
+    def test_standing_dead_raises_error(self):
+        with pytest.raises(ValueError, match="not yet implemented"):
+            stock_change(MockDB(), pool="standing_dead")
+
+    def test_invalid_pool_raises_error(self):
+        with pytest.raises(ValueError, match="Invalid pool"):
+            stock_change(MockDB(), pool="invalid")
+
+    def test_valid_pools_accepted(self):
+        """Valid pools should pass pool validation (fail later on DB access)."""
+        for pool in _VALID_POOLS:
+            with pytest.raises(Exception):
+                stock_change(MockDB(), pool=pool)
+
+    def test_all_pool_accepted(self):
+        with pytest.raises(Exception):
+            stock_change(MockDB(), pool="all")
+
+    def test_list_of_pools_accepted(self):
+        with pytest.raises(Exception):
+            stock_change(MockDB(), pool=["downed_dead", "litter"])
+
+    def test_list_with_tree_pool_raises(self):
+        with pytest.raises(ValueError, match="not yet implemented"):
+            stock_change(MockDB(), pool=["downed_dead", "live_tree"])
+
+
+class TestEstimatorLabel:
+    def test_label(self):
+        config = {"pool": "downed_dead"}
+        estimator = CarbonStockChangeEstimator(MockDB(), config)
+        assert estimator._estimator_label == "CarbonStockChange"
+
+
+class TestPoolColumnMapping:
+    def test_all_valid_pools_have_columns(self):
+        for pool in _VALID_POOLS:
+            assert pool in _POOL_COLUMNS
+            assert len(_POOL_COLUMNS[pool]) > 0
+
+    def test_understory_has_ag_and_bg(self):
+        cols = _POOL_COLUMNS["understory"]
+        assert "CARBON_UNDERSTORY_AG" in cols
+        assert "CARBON_UNDERSTORY_BG" in cols
diff --git a/tests/unit/test_total_ecosystem.py b/tests/unit/test_total_ecosystem.py
new file mode 100644
index 00000000..5f4c73a7
--- /dev/null
+++ b/tests/unit/test_total_ecosystem.py
@@ -0,0 +1,262 @@
+"""Unit tests for total_ecosystem function.
+
+Tests the pool-summing logic in isolation using monkeypatched pool functions.
+No database connection required.
+"""
+
+from unittest.mock import patch
+
+import polars as pl
+import pytest
+
+from pyfia.carbon.total_ecosystem import total_ecosystem
+
+
+class MockDB:
+    """Mock database for testing."""
+
+    def __init__(self):
+        self.db_path = "/fake/path"
+        self.tables = {}
+        self.evalid = [132301]
+        self.evalids = None
+        self._state_filter = None
+
+
+def _make_pool_result(
+    carbon_acre: float, carbon_total: float, n_plots: int = 100, year: int = 2023
+) -> pl.DataFrame:
+    """Build a minimal pool result DataFrame."""
+    return pl.DataFrame(
+        {
+            "YEAR": [year],
+            "POOL": ["TOTAL"],
+            "CARBON_ACRE": [carbon_acre],
+            "CARBON_TOTAL": [carbon_total],
+            "N_PLOTS": [n_plots],
+            "N_TREES": [0],
+        }
+    )
+
+
+def _make_empty_result() -> pl.DataFrame:
+    return pl.DataFrame(
+        {
+            "YEAR": pl.Series([], dtype=pl.Int64),
+            "POOL": pl.Series([], dtype=pl.Utf8),
+            "CARBON_ACRE": pl.Series([], dtype=pl.Float64),
+            "CARBON_TOTAL": pl.Series([], dtype=pl.Float64),
+            "N_PLOTS": pl.Series([], dtype=pl.Int64),
+            "N_TREES": pl.Series([], dtype=pl.Int64),
+        }
+    )
+
+
+# Patch at the source modules where the functions are defined
+_POOL_PATCHES = {
+    "live_tree": "pyfia.carbon.live_tree.live_tree",
+    "standing_dead": "pyfia.carbon.standing_dead.standing_dead",
+    "understory": "pyfia.carbon.understory.understory",
+    "downed_dead": "pyfia.carbon.downed_dead.downed_dead",
+    "litter": "pyfia.carbon.litter.litter",
+    "soil_organic": "pyfia.carbon.soil_organic.soil_organic",
+}
+
+
+def _start_pool_patches(pool_results: dict) -> list:
+    """Start patches for all six pool functions. Returns list of patchers."""
+    patchers = []
+    for name, target in _POOL_PATCHES.items():
+        p = patch(target, return_value=pool_results.get(name, _make_pool_result(1.0, 1000.0)))
+        p.start()
+        patchers.append(p)
+    return patchers
+
+
+def _stop_pool_patches(patchers: list):
+    for p in patchers:
+        p.stop()
+
+
+@pytest.fixture
+def mock_infra():
+    """Patch ensure_fia_instance and ensure_evalid_set."""
+    with patch(
+        "pyfia.carbon.total_ecosystem.ensure_fia_instance",
+        return_value=(MockDB(), False),
+    ), patch("pyfia.carbon.total_ecosystem.ensure_evalid_set"):
+        yield
+
+
+class TestTotalEcosystemSumming:
+    """Tests for the pool-summing logic."""
+
+    def test_sums_carbon_acre_correctly(self, mock_infra):
+        results = {
+            "live_tree": _make_pool_result(10.0, 100000.0),
+            "standing_dead": _make_pool_result(2.0, 20000.0),
+            "understory": _make_pool_result(1.0, 10000.0),
+            "downed_dead": _make_pool_result(3.0, 30000.0),
+            "litter": _make_pool_result(4.0, 40000.0),
+            "soil_organic": _make_pool_result(20.0, 200000.0),
+        }
+        patchers = _start_pool_patches(results)
+        try:
+            result = total_ecosystem(MockDB())
+            total_row = result.filter(pl.col("POOL") == "TOTAL_ECOSYSTEM")
+            assert abs(float(total_row["CARBON_ACRE"][0]) - 40.0) < 1e-10
+            assert abs(float(total_row["CARBON_TOTAL"][0]) - 400000.0) < 1e-10
+        finally:
+            _stop_pool_patches(patchers)
+
+    def test_result_has_seven_rows(self, mock_infra):
+        """6 pool rows + 1 TOTAL_ECOSYSTEM row."""
+        results = {
+            name: _make_pool_result(1.0, 1000.0)
+            for name in _POOL_PATCHES
+        }
+        patchers = _start_pool_patches(results)
+        try:
+            result = total_ecosystem(MockDB())
+            assert len(result) == 7
+            pools = result["POOL"].to_list()
+            for expected in [
+                "TOTAL_ECOSYSTEM", "LIVE_TREE", "STANDING_DEAD",
+                "UNDERSTORY", "DOWNED_DEAD", "LITTER", "SOIL_ORGANIC",
+            ]:
+                assert expected in pools
+        finally:
+            _stop_pool_patches(patchers)
+
+    def test_handles_all_empty_results(self, mock_infra):
+        """If all pools return empty, total_ecosystem returns a fallback row."""
+        results = {name: _make_empty_result() for name in _POOL_PATCHES}
+        patchers = _start_pool_patches(results)
+        try:
+            result = total_ecosystem(MockDB())
+            assert len(result) > 0
+            assert result["CARBON_ACRE"][0] == 0.0
+        finally:
+            _stop_pool_patches(patchers)
+
+    def test_year_propagates(self, mock_infra):
+        results = {
+            name: _make_pool_result(1.0, 1000.0, year=2023)
+            for name in _POOL_PATCHES
+        }
+        patchers = _start_pool_patches(results)
+        try:
+            result = total_ecosystem(MockDB())
+            total_row = result.filter(pl.col("POOL") == "TOTAL_ECOSYSTEM")
+            assert int(total_row["YEAR"][0]) == 2023
+        finally:
+            _stop_pool_patches(patchers)
+
+
+class TestTotalEcosystemSignature:
+    def test_no_pool_parameter(self):
+        """total_ecosystem has no pool parameter."""
+        import inspect
+        sig = inspect.signature(total_ecosystem)
+        assert "pool" not in sig.parameters
+
+    def test_has_grp_by_parameter(self):
+        """total_ecosystem accepts grp_by, matching sibling estimators."""
+        import inspect
+        sig = inspect.signature(total_ecosystem)
+        assert "grp_by" in sig.parameters
+        assert sig.parameters["grp_by"].default is None
+
+
+def _make_grouped_pool_result(
+    rows: list[tuple],
+    group_col: str = "FORTYPCD",
+    year: int = 2023,
+) -> pl.DataFrame:
+    """Build a multi-row pool result keyed by a grouping column.
+
+    Each ``rows`` entry is ``(group_value, carbon_acre, carbon_total)``.
+    """
+    return pl.DataFrame(
+        {
+            "YEAR": [year] * len(rows),
+            group_col: [r[0] for r in rows],
+            "POOL": ["TOTAL"] * len(rows),
+            "CARBON_ACRE": [r[1] for r in rows],
+            "CARBON_TOTAL": [r[2] for r in rows],
+            "N_PLOTS": [100] * len(rows),
+            "N_TREES": [0] * len(rows),
+        }
+    )
+
+
+class TestTotalEcosystemGrpBy:
+    """Tests for grp_by forwarding and per-group summing."""
+
+    def test_grp_by_forwarded_to_each_pool(self, mock_infra):
+        """grp_by must be passed through to every pool estimator."""
+        result_df = _make_grouped_pool_result([("A", 1.0, 100.0)])
+        mocks: dict[str, object] = {}
+        patchers = []
+        try:
+            for name, target in _POOL_PATCHES.items():
+                p = patch(target, return_value=result_df)
+                mocks[name] = p.start()
+                patchers.append(p)
+            total_ecosystem(MockDB(), grp_by="FORTYPCD")
+            for name, mock in mocks.items():
+                assert mock.call_args is not None, f"pool {name} was not called"
+                kwargs = mock.call_args.kwargs
+                assert kwargs.get("grp_by") == "FORTYPCD", (
+                    f"pool {name} did not receive grp_by; got {kwargs}"
+                )
+        finally:
+            for p in patchers:
+                p.stop()
+
+    def test_grp_by_sums_per_group(self, mock_infra):
+        """TOTAL_ECOSYSTEM row is summed within each group, not collapsed."""
+        # Two forest type codes, each pool reports both.
+        results = {
+            name: _make_grouped_pool_result(
+                [("A", 1.0, 100.0), ("B", 2.0, 200.0)]
+            )
+            for name in _POOL_PATCHES
+        }
+        patchers = _start_pool_patches(results)
+        try:
+            result = total_ecosystem(MockDB(), grp_by="FORTYPCD")
+            totals = result.filter(pl.col("POOL") == "TOTAL_ECOSYSTEM").sort(
+                "FORTYPCD"
+            )
+            # 2 groups × 1 TOTAL row each
+            assert len(totals) == 2
+            assert totals["FORTYPCD"].to_list() == ["A", "B"]
+            # 6 pools × 1.0 = 6.0 for group A, 6 × 2.0 = 12.0 for group B
+            assert abs(float(totals["CARBON_ACRE"][0]) - 6.0) < 1e-10
+            assert abs(float(totals["CARBON_ACRE"][1]) - 12.0) < 1e-10
+            # 6 × 100 = 600, 6 × 200 = 1200
+            assert abs(float(totals["CARBON_TOTAL"][0]) - 600.0) < 1e-10
+            assert abs(float(totals["CARBON_TOTAL"][1]) - 1200.0) < 1e-10
+        finally:
+            _stop_pool_patches(patchers)
+
+    def test_grp_by_preserves_pool_rows(self, mock_infra):
+        """Pool rows survive grp_by — output has pool rows + TOTAL rows per group."""
+        results = {
+            name: _make_grouped_pool_result(
+                [("A", 1.0, 100.0), ("B", 2.0, 200.0)]
+            )
+            for name in _POOL_PATCHES
+        }
+        patchers = _start_pool_patches(results)
+        try:
+            result = total_ecosystem(MockDB(), grp_by="FORTYPCD")
+            # 6 pools × 2 groups + 2 TOTAL rows = 14 rows
+            assert len(result) == 14
+            assert "FORTYPCD" in result.columns
+            # Each pool appears for each group
+            pool_a = result.filter(pl.col("FORTYPCD") == "A")
+            assert len(pool_a) == 7  # 6 pools + 1 TOTAL
+        finally:
+            _stop_pool_patches(patchers)
diff --git a/tests/unit/test_understory_estimator.py b/tests/unit/test_understory_estimator.py
new file mode 100644
index 00000000..93d3fffb
--- /dev/null
+++ b/tests/unit/test_understory_estimator.py
@@ -0,0 +1,210 @@
+"""Unit tests for UnderstoryEstimator class.
+
+Tests the UnderstoryEstimator methods in isolation using mock data.
+No database connection required.
+"""
+
+import polars as pl
+import pytest
+
+from pyfia.carbon.understory import UnderstoryEstimator, understory
+
+
+class MockDB:
+    """Mock database for testing estimator methods in isolation."""
+
+    def __init__(self):
+        self.db_path = "/fake/path"
+        self.tables = {}
+        self.evalid = None
+        self.evalids = None
+        self._state_filter = None
+
+
+class TestGetRequiredTables:
+    """Tests for get_required_tables method."""
+
+    def test_returns_condition_level_tables(self):
+        config = {"pool": "ag", "land_type": "forest"}
+        estimator = UnderstoryEstimator(MockDB(), config)
+        tables = estimator.get_required_tables()
+
+        assert "COND" in tables
+        assert "PLOT" in tables
+        assert "POP_PLOT_STRATUM_ASSGN" in tables
+        assert "POP_STRATUM" in tables
+
+    def test_tree_table_not_required(self):
+        config = {"pool": "ag", "land_type": "forest"}
+        estimator = UnderstoryEstimator(MockDB(), config)
+        tables = estimator.get_required_tables()
+        assert "TREE" not in tables
+
+    def test_tables_consistent_across_pools(self):
+        for pool in ["ag", "bg", "total"]:
+            config = {"pool": pool}
+            estimator = UnderstoryEstimator(MockDB(), config)
+            tables = estimator.get_required_tables()
+            assert "TREE" not in tables
+            assert "COND" in tables
+
+
+class TestGetTreeColumns:
+    def test_returns_empty_list(self):
+        config = {"pool": "ag"}
+        estimator = UnderstoryEstimator(MockDB(), config)
+        cols = estimator.get_tree_columns()
+        assert cols == []
+
+
+class TestGetCondColumns:
+    def test_includes_understory_columns(self):
+        config = {"land_type": "forest"}
+        estimator = UnderstoryEstimator(MockDB(), config)
+        cols = estimator.get_cond_columns()
+
+        assert "CARBON_UNDERSTORY_AG" in cols
+        assert "CARBON_UNDERSTORY_BG" in cols
+        assert "PLT_CN" in cols
+        assert "CONDID" in cols
+        assert "CONDPROP_UNADJ" in cols
+        assert "COND_STATUS_CD" in cols
+
+
+class TestCalculateValues:
+    """Tests for calculate_values method."""
+
+    @pytest.fixture
+    def mock_db(self):
+        return MockDB()
+
+    def test_ag_pool(self, mock_db):
+        config = {"pool": "ag"}
+        estimator = UnderstoryEstimator(mock_db, config)
+
+        data = pl.DataFrame(
+            {
+                "CARBON_UNDERSTORY_AG": [1.5, 0.8],
+                "CARBON_UNDERSTORY_BG": [0.15, 0.08],
+            }
+        ).lazy()
+
+        result = estimator.calculate_values(data).collect()
+        assert abs(result["CARBON_ACRE"][0] - 1.5) < 1e-10
+        assert abs(result["CARBON_ACRE"][1] - 0.8) < 1e-10
+
+    def test_bg_pool(self, mock_db):
+        config = {"pool": "bg"}
+        estimator = UnderstoryEstimator(mock_db, config)
+
+        data = pl.DataFrame(
+            {
+                "CARBON_UNDERSTORY_AG": [1.5, 0.8],
+                "CARBON_UNDERSTORY_BG": [0.15, 0.08],
+            }
+        ).lazy()
+
+        result = estimator.calculate_values(data).collect()
+        assert abs(result["CARBON_ACRE"][0] - 0.15) < 1e-10
+        assert abs(result["CARBON_ACRE"][1] - 0.08) < 1e-10
+
+    def test_total_pool(self, mock_db):
+        config = {"pool": "total"}
+        estimator = UnderstoryEstimator(mock_db, config)
+
+        data = pl.DataFrame(
+            {
+                "CARBON_UNDERSTORY_AG": [1.5, 0.8],
+                "CARBON_UNDERSTORY_BG": [0.15, 0.08],
+            }
+        ).lazy()
+
+        result = estimator.calculate_values(data).collect()
+        assert abs(result["CARBON_ACRE"][0] - 1.65) < 1e-10
+        assert abs(result["CARBON_ACRE"][1] - 0.88) < 1e-10
+
+    def test_null_values_treated_as_zero(self, mock_db):
+        config = {"pool": "total"}
+        estimator = UnderstoryEstimator(mock_db, config)
+
+        data = pl.DataFrame(
+            {
+                "CARBON_UNDERSTORY_AG": [None, 1.0],
+                "CARBON_UNDERSTORY_BG": [0.1, None],
+            }
+        ).lazy()
+
+        result = estimator.calculate_values(data).collect()
+        assert abs(result["CARBON_ACRE"][0] - 0.1) < 1e-10
+        assert abs(result["CARBON_ACRE"][1] - 1.0) < 1e-10
+
+    def test_both_null_gives_zero(self, mock_db):
+        config = {"pool": "total"}
+        estimator = UnderstoryEstimator(mock_db, config)
+
+        data = pl.DataFrame(
+            {
+                "CARBON_UNDERSTORY_AG": [None],
+                "CARBON_UNDERSTORY_BG": [None],
+            }
+        ).lazy()
+
+        result = estimator.calculate_values(data).collect()
+        assert result["CARBON_ACRE"][0] == 0.0
+
+    def test_empty_dataframe(self, mock_db):
+        config = {"pool": "ag"}
+        estimator = UnderstoryEstimator(mock_db, config)
+
+        data = pl.DataFrame(
+            {
+                "CARBON_UNDERSTORY_AG": pl.Series([], dtype=pl.Float64),
+                "CARBON_UNDERSTORY_BG": pl.Series([], dtype=pl.Float64),
+            }
+        ).lazy()
+
+        result = estimator.calculate_values(data).collect()
+        assert len(result) == 0
+
+    def test_bg_is_ten_percent_of_total(self, mock_db):
+        """BG should be ~10% of total per the Smith et al. 2006 convention."""
+        # Using realistic values where BG ≈ AG / 9
+        ag = 1.08
+        bg = 0.12  # 10% of total (ag + bg = 1.2)
+        config_total = {"pool": "total"}
+        config_bg = {"pool": "bg"}
+
+        estimator_total = UnderstoryEstimator(mock_db, config_total)
+        estimator_bg = UnderstoryEstimator(mock_db, config_bg)
+
+        data = pl.DataFrame(
+            {
+                "CARBON_UNDERSTORY_AG": [ag],
+                "CARBON_UNDERSTORY_BG": [bg],
+            }
+        ).lazy()
+
+        total = estimator_total.calculate_values(data).collect()["CARBON_ACRE"][0]
+        bg_val = estimator_bg.calculate_values(data).collect()["CARBON_ACRE"][0]
+        assert abs(bg_val / total - 0.1) < 1e-10
+
+
+class TestPoolValidation:
+    def test_invalid_pool_raises_error(self):
+        with pytest.raises(ValueError, match="Invalid pool"):
+            understory(MockDB(), pool="invalid")
+
+    def test_valid_pools_accepted(self):
+        # These should not raise ValueError on pool validation
+        # (they'll fail on DB access later)
+        for pool in ["ag", "bg", "total", "AG", "BG", "TOTAL"]:
+            with pytest.raises(Exception):
+                # Will fail on DB access, but pool validation passes
+                understory(MockDB(), pool=pool)
+
+
+class TestEstimatorLabel:
+    def test_label_is_understory(self):
+        config = {"pool": "ag"}
+        estimator = UnderstoryEstimator(MockDB(), config)
+        assert estimator._estimator_label == "Understory"
diff --git a/tests/validation/test_live_tree_nsvb.py b/tests/validation/test_live_tree_nsvb.py
new file mode 100644
index 00000000..cab541fe
--- /dev/null
+++ b/tests/validation/test_live_tree_nsvb.py
@@ -0,0 +1,431 @@
+"""NSVB live tree carbon parity validation against FIADB TREE.CARBON_AG.
+
+This is the PR 2+ validation gate — see PR 2 (``pyfia.carbon.live_tree``)
+and the ongoing PR 3 closure work. The test answers the question the
+in-repo unit/equivalence suite cannot: *how closely does the NSVB
+pipeline agree with FIADB's pre-computed carbon values on real inventory
+data for the official Georgia 2024 EXPVOL evaluation (EVALID 132401)?*
+
+History of the ratchet thresholds in this file:
+
+**Phase 1 baseline** (species-level fallback only, no EVALID filter,
+measured before the ECOSUBCD lookup landed — commit e1f0254):
+
+- Biomass ratio (pyfia NSVB AGB / FIADB DRYBIO_AG):
+  median **1.030**, mean **1.093**, p95 **1.411**
+- ~50% of trees agree within 5%; ~34% within 1%; ~29% within 0.1%
+- Rooted in the PR 2 Phase 1 choice to skip DIVISION-specific
+  coefficient rows (S1a-S8a columns keyed on Bailey ecoprovince)
+
+**Phase 1.5 closure** (no EVALID filter, ECOSUBCD → DIVISION lookup
+wired in — commit adf3635):
+
+- Joins ``PLOTGEOM.ECOSUBCD`` → derives the Bailey DIVISION via
+  :func:`pyfia.carbon.nsvb.coefficients.ecosubcd_to_division` →
+  passes the ``DIVISION`` column through ``compute_nsvb_biomass``, which
+  then activates the Level 2 coalesce.
+- median rel_err 4.87% → 3.55%, biomass ratio median 1.0179 → 1.0000.
+
+**Phase 1.6 finding — validation scope correction** (this commit):
+
+- The Phase 1 / 1.5 numbers above were measured on the FULL Georgia DB
+  (1.46M live trees), which includes 575k pre-1989 periodic-inventory
+  trees (1972, 1982, 1989 panels) NOT in the current annual evaluation.
+- Those periodic-era trees have FIADB ``CARBON_AG`` / ``DRYBIO_AG``
+  computed via the legacy Component Ratio Method (CRM, flat 0.5 carbon
+  fraction; see Appendix K of FIADB User Guide v9.1, K-1) rather than
+  NSVB. Comparing pyfia's NSVB recompute against legacy-CRM data was
+  producing spurious 1,000-12,000% rel_err outliers — almost all
+  CULL ≥ 90 TREECLCD=4 hardwoods from the periodic panels.
+- The Phase 1.6 task as originally framed (TREECLCD=4 cull formula
+  investigation) was misdiagnosed: the pyfia cull formula
+  ``(1 - (1 - DENSITY_PROP) * CULL/100) * Stem Wood`` matches Appendix K
+  exactly, with no TREECLCD dispatch. The actual fix is to scope the
+  validation test to the EVALID set, which we now do via
+  ``JOIN POP_PLOT_STRATUM_ASSGN ppsa ON t.PLT_CN = ppsa.PLT_CN
+  AND ppsa.EVALID = 132401`` in the SQL above.
+- Massive measured improvement on the EVALID-filtered set
+  (130,952 trees): median rel_err **3.55% → 0.085%** (42x), within-1%
+  **43% → 63%**, within-0.1% **39% → 58%**, max rel_err
+  **12,425% → 478%**, mean **9.6% → 4.4%**.
+
+FIADB implied carbon fraction (``CARBON_AG / DRYBIO_AG``) on the
+EVALID-filtered set is **0.42-0.53** with median 0.477, exactly the
+S10a range — confirming the EVALID 132401 trees are all NSVB-era and
+the fraction layer is not the source of any residual gap.
+
+**Requires** the ``PLOTGEOM`` table in the test database. ``PLOTGEOM``
+is in ``pyfia.downloader.COMMON_TABLES`` as of the Phase 1.5 PR, so any
+fresh ``pyfia.download(state)`` call pulls it automatically. Existing
+test databases downloaded *before* that change need a one-off import:
+
+.. code-block:: python
+
+    # Only needed for test databases downloaded before PLOTGEOM was
+    # added to COMMON_TABLES. New downloads include it automatically.
+    from pyfia.downloader.client import DataMartClient
+    import duckdb, tempfile
+    from pathlib import Path
+
+    client = DataMartClient()
+    with tempfile.TemporaryDirectory() as tmp:
+        p = Path(tmp)
+        client.download_tables("GA", tables=["PLOTGEOM"], common=False, dest_dir=p)
+        conn = duckdb.connect("data/georgia.duckdb")
+        csv = next(p.glob("*.csv"))
+        conn.execute(f\"\"\"
+            CREATE OR REPLACE TABLE PLOTGEOM AS
+            SELECT * FROM read_csv_auto('{csv}', header=true, ignore_errors=true)
+        \"\"\")
+        conn.close()
+
+If ``PLOTGEOM`` is missing the test skips with a helpful message.
+"""
+
+from __future__ import annotations
+
+import duckdb
+import polars as pl
+import pytest
+
+from pyfia.carbon.nsvb.carbon_fractions import (
+    _compute_default_live_carbon_fraction,
+    load_carbon_fractions_live_df,
+)
+from pyfia.carbon.nsvb.coefficients import ecosubcd_to_division_expr
+from pyfia.carbon.nsvb.equations import compute_nsvb_biomass
+
+# Ratchet thresholds — locked at the Phase 1.6 baseline (EVALID-filtered
+# scope, ECOSUBCD → DIVISION lookup active). Any commit that LOOSENS these
+# is a regression; any commit that tightens them is a measurable improvement.
+#
+# Phase 1 baseline (no EVALID filter, before DIVISION lookup, commit e1f0254):
+#   median rel_err = 4.87%, p99 = 65.23%, within 5% = 50.36%,
+#   biomass ratio median = 1.0179
+#
+# Phase 1.5 baseline (no EVALID filter, DIVISION lookup wired, commit adf3635):
+#   median rel_err = 3.55% (-27%), p99 = 65.55% (~unchanged), within 5% = 53.90%
+#   biomass ratio median = 1.0000, within 1% = 43.07%, within 0.1% = 38.83%
+#
+# Phase 1.6 baseline (EVALID 132401 filter applied, this commit):
+#   The unfiltered baselines above were polluted by 575k pre-1989
+#   periodic-inventory trees that FIADB computed via legacy CRM. Filtering
+#   to the official EVALID 132401 evaluation (130,952 NSVB-era trees)
+#   reveals the real Phase 1.5/1.7 quality:
+#   median rel_err = 0.0846% (42x better), p99 = 40.37% (1.6x better),
+#   within 5% = 73.32%, within 1% = 62.91%, within 0.1% = 58.20%,
+#   max rel_err = 478.62% (down from 12,425%), biomass ratio median = 1.0000
+_BASELINE_MEDIAN_REL_ERR = 0.005  # Phase 1.6: 0.000846
+_BASELINE_P99_REL_ERR = 0.50  # Phase 1.6: 0.4037
+_BASELINE_WITHIN_5PCT_FRAC = 0.70  # Phase 1.6: 0.7332 (lower bound)
+_BASELINE_WITHIN_1PCT_FRAC = 0.60  # Phase 1.6: 0.6291 (lower bound)
+_BASELINE_WITHIN_0P1PCT_FRAC = 0.55  # Phase 1.6: 0.5820 (lower bound)
+_BASELINE_BIOMASS_RATIO_MEDIAN_MIN = 0.999  # Phase 1.6: 1.0000
+_BASELINE_BIOMASS_RATIO_MEDIAN_MAX = 1.001  # Phase 1.6: 1.0000
+
+# Trees with SPCD not covered by any NSVB coefficient path (species-level
+# OR Jenkins fallback) will get ``agb=None`` from compute_nsvb_biomass.
+# Allow a little headroom — the EVALID-filtered set has even fewer null
+# trees than the unfiltered set since it's all annual-inventory data.
+_MAX_NULL_FRAC = 0.005  # Phase 1.6 EVALID-filtered: well below 0.001
+
+
+class TestLiveTreeNSVBParity:
+    """Per-tree NSVB AG vs FIADB ``TREE.CARBON_AG`` on real Georgia data.
+
+    The baseline locks PR 2's species-level-only Phase 1 behavior. Each
+    subsequent commit that improves the NSVB pipeline's fidelity (DIVISION
+    lookup, missing SPCD coverage, etc.) should tighten the thresholds
+    below.
+    """
+
+    def test_per_tree_ag_agrees_with_fiadb(self, fia_db):
+        """Per-tree AG carbon parity between PR 2 and FIADB.
+
+        Runs the vectorized NSVB pipeline on every eligible live Georgia
+        tree, converts to carbon via species-specific S10a fractions, and
+        diffs against FIADB's pre-computed ``TREE.CARBON_AG`` column.
+
+        Reports the relative-error distribution plus the biomass ratio
+        (pyfia / FIADB) and the FIADB-implied carbon fraction as separate
+        diagnostic layers — if future validation reveals a regression,
+        the layered stats localize which layer moved.
+        """
+        # Load every eligible live tree in one go, joined to PLOTGEOM to
+        # get ECOSUBCD for the Phase 1.5 DIVISION lookup. Skip the test
+        # gracefully when PLOTGEOM is missing so CI runs without it don't
+        # fail — this file is gated on the ``validation`` marker and is
+        # opt-in by design.
+        conn = duckdb.connect(fia_db, read_only=True)
+        try:
+            existing = set(
+                conn.execute("SELECT table_name FROM information_schema.tables")
+                .pl()["table_name"]
+                .to_list()
+            )
+            if "PLOTGEOM" not in existing:
+                pytest.skip(
+                    "PLOTGEOM table missing from the test database. "
+                    "Phase 1.5 validation requires ECOSUBCD. As of the "
+                    "Phase 1.5 PR, PLOTGEOM is in pyfia.downloader"
+                    ".COMMON_TABLES and new pyfia.download() calls "
+                    "include it automatically. For older test databases, "
+                    "see this file's module docstring for the one-off "
+                    "PLOTGEOM import script."
+                )
+
+            # Phase 1.6 fix: filter to EVALID 132401 (the official Georgia
+            # 2024 EXPVOL evaluation, ~131k trees). Without this filter, the
+            # query pulled in 575k pre-1989 periodic-inventory trees from
+            # 1972/1982/1989 panels — those have FIADB CARBON_AG/DRYBIO_AG
+            # computed via the legacy Component Ratio Method (CRM, flat 0.5
+            # carbon fraction; see Appendix K of FIADB User Guide v9.1, K-1)
+            # rather than NSVB. Comparing pyfia's NSVB recompute against
+            # legacy-CRM data was producing spurious 1,000-12,000% rel_err
+            # outliers (almost all CULL ≥ 90 TREECLCD=4 hardwoods from the
+            # periodic inventory) that masked the real Phase 1.5/1.7 quality.
+            # The DISTINCT is required because POP_PLOT_STRATUM_ASSGN can
+            # have duplicate (PLT_CN, EVALID) rows in some states.
+            trees = conn.execute("""
+                SELECT DISTINCT
+                    t.CN,
+                    t.SPCD,
+                    t.DIA,
+                    t.HT,
+                    t.CULL,
+                    t.CARBON_AG AS FIADB_CARBON_AG,
+                    t.DRYBIO_AG AS FIADB_DRYBIO_AG,
+                    rs.JENKINS_SPGRPCD,
+                    rs.WOOD_SPGR_GREENVOL_DRYWT AS WDSG,
+                    pg.ECOSUBCD
+                FROM TREE t
+                JOIN POP_PLOT_STRATUM_ASSGN ppsa
+                  ON t.PLT_CN = ppsa.PLT_CN
+                LEFT JOIN REF_SPECIES rs ON t.SPCD = rs.SPCD
+                LEFT JOIN PLOTGEOM pg ON t.PLT_CN = pg.CN
+                WHERE ppsa.EVALID = 132401
+                  AND t.STATUSCD = 1
+                  AND t.DIA IS NOT NULL AND t.DIA >= 1.0
+                  AND t.HT IS NOT NULL
+                  AND t.SPCD IS NOT NULL
+                  AND t.CARBON_AG IS NOT NULL
+                  AND rs.JENKINS_SPGRPCD IS NOT NULL
+                  AND rs.WOOD_SPGR_GREENVOL_DRYWT IS NOT NULL
+            """).pl()
+        finally:
+            conn.close()
+
+        n_total = trees.height
+        assert n_total > 0, f"no eligible trees found in {fia_db}"
+
+        # Derive the Bailey DIVISION from ECOSUBCD. ``ecosubcd_to_division``
+        # returns None for null/malformed ECOSUBCDs, which the downstream
+        # coalesce handles correctly by falling through to species-level
+        # and Jenkins.
+        trees = trees.with_columns(
+            ecosubcd_to_division_expr("ECOSUBCD").alias("DIVISION")
+        )
+        n_with_division = int(trees["DIVISION"].is_not_null().sum())
+
+        # Normalize dtypes — see PR 2 commit 12a87c9 for why SPCD must be
+        # cast to Int64 here (CSV-loaded TREE.SPCD lands as Float64).
+        trees = trees.with_columns(
+            [
+                pl.col("SPCD").cast(pl.Int64),
+                pl.col("JENKINS_SPGRPCD").cast(pl.Int64),
+                pl.col("DIA").cast(pl.Float64),
+                pl.col("HT").cast(pl.Float64),
+                pl.col("CULL").cast(pl.Float64),
+                pl.col("WDSG").cast(pl.Float64),
+                pl.col("FIADB_CARBON_AG").cast(pl.Float64),
+                pl.col("FIADB_DRYBIO_AG").cast(pl.Float64),
+            ]
+        )
+
+        # Run the vectorized NSVB biomass pipeline (same entry point
+        # LiveTreeEstimator.calculate_values uses).
+        result = compute_nsvb_biomass(trees.lazy()).collect()
+
+        # Count trees with null agb (SPCD coverage gaps). Report then drop.
+        n_null = int(result["agb"].null_count())
+        null_frac = n_null / n_total
+        print(f"\n=== Live tree NSVB parity vs FIADB (Georgia, {n_total:,} trees) ===")
+        print(
+            f"  trees with DIVISION resolved from ECOSUBCD: "
+            f"{n_with_division:,} ({n_with_division / n_total:.2%})"
+        )
+        print(
+            f"  trees with null NSVB agb (SPCD coverage gap): "
+            f"{n_null:,} ({null_frac:.3%})"
+        )
+
+        result = result.filter(pl.col("agb").is_not_null())
+
+        # Apply species-specific S10a carbon fractions with default-mean
+        # fallback, matching LiveTreeEstimator.calculate_values exactly.
+        cf_df = load_carbon_fractions_live_df()
+        default_frac = _compute_default_live_carbon_fraction()
+        result = result.join(cf_df, on="SPCD", how="left")
+        result = result.with_columns(
+            [
+                pl.col("CARBON_FRAC_LIVE")
+                .fill_null(default_frac)
+                .alias("CARBON_FRAC_LIVE"),
+            ]
+        )
+        result = result.with_columns(
+            [
+                (pl.col("agb") * pl.col("CARBON_FRAC_LIVE")).alias("pyfia_CARBON_AG"),
+                # Biomass ratio: localizes whether disagreement is in the
+                # biomass layer or the carbon-fraction layer.
+                (pl.col("agb") / pl.col("FIADB_DRYBIO_AG")).alias("biomass_ratio"),
+                # FIADB implied carbon fraction — should be in the S10a
+                # range [0.40, 0.55]. If it's flat 0.50, FIADB is pre-NSVB.
+                (pl.col("FIADB_CARBON_AG") / pl.col("FIADB_DRYBIO_AG")).alias(
+                    "fiadb_implied_frac"
+                ),
+            ]
+        )
+
+        # Only compute rel-error stats on trees with valid pyfia output and
+        # positive FIADB CARBON_AG (a zero denominator is degenerate).
+        result = result.filter(
+            (pl.col("pyfia_CARBON_AG").is_not_null()) & (pl.col("FIADB_CARBON_AG") > 0)
+        )
+        n_compared = result.height
+
+        result = result.with_columns(
+            [
+                (
+                    (pl.col("pyfia_CARBON_AG") - pl.col("FIADB_CARBON_AG")).abs()
+                    / pl.col("FIADB_CARBON_AG").abs()
+                ).alias("rel_error"),
+            ]
+        )
+
+        rel = result["rel_error"]
+        n_within_0p1 = int((rel < 0.001).sum())
+        n_within_1 = int((rel < 0.01).sum())
+        n_within_5 = int((rel < 0.05).sum())
+
+        stats = {
+            "mean": float(rel.mean()),
+            "median": float(rel.median()),
+            "p95": float(rel.quantile(0.95)),
+            "p99": float(rel.quantile(0.99)),
+            "max": float(rel.max()),
+        }
+        br = result["biomass_ratio"]
+        biomass_stats = {
+            "median": float(br.median()),
+            "mean": float(br.mean()),
+            "p5": float(br.quantile(0.05)),
+            "p95": float(br.quantile(0.95)),
+        }
+        frac_stats = {
+            "median": float(result["fiadb_implied_frac"].median()),
+            "min": float(result["fiadb_implied_frac"].min()),
+            "max": float(result["fiadb_implied_frac"].max()),
+        }
+
+        print(
+            f"  trees compared (non-null pyfia, positive FIADB_CARBON_AG): "
+            f"{n_compared:,}"
+        )
+        print()
+        print("  === per-tree carbon rel_error ===")
+        print(f"    mean    : {stats['mean']:.4%}")
+        print(f"    median  : {stats['median']:.4%}")
+        print(f"    p95     : {stats['p95']:.4%}")
+        print(f"    p99     : {stats['p99']:.4%}")
+        print(f"    max     : {stats['max']:.4%}")
+        print(
+            f"    within 0.1% : {n_within_0p1:>10,} / {n_compared:,} "
+            f"({n_within_0p1 / n_compared:.2%})"
+        )
+        print(
+            f"    within 1%   : {n_within_1:>10,} / {n_compared:,} "
+            f"({n_within_1 / n_compared:.2%})"
+        )
+        print(
+            f"    within 5%   : {n_within_5:>10,} / {n_compared:,} "
+            f"({n_within_5 / n_compared:.2%})"
+        )
+        print()
+        print("  === biomass ratio (pyfia_NSVB_AGB / FIADB_DRYBIO_AG) ===")
+        print(f"    median  : {biomass_stats['median']:.4f}  (target: 1.000)")
+        print(f"    mean    : {biomass_stats['mean']:.4f}")
+        print(f"    p5      : {biomass_stats['p5']:.4f}")
+        print(f"    p95     : {biomass_stats['p95']:.4f}")
+        print()
+        print("  === FIADB implied carbon fraction (CARBON_AG / DRYBIO_AG) ===")
+        print(
+            f"    median  : {frac_stats['median']:.4f}  "
+            f"(S10a range 0.40-0.55 → NSVB, flat 0.50 → pre-NSVB)"
+        )
+        print(f"    min     : {frac_stats['min']:.4f}")
+        print(f"    max     : {frac_stats['max']:.4f}")
+
+        # Top-10 worst offenders for diagnosis, filtering out any lingering
+        # nulls (shouldn't be any after the earlier filter, but belt-and-
+        # suspenders against NaN pass-through).
+        worst = (
+            result.filter(pl.col("rel_error").is_not_null())
+            .sort("rel_error", descending=True)
+            .head(10)
+        )
+        if worst.height > 0:
+            print("\n  Top 10 worst disagreements:")
+            print(
+                "    SPCD    DIA    HT   CULL      pyfia_AG       FIADB_AG    rel_err"
+            )
+            for row in worst.iter_rows(named=True):
+                cull_val = row["CULL"] if row["CULL"] is not None else 0.0
+                print(
+                    f"    {row['SPCD']:>4}  {row['DIA']:>5.1f}  "
+                    f"{row['HT']:>4.1f}  {cull_val:>5.1f}  "
+                    f"{row['pyfia_CARBON_AG']:>12.2f}  "
+                    f"{row['FIADB_CARBON_AG']:>12.2f}  "
+                    f"{row['rel_error']:>8.2%}"
+                )
+
+        # === Ratchet assertions — locked at the Phase 1.5 baseline. ===
+        # Each assertion failure should be interpreted as either a
+        # regression (if the violation is on the wrong side) or an
+        # opportunity to tighten the constant (if the new measurement
+        # beats the baseline).
+        assert null_frac < _MAX_NULL_FRAC, (
+            f"SPCD coverage null rate {null_frac:.3%} exceeds "
+            f"{_MAX_NULL_FRAC:.1%} — new SPCDs falling out of the "
+            "coefficient tables?"
+        )
+        assert stats["median"] < _BASELINE_MEDIAN_REL_ERR, (
+            f"median rel error {stats['median']:.4%} exceeds baseline "
+            f"{_BASELINE_MEDIAN_REL_ERR:.2%} — PR 2 NSVB regression?"
+        )
+        assert stats["p99"] < _BASELINE_P99_REL_ERR, (
+            f"p99 rel error {stats['p99']:.4%} exceeds baseline "
+            f"{_BASELINE_P99_REL_ERR:.2%} — regression in the tail?"
+        )
+        assert n_within_5 / n_compared > _BASELINE_WITHIN_5PCT_FRAC, (
+            f"only {n_within_5 / n_compared:.2%} of trees agree within 5% "
+            f"(baseline: ≥{_BASELINE_WITHIN_5PCT_FRAC:.0%}) — regression?"
+        )
+        assert n_within_1 / n_compared > _BASELINE_WITHIN_1PCT_FRAC, (
+            f"only {n_within_1 / n_compared:.2%} of trees agree within 1% "
+            f"(baseline: ≥{_BASELINE_WITHIN_1PCT_FRAC:.0%}) — regression?"
+        )
+        assert n_within_0p1 / n_compared > _BASELINE_WITHIN_0P1PCT_FRAC, (
+            f"only {n_within_0p1 / n_compared:.2%} of trees agree within "
+            f"0.1% (baseline: ≥{_BASELINE_WITHIN_0P1PCT_FRAC:.0%}) — "
+            "regression?"
+        )
+        assert (
+            _BASELINE_BIOMASS_RATIO_MEDIAN_MIN
+            <= biomass_stats["median"]
+            <= _BASELINE_BIOMASS_RATIO_MEDIAN_MAX
+        ), (
+            f"biomass ratio median {biomass_stats['median']:.4f} outside "
+            f"baseline window [{_BASELINE_BIOMASS_RATIO_MEDIAN_MIN:.3f}, "
+            f"{_BASELINE_BIOMASS_RATIO_MEDIAN_MAX:.3f}] — systematic shift?"
+        )
diff --git a/tests/validation/test_standing_dead_nsvb.py b/tests/validation/test_standing_dead_nsvb.py
new file mode 100644
index 00000000..9d92c242
--- /dev/null
+++ b/tests/validation/test_standing_dead_nsvb.py
@@ -0,0 +1,429 @@
+"""NSVB standing dead carbon parity validation against FIADB TREE.CARBON_AG.
+
+Phase 2.5 validation gate for ``pyfia.carbon.standing_dead`` — the dead-tree
+analogue of ``test_live_tree_nsvb.py``. The test answers the question the
+in-repo unit/equivalence suite cannot: *how closely does the NSVB dead-tree
+pipeline + broken-top corrections + decay reductions + S10b carbon fractions
+agree with FIADB's pre-computed CARBON_AG values on real Georgia inventory
+data for the official 2024 EXPVOL evaluation (EVALID 132401)?*
+
+**Validation scope** (locked from PR 3 Phase 1.6 lessons):
+
+The query joins ``POP_PLOT_STRATUM_ASSGN`` and filters to ``EVALID =
+132401`` from the start. Without that filter, the query would pull in
+pre-1989 periodic-inventory standing dead trees (1972/1982/1989 panels)
+that have FIADB ``CARBON_AG`` / ``DRYBIO_AG`` computed via the legacy
+Component Ratio Method (CRM, flat 0.5 carbon fraction; see Appendix K of
+FIADB User Guide v9.1, K-1) rather than NSVB. See PR 3 commit ``4ae5bd0``
+for the full rationale.
+
+**Population filter** for standing dead trees:
+
+- ``STATUSCD = 2`` (dead)
+- ``STANDING_DEAD_CD = '1'`` (excludes downed dead trees, which are part
+  of the down dead wood pool, and dead saplings, which FIADB tracks but
+  doesn't compute biomass for)
+- ``DECAYCD IS NOT NULL`` (the join key for ``REF_TREE_DECAY_PROP``)
+- ``DIA >= 1.0`` (NSVB Models 1-5 are not parameterized below 1.0")
+
+This filter recovers the ~6,870 EVALID 132401 standing dead Georgia trees
+that have a FIADB ``CARBON_AG`` value to compare against.
+
+**Broken-top corrections (Phase 2.5):**
+
+~75% (5,132 / 6,870) of EVALID 132401 standing dead Georgia trees have
+``ACTUALHT < HT`` (broken top). The pipeline applies two adjustments:
+
+- Branch biomass is multiplied by ``Broken_crn_prop`` — the fraction of
+  the intact crown remaining below the break, using the mean intact crown
+  ratio from Table S11 (``REF_TREE_STND_DEAD_CR_PROP``) keyed on Bailey
+  ecoregion province × hw/sw.
+- Wood and bark are multiplied by ``(ACTUALHT/HT)^(2/3)`` — a paraboloid
+  taper approximation of the stem volume ratio below the break. This
+  replaces the Schumacher-Hall Model 6 volume-ratio computation that
+  FIADB uses.
+
+**Phase 2.5 measurement (with broken-top corrections):**
+
+- 6,870 trees compared (100% with DIVISION resolved from ECOSUBCD)
+- median rel_err: **10.89%**, mean: 12.14%, p95: 27.5%, p99: 36.5%
+- biomass ratio (pyfia/FIADB) median: **0.905** (~10% under-estimate)
+- within 1%: 15.02%, within 10%: 47.74%, within 50%: 99.88%
+- 0 null trees (full SPCD/DECAYCD coverage on the EVALID 132401 set)
+
+The remaining ~10% under-estimate (biomass ratio 0.905) comes from the
+paraboloid taper approximation vs FIADB's Model 6. Top-10 worst offenders
+are now small trees with peculiar HT/ACTUALHT configurations, not the
+extreme broken-top snags that dominated the Phase 2 tail.
+
+**Requires** the ``PLOTGEOM`` table in the test database for the
+DIVISION lookup. Same setup as ``test_live_tree_nsvb.py`` — see that
+file's docstring for the one-off PLOTGEOM import script for older test
+databases. ``PLOTGEOM`` ships in fresh ``pyfia.download()`` outputs as
+of the Phase 1.5 ``COMMON_TABLES`` PR.
+"""
+
+from __future__ import annotations
+
+import duckdb
+import polars as pl
+import pytest
+
+from pyfia.carbon.nsvb.carbon_fractions import (
+    load_carbon_fractions_dead_df,
+    load_dead_cr_prop_df,
+    load_dead_decay_proportions_df,
+)
+from pyfia.carbon.nsvb.coefficients import ecosubcd_to_division_expr
+from pyfia.carbon.nsvb.equations import compute_nsvb_dead_biomass
+
+# Ratchet thresholds — tightened for Phase 2.5 broken-top corrections.
+#
+# Phase 2.5 measurement (Georgia EVALID 132401, 6,870 trees, with
+# Appendix K broken-top corrections — crown proportion + paraboloid
+# volume ratio):
+#   median rel_err = 10.89%, mean = 12.14%, p95 = 27.51%, p99 = 36.49%,
+#   max = 90.59%, within 1% = 15.02%, within 10% = 47.74%,
+#   within 50% = 99.88%, biomass ratio median = 0.905,
+#   null fraction = 0.000%
+#
+# Improvement over Phase 2 baseline (intact-HT, no corrections):
+#   median 17.89% → 10.89%  (-39%)
+#   p99    441.5% → 36.5%   (-92%)
+#   within-10% 36.8% → 47.7% (+30%)
+#   within-50% 71.6% → 99.9% (+39%)
+#   biomass ratio 1.174 → 0.905 (moved from 17% over to 10% under)
+#
+# The remaining ~10% under-estimate (biomass ratio 0.905) is the
+# paraboloid taper approximation (exponent 2/3) vs FIADB's Model 6
+# Schumacher-Hall volume-ratio model. Top-10 worst offenders are now
+# small trees with peculiar HT/ACTUALHT configurations rather than the
+# extreme broken-top snags that dominated the Phase 2 tail.
+_BASELINE_MEDIAN_REL_ERR = 0.12  # Phase 2.5: 0.1089
+_BASELINE_P99_REL_ERR = 0.40  # Phase 2.5: 0.3649
+_BASELINE_WITHIN_50PCT_FRAC = 0.99  # Phase 2.5: 0.9988 (lower bound)
+_BASELINE_WITHIN_10PCT_FRAC = 0.45  # Phase 2.5: 0.4774 (lower bound)
+_BASELINE_WITHIN_1PCT_FRAC = 0.13  # Phase 2.5: 0.1502 (lower bound)
+_BASELINE_BIOMASS_RATIO_MEDIAN_MIN = 0.85  # Phase 2.5: 0.9052
+_BASELINE_BIOMASS_RATIO_MEDIAN_MAX = 0.95  # Phase 2.5: 0.9052
+
+# Trees with SPCD not covered by any NSVB coefficient path (species-level
+# OR Jenkins fallback) get null agb. Allow a little headroom — the
+# EVALID-filtered dead set is small (~6,870 trees) so a few nulls are OK.
+_MAX_NULL_FRAC = 0.005
+
+
+class TestStandingDeadNSVBParity:
+    """Per-tree NSVB AG dead vs FIADB ``TREE.CARBON_AG`` on real Georgia data.
+
+    The Phase 2 baseline locks the intact-HT approximation behavior. Each
+    subsequent commit that improves the NSVB dead pipeline's fidelity
+    (broken-top corrections, etc.) should tighten the thresholds below.
+    """
+
+    def test_per_tree_ag_agrees_with_fiadb(self, fia_db):
+        """Per-tree AG dead carbon parity between pyfia and FIADB.
+
+        Runs the vectorized NSVB dead pipeline (intact-HT + decay
+        reductions + S10b fractions) on every eligible standing dead
+        Georgia tree and diffs against FIADB's pre-computed
+        ``TREE.CARBON_AG`` column.
+
+        Reports the relative-error distribution plus the biomass ratio
+        (pyfia / FIADB) and the FIADB-implied dead carbon fraction as
+        separate diagnostic layers — if future validation reveals a
+        regression, the layered stats localize which layer moved.
+        """
+        conn = duckdb.connect(fia_db, read_only=True)
+        try:
+            existing = set(
+                conn.execute("SELECT table_name FROM information_schema.tables")
+                .pl()["table_name"]
+                .to_list()
+            )
+            if "PLOTGEOM" not in existing:
+                pytest.skip(
+                    "PLOTGEOM table missing from the test database. The "
+                    "DIVISION lookup requires ECOSUBCD. As of the Phase 1.5 "
+                    "PR, PLOTGEOM is in pyfia.downloader.COMMON_TABLES and "
+                    "new pyfia.download() calls include it automatically. "
+                    "For older test databases, see the live-tree validation "
+                    "test's module docstring for the one-off PLOTGEOM "
+                    "import script."
+                )
+
+            # Phase 2 SD-specific filter:
+            #   STATUSCD = 2          (dead)
+            #   STANDING_DEAD_CD='1'  (excludes downed dead + dead saplings)
+            #   DECAYCD IS NOT NULL   (the REF_TREE_DECAY_PROP join key)
+            #   DIA  >= 1.0           (NSVB Models 1-5 floor)
+            #   CARBON_AG > 0         (FIADB has a value to compare against)
+            # Plus the same EVALID 132401 scope filter the live-tree test
+            # uses (see PR 3 Phase 1.6 commit ``4ae5bd0`` for why).
+            trees = conn.execute("""
+                SELECT DISTINCT
+                    t.CN,
+                    t.SPCD,
+                    t.DIA,
+                    t.HT,
+                    t.ACTUALHT,
+                    t.DECAYCD,
+                    t.STANDING_DEAD_CD,
+                    t.CARBON_AG AS FIADB_CARBON_AG,
+                    t.DRYBIO_AG AS FIADB_DRYBIO_AG,
+                    rs.JENKINS_SPGRPCD,
+                    rs.WOOD_SPGR_GREENVOL_DRYWT AS WDSG,
+                    pg.ECOSUBCD
+                FROM TREE t
+                JOIN POP_PLOT_STRATUM_ASSGN ppsa
+                  ON t.PLT_CN = ppsa.PLT_CN
+                LEFT JOIN REF_SPECIES rs ON t.SPCD = rs.SPCD
+                LEFT JOIN PLOTGEOM pg ON t.PLT_CN = pg.CN
+                WHERE ppsa.EVALID = 132401
+                  AND t.STATUSCD = 2
+                  AND t.STANDING_DEAD_CD = '1'
+                  AND t.DECAYCD IS NOT NULL
+                  AND t.DIA IS NOT NULL AND t.DIA >= 1.0
+                  AND t.HT IS NOT NULL
+                  AND t.SPCD IS NOT NULL
+                  AND t.CARBON_AG IS NOT NULL AND t.CARBON_AG > 0
+                  AND rs.JENKINS_SPGRPCD IS NOT NULL
+                  AND rs.WOOD_SPGR_GREENVOL_DRYWT IS NOT NULL
+            """).pl()
+        finally:
+            conn.close()
+
+        n_total = trees.height
+        assert n_total > 0, f"no eligible standing dead trees found in {fia_db}"
+
+        # Derive Bailey DIVISION from ECOSUBCD (Level 2 NSVB lookup).
+        trees = trees.with_columns(
+            ecosubcd_to_division_expr("ECOSUBCD").alias("DIVISION")
+        )
+        n_with_division = int(trees["DIVISION"].is_not_null().sum())
+
+        # Normalize dtypes. DECAYCD comes off DuckDB as Utf8 because the
+        # source CSV column has nulls; cast to Int64 for the join. ACTUALHT
+        # is cast to Float64 for the broken-top correction pipeline.
+        trees = trees.with_columns(
+            [
+                pl.col("SPCD").cast(pl.Int64),
+                pl.col("JENKINS_SPGRPCD").cast(pl.Int64),
+                pl.col("DIA").cast(pl.Float64),
+                pl.col("HT").cast(pl.Float64),
+                pl.col("ACTUALHT").cast(pl.Float64, strict=False),
+                pl.col("DECAYCD").cast(pl.Int64, strict=False),
+                pl.col("WDSG").cast(pl.Float64),
+                pl.col("FIADB_CARBON_AG").cast(pl.Float64),
+                pl.col("FIADB_DRYBIO_AG").cast(pl.Float64),
+            ]
+        )
+
+        # Diagnostic: how many of the SDs are broken-top vs intact?
+        broken_top_count = int(
+            trees.filter(
+                pl.col("ACTUALHT").cast(pl.Float64, strict=False) < pl.col("HT")
+            ).height
+        )
+
+        # Run the vectorized NSVB dead biomass pipeline with broken-top
+        # corrections (Phase 2.5).
+        decay_props = load_dead_decay_proportions_df()
+        cr_prop_table = load_dead_cr_prop_df()
+        result = compute_nsvb_dead_biomass(
+            trees.lazy(), decay_props, cr_prop_table=cr_prop_table
+        ).collect()
+
+        # Count trees with null agb (SPCD coverage gaps OR DECAYCD outside 1-5).
+        n_null = int(result["agb"].null_count())
+        null_frac = n_null / n_total
+        print(
+            f"\n=== Standing dead NSVB parity vs FIADB "
+            f"(Georgia EVALID 132401, {n_total:,} trees) ==="
+        )
+        print(
+            f"  trees with DIVISION resolved from ECOSUBCD: "
+            f"{n_with_division:,} ({n_with_division / n_total:.2%})"
+        )
+        print(
+            f"  trees with broken top (ACTUALHT < HT): "
+            f"{broken_top_count:,} ({broken_top_count / n_total:.2%})"
+        )
+        print(
+            f"  trees with null NSVB agb (SPCD/DECAYCD coverage gap): "
+            f"{n_null:,} ({null_frac:.3%})"
+        )
+
+        result = result.filter(pl.col("agb").is_not_null())
+
+        # Apply S10b dead carbon fractions joined on (hw_sw, DECAYCD).
+        # The hw_sw expression mirrors the SPCD<300 rule used inside
+        # compute_nsvb_dead_biomass for consistency.
+        cf_df = load_carbon_fractions_dead_df()
+        result = result.with_columns(
+            [
+                pl.when(pl.col("SPCD") >= 300)
+                .then(pl.lit("hardwood"))
+                .otherwise(pl.lit("softwood"))
+                .alias("_hw_sw_cf"),
+            ]
+        )
+        result = result.join(
+            cf_df.rename({"hw_sw": "_hw_sw_cf"}),
+            on=["_hw_sw_cf", "DECAYCD"],
+            how="left",
+        )
+        result = result.with_columns(
+            [
+                (pl.col("agb") * pl.col("CARBON_FRAC_DEAD")).alias("pyfia_CARBON_AG"),
+                # Biomass ratio: localizes whether disagreement is in the
+                # biomass layer or the carbon-fraction layer.
+                (pl.col("agb") / pl.col("FIADB_DRYBIO_AG")).alias("biomass_ratio"),
+                # FIADB implied dead carbon fraction — should be in the S10b
+                # range (0.47 hardwood / 0.50-0.53 softwood). If it's flat 0.50,
+                # FIADB is using legacy CRM rather than NSVB S10b.
+                (pl.col("FIADB_CARBON_AG") / pl.col("FIADB_DRYBIO_AG")).alias(
+                    "fiadb_implied_frac"
+                ),
+            ]
+        )
+
+        # Only compute rel-error stats on trees with valid pyfia output and
+        # positive FIADB CARBON_AG (a zero denominator is degenerate).
+        result = result.filter(
+            (pl.col("pyfia_CARBON_AG").is_not_null()) & (pl.col("FIADB_CARBON_AG") > 0)
+        )
+        n_compared = result.height
+
+        result = result.with_columns(
+            [
+                (
+                    (pl.col("pyfia_CARBON_AG") - pl.col("FIADB_CARBON_AG")).abs()
+                    / pl.col("FIADB_CARBON_AG").abs()
+                ).alias("rel_error"),
+            ]
+        )
+
+        rel = result["rel_error"]
+        n_within_1 = int((rel < 0.01).sum())
+        n_within_10 = int((rel < 0.10).sum())
+        n_within_50 = int((rel < 0.50).sum())
+
+        stats = {
+            "mean": float(rel.mean()),
+            "median": float(rel.median()),
+            "p95": float(rel.quantile(0.95)),
+            "p99": float(rel.quantile(0.99)),
+            "max": float(rel.max()),
+        }
+        br = result["biomass_ratio"]
+        biomass_stats = {
+            "median": float(br.median()),
+            "mean": float(br.mean()),
+            "p5": float(br.quantile(0.05)),
+            "p95": float(br.quantile(0.95)),
+        }
+        frac_stats = {
+            "median": float(result["fiadb_implied_frac"].median()),
+            "min": float(result["fiadb_implied_frac"].min()),
+            "max": float(result["fiadb_implied_frac"].max()),
+        }
+
+        print(
+            f"  trees compared (non-null pyfia, positive FIADB_CARBON_AG): "
+            f"{n_compared:,}"
+        )
+        print()
+        print("  === per-tree carbon rel_error ===")
+        print(f"    mean    : {stats['mean']:.4%}")
+        print(f"    median  : {stats['median']:.4%}")
+        print(f"    p95     : {stats['p95']:.4%}")
+        print(f"    p99     : {stats['p99']:.4%}")
+        print(f"    max     : {stats['max']:.4%}")
+        print(
+            f"    within 1%   : {n_within_1:>6,} / {n_compared:,} "
+            f"({n_within_1 / n_compared:.2%})"
+        )
+        print(
+            f"    within 10%  : {n_within_10:>6,} / {n_compared:,} "
+            f"({n_within_10 / n_compared:.2%})"
+        )
+        print(
+            f"    within 50%  : {n_within_50:>6,} / {n_compared:,} "
+            f"({n_within_50 / n_compared:.2%})"
+        )
+        print()
+        print("  === biomass ratio (pyfia_NSVB_dead_AGB / FIADB_DRYBIO_AG) ===")
+        print(f"    median  : {biomass_stats['median']:.4f}  (target: 1.000)")
+        print(f"    mean    : {biomass_stats['mean']:.4f}")
+        print(f"    p5      : {biomass_stats['p5']:.4f}")
+        print(f"    p95     : {biomass_stats['p95']:.4f}")
+        print()
+        print("  === FIADB implied dead carbon fraction (CARBON_AG / DRYBIO_AG) ===")
+        print(
+            f"    median  : {frac_stats['median']:.4f}  "
+            f"(S10b range 0.47-0.53 → NSVB, flat 0.50 → pre-NSVB CRM)"
+        )
+        print(f"    min     : {frac_stats['min']:.4f}")
+        print(f"    max     : {frac_stats['max']:.4f}")
+
+        # Top-10 worst offenders for diagnosis. The intact-HT approximation
+        # should make broken-top trees the worst offenders — they should
+        # cluster at the high end with pyfia / FIADB ratios well above 1.
+        worst = (
+            result.filter(pl.col("rel_error").is_not_null())
+            .sort("rel_error", descending=True)
+            .head(10)
+        )
+        if worst.height > 0:
+            print("\n  Top 10 worst disagreements:")
+            print("    SPCD    DIA   HT  ACTHT  DC   pyfia_AG    FIADB_AG    rel_err")
+            for row in worst.iter_rows(named=True):
+                actht_str = row["ACTUALHT"] if row["ACTUALHT"] else "-"
+                print(
+                    f"    {row['SPCD']:>4}  {row['DIA']:>5.1f} "
+                    f"{row['HT']:>4.0f}  {actht_str:>5}  "
+                    f"{row['DECAYCD']:>2}  "
+                    f"{row['pyfia_CARBON_AG']:>10.2f}  "
+                    f"{row['FIADB_CARBON_AG']:>10.2f}  "
+                    f"{row['rel_error']:>8.2%}"
+                )
+
+        # === Ratchet assertions — locked at the Phase 2 baseline ===
+        # See the comment block at the top of this file for the full
+        # rationale on why these are loose. Future commits that improve
+        # broken-top handling should tighten them.
+        assert null_frac < _MAX_NULL_FRAC, (
+            f"SPCD/DECAYCD coverage null rate {null_frac:.3%} exceeds "
+            f"{_MAX_NULL_FRAC:.1%} — new SPCDs falling out of the "
+            "coefficient tables, or new DECAYCD outside the 1-5 range?"
+        )
+        assert stats["median"] < _BASELINE_MEDIAN_REL_ERR, (
+            f"median rel error {stats['median']:.4%} exceeds baseline "
+            f"{_BASELINE_MEDIAN_REL_ERR:.2%} — Phase 2 NSVB dead regression?"
+        )
+        assert stats["p99"] < _BASELINE_P99_REL_ERR, (
+            f"p99 rel error {stats['p99']:.4%} exceeds baseline "
+            f"{_BASELINE_P99_REL_ERR:.2%} — regression in the tail?"
+        )
+        assert n_within_50 / n_compared > _BASELINE_WITHIN_50PCT_FRAC, (
+            f"only {n_within_50 / n_compared:.2%} of trees agree within 50% "
+            f"(baseline: ≥{_BASELINE_WITHIN_50PCT_FRAC:.0%}) — regression?"
+        )
+        assert n_within_10 / n_compared > _BASELINE_WITHIN_10PCT_FRAC, (
+            f"only {n_within_10 / n_compared:.2%} of trees agree within 10% "
+            f"(baseline: ≥{_BASELINE_WITHIN_10PCT_FRAC:.0%}) — regression?"
+        )
+        assert n_within_1 / n_compared > _BASELINE_WITHIN_1PCT_FRAC, (
+            f"only {n_within_1 / n_compared:.2%} of trees agree within 1% "
+            f"(baseline: ≥{_BASELINE_WITHIN_1PCT_FRAC:.0%}) — regression?"
+        )
+        assert (
+            _BASELINE_BIOMASS_RATIO_MEDIAN_MIN
+            <= biomass_stats["median"]
+            <= _BASELINE_BIOMASS_RATIO_MEDIAN_MAX
+        ), (
+            f"biomass ratio median {biomass_stats['median']:.4f} outside "
+            f"baseline window [{_BASELINE_BIOMASS_RATIO_MEDIAN_MIN:.3f}, "
+            f"{_BASELINE_BIOMASS_RATIO_MEDIAN_MAX:.3f}] — systematic shift?"
+        )
diff --git a/uv.lock b/uv.lock
index 0bfd5802..998a0d78 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 1
+revision = 3
 requires-python = ">=3.11"
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