diff --git a/.agents/skills/crc-assess-agricultural-climate-risk/SKILL.md b/.agents/skills/crc-assess-agricultural-climate-risk/SKILL.md
new file mode 100644
index 0000000..e950295
--- /dev/null
+++ b/.agents/skills/crc-assess-agricultural-climate-risk/SKILL.md
@@ -0,0 +1,47 @@
+---
+name: crc-assess-agricultural-climate-risk
+description: Assess bounded USDA crop-cover or global FTW field evidence against CRC canonical flood or drought hazards. Use for agricultural sourcing, crop exposure, field screening, and open climate-risk baselines; not for yield prediction, ownership, or a synthetic farm-risk score.
+---
+
+# CRC agricultural climate risk
+
+Produce an auditable agricultural evidence layer and a separate per-hazard
+evaluation. Never infer ownership, yield, production, or a combined risk score.
+
+## Intake and source choice
+
+Reuse a supplied geography, year, crops, hazard, and decision context. If the
+target or decision is absent, ask: “Which sourcing area or agricultural region
+should I assess, and what decision should the screen inform?” Resolve and show
+a narrow WGS84 AOI.
+
+- In CONUS, default to USDA CDL 30 m and the latest complete requested year.
+ Use 10 m only for 2024–2025 when the added detail matters.
+- Elsewhere, use FTW field boundaries for the requested ISO country and retain
+ continuous confidence. State that crop type is unavailable.
+- Default flood to EFAS in Europe and GloFAS elsewhere. Add EDO SMI only when
+ drought is relevant and covered.
+
+## Workflow
+
+1. Read [the agricultural playbook](../../../ai-playbooks/agricultural-climate-risk.md).
+2. Materialize each applicable hazard into its own canonical Parquet and read
+ its embedded source, unit, semantics, pathway, horizon, tail, support, and
+ H3 resolution.
+3. Run `pipelines/agricultural_climate_pipeline.py` with a bounded AOI. Let the
+ hazard metadata choose H3 resolution. The notebook twin is
+ `notebooks/agricultural_climate_risk.ipynb`.
+4. Preserve USDA year/class and sampled crop share, or FTW year/confidence/area.
+ Preserve match status and missingness in each hazard evaluation.
+5. Keep hazards with incompatible units, horizons, or semantics separate.
+ Flag return-period extrapolation and low-confidence or missing agricultural
+ coverage.
+
+## Deliverable
+
+Return the agricultural-units Parquet, one canonical evaluation Parquet per
+hazard, map and summary Parquets, a PMTiles presentation artifact, and a
+manifest with versions, filters, hashes, row counts, metadata, and warnings.
+Show a map plus a per-crop or confidence-stratified exposure summary. Label
+USDA crop share as sampled pixels—not acreage—and FTW polygons as
+remote-sensing field units—not parcels or proof of ownership.
diff --git a/.agents/skills/crc-assess-agricultural-climate-risk/agents/openai.yaml b/.agents/skills/crc-assess-agricultural-climate-risk/agents/openai.yaml
new file mode 100644
index 0000000..b0f8fd6
--- /dev/null
+++ b/.agents/skills/crc-assess-agricultural-climate-risk/agents/openai.yaml
@@ -0,0 +1,5 @@
+interface:
+ display_name: "CRC Agricultural Climate Risk"
+ short_description: "Screen crop and field layers against hazards"
+ default_prompt: "Use $crc-assess-agricultural-climate-risk for my sourcing region."
+
diff --git a/AGENTS.md b/AGENTS.md
index 1f40b5d..9605527 100644
--- a/AGENTS.md
+++ b/AGENTS.md
@@ -14,6 +14,7 @@ repository discovery; see README.md's "Platform discovery" section.
| crc-screen-mortgage-flood | [.agents/skills/crc-screen-mortgage-flood/SKILL.md](.agents/skills/crc-screen-mortgage-flood/SKILL.md) |
| crc-model-flood-insurance-loss | [.agents/skills/crc-model-flood-insurance-loss/SKILL.md](.agents/skills/crc-model-flood-insurance-loss/SKILL.md) |
| crc-assess-asset-portfolio-risk | [.agents/skills/crc-assess-asset-portfolio-risk/SKILL.md](.agents/skills/crc-assess-asset-portfolio-risk/SKILL.md) |
+| crc-assess-agricultural-climate-risk | [.agents/skills/crc-assess-agricultural-climate-risk/SKILL.md](.agents/skills/crc-assess-agricultural-climate-risk/SKILL.md) |
| velo-underwrite-property-climate | [.agents/skills/velo-underwrite-property-climate/SKILL.md](.agents/skills/velo-underwrite-property-climate/SKILL.md) |
| velo-triage-portfolio-insurability | [.agents/skills/velo-triage-portfolio-insurability/SKILL.md](.agents/skills/velo-triage-portfolio-insurability/SKILL.md) |
| velo-assess-company-climate-risk | [.agents/skills/velo-assess-company-climate-risk/SKILL.md](.agents/skills/velo-assess-company-climate-risk/SKILL.md) |
diff --git a/README.md b/README.md
index 303aa2a..5186c19 100644
--- a/README.md
+++ b/README.md
@@ -42,6 +42,7 @@ Note: to run an enterprise twin, connect the [`CDT Express MCP server`](https://
| **Mortgage / collateral** | **Start:** `Use $crc-screen-mortgage-flood.`
**Optional:** `Use $crc-screen-mortgage-flood for Toronto, Canada.` | **Start:** `Use $velo-underwrite-property-climate.`
**Optional:** `Use $velo-underwrite-property-climate for 392 Markham Street, Toronto.` |
| **Insurance** | **Start:** `Use $crc-model-flood-insurance-loss.`
**Optional:** `Use $crc-model-flood-insurance-loss for Rotterdam using my attached portfolio and approved depth-damage curve.` | **Start:** `Use $velo-triage-portfolio-insurability.`
**Optional:** `Use $velo-triage-portfolio-insurability for Example Insurance Holdings.` |
| **Corporate finance / investment** | **Start:** `Use $crc-assess-asset-portfolio-risk.`
**Optional:** `Use $crc-assess-asset-portfolio-risk for Frankfurt, with flood and drought.` | **Start:** `Use $velo-assess-company-climate-risk.`
**Optional:** `Use $velo-assess-company-climate-risk for the S&P 500.` |
+| **Agricultural sourcing / crop exposure** | **Start:** `Use $crc-assess-agricultural-climate-risk.`
**Optional:** `Use $crc-assess-agricultural-climate-risk for corn and soybeans near Ames, Iowa.` | Use the company/portfolio twin when ownership, suppliers, values, or enterprise scenarios are required. |
After running both sides for the same real target:
@@ -90,6 +91,7 @@ both outputs exist.
| Mortgage / collateral | [`crc-screen-mortgage-flood`](.agents/skills/crc-screen-mortgage-flood/SKILL.md) → [`velo-underwrite-property-climate`](.agents/skills/velo-underwrite-property-climate/SKILL.md) |
| Insurance | [`crc-model-flood-insurance-loss`](.agents/skills/crc-model-flood-insurance-loss/SKILL.md) → [`velo-triage-portfolio-insurability`](.agents/skills/velo-triage-portfolio-insurability/SKILL.md) |
| Corporate finance / investment | [`crc-assess-asset-portfolio-risk`](.agents/skills/crc-assess-asset-portfolio-risk/SKILL.md) → [`velo-assess-company-climate-risk`](.agents/skills/velo-assess-company-climate-risk/SKILL.md) |
+| Agricultural sourcing / crop exposure | [`crc-assess-agricultural-climate-risk`](.agents/skills/crc-assess-agricultural-climate-risk/SKILL.md) plus the [agricultural playbook](ai-playbooks/agricultural-climate-risk.md) |
| Reconciliation / sales engineering | [`compare-crc-velo-assessments`](.agents/skills/compare-crc-velo-assessments/SKILL.md) |
For an open baseline without supplied files, the AI should:
@@ -202,6 +204,7 @@ These workflows acquire and canonicalize hazard data for a selected geography.
| [`notebooks/flood_risk_by_province.ipynb`](notebooks/flood_risk_by_province.ipynb) | [`pipelines/flood_admin_pipeline.py`](pipelines/flood_admin_pipeline.py) | EFAS area selection → pinned AOI cache → canonical curves → H3/admin join → Overture places → PMTiles |
| [`notebooks/jrc_global_flood_hazard.ipynb`](notebooks/jrc_global_flood_hazard.ipynb) | [`pipelines/jrc_flood_pipeline.py`](pipelines/jrc_flood_pipeline.py) | EFAS/GloFAS selection → pinned AOI cache → fitted curves → return-period evaluation |
| [`notebooks/jrc_edo_drought_index.ipynb`](notebooks/jrc_edo_drought_index.ipynb) | [`pipelines/jrc_drought_pipeline.py`](pipelines/jrc_drought_pipeline.py) | EDO SMI area/year selection → annual-minimum AOI cache → lower-tail curves → support-aware evaluation |
+| [`notebooks/agricultural_climate_risk.ipynb`](notebooks/agricultural_climate_risk.ipynb) | [`pipelines/agricultural_climate_pipeline.py`](pipelines/agricultural_climate_pipeline.py) | Bounded USDA crop samples or FTW field polygons → canonical hazard evaluation → source-aware maps and weighted summaries → PMTiles |
### Asset portfolio analytics
diff --git a/ai-playbooks/agricultural-climate-risk.md b/ai-playbooks/agricultural-climate-risk.md
new file mode 100644
index 0000000..5076bc1
--- /dev/null
+++ b/ai-playbooks/agricultural-climate-risk.md
@@ -0,0 +1,72 @@
+# Agricultural climate-risk playbook
+
+Use this playbook to answer location-specific questions such as:
+
+- Which sampled crop classes in an Iowa procurement area overlap modeled
+ riverine-flood exposure?
+- Which high-confidence predicted fields in a French sourcing region intersect
+ lower-tail soil-moisture drought conditions?
+- Where is agricultural coverage missing, uncertain, or spatially mismatched?
+
+## Choose the agricultural evidence layer
+
+Use USDA NASS CDL inside the conterminous United States when crop class matters.
+Select a narrow AOI, one or more years, and only relevant class codes. Use the
+30 m series for 2008–2025 continuity; use 10 m only for 2024–2025 questions
+that need the native newer product. Treat code 0 as background and code 81 as
+a real Clouds/No Data class.
+
+Use FTW outside the US, or when field boundaries rather than crop type are the
+needed unit. Preserve continuous confidence, the observation year, and field
+area. FTW does not supply crop type, cadastral boundaries, tenure, ownership,
+yield, or financial materiality. A confidence threshold is a screening policy,
+not a statement that excluded places contain no fields.
+
+## Pair with climate evidence
+
+Choose EFAS flood for Europe, GloFAS flood elsewhere, and EDO Soil Moisture
+Index where its coverage and lower-tail semantics answer the decision. Build
+one canonical hazard Parquet per source, then let its embedded H3 resolution
+control agricultural indexing. Do not relabel a historical hazard as a future
+scenario, and do not compare flood depth with SMI numerically.
+
+The agricultural pipeline materializes bounded GloFAS curves automatically when
+`--hazard` is omitted, so the Iowa crop/flood example is one command:
+
+```shell
+uv run python pipelines/agricultural_climate_pipeline.py \
+ --source usda --bounds -93.965 42.03 -93.91 42.085 --year 2025 \
+ --crop-codes 1 5 --return-periods 10 100
+```
+
+For Europe, materialize EDO with `pipelines/jrc_drought_pipeline.py`, then run
+the agricultural pipeline with `--source ftw --country-code `.
+
+## Interpret the outputs
+
+Keep agricultural observations, canonical hazard curves, and evaluated hazard
+values as separate artifacts. Report:
+
+- agricultural source, version, year, resolution, license, and filters;
+- hazard source, release, pathway, horizon, unit, probability convention, and
+ source support from embedded metadata;
+- matched and missing agricultural units, with exact/H3 match labels;
+- USDA sampled crop composition or FTW confidence/field area;
+- return-period hazard values with interpolation/extrapolation warnings.
+
+Use the source-aware summaries: USDA reports sampled pixels, crop share, and
+pixel-weighted hazard values by crop and coverage status; FTW reports field
+counts, observed field area, confidence, and area-weighted hazard values by
+country, year, and coverage status. Do not create a combined "farm risk score"
+without an authorized impact model, exposure values, and a documented
+aggregation policy. Observed crop cover is not yield, production, revenue,
+ownership, or vulnerability.
+
+## Reproducible bundle
+
+Retain `*-agricultural-units.parquet`, `*-hazard-evaluation.parquet`, the
+agricultural/hazard map and source-aware summary Parquets, PMTiles, and
+`manifest.json`. Add a CSV or GeoJSON only as a presentation derivative. The
+notebook `notebooks/agricultural_climate_risk.ipynb` and pipeline
+`pipelines/agricultural_climate_pipeline.py` are twins: use the notebook to
+explore assumptions and the pipeline for repeatable/headless execution.
diff --git a/ai-playbooks/docs/capability-matrix.md b/ai-playbooks/docs/capability-matrix.md
index ec0886f..677e9ce 100644
--- a/ai-playbooks/docs/capability-matrix.md
+++ b/ai-playbooks/docs/capability-matrix.md
@@ -7,6 +7,7 @@ Status reflects the public package surfaces verified on 2026-08-18 plus the prod
| Computation core | Distributions, explicit curve fitting, impact transforms, microscores, spanning sets, VaR/CVaR, attribution | Enterprise scores and impacts are delivered through VELO/CDT | Explain CRC as the open computational foundation and VELO/CDT as the industrialized data/product layer |
| Flood data | JRC GloFAS and EFAS lazy ingestion and canonicalization | Broader proprietary hazard catalogue and finer coverage | Pair every open flood screen with a multi-hazard enterprise run |
| Drought data | JRC EDO Soil Moisture Index workflow | Broader hazard/scenario coverage | Use CRC for transparent drought screening; use VELO/CDT when business impact needs multiple interacting factors |
+| Agricultural layers | Bounded USDA CDL crop-class scans (2008–2025) and FTW field-boundary scans (2024–2025) through the lazy DuckDB process seam | Entity, ownership, supplier, value, and broader scenario context where available | Treat crop cover and predicted field units as separate evidence—not yield, ownership, or a combined farm-risk score |
| Arbitrary data | Canonical Parquet; GeoTIFF/COG, Zarr, NetCDF, DuckDB/Arrow and geometry primitives | API/MCP-managed datasets and assets | CRC can onboard customer/open data, but needs more high-level recipes and validation |
| OS-Climate | Provider code exists; project context says the feed is currently unplugged | Not required for the proprietary route | Mark unavailable until a live integration test passes |
| Asset input | Caller supplies assets, or AI sources Overture candidate locations by AOI/category for an open demonstration | Public and organization companies/assets, ownership, indexes | Overture supports geographic screening but does not establish ownership, materiality, collateral, value, or portfolio membership |
diff --git a/notebooks/agricultural_climate_risk.ipynb b/notebooks/agricultural_climate_risk.ipynb
new file mode 100644
index 0000000..1cb3e4e
--- /dev/null
+++ b/notebooks/agricultural_climate_risk.ipynb
@@ -0,0 +1,4823 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "title",
+ "metadata": {},
+ "source": [
+ "# Corn, soybeans, and river-flood exposure near the Des Moines River\n",
+ "\n",
+ "This practical sourcing screen combines **2025 USDA Cropland Data Layer** pixels with a bounded, canonical **JRC GloFAS riverine-flood hazard**. The question is intentionally narrow: *which sampled corn and soybean H3 units overlap modeled flood depths, and how does that overlap change from a 10-year to a 100-year event?*\n",
+ "\n",
+ "The workflow never downloads a national crop raster or a global flood layer. Icechunk supplies bounded Arrow batches; DuckDB aggregates them out of core; only the small result is materialized. Crop pixels are evidence of land cover—not acreage, yield, ownership, or vulnerability—and flood depth remains a separate hazard measurement rather than a synthetic risk score."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "setup",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-24T22:46:27.533179Z",
+ "iopub.status.busy": "2026-08-24T22:46:27.533016Z",
+ "iopub.status.idle": "2026-08-24T22:46:29.793383Z",
+ "shell.execute_reply": "2026-08-24T22:46:29.792879Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import json\n",
+ "import subprocess\n",
+ "from argparse import Namespace\n",
+ "from pathlib import Path\n",
+ "\n",
+ "import h3\n",
+ "import pyarrow.parquet as pq\n",
+ "import plotly.express as px\n",
+ "import plotly.graph_objects as go\n",
+ "import plotly.io as pio\n",
+ "from plotly.subplots import make_subplots\n",
+ "\n",
+ "REPO_ROOT = Path.cwd().parent if Path.cwd().name == 'notebooks' else Path.cwd()\n",
+ "\n",
+ "from crc_sdk.workflows import HazardDataset\n",
+ "\n",
+ "# GitHub's notebook preview only renders static HTML/images (no JS execution),\n",
+ "# so every fig.show() below emits both an interactive widget and a static PNG fallback.\n",
+ "pio.renderers.default = \"jupyterlab+png\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "configuration-note",
+ "metadata": {},
+ "source": [
+ "## 1. A bounded, river-adjacent agricultural area\n",
+ "\n",
+ "The roughly 5 × 6 km AOI is deliberately small enough for a quick live run but crosses modeled GloFAS flood cells and active corn/soy cover. H3 resolution 7 keeps the crop and hazard layers on the same spatial key."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "configuration",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-24T22:46:29.795077Z",
+ "iopub.status.busy": "2026-08-24T22:46:29.794910Z",
+ "iopub.status.idle": "2026-08-24T22:46:29.799264Z",
+ "shell.execute_reply": "2026-08-24T22:46:29.798832Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Namespace(source='usda', bounds=[-93.965, 42.03, -93.91, 42.085], year=2025, crop_codes=[1, 5], country_code=None, minimum_confidence=80.0, h3_resolution=7, hazard=None, return_periods=[10, 100], cache_mode='reuse', output_dir=PosixPath('/Users/woozyking/rtai/crc-docs/pipeline_output/agriculture-notebook'), skip_pmtiles=True, pmtiles_output=PosixPath('/Users/woozyking/rtai/crc-docs/notebooks/artifacts/agricultural_crop_flood.pmtiles'), pmtiles_threads=1)"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "BOUNDS = (-93.965, 42.030, -93.910, 42.085)\n",
+ "OUTPUT_DIR = REPO_ROOT / 'pipeline_output' / 'agriculture-notebook'\n",
+ "ARTIFACTS_DIR = REPO_ROOT / 'notebooks' / 'artifacts'\n",
+ "OUTPUT_DIR.mkdir(parents=True, exist_ok=True)\n",
+ "ARTIFACTS_DIR.mkdir(parents=True, exist_ok=True)\n",
+ "\n",
+ "args = Namespace(\n",
+ " source='usda', bounds=list(BOUNDS), year=2025, crop_codes=[1, 5],\n",
+ " country_code=None, minimum_confidence=80.0, h3_resolution=7,\n",
+ " hazard=None, return_periods=[10, 100], cache_mode='reuse',\n",
+ " output_dir=OUTPUT_DIR, skip_pmtiles=True,\n",
+ " pmtiles_output=ARTIFACTS_DIR / 'agricultural_crop_flood.pmtiles',\n",
+ " pmtiles_threads=1,\n",
+ ")\n",
+ "args"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "hazard-note",
+ "metadata": {},
+ "source": [
+ "## 2. Materialize real GloFAS flood curves\n",
+ "\n",
+ "With no pre-supplied hazard file, the twin pipeline resolves the latest GloFAS release, fetches only intersecting source tiles, crops them to the AOI, fits canonical return-period curves, and embeds the source version and probability semantics in Parquet metadata. Running the twin in a clean process also keeps native Icechunk state out of the notebook kernel, so Plotly/Kaleido can reliably emit GitHub-visible PNG fallbacks."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "hazard",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-24T22:46:29.800342Z",
+ "iopub.status.busy": "2026-08-24T22:46:29.800272Z",
+ "iopub.status.idle": "2026-08-24T22:46:36.847848Z",
+ "shell.execute_reply": "2026-08-24T22:46:36.846890Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "agricultural units: /Users/woozyking/rtai/crc-docs/pipeline_output/agriculture-notebook/usda-agricultural-units.parquet\n",
+ "hazard evaluation: /Users/woozyking/rtai/crc-docs/pipeline_output/agriculture-notebook/usda-hazard-evaluation.parquet\n",
+ "map data: /Users/woozyking/rtai/crc-docs/pipeline_output/agriculture-notebook/usda-crop-flood-map.parquet\n",
+ "summary: /Users/woozyking/rtai/crc-docs/pipeline_output/agriculture-notebook/usda-crop-flood-summary.parquet\n",
+ "pmtiles: skipped\n",
+ "manifest: /Users/woozyking/rtai/crc-docs/pipeline_output/agriculture-notebook/manifest.json\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "pmtiles: /Users/woozyking/rtai/crc-docs/notebooks/artifacts/agricultural_crop_flood.pmtiles\n",
+ "{\n",
+ " \"schema_version\": \"1.1\",\n",
+ " \"h3_resolution\": 7,\n",
+ " \"probability_convention\": \"non_exceedance\",\n",
+ " \"return_period_tail\": \"upper\",\n",
+ " \"return_period_support\": [\n",
+ " 10.0,\n",
+ " 500.0\n",
+ " ],\n",
+ " \"source_probability_support\": null,\n",
+ " \"value_unit\": \"m\",\n",
+ " \"value_semantics\": \"riverine flood depth\",\n",
+ " \"geometry_encoding\": \"WKB\",\n",
+ " \"geometry_crs\": \"EPSG:4326\",\n",
+ " \"producer\": \"crc-sdk\",\n",
+ " \"source\": {\n",
+ " \"provider\": \"jrc\",\n",
+ " \"dataset\": \"RiverineInundation:flood_depth\",\n",
+ " \"uri\": \"cems-glofas-river-flood/aoi?bounds=-93.965%2C42.03%2C-93.91%2C42.085&sources=ID36_N50_W100&source_periods=10%2C20%2C50%2C75%2C100%2C200%2C500\",\n",
+ " \"version\": \"2.1.2\"\n",
+ " },\n",
+ " \"fitting\": null,\n",
+ " \"creation_version\": \"0.2.0\",\n",
+ " \"row_key\": [\n",
+ " \"hazard_name\",\n",
+ " \"horizon\",\n",
+ " \"pathway\",\n",
+ " \"cell_index\",\n",
+ " \"source_id\"\n",
+ " ],\n",
+ " \"sort_order\": [\n",
+ " \"hazard_name\",\n",
+ " \"horizon\",\n",
+ " \"pathway\",\n",
+ " \"cell_index\",\n",
+ " \"source_id\"\n",
+ " ]\n",
+ "}\n"
+ ]
+ }
+ ],
+ "source": [
+ "pipeline_command = [\n",
+ " str(REPO_ROOT / '.venv' / 'bin' / 'python'),\n",
+ " str(REPO_ROOT / 'pipelines' / 'agricultural_climate_pipeline.py'),\n",
+ " '--source', 'usda', '--bounds', *map(str, BOUNDS),\n",
+ " '--year', str(args.year), '--crop-codes', *map(str, args.crop_codes),\n",
+ " '--return-periods', *map(str, args.return_periods),\n",
+ " '--h3-resolution', str(args.h3_resolution),\n",
+ " '--cache-mode', args.cache_mode, '--output-dir', str(OUTPUT_DIR),\n",
+ " '--pmtiles-output', str(args.pmtiles_output),\n",
+ "]\n",
+ "subprocess.run(pipeline_command, check=True)\n",
+ "hazard_path = OUTPUT_DIR / 'glofas-flood-curves.parquet'\n",
+ "hazard = HazardDataset.local(hazard_path)\n",
+ "hazard_metadata = hazard.metadata()\n",
+ "print(hazard_metadata.model_dump_json(indent=2))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "execution-note",
+ "metadata": {},
+ "source": [
+ "## 3. Stream crops, evaluate hazard, and publish map artifacts\n",
+ "\n",
+ "The remote Icechunk scan is consumed once in the clean pipeline process. DuckDB writes per-H3 crop counts, computes sampled crop shares from that small local aggregate, evaluates only cells covered by the hazard while retaining the unmatched count, and publishes map-ready GeoParquet, summary, manifest, and PMTiles outputs."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "execute",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-24T22:46:36.850056Z",
+ "iopub.status.busy": "2026-08-24T22:46:36.849927Z",
+ "iopub.status.idle": "2026-08-24T22:46:36.853745Z",
+ "shell.execute_reply": "2026-08-24T22:46:36.853346Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
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+ "text": [
+ "21 crop/H3 units\n",
+ "17 modeled, 4 outside modeled hazard coverage\n",
+ "PMTiles: /Users/woozyking/rtai/crc-docs/notebooks/artifacts/agricultural_crop_flood.pmtiles\n"
+ ]
+ }
+ ],
+ "source": [
+ "agricultural_path = OUTPUT_DIR / 'usda-agricultural-units.parquet'\n",
+ "evaluation_path = OUTPUT_DIR / 'usda-hazard-evaluation.parquet'\n",
+ "map_path = OUTPUT_DIR / 'usda-crop-flood-map.parquet'\n",
+ "summary_path = OUTPUT_DIR / 'usda-crop-flood-summary.parquet'\n",
+ "manifest_path = OUTPUT_DIR / 'manifest.json'\n",
+ "pmtiles_path = args.pmtiles_output\n",
+ "manifest = json.loads(manifest_path.read_text())\n",
+ "matched_rows = manifest['hazard_matched_agricultural_rows']\n",
+ "missing_rows = manifest['hazard_unmatched_agricultural_rows']\n",
+ "print(f'{pq.read_metadata(agricultural_path).num_rows} crop/H3 units')\n",
+ "print(f'{matched_rows} modeled, {missing_rows} outside modeled hazard coverage')\n",
+ "print(f'PMTiles: {pmtiles_path}')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "map-note",
+ "metadata": {},
+ "source": [
+ "## 4. See crop composition and flood overlap\n",
+ "\n",
+ "Each hex is labeled by its dominant sampled crop on the left. The right map shows the fitted 100-year riverine-flood depth for modeled cells; gray cells are explicit agricultural observations outside this bounded hazard support. Hovering retains crop share, pixel count, and hazard depth together without collapsing them into a score."
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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "mapped = pq.read_table(map_path).to_pandas()\n",
+ "dominant = (mapped.sort_values('sampled_crop_share', ascending=False)\n",
+ " .drop_duplicates('cell_index').copy())\n",
+ "dominant['cell_id'] = dominant['cell_index'].astype(str)\n",
+ "dominant['crop_index'] = dominant['crop_name'].map({'Corn': 0, 'Soybeans': 1})\n",
+ "features = []\n",
+ "for cell in dominant['cell_index']:\n",
+ " boundary = h3.cell_to_boundary(h3.int_to_str(int(cell)))\n",
+ " coordinates = [[lon, lat] for lat, lon in boundary]\n",
+ " coordinates.append(coordinates[0])\n",
+ " features.append({'type': 'Feature', 'id': str(cell), 'properties': {},\n",
+ " 'geometry': {'type': 'Polygon', 'coordinates': [coordinates]}})\n",
+ "hex_geojson = {'type': 'FeatureCollection', 'features': features}\n",
+ "custom = dominant[['crop_name', 'sampled_crop_share', 'pixel_count',\n",
+ " 'value_rp10', 'value_rp100', 'hazard_coverage']].to_numpy()\n",
+ "center = {'lon': (BOUNDS[0] + BOUNDS[2]) / 2,\n",
+ " 'lat': (BOUNDS[1] + BOUNDS[3]) / 2}\n",
+ "fig = make_subplots(rows=1, cols=2,\n",
+ " specs=[[{'type': 'map'}, {'type': 'map'}]],\n",
+ " subplot_titles=('Dominant sampled crop', '100-year riverine-flood depth'))\n",
+ "fig.add_trace(go.Choroplethmap(\n",
+ " geojson=hex_geojson, locations=dominant['cell_id'], z=dominant['crop_index'],\n",
+ " colorscale=[[0, '#f2c94c'], [0.499, '#f2c94c'], [0.5, '#2f9e72'], [1, '#2f9e72']],\n",
+ " zmin=0, zmax=1, marker_opacity=0.82, marker_line_width=1,\n",
+ " colorbar={'title': 'Crop', 'tickvals': [0, 1],\n",
+ " 'ticktext': ['Corn', 'Soybeans'], 'x': 0.46},\n",
+ " customdata=custom,\n",
+ " hovertemplate='%{customdata[0]}
sampled crop share %{customdata[1]:.0%}'\n",
+ " '
sampled pixels %{customdata[2]:,.0f}'\n",
+ "), row=1, col=1)\n",
+ "fig.add_trace(go.Choroplethmap(\n",
+ " geojson=hex_geojson, locations=dominant['cell_id'], z=[0] * len(dominant),\n",
+ " colorscale=[[0, '#d9dde3'], [1, '#d9dde3']], showscale=False,\n",
+ " marker_opacity=0.55, marker_line_width=1, hoverinfo='skip'\n",
+ "), row=1, col=2)\n",
+ "modeled = dominant[dominant['value_rp100'].notna()]\n",
+ "fig.add_trace(go.Choroplethmap(\n",
+ " geojson=hex_geojson, locations=modeled['cell_id'], z=modeled['value_rp100'],\n",
+ " colorscale='YlOrRd', marker_opacity=0.82, marker_line_width=1,\n",
+ " colorbar={'title': 'RP100 depth (m)', 'x': 1.01},\n",
+ " customdata=modeled[['crop_name', 'sampled_crop_share',\n",
+ " 'value_rp10', 'value_rp100']].to_numpy(),\n",
+ " hovertemplate='%{customdata[0]}
sampled crop share %{customdata[1]:.0%}'\n",
+ " '
RP10 %{customdata[2]:.2f} m'\n",
+ " '
RP100 %{customdata[3]:.2f} m'\n",
+ "), row=1, col=2)\n",
+ "fig.update_layout(\n",
+ " title='Crop evidence and modeled flood hazard share one H3 key',\n",
+ " height=560, margin={'l': 0, 'r': 0, 't': 70, 'b': 0},\n",
+ " map={'style': 'carto-positron', 'center': center, 'zoom': 10.7},\n",
+ " map2={'style': 'carto-positron', 'center': center, 'zoom': 10.7},\n",
+ ")\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "summary-note",
+ "metadata": {},
+ "source": [
+ "## 5. Compare coverage and return-period implications by crop\n",
+ "\n",
+ "The first chart makes missing hazard coverage visible rather than silently dropping it. The second weights flood depth by sampled crop pixels within the modeled overlap; it is an exposure summary, not expected loss."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "charts",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-08-24T22:46:47.647754Z",
+ "iopub.status.busy": "2026-08-24T22:46:47.647639Z",
+ "iopub.status.idle": "2026-08-24T22:46:52.347613Z",
+ "shell.execute_reply": "2026-08-24T22:46:52.347096Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.plotly.v1+json": {
+ "config": {
+ "plotlyServerURL": "https://plot.ly"
+ },
+ "data": [
+ {
+ "alignmentgroup": "True",
+ "hovertemplate": "Hazard coverage=modeled_hazard
Crop=%{x}
Sampled CDL pixels=%{y}",
+ "legendgroup": "modeled_hazard",
+ "marker": {
+ "color": "#d95f0e",
+ "pattern": {
+ "shape": ""
+ }
+ },
+ "name": "modeled_hazard",
+ "offsetgroup": "modeled_hazard",
+ "orientation": "v",
+ "showlegend": true,
+ "textposition": "auto",
+ "texttemplate": "%{y:,.0f}",
+ "type": "bar",
+ "x": [
+ "Corn",
+ "Soybeans"
+ ],
+ "xaxis": "x",
+ "y": {
+ "bdata": "AAAAAAAyqUAAAAAAAAyTQA==",
+ "dtype": "f8"
+ },
+ "yaxis": "y"
+ },
+ {
+ "alignmentgroup": "True",
+ "hovertemplate": "Hazard coverage=outside_modeled_hazard
Crop=%{x}
Sampled CDL pixels=%{y}",
+ "legendgroup": "outside_modeled_hazard",
+ "marker": {
+ "color": "#9aa1aa",
+ "pattern": {
+ "shape": ""
+ }
+ },
+ "name": "outside_modeled_hazard",
+ "offsetgroup": "outside_modeled_hazard",
+ "orientation": "v",
+ "showlegend": true,
+ "textposition": "auto",
+ "texttemplate": "%{y:,.0f}",
+ "type": "bar",
+ "x": [
+ "Corn",
+ "Soybeans"
+ ],
+ "xaxis": "x",
+ "y": {
+ "bdata": "AAAAAABoi0AAAAAAAGSSQA==",
+ "dtype": "f8"
+ },
+ "yaxis": "y"
+ }
+ ],
+ "layout": {
+ "barmode": "group",
+ "height": 430,
+ "legend": {
+ "title": {
+ "text": ""
+ },
+ "tracegroupgap": 0
+ },
+ "template": {
+ "data": {
+ "bar": [
+ {
+ "error_x": {
+ "color": "#2a3f5f"
+ },
+ "error_y": {
+ "color": "#2a3f5f"
+ },
+ "marker": {
+ "line": {
+ "color": "#E5ECF6",
+ "width": 0.5
+ },
+ "pattern": {
+ "fillmode": "overlay",
+ "size": 10,
+ "solidity": 0.2
+ }
+ },
+ "type": "bar"
+ }
+ ],
+ "barpolar": [
+ {
+ "marker": {
+ "line": {
+ "color": "#E5ECF6",
+ "width": 0.5
+ },
+ "pattern": {
+ "fillmode": "overlay",
+ "size": 10,
+ "solidity": 0.2
+ }
+ },
+ "type": "barpolar"
+ }
+ ],
+ "carpet": [
+ {
+ "aaxis": {
+ "endlinecolor": "#2a3f5f",
+ "gridcolor": "white",
+ "linecolor": "white",
+ "minorgridcolor": "white",
+ "startlinecolor": "#2a3f5f"
+ },
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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "summary = pq.read_table(summary_path).to_pandas()\n",
+ "coverage_fig = px.bar(\n",
+ " summary, x='crop_name', y='sampled_pixels', color='hazard_coverage',\n",
+ " barmode='group', text_auto=',.0f',\n",
+ " color_discrete_map={'modeled_hazard': '#d95f0e',\n",
+ " 'outside_modeled_hazard': '#9aa1aa'},\n",
+ " labels={'crop_name': 'Crop', 'sampled_pixels': 'Sampled CDL pixels',\n",
+ " 'hazard_coverage': 'Hazard coverage'},\n",
+ " title='Sampled crop pixels inside and outside modeled flood coverage',\n",
+ ")\n",
+ "coverage_fig.update_layout(height=430, legend_title_text='')\n",
+ "coverage_fig.show()\n",
+ "evaluated = pq.read_table(evaluation_path).to_pandas()\n",
+ "long = evaluated.melt(\n",
+ " id_vars=['crop_name', 'pixel_count'], value_vars=['value_rp10', 'value_rp100'],\n",
+ " var_name='return_period', value_name='depth_m')\n",
+ "long['return_period_years'] = (long['return_period']\n",
+ " .str.replace('value_rp', '', regex=False).astype(int))\n",
+ "weighted = (long.assign(weighted_depth=lambda d: d.depth_m * d.pixel_count)\n",
+ " .groupby(['crop_name', 'return_period_years'], as_index=False)\n",
+ " .agg(weighted_depth=('weighted_depth', 'sum'),\n",
+ " sampled_pixels=('pixel_count', 'sum')))\n",
+ "weighted['pixel_weighted_depth_m'] = (\n",
+ " weighted['weighted_depth'] / weighted['sampled_pixels'])\n",
+ "depth_fig = px.line(\n",
+ " weighted, x='return_period_years', y='pixel_weighted_depth_m',\n",
+ " color='crop_name', markers=True, text='pixel_weighted_depth_m',\n",
+ " color_discrete_map={'Corn': '#d8a600', 'Soybeans': '#2f9e72'},\n",
+ " labels={'return_period_years': 'Return period (years)',\n",
+ " 'pixel_weighted_depth_m': 'Pixel-weighted flood depth (m)',\n",
+ " 'crop_name': 'Crop'},\n",
+ " title='Modeled riverine-flood depth across event rarity',\n",
+ ")\n",
+ "depth_fig.update_traces(texttemplate='%{text:.2f} m', textposition='top center')\n",
+ "depth_fig.update_layout(height=430, legend_title_text='')\n",
+ "depth_fig.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "artifacts",
+ "metadata": {},
+ "source": [
+ "## Reproducible outputs\n",
+ "\n",
+ "- **[Download the layered crop + flood PMTiles archive](artifacts/agricultural_crop_flood.pmtiles)** and open it in [pmtiles.io](https://pmtiles.io). Its `crop_flood` layer retains crop, sampled share, coverage status, and RP10/RP100 depth properties.\n",
+ "- The output directory also contains canonical GloFAS curves, agricultural units, hazard-covered evaluation candidates, the evaluation Parquet, map GeoParquet, summary Parquet, and a checksummed manifest.\n",
+ "- [`pipelines/agricultural_climate_pipeline.py`](../pipelines/agricultural_climate_pipeline.py) is the headless twin.\n",
+ "\n",
+ "Interpretation remains deliberately limited: sampled crop composition is not acreage or yield; modeled depth is not damage or loss; gray cells mean *outside this bounded modeled hazard support*, not zero flood risk."
+ ]
+ }
+ ],
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+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
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+ "codemirror_mode": {
+ "name": "ipython",
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+}
diff --git a/notebooks/artifacts/agricultural_crop_flood.pmtiles b/notebooks/artifacts/agricultural_crop_flood.pmtiles
new file mode 100644
index 0000000..8393508
Binary files /dev/null and b/notebooks/artifacts/agricultural_crop_flood.pmtiles differ
diff --git a/pipelines/agricultural_climate_pipeline.py b/pipelines/agricultural_climate_pipeline.py
new file mode 100644
index 0000000..8900e4b
--- /dev/null
+++ b/pipelines/agricultural_climate_pipeline.py
@@ -0,0 +1,543 @@
+"""Build bounded agricultural H3 units and optionally evaluate a CRC hazard.
+
+Examples:
+
+ uv run python pipelines/agricultural_climate_pipeline.py \
+ --source usda --bounds -93.7 41.9 -93.2 42.3 --year 2025 \
+ --crop-codes 1 5 --hazard pipeline_output/iowa-glofas.parquet
+
+ uv run python pipelines/agricultural_climate_pipeline.py \
+ --source ftw --country-code FR --bounds 2.0 47.5 3.0 48.5 \
+ --year 2024 --minimum-confidence 80 \
+ --hazard pipeline_output/france-edo-smi.parquet
+"""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import subprocess
+import sys
+from contextlib import closing
+from pathlib import Path
+from typing import Any
+
+import pyarrow.parquet as pq
+from crc_sdk.connectors.duckdb import DuckDBConnection, sql_quote
+from crc_sdk.geometry.pmtiles import AREAS, PMTilesBuild
+from crc_sdk.workflows import (
+ AgriculturalLayer,
+ AssetPortfolio,
+ CellColumn,
+ HazardDataset,
+ JRCFloodPolicy,
+)
+
+
+def parse_args() -> argparse.Namespace:
+ parser = argparse.ArgumentParser(description=__doc__)
+ parser.add_argument("--source", choices=("usda", "ftw"))
+ parser.add_argument(
+ "--bounds",
+ type=float,
+ nargs=4,
+ metavar=("MIN_LON", "MIN_LAT", "MAX_LON", "MAX_LAT"),
+ )
+ parser.add_argument("--year", type=int, default=2025)
+ parser.add_argument("--country-code", help="Required for FTW")
+ parser.add_argument("--crop-codes", type=int, nargs="+", default=[1, 5])
+ parser.add_argument("--minimum-confidence", type=float, default=80.0)
+ parser.add_argument("--h3-resolution", type=int)
+ parser.add_argument(
+ "--hazard",
+ type=Path,
+ help="Existing canonical CRC hazard Parquet; otherwise materialize GloFAS",
+ )
+ parser.add_argument(
+ "--cache-mode", choices=("reuse", "offline", "refresh"), default="reuse"
+ )
+ parser.add_argument(
+ "--return-periods", type=float, nargs="+", default=[10, 25, 100]
+ )
+ parser.add_argument(
+ "--output-dir", type=Path, default=Path("pipeline_output/agriculture")
+ )
+ parser.add_argument(
+ "--skip-pmtiles",
+ action="store_true",
+ help="Skip the final map archive (requires tippecanoe on PATH)",
+ )
+ parser.add_argument(
+ "--pmtiles-output",
+ type=Path,
+ help="Optional PMTiles destination; defaults inside --output-dir",
+ )
+ parser.add_argument(
+ "--pmtiles-threads",
+ type=int,
+ help="Optional tippecanoe thread limit (useful inside notebook kernels)",
+ )
+ parser.add_argument(
+ "--pmtiles-from-map",
+ type=Path,
+ help="Tile an existing map GeoParquet and exit (notebook-safe helper)",
+ )
+ return parser.parse_args()
+
+
+def _sha256(path: Path) -> str:
+ digest = hashlib.sha256()
+ with path.open("rb") as source:
+ for chunk in iter(lambda: source.read(1024 * 1024), b""):
+ digest.update(chunk)
+ return digest.hexdigest()
+
+
+def _resolution(args: argparse.Namespace) -> int:
+ if args.hazard:
+ hazard_resolution = HazardDataset.local(args.hazard).metadata().h3_resolution
+ if args.h3_resolution is not None and args.h3_resolution != hazard_resolution:
+ raise ValueError(
+ "--h3-resolution must match the canonical hazard metadata "
+ f"({hazard_resolution})"
+ )
+ return hazard_resolution
+ return args.h3_resolution if args.h3_resolution is not None else 7
+
+
+def ensure_hazard(args: argparse.Namespace) -> Path:
+ """Use a supplied canonical hazard or materialize bounded GloFAS flood."""
+ if args.hazard is not None:
+ return args.hazard
+ resolution = args.h3_resolution if args.h3_resolution is not None else 7
+ hazard_path = args.output_dir / "glofas-flood-curves.parquet"
+ hazard = (
+ HazardDataset.glofas(version="latest")
+ .for_area(tuple(args.bounds))
+ .cache(args.output_dir / "glofas-source-cache", mode=args.cache_mode)
+ .source_periods("all")
+ .canonicalize(policy=JRCFloodPolicy.curated(h3_resolution=resolution))
+ .materialize(hazard_path)
+ )
+ args.hazard = Path(hazard.provider.source)
+ return args.hazard
+
+
+def build_agricultural_units(
+ args: argparse.Namespace, connection: DuckDBConnection, resolution: int
+) -> Path:
+ bounds = tuple(args.bounds)
+ output = args.output_dir / f"{args.source}-agricultural-units.parquet"
+ if args.source == "usda":
+ scan = (
+ AgriculturalLayer.usda_cdl()
+ .resolution("30m")
+ .for_area(bounds)
+ .years(args.year)
+ .classes(args.crop_codes)
+ .scan()
+ )
+ pipeline = (
+ scan.pipeline(connection=connection)
+ .select(
+ "*, CAST(h3_latlng_to_cell(latitude, longitude, "
+ f"{resolution}) AS UBIGINT) AS cell_index"
+ )
+ .aggregate(
+ "count(*) AS pixel_count, avg(longitude) AS longitude, "
+ "avg(latitude) AS latitude, any_value(source_path) AS source_path",
+ groups="cell_index, year, crop_code, crop_name",
+ )
+ .select("concat(year, ':', crop_code, ':', cell_index) AS asset_id, *")
+ )
+ else:
+ if not args.country_code:
+ raise ValueError("--country-code is required for --source ftw")
+ scan = (
+ AgriculturalLayer.ftw_fields()
+ .in_country(args.country_code)
+ .for_area(bounds)
+ .years(args.year)
+ .confidence_at_least(args.minimum_confidence)
+ .scan()
+ )
+ pipeline = scan.pipeline(connection=connection).select(
+ "id AS asset_id, year, confidence, area_m2, perimeter_m, "
+ "geometry, "
+ "ST_X(ST_Centroid(geometry)) AS longitude, "
+ "ST_Y(ST_Centroid(geometry)) AS latitude, "
+ "CAST(h3_latlng_to_cell(ST_Y(ST_Centroid(geometry)), "
+ f"ST_X(ST_Centroid(geometry)), {resolution}) AS UBIGINT) AS cell_index, "
+ "country_code, source_path"
+ )
+ if args.source != "usda":
+ return pipeline.write_parquet(output, overwrite=True)
+
+ # DuckDB 1.5.x does not currently execute window expressions correctly
+ # over a Python Arrow batch reader. Keep the remote scan one-pass and
+ # bounded, then compute crop shares from the small local aggregate.
+ staged = output.with_name(f".{output.stem}-counts.parquet")
+ pipeline.write_parquet(staged, overwrite=True)
+ try:
+ with closing(connection.connect()) as con:
+ con.execute(
+ f"""
+ COPY (
+ SELECT *, pixel_count::DOUBLE / sum(pixel_count) OVER
+ (PARTITION BY cell_index, year) AS sampled_crop_share
+ FROM read_parquet({sql_quote(str(staged))})
+ ) TO {sql_quote(str(output))}
+ (FORMAT PARQUET, COMPRESSION ZSTD, OVERWRITE_OR_IGNORE true)
+ """
+ )
+ finally:
+ staged.unlink(missing_ok=True)
+ return output
+
+
+def filter_hazard_covered_units(
+ args: argparse.Namespace,
+ agricultural_path: Path,
+ connection: DuckDBConnection,
+) -> tuple[Path, int, int]:
+ """Retain evaluation candidates while preserving overall coverage counts."""
+ assert args.hazard is not None
+ matched = args.output_dir / f"{args.source}-hazard-covered-units.parquet"
+ with closing(connection.connect()) as con:
+ total = con.execute(
+ f"SELECT count(*) FROM read_parquet({sql_quote(str(agricultural_path))})"
+ ).fetchone()[0]
+ con.execute(
+ f"""
+ COPY (
+ SELECT a.*
+ FROM read_parquet({sql_quote(str(agricultural_path))}) a
+ SEMI JOIN (
+ SELECT DISTINCT cell_index
+ FROM read_parquet({sql_quote(str(args.hazard))})
+ ) h USING (cell_index)
+ ) TO {sql_quote(str(matched))}
+ (FORMAT PARQUET, COMPRESSION ZSTD, OVERWRITE_OR_IGNORE true)
+ """
+ )
+ matched_rows = con.execute(
+ f"SELECT count(*) FROM read_parquet({sql_quote(str(matched))})"
+ ).fetchone()[0]
+ return matched, int(matched_rows), int(total - matched_rows)
+
+
+def evaluate_hazard(args: argparse.Namespace, agricultural_path: Path) -> Path | None:
+ if args.hazard is None:
+ return None
+ columns = pq.read_schema(agricultural_path).names
+ passthrough = tuple(
+ name
+ for name in columns
+ if name not in ("asset_id", "cell_index", "geometry")
+ )
+ assets = AssetPortfolio(
+ agricultural_path,
+ id_column="asset_id",
+ location=CellColumn(),
+ passthrough_columns=passthrough,
+ )
+ output = args.output_dir / f"{args.source}-hazard-evaluation.parquet"
+ HazardDataset.local(args.hazard).for_assets(assets).return_periods(
+ args.return_periods
+ ).write_parquet(output)
+ return output
+
+
+def build_map_artifacts(
+ args: argparse.Namespace,
+ agricultural_path: Path,
+ evaluation_path: Path,
+ connection: DuckDBConnection,
+) -> tuple[Path, Path, Path | None]:
+ """Create a source-aware agricultural/hazard map, summary, and archive."""
+ map_path = args.output_dir / f"{args.source}-crop-flood-map.parquet"
+ summary_path = args.output_dir / f"{args.source}-crop-flood-summary.parquet"
+ value_columns = [
+ name
+ for name in pq.read_schema(evaluation_path).names
+ if name.startswith("value_")
+ ]
+ value_projection = ", ".join(f"e.{name}" for name in value_columns)
+ if value_projection:
+ value_projection = ", " + value_projection
+ weight_column = "pixel_count" if args.source == "usda" else "area_m2"
+ weight_prefix = "pixel" if args.source == "usda" else "area"
+ weighted_values = ", ".join(
+ f"sum({weight_column} * {name}) "
+ f"FILTER (WHERE {name} IS NOT NULL AND {weight_column} IS NOT NULL) / "
+ f"nullif(sum({weight_column}) "
+ f"FILTER (WHERE {name} IS NOT NULL AND {weight_column} IS NOT NULL), 0) "
+ f"AS {weight_prefix}_weighted_{name}"
+ for name in value_columns
+ )
+ if weighted_values:
+ weighted_values = ", " + weighted_values
+
+ if args.source == "usda":
+ agricultural_projection = "a.*"
+ geometry_projection = (
+ "h3_cell_to_boundary_wkb(CAST(a.cell_index AS UBIGINT)) AS geometry"
+ )
+ summary_projection = f"""
+ SELECT crop_code, crop_name, hazard_coverage,
+ count(*) AS h3_crop_units,
+ sum(pixel_count) AS sampled_pixels,
+ sum(pixel_count * sampled_crop_share) /
+ nullif(sum(pixel_count), 0) AS pixel_weighted_crop_share
+ {weighted_values}
+ FROM read_parquet({sql_quote(str(map_path))})
+ GROUP BY crop_code, crop_name, hazard_coverage
+ ORDER BY crop_code, hazard_coverage
+ """
+ properties = (
+ "asset_id",
+ "year",
+ "crop_code",
+ "crop_name",
+ "pixel_count",
+ "sampled_crop_share",
+ "hazard_coverage",
+ "spatial_match",
+ *value_columns,
+ )
+ else:
+ agricultural_projection = "a.* EXCLUDE (geometry)"
+ geometry_projection = "a.geometry AS geometry"
+ summary_projection = f"""
+ SELECT country_code, year, hazard_coverage,
+ count(*) AS field_units,
+ sum(area_m2) AS observed_field_area_m2,
+ avg(confidence) AS average_confidence,
+ sum(area_m2 * confidence) /
+ nullif(sum(area_m2), 0) AS area_weighted_confidence
+ {weighted_values}
+ FROM read_parquet({sql_quote(str(map_path))})
+ GROUP BY country_code, year, hazard_coverage
+ ORDER BY country_code, year, hazard_coverage
+ """
+ properties = (
+ "asset_id",
+ "year",
+ "confidence",
+ "area_m2",
+ "perimeter_m",
+ "country_code",
+ "hazard_coverage",
+ "spatial_match",
+ *value_columns,
+ )
+
+ with closing(connection.connect()) as con:
+ con.execute(
+ f"""
+ COPY (
+ SELECT {agricultural_projection},
+ CASE WHEN e.asset_id IS NULL
+ THEN 'outside_modeled_hazard'
+ ELSE 'modeled_hazard' END AS hazard_coverage,
+ e.spatial_match{value_projection},
+ {geometry_projection}
+ FROM read_parquet({sql_quote(str(agricultural_path))}) a
+ LEFT JOIN read_parquet({sql_quote(str(evaluation_path))}) e
+ USING (asset_id)
+ ) TO {sql_quote(str(map_path))}
+ (FORMAT PARQUET, COMPRESSION ZSTD, OVERWRITE_OR_IGNORE true)
+ """
+ )
+ con.execute(
+ f"""
+ COPY (
+ {summary_projection}
+ ) TO {sql_quote(str(summary_path))}
+ (FORMAT PARQUET, COMPRESSION ZSTD, OVERWRITE_OR_IGNORE true)
+ """
+ )
+ pmtiles_path: Path | None = None
+ if not args.skip_pmtiles:
+ pmtiles_path = args.pmtiles_output or (
+ args.output_dir / f"{args.source}-crop-flood.pmtiles"
+ )
+ write_pmtiles_archive(
+ map_path,
+ pmtiles_path,
+ args.output_dir / "pmtiles-work",
+ connection=con,
+ properties=properties,
+ threads=args.pmtiles_threads,
+ )
+ return map_path, summary_path, pmtiles_path
+
+
+def write_pmtiles_archive(
+ map_path: Path,
+ output: Path,
+ work_dir: Path,
+ *,
+ connection: Any | None = None,
+ properties: tuple[str, ...] | None = None,
+ threads: int | None = None,
+) -> Path:
+ """Write the local agricultural/hazard GeoParquet as a vector-tile layer."""
+ (
+ PMTilesBuild(
+ con=connection,
+ work_dir=work_dir,
+ tippecanoe_threads=threads,
+ )
+ .layer(
+ map_path,
+ name="crop_flood",
+ zooms=(5, 13),
+ preset=AREAS,
+ property_columns=properties,
+ )
+ .write(output)
+ )
+ return output
+
+
+def write_manifest(
+ args: argparse.Namespace,
+ resolution: int,
+ agricultural_path: Path,
+ evaluation_path: Path | None,
+ matched_rows: int | None,
+ missing_rows: int | None,
+ map_path: Path,
+ summary_path: Path,
+ pmtiles_path: Path | None,
+) -> Path:
+ manifest: dict[str, Any] = {
+ "agricultural_source": args.source,
+ "bounds_wgs84": args.bounds,
+ "year": args.year,
+ "h3_resolution": resolution,
+ "agricultural_output": str(agricultural_path),
+ "agricultural_sha256": _sha256(agricultural_path),
+ "agricultural_rows": pq.read_metadata(agricultural_path).num_rows,
+ "hazard_input": str(args.hazard) if args.hazard else None,
+ "hazard_evaluation": str(evaluation_path) if evaluation_path else None,
+ "hazard_evaluation_sha256": (
+ _sha256(evaluation_path) if evaluation_path else None
+ ),
+ "hazard_matched_agricultural_rows": matched_rows,
+ "hazard_unmatched_agricultural_rows": missing_rows,
+ "map_output": str(map_path),
+ "map_output_sha256": _sha256(map_path),
+ "summary_output": str(summary_path),
+ "summary_output_sha256": _sha256(summary_path),
+ "pmtiles_output": str(pmtiles_path) if pmtiles_path else None,
+ "pmtiles_sha256": _sha256(pmtiles_path) if pmtiles_path else None,
+ "warnings": [
+ (
+ "Agricultural observations and hazard curves are separate evidence "
+ "layers; no combined risk score was inferred."
+ ),
+ (
+ "FTW polygons are remote-sensing field units, not legal parcels or "
+ "proof of ownership."
+ ),
+ ("USDA CDL pixel_count is a bounded pixel sample, not acreage or yield."),
+ ],
+ }
+ if args.hazard:
+ manifest["hazard_metadata"] = (
+ HazardDataset.local(args.hazard).metadata().model_dump(mode="json")
+ )
+ path = args.output_dir / "manifest.json"
+ path.write_text(json.dumps(manifest, indent=2, sort_keys=True) + "\n")
+ return path
+
+
+def main() -> None:
+ args = parse_args()
+ if args.pmtiles_from_map is not None:
+ output = args.pmtiles_output or args.pmtiles_from_map.with_suffix(".pmtiles")
+ write_pmtiles_archive(
+ args.pmtiles_from_map,
+ output,
+ args.output_dir / "pmtiles-work",
+ threads=args.pmtiles_threads,
+ )
+ print(f"pmtiles: {output}")
+ return
+ if args.source is None or args.bounds is None:
+ raise SystemExit("--source and --bounds are required for an agricultural run")
+ args.output_dir.mkdir(parents=True, exist_ok=True)
+ if not args.skip_pmtiles:
+ # Keep tippecanoe out of the process that opens the native Icechunk
+ # repository. The clean parent orchestrates a bounded data-only child,
+ # then tiles its small local GeoParquet after that child exits.
+ subprocess.run(
+ [
+ sys.executable,
+ str(Path(__file__).resolve()),
+ *sys.argv[1:],
+ "--skip-pmtiles",
+ ],
+ check=True,
+ )
+ map_path = args.output_dir / f"{args.source}-crop-flood-map.parquet"
+ pmtiles_path = args.pmtiles_output or (
+ args.output_dir / f"{args.source}-crop-flood.pmtiles"
+ )
+ write_pmtiles_archive(
+ map_path,
+ pmtiles_path,
+ args.output_dir / "pmtiles-work",
+ threads=args.pmtiles_threads,
+ )
+ manifest_path = args.output_dir / "manifest.json"
+ manifest = json.loads(manifest_path.read_text())
+ manifest["pmtiles_output"] = str(pmtiles_path)
+ manifest["pmtiles_sha256"] = _sha256(pmtiles_path)
+ manifest_path.write_text(json.dumps(manifest, indent=2, sort_keys=True) + "\n")
+ print(f"pmtiles: {pmtiles_path}")
+ return
+ ensure_hazard(args)
+ resolution = _resolution(args)
+ connection = DuckDBConnection.for_analytics(
+ args.output_dir / "duckdb-spill",
+ extensions=("spatial", "httpfs", "h3"),
+ config={"threads": 1} if args.source == "usda" else None,
+ )
+ agricultural_path = build_agricultural_units(args, connection, resolution)
+ evaluation_input = agricultural_path
+ matched_rows = missing_rows = None
+ if args.hazard:
+ evaluation_input, matched_rows, missing_rows = filter_hazard_covered_units(
+ args, agricultural_path, connection
+ )
+ evaluation_path = evaluate_hazard(args, evaluation_input)
+ assert evaluation_path is not None
+ map_path, summary_path, _ = build_map_artifacts(
+ args, agricultural_path, evaluation_path, connection
+ )
+ pmtiles_path = None
+ manifest_path = write_manifest(
+ args,
+ resolution,
+ agricultural_path,
+ evaluation_path,
+ matched_rows,
+ missing_rows,
+ map_path,
+ summary_path,
+ pmtiles_path,
+ )
+ print(f"agricultural units: {agricultural_path}")
+ print(f"hazard evaluation: {evaluation_path or 'not requested'}")
+ print(f"map data: {map_path}")
+ print(f"summary: {summary_path}")
+ print(f"pmtiles: {pmtiles_path or 'skipped'}")
+ print(f"manifest: {manifest_path}")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/pyproject.toml b/pyproject.toml
index 5eafbfe..28a9aab 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -9,7 +9,7 @@ dependencies = [
"kaleido>=0.2.1",
"pandas>=3.0.5",
"plotly>=6.9.0",
- "crc-sdk[zarr,raster,netcdf,geometry]>=0.3.0",
+ "crc-sdk[agriculture,zarr,raster,netcdf,geometry]>=0.5.0",
]
[project.optional-dependencies]
diff --git a/tests/test_agricultural_climate_pipeline.py b/tests/test_agricultural_climate_pipeline.py
new file mode 100644
index 0000000..7e68344
--- /dev/null
+++ b/tests/test_agricultural_climate_pipeline.py
@@ -0,0 +1,98 @@
+from argparse import Namespace
+from pathlib import Path
+
+import pyarrow as pa
+import pyarrow.parquet as pq
+from crc_sdk.connectors.duckdb import DuckDBConnection
+
+from pipelines.agricultural_climate_pipeline import (
+ build_map_artifacts,
+ write_manifest,
+)
+
+
+def test_ftw_publication_uses_field_schema_and_writes_manifest(tmp_path: Path) -> None:
+ agricultural_path = tmp_path / "ftw-agricultural-units.parquet"
+ evaluation_path = tmp_path / "ftw-hazard-evaluation.parquet"
+ connection = DuckDBConnection.for_analytics(
+ tmp_path / "duckdb-spill",
+ extensions=("spatial",),
+ )
+ with connection.connect() as con:
+ con.execute(
+ f"""
+ COPY (
+ SELECT *
+ FROM (VALUES
+ ('field-1', 2024, 80.0, 100.0, 40.0,
+ ST_GeomFromText('POLYGON((0 0, 0 1, 1 1, 1 0, 0 0))'),
+ 0.5, 0.5, 1::UBIGINT, 'FR', 's3://example/field-1'),
+ ('field-2', 2024, 100.0, 300.0, 80.0,
+ ST_GeomFromText('POLYGON((1 0, 1 1, 2 1, 2 0, 1 0))'),
+ 1.5, 0.5, 2::UBIGINT, 'FR', 's3://example/field-2')
+ ) AS fields(
+ asset_id, year, confidence, area_m2, perimeter_m, geometry,
+ longitude, latitude, cell_index, country_code, source_path
+ )
+ ) TO '{agricultural_path}' (FORMAT PARQUET)
+ """
+ )
+ pq.write_table(
+ pa.table(
+ {
+ "asset_id": ["field-1"],
+ "spatial_match": ["exact"],
+ "value_rp10": [2.0],
+ }
+ ),
+ evaluation_path,
+ )
+ args = Namespace(
+ source="ftw",
+ output_dir=tmp_path,
+ skip_pmtiles=True,
+ pmtiles_output=None,
+ pmtiles_threads=None,
+ bounds=[0.0, 0.0, 2.0, 1.0],
+ year=2024,
+ hazard=None,
+ )
+
+ map_path, summary_path, pmtiles_path = build_map_artifacts(
+ args, agricultural_path, evaluation_path, connection
+ )
+ manifest_path = write_manifest(
+ args,
+ 7,
+ agricultural_path,
+ evaluation_path,
+ 1,
+ 1,
+ map_path,
+ summary_path,
+ pmtiles_path,
+ )
+
+ mapped = pq.read_table(map_path)
+ summary = pq.read_table(summary_path)
+ assert "geometry" in mapped.column_names
+ assert "crop_code" not in mapped.column_names
+ assert set(summary.column_names) >= {
+ "field_units",
+ "observed_field_area_m2",
+ "average_confidence",
+ "area_weighted_confidence",
+ "area_weighted_value_rp10",
+ }
+ assert summary["field_units"].to_pylist() == [1, 1]
+ by_coverage = {
+ row["hazard_coverage"]: row for row in summary.to_pylist()
+ }
+ assert float(by_coverage["modeled_hazard"]["observed_field_area_m2"]) == 100.0
+ assert (
+ float(by_coverage["outside_modeled_hazard"]["observed_field_area_m2"])
+ == 300.0
+ )
+ assert by_coverage["modeled_hazard"]["area_weighted_value_rp10"] == 2.0
+ assert by_coverage["outside_modeled_hazard"]["area_weighted_value_rp10"] is None
+ assert manifest_path.is_file()
diff --git a/uv.lock b/uv.lock
index 72b499c..4c5d525 100644
--- a/uv.lock
+++ b/uv.lock
@@ -307,12 +307,6 @@ wheels = [
{ url = "https://files.pythonhosted.org/packages/ed/c9/d7977eaacb9df673210491da99e6a247e93df98c715fc43fd136ce1d3d33/arrow-1.4.0-py3-none-any.whl", hash = "sha256:749f0769958ebdc79c173ff0b0670d59051a535fa26e8eba02953dc19eb43205", size = 68797, upload-time = "2025-10-18T17:46:45.663Z" },
]
-[[package]]
-name = "asciitree"
-version = "0.3.3"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/2d/6a/885bc91484e1aa8f618f6f0228d76d0e67000b0fdd6090673b777e311913/asciitree-0.3.3.tar.gz", hash = "sha256:4aa4b9b649f85e3fcb343363d97564aa1fb62e249677f2e18a96765145cc0f6e", size = 3951, upload-time = "2016-09-05T19:10:42.681Z" }
-
[[package]]
name = "asttokens"
version = "3.0.2"
@@ -618,7 +612,7 @@ name = "crc-docs"
version = "0.1.0"
source = { virtual = "." }
dependencies = [
- { name = "crc-sdk", extra = ["geometry", "netcdf", "raster", "zarr"] },
+ { name = "crc-sdk", extra = ["agriculture", "geometry", "netcdf", "raster", "zarr"] },
{ name = "jupyter" },
{ name = "kaleido" },
{ name = "pandas" },
@@ -632,7 +626,7 @@ velo = [
[package.metadata]
requires-dist = [
- { name = "crc-sdk", extras = ["zarr", "raster", "netcdf", "geometry"], specifier = ">=0.3.0" },
+ { name = "crc-sdk", extras = ["agriculture", "zarr", "raster", "netcdf", "geometry"], specifier = ">=0.5.0" },
{ name = "jupyter", specifier = ">=1.1.1" },
{ name = "kaleido", specifier = ">=0.2.1" },
{ name = "pandas", specifier = ">=3.0.5" },
@@ -643,28 +637,28 @@ provides-extras = ["velo"]
[[package]]
name = "crc-framework"
-version = "0.2.0"
+version = "0.2.1"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "numpy" },
]
-sdist = { url = "https://files.pythonhosted.org/packages/f5/55/7c48f133effc81b50ddb4ad8560b021569aad2fe786fa00abe731794fca1/crc_framework-0.2.0.tar.gz", hash = "sha256:96466d9261432db56238b16f3cd76d911a2d3455e0928e3a3feac8548bd1d31c", size = 5845607, upload-time = "2026-07-31T20:39:08.956Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/b7/6b/12a1779893d3b7aa7f36b273c132fc6fda8fc5f09e471e272427d20f1f73/crc_framework-0.2.1.tar.gz", hash = "sha256:6ff21feb2b8964c63a8339734d6ab31f129cbebc5a6ba43940d3fe1985b6195e", size = 5846297, upload-time = "2026-08-20T22:01:00.579Z" }
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+ { url = "https://files.pythonhosted.org/packages/6a/52/72974ecfac72c79ebecc463dd9a14f77430e2502f4a28d7c2e8c060a0c80/crc_framework-0.2.1-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:40d95a609b8ba7223e4c2e3321067b25b6dcec1cbe4f734fe83d5d0e6196c109", size = 3079279, upload-time = "2026-08-20T22:00:59.024Z" },
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[[package]]
name = "crc-sdk"
-version = "0.3.0"
+version = "0.5.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "crc-framework" },
@@ -676,12 +670,16 @@ dependencies = [
{ name = "pydantic" },
{ name = "s3fs" },
]
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[package.optional-dependencies]
+agriculture = [
+ { name = "icechunk" },
+ { name = "pyproj" },
+ { name = "zarr" },
+]
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{ name = "h3" },
{ name = "h3ronpy" },
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+[[package]]
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+version = "0.8.1.post1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+ { name = "pyyaml" },
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+source = { registry = "https://pypi.org/simple" }
+dependencies = [
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