terraflow-agro 0.2.2

TerraFlow: a reproducible workflow for geospatial agricultural modeling.

TerraFlow: Reproducible Geospatial Agricultural Modeling

TerraFlow is a reproducible, config-driven geospatial workflow for agricultural suitability modeling. Give it a land-cover raster, a climate CSV, and a YAML config — it returns a scored, location-stamped results table with full provenance.

Documentation: terraflow.marupilla.dev

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Installation

macOS (Homebrew) — handles GDAL and PROJ automatically:

brew tap gmarupilla/terraflow
brew install terraflow

pip / uv:

uv pip install terraflow-agro
# or
pip install terraflow-agro

For kriging-based interpolation:

pip install terraflow-agro pykrige

See Homebrew install docs for update/uninstall instructions and troubleshooting.

Quickstart

terraflow --config config.yml

A minimal config:

raster_path: "data/land_cover.tif"
climate_csv: "data/climate.csv"
output_dir: "outputs"
roi:
  type: bbox
  xmin: -120.5
  ymin: 34.0
  xmax: -118.0
  ymax: 35.5
model_params:
  v_min: 0.0
  v_max: 1.0
  t_min: 10.0
  t_max: 35.0
  r_min: 100.0
  r_max: 800.0
  w_v: 0.4
  w_t: 0.3
  w_r: 0.3

Results are written to outputs/runs/<fingerprint>/:

features.parquet   — scored cells (lat, lon, score, label, …)
results.csv        — same data in CSV
manifest.json      — full provenance record
report.json        — QA stats and timings

CLI subcommands

Subcommand	Purpose
terraflow run -c config.yml	Run the full pipeline
terraflow sensitivity -c config.yml	Sobol' / Morris sensitivity indices for model weights
terraflow validate -c config.yml	Spatial block CV, Cohen's kappa, Moran's I on residuals
terraflow export --format h3 -c config.yml	H3-indexed export for interop with H3-native visualization tools (pip install terraflow-agro[h3])

See CLI docs for full reference.

Climate interpolation

Three spatial algorithms are available via interpolation_method:

Method	Notes
linear (default)	scipy.griddata — fast, no extra deps
kriging	Ordinary Kriging via pykrige; adds {var}_krig_std uncertainty columns
idw	Inverse Distance Weighting (power=2) — faster than kriging, no uncertainty

Combine interpolation_method: kriging with uncertainty_samples: N in model_params to get Monte Carlo score confidence intervals (score_ci_low / score_ci_high).

See Config Schema for the full reference.

Python API

from terraflow.pipeline import run_pipeline

results_df = run_pipeline("config.yml")

Development

git clone https://github.com/gmarupilla/AgroTerraFlow.git
cd AgroTerraFlow
make dev       # create .venv and install dev deps
make test      # run test suite
make lint      # ruff + black
make docs-build

Architecture

Core modules: cli, config, climate, geo, ingest, model, pipeline, stats, viz.

Key design decisions are documented in Architecture Decision Records under docs/architecture/.

Project Scope

TerraFlow is a reproducible pipeline for geospatial agricultural modeling. It handles raster ingestion, ROI clipping, climate interpolation, suitability scoring, and deterministic artifact generation.

In scope:

• Configuration-driven pipeline execution (YAML → Parquet + provenance artifacts)
• Spatial interpolation of point climate observations (linear, kriging, IDW)
• Per-cell suitability scoring with uncertainty quantification (Monte Carlo)
• Deterministic run fingerprinting and artifact caching

Out of scope:

• Real-time data ingestion or streaming workflows
• General-purpose raster analysis (use rioxarray or rasterstats instead)
• Cloud-scale distributed processing (no Dask/Spark integration planned)
• Web application or GUI layer

Maintenance & Support

TerraFlow is actively maintained. Bug fixes are prioritized; the test suite and CI pipeline are kept green on every commit.

Feature requests are evaluated against project scope — open an issue to discuss before building. Not all requests will be accepted.

Support is provided on a best-effort basis via GitHub Issues. Response time is typically within a week. There is no paid support tier.

Contributing

See CONTRIBUTING.md.

Citation

If you use TerraFlow in your research, please cite our JOSS paper (manuscript in preparation).

License

MIT License — free for academic, commercial, and open-source use.