🌾 CropProphEU — EU Crop Intelligence MCP Server

13 MCP Tools, 5 Crops, 26 EU Countries, 123 NUTS2 Regions — Yield forecasts, market values (€/ha), risk analysis, environmental risk scoring & portfolio optimization for European agriculture. Built for AI agents, by AI agents.

> "How will wheat perform in Sachsen-Anhalt this year? What's my best €/ha allocation across 100 ha?"

pip install git+https://github.com/DasClown/CropProphEU.git

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Features (13 MCP Tools)

| # | Tool | What it does | V |
|---|------|-------------|---|
| 1 | yield_and_value | Yield + market value (€/ha) + plain-language summary (DE/EN) | V4.6 |
| 2 | europe_yield_forecast | Pan-European RF forecast with Yield-at-Risk (P10/P50/P90) + NDVI correction | V4.3 |
| 3 | crop_forecast | Current season: GDD, rain, soil moisture, drought index, frost warnings | V4.0 |
| 4 | compare_regions | Batch-compare 20 regions × 5 crops with live market prices | V5.1 |
| 5 | portfolio_optimizer | AI investment engine: budget → optimal allocation across regions × crops | V5.1e |
| 6 | season_comparison | Compare this season to historical years | V4.0 |
| 7 | region_health | All crops for one region, single call | V4.5 |
| 8 | weather_outlook | 16-day weather forecast | V4.0 |
| 9 | climate_scenario | What-if: +2°C, -20% rain, etc. | V4.4 |
| 10 | yield_forecast | Analog-year yield matching (DE-focused) | V3.0 |
| 11 | list_regions | 123 NUTS2 regions across 26 countries | V4.2 |
| 12 | list_crops | Crop parameters (GDD base, season, frost sensitivity) | V4.5 |
| 13 | environmental_risk | NEW V5.4 — ERS (forest, erosion, storm, hail) + wild boar damage risk for DE | V5.4 |

---

Quick Start

1. Install

pip install git+https://github.com/DasClown/CropProphEU.git

2. Use as MCP Server

CLI (stdio):

crop-mcp

Python API:

from crop_mcp import predict_europe_yield

result = predict_europe_yield("DE11", "DE", crop="wheat", gdd=3050, precip_mm=650)

print(f"Yield: {result['predicted_yield_t_ha']} t/ha")

print(f"Revenue: ~{result['predicted_yield_t_ha']  239:.0f} €/ha")

{
  "mcpServers": {
    "crop": {
      "command": "python3",
      "args": ["-m", "crop_mcp.server"]
    }
  }
}

pip install crop-mcp[http]
crop-mcp --http --port 8080

docker build -t crop-mcp .
docker run -p 8080:8080 crop-mcp crop-mcp --http --port 8080

{

  "overall_risk": "🔴 high",

  "ers_level": "🟡 moderate",

  "wild_boar_risk": {"level": "🔴 high", "loss_eur_ha": 158},

  "management": ["Waldrandstreifen 3-6m", "Drückjagd Nov-Dez", "8-Tage-Anzeigefrist §36 BayJG"]

}

| Maßnahme | Status |
|:---------|:------:|
| pytest | 16 Tests, alle passing (tests/test_crop_mcp.py) |
| CI (GitHub Actions) | ✅ Aktiv bei jedem Push — Badge im Header grün |
| Git LFS | .pkl + große .json via LFS (aus Git-Tree entfernt) |

Run tests:

pip install -e ".[test]"

pytest tests/ -v

---

Model Accuracy

| Metric | Value |
|--------|:-----:|
| LOYO MAE (Wheat) | 0.599 t/ha (11.5%) |
| Forward Validation (Train ≤2022, Test 2023-24) | 0.794 t/ha (15.0%) |
| R² (LOYO) | 0.871 |
| R² (Forward) | 0.628 |

Most accurate for core EU (DE, FR, BE, NL, AT, CZ) where training data is dense.

Per-Crop Performance (V5.2)

| Crop | Algorithm | Top Feature | Key Insight |
|------|:---------:|:-----------:|-------------|
| 🌾 Wheat | RF 200 trees | solar_kwh (35%) | Nord/Süd gradient dominates |
| 🌽 Corn | RF 200 trees | clay_pct (42%) | Maize is extremely soil-sensitive |
| 🌿 Barley | Ridge | clay_pct (27%) | Best coverage of all crops |
| 🌻 Rapeseed | RF 200 trees | coarse_pct (28%) | Corrected — now 1,825 real samples |
| 🌻 Sunflower | Ridge | silt_pct (24%) | 17 countries (post-fix) |

---

Live Market Prices

| Crop | Source | €/t (Mai 2026) | Market |
|------|:------:|:---------------:|--------|
| Wheat | ✅ CBOT ZW=F + MATIF premium | 239 | Euronext MATIF |
| Corn | ✅ CBOT ZC=F + MATIF premium | 189 | Euronext MATIF |
| Barley | ✅ Reference (AMI regional) | 190 | AMI regional exchanges |
| Rapeseed | ✅ Reference | 470 | Euronext MATIF (ECO) |
| Sunflower | ✅ Reference | 420 | ICE / Black Sea |

Production costs are country- and crop-specific (V5.3+). See market_prices.py → COUNTRY_PRODUCTION_COSTS (28 EU countries × 5 crops). Sources: FADN, KTBL, ARVALIS. Examples:

| Country | Wheat | Corn | Barley | Rapeseed | Sunflower |
|:--------|:-----:|:----:|:------:|:--------:|:---------:|
| 🇩🇪 DE | 650 | 700 | 600 | 780 | 650 |
| 🇫🇷 FR | 700 | 650 | 600 | 750 | 600 |
| 🇵🇱 PL | 550 | 600 | 500 | 650 | 550 |
| 🇷🇴 RO | 450 | 500 | 400 | 550 | 450 |
| 🇪🇸 ES | 600 | 650 | 550 | 700 | — |

> ℹ️ Full table: COUNTRY_PRODUCTION_COSTS dict in crop_mcp/market_prices.py

---

Data Sources

| Source | Data | Access |
|--------|------|--------|
| Eurostat | Crop yields (apro_cpshr) — 25+ years, verified codes | Free, no key |
| NASA POWER | GDD, precip, solar, soil moisture | Free, no rate limits |
| Open-Meteo | 16-day forecast, GDD | Free, no key |
| SoilGrids v2 (ISRIC) | 11 properties: SOC, pH, N, CEC, clay, sand, silt, bdod (bulk density), cfvo (coarse fragments), AWC, coarse | Free REST API |
| LUCAS Soil (ESDAC) | Texture ~20K field points + coarse fragments | Free download |
| Sentinel-2 NDVI | Vegetation index (Copernicus STAC + Planetary Computer fallback) | Free, no auth |
| Yahoo Finance | Live CBOT wheat/corn futures, EUR/USD | Free, no key |

Zero API keys required — all sources are free and public.

---

Example Output

German (default):

Weizen – Region DEE0 (DE)

Ertrag: 7.35 t/ha (Spanne 6.50–8.20)

Temperatur: warm (2950°C Wärmesumme)

Niederschlag: ausreichend (480 mm)

Bodenfeuchte: feucht (48%)

Modellabweichung: ±11.5% (1603 Samples, 26 Länder)

Vergleich zu 2024: +0.15 t/ha (im Rahmen des Vorjahres)

Marktwert: 1.757 €/ha @ 239 €/t

Kosten: 650 €/ha → Deckungsbeitrag: 1.107 €/ha

English (with language="en"):

Wheat – Region DEE0 (DE)

Yield: 7.35 t/ha (range 6.50–8.20)

Temperature: warm (2950°C GDD)

...

---

Architecture

crop-mcp/
├── crop_mcp/
|├── server.py                 # 857 Zeilen — Pydantic-Modelle + Tool-Registry + MCP-Init
│   ├── tools/                  # V5.4e — Handler in logische Module aufgeteilt
│   │   ├── weather.py          # weather_outlook, crop_forecast, season_comparison, region_health
│   │   ├── yield_tools.py      # yield_forecast, europe_yield_forecast, yield_and_value, climate_scenario
│   │   ├── market.py           # compare_regions, portfolio_optimizer
│   │   ├── info.py             # list_regions, list_crops
│   │   ├── environmental.py    # environmental_risk (V5.4)
│   │   └── helpers.py          # Shared utilities (NDVI correction, frost, language)
│   ├── europe_model_api.py       # RF (200 trees) + Yield-at-Risk + NDVI correction
│   ├── environmental_risk.py     # V5.4 — ERS + Wildschaden DE
│   ├── ndvi_correction.py        # Sentinel-2 NDVI correction factor (±30%)
│   ├── market_prices.py          # Live CBOT/MATIF via Yahoo Finance
│   ├── feature_cache.py          # Sub-second historical queries
│   ├── simulate_yield.py         # Analog-year matching
│   ├── auto_update.py            # Monthly retrain cron
│   ├── core/regions.py           # 123 NUTS2 regions
│   └── sources/                  # Weather, soil, NDVI, Eurostat, FAOSTAT fetchers
├── models/                       # .pkl files ➜ download from Releases
├── data/                         # Training data ➜ download from Releases (or generated by build)
├── tests/                        # V5.4 — 16 pytest tests
├── .github/workflows/ci.yml      # V5.4 — CI workflow (lokal, benötigt PAT mit workflow-Scope für Push)
├── pyproject.toml
└── README.md

Key design principles:
- No hallucination — every yield prediction traces to verified Eurostat data
- Live prices — CBOT wheat/corn via Yahoo Finance, updated hourly
- Self-updating — monthly cron rebuilds models with latest Eurostat data
- Zero API keys — all data sources are free and public
- AI-for-AI — built for agents, no dashboards

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Building & Training

```bash