Provides access to NASA POWER data for weather variables (GDD, precipitation, solar radiation, soil moisture) used in crop yield forecasting and risk analysis.
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# | Tool | What it does | V |
1 |
| Yield + market value (€/ha) + plain-language summary (DE/EN) | V4.6 |
2 |
| Pan-European RF forecast with Yield-at-Risk (P10/P50/P90) + NDVI correction | V4.3 |
3 |
| Current season: GDD, rain, soil moisture, drought index, frost warnings | V4.0 |
4 |
| Batch-compare 20 regions × 5 crops with live market prices | V5.1 |
5 |
| AI investment engine: budget → optimal allocation across regions × crops | V5.1e |
6 |
| Compare this season to historical years | V4.0 |
7 |
| All crops for one region, single call | V4.5 |
8 |
| 16-day weather forecast | V4.0 |
9 |
| What-if: +2°C, -20% rain, etc. | V4.4 |
10 |
| Analog-year yield matching (DE-focused) | V3.0 |
11 |
| 123 NUTS2 regions across 26 countries | V4.2 |
12 |
| Crop parameters (GDD base, season, frost sensitivity) | V4.5 |
13 |
| NEW V5.4 — ERS (forest, erosion, storm, hail) + wild boar damage risk for DE | V5.4 |
Related MCP server: agriculture-mcp-server
pip install git+https://github.com/DasClown/CropProphEU.gitCLI (stdio):
crop-mcpPython 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 8080Connect via SSE: http://your-server:8080/sse
docker build -t crop-mcp .
docker run -p 8080:8080 crop-mcp crop-mcp --http --port 8080Every prediction traces to a verified Eurostat crop code. No hallucinations, no silent wrong-crop training.
Crop | Eurostat Code | Samples | Countries | MAE (LOYO) | R² |
🌾 Wheat | C1100 | 1,603 | 26 (🇪🇺+🇺🇦) | 11.5% | 0.87 |
🌽 Corn (Maize) | C1500 | 1,797 | 21 (🇪🇺+🇬🇧) | 11.6% | 0.72 |
🌿 Barley | C1300 | 1,885 | 26 (🇪🇺+🇬🇧) | 11.2% | 0.85 |
🌻 Rapeseed | I1110 | 1,825 | 25 | 10.8% | 0.83 |
🌻 Sunflower | I1120 | 1,229 | 17 | 16.1% | 0.74 |
⚠️ V5.1d Data Fix: Rapeseed + Sunflower were previously trained on RICE data (wrong Eurostat codes C2000/C2200). Now corrected to Industrial crop codes I1110/I1120. DE rapeseed prediction fell from 7.21t to 2.63t — real, not extrapolated.
Capability | CropProphEU | Open-Meteo MCP | Gro Intelligence |
Yield forecasts | ✅ 5 crops | ❌ | ✅ $10K+/yr |
Soil features | ✅ 11 properties | ❌ | ✅ |
Yield-at-Risk (P10/P90) | ✅ | ❌ | ✅ |
Live market prices (€/ha) | ✅ CBOT + MATIF | ❌ | ✅ |
Climate what-if | ✅ | ❌ | ✅ |
Frost warnings | ✅ | ✅ | ❌ |
NDVI satellite correction | ✅ | ❌ | ❌ |
Portfolio optimizer | ✅ | ❌ | ❌ |
Multi-language (DE/EN) | ✅ | ❌ | ❌ |
Environmental Risk | ✅ V5.4 | ❌ | ❌ |
Price | Free | Free | $10K+/yr |
Unique: Only free MCP server covering EU agriculture with soil → yield → market value → environmental risk → portfolio optimization in one pipeline.
Feature | Beschreibung |
Environmental Risk Score | Komposit aus Waldanteil, Maisfläche, Bodenerosion, Sturm- + Hagelrisiko → 🟢🟡🔴 |
Wildschaden DE | DJV-Jagdstreckendaten + Waldrandindex + Maisflächenanteil → €/ha-Verlustschätzung |
Ampel-System |
|
MCP Tool |
|
Beispiel Maßbach (DE26 Unterfranken):
{
"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 ( |
CI (GitHub Actions) | ✅ Aktiv bei jedem Push — Badge im Header grün |
Git LFS |
|
Run tests:
pip install -e ".[test]"
pytest tests/ -vMetric | 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.
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) |
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_COSTSdict incrop_mcp/market_prices.py
Source | Data | Access |
Eurostat | Crop yields ( | 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.
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 €/haEnglish (with language="en"):
Wheat – Region DEE0 (DE)
Yield: 7.35 t/ha (range 6.50–8.20)
Temperature: warm (2950°C GDD)
...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.mdKey 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
# Build training data (25 min per crop)
python3 build_europe.py --crop corn
# Train model (2 min)
python3 train_europe_fast.py --crop corn
# Auto-update monthly (cron: 1st of month at 06:00)Agri-trading desks: "What's wheat worth in Picardie at current MATIF prices?"
Farm advisory: "How does this season compare to the last 5 years?"
Insurance / Risk: Yield-at-Risk (P10/P50/P90) per region + crop
EU policy analysis: Climate scenario impact on national yields
Investment: Portfolio optimizer for 100+ ha allocation decisions
Channel | Purpose |
Questions before coding, feature ideas, community chat | |
Bug reports, confirmed feature requests | |
Development setup, branch naming, commit conventions, code style |
New contributors welcome! See CONTRIBUTING.md to get started.
MIT — free to use, modify, and distribute.
Built with ❤️ for AI agents that need real, verifiable crop intelligence.
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