Landbrugsdata

The purpose of this project is to organize information about the Danish agricultural sector and make it universally accessible and useful.

🚀 Quick Start

Backend Setup

cd backend
python -m venv venv
source venv/bin/activate # or venv\Scripts\activate on Windows
pip install -r requirements.txt
uvicorn src.main:app --reload

Frontend Setup

cd frontend
npm install
npm start

Visit:

• Frontend: http://localhost:3000
• API Documentation: http://localhost:8000/docs

🏗️ Architecture

Backend (FastAPI + Python)

├── src/
│ ├── sources/ # Data source implementations
│ │ ├── base.py # Base source class
│ │ ├── parsers/ # API/WFS sources
│ │ └── static/ # Static file sources
│ ├── main.py # FastAPI application
│ └── config.py # Configuration

Frontend (React + TypeScript)

frontend/
└── src/
├── components/ # React components
├── api/ # Backend API client
├── hooks/ # Custom React hooks
└── types/ # TypeScript definitions

📊 Data Sources

Data should have one or more of the following attributes to be useful:

• CVR number (company registration number)
• CHR number (herd registration number)
• geospatial coordinates (point or polygon)
• enhedsnummer (CVR individual identifier)
• bfe number (cadaster number)

Live Sources

1. Agricultural Fields (WFS)
   • Updates: Weekly (Mondays 2 AM UTC)
   • Content: Field boundaries, crop types

Static Sources

All static sources are updated through manual pull requests:

• Animal Welfare: Inspection reports and focus areas
• Biogas: Production data and methane leakage reports
• Fertilizer: Nitrogen data and climate calculations
• Herd Data: CHR (Central Husbandry Register)
• Pesticides: Usage statistics (2021-2023)
• Pig Movements: International transport (2017-2024)
• Subsidies: Support schemes and project grants
• Visa: Agricultural visa statistics
• Wetlands: Areas and carbon content

🤝 Contributing

Prerequisites

• Python 3.9+
• Node.js 16+
• GDAL library
• Git

Development Workflow

1. Fork the repository
2. Create a feature branch:

   git checkout -b feature/your-feature-name

3. Make your changes
4. Run tests:

   # Backend
   cd backend
   pytest
   
   # Frontend
   cd frontend
   npm test

5. Submit a pull request

Adding New Data Sources

1. Choose the appropriate directory:

   • backend/src/sources/parsers/ for API/WFS sources
   • backend/src/sources/static/ for static files

2. Create a new parser class:

   from ..base import Source
   
   class YourSource(Source):
       async def fetch(self) -> pd.DataFrame:
           # Implement data fetching
           pass

3. Add configuration in backend/src/config.py

4. Update frontend types in frontend/src/types/

5. Add visualization in frontend/src/components/

🔄 Data Updates

• WFS Sources: Automatic weekly updates (Mondays 2 AM UTC)
• Static Sources: Manual updates via pull requests
• Deployment: Automatic to Google Cloud Run

🔧 Environment Setup

Backend (.env)

GOOGLE_CLOUD_PROJECT=your-project-id
GCS_BUCKET=your-bucket-name

Frontend (.env)

REACT_APP_API_URL=http://localhost:8000

📚 Documentation

• Backend API Documentation
• Frontend Component Documentation
• Data Source Specifications

🐛 Troubleshooting

Common issues and solutions:

1. GDAL Installation

   # Ubuntu/Debian
   sudo apt-get install gdal-bin libgdal-dev
   
   # macOS
   brew install gdal

2. API Connection Issues

   • Verify backend is running
   • Check CORS settings
   • Confirm environment variables

📝 License

This work is licensed under a Creative Commons Attribution 4.0 International License (CC-BY).

🙏 Acknowledgments

• Danish Agricultural Agency
• Danish Environmental Protection Agency
• Danish Energy Agency
• SIRI