IMEA-Crop_DATA EXTRACTOR

IMEA Direct Data Extractor 🌾📈More actions

A simple Python tool for extracting real-time agricultural data from IMEA (Instituto Mato-grossense de Economia Agropecuária) API. Providing crucial insights into Brazil's agricultural sector for equity analysts, traders, and researchers.

🎯 Why This Matters for Equity Analysts

Critical Market Intelligence for Agricultural Commodities

Brazil is the world's largest exporter of soybeans and a major producer of corn and cotton. Mato Grosso alone accounts for:

• 32% of Brazil's soybean production
• 28% of Brazil's corn production
• 65% of Brazil's cotton production

This data extractor provides real-time crop progress data that directly impacts:

📊 Commodity Price Movements

• Planting Progress: Early indicators of potential supply
• Harvest Progress: Real-time production estimates
• Commercialization Progress: Market flow and pricing pressure

🏢 Equity Impact Analysis

• Agricultural Companies: ADM, Cargill, Bunge, Amaggi
• Equipment Manufacturers: John Deere, CNH Industrial, AGCO
• Fertilizer Companies: Nutrien, Mosaic, Yara
• Food & Beverage: Tyson Foods, JBS, BRF
• Biofuel Producers: Renewable Energy Group, Archer Daniels

🚀 Quick Start (5 minutes)

1. Clone and Install

git clone https://github.com/yourusername/imea-direct-extractor.git
cd imea-direct-extractor
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

2. Set Your Credentials

Create a .env file:

IMEA_USERNAME=your_email@example.com
IMEA_PASSWORD=your_password

3. Extract Data

python imea_extractor.py

That's it! You'll get 10 CSV files with all the agricultural data.

📊 What You Get

Main Summary File

• BR_IMEA_CROP_PERCENTAGE_PROGRESS.csv - All crops and activities combined

Individual Crop Files (9 files)

• BR_IMEA_SOY_PLANTING_PERCENTAGE.csv
• BR_IMEA_SOY_HARVEST_PERCENTAGE.csv
• BR_IMEA_SOY_COMMERCIALIZATION_PERCENTAGE.csv
• BR_IMEA_CORN_PLANTING_PERCENTAGE.csv
• BR_IMEA_CORN_HARVEST_PERCENTAGE.csv
• BR_IMEA_CORN_COMMERCIALIZATION_PERCENTAGE.csv
• BR_IMEA_COTTON_PLANTING_PERCENTAGE.csv
• BR_IMEA_COTTON_HARVEST_PERCENTAGE.csv
• BR_IMEA_COTTON_COMMERCIALIZATION_PERCENTAGE.csv

Data Structure

date,year,month,crop,state,harvest_season,planted_percentage,harvested_percentage,commercialized_percentage
2024-01-15,2024,1,Soy,Mato Grosso,Safra 2023/24,98.5,15.2,75.8

📈 Real-World Applications

1. Earnings Forecasting: Predict quarterly results for agribusiness companies
2. Risk Management: Hedge positions based on crop progress data
3. Sector Rotation: Time rotation into/out of agricultural stocks
4. Weather Impact: Correlate planting delays with price volatility
5. Supply Chain Analysis: Track commercialization for logistics companies

🔧 Technical Features

• ✅ Direct API Access: No email workflows, real-time data
• ✅ Comprehensive Coverage: Soy, Corn, and Cotton from 2021+
• ✅ Clean CSV Output: Ready for Excel, Python, R analysis
• ✅ Parallel Processing: Fast bulk data extraction
• ✅ Robust Authentication: Handles IMEA's SSL requirements
• ✅ Simple Setup: One file, minimal dependencies

📊 Sample Data Analysis

import pandas as pd

# Load main summary
df = pd.read_csv('datasets/BR_IMEA_CROP_PERCENTAGE_PROGRESS.csv')

# Latest crop status
latest = df[df['date'] == df['date'].max()]
print("Current Crop Progress:")
for crop in ['Soy', 'Corn', 'Cotton']:
    crop_data = latest[latest['crop'] == crop]
    if not crop_data.empty:
        row = crop_data.iloc[0]
        print(f"{crop}: {row['planted_percentage']:.1f}% planted, {row['harvested_percentage']:.1f}% harvested")

# Seasonal trends
import matplotlib.pyplot as plt
soy_data = df[df['crop'] == 'Soy']
plt.plot(soy_data['date'], soy_data['planted_percentage'], label='Soy Planting')
plt.title('Soy Planting Progress Over Time')
plt.show()

🏗️ File Structure

imea-direct-extractor/
├── imea_extractor.py     # Main extractor (run this!)
├── requirements.txt      # Dependencies
├── .env                 # Your credentials (create this)
├── datasets/            # Output CSV files (auto-created)
├── README.md           # This file
├── LICENSE             # MIT License
└── .gitignore          # Git ignore (.env protected)

🔒 Security

• ✅ Credentials stored in .env file (git-ignored)
• ✅ No hardcoded passwords in code
• ✅ SSL warnings handled appropriately
• ✅ Environment variable loading

🤝 Contributing

We welcome contributions! Key areas:

• Additional Brazilian states
• More crop types (wheat, coffee, sugarcane)
• Data visualization features
• Performance optimizations

📄 License

MIT License - see LICENSE file for details.

⚠️ Disclaimer

This tool is for informational purposes only. Users are responsible for:

• Complying with IMEA's Terms of Service
• Ensuring proper API usage
• Validating data accuracy
• Making independent investment decisions

🆘 Support

• 🐛 Issues: GitHub Issues
• 📧 Email: support@yourproject.com

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Made with ❤️ for the agricultural finance community

"Transforming agricultural data into actionable insights for better investment decisions." "Transforming agricultural data into actionable insights for better investment decisions."