Data Collection & Management

2.1 Historical Yield Data

The foundation of any yield prediction system is high-quality historical yield data. This includes actual harvest yields measured over multiple growing seasons.

Essential Data Points

• Crop type and variety
• Geographic location (GPS coordinates)
• Farm/field area
• Planting and harvest dates
• Actual yield (kg/ha or tons/ha)
• Input data (fertilizer, irrigation, pesticides)

2.2 Weather Data Integration

Weather is the single most important factor affecting crop yields. Comprehensive weather data collection is critical.

Key Weather Variables

• Temperature (daily min, max, average)
• Rainfall/precipitation
• Sunshine hours or solar radiation
• Humidity levels
• Wind speed
• Evapotranspiration rates

Data Sources

Weather data can be obtained from:

• National meteorological services (e.g., KMA in Korea)
• Commercial weather APIs (OpenWeatherMap, Weather Underground)
• On-farm weather stations
• Satellite-based weather estimates

2.3 Soil Data Collection

Soil properties significantly impact yield potential. Key soil parameters include:

• Soil type and texture
• pH levels
• Organic matter content
• Nutrient levels (N, P, K)
• Water holding capacity
• Drainage characteristics

2.4 Korean Agricultural Data

In Korea, yield data follows specific standards from 통계청 (Statistics Korea - KOSIS):

Korean Units

• 재배면적: Cultivated area (hectares)
• 단수: Yield per unit area (kg/10a)
• 생산량: Total production (tons)

Conversion

Korean agriculture traditionally uses 10a (1,000 m²) as the area unit. To convert:

1 hectare = 10 × 10a
1 kg/10a = 10 kg/ha

Example: Rice yield of 520 kg/10a = 5,200 kg/ha

2.5 Data Quality Standards

The WIA-AGRI-008 standard defines quality requirements:

Mandatory Fields

• Farm/field unique identifier
• Crop type
• GPS coordinates (±10m accuracy)
• Harvest year
• Actual yield with units
• Data collection timestamp

Validation Rules

• Yield values within realistic ranges for crop type
• Planting date before harvest date
• Geographic coordinates valid
• No duplicate records
• Missing data < 10% per record

2.6 Data Storage Formats

Standardized formats ensure interoperability:

JSON Format (Recommended)

{
  "farmId": "KR-FARM-2025-001",
  "crop": "rice",
  "year": 2025,
  "yield": {
    "amount": 5200,
    "unit": "kg/ha"
  },
  "location": {
    "lat": 36.5184,
    "lon": 127.2158
  }
}

CSV Format

For bulk historical data, CSV files with proper headers:

farmId,crop,year,lat,lon,yield_kg_ha,rainfall_mm
KR-001,rice,2025,36.5184,127.2158,5200,850