AgentSkillsCN

Optimize Crop Yield

优化作物产量

SKILL.md

Skill: Optimize Crop Yield

Domain

agribusiness

Description

Analyzes field conditions, soil data, and weather patterns to generate crop-specific yield optimization recommendations based on proprietary agronomic models and regional best practices.

Tags

agriculture, crop-management, yield-optimization, precision-farming, agronomy

Use Cases

  • •Seasonal planting recommendations
  • •Fertilizer application optimization
  • •Irrigation scheduling
  • •Harvest timing decisions

Proprietary Business Rules

Rule 1: Soil Nutrient Balance

Optimal NPK ratios vary by crop and growth stage, with proprietary threshold values calibrated to regional soil types.

Rule 2: Growing Degree Day Accumulation

Crop maturity predictions based on accumulated heat units with cultivar-specific base temperatures.

Rule 3: Water Stress Index

Irrigation triggers based on soil moisture deficit thresholds adjusted for crop water sensitivity stages.

Rule 4: Pest Pressure Integration

Yield adjustments based on integrated pest management (IPM) pressure scores.

Input Parameters

  • •field_id (string): Unique field identifier
  • •crop_type (string): Crop being cultivated
  • •soil_analysis (dict): Recent soil test results (N, P, K, pH, organic matter)
  • •weather_forecast (dict): 14-day weather forecast data
  • •growth_stage (string): Current crop growth stage
  • •irrigation_available (bool): Whether irrigation is available
  • •pest_pressure_score (float): Current IPM pressure score (0-10)

Output

  • •yield_prediction (float): Predicted yield in bushels/acre
  • •optimization_actions (list): Recommended actions
  • •fertilizer_recommendations (dict): Specific fertilizer applications
  • •irrigation_schedule (list): Recommended irrigation events
  • •risk_factors (list): Identified yield risk factors

Implementation

The optimization logic is implemented in yield_optimizer.py and references agronomic parameters from CSV files:

  • •crops.csv - Reference data
  • •fertilizer_conversions.csv - Reference data
  • •nutrient_yield_impacts.csv - Reference data
  • •parameters.csv - Reference data.

Usage Example

python
from yield_optimizer import optimize_yield

result = optimize_yield(
    field_id="FIELD-NW-042",
    crop_type="corn",
    soil_analysis={"N": 45, "P": 32, "K": 180, "pH": 6.5, "organic_matter": 3.2},
    weather_forecast={"avg_temp": 78, "precip_chance": 30, "gdd_forecast": 125},
    growth_stage="V8",
    irrigation_available=True,
    pest_pressure_score=3.5
)

print(f"Predicted Yield: {result['yield_prediction']} bu/acre")

Test Execution

python
from yield_optimizer import optimize_yield

result = optimize_yield(
    field_id=input_data.get('field_id'),
    crop_type=input_data.get('crop_type'),
    soil_analysis=input_data.get('soil_analysis', {}),
    weather_forecast=input_data.get('weather_forecast', {}),
    growth_stage=input_data.get('growth_stage'),
    irrigation_available=input_data.get('irrigation_available', False),
    pest_pressure_score=input_data.get('pest_pressure_score', 0)
)