field-history-intelligence

name	field-history-intelligence
description	Comprehensive field history tracking, analysis, and decision intelligence system for agricultural operations. Use when the user asks about field history intelligence.

Field History Intelligence

Comprehensive field history tracking, analysis, and decision intelligence system for agricultural operations.

Purpose

Capture, organize, and analyze complete field history data across seasons, enabling data-driven decisions that improve soil health, optimize input usage, and increase profitability. Transform scattered notes, receipts, weather data, and equipment records into actionable intelligence without requiring expensive farm management software subscriptions.

Problem Solved

Farmers make critical decisions based on fragmented information spread across notebooks, spreadsheets, paper receipts, and various software systems. This scattered approach leads to:

• Repeating mistakes from previous seasons
• Missing patterns in crop performance
• Over-application of inputs due to lack of historical context
• Inability to track soil health trends over time
• Difficulty justifying decisions to lenders or inspectors
• Lost opportunities to improve yields through pattern recognition
• Inability to demonstrate compliance with regulatory requirements

Field History Intelligence centralizes all field data in a format AI can analyze, providing insights that would take hours to discover manually.

Capabilities

• Multi-Source Data Ingestion: Import from CSV, JSON, PDF, images, spreadsheets, and manual entry
• Seasonal Tracking: Organize data by planting, growing, and harvest seasons
• Crop Performance Analysis: Compare yields, varieties, and planting dates across years
• Input Optimization: Track fertilizer, chemical, and seed usage with cost analysis
• Weather Correlation: Correlate weather patterns with crop performance
• Soil Health Trending: Track soil test results and amendment history
• Equipment Performance: Analyze equipment usage and efficiency per field
• Profitability Tracking: Calculate ROI per field, per crop, per season
• Pattern Recognition: Identify recurring issues (pest pressure, drainage problems)
• Predictive Insights: Suggest optimal planting times and input rates based on history
• Compliance Reporting: Generate required reports for regulators and certifiers
• Decision Support: Provide AI-analyzed recommendations for upcoming season
• Visualization: Create charts, maps, and timelines from field data
• Export Capabilities: Export data for analysis in external tools

Instructions

Usage by AI Agent

1. Initialize Field Database

   • Load or create SQLite database for field data
   • Import existing field boundaries and layouts
   • Set up data categories: crops, inputs, equipment, weather, soil, observations
   • Configure units of measurement (acres, bushels, pounds, gallons)

2. Ingest Historical Data

   • Scan and import existing records (spreadsheets, notebooks, receipts)
   • Parse CSV files from equipment GPS logs
   • Import soil test reports from PDF or images
   • Extract data from equipment software exports
   • Record historical weather data for field location
   • Tag and categorize all imported data by season and field

3. Organize and Normalize

   • Standardize data formats (dates, units, naming conventions)
   • Link related records (e.g., fertilizer application to crop yield)
   • Create field hierarchy (farm, field, subfield, zone)
   • Tag records with metadata (weather events, pest outbreaks, equipment issues)
   • Validate data consistency and flag anomalies

4. Analyze Patterns

   • Compare yields across years and varieties
   • Correlate input applications with outcomes
   • Identify recurring pest pressure patterns
   • Analyze soil health trends from test results
   • Map drainage or irrigation issues
   • Calculate input efficiency (cost per bushel)
   • Track equipment effectiveness per operation type

5. Generate Insights

   • Identify best-performing crop varieties per field
   • Determine optimal planting windows based on historical success
   • Calculate input rate optimization opportunities
   • Highlight fields requiring soil health intervention
   • Flag recurring equipment issues requiring maintenance
   • Generate profitability analysis by field and crop

6. Support Decision Making

   • Provide recommendations for upcoming planting season
   • Suggest input rate adjustments based on field history
   • Alert to potential issues based on historical patterns
   • Generate ROI projections for different scenarios
   • Create comparison reports for decision support

7. Maintain and Update

   • Record current season data as it occurs

Data Categories to Capture

Crop Production:

• Crop type and variety
• Planting date and rate
• Harvest date and yield
• Moisture content at harvest
• Quality metrics (test weight, protein, etc.)
• Stand establishment counts
• Pest and disease pressure

Input Applications:

• Fertilizer type, rate, timing, and method
• Chemical/herbicide applications (product, rate, timing, conditions)
• Seed variety, treatment, and planting rate
• Irrigation applications (timing, volume, method)
• Soil amendments (lime, gypsum, organic matter)

Soil Health:

• Soil test results (pH, N-P-K, organic matter, micronutrients)
• Texture classification
• Cation exchange capacity
• Biological activity indicators
• Compaction assessments
• Drainage ratings

Equipment Usage:

• Equipment used per operation
• Fuel consumption per operation
• Operating hours per field
• Maintenance records
• Efficiency metrics (acres/hour, bushels/hour)

Environmental Conditions:

• Daily weather data (precipitation, temperature, growing degree days)
• Soil moisture and temperature readings
• Frost dates and growing season length
• Extreme weather events

Financial Data:

• Input costs per application
• Equipment operating costs
• Crop prices received
• Insurance claims
• Government program payments

Observations and Notes:

• Field observations during scouting
• Equipment operator notes
• Weather impacts and unusual conditions
• Experimental trials and results
• Lessons learned

Analysis Types

Trend Analysis:

• Multi-year yield trends by field
• Soil health parameter changes over time
• Input efficiency trends
• Cost per unit production changes
• Weather pattern shifts

Comparative Analysis:

• Variety performance comparison
• Field-to-field performance comparison
• Input rate vs. yield correlation
• Equipment efficiency comparison
• Timing effect analysis (early vs. late planting)

Pattern Recognition:

• Recurring pest pressure hotspots
• Drainage problem identification
• Yield-limiting factor identification
• Optimal planting window identification
• Input interaction effects

Predictive Analysis:

• Yield projections based on historical data
• Input requirement predictions
• Risk assessment for different scenarios
• Weather impact projections
• ROI projections

Tools

• Python 3.8+ for data analysis and processing
• SQLite for structured data storage
• Pandas for data manipulation and analysis
• NumPy for numerical computing
• Matplotlib/Plotly for data visualization
• OpenCV/PIL for image and document processing
• PyPDF2/Tabula for PDF extraction
• Requests for weather data API calls
• ReportLab for PDF report generation
• GeoJSON/Fiona for spatial data handling

Environment Variables

# Database Configuration
DATABASE_PATH=/opt/field-history/field-data.db
BACKUP_PATH=/opt/field-history/backups
AUTO_BACKUP_ENABLED=true
AUTO_BACKUP_INTERVAL=daily

# Data Import Settings
IMPORT_DIR=/opt/field-history/imports
IMAGE_OCR_ENABLED=true
PDF_EXTRACTION_ENABLED=true
CSV_DELIMITER=comma

# Weather Data
WEATHER_API_KEY=
WEATHER_SOURCE=noaa  # noaa | openweathermap | weather.gov
DEFAULT_LATITUDE=41.8781
DEFAULT_LONGITUDE=-87.6298

# Analysis Settings
DEFAULT_UNIT_SYSTEM=imperial  # imperial | metric
CURRENCY=USD
ANALYSIS_DEPTH=5  # years of history to analyze

# Visualization Settings
CHART_FORMAT=png
CHART_DPI=150
MAP_STYLE=satellite

# Export Settings
EXPORT_FORMAT=csv  # csv | json | excel
EXPORT_INCLUDE_IMAGES=true

# Logging
LOG_LEVEL=info
LOG_FILE=/var/log/field-history-intelligence.log

# Notification Settings
NOTIFICATION_ENABLED=false
NOTIFICATION_EMAIL=
NOTIFICATION_SMS=
SMTP_SERVER=
SMTP_PORT=587
SMTP_USERNAME=
SMTP_PASSWORD=

Data Schema

Core Tables

Fields:

CREATE TABLE fields (
  id INTEGER PRIMARY KEY AUTOINCREMENT,
  name TEXT NOT NULL UNIQUE,
  farm_name TEXT,
  acres REAL,
  soil_type TEXT,
  drainage_rating TEXT,
  gps_boundary TEXT,  -- GeoJSON
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
  updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

Seasons:

CREATE TABLE seasons (
  id INTEGER PRIMARY KEY AUTOINCREMENT,
  name TEXT NOT NULL,  -- e.g., "2024 Corn"
  year INTEGER NOT NULL,
  crop_type TEXT,
  planting_date DATE,
  harvest_date DATE,
  description TEXT,
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

Planting:

CREATE TABLE planting (
  id INTEGER PRIMARY KEY AUTOINCREMENT,
  field_id INTEGER NOT NULL,
  season_id INTEGER NOT NULL,
  variety TEXT NOT NULL,
  planting_date DATE NOT NULL,
  seeding_rate REAL,  -- seeds/acre
  population REAL,  -- plants/acre
  row_spacing REAL,  -- inches
  depth REAL,  -- inches
  method TEXT,  -- no-till, conventional, strip-till
  FOREIGN KEY (field_id) REFERENCES fields(id),
  FOREIGN KEY (season_id) REFERENCES seasons(id)
);

Harvest:

CREATE TABLE harvest (
  id INTEGER PRIMARY KEY AUTOINCREMENT,
  field_id INTEGER NOT NULL,
  season_id INTEGER NOT NULL,
  variety TEXT,
  harvest_date DATE NOT NULL,
  yield REAL,  -- bushels/acre
  moisture REAL,  -- percentage
  test_weight REAL,  -- lbs/bushel
  gross_yield REAL,
  dockage REAL,
  quality_notes TEXT,
  FOREIGN KEY (field_id) REFERENCES fields(id),
  FOREIGN KEY (season_id) REFERENCES seasons(id)
);

Input Applications:

CREATE TABLE inputs (
  id INTEGER PRIMARY KEY AUTOINCREMENT,
  field_id INTEGER NOT NULL,
  season_id INTEGER NOT NULL,
  application_date DATE NOT NULL,
  input_type TEXT NOT NULL,  -- fertilizer, chemical, seed, irrigation, amendment
  product_name TEXT,
  rate REAL,
  rate_unit TEXT,  -- lbs/acre, gal/acre, oz/acre
  method TEXT,  -- broadcast, injected, banded, foliar
  total_quantity REAL,
  cost_per_unit REAL,
  total_cost REAL,
  equipment_id INTEGER,
  weather_conditions TEXT,
  notes TEXT,
  FOREIGN KEY (field_id) REFERENCES fields(id),
  FOREIGN KEY (season_id) REFERENCES seasons(id)
);

Soil Tests:

CREATE TABLE soil_tests (
  id INTEGER PRIMARY KEY AUTOINCREMENT,
  field_id INTEGER NOT NULL,
  test_date DATE NOT NULL,
  lab_name TEXT,
  sample_depth TEXT,  -- e.g., "0-6 inches"
  ph REAL,
  organic_matter REAL,
  nitrogen REAL,
  phosphorus REAL,
  potassium REAL,
  calcium REAL,
  magnesium REAL,
  sulfur REAL,
  boron REAL,
  zinc REAL,
  iron REAL,
  manganese REAL,
  copper REAL,
  cec REAL,
  textural_class TEXT,
  notes TEXT,
  FOREIGN KEY (field_id) REFERENCES fields(id)
);

Weather Data:

CREATE TABLE weather (
  id INTEGER PRIMARY KEY AUTOINCREMENT,
  field_id INTEGER NOT NULL,
  date DATE NOT NULL,
  precipitation REAL,  -- inches
  max_temp REAL,  -- F
  min_temp REAL,  -- F
  avg_temp REAL,  -- F
  humidity REAL,  -- percentage
  wind_speed REAL,  -- mph
  soil_temp_2in REAL,  -- F
  soil_temp_4in REAL,  -- F
  growing_degree_days REAL,
  notes TEXT,
  FOREIGN KEY (field_id) REFERENCES fields(id)
);

Equipment Usage:

CREATE TABLE equipment (
  id INTEGER PRIMARY KEY AUTOINCREMENT,
  name TEXT NOT NULL,
  type TEXT NOT NULL,  -- tractor, combine, planter, sprayer
  make TEXT,
  model TEXT,
  year INTEGER,
  purchase_date DATE
);

CREATE TABLE equipment_usage (
  id INTEGER PRIMARY KEY AUTOINCREMENT,
  equipment_id INTEGER NOT NULL,
  field_id INTEGER NOT NULL,
  season_id INTEGER NOT NULL,
  operation_date DATE NOT NULL,
  operation_type TEXT,  -- planting, spraying, harvesting, tillage
  hours REAL,
  acres_covered REAL,
  fuel_used REAL,
  efficiency REAL,  -- acres/hour
  notes TEXT,
  FOREIGN KEY (equipment_id) REFERENCES equipment(id),
  FOREIGN KEY (field_id) REFERENCES fields(id),
  FOREIGN KEY (season_id) REFERENCES seasons(id)
);

Observations:

CREATE TABLE observations (
  id INTEGER PRIMARY KEY AUTOINCREMENT,
  field_id INTEGER NOT NULL,
  season_id INTEGER,
  observation_date DATE NOT NULL,
  category TEXT,  -- scouting, weather_damage, pest_issue, disease, equipment_problem
  severity TEXT,  -- low, medium, high
  description TEXT NOT NULL,
  location_in_field TEXT,  -- GPS or relative position
  photos TEXT,  -- comma-separated file paths
  action_taken TEXT,
  resolved BOOLEAN DEFAULT FALSE,
  resolution_date DATE,
  created_by TEXT,
  FOREIGN KEY (field_id) REFERENCES fields(id),
  FOREIGN KEY (season_id) REFERENCES seasons(id)
);

Financial Data:

CREATE TABLE financial_records (
  id INTEGER PRIMARY KEY AUTOINCREMENT,
  field_id INTEGER NOT NULL,
  season_id INTEGER NOT NULL,
  record_date DATE NOT NULL,
  category TEXT,  -- input_cost, equipment_cost, labor_cost, insurance_payment, crop_sale
  description TEXT,
  amount REAL,
  unit TEXT,
  payment_method TEXT,
  receipt_image TEXT,
  notes TEXT,
  FOREIGN KEY (field_id) REFERENCES fields(id),
  FOREIGN KEY (season_id) REFERENCES seasons(id)
);

Import Formats

CSV Format Examples

Planting Data:

field_name,season,variety,planting_date,seeding_rate,row_spacing,depth,method
North 40,2024 Corn,Pioneer P1234,2024-04-15,32000,30,2.5,no-till
East 80,2024 Soybeans,Asgrow A2632,2024-05-10,140000,15,1.5,conventional

Input Applications:

field_name,season,application_date,input_type,product_name,rate,rate_unit,method,total_cost
North 40,2024 Corn,2024-04-10,fertilizer,28-0-0,30,gal/acre,injected,45.00
North 40,2024 Corn,2024-04-12,fertilizer,MAP,150,lbs/acre,broadcast,67.50

Harvest Data:

field_name,season,harvest_date,variety,yield,moisture,test_weight
North 40,2024 Corn,2024-10-15,Pioneer P1234,215,16.5,58.2
East 80,2024 Soybeans,2024-10-01,Asgrow A2632,55,13.0,55.0

JSON Format

{
  "field_name": "North 40",
  "season": "2024 Corn",
  "crop_type": "corn",
  "planting": {
    "variety": "Pioneer P1234",
    "planting_date": "2024-04-15",
    "seeding_rate": 32000,
    "row_spacing": 30,
    "depth": 2.5,
    "method": "no-till"
  },
  "inputs": [
    {
      "application_date": "2024-04-10",
      "input_type": "fertilizer",
      "product_name": "28-0-0",
      "rate" 
       
       
       
    
  
   
     
     
     
     
  

Analysis Queries

Yield Comparison by Year

SELECT
  f.name AS field_name,
  s.year,
  h.variety,
  h.yield,
  h.moisture,
  p.planting_date
FROM harvest h
JOIN fields f ON h.field_id = f.id
JOIN seasons s ON h.season_id = s.id
LEFT JOIN planting p ON h.field_id = p.field_id AND h.season_id = p.season_id
WHERE s.crop_type = 'corn'
ORDER BY f.name, s.year DESC;

Input Cost Analysis

SELECT
  s.year,
  f.name AS field_name,
  s.crop_type,
  SUM(i.total_cost) AS total_input_cost,
  h.yield,
  CASE
    WHEN h.yield > 0 THEN i.total_cost / h.yield
    ELSE 0
  END AS cost_per_bushel
FROM inputs i
JOIN fields f ON i.field_id = f.id
JOIN seasons s ON i.season_id = s.id
LEFT JOIN harvest h ON h.field_id = f.id AND h.season_id = s.id
GROUP BY s.year, f.name, s.crop_type, h.yield
ORDER BY s.year DESC, f.name;

Soil Health Trends

SELECT
  f.name AS field_name,
  st.test_date,
  st.ph,
  st.organic_matter,
  st.nitrogen,
  st.phosphorus,
  st.potassium
FROM soil_tests st
JOIN fields f ON st.field_id = f.id
WHERE f.name = 'North 40'
ORDER BY st.test_date DESC
LIMIT 10;

Variety Performance Comparison

SELECT
  h.variety,
  COUNT(*) AS field_count,
  AVG(h.yield) AS avg_yield,
  MIN(h.yield) AS min_yield,
  MAX(h.yield) AS max_yield,
  STDDEV(h.yield) AS yield_variance,
  AVG(h.moisture) AS avg_moisture
FROM harvest h
JOIN seasons s ON h.season_id = s.id
WHERE s.crop_type = 'corn'
  AND s.year >= 2020
GROUP BY h.variety
HAVING COUNT(*) >= 3
ORDER BY avg_yield DESC;

Visualizations

Yield Trend Chart

Create line charts showing yield trends over time for each field and variety.

Input Efficiency Chart

Bar charts comparing cost per bushel across fields and years.

Soil Health Timeline

Multi-line charts showing pH, organic matter, and nutrient levels over time.

Variety Performance Matrix

Heat map comparing variety performance across different fields and years.

Planting Date Impact

Scatter plot showing yield vs. planting date with regression line.

Weather Correlation

Correlation plots between weather variables and yield outcomes.

Report Templates

End-of-Season Summary Report

Sections:

1. Executive Summary
2. Field-by-Field Performance
3. Input Usage and Efficiency
4. Variety Comparison
5. Weather Impact Analysis
6. Soil Health Assessment
7. Equipment Performance
8. Financial Summary
9. Lessons Learned
10. Recommendations for Next Season

Crop Comparison Report

Sections:

1. Yield Comparison Across Fields
2. Cost Comparison Across Fields
3. Variety Performance Rankings
4. Planting Date Impact Analysis
5. Soil Type Correlation
6. Weather Impact by Field

Input Efficiency Report

Sections:

1. Total Input Costs by Category
2. Cost Per Unit Production
3. Input Rate vs. Yield Analysis
4. Application Timing Impact
5. Input Product Comparison
6. Optimization Opportunities

AI Analysis Prompts

Pattern Recognition

"Analyze 5 years of field history for [FIELD NAME] and identify:

• Recurring yield-limiting factors
• Pest pressure patterns and timing
• Weather events with significant impact
• Soil health trends requiring attention
• Input application efficiency patterns"

Variety Recommendations

"Based on historical yield data from [FIELDS] over [YEARS], recommend:

• Top 3 corn varieties for next season with justification
• Varieties to avoid based on past performance
• Optimal planting window for each recommended variety
• Expected yield range and confidence intervals"

Input Optimization

"Analyze input application history for [FIELD] and recommend:

• Fertilizer rate adjustments based on soil test trends and yield response
• Optimal application timing based on historical effectiveness
• Opportunities to reduce input costs without yield impact
• Soil amendment priorities based on test results"

Risk Assessment

"Identify risks for upcoming season in [FIELD] based on:

• Historical pest pressure patterns
• Weather event probability
• Soil health concerns
• Equipment performance history
• Past crop rotation issues"

Data Privacy and Security

• Store database locally on farm equipment
• No cloud storage or data transmission unless explicitly enabled
• Encrypt database at rest using AES-256 encryption
• Create regular automated backups
• Allow selective data export for analysis tools
• Comply with agricultural data ownership standards (Ag Data Coalition)
• Maintain data portability standards

Examples

See examples/ directory for:

• Basic field history import and analysis
• Multi-year yield trend analysis
• Input efficiency optimization
• Variety selection process
• Soil health trend analysis

References

Research and Standards

• Precision Agriculture: Studies on data-driven decision making
• Soil Health: NRCS soil health assessment protocols
• Crop Modeling: University extension research on yield prediction
• Ag Data Coalition: Agricultural data ownership standards
• ISO 11783: Equipment data export standards

Manufacturer Documentation

See references/manufacturer-docs.md for:

• Equipment software export formats
• GPS log file specifications
• Soil test lab report formats
• Application controller data exports

Academic Research

• Big data in agriculture literature
• Precision agriculture decision support systems
• Soil health monitoring research
• Crop modeling and prediction studies

Troubleshooting

Import Issues

CSV Import Fails:

• Verify delimiter matches file format
• Check for special characters in data
• Ensure date formats are consistent
• Validate column headers match expected schema

PDF Extraction Fails:

• Try OCR conversion to text first
• Verify PDF is not scanned image without text layer
• Check for password protection on file

Data Validation Errors:

• Review error log for specific validation failures
• Check for duplicate records
• Verify foreign key relationships exist

Analysis Issues

Unexpected Yield Trends:

• Verify data accuracy for outlier years
• Check for units conversion errors
• Confirm field boundaries haven't changed
• Review weather data for anomalous years

Missing Correlations:

• Ensure sufficient historical data exists
• Check for missing data in key periods
• Verify consistent data collection methods
• Consider confounding variables

Soil Test Inconsistencies:

• Verify lab methods are consistent across tests
• Check sample depth and location consistency
• Review seasonal timing of soil tests
• Consider environmental conditions at sampling

Performance Issues

Slow Database Queries:

• Add indexes on frequently queried columns
• Archive old data to separate tables
• Optimize complex analytical queries
• Consider database maintenance (VACUUM, ANALYZE)

Large Memory Usage:

• Process data in chunks for large datasets
• Use database aggregation instead of loading all data
• Clear cached data after analysis
• Increase system memory if processing large farms

Testing

1. Unit Testing

   • Test data import from various formats
   • Verify data validation rules
   • Test analytical query accuracy
   • Validate visualization generation

2. Integration Testing

   • Test end-to-end workflow from import to analysis
   • Verify database integrity
   • Test backup and restore procedures
   • Validate export functionality

3. Data Quality Testing

   • Validate against known good datasets
   • Check for edge cases (null values, extreme values)
   • Test with incomplete data scenarios
   • Verify consistency checks

Version History

• 1.0.0 - Initial release with core tracking and analysis features

License

MIT License - Open source, free to use, modify, and distribute.

Support

For issues, questions, or contributions:

• GitHub Issues: https://github.com/farmfriend-labs/agent-skills-farming/issues
• Email: [e-mail]