Foundation for Decisions, Compliance, and Trust

Data quality determines everything downstream. Regulatory compliance, clinical diagnosis, biosecurity effectiveness, research validity, market access all depend on data quality. Poor data quality cascades: inaccurate GPS invalidates movement tracking → rejected export permits. Incomplete observations → missed diagnosis → animal welfare failure. Delayed entry → obscured patterns → ineffective management.

This section establishes data quality standards for GPS recording, observation documentation, photo evidence, and timely data entry. These are proven practices ensuring Kora data meets professional requirements across animal management contexts.

1. Accurate GPS Recording

Why GPS Accuracy Matters

GPS coordinates serve multiple critical functions:

• Movement tracking: Proving animals moved between documented locations
• Biosecurity contact tracing: Identifying animals sharing locations during disease exposure windows
• Wildlife population monitoring: Mapping species distribution for conservation research
• Regulatory compliance: Export health certificates require documented location history
• Legal defense: GPS timestamps prove animal presence/absence during incidents

Example: Cattle export to EU requires 60-day pre-export quarantine at documented location. GPS coordinates proving animal never left quarantine paddock = compliance evidence. Inaccurate coordinates suggesting animal moved = rejected health certificate = export denied.

GPS Precision Standards

Kora stores GPS coordinates with high precision:

• Latitude: Decimal format with up to 8 decimal places (approximately 1.1 mm precision)
• Longitude: Decimal format with up to 8 decimal places (approximately 1.1 mm precision)
• Accuracy: Measured in metres with 2 decimal places
• Elevation: Measured in metres with 2 decimal places (optional but valuable for terrain-aware management)

Database capacity vs. practical precision: System stores 8 decimal places, but mobile device GPS rarely achieves better than 5-10 metre accuracy under ideal conditions. High-precision storage accommodates future GPS improvements and specialised equipment (survey-grade GPS, differential correction).

Recommended GPS Accuracy Thresholds

How to check GPS accuracy: Modern smartphones display GPS accuracy in location services. Wait for accuracy to stabilise (usually 30-60 seconds under open sky) before recording observations.

Optimal GPS Capture Conditions

Best practices for accurate GPS:

DO:

• Wait for GPS lock: Allow 30-60 seconds for device to acquire satellite signals
• Open sky visibility: Record observations in areas with clear view of sky
• Stationary capture: Stand still while GPS stabilises
• Modern devices: Use smartphones ≤ 5 years old for best GPS chipsets
• Check accuracy indicator: Only record when accuracy meets your threshold

AVOID:

• Dense tree canopy: Satellite signals blocked, accuracy degrades to 50-100+ metres
• Indoor/underground: No satellite visibility = no GPS or wildly inaccurate coordinates
• Urban canyons: Tall buildings reflect signals (multipath error), creating 20-50m inaccuracy
• Near large metal structures: Barns, silos, vehicles interfere with GPS signals
• Moving capture: GPS accuracy degrades while moving vs. stationary

Mobile device tips:

• Enable "High Accuracy" GPS mode (uses GPS + WiFi + cellular triangulation)
• Keep location services enabled (GPS "cold start" from disabled state takes longer)
• Update device to latest OS version (GPS algorithm improvements)
• Avoid low-battery mode (may reduce GPS accuracy to save power)

Location Validation

Kora validates GPS coordinates against defined property boundaries:

When you record an observation with GPS coordinates, the system can verify:

• Does this coordinate fall within your registered property boundary?
• If subdivisions defined (paddocks, enclosures, zones), which subdivision contains this coordinate?
• Is the animal currently assigned to a location matching the GPS coordinate location?

Example validation workflow:

1. Field staff records health observation for cattle in "North Paddock"
2. GPS coordinates: -33.8683°, 151.2093°
3. System checks: Does this coordinate fall within North Paddock boundary polygon?
4. Result: Coordinate outside North Paddock → Warning: "GPS location doesn't match assigned paddock. Animal may have moved."
5. Staff verifies: Animal actually in East Paddock → Updates location assignment

This validation prevents:

• Incorrect location assignments
• Movement tracking gaps
• Contact tracing errors

2. Complete Observation Documentation

Required vs. Optional Fields

Understanding field requirements prevents incomplete records:

Observation Categories

Select the most specific category (don't default to "Other"):

Category selection impacts workflows: "Health Symptom" observations with high severity trigger veterinary notification workflows. "Behaviour" observations feed into welfare assessments. Choose accurately for proper automatic handling.

Description Quality Standards

Effective descriptions are specific, observable, measurable:

Length recommendations:

• Minimum: 20 characters (absolute floor for meaningful information)
• Recommended: 50-500 characters (enough detail for context, not overwhelming)
• Maximum: 1,000 characters (system limit)

Structure your description:

1. What you observed (specific signs/behaviours): "Left front leg swelling at knee joint"
2. When it occurred (time context): "Noticed this morning during feeding, not present yesterday"
3. How severe/frequent (quantification): "Moderate swelling, animal bears weight but limps noticeably"
4. What you did (immediate response): "Isolated from group, will monitor today, requesting vet check"

Examples:

Poor: "Sick" (not specific, no context, no actionable information)

Good: "Cow #142 showed reduced appetite this morning. Ate approximately half normal feed ration. Previously normal appetite yesterday. Body condition appears unchanged. No visible fever, discharge, or other symptoms. Will monitor feed intake through today."

Excellent (for critical observation): "Bull #87 showing acute lameness in right front leg, first observed at 6:30 AM during yard movement. Animal reluctant to bear weight, holding leg slightly elevated when stationary. Visible heat and moderate swelling at fetlock joint. No obvious external wounds. Animal isolated in medical pen, veterinarian notified (Dr. Sarah, mobile: 0412-XXX-XXX). Monitoring for deterioration."

Description best practices:

• Use measurements when possible: "Temperature 40.2°C" not "feels warm"
• Include affected individuals: "3 out of 20 sheep showing symptom" not "some sheep sick"
• Note changes from baseline: "Previously ate 5kg feed daily, now eating 2kg" not just "poor appetite"
• Document negative findings: "No discharge, no coughing, no fever" (rules out differential diagnoses)

Severity Level Guidelines

Severity classification directly impacts response:

Severity assessment considers:

• Animal welfare impact: Pain, suffering, functional impairment
• Biosecurity risk: Contagious disease signs, unusual deaths, multi-animal symptoms
• Productivity impact: Severe symptoms affecting production
• Urgency: How quickly does this need intervention to prevent deterioration?

When in doubt, escalate severity: Better to mark "High" and have veterinarian determine it's "Medium" than mark "Low" and miss critical window for treatment.

3. Photo Evidence

Why Photo Documentation Matters

Photos provide:

• Objective evidence: Visual record not dependent on memory or description accuracy
• Baseline comparison: "Before" photos enable "after" assessment
• Diagnostic support: Veterinarians diagnose more accurately with visual evidence
• Regulatory compliance: Export health certificates often require photo documentation
• Legal defense: Photographic evidence resolves disputes
• Training: Photos educate new staff on what conditions look like

Photo Quality Standards

Minimum standards:

• Resolution: ≥ 1 megapixel (1280x720 pixels minimum for clear detail)
• Focus: Sharp focus on subject
• Lighting: Adequate light to see detail
• Subject visibility: Animal/area of concern clearly visible
• Orientation: Photo right-side-up

Recommended standards:

• Resolution: 3-8 megapixels (modern smartphone default)
• Multiple angles: At least 2 photos showing different perspectives
• Scale reference: Include ruler, coin, hand, or familiar object for size context
• Close-up + wide view: Detail photo + context photo showing whole animal/location

Avoid:

• Excessive resolution: ≥ 12 megapixels creates large files slowing uploads
• Poor compression: Save photos as JPEG
• Excessive editing: Don't enhance/filter photos (may obscure diagnostic detail)

What to Photograph

Health observations:

• Injuries: Wound location, size, depth, discharge
• Swelling: Affected joint/limb, comparison to unaffected limb
• Skin conditions: Lesions, hair loss, discolouration, parasites
• Discharge: Eyes, nose, mouth (colour, consistency, volume)
• Body condition: Whole-body photo showing rib visibility, hip bones
• Gait abnormalities: Video if possible

Location-based observations:

• Unidentified animals: Wildlife, escaped livestock
• Habitat context: Surrounding environment, vegetation
• Animal groups: Population estimates
• Infrastructure issues: Broken fences, damaged water troughs

Regulatory documentation:

• Pre-export condition: Full-body photos before shipment
• Quarantine facilities: Photo documentation of isolation areas
• Transport conditions: Vehicle condition, loading/unloading

Photo Attachment Best Practices

Attach photos immediately after observation:

• Record observation → Attach photos before saving → Photos linked automatically
• Delayed attachment risks: Photo-observation mismatches, forgotten attachments, incomplete records

Organise multiple photos:

• Name photos descriptively: "Animal142_FetlockSwelling_Close_2024-12-01.jpg" not "IMG_8472.jpg"
• If uploading multiple photos, sequence them logically (overview → detail → additional angles)

Storage limits (check your Kora instance configuration):

• Typical limit: 10 MB per file
• Number of photos per observation: Usually unlimited, recommend ≤ 10 per observation
• Supported formats: JPEG, PNG (most universal), some systems support HEIC

4. Timely Data Entry

Real-Time vs. Retrospective Entry

Understanding entry types:

System handles all types. Kora distinguishes "Observation Date" (when event occurred) from "Record Created Date" (when you entered it). Both timestamps preserved for audit trail.

Why Timeliness Matters

Immediate entry (< 1 hour) provides:

• Real-time decision support: Veterinarians notified immediately, enabling rapid response
• Accurate timestamps: No reliance on memory for exact times
• Team coordination: Other staff see observations in real-time
• Biosecurity triggers: Automatic contact tracing activates immediately

Same-day entry (< 8 hours) provides:

• Reliable context: Environmental conditions, other events still fresh in memory
• Sufficient freshness: Health trends still detectable
• Regulatory acceptance: Most compliance frameworks accept same-day documentation

Delayed entry risks:

• Memory decay: Details forgotten, times approximated, context lost
• Obscured patterns: Multi-day delay = temporal patterns undetectable
• Delayed response: Critical observations entered late = delayed veterinary intervention
• Compliance gaps: Export health certificates require continuous monitoring documentation

Example: Cattle showing mild respiratory symptoms Monday morning. If documented Monday (real-time), veterinarian reviews same day, diagnoses early pneumonia, treatment starts Monday evening. If documented Thursday (3-day delay), disease progressed, pneumonia now severe, treatment longer/more expensive, productivity loss greater. Three days = critical difference.

Data Staleness Thresholds

Recommended entry windows:

Managing staleness:

• Flagging delayed entry: If entering observation from yesterday, add note explaining delay
• Approximate times: If exact time unknown, note: "Symptoms noticed mid-morning, exact time uncertain"
• Source documentation: When entering historical data, reference source

Timestamp Validation

Kora validates timestamps to prevent errors:

Future date prevention:

• Rule: Observation Date ≤ Today + 1 day
• Rationale: Observations can't occur in future (allows minor timezone flexibility)

Extreme past prevention:

• Rule: Observation Date ≥ 10 years ago
• Rationale: Observations older than 10 years likely data entry errors

Duplicate prevention:

• Rule: No duplicate observations for same animal within 5-minute window
• Rationale: Prevents accidental double-entry

Audit Trail Requirements

Every observation automatically captures:

• User ID: Who created the observation (accountability)
• Record Created Date: When entered (UTC timestamp)
• Updated At: If modified, when last changed
• Device metadata (if mobile app): GPS accuracy, device type, app version

Best practices for audit integrity:

• Never delete observations: Mark as resolved, dismissed, or corrected instead
• Corrections via new observations: If initial observation incorrect, create new observation noting correction
• Document modifications: If editing observation, add note explaining change
• User identification: Ensure correct user logged in when recording (individual accountability)