Why it matters
Without it, mismanaged grazing, late detection of metabolic issues, and unbalanced vaccination and monitoring can lead to flare-ups, delayed intervention, and compromised horse welfare across the yard.
From design to safety
Design
Inform design and risk thresholds
The AI defines data requirements, risk thresholds, and a monitoring plan from baseline records, lab results, and grazing data to shape stable layout, feeding rules, and turnout protocols.
- insulin tests
- cresty neck scores
- PPID results
Build
Install sensors and data streams
Install feeds from lab systems, grass growth models, weather stations, and hoof health logs; validate data privacy, quality, and integration during yard construction.
- insulin tests
- digital pulses
- spring grass
Operation
Continuous risk screening in daily care
The AI analyzes daily feeding plans, grazing rotations, turnout logs, body condition scores, and pulse trends to flag EMS or laminitis risk and suggest turnout or feeding adjustments.
- grazing access
- spring grass
- cresty neck scores
- digital pulses
Maintenance
Maintain data quality and calibration
Recalibrate sensors, refresh insulin and PPID records, update grass growth models, and verify thresholds against recent BCS, activity data, and veterinary notes.
- insulin tests
- digital pulses
- PPID results
- spring grass
Safety
Safety checks and approvals
Provide auditable welfare reports for licensing, audits, and veterinary sign-off; ensure action logs and biosecurity adherence accompany every decision and record.
- grazing access
- cresty neck scores
- PPID results
Part by part
- 1
insulin tests
- Watch for
- Data gaps from lab delays or missing sample IDs that obscure current metabolic status. Ensure chain of custody and timestamped results for every horse.
- AI action
- If insulin result is outside threshold, trigger alert and automatically adjust feeding plan with per-hour carbohydrate limits and alternative forage.]
- 2
grazing access
- Watch for
- Uncontrolled turnout during high-risk grass growth periods or wet weather; monitor fence breaks and posted grazing maps for accuracy.
- AI action
- Recommend temporary grazing restriction and adjust turnout schedule; log justifications and notify yard manager when thresholds crossed.
- 3
cresty neck scores
- Watch for
- Inconsistent scoring or delayed rechecks that mask regional fat deposition changes; ensure standardized scoring protocol and time-stamped records.
- AI action
- Flag elevated score and schedule recheck with yard vet; auto-create recheck appointment and update care plan.
- 4
digital pulses
- Watch for
- Sensor drift or intermittent readings during exercise and rest; verify sensor calibration and placement daily.
- AI action
- Flag abnormal pulse continuity and prompt clinical assessment; trigger vet review if pulse remains elevated beyond 2 hours.
- 5
PPID results
- Watch for
- Outdated or missing PPID testing records; validate lab slab IDs with horse tag numbers.
- AI action
- Prompt re-test scheduling and adjust monitoring cadence; generate notification to yard vet for annual review.
- 6
spring grass
- Watch for
- Rapid grass growth causing sudden energy surges in susceptible horses; track digestible energy indices and grazing exposure hours.
- AI action
- Trigger grazing limits and adjust carbohydrate allowance; update energy budget and send prompts to feed room.
Worked example
Managing EMS risk on a medium-yard with 40 stables
Illustrative scenario with sample data.
A mid-season EMS risk event triggers AI to review metabolic indicators and grazing access across the herd, forming a control plan.
Signal in
insulinTests.date, insulinTests.level, crestyNeckScore.value, ppIDResults.status, digitalPulse.value, springGrass.growthRate, grazingAccess.isAllowed
System out
riskFlag: EMS_high; action: restrict turnout for 12 hours; schedule vet review; update feeding plan; document in welfare log; notify owner group.
Human sign-off: Yard manager reviews the AI-generated risk flag, confirms turnout restriction, vets appointment, and authorizes feeding adjustments.
How to roll it out
Phase 1
Weeks 1 to 2
Baseline data import from existing records and initial risk threshold validation with sample cases.
Phase 2
Weeks 3 to 6
Live data feeds established; initial alerts and dashboards tested with controlled case scenarios and cross-checked against vet notes.
Phase 3
Weeks 7 to 10
Full operation with staff training; routine checks and sign-offs consolidated in welfare management records; audit trail enabled.
Where people stay in charge
- All decisions affecting turnout or medical testing must be signed off by the attending equine veterinarian.
- Only licensed personnel may adjust PPID-related monitoring schedules or insulin-related treatment plans after AI recommendations.
- Critical alerts requiring immediate action must be reviewed within 24 hours by the yard manager and veterinarian.
Questions owners and crews ask
What data streams are used to predict laminitis risk?
The system combines insulin test results, PPID status, cresty neck measurements, digital pulse readings, grazing access logs, and spring grass growth to assess risk and trigger actions.
Who signs off on AI-driven care decisions?
The attending veterinarian and yard manager sign off on AI-generated recommendations, ensuring clinical suitability and welfare compliance before actioning changes.
How does AI handle grazing during high-risk grass growth?
AI flags high-risk periods, suggests turnout restrictions, adjusts feeding carbohydrates, and logs decisions for inspection and welfare records.
What records are produced for compliance?
Audit-ready logs include test dates, scores, sensor readings, turnout changes, veterinary visits, and actions taken, all time-stamped and signed.