farm resilience / Farmers, operators, trial coordinators, agronomists, livestock professionals, researchers, statisticians, sponsors, reviewers, and data stewards
Agricultural trial deviation and exclusion audit
Audit a farm trial's departures from protocol and data exclusions by preserving what happened, where, when, why, who knew, treatment visibility, decision authority, analysis effect and retained evidence.
See the whole mission
Orient before entering the field.
Connect the intended outcomes, operating stages, stop conditions, and supporting technology concepts before opening the detailed action sequence.
- 01Distinguish protocol deviations, operational incidents, measurement failures, missing data and exclusions
- 02Preserve original observations and a time-stamped decision trail instead of silently cleaning evidence
- 03Test whether treatment knowledge or observed results influenced exclusion decisions
- 04Show how deviations alter analysis, uncertainty, applicability and downstream claims
- 01Reconstruct the approved baseline
A deviation is impossible to classify without the protocol version, treatment map, measurement plan and predefined rules active at the time.
3 field actions ↓ - 02Build the deviation ledger
Weather, missed passes, carryover, equipment changes, movement, contamination and data loss can affect different units and times in different ways.
3 field actions ↓ - 03Audit exclusion decisions
Removing inconvenient units after results are known can bias the comparison even when the operational reason sounds plausible.
3 field actions ↓ - 04Reconcile analysis and disclosure
A clean final table can conceal how operational reality changed the supported inference.
3 field actions ↓
- G01
This guide provides no exclusion rule, statistical method, missing-data treatment, agronomic recommendation, safety procedure or legal conclusion.
- G02
Never hide unfavorable results, failed units, protocol changes, treatment crossover, missing data, sponsor influence or decisions made after outcomes were known.
- G03
Use qualified statistical and domain review when deviations may change independence, comparison, uncertainty, safety or the supported claim.
Reconstruct the approved baseline
A deviation is impossible to classify without the protocol version, treatment map, measurement plan and predefined rules active at the time.
- 01Retrieve the approved question, design, unit map, treatment identity, execution plan, primary outcome, measurement method, analysis plan, exclusion rules and amendments
- 02Confirm version, effective date, owners, approvals, access history and whether the rule existed before treatment assignment or results were visible
- 03Separate mandatory safety or welfare action from evidence-preservation decisions and never delay protective action for trial completeness
Build the deviation ledger
Weather, missed passes, carryover, equipment changes, movement, contamination and data loss can affect different units and times in different ways.
- 01Record event identity, affected units and observations, occurrence and discovery time, location, treatment visibility, people, equipment, environment, immediate action and source evidence
- 02Classify without erasing detail: execution deviation, intervention crossover, interference, measurement failure, missing data, external event, safety response, amendment or unknown
- 03Keep photographs, machine files, logs, messages, samples, raw records and contradictory accounts with provenance, access and retention controls
Audit exclusion decisions
Removing inconvenient units after results are known can bias the comparison even when the operational reason sounds plausible.
- 01For each exclusion or status change, record rule, rationale, author, authority, timing, treatment knowledge, result knowledge, affected outcomes and dataset versions
- 02Retain raw, included, excluded and corrected states with machine-readable reason codes and human explanation rather than deleting records
- 03Require qualified review for unplanned exclusions, unequal treatment effects, ambiguous independence, missing-not-at-random concerns or major protocol changes
Reconcile analysis and disclosure
A clean final table can conceal how operational reality changed the supported inference.
- 01Compare planned and executed units, treatment exposure, measurements, missingness, exclusions, analysis populations, transformations and sensitivity results
- 02Update uncertainty, applicability, practical interpretation and prohibited claims; distinguish robust conclusions from unresolved or exploratory observations
- 03Publish material deviations, exclusions, amendments, conflicts, limitations and retained evidence and assign corrective actions for future trials
Continue through the operation
See where this field guide fits.
Move beyond one task into the complete evidence, technology, operating, and review sequence around it.
Run the farm disaster resilience chain
Move from authoritative hazard information and farm exposure mapping through warning delivery, accountability, continuity, critical-load review, safe damage evidence, authorized reporting, recovery acceptance and governed records.
- 01 / RECEIVEUnderstand agricultural hazard alertsTechnology→
- 02 / DRILLExercise the severe-weather warning chainField guide→
- 03 / MAP CONNECTIVITYUnderstand farm coverage mappingTechnology→
- 04 / SURVEYSurvey one farm workflowField guide→
- 05 / ARCHITECTUnderstand IoT connectivity dependenciesTechnology→
- 06 / REVIEW CHAINReview one connected endpointField guide→
- 07 / CONTINUEUnderstand communications continuityTechnology→
- 08 / EXERCISE OUTAGERun a connectivity outage tabletopField guide→
- 09 / COORDINATEUnderstand farm emergency coordinationTechnology→
- 10 / HAND OFFRun the communication and continuity drillField guide→
- 11 / KEEP ALIVETest farm critical-load resilienceField guide→
- 12 / DOCUMENTUnderstand disaster damage evidenceTechnology→
- 13 / AUDITAudit the post-disaster evidence packageField guide→
- 14 / DEFINE TECHNOLOGYUnderstand technology requirementsTechnology→
- 15 / WORKSHOPRun the requirements workshopField guide→
- 16 / PILOTUnderstand pilot acceptanceTechnology→
- 17 / TESTDesign the pilot evidence planField guide→
- 18 / COSTUnderstand lifecycle cost evidenceTechnology→
- 19 / REVIEW COSTReview the lifecycle modelField guide→
- 20 / RETAINReconnect the operational historyTechnology→
- 21 / GOVERNGovern sensitive incident evidenceTechnology→
- 22 / CONTROL ACCESSUnderstand identity and access lifecycleTechnology→
- 23 / AUDIT ACCESSTrace one role end to endField guide→
- 24 / ASSURE RECOVERYUnderstand backup and recovery assuranceTechnology→
- 25 / TEST RESTOREReview restore readinessField guide→
- 26 / CONTROL CHANGEUnderstand technology change controlTechnology→
- 27 / REVIEW CHANGEPrepare one bounded changeField guide→
- 28 / EXPORTUnderstand agricultural data portabilityTechnology→
- 29 / AUDIT PORTABILITYTest one representative exportField guide→
- 30 / GOVERN EXITUnderstand vendor exit governanceTechnology→
- 31 / REVIEW EXITBuild the supplier exit registerField guide→
- 32 / RETIREUnderstand secure decommissioningTechnology→
- 33 / VERIFY RETIREMENTReview one connected assetField guide→
- 34 / TRANSFERUnderstand ownership-transfer assuranceTechnology→
- 35 / HAND OVERRun the connected-equipment transfer reviewField guide→
- 36 / IDENTIFYUnderstand master data and identifiersTechnology→
- 37 / CROSSWALKReconcile one entity classField guide→
- 38 / ORDER TIMEUnderstand event-time integrityTechnology→
- 39 / REVIEW TIMEReconstruct one farm timelineField guide→
- 40 / REFERENCE SPACEUnderstand geospatial assuranceTechnology→
- 41 / REVIEW SPACEAudit one spatial handoffField guide→
- 42 / TRACEUnderstand data provenance and lineageTechnology→
- 43 / AUDIT LINEAGETrace one agricultural recordField guide→
- 44 / GOVERN AIUnderstand agricultural AI governanceTechnology→
- 45 / REVIEW AIReview one proposed AI use caseField guide→
- 46 / EVALUATE AIUnderstand performance evidenceTechnology→
- 47 / AUDIT CLAIMAudit one AI performance claimField guide→
- 48 / MONITOR AIUnderstand model drift assuranceTechnology→
- 49 / REVIEW DRIFTReview one deployed modelField guide→
- 50 / HAND OFFUnderstand human–AI decision handoffTechnology→
- 51 / EXERCISERun the human–AI handoff tabletopField guide→
- 52 / DESIGN TRIALUnderstand on-farm trial designTechnology→
- 53 / REVIEW TRIALReview one treatment protocolField guide→
- 54 / ASSURE MEASUREMENTUnderstand measurement assuranceTechnology→
- 55 / CHECK REPEATABILITYAudit one measurement chainField guide→
- 56 / GOVERN DEVIATIONSUnderstand deviation governanceTechnology→
- 57 / AUDIT EXCLUSIONSAudit deviations and exclusionsField guide→
- 58 / SYNTHESIZEUnderstand evidence transferTechnology→
- 59 / REVIEW TRANSFERReview multi-site evidenceField guide→
- 60 / BOUND AUTONOMYUnderstand autonomy operating domainsTechnology→
- 61 / REVIEW DOMAINReview one autonomous missionField guide→
- 62 / COVER PERCEPTIONUnderstand robot perception coverageTechnology→
- 63 / AUDIT VISIONAudit one perception claimField guide→
- 64 / SUPERVISEUnderstand autonomous mission supervisionTechnology→
- 65 / EXERCISE CONTROLRun the supervision tabletopField guide→
- 66 / FALL BACKUnderstand robot fallback and recoveryTechnology→
- 67 / REHEARSE RECOVERYRun the fallback and recovery tabletopField guide→
- 68 / AUDIT POLICYAudit the crop-insurance registerField guide→
- 69 / AUDIT ACRESAudit the acreage reportField guide→
- 70 / AUDIT PRODUCTIONAudit production historyField guide→
- 71 / AUDIT NOTICEAudit loss notice and inspectionField guide→
- 72 / AUDIT CLAIMAudit claim settlementField guide→
- 73 / REVIEW SUPPLIERReview input supplier resilienceField guide→
- 74 / AUDIT ORDERAudit the input orderField guide→
- 75 / AUDIT DELIVERYAudit input receivingField guide→
- 76 / RECONCILE INVENTORYAudit input custodyField guide→
- 77 / CLOSE PAYMENTAudit procurement paymentField guide
Audit deviations and exclusions
Reconstruct the approved baseline, event ledger, retained evidence, dataset states and disclosure limits.