Preserve original records
Keep an unmodified source export and identify every derived version before performing a review.
MONITOR RECORD · QUALITY REVIEW · TRACEABLE MAP
Yield Data
Cleaning
Yield-data cleaning is a review process for making potentially unreliable monitor records visible before a map is interpreted. It is not a way to certify a map as true, erase inconvenient variation, infer a crop-management cause, or generate a prescription.
5CHAPTERS4VISUAL BLOCKS1GRAPH LINKS2SOURCES
DECIDE / SELECTED CONCEPTYield Data CleaningStart with the role, then move through the editorial sequence.
HOW TO READ THIS PAGE
Visual explanationA diagram or operating scene makes the relationship visible.
Structured modelA flow, comparison, capability set, or boundary map organizes the idea.
Guided explanationOriginal prose connects the concept to its operating context.
01 / Overview
Cleaning reveals
uncertainty; it does not remove it.
USDA ARS describes Yield Editor as software for removing errors from crop yield maps, while Iowa State Extension discusses yield-monitor calibration. Those sources support an evidence-aware distinction between a recorded value, its machine context, and the confidence a reviewer can reasonably place in it.
A responsible workflow preserves the original data, records the review logic, and keeps excluded or questionable observations available for audit rather than silently making them disappear.
02 / Review loop
Preserve the original,
then qualify the map.
01PRESERVE / 01Keep source records intactOriginal export, machine and monitor identity, season, field identity, position context, metadata, and processing copy
02REVIEW / 02Identify data-quality questionsGaps, transitions, implausible context, calibration history, machine state, header consistency, spatial alignment, and uncertainty
03QUALIFY / 03Document review decisionsFlags, retained or excluded observations, reason codes, processing version, reviewer notes, and evidence boundary
04INTERPRET / 04Use a qualified map carefullyComparison with independent context, field observations, uncertainty, hypotheses, and decisions that need further evidence
03 / Evidence layers
A cleaner map
is not a complete explanation.
LayerCan supportCannot establish alone
Raw recordA traceable monitor observationAccurate yield everywhere
Cleaning logA transparent review historyThat every retained point is true
Qualified mapA pattern for investigationThe cause of a yield difference
Independent evidenceBetter field questionsA universal management action
04 / Practice
Make every change
auditable.
Review machine provenance
Retain monitor, combine, calibration, position, crop, field, date, and operational context with the data.
Document flags and exclusions
Record why an observation is questioned, retained, or excluded so another reviewer can understand the decision.
Treat patterns as hypotheses
Use qualified maps alongside field evidence and other independent context; do not convert a visual pattern into a cause or prescription.
05 / Boundaries
Review does not
certify the field.
No thresholds or certification claim is provided.World Farm Tech does not specify cleaning thresholds or certify any yield map as accurate.
Map variation has multiple possible causes.Monitor behavior, crop, terrain, weather, operations, spatial context, and data processing can all affect an observed pattern.
A cleaned map is not a prescription input by itself.Material management decisions need relevant independent evidence and qualified local interpretation.
06 / Connections
See the system around this concept.
Follow incoming and outgoing relationship records to understand what supplies, informs, enables, coordinates with, or extends this technology in the published knowledge graph.
Relationship radar / published edges1 records / 1 neighboring systems
Incoming00records point toward this concept
decide roleYield Data CleaningSelected technology
Outgoing01records point from this concept
01connections visible
observe / Field sensingYield Monitoring and Mapping ↗improves the interpretability of
Yield-data cleaning can expose delays, calibration issues, impossible values, swath and boundary artifacts, duplicates, gaps, and transformation history before maps are interpreted.
1CONNECTED ROUTE5–5STEP POSITIONS6ROUTE SOURCE LINKS
Operating practice
Move from harvest loss to a qualified yield record
Verify machine adjustment and field loss before accepting, cleaning, and organizing yield data for later decisions.
- 01 / ADJUSTVerify combine adjustmentsField guide→
- 02 / MEASURE LOSSDiagnose harvest lossField guide→
- 03 / MAPUnderstand yield monitoringTechnology→
- 04 / QUALIFYAudit harvest data qualityField guide→
- 05 / CLEANClean without hiding uncertaintyTechnology→
- 06 / ORGANIZEReturn the record to the FMISTechnology
CURRENT POSITION05
05 / CLEAN
Open the complete route ↗Clean without hiding uncertainty
Flag implausible or context-poor observations while preserving rules, exclusions, versions, and the difference between raw and derived records.
07 / Evidence
Primary sources.
This original briefing uses USDA ARS yield-map error-removal research and Iowa State Extension yield-monitor calibration context. It offers no threshold, certification, causal claim, or prescription.
01
Yield Editor: Software for Removing Errors from Crop Yield MapsUSDA Agricultural Research Service · Accessed 2026-07-11
02
Tips for Calibrating Your Combine's Yield MonitorIowa State University Extension and Outreach · Accessed 2026-07-15