Calibration
Sensor response should be checked using appropriate reference weights, crop conditions, and manufacturer procedures.
HARVEST-TIME SPATIAL MEASUREMENT
Yield Monitoring
and Mapping
A yield-monitoring system estimates harvested crop flow and combines it with position and machine data to create a spatial record of yield across a field.
5CHAPTERS3VISUAL BLOCKS6GRAPH LINKS2SOURCES
OBSERVE / SELECTED CONCEPTYield Monitoring and MappingStart 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
Harvest becomes
a spatial dataset.
Yield monitors sample crop flow during harvest rather than directly weighing every map cell. Position, travel speed, harvested width, moisture or quality measurements, and machine states are combined to estimate yield at locations along the pass.
USDA treats yield maps as an important precision-agriculture information layer. USDA ARS also documents that raw yield-monitor datasets can contain errors requiring filtering before interpretation.
02 / Measurement chain
Sensor time must match
field position.
01HARVEST / 01Crop enters machineHeader width and machine state define harvested area
02SENSE / 02Flow and moistureSensors estimate harvested material and condition
03ALIGN / 03Time and positionFlow delay is associated with the originating field location
04MAP / 04Cleaned spatial dataFiltered points support comparison and interpretation
03 / Data quality
A colorful map can hide
measurement errors.
Flow delay
Material reaches the yield sensor after entering the header, so timestamps and positions require alignment.
Pass geometry
Partial swaths, headlands, overlaps, stops, turns, and header state can distort calculated area and yield.
Cleaning
Outliers, impossible values, start and stop effects, position errors, and configuration mistakes need traceable review.
04 / Interpretation
Yield shows an outcome,
not a single cause.
A yield pattern can reflect soil, weather, drainage, pest pressure, crop establishment, management, machine operation, or data artifacts. Multiple seasons and independent layers help separate persistent patterns from one-year effects.
Absolute comparison across machines, crops, fields, or seasons requires consistent calibration, units, processing, boundaries, and documented transformations.
05 / Boundaries
Do not turn correlation
into a prescription.
Yield maps are estimates.They depend on sensor calibration, machine configuration, area calculation, timing, positioning, and filtering.
A pattern is not a diagnosis.Causal claims need field evidence, agronomic context, and preferably repeated observations or controlled comparisons.
Cleaning must remain auditable.Keep raw data and record filters so later users can distinguish observations from processing decisions.
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 edges6 records / 6 neighboring systems
Incoming02records point toward this concept
observe roleYield Monitoring and MappingSelected technology
Outgoing04records point from this concept
06connections visible
decide / Decision dataPrescription Maps ↗can inform
Cleaned, interpreted yield maps can contribute historical field evidence when agronomic teams prepare later management decisions.
observe / Harvest systemsCrop Insurance Production-History Evidence Assurance ↗adds field and machine harvest evidence to
Yield-monitor data supports reconciliation but cannot independently establish certified insurance production.
observe / Agricultural research evidenceOn-Farm Variety Trial Evidence ↗provides qualified plot response evidence to
Calibrated and traceable yield observations can inform plot responses when harvest geometry, quality controls and exclusions remain visible; a yield map alone is not a trial result.
act / Harvest systemsRice Harvest Mechanization ↗adds crop- and machine-specific context to
Rice harvesting adds crop condition, header, threshing, separation, grain handling, loss, moisture, calibration, and regional machine context to yield-monitoring review.
decide / Field sensingYield Data Cleaning ↗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.
act / Post-harvest systemsGrain Drying Process Assurance ↗provides field, harvest and load context to
Yield and harvest records can inform incoming grain context while the drying process still requires independent lot, quantity and moisture evidence.
3CONNECTED ROUTES1–5STEP POSITIONS25ROUTE SOURCE LINKS
Technology system
From field evidence to application
Follow a complete decision loop from measured field variation through management software and spatial intent to machine-side application control.
- 01 / OBSERVEMeasure with yield mappingTechnology→
- 02 / ORGANIZEPut the record in an FMISTechnology→
- 03 / DECIDEBuild prescription intentTechnology→
- 04 / VARYExecute variable rateTechnology→
- 05 / COVERCoordinate section controlTechnology→
- 06 / MODULATEControl flow at the nozzleTechnology→
- 07 / CANOPYCoordinate liquid, airflow, and chargeTechnology
CURRENT POSITION01
01 / OBSERVE
Open the complete route ↗Measure with yield mapping
Begin with field observations and the cleaning needed before a map becomes useful evidence.
07 / Evidence
Primary sources.
This briefing uses USDA ERS for yield mapping within precision-agriculture information flows and USDA ARS research for documented yield-data error and cleaning concerns. Monitor installation, calibration, and processing must follow crop- and machine-specific guidance.
01
Precision Agriculture in the Digital Era: Recent Adoption on U.S. FarmsUSDA Economic Research Service · Accessed 2026-07-11
02
Yield Editor: Software for Removing Errors from Crop Yield MapsUSDA Agricultural Research Service · Accessed 2026-07-11