🧭 Concept Agriculture & vegetation Data cleaning Agriculture & Crops · step 5 of 20 Updated

Yield monitor data explained: what the numbers really are

Problem statement

A yield map looks like a measurement of the field. It is a measurement of a machine moving through the field, and the difference produces artefacts that dominate the map if they are not removed: a stripe along every start and stop, a halo at every headland turn, a band down every pass where the header was not full, and a smear of about twenty metres wherever the crop changed.

None of these is noise in the statistical sense. They are systematic, they repeat in every year's map from the same machine, and a clustering algorithm will happily build management zones out of them.

This guide explains where each artefact comes from, which of them can be corrected and which can only be removed.

Quick answer

A yield point is a mass flow measured at the top of the elevator, divided by an area inferred from speed and an assumed header width:

yield = mass_flow / (speed × effective_width × moisture_correction)

Every term contributes an error:

term error effect on the map
mass flow sensor lag of 10–20 s yield attributed 20–60 m behind where it grew
speed GPS noise, wheel slip spikes at low speed, stripes at changes
effective width assumed full, often is not a band of inflated yield down every partial pass
moisture measured or assumed a systematic offset between passes
position receiver offset from the header the whole map shifted by metres
Flow from mass flow, speed, width and moisture to a yield value, with the error each contributes.
Four measurements and an assumption; the assumption is usually the biggest error.

Step-by-step solution

1. Understand the lag

Grain takes ten to twenty seconds to travel from the header to the mass-flow sensor. At a typical 5 km/h that is 14–28 m, and at 8 km/h it is 22–44 m. Unless the data has been lag-corrected, every value is attributed to ground the machine had already left — which smears every real boundary in the direction of travel.

2. Understand the partial-width problem

The monitor assumes the full header width unless the operator tells it otherwise. On the last pass of a land, around obstacles and on tapered headlands, the header is part full, so the same grain flow is divided by too large an area and the recorded yield is too low — or, where the machine overlaps a previous pass, grain is counted twice over ground already cut.

3. Understand the start and stop transients

Flow builds up over several seconds after the machine starts moving and decays after it stops. Points recorded during either are meaningless, and they cluster at the ends of every pass, which is exactly where headland zones get drawn.

4. Understand the moisture correction

Yield is reported at a standard moisture. If the moisture sensor is out or the correction is applied inconsistently between passes, whole passes shift relative to each other by several percent, producing stripes that follow the machine rather than the field.

5. Understand the position offset

The GNSS antenna is on the cab roof, the crop entered the header several metres ahead. A constant offset moves the entire map, which matters when comparing with a prescription or a soil sample.

6. Know which artefacts can be corrected

  • Lag — correctable if the delay is known, by shifting values back along the track.
  • Position offset — correctable with a constant translation.
  • Moisture — correctable if the raw moisture is recorded.
  • Partial width — correctable only if the true swath width was logged; otherwise the affected points have to go.
  • Start/stop transients — not correctable; remove them.

7. Expect to remove a substantial fraction of the points

A defensible cleaning pass removes start and stop transients, overlapping passes, extreme speeds, extreme flows and impossible yields. Losing 10–25% of the points is normal, and the number should be reported.

Four scenes showing lag smear, headland halo, partial-width stripe and start-stop transients on a yield map.
Four artefacts, all systematic, all repeating in every year from the same machine.

Code examples

Example 1 — what a raw yield file contains 

import pandas as pd, numpy as np, geopandas as gpd

y = pd.read_csv("harvest_2025.csv")
print(y.columns.tolist())
print(y[["yield_t_ha", "speed_kmh", "swath_m", "moisture_pct", "flow_kg_s"]]
      .describe(percentiles=[.01, .5, .99]).round(2))

print(f"\npoints {len(y):,}")
print(f"zero or negative yield: {(y.yield_t_ha <= 0).sum():,}")
print(f"speed under 1 km/h: {(y.speed_kmh < 1).sum():,}")
print(f"swath less than full: {(y.swath_m < y.swath_m.max()).mean():.1%}")

The last line is the one that surprises people. A quarter to a third of points recorded at less than full header width is normal, and every one of them has an area error.

Example 2 — reconstruct the geometry of each pass 

import numpy as np, pandas as pd, geopandas as gpd

def add_pass_geometry(y, heading_col="heading_deg", gap_s=10):
    y = y.sort_values("timestamp").copy()
    dt = y["timestamp"].diff().dt.total_seconds()
    turn = y[heading_col].diff().abs().mod(360)
    turn = np.minimum(turn, 360 - turn)
    y["pass_id"] = ((dt > gap_s) | (turn > 60)).cumsum()

    sizes = y.groupby("pass_id").size()
    print(f"{len(sizes):,} passes, median {sizes.median():.0f} points, "
          f"{int((sizes < 10).sum()):,} passes with fewer than 10 points")

    y["point_in_pass"] = y.groupby("pass_id").cumcount()
    y["from_end"] = y.groupby("pass_id")["point_in_pass"].transform("max") - y["point_in_pass"]
    return y

Once the passes are reconstructed, the start and stop transients are the first and last few points of each one, and the headland is where the passes turn.

Example 3 — correct the lag 

import numpy as np, pandas as pd

def correct_lag(y, lag_s=12.0):
    """Shift each yield value back along its own pass by the sensor delay."""
    out = []
    for pid, g in y.groupby("pass_id", sort=False):
        g = g.sort_values("timestamp").copy()
        t = (g["timestamp"] - g["timestamp"].iloc[0]).dt.total_seconds().values
        shifted = np.interp(t, t - lag_s, g["yield_t_ha"].values,
                            left=np.nan, right=np.nan)
        g["yield_lag_corrected"] = shifted
        out.append(g)
    return pd.concat(out)

y = correct_lag(y, lag_s=12.0)
moved = (y["yield_t_ha"] - y["yield_lag_corrected"]).abs()
print(f"median change from lag correction: {moved.median():.2f} t/ha")

The lag is a machine constant that can be estimated by cross-correlating the yield signal against a known boundary — the edge of a trial strip, or a change of variety — and the estimate is worth making once per combine rather than taking a default.

Explanation

Why the lag smears boundaries rather than shifting them

If the delay were constant in distance, the map would simply be translated. It is constant in time, so the distance depends on the speed, which varies within and between passes. A boundary is therefore displaced by different amounts in different places, which blurs it rather than moving it — and it is why a lag-uncorrected map cannot resolve a trial strip narrower than about forty metres.

Why partial width is the largest single error

The monitor divides a measured mass by an assumed area. If the header is half full and the monitor assumes it is full, the recorded yield is half the true value — a 50% error, far larger than anything the sensors contribute. It happens on every last pass, around every obstacle and on every tapered headland, which in an irregular field can be a quarter of the points.

Why moisture produces stripes

The reported yield is corrected to a standard moisture, typically 15% for cereals. If the moisture reading drifts or the correction is applied from a manual entry that was changed part way through the day, the correction differs between passes cut at different times. The result is a stripe pattern aligned with the machine's direction rather than with anything in the field.

Why these artefacts survive into management zones

They are spatially structured: headlands are at the edges, partial passes are along one side, start-stop transients are in rows. Clustering finds spatial structure, so a zoning built on uncleaned yield data reproduces the harvest pattern. The give-away is a zone boundary that runs parallel to the direction of travel.

Two panels separating yield map artefacts that can be corrected from those that can only be removed.
Whether an artefact is correctable depends on what the monitor logged.

Edge cases or notes

  • Formats vary by manufacturer. A yield file is a vendor format with a common shape.
  • Multiple harvests in one file. Split by date before anything else.
  • Two combines in one field have two lags and two calibrations.
  • Calibration drift within a day is common.
  • Grain cart weights are the reference. Scale the map to the weighbridge total.
  • Dry and wet grain are different products. Record the moisture basis.
  • The first year of a new monitor is the worst. Calibration improves.
  • Report the cleaning statistics. Removing 20% of points is a result.

FAQ

What does a yield monitor actually measure?

Mass flow at the top of the clean grain elevator, divided by an area inferred from ground speed and an assumed header width, corrected to a standard moisture.

Why is my yield map smeared along the direction of travel?

Sensor lag. Grain takes ten to twenty seconds to reach the sensor, which at 5–8 km/h is 14–44 m of travel.

Why are there stripes down my field?

Usually partial header width being recorded as full width, or an inconsistent moisture correction between passes cut at different times.

Why is the headland always different?

Start and stop transients, turning, and partial width all concentrate there. Headland points are the least reliable in the file.

How many points should I expect to remove?

Ten to twenty-five per cent is normal for a defensible cleaning pass. Report the number.

Can I fix all of this?

Lag, position offset and moisture are correctable if the raw values were logged. Partial width is correctable only if the true swath was recorded; start and stop transients can only be removed.