Yield Monitoring and Mapping

Insight Monitor

Yield monitoring equipment was introduced in the early 1990s and is increasingly considered a conventional practice in modern agriculture. The pioneers of precision agriculture already have generated several years of yield history and have examined different ways of interpreting and processing these data.

Yield Mapping Concept

Yield mapping refers to the process of collecting georeferenced data on crop yield and characteristics, such as moisture content, while the crop is being harvested. Various methods, using a range of sensors, have been developed for mapping crop yields.

 The basic components of a grain yield mapping system include:

  • Grain flow sensor - determines grain volume harvested
  • Grain moisture sensor - compensates for grain moisture variability
  • Clean grain elevator speed sensor - used by some mapping sytems to improve accuracy of grain flow measurements
  • GPS antenna - receives satellite signal
  • Yield monitor display with a GPS receiver - georeference and record data
  • Header position sensor - distinguishes measurements logged during turns
  • Travel speed sensor - determines the distance the combine travels during a certain logging interval (Sometimes travel speed is measured with a GPS receiver or a radar or ultrasonic sensor.)

Each sensor has to be properly calibrated according to the operator’s manual. Calibration converts the sensor’s signal to physical parameters. A proprietary binary log file is created during harvest to record the output of all sensors as a function of time. This file can be converted to a text format or displayed as a map using the yield monitor vendor’s software.

Processing Yield Maps

The yield calculated at each field location can be displayed on a map using a Geographic Information System (GIS) software package. The raw log file, however, contains points recorded during turns and the sensor measurements do not correspond to the exact harvest locations because grain flow through a combine is a delayed process (unless real-time correction is applied). To eliminate these obvious errors, the raw data is shifted to compensate for the combining delay, and the points corresponding to the header up position are removed. Settings for grain flow delay are combine- and sometimes even crop-specific, but typical values for grain crops range from about 10 to 12 seconds.

Usually a few points at the beginning and at the end of a pass should be removed as well. These are referred to as start-and end-pass delays. Start-pass delays occur when the combine starts harvesting the crop, but grain flow has not stabilized because the elevator is gradually filling up. Similarly, end-pass delays occur when the combine moves out of the crop and grain flow gradually declines to zero when the elevator is completely emptied.  Consult the manufacturer of your yield monitor for the most appropriate settings to use with your combine.

Shifting of raw data to correct for grain flow delay as well as deletion of points that represent header status up and start-and end-pass delays is the primary data filtering procedure built into software supplied with yield mapping systems.

Yield History Evaluation

Evaluating the temporal (year-to-year) variation of yield distribution within the field is an essential step in defining field areas with potentially high and low yields. Several approaches can be used to evaluate temporal effects on yield. One approach is to calculate the relative (normalized) yield for each point or grid cell. Normalized yield can be defined as the ratio of the actual yield to the field average:

When growing conditions in a field vary considerably, such as irrigated and dryland areas or different crops or varieties grown in different areas, normalization should be done separately for those areas, with the resulting relative yields recombined into one data file for the whole field. The following figure shows a relative yield history for a field with corn (soybean in the southern half in 2000) grown using furrow-irrigation (until 2001) and center-pivot irrigation (in 2002).

Relative Corn Yields

Maps of relative yield of corn and soybean grown during a seven-year period (red indicates low-yielding areas and green indicates higher than average yields).

Potential Applications

Yield maps represent the output of crop production. On one hand this information can be used to investigate the existence of spatially variable yield limiting factors. On the other hand, the yield history can be used to define spatially variable yield goals that may allow varying inputs according to expected field productivity.

The following flowchart illustrates the process one might follow in deciding whether to invest in site-specific crop management, based on analysis of yield maps. If yield variability across the field cannot be explained by any spatially inconsistent field property, uniform management may be appropriate. Site-specific management becomes a promising strategy if yield patterns are consistent from year to year and can be correlated to one or more field properties (e.g. nutrient supply, topography, past management, etc.).

Yield Map Decision Making Flowchart

If the causes for yield variation are known and can be eliminated permanently, the entire area could be brought to similar growing conditions and managed uniformly thereafter. This concept was one of the earliest philosophies behind precision agriculture, but is likely only feasible for certain field properties. For example, variable rate liming can be used to correct acidic areas in a field. In this case, the yield map is used only to investigate whether low soil pH is a yield-limiting factor, and the soil map is used to prescribe variable application rates. Another example would be localized deep soil tillage to alleviate compaction in selected field areas.

Most yield limiting factors cannot be modified permanently through single measures because of economic or practical constraints. Consequently, site-specific crop management may be used to appropriately account for the existing spatial variability in attainable yield and/or soil properties.


Yield maps are one of the most valuable sources of spatial data for precision agriculture. In developing these maps, it is essential to remove the data points that do not accurately represent the yield at a corresponding location. Map averaging or smoothing is usually done to aid data interpretation. A long yield history is essential to avoid drawing conclusions that are affected by the weather or other unpredictable factors during a particular year. Typically, at least five years of yield maps are desired. Processed yield maps can be used to investigate factors affecting the yield or to prescribe variable rate applications of agricultural inputs according to spatially variable yield goals (yield potential). Producers interested in precision farming should, however, always evaluate different management approaches to identify those that provide the greatest benefit at a particular site.


For additional informaton, see UNL Extension Circular, Listening to the Story Told by Yield Maps - EC 704 (362 KB; 8 pages)

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