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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #105288
Title: ACCURACY ISSUES IN ELECTROMAGNETIC INDUCTION SENSING OF SOIL ELECTRICAL CONDUCTIVITY FOR PRECISION AGRICULTURE

Author
item Sudduth, Kenneth - Ken
item Drummond, Scott
item Kitchen, Newell

Submitted to: Computers and Electronics in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/10/2000
Publication Date: 5/1/2001
Citation: Sudduth, K.A., Drummond, S.T., Kitchen, N.R. 2001. Accuracy issues in electromagnetic induction sensing of soil electrical conductivity for precision agriculture. Computers and Electronics in Agriculture. 31:239-264.

Interpretive Summary: Precision agriculture is a crop management strategy which seeks to address within-field variability. Variability in mapped yield data has often corresponded well with landscape and soil physical properties that relate to water distribution and water availability. Apparent soil electrical conductivity (ECa) is one sensor-based measurement that can provide an indirect indicator of some of these water-related soil properties. In non- saline soils, ECa variations are primarily a function of soil texture, moisture content, and cation exchange capacity. Commercial sensors are available to measure ECa, and researchers, crop consultants, and producers are currently collecting and using ECa data to aid in precision agriculture decisions. In this research, we investigated issues related to the accuracy of a commercial electromagnetic induction ECa sensor. We found that the output of the sensor could drift a significant amount in the time required to survey a field and suggested methods to compensate for this source of error. We also suggested methods to evaluate time lags in the data collection system. Methods were developed to calibrate ECa readings to topsoil depth on claypan soils, and the effect of moisture and temperature variations on those calibrations was investigated. Moisture and temperature effects were minor within a single measurement date but could be significant from one survey date to another. These results will benefit researchers, producers, and agricultural consultants by making them aware of potential error sources in ECa data collection and by recommending ways for them to minimize the effects of these error sources in their own data.

Technical Abstract: Soil apparent electrical conductivity (ECa) has been used as a surrogate measure for such soil properties as salinity, moisture content, topsoil depth, and clay content. The objective of this research was to investigate accuracy issues in the collection of soil ECa. A mobile data acquisition system for ECa was developed using the Geonics EM38 sensor. The sensor was mounted on a wooden cart pulled behind an all-terrain vehicle, which also carried a GPS receiver and data collection computer. Tests showed that drift of the EM38 could be a significant fraction of within-field ECa variation. Use of a calibration transect to document and adjust for this drift was recommended. A procedure was developed to evaluate positional offset of the mobile EM38 data. Positional offset was due to both the distance from the sensor to the GPS antenna and data acquisition system time lags. Sensitivity of ECa to variations in sensor operating speed and height was relatively minor. Procedures were developed to estimate topsoi depth on claypan soils from ECa. Linear equations of an inverse or power function transformation of ECa provided the best estimates of topsoil depth. Collection of individual calibration datasets within each surveyed field was necessary for best results. Multiple measurements of ECa on a field were similar if they were obtained at the same time of year. Whole- field maps of ECa-determined topsoil depth from multiple surveys were similar but not identical. There was a significant effect of soil moisture and temperature differences across measurement dates. Classification of measurement dates as hot vs. cold and wet vs. dry provided topsoil depth estimations nearly as accurate as when individual point soil moisture and temperature data were included in the calibration equation.