Using yield and soil electrical conductivity (EC) maps to derive crop production performance information.

Abstract

Of the various factors that affect crop yield, soil water-holding capacity is usually a significant contributor. Soil electrical conductivity (EC) measurements in non-saline soils are driven primarily by soil texture and soil moisture. Those same factors correlate highly to the soil’s water-holding capacity. Thus, an EC map can serve as a proxy for soil water-holding capacity, resulting in soil EC and yield maps that frequently exhibit similar spatial patterns. Numerous commercial EC mapping systems are being used in precision agriculture, and many of the maps generated by these units are being layered in a GIS with yield data in an attempt to explain yield variability. A common tool being employed in yield-EC analyses is bi-variate linear regression. While this analysis frequently explains a larger percentage of yield variability than is explained by other available layers of soil sample information, it ignores the more complex relationships between soil physical properties and yield. Moving to a non-linear curve-fit may improve the correlation co-efficient but rarely explains more than 50% of the yield variability within a field. This paper presents an analysis technique that sorts through the cloud of yield data points to establish a yield benchmark for each soil EC level. Further analysis generates maps that can be used to investigate areas that are performing below the benchmark.