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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #375920

Research Project: Sustainable Intensification of Cropping Systems on Spatially Variable Landscapes and Soils

Location: Cropping Systems and Water Quality Research

Title: Estimating soil water content using electrical conductivity sensing

item LEE, KYUHO - University Of Missouri
item Sudduth, Kenneth - Ken
item Drummond, Scott

Submitted to: ASABE Annual International Meeting
Publication Type: Abstract Only
Publication Acceptance Date: 6/29/2020
Publication Date: 7/13/2020
Citation: Lee, K., Sudduth, K.A., Drummond, S.T. 2020. Estimating soil water content using electrical conductivity sensing [abstract]. ASABE Annual International Virtual Meeting, July 13-15, 2020.

Interpretive Summary:

Technical Abstract: In precision agriculture, the most broadly used proximal soil sensing technology is apparent soil electrical conductivity (ECa). Although spatial patterns of ECa are quite stable across measurement dates and ambient conditions, the quantitative calibration of ECa to stable soil properties will vary, primarily due to the soil moisture at the time of sensing. For this reason, the objective of this research was to determine the relationship of soil moisture content to ECa at a set of fixed locations throughout a growing season. Data were collected in a cropping systems experiment in central Missouri in tilled and no-till corn at three different landscape positions (i.e., summit, side and toe slope) from July to November of 2019. Volumetric water content sensors were inserted at each of the six locations to provide integrated readings over four 16.5 cm depth increments starting at the soil surface, and data were logged every 15 minutes. Soil ECa data were collected in triplicate at each location approximately once per week using an EM38 electromagnetic induction ECa sensor in both horizontal and vertical dipole orientation. The relationship of ECa to soil water content was investigated using multiple regression techniques. The relationship was strong over measurement dates in the growing season; however, later in the year the relationship degraded, possibly due to installation problems with the soil water content sensors. These results show the potential to estimate changes in soil moisture content using models based on the changes in ECa over time.