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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #307240

Research Project: LANDSCAPE-BASED CROP MANAGEMENT FOR FOOD, FEED, AND BIOENERGY

Location: Cropping Systems and Water Quality Research

Title: Evaluation of a commercial multi-sensor system for soil electrical conductivity, organic matter, and pH

Author
item Sudduth, Kenneth - Ken
item Vories, Earl - Earl
item Kitchen, Newell
item Myers, David - University Of Missouri
item Drummond, Scott

Submitted to: ASABE Annual International Meeting
Publication Type: Abstract Only
Publication Acceptance Date: 7/10/2014
Publication Date: 7/13/2014
Citation: Sudduth, K.A., Vories, E.D., Kitchen, N.R., Myers, D.B., Drummond, S.T. 2014. Evaluation of a commercial multi-sensor system for soil electrical conductivity, organic matter, and pH. ASABE Annual International Meeting [Availble online].

Interpretive Summary:

Technical Abstract: Efficient and accurate spatial quantification of soil properties is recognized as an important aspect of precision agriculture. With the current standard practice of in-field sample collection and subsequent laboratory analysis it is often prohibitively expensive to obtain data at the spatial density required to accurately characterize variability. Mobile proximal soil sensors are one way to overcome this problem. Many such sensors have been developed over the years and a few are commercially available. One such sensor is the Veris MSP3, which provides simultaneous measurement of soil apparent electrical conductivity (ECa), organic matter, and pH. The objective of this research was to evaluate results obtained with the MSP3 in comparison to laboratory analysis of collected soil samples. Data were collected at multiple field sites across the state of Missouri, spanning different soil regions. Sensor data collected on transects were spatially matched with soil sampling points and the two datasets were compared statistically. Specific conditions (e.g., soil type, tillage system, degree of spatial variability) affecting the performance of the sensor system were identified. The results of this research will provide guidance to those interested in applying mobile proximal soil sensing for precision agriculture.