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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #324094

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

Location: Cropping Systems and Water Quality Research

Title: Soil physical property estimation from soil strength and apparent electrical conductivity sensor data

Author
item Cho, Yong Jin - University Of Missouri
item Sudduth, Kenneth - Ken
item Chung, Sun-ok - Chungnam National University

Submitted to: Biosystems Engineering
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/3/2016
Publication Date: 11/29/2016
Citation: Cho, Y., Sudduth, K.A., Chung, S. 2016. Soil physical property estimation from soil strength and apparent electrical conductivity sensor data. Biosystems Engineering. 152:68-78. doi: 10.1016/j.biosystemseng.2016.07.003.

Interpretive Summary: Quantification of soil physical properties such as bulk density, water content, and texture fractions is traditionally accomplished by collection of soil samples in the field and subsequent laboratory analysis. This process is inefficient and may be impractical when measurements are needed at many locations across a field or landscape, for example to develop precision agriculture management plans, to quantify spatial variability in soil quality, or to drive spatially-explicit process-based models. Mobile proximal sensors that can collect spatially dense data could be a potential solution to this problem. The purpose of this research was to examine the use of data from soil electrical conductivity (ECa) and soil strength sensors to estimate bulk density, soil water content, and soil clay fraction. Data used included two different ECa measurements and two different soil strength measurements for three field sites in central Missouri. Using correlation and regression analysis, we found that soil clay at discrete soil depths was well-estimated by combinations of the two ECa variables, depending on the depth in question. Sensor-based soil water content estimates were of variable accuracy. Bulk density estimates were not good unless laboratory-measured water content was included as an explanatory variable along with sensor data. Fusion of data from ECa and soil strength sensors showed potential for estimating soil physical properties, but inclusion of a soil water content sensor would be required for better results. These results will benefit researchers and practitioners interested in applying sensors to improve the efficiency of quantifying spatially-variable soil physical properties.

Technical Abstract: Quantification of soil physical properties through soil sampling and laboratory analyses is time-, cost-, and labor-consuming, making it difficult to obtain the spatially-dense data required for precision agriculture. Proximal soil sensing is an attractive alternative, but many currently available sensors do not respond to a single soil property. For example, soil strength and apparent electrical conductivity (ECa) sensor measurements are significantly affected by soil texture, bulk density (BD), and water content (WC). The objective of this study was to explore the potential for estimating soil texture, BD, and WC using combinations of sensor-based soil strength and ECa data obtained from sites with varying soil physical properties. Data collected from three research sites in Missouri included on-the-go horizontal soil strength at five depths up to 0.5 m on a 0.1-m interval, cone index measurements at the same depths, ECa measured by a Veris 3100, and depth-dependent, laboratory-determined soil properties. Accuracy of all-depth regression models for WC estimation was low (R2 = 0.01–0.48), with better results when using combining soil strength and ECa data, when including depth as a variable, and when using quadratic instead of linear models. Best BD estimation models (R2 = 0.14–0.53) for a single depth generally included ECa, but soil strength was only included in less than 25 percent of the models. BD estimates were improved considerably by adding lab-measured WC to the model (R2 = 0.57–0.83), suggesting the need for a WC sensor. Soil clay texture fraction was well-estimated at most measurement depths by ECa (R2 = 0.70–0.81). This study showed the potential of fusing data from multiple mobile proximal sensors to estimate important soil physical properties.