Feasibility assessment on use of proximal geophysical sensors to support precision management

Authors

Sophia M. Becker, University of Nebraska-LincolnFollow
Trenton E. Franz, University of Nebraska-LincolnFollow
Olufemi P. Abimbola, University of Nebraska - LincolnFollow
Dean D. Steele, North Dakota State University--FargoFollow
J. Paulo Flores, North Dakota State University--FargoFollow
Xinhua Jia, North Dakota State University--FargoFollow
Thomas F. Scherer, North Dakota State University--FargoFollow
Daran Rudnick, University of Nebraska - LincolnFollow
Christopher M.U. Neale, University of Nebraska & Daugherty Water for Food Global InstituteFollow

Document Type

Article

Date of this Version

2022

Citation

Vadose Zone J. 2022;e20228.

DOI: 10.1002/vzj2.20228

Comments

This is an open access article under the terms of the Creative Commons Attribution License,

Abstract

A study was conducted at three sites in North Dakota to strengthen understanding

of the usefulness of different proximal geophysical data types in agricultural contexts of varying pedology. This study hypothesizes that electromagnetic induction (EMI), gamma-ray sensor (GRS), cosmic-ray neutron sensor (CRNS), and elevation data layers are all useful in multiple linear regression (MLR) predictions of soil properties that meet expert criteria at three agricultural sites. In addition to geophysical data collection with vehicle-mounted sensors, 15 soil samples were collected at each site and analyzed for nine soil properties of interest. A set of model training data was compiled by pairing the sampled soil property measurements with the nearest geophysical data. Eleven models passed expert-defined uncertainty criteria at Site 1, 16 passed at Site 2, and 14 passed at Site 3. Electrical conductivity (EC), organic matter (OM), available water holding capacity, silt, and clay were predicted at Site 1 with an R-squared of prediction (𝑅²_{𝑝𝑟𝑒𝑑} ) > .50 and acceptable root mean square error of prediction (RMSEP). Bulk density (BD), OM, available water capacity, silt, and clay were predicted with 𝑅²_{𝑝𝑟𝑒𝑑}> .50 and acceptable RMSEP at Site 2. At Site 3, no soil properties were predicted with acceptable RMSEP and an 𝑅²_{𝑝𝑟𝑒𝑑}> .50. These results confirm feasibility of our method, and the authors recommend the prioritization of EMI data collection if geophysical data collection is limited to a single mapping effort and calibration soil samples are few.