Natural Resources, School of


First Advisor

Trenton E. Franz

Date of this Version



Becker, S.M. (2022). Feasibility assessment on use of proximal geophysical sensors to support precision management (MS Thesis). University of Nebraska-Lincoln.


A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science, Major: Natural Resource Sciences, Under the Supervision of Professor Trenton E. Franz. Lincoln, Nebraska: May 2022

Copyright (c) 2022 Sophia M. Becker


Soil property maps provide information for field management activities such as irrigation, fertilization, and seeding. Many on-the-go proximal geophysical sensors have been developed in recent decades that can help map agricultural fields without dense soil sampling. To utilize these technologies most profitably in precision management, scientists and precision agriculture dealers must better understand sensors’ performances in given field conditions and the economic value of different proximal soil sensing methods. Chapter two reports the study that was conducted at three sites in North Dakota, United States to strengthen understanding of the usefulness of different proximal geophysical data types in agricultural contexts of varying pedology. This study hypothesizes that electro-magnetic 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, organic matter, available water holding capacity, silt, and clay were predicted at site 1 with an Rpred2 > .50 and acceptable RMSEP. Bulk density, organic matter, available water capacity, silt, and clay were predicted with Rpred2 > .50 and acceptable RMSEP at site 2. At site 3, no soil properties were predicted with acceptable RMSEP and an Rpred2 > .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. Strategies for addressing the remaining needs for better prediction of sensor performance and evaluation of sensing methods’ economic value are discussed in chapter three. Several potential methods for future research from the literature are summarized that can advance understanding of sensors’ best use, sophisticated cost-benefit analysis, and soil sampling optimization.

Advisor: Trenton Franz