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

A study 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. Included is the data from proximal geophysical sensors for each site (one file for each site) and a file containing all the sampled soil property data. The sampled soil properties include pH, electrical conductivity, cation exchange capacity, organic matter, available water holding capacity, bulk density, percent sand, percent silt, and percent clay.