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

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