Predicting spatial structure of soil physical and chemical properties of golf course fairways using an apparent electrical conductivity sensor

Soil apparent electrical conductivity (EC a ) has been used to map spatial variability of soil properties in multiple cropping systems and may have applications in precision turfgrass management (PTM). The objective of this research was to determine whether EC a data could predict the spatial structure of soil properties relevant to turfgrass management. Research was conducted at the University of Georgia (UGA) and the Georgia Club (GC) golf courses in North GA during the summer of 2016. A mobile Veris Q1000 device was used to collect georeferenced EC a data from six golf course fairways (three per course). Soil samples were collected from each fairway using a georeferenced grid to determine clay content, soil pH, cation exchange capacity (CEC) and organic matter (OM). To understand the predictive relationship between EC a and soil properties, correlation coefficients and multiple linear regression models were generated for each fairway. Spatial maps were used to visually demonstrate these relationships. Though some relationships were observed between EC a and soil properties (primarily clay, soil pH and OM on the UGA course), measured parameters were insufficient to fully explain spatial variability in EC a . Findings from this study suggest that spatial variability of soil properties in turfgrass can be significant enough to warrant PTM. Though EC a may be used to partially predict clay content, CEC, OM and soil pH, additional research is required to better understand EC a variability and its applications for PTM. Future research exploring EC a for PTM should consider the roles of soil moisture, temporal variability and topography.