Toward an Improved Rapid Urban Site Index - Scheberl, Luke L. (University of Wisconsin-Stevens Point, College of Natural Resources, 2018)
The ability of the rapid urban site index (RUSI) model to predict urban tree health was tested in three cities in Wisconsin, USA. While the RUSI model was found to significantly correlate to tree growth and health (P = <0.01; R2 = 0.09-0.10), it did so while explaining less variation than the previous study (P = <0.0001; R2 = 0.18-0.40). To increase the strength of this correlation, weighting schemes on RUSI parameters were investigated but resulted in no significant correlation with tree performance. The RUSI models’ sensitivity to the application of biosolids was also tested. To increase this sensitivity, four different labile organic carbon assessments were added. Only the RUSI + permanganate oxidizable carbon model showed a significant mean change as a result of the soil amendment application (P = 0.04; F = 3.47). Future research should continue to expand the models geographic extent and tree species evaluated as well as investigate other potential parameters to aid in identifying site quality. This thesis continues with an evaluation of popular low-cost soil pH and moisture field sensors. Twenty-two soil pH and moisture sensors were tested for their ability to accurately and precisely measure soil pH, volumetric soil moisture content (VMC), or both. This research was conducted on four different soil texture classes (loamy sand, sandy loam, clay loam, and clay) at three different moisture levels (air dry, ≈ 0.5 field capacity, and ≈ field capacity). Glass-electrode pH sensors measuring a 1:2 (soil:deionized water) solution were found to be both accurate and precise (P = <0.0001; ρc = >0.95). However, metal electrode sensors inserted into the soil had no significant correlation to soil pH levels (P = >0.1; ρc = <0.2). When selecting a soil pH sensor, measurement method may be the most important variable. Soil VMC sensors performed best when measuring time domain reflectometry and frequency domain reflectometry (P = <0.0001; ρc = >0.76). Sensors measuring electrical conductivity were highly variable in cost, accuracy, and precision. When selecting a soil VMC sensor, measurement method and cost are both important variables. These field sensors may improve urban site management and could lead to the addition of an available water holding capacity parameter to the RUSI model.