©2023 International Society of Arboriculture Arboriculture & Urban Forestry 49(2): March 2023 97 labile organic matter for improving the RUSI model for more accurate site assessments for urban trees. It is also important to recognize that nonsite factors also influence the health and growth of urban trees. Understanding site conditions is important for urban tree management, but other factors (e.g., nursery practices, pruning) also impact urban tree condition. Significant but relatively low correlations between RUSI scores and urban tree condition parameters in this study provide evidence for this statement. Urban forests, soils, and sites are diverse. Our under- standing for assessing those conditions, and then evaluating the results, is evolving. Future work on the RUSI model should be directed at tailoring and test- ing the RUSI model in more urban tree populations, soils, and site conditions. The corresponding author of the current study has begun working with individ- ual urban forest managers to develop tailored RUSI models for specific cities. Data from these case stud- ies will be critical for improving the RUSI model and further demonstration of how it can be practically applied. If interested in participating in this effort, please contact the corresponding author. dynamic labile organic matter parameter that is more sensitive to a soil amendment did appear to provide an early indication of potential site quality improve- ments leading to improved tree growth and health. CONCLUSION This study showed that the RUSI can be used in Wis- consin to relate urban site conditions and urban tree performance. The study also demonstrated the value of parameter weighting to improve the RUSI model. Lastly, the study identified a labile organic matter parameter that might be used to make the RUSI model more dynamic and detect soil management. It is important to recognize that the RUSI model was developed as an approach, not a “one-size-fits-all” model. The approach allows for sensible and mean- ingful tailoring of RUSI to specific site conditions and urban tree populations. The RUSI approach involves understanding the site conditions affecting an urban tree population, tailoring an assessment to those con- ditions, assessing those conditions, and then evaluat- ing the results for management. The results from the current study demonstrate the value of parameter weighting, adding or removing parameters, and using Table 6. Mean (n = 30) and standard error of the mean (SE) for total soil organic matter (SOM), particulate organic matter (POM), potassium permanganate oxidizable carbon (POXC), potentially mineralizable carbon (PMC), Solvita® (Solvita, Woodsend Laboratories, Augusta, ME, USA) soil respiration (SOLV), microbial biomass carbon (MBC), and microbial biomass nitrogen (MBN). Property P-value Mean SE Mean SE Mean SE SOM (%) 6.9 0.6 7.2 0.6 7.4 0.7 0.9012 POM (%) 1.4 0.1 1.6 0.1 1.5 0.1 0.6372 POXC (mg/kg) 1,020 54.9 850 57.5 991 53.5 0.0793 PMC (mg/kg/d) 77.4 7.6 91.6 5.5 86.3 7.3 0.3492 SOLV (mg/kg/d) 32.4b 0.5 33.8ab 0.4 33.9a 0.4 0.0275* MBC (mg/kg) 39.5 3.7 45.9 5.8 48.3 4.4 0.4167 MBN (mg/kg) 8.6 0.7 9.9 1.4 8.9 0.8 0.6717 a Data is from the fall sampling period. Treatments are high, low, and no biosolids application (null). The P-values for the analysis of variance (ANOVA) are given. Letters identify Tukey-Kramer Honestly Significant Difference (HSD) mean separations with unique letters identifying significant differences. *P ≤ 0.05 Nulla Lowa Higha
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