16 targeted dose and deflection measured at each wind speed, the tree was pruned at the 30% targeted dose and deflection mea- sured at each speed, and then at the 45% targeted dose and deflection measured. Trees returned to prewind crown structure before blowing at the next pruning dose; crossing branches were untangled and no branches broke. Therefore, data were collected 24 different times (six targeted wind speeds × four pruning doses) on each tree. The first four lion’s tailed, reduced, and thinned trees, and the first three raised trees, were blocked in time forming an incomplete block design; then the last three lion’s tailed, raised, and reduced trees, and the two structurally pruned trees were blown in no particular order. Measurements from CETs and anemometer were taken at 0.5 Hertz (every 2 sec). This infrequent interval could have resulted in losing data resolution, especially in gusts of wind. The data acquisition system (DAQ) consisted of a Campbell Scientific CR10X data logger used in combination with a Campbell AM 416 multiplexer and a program written in Loggernet 2.1 (Camp- bell Scientific, Inc.). The DAQ system was powered from a standard 120 VAC socket through a 12 VDC converter. Data Analysis Strategy Measured (not targeted) pruning dose and wind speed for each tree individually were regressed onto trunk movement using a complete two-factor linear and quadratic model (equation 1) pre- dicting trunk movement from measured pruning dose and wind speed. Orthogonal (equal spaced) combinations of dose (0%, 15%, 30%, 45%, and 60% foliage removed) and wind speed [0, 6.7, 13.4, 20.1, 26.8 m/s (0, 15, 30, 45, 60 mph)] were selected along the linear and quadratic model response surface (25 dose × speed combinations per tree) to calculate predicted trunk movement for each tree. We were able to add the 26.8 m/s (60 mph) wind speed because winds gusted above this speed when we blew trees. We were able to add the 60% pruning dose because some trees were pruned that much as determined by weighting removed foliage. Equation 1. Predicted trunk movement (PTM) b0 +b1 wind speed (W) + b2 pruning dose (D) + b3 W2 +b4 D2 +W × D. b’s represent coefficients for each term. Predicted trunk movement was calculated from the equation generated for each tree. Three-way analysis of variance of PTM evaluated the effects of pruning type, dose, and wind speed. Analysis of variance (ANOVA) was run as a completely ran- domized design because all replicate trees within a pruning type were not included in the original block design. Least squared means were separated with Tukey’s multiple range test. Data were analyzed using the SAS system for windows release 8.02 (SAS Institute Inc., Cary, NC). RESULTS AND DISCUSSION Actual trunk movement was regressed against measured wind speed and measured pruning dose for each tree tested (Table 1), and the resulting equation was used to predict trunk movement for that tree at orthogonal levels of dose and wind speed. Linear and quadratic terms were significant. Rudnicki et al. (2004) and Vollsinger et al. (2005) found that crown drag was related to wind speed squared when accounting for reduction in crown area attributable to reconfiguration, and Hoag et al. (1971) and Smiley and Kane (2006) both found drag related to the 1.4 power ©2008 International Society of Arboriculture Gilman et al.: Effects of Pruning Dose and Type of wind speed for broad-leaved trees. Three-way ANOVA of predicted trunk movement showed that the factors pruning type, pruning dose, and wind speed had a significant effect on trunk movement (Table 2). However, interactions were significant so conclusions about each factor depended on the level of another factor. Despite the interactions described subsequently, increas- ing wind speed increased trunk movement (indicated by a posi- tive b1 coefficient for every tree; Table 1), and the magnitude of the increase depended on pruning dose and pruning type (Fig- ure 3; Table 3) similar to findings of Smiley and Kane (2006). Increasing pruning dose reduced trunk movement (indicated by a negative b2 or b4 coefficient for most trees; Table 2), and the magnitude of the reduction was greater at higher wind speeds. The pruning dose × pruning type interaction was not signifi- cant indicating that the impact of either factor on trunk move- ment was independent of the other. In other words, averaged across all wind speeds, increasing the amount of foliage removed (pruning dose) on one pruning type resulted in the same reduc- tion in trunk movement as all other pruning types (data not shown). This might lead us to conclude that removing foliage from anywhere in the crown on trees of this size was equally effective at reducing trunk movement in windy weather. How- ever, pruning type interacted with wind speed so further inter- pretation was needed. The pruning type × wind speed interaction was significant (Table 2) indicating that averaged across pruning dose, the effect of wind speed on trunk movement was not the same for all pruning types (Table 3). Predicted trunk movement of thinned trees was statistically greater than movement of trees pruned by all other types except reduction at wind speeds of 20 m/s (45 mph). This agrees with Smiley and Kane (2006) who found reduced trees responded to wind similar to thinned trees up to 20.1 m/s (45 mph). However, we found that at 26.8 m/s (60 mph), thinned trees moved more than trees in all pruning types, including those that were reduced. Perhaps if the tops of our thinned trees were within the main wind field, they would have bent more than reduced trees (which were totally inside the wind field) at a wind speed lower than 26.8 m/s (60 mph). However, if the crown top extending outside the main wind field had a significant effect on trunk movement, then other pruning types might be expected to bend less than reduced trees. Because this did not happen, differences we are reporting here appear defendable. The limited size of generated wind fields is one of the most conspicuous obstacles in the study of wind and trees with intact root systems. Mayhead (1973) noted that the leading shoots of several of the trees he tested were outside the wind flow and commented that it was not likely a significant source of error. The leading shoots in the upper crown in our trees were out- side the primary wind flow before pruning (Jones 2005). The effect of the shortened wind field might be most noticeable on the dimensionally pruned raised trees (i.e., the first four raised trees) because they were pruned with the highest dose and the crown was removed from the portion of the wind field with the highest wind speed (Figure 1); however, trunk movement of the raised trees was not statistically different from movement of lion’s tailed, reduced, or structurally pruned trees. When ANOVA was carried out with the four dimensionally pruned trees removed from the data set (data not shown), the results did not change. Therefore, the size of the wind field likely did not affect comparisons among pruning types or doses.
January 2008
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