Table 1. The number of experimental sites in the pre-commercial thinning study according to the age of the stand and early cleaning method (total, point) applied in the site.
Stand age in early cleaning, years 5 6 7 8 10  
Establishment year 2005 2004 2003 2002 2000 Overall
Total cleaning 4 3 0 1 1 9
Point cleaning 3 7 3 0 0 13
Overall 7 10 3 1 1 22
Table 2. The main characteristics of the variables measured in the experimental sites for the pre-commercial thinning (PCT) study in southern Finland (EC = early cleaning, TWI = topographic wetness index).
   Min Mean SD Max
Site level, n = 22        
  Site area, ha 1.8 3.5 1.5 6.9
  Density, crop spruces ha–1 1150 1937 342 2812
  Height of crop spruce, cm 74 129 35 232
  Diameter0.15 of crop spruce, cm 1.42 2.02 0.47 3.56
  Total stand density before EC, trees ha–1 10 350 22 222 11 294 59 475
  Diameter0.15 of all trees before EC, cm 0.82 1.11 0.20 1.73
Experimental unit level, n = 132        
  Experimental unit size, ha 0.16 0.59 0.27 1.40
  Time consumption in EC (recorded), pwh ha–1 1.7 5.8 2.6 13.5
  Total stand density before EC, trees ha–1 5200 22 810 11 902 63 750
  Diameter0.15 of all trees before EC, cm 0.77 1.12 0.23 2.09
  Range of elevation change, m 0.0 11.0 7.2 30.0
  Vegetation coverage, % 0.4 9.8 7.0 38.8
  TWI 5098 7018 1044 10 818
  Height growth of removal between EC and PCT, cm year–1 18.2 37.8 9.3 62.9
  Time consumption in PCT (calculated), twh ha–1 4.5 11.0 4.4 26.1
  Diameter of removal in PCT, cm 0.83 1.36 0.34 2.93
  Density of removal in PCT, trees ha–1 4065 18 823 12 342 65 381
1

Fig. 1. The cumulative distribution function of the diameter growth of birch within a growing season (line). The function was modeled to estimate the proportion of the growing season that sprouts emerging after early cleaning treatment can still utilize for growth during that year. The initial data (dots) were acquired from Niemistö et al. (2008).

Table 3. Mixed models (EC1 and EC2) for worktime consumption in early cleaning. EC1 is the primary analysis including removal figures as covariates. The secondary analysis (EC2) replaces the direct figures of removal with its indirect indications, the TWI (topographic wetness index) of a site. The worker effect is redundant in EC2, because it cannot be separated from the site-level variation in the model. The dependent variable is worktime consumption in productive working hours (pwh) ha–1 in both models (N = density, D0.15 = diameter at stump height, EC = early cleaning). All the continuous independent variables are centered around the mean. P-values in bold represent statistically significant differences.
        EC1 EC2
Estimate Std. Error P-value Estimate Std. Error P-value
Intercept 5.303 0.509 <0.001 5.343 0.554 <0.001
N before EC, 1000 trees ha–1 0.175 0.015 <0.001      
D0.15 before EC, cm 1.921 0.771 0.016      
Season, ref. Spring            
  Summer 1.970 0.255 <0.001 1.955 0.318 <0.001
  Autumn 0.939 0.261 <0.001 0.884 0.317 0.006
Ln(Area), ha –1.545 0.345 <0.001 –1.731 0.453 <0.001
Elevation change, m 0.256 0.119 0.035 0.103 0.167 0.538
EC-method, ref. Total cleaning            
  Point cleaning –0.774 0.412 0.079 –0.742 0.689 0.293
Vegetation cover, % –0.017 0.027 0.516 –0.005 0.033 0.892
Season:Vegetation cover, ref. Spring            
  Summer:Vegetation cover 0.086 0.037 0.024 0.107 0.047 0.025
  Autumn:Vegetation cover 0.057 0.038 0.138 0.004 0.047 0.939
TWI, in thousands       0.549 0.221 0.014
Random effects   Variance Std.Dev.   Variance Std.Dev.
Stand:Worker   0.317 0.563   2.104 1.451
Worker   1.267 1.126   0.000 0.000
Residual   1.381 1.175   2.081 1.443
2

Fig. 2. Estimate of productive worktime consumption in early cleaning in spruce stands according to the EC1 model. Cleanings were applied in different seasons and with varying degrees of ground vegetation coverage. Vegetation cover was determined in the summer or autumn in all treatments, so not necessarily during or close to the application of the treatment. The main effects differed significantly between all seasons. The slope was statistically significant in the summer, but not in the autumn or spring.

Table 4. Mixed models for worktime consumption (model PCT1, total work hours ha–1), removal of density (model PCTden, Ln[trees ha–1]), and diameter of density (model PCTdiam, cm) in PCT in studied spruce stands (N = density, D0.15 = diameter at stump height). All the continuous independent variables are centered around the mean. P-values in bold represent statistically significant differences.
PCT1 PCTden a PCTdiam
Estimate Std. Error P-value Estimate Std. Error P-value Estimate Std. Error P-value
Intercept 10.698 0.521 <0.001 2.940 0.102 <0.001 1.368 0.062 <0.001
Season, ref. Spring                  
  Summer –1.000 0.410 0.016 –0.136 0.066 0.043 –0.091 0.062 0.146
  Autumn –0.291 0.421 0.490 –0.014 0.069 0.839 –0.112 0.064 0.082
EC-method, ref. Total cleaning                  
  Point cleaning b –1.456 0.622 0.028 –0.241 0.126 0.068 0.099 0.068 0.158
N before EC, 1000 trees ha–1 0.200 0.023 <0.001 0.041 0.004 <0.001 –0.012 0.003 <0.001
D0.15 before EC, cm –1.503 1.191 0.212 –0.125 0.218 0.566 –0.103 0.144 0.482
Random effects:   Variance Std.Dev.   Variance Std.Dev.   Variance Std.Dev.
Stand   1.244 1.116   0.007 0.086   0.007 0.086
Residual   3.725 1.930   0.086 0.293   0.086 0.293
a) Log-transformed dependent variable used in PCTden.
b) It should be noted that placement of experiment plots differed between EC methods (see discussion).
Table 5. Mixed model (Hremo) for height growth (cm year–1) of the removal between early cleaning (EC) and pre-commercial thinning (PCT) in spruce stands studied in southern Finland (N = density, D0.15 = diameter at stump height). All the continuous independent variables are centered around the mean. P-values in bold represent statistically significant differences.
Estimate Std. Error P-value
Intercept 34.190 1.814 <0.001
Season, ref. Spring      
  Summer 0.397 0.345 0.249
  Autumn 7.669 0.361 <0.001
EC-method, ref. Total cleaning      
  Point cleaning 2.554 2.338 0.286
Species, ref. Downy birch      
  Conifer –1.417 0.544 0.009
  Silver birch 0.432 0.461 0.349
  Aspen –5.966 1.015 <0.001
  Other 7.850 2.571 0.002
  Sorbus –2.025 0.438 <0.001
  Salix –1.795 0.615 0.004
N before EC, 1000 trees ha–1 –0.142 0.033 <0.001
D0.15 before EC, cm 2.367 1.514 0.118
Random effects:   Variance Std.Dev.
Stand   28.350 5.324
Residual   38.110 6.173
3

Fig. 3. Post early cleaning (EC) growth rate of the trees removed in pre-commercial thinning (mainly resprouts from EC) according to the season of application (A) or the method (B) of EC in the studied spruce stands. The error bars are the 95% confidence intervals of the parameter estimates compared to the reference category (Spring in A or Total in B) in the Hremo model.

4

Fig. 4. Post early cleaning growth rate of the trees of different species removed in pre-commercial thinning (mainly resprouts from EC) of the studied spruce stands (DoB = downy birch, SiB = silver birch). The error bars are the 95% confidence intervals of the parameter estimates compared to the reference category (DoB) in the Hremo model.

5

Fig. 5. The effect of the season of application of early cleaning (EC) on the time consumption of the juvenile stand management program in total working hours (twh) ha–1, i.e. on the combined time consumption of EC and 4–5 growing seasons later following pre-commercial thinning (PCT).

Table 6. Costs of early cleaning (EC) and pre-commercial thinning (PCT) with a nominal rate (0%) and with a 3% discounted rate in totally cleaned and point cleaned spruce stands when cleaning was applied in the spring, summer, or autumn. The present values have been calculated to the timepoint when the first EC treatment was applied in the spring.
  EC,
€ ha–1 		PCT,
€ ha–1 		Overall,
€ ha–1 		EC time,
years 		PCT time,
years
Nominal rate, 0%          
Spring - Total 266 326 592    
Summer - Total 365 295 661    
Autumn - Total 313 317 630    
Discount rate, 3%          
Spring - Total 266 312 578 0.00 4.40
Summer - Total 365 283 647 0.14 4.40
Autumn - Total 312 303 615 0.39 4.40