1

Fig. 1. Location of the study worksites in Finland. The long-term (1971–2000) average effective temperature sum is showed on the map.

Table 1. Main categorical characteristics of the worksites (N = 11 848) extracted from forest resources data (FRD) and used in modelling.
Year Month Site type a Main tree species b Soil preparation method
2008 359 Dec–Mar 216 OMT 1316 Pinus 3936 Spot mounding 4637
2009 454 Apr 522 MT 6812 Picea 6184 Disc trenching 2689
2010 737 May 1625 VT 1869 Betula pendula 869 No preparation 45
2011 863 Jun 1618 CT or ClT 82 Betula pubescens 812 Inverting 68
2012 1120 Jul 1304 Rhtkg or Mtkg 1335 Other broadleaves 47 Excavator patching 1392
2013 995 Aug 1793 Ptkg 397     Continuous patching 1380
2014 1574 Sep 1489 Vtkg or Jätkg 37 Establishment method Ditch mounding 1258
2015 1476 Oct 1967 Planting 9780 Not known 379
2016 1490 Nov 1314 JSM c Direct seeding 1358            
2017 1533 Yes 1418 Natural 710
2018 1247 No 10430    
a Oxalis–Myrtillus type (OMT), Myrtillus type (MT), Vaccinium type (VT), Calluna type (CT), and Cladonia type (ClT) on   mineral soils, and the corresponding site types on peatlands herb-rich type (Rhtkg), Myrtillus type (Mtkg), Vaccinium
  type (Ptkg), dwarf shrub type (Vtkg) and Cladonia type (Jätkg).
b Picea includes also Larix and Abies, and Pinus includes both P. sylvestris and P. contorta as main tree species.
c Juvenile stand management (JSM) has been done earlier or not.
Table 2. Main continuous characteristics of the worksites (N = 11 848) used in modelling.
  Min Median Mean   SD Max
TC, h ha–1 0.40 8.80 9.93 ± 5.47 46.10
Worksite area, ha 0.03 1.34 1.89 ± 1.75 27.73
Stand age, years 2 8 8.40 ± 3.40 24
Establishment delay a, years 0 1 0.58 ± 0.59 9
Stand age in previous JSM b, years 1 7 6.90 ± 2.00 13
Birch in final harvest, m3 ha–1 0 5 13.78 ± 0.24 233
Topographic wetness index 3891 6797 7263 ± 1872 21846
Temperature sum, d.d. 935 1147 1161 ± 98 1388
Latitude ETRS-TM35FIN 6699759   6930960     7331801
Longitude ETRS-TM35FIN 378536   596313     725856
a Time from the soil preparation to the artificial regeneration.
b Only worksites with previous juvenile stand management (JSM) (N = 1418).
Table 3. Worksite difficulty factors (N = 7595) determined in field-assessed worksite classification (WSC) by forestry officer or worker and used in modelling. Reference productivity guidelines for removal quantity (ha day–1), terrain difficulty (multiplier) and removal type (multiplier) classes are given in parentheses.
Removal quantity Terrain difficulty Removal type
Class N Class N Class N
Sparse (1.80 ha day–1) 444 Normal (1.00) 5590 Easy (1.00) 178
Easy (1.25 ha day–1) 1657 Difficult (0.95) 1836 Normal (0.95) 7286
Normal (0.67 ha day–1) 3779 Extreme (0.90) 169 Difficult (0.90) 131
Difficult (0.32 ha day–1) 1569        
Extreme (0.20 ha day–1) 146        
Table 4. Linear mixed model (Model 1, Eq. 1) for the time consumption (h ha–1) in pre-commercial thinning (PCT) based on forest resources data (N = 11 848). F-values were calculated to test the significance of the categorical variables in the model. The bias correction ratio was 1.0037.
Variable Estimate Std. Error t-value p
Intercept 2.0505 0.0425 48.2 < 0.001
JSM-program        
  Stand age, (years from estab.) 0.0614 0.0056 10.9 < 0.001
  Stand age^2, years –0.0009 0.0003 –3.2 0.001
  Previous JSM (1 yes, 0 No) 0.2625 0.0571 4.6 < 0.001
    Previous JSM * Stand age 0.0215 0.0074 2.9 0.004
    Previous JSM * Stand age in previous JSM –0.0672 0.0095 –7.1 < 0.001
ln(site area), ha –0.0582 0.0044 –13.2 < 0.001
Site type, ref MT     F = 108.0 < 0.001
  OMT 0.0598 0.0124 4.8 < 0.001
  VT –0.2535 0.0133 –19.0 < 0.001
  CT-ClT –0.5344 0.0450 –11.9 < 0.001
  Rhtkg-Mtkg 0.1731 0.0141 12.3 < 0.001
  Ptkg 0.0074 0.0231 0.3 0.747
  Vtkg-Jätkg –0.1973 0.0673 –2.9 0.003
Month, ref Aug.     F = 83.7 < 0.001
  Dec.-Mar. –0.1503 0.0286 –5.3 < 0.001
  Apr. –0.3440 0.0200 –17.2 < 0.001
  May. –0.2876 0.0137 –21.1 < 0.001
  Jun. –0.1626 0.0136 –12.0 < 0.001
  Jul. –0.0494 0.0144 –3.4 < 0.001
  Sept. –0.0795 0.0138 –5.7 < 0.001
Oct. –0.1457 0.0130 –11.2 < 0.001
  Nov. –0.1390 0.0145 –9.6 < 0.001
Ln(TWI), in tens of thousands 0.1491 0.0172 8.7 < 0.001
Establishment method, ref planting     F = 50.4 < 0.001
  Direct-seeding –0.1614 0.0162 –10.0 < 0.001
  Natural –0.0842 0.0182 –4.6 < 0.001
Establishment dealy, years 0.0388 0.0068 5.7 < 0.001
Site preparation, ref spot mounding     F = 14.4 < 0.001
  Disc trenching –0.0596 0.0131 –4.6 < 0.001
  No soil preparation –0.2030 0.0593 –3.4 < 0.001
  Inverting 0.0345 0.0514 0.7 0.502
  Excavator patching –0.0032 0.0140 –0.2 0.821
  Continuous patching –0.0512 0.0146 –3.5 < 0.001
  Ditch mounding 0.1083 0.0151 7.2 < 0.001
  Not known –0.0384 0.0241 –1.6 0.112
Main species, ref Picea     F = 13.2 < 0.001
  Pinus –0.0504 0.0096 –5.3 < 0.001
  Betula pendula 0.0335 0.0148 2.3 0.024
  Betula pubescens 0.0349 0.0154 2.3 0.023
  Other broadleaf 0.0829 0.0579 1.4 0.152
Random effects at: Variance      
    Forest worker level (N = 163) 0.0848      
    Year level (N = 11) 0.0047      
    Stand level (N = 11 848) 0.1506      
a JSM = juvenile stand management, TWI = topographic wetness index, TS = effective   temperature sum.
2

Fig. 2. Effect of stand age and the previous juvenile stand management (JSM) on the time consumption (TC) in pre-commercial thinning (PCT) done in August in planted spruce stand on spot mounded MT site type. Other predictor values fixed to: establishment delay = 0, TWI = 10 000, area = 1.5 ha.

3

Fig. 3. Relative time consumptions (TC) for PCT as functions of the size of worksite area (1 ha = 100) and topographic wetness index (TWI 10 000 = 100).

4

Fig. 4. Relative time consumption (TC) in PCT on different site types (MT = 100).

5

Fig. 5. The effect of the seasonal timing of PCT on the relative time consumption (TC) for PCT (August = 100).

Table 5. Fitting statistics of three models for the time consumption in PCT. Fitting statistics were based on back-transformed predictions. In the modelling data sets, the models were unbiased due to Snowdon’s bias correction.
    Model 1 (FRD) WSC-comparison
Modelling data set Validation data set Model 2 (WSC) Model 3 (FRD)
No. of worksites 11 848 3035 7595 7595
Bias, h ha–1 0 –0.16 0 0
Bias%, % 0 –1.6 0 0
RMSE, h ha–1 4.89 4.75 4.11 4.91
RMSE%, % 49.3 48.9 43.2 51.6
R2, % 19.9 19.3 43.0 18.6
Models 1 and 3 were fitted by using variables such as site and stand characteristics and previous silvicultural management available in forest resources data (FRD), whereas Model 2 was based on field-assessed worksite classification (WSC).
6

Fig. 6. Measured time consumption (TC) in PCT in validation data compared to forest resource data (FRD) based predictions (Model 1). Solid grey line is the regression line between the variables, dashed grey line is the diagonal where measured and predicted values are equal. The density of plots is illustrated with a density index as numbers of near neighbors (n_neighbors) and density based contours are marked with light grey contour lines each representing 20% share of the worksites, the outermost contour includes 80% of the observations.

Table 6. Linear mixed model (Model 2, Eq. 1) for the time consumption (h ha–1) in PCT based on worksite difficulty classification (N = 7595). F-values were calculated to test the significance of the categorical variables in the model. The bias correction ratio was 0.98302.
  Value Std.Error t-value p
Intercept 1.2410 0.0480 25.8 <0.001
Removal quantity class, ref Sparse     F = 2055.4 <0.001
   Easy 0.3574 0.0183 19.5 <0.001
   Normal 0.8298 0.0175 47.4 <0.001
   Difficult 1.2944 0.0190 68.2 <0.001
   Extreme 1.5388 0.0330 46.6 <0.001
Terrain difficulty class, ref Normal     F = 70.2 <0.001
   Difficult 0.1018 0.0101 10.1 <0.001
   Extreme 0.2202 0.0272 8.1 <0.001
Removal type class, ref Easy     F = 15.9 <0.001
   Normal 0.1396 0.0268 5.2 <0.001
   Difficult 0.1946 0.0399 4.9 <0.001
Random effects at: Variance      
   Forest worker level (N = 100) 0.1202      
   Year level (N = 5) 0.0006      
   Worksite level (N = 7595) 0.1089