Table 1. The number of suitable planting spots (good and acceptable after additional compression) and the main characteristics on the 50 m2 sample plots in the modelling data (i.e., the sample plots with mineral soil, N = 656).
Variable Mean Std dev. Range
Planting spots per 50 m2 7.0 2.8 0–17
Thickness of humus, cm 6.5 2.4 2–20
N %
Logging residues Removed 400 61
Fresh 83 13
Dry 173 26
Site fertility a Rich 64 10
Medium 568 87
Sub-dry 24 4
Soil texture Coarse 8 1
Medium 474 72
Fine 174 27
Stoniness Stoneless 193 29
Stony 389 59
Very stony 74 11
Terrain Sloping 150 23
Flat 480 73
Valley 26 4
Wetness b Not affect 604 92
Affect 51 8
a Site fertility classes: rich, medium and sub-dry = Oxalis-Myrtillus, Myrtillus and Vaccinium site type, respectively, in the Finnish system of classification (Cajander 1926).
b Whether the soil was wet enough to affect regeneration or not.
Table 2. Linear mixed model (Eq. 1) for the number of suitable planting spots (i.e., good and acceptable after additional compression) on the 50 m2 sample plot. F-values are calculated to test the significance of the categorical variables in the model. The fitting statistics using both fixed and random effects are given in parentheses. The modelling data consist of 656 sample plots with mineral soil.
Variable Estimate Std err. t-value p
Intercept 5.741 0.941 6.10 <0.001
Slash conditions (ref. Dry) F = 3.16 0.056
   Fresh –0.956 0.543 –1.76 0.085
   Removed 0.339 0.438 0.78 0.451
Thickness of humus, cm –0.148 0.052 –2.84 0.005
Soil texture (ref. Fine) F = 3.17 0.043
   Medium –0.626 0.294 –2.13 0.034
   Coarse 0.979 0.983 1.00 0.320
Stoniness (ref. Very stony) F = 4.68 0.010
   Stony 0.444 0.339 1.31 0.191
   Stoneless 1.025 0.370 2.77 0.006
Terrain (ref. Valley) F = 11.74 <0.001
   Sloping 1.467 0.573 2.56 0.011
   Flat 2.233 0.551 4.05 <0.001
Random effect Variance
   Operator (N = 4) 0.010
   Forest estate (N = 29) 1.179
   Regeneration area (N = 66) 0.317
   Sample plot (N = 656) 5.502
Fitting statistics
   R2, % 13.6 (34.4)
   Bias, spots per 50 m2 0.06 (0.00)
   Bias%, % 0.8 (0.0)
   RMSE, spots per 50 m2 2.60 (2.27)
   RMSE%, % 37.4 (32.3)
1

Fig. 1. Predicted number of suitable planting spots ha–1 (i.e., good and acceptable for planting after additional compression) as a function of thickness of humus layer, if logging residues are removed, dry or fresh. Other predictors: A – flat (solid lines) or sloping (broken lines) terrain, medium soil texture and stony soil; B – stony (solid lines) or stoneless (broken lines) soil, flat terrain and medium soil texture.

Table 3. Logistic mixed model (Eq. 2) for the probability of successful mounding, i.e. the number of suitable planting spots on the 50 m2 sample plot is at least eight (1600 spots ha–1). F-values are calculated to test the significance of the categorical variables in the model. The modelling data consist of 656 sample plots with mineral soil.
Variable Estimate Std err. t-value p Exp(Est.)
Intercept 0.140 1.020 0.14 0.891 1.150
Slash conditions (ref. Dry) F = 5.22 0.006
   Fresh –1.255 0.548 –2.29 0.022 0.285
   Removed 0.326 0.433 0.75 0.453 1.385
Thickness of humus, cm –0.160 0.055 –2.89 0.004 0.852
Soil texture (ref. Fine) F = 1.55 0.213
   Medium –0.185 0.289 –0.64 0.523 0.831
   Coarse 2.066 1.323 1.56 0.119 7.894
Stoniness (ref. Very stony) F = 1.55 0.214
   Stony 0.158 0.328 0.48 0.630 1.171
   Stoneless 0.523 0.359 1.46 0.146 1.687
Terrain (ref. Valley) F = 6.71 0.001
   Sloping –0.007 0.640 –0.01 0.991 0.993
   Flat 0.824 0.618 1.33 0.183 2.280
Random effect Variance
   Operator (N = 4) 0.466
   Forest estate (N = 29) 0.443
   Regeneration area (N = 66) 0.235
Exp(Est.) is the exponentiated parameter estimate (i.e. odds ratio) for the corresponding variable.
2

Fig. 2. Predicted probability of successful mounding (i.e., ≥1600 suitable planting spots ha–1) as a function of thickness of humus layer, if logging residues are removed, dry or fresh. Other predictors: A – flat (solid lines) or sloping (broken lines) terrain, medium soil texture and stony soil; B – stony (solid lines) or stoneless (broken lines) soil, flat terrain and medium soil texture.

Table 4. Accuracy of the classification of the 50 m2 sample plots as successfully mounded, i.e. the number of suitable planting spots per plot is at least eight (1600 spots ha–1). The predicted categories have been calculated using the fixed part of the model (Eq. 2); the classification using both fixed and random effects are given in parentheses.
Observed Predicted Total Accuracy
Success Failure
Success 218 (218) 81 (81) 299 73% (73%)
Failure 173 (49) 184 (308) 357 52% (86%)
Total 391 (286) 265 (389) 656 61% (80%)
Table 5. Logistic mixed model (Eq. 2) for the probability that the quality of mound was good or acceptable for planting after additional compression. F-values are calculated to test the significance of the categorical variables in the model. The modelling data consist of 6217 mounds on 656 sample plots with mineral soil.
Variable Estimate Std err. t-value p Exp(Est.)
Intercept 1.263 0.435 2.91 0.004 3.536
Slash conditions (ref. Dry) F = 2.57 0.076
   Fresh –0.385 0.213 –1.81 0.070 0.680
   Removed 0.035 0.208 0.17 0.866 1.036
Thickness of humus, cm –0.055 0.020 –2.78 0.005 0.946
Soil texture (ref. Fine) F = 2.05 0.129
   Medium –0.227 0.120 –1.88 0.060 0.797
   Coarse 0.160 0.388 0.41 0.681 1.174
Stoniness (ref. Very stony) F = 1.96 0.141
   Stony 0.029 0.133 0.22 0.829 1.029
   Stoneless 0.203 0.144 1.41 0.159 1.225
Terrain (ref. Valley) F = 4.68 0.009
   Sloping 0.394 0.215 1.83 0.067 1.483
   Flat 0.560 0.207 2.71 0.007 1.751
Random effect Variance
   Operator (N = 4) 0.239
   Forest estate (N = 29) 0.225
   Regeneration area (N = 66) 0.034
   Sample plot (N = 656) 0.191
Exp(Est.) is the exponentiated parameter estimate (i.e. odds ratio) for the corresponding variable.
3

Fig. 3. Predicted probability of suitable mound as a function of thickness of humus layer, if logging residues are removed, dry or fresh. Other predictors: A – flat (solid lines) or sloping (broken lines) terrain, medium soil texture and stony soil; B – stony (solid lines) or stoneless (broken lines) soil, flat terrain and medium soil texture. Note that the lines for Slash removed and Dry slash are overlapping.

Table 6. Accuracy of the classification of the mounds as unsuitable or suitable (i.e., good or acceptable after additional compression) for planting. The predicted categories have been calculated using the fixed part of the model; the classification using both fixed and random effects are given in parentheses.
Observed Predicted quality of mound Total Accuracy
Unsuitable Suitable
Unsuitable 313 (1155) 1302 (460) 1615 19% (72%)
Suitable 452 (1703) 4150 (2899) 4602 90% (63%)
Total 765 (2858) 5452 (3359) 6217 72% (65%)