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Table 2 Results calculated from 1000 stochastic simulations with the optimal values of decision variables for a risk neutral decision maker in different problem formulations when tree growth, ingrowth and timber price are stochastic (Det = deterministic optimization, Anti = stochastic anticipatory optimization, Ada = stochastic adaptive optimization)

From: Optimizing continuous cover management of boreal forest when timber prices and tree growth are stochastic

Number of the cutting Young spruce Mature spruce Young pine Mature pine
Det Anti Ada Det Anti Ada Det Anti Ada Det Anti Ada
  Cutting year
1st 20 20 21.9 0 0 4.6 20 20 25.3 0 0 5.6
2nd 35 35 42.1 20 15 19.6 35 35 47.4 15 15 21.2
3rd 55 50 62.9 55 45 30.8 55 50 70.3 45 25 39.2
  Diameter before cutting (cm)
1st 19.1 19.1 19.6 23.0 23.0 24.4 17.5 17.6 18.6 21.0 21.0 22.2
2nd 19.9 19.7 21.9 28.3 26.6 27.7 17.8 18.9 20.0 23.5 23.5 23.8
3rd 23.3 22.3 25.3 31.5 30.5 29.9 19.9 19.1 21.6 24.8 24.3 23.7
  Basal area before cutting (m2 · ha−1)
1st 35.7 35.7 37.1 28.1 28.1 31.3 32.1 32.2 35.8 25.1 25.1 28.1
2nd 29.5 27.1 33.6 21.6 19.8 19.7 26.7 26.6 33.2 16.4 19.4 19.5
3rd 27.6 25.7 29.0 26.7 21.6 14.9 27.6 26.0 32.0 23.9 14.1 19.6
  Basal area after cutting (m2 · ha−1)
1st 14.6 12.4 14.5 9.8 10.8 11.1 13.5 13.4 14.4 9.3 11.8 11.5
2nd 10.6 12.7 12.5 6.4 5.5 9.2 10.5 12.8 12.5 7.1 9.4 8.7
3rd 10.5 12.6 10.1 11.6 7.2 5.1 11.3 12.1 11.2 9.2 6.2 9.3
  Removed volume (m3 · ha−1)
1st 176 192 192 197 187 219 140 141 167 150 126 162
2nd 163 126 190 164 153 113 129 111 175 93 99 109
3rd 160 122 182 167 157 109 141 118 181 153 78 93
Total 499 440 564 528 497 441 410 370 523 396 303 364
  Average roadside saw log price obtained (€ · m−3)
1st 56.8 55.8 64.6 56.1 56.3 61.9 56.7 56.4 66.8 55.7 56.3 62.9
2nd 56.2 56.5 65.0 56.5 56.4 66.2 56.1 55.8 67.0 56.2 56.5 67.5
3rd 56.5 55.6 65.1 54.5 55.1 67.4 55.6 55.8 65.2 54.5 55.7 68.1