Effects of forest management on timber production in Scots pine, Norway spruce and Silver birch stands in Moscow area based on forest ecosystem model simulations

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Faculty of Science and Forestry

EFFECTS OF FOREST MANAGEMENT ON TIMBER PRODUCTION IN SCOTS PINE, NORWAY SPRUCE AND SILVER BIRCH STANDS IN MOSCOW AREA BASED ON FOREST ECOSYSTEM MODEL SIMULATIONS

Kseniia Ulianova

MASTER THESIS IN FOREST SCIENCES CBU PROGRAM SPECILIZATION IN FOREST INFORMATION TECHNOLOGY AND RESOURCE MANAGEMENT

JOENSUU 2018

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Ulianova Kseniia. 2018. Effects of forest management on timber production in Scots pine, Norway spruce and Silver birch stands in Moscow area based on forest ecosystem model simulations. University of Eastern Finland, Faculty of Science and Forestry, School of Forest Sciences. Master’s thesis in Forest Science, 43 pages.

ABSTRACT

Russian forest sector has a great development potential. However, the currently applied forest management strategies may need to be modified in Russia in order to result in more favorable forest succession than nowadays and to increase the profitability of forestry. In this work, it was studied the effects of alternative forest management regimes on development of volume of growing stock and timber yield (pulp wood and saw logs) and its economic profitability (NPV with different interest rates) in Scots pine (Pinus sylvestris), Norway spruce (Picea abies) and Silver birch (Betula pendula) stands on different site fertility types under the current climate in Moscow area. This was done based on simulations with a forest ecosystem model (SIMA), which was originally developed in Finland (by Kellomäki et al. 2008), and calibrated in this study for the Moscow area.

In simulations, it was used as inputs, stand characteristics reported in old growth and yield tables, and Finnish management (thinning) recommendations for practical forestry and Russian management (thinning) recommendations. Additionally, no management (thinning) option was simulated. In all cases, the final felling (clear cut) was done at same stand age. Based on this work, it was aimed to find out if the current Russian forest management regulations are the most beneficial from the economical point of view or are there better management options available. As a result, the Finnish management recommendations resulted in higher amount of harvested timber and NPV compared to the Russian ones, regardless of tree species. When considering costs of forest regeneration and pre-commercial thinning in NPV calculations with high interest rate (7,25%), then no thinning regime (only final felling) became reasonable option. If considering lower interest rate (4%), then it may be reasonable to modify current Russian forest management (thinning) strategies. In future studies, it should also be taken into account risks associated with climate change and natural disturbances.

Keywords: boreal tree species, economic profitability, forest ecosystem model, rotation length, thinning regime, timber yield

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FOREWORD

I would like to thank professor Timo Karjalainen, who passed away in summer 2018, for helping to come up with this research idea, and professor Heli Peltola for supervising me during the work and Mr Harri Strandman for helping me with SIMA model calibration. I also appreciate the help of Shelkovsky training forest administration, who provided forest inventory data essential for this work.

Ulianova Kseniia

Joensuu, December 2018

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Forests in Russia cover about 8 mill. km² and over 20% of the global forest area (FAO 2015). Russian forests provide huge possibilities for wood harvesting and development of forest sector. However, since the Soviet Union times clear cut approach followed by insufficient thinning and regeneration practices have been widely used. As a consequence, the area of economically accessible forests has been reducing (Karjalainen et al. 2007, 2009). The lack of proper regeneration practices have also led to unfavorable forest succession, i.e. expansion of soft-laved deciduous tree species dominated stands (FAO 2012). Large availability of forest resources and market demand were favorable for such a development. However, new sustainability requirements ask for to improve existing management strategies.

In Moscow area, in forests hardwood tree species are dominant (about 58% of total growing stock volume), of which birches account 38%. The rest is softwood, the share of Norway spruce is 26% and that of Scots pine 21% of total volume of growing stock. The age structure of forests is following: 41 % of forests belong to the 2^nd (41 – 80 years for conifers, 21 – 40 years for deciduous species) age class, whereas 3^rd (81 – 120 years for conifers, 41 – 60 years for deciduous species) and 4^th (> 121 years for conifers, > 61 years for deciduous species) age classes accounts about 20% each, and 1^st (1 – 40 years for conifers, 1 – 20 years for deciduous species) age class only 15%, respectively (Forest plan of Moscow area 2008). Based on this, the current age structure of forests may not be optimal, which could be affected, however, by intensifying forest management.

Forest growth and, hence, also timber production and its economic profitability (NPV), are affected by many factors. First of all, prevailing climatic conditions and site fertility determine forest growth potential. However, various tree species have also different preferences to climatic and site conditions, which affect nutrient and water availability of trees, respectively. Thus, proper site-specific choice of tree species in forest regeneration, considering environmental (climate, site) conditions is essential (Melekhov 2003). Different forest management options are also available to improve the growth and structure of forests. Thinnings may be used to enhance growth of crop trees and to decrease natural mortality. Different rotation lengths may be used to achieve desired size (diameter) of trees at final cut.

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Management regimes used affect also the profitability of forestry. Reasonable shortening of rotation periods and use of commercial thinnings help to get earlier incomes, and may increase the profitability of forestry, especially in case of high interest rate. Though, forests are also vulnerable to many damages due to natural disturbances. In young seedling stands, especially frost, insects, and mammals, may cause damage. In older stands, there may exist wind- and snow induced damages, pests outbreaks and wood decay by pathogen species. Additionally, forest fires may cause damage regardless of forest age. All these risks could be at least partially diminished by means of appropriate forest management practices (Seidl et al. 2017, Reyer et al. 2017).

Forest management is in Russian Federation regulated by Forest code (so called forest law), and forest management rules, which contain region specific forest management recommendations. Those documents determine appropriate methods for forest regeneration, timing and intensity of thinning and rotation lengths. According to official statistics, 88% of reported forest regeneration activities in Moscow area are done artificially, however, resulting in an insufficient growth of the main tree species, especially Scots pine (Forest plan of Moscow area 2008). Artificial regeneration is done by planting of seedlings or seed sowing, latter one on sites with a weak development of grass cover. Natural regeneration includes protection of viable undergrowth of the main tree species, seed trees and soil preparation (by harrowing, scarification and moulding).

Recommendations for choice of regenerable tree species are following: pine and birch could be regenerated regardless of site fertility type, but spruce is preferable on more fertile sites. Then, tending of seedlings is recommended to be done to enhance crop trees growth, 2 or 3 times until 20 years age.

Commercial thinning operations could be done many times during rotation, however, at the latest 20 – 30 years before final felling (clear cut). Intensity of thinning varies from 10 to 30% of volume of growing stock, the actual value being determined by basal area thresholds. Rotation lengths vary depending on tree species, forest usage category and region. Generally there are two types of forest usage categories: protection forests and production forests. The main function of production forests is to produce high quality wood and other forest resources. Rotation length in this forest category is 80 – 100 years for conifers and 60 – 70 years for birch in a mixed forests zone (Ob ustanovlenii vozrastov rubok 2015). Forests related to protection category should be maintained primarily in order to preserve environmental, water-conservation, protective, sanitary-

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and-hygienic and other useful functions of forests (Forest code of Russian Federation 2006). These forests have longer rotation periods. All forests in Moscow area refer to protection category. Thus rotation length for conifers is 100 – 120 years and for birch 70 – 80 years (Forest plan of Moscow area 2008).

In general, recommendations for forest management practices in Russia and Finland, and particularly in Moscow area and Central Finland, are rather same. However, artificial regeneration is more common in Finland compared to natural regeneration.

Also there are differences in implementation of commercial thinnings. In Finland, they are less frequent, but more intensive. Rotation lengths are shorter in Finland, i.e. 70 – 80 years for conifers and 60 years for birch, depending on site fertility, e.g. in central Finland (Äijälä et al. 2014).

Forest management recommendations in Finland are affected by targets given for timber production (high saw log yield) and its profitability, considering interest rate of about 2-3%. Oppositely, there is no assessment of the cost-effectiveness of the proposed activities in Russian forest management plans (Karjalainen et al. 2007). Decisions made in forestry, based on old Growth and Yield tables and regulations, don’t take either into account economic profitability. Thus, to improve the profitability of forestry and to fulfill the sustainability requirements of forest management practices, new decision support tools are needed.

One of the goals mentioned in Forest plan of Moscow area, as published by the Federal forest agency, is to enhance forest productivity and profitability of forestry. Decision support tools may help to choose an appropriate management regime in a given forest ecosystem and economic conditions. The lack of decision support tools in forest management planning in Russia makes profitability analysis and comparison of different forest management options quite difficult.

However, impacts Russian forest management regulations may be possibly evaluated also using modeling tools developed in other countries, if they are suitable and calibrated to Russian conditions. The information available for past growth and yield tables for forests, e.g. in Moscow area, could be used to support the calibration of available forest growth and yield models in order to predict future forest growth and timber supply potential and economic profitability of different forest management options. So far, it has not been widely used growth simulators to predict timber yield in

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practical forestry in Russia. However, there have been some previous attempts to adapt existing foreign decision support tools for Russian conditions. For example, some previous attempts exist using Finnish MOTTI stand simulator, under “KARELIA ENPI CBC PROGRAMME 2007-2013” (Sukhanov et all. 2012).

1.2 Aims of the work

In this work, it was studied the effects of alternative forest management regimes on development of volume of growing stock and timber yield (pulp wood and saw logs) and its economic profitability (NPV with different interest rates) in Scots pine (Pinus sylvestris), Norway spruce (Picea abies) and Silver birch (Betula pendula) stands on different site fertility types under the current climate in Moscow area. This was done based on simulations with a forest ecosystem model (SIMA), which was originally developed in Finland (by Kellomäki et al. 2008), and calibrated in this study for the Moscow area. In simulations, it was used as inputs, stand characteristics reported in old growth and yield tables, and Finnish management (thinning) recommendations for practical forestry and Russian management (thinning) recommendations. Additionally, no management (thinning) option was simulated. In all cases, the final felling (clear cut) was done at same stand age. Based on this work, it was aimed to find out if the current Russian forest management regulations are the most beneficial from the economical point of view or are there better management options available.

2. MATERIALS AND METHODS

2.1 Outline for forest ecosystem model SIMA

In this study a gap-type forest ecosystem model, called SIMA (Kellomäki et al. 2008) was applied for simulation of forest stand dynamics. The model was developed for the main forest tree species (Scots pine, Norway spruce, birch spp) growing on upland forest sites in Finland between N 60° and N 70° latitudes and E 20° and E 32° longitudes. In SIMA model, environmental conditions of the site are described by temperature sum, soil moisture, nitrogen and light availability. Optimal conditions for growth and regeneration assume no shade and no limitations in soil moisture and nitrogen availability. Environmental factors are linked to the demographic processes (birth,

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growth, death) by multipliers as G = G0 x M1, …, Mn, where G is growth, G0 is growth under optimal conditions, and M1, …, Mn are multipliers of environmental conditions, i.e.

temperature sum, light conditions and soil water and nitrogen supply. At the same time such factors as atmospheric CO2 concentration, nitrogen deposition and diameter of tree at breast height (1.3 m above the ground) also affect tree growth. Height and mass of tree are calculated based on diameter.

Fig. 1 Outlines for forest ecosystem model SIMA used in the simulations (adopted from Kellomäki et al. 2008).

Growth responses of different tree species to the temperature sum are modeled according to tree species-specific minimum, optimum and maximum temperature sum values for growth. Temperature sum, site fertility type and input of litter and dead wood define amount of soil organic matter and available nitrogen. Soil moisture available for tree growth on different soil and site types is affected by the precipitation and evaporation.

Simulation of forest stand dynamics require initial stand density and average tree diameter and tree species as input data. Different management operations such as intensity and type of thinnings and timing of final cut need to be specified. Model calculations are based on Monte Carlo technique with one year time step and performed on an area of 100m². In this study, average simulation results were obtained using 50 iterations for simulations.

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2.2 Model calibration for Russian sites

The use of SIMA model for growth and yield predictions of forests in Moscow area required some changes in model parameters depending on geographical location. This was due to the fact, that Moscow area has lower latitude than Finland, so the sun radiation activity also differs. That affects the growth of trees.

The study area was - located in central Russia federal region in the North-East part of Moscow area. Forest inventory and climate data used in model calibration were taken from Shelkovsky training forest compartment managed by Moscow State Forest University.

Climate data represented by mean monthly temperatures and precipitations measured by the local meteorological station over the period 1962-1984. Climate of Shelkovsky area was characterized by warm summer and cold winter. Mean annual maximum temperature was in July (17,4°) and mean annual minimum in January (-10,3°).

Fig. 2 Average temperature and precipitation curves. Moscow area climate = solid line, Joensuu climate = dot line.

Precipitation is highest during the growing season from May to October (325mm on average). While mean annual amount of precipitation is around 549mm. The highest amount of precipitation refers to July. In general, climate of Moscow area is very similar to that of Joensuu, in Eastern Finland (Fig. 2). Thus, finally Joensuu climate was used in all calculations.

-15 -10 -5 0 5 10 15 20

I III V VII IX XI

Temperature, C

Months

Annual mean temperature

0 10 20 30 40 50 60 70 80 90

I III V VII IX XI

Precipitations, mm

Months

Annual mean precepitations

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The growth parameters used in SIMA model were originally estimated based on old growth and yield tables in Finland (Kellomäki et all. 1992). To make model calibration for Moscow area, the growth and yield tables representing main forest tree species of Moscow area (Scots pine, Norway spruce and Silver birch) were available from Shelkovsky training forest (see Appendx A). These growth and yield tables were based on data from sample plots established in 1936-1940 and 1946 and contain the information on stand characteristics such as average tree height and diameter, stand density, basal area and stand volume for every tenth year during rotation, starting from 20 years age for conifers and 10 years age for birch. The data was organized by tree species and yield classes (bonitet). To relate Russian forest stand classification based on yield classes to Finnish classification based on site fertility types it was used “Forestry organization and development project of Shelkovsky training forest compartment 1984- 1985”, which includes characteristics of forest undergrowth. As a help, it was used detailed description of Finnish site fertility types (Cajander 1926).

The model parameters were calibrated by comparison between SIMA simulation results and growth and yield table values. Simulations were initiated using initial stand data from growth and yield tables. As a result, some changes were done for potential growth, height and mortality parameters.

For the potential growth calculation, following equation was used:

PotGr = EXP(-1,307-1,643/(CO2/100))*DBH*EXP(DGRO*DBH) (1)

where DBH is the stem diameter at the breast height [cm] and DGRO is a species specific parameter, which has been modified (Table 1) and CO2 is atmospheric CO2 content (ppm).

Table 1. Species specific values of DGRO parameter before and after modifications.

Parameter DGRO Pine Spruce Birch

Initial value -0.0719 -0.0502 -0.0956

Modified value -0.0590 -0.0500 -0.0950

In a height growth equation, parameter C was modified (Table 2).

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where TS is local temperature sum (d.d), DBH is diameter at breast height (1,3 m form stem base), and A, B and C parameters.

Table 2. Species specific values of C parameter before and after modifications.

Parameter C Pine Spruce Birch

Initial value 0.4354000 0.6689000 0.7302000

Modified value 2.0856 2.94316 3.13986

For calculation of natural mortality, following equation was used

Mortality factor = (BA*density**a/37)/agemx(i) (3) where agemx(i) is a species specific parameter (for pine=350, spruce=180 and birch=110), BA is a basal area (m²ha^-1), density is a stand density (stems ha^-1) and a is a modified parameter, which was 0,7 before change and 1,05 after modification. According to the model design, tree dies if random number from 0 to 1 is smaller than factor.

2.3 Tree stands and climate data and management regimes used in simulations Climate and forest stand (initial) data used in simulations was the same as used for model calibration (see 2.2). To start simulations the initial stand data for pure even-aged Scots pine, Norway spruce and Silver birch stands growing on Oxalis - Myrtillus (OMT) and Myrtillus (MT) sites were used as input data. In total, the data represented 5 different forest stands.

Three alternative management scenarios: No management scenario, management according Finnish management guidelines and Russian forest management regulations were applied for each of 5 forest stands. In No management scenario (no thinning) only final cut was done. For Finnish recommendations for practical forestry (Äijälä et al.

2014) and Russian forest management regulations (Pravila uhoda za lesami 2017), timing, and intensity (and thus also frequency) of thinning operations were determined based on given thresholds for a basal area. Rotation lengths were taken according to

Height = 𝑇𝑆 1000

𝐶

𝑥 [1.3 + 𝐷𝐵𝐻²

𝐴 + 𝐵𝑥𝐷𝐵𝐻] (2)

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Russian forest management recommendations in all cases, i.e. 100 years rotation was used for spruce and pine and 70 year rotation for birch (Ob ustanovlenii vozrastov rubok 2015).

2.4 Data analysis

First, reliability of model calibration results was analyzed based on average, minimum and maximum difference in percents between simulated values and growth and yield table’s values for each stand characteristic. Also standard deviation of the average difference was calculated using the following formula:

where xi s the average difference [%], µ s the mean of average difference [%], N is the total number of values.

Secondly, differences between management regimes were analyzed based on intensity and number of thinnings (Finnish and Russian ones) and the average volume of growing stock, volume growth and mortality, as well as volume of harvested saw and pulp wood in thinnings and final cut. Stand volume, average tree height and diameter at the end of rotation were also compared in all regimes.

The productivity of forest stands under different management regimes were compared based on total amount of harvested wood. Economic profitability assessment was based on Net Present Value (NPV) using following equation:

NPV = ∑ Ct/(1+r)^t (5)

Where Ct is the net cash inflow during the period t; r = discount rate, and t = time of thinning or final cut. The net cash inflow was calculated as stumpage price of saw and pulp wood, for wood harvested during the thinning or final cut. For the calculations, an average prices for stumpage wood in Central Federal Region of Russia in spring 2018 were used (LesOnlajn 2018). Prices were found in rubbles and converted into euros at 73 rub to 1 euro conversion rate.

STD = 1

𝑁 (x_i−μ)²

N

i=1

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To consider uncertainties in Russian economy, two discount rates were applied in the profitability analysis. Current discount rate according to the Central Bank of Russian Federation is 7,25%, which is very high and not favorable for the long-term investments.

However, it is forecasted to decrease to the 4% rate in early 2019 (Trading economics 2018). It is still higher then typically used in Finnish forecasts, i.e. 2-3%.

Operational costs of commercial thinnings are higher than in final felling. Therefore, the price of thinning wood is 50% of that in final cut. Tending of seedlings was considered to be done manually by the forest owner, thus costs of these operations were excluded from calculations. Regeneration was also expected to happen naturally and its cost (incl.

possible soil preparation) were not considered. However, the average cost for tending of seedlings in Moscow area is about 200 euroha-1 (LesOnlajn 2014).

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3. RESULTS

3.1 Model calibration results

We used Growth and Yield tables (GYT) of main forest tree species, Scots pine, Norway spruce and Silver birch, from Moscow area to calibrate SIMA model (see Appendix A).

Development of mean height, breast height diameter, stand density, basal area and stem volume for each study case were simulated over rotation lengths given in GYT. Then average, maximum and minimum difference and standard deviation between predictions by SIMA and GYT values were calculated to compare them (Table 3). If SIMA gives higher values than GYT the difference >0, and vice versa.

On average, SIMA gives from 1,6% to 10,6% lower values of all above mentioned stand characteristics compared to GYT in Pine on OMT site. The difference is smaller on MT site, i.e. from 0.1% to 4.8%. In both cases the highest average difference was found in stem volume (Fig. 4). In spruce on OMT site the average SIMA results are from 4,1%

lower to 9,0% higher for different stand characteristics compared to GYT (Fig. 3). For birch on OMT site the average difference of SIMA results compared to GYT ranges from 13% lower to 15% higher values, and from 15% lower to 12% higher values for birch on MT site (Fig. 5).

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Table 3. Comparison of difference between SIMA simulation results and Growth and Yield table values for pine, spruce and birch on different site fertility types.

Pine OMT Average

difference, %

Min difference, %

Max difference, %

Standard dev.,

%

Height, m -6.50 0.39 -9.82 2.93

Diameter, cm -1.64 1.35 14.01 7.99

Stand density, stems ha^-1 -3.07 0.15 -24.03 11.58

Basal area, m²ha^-1 -6.18 -0.13 -11.66 3.91

Volume, m³ha^-1 -10.58 -0.80 -14.63 3.81

Pine MT

Height, m -1.93 0.46 -8.10 5.06

Diameter, cm -2.72 1.86 15.94 9.11

Stand density, stems ha^-1 -0.13 -0.46 -20.83 12.64

Basal area, m²ha^-1 -4.20 -1.72 -10.27 5.05

Volume, m³ha^-1 -4.84 0.28 -9.31 3.96

Spruce OMT

Height, m 9.01 2.03 15.39 4.64

Diameter, cm 2.65 0.76 18.22 7.75

Stand density, stems Fha^-1 -4.13 1.01 -27.24 14.90

Basal area, m²ha^-1 -0.94 -0.01 -10.63 4.25

Volume, m³ha^-1 5.89 -0.05 13.00 4.29

Birch OMT

Height, m 14.59 12.05 20.32 2.77

Diameter, cm -5.61 -0.50 -13.91 6.47

Stand density, stems ha^-1 -1.06 -2.36 -24.13 13.54

Basal area, m²ha^-1 -12.91 -9.10 -15.24 2.00

Volume, m³ha^-1 1.54 0.52 4.30 1.50

Birch MT

Height, m 6.89 -1.99 21.39 11.14

Diameter, cm -11.83 0.94 -29.73 13.40

Stand density, stems ha^-1 12.28 5.16 37.15 21.87

Basal area, m²ha^-1 -15.57 -7.23 -32.19 8.44

Volume, m³ha^-1 -10.10 3.33 -42.75 17.91

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Spruce OMT

0 5 10 15 20 25 30 35

20 40 60 80 100 120

Height, m

0 10 20 30 40 50

20 40 60 80 100 120

Diameter, cm

0 10 20 30 40 50 60 70

20 40 60 80 100 120

Density, (stems ha-1)*100

0 10 20 30 40 50

20 40 60 80 100 120

Basal area, m2ha-1

0 100 200 300 400 500 600 700

20 40 60 80 100 120

Volume, m3ha-1

Fig. 3 SIMA model calibration results for spruce on OMT site.

SIMA = dot line, GYT = solid line.

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Pine OMT Pine MT

Fig. 4 SIMA model calibration results for pine on OMT and MT sites. SIMA = dot line, GYT

= solid line.

0 5 10 15 20 25 30 35

20 40 60 80 100 120

Height, m

0 5 10 15 20 25 30 35

20 40 60 80 100 120 140

Height, m

0 10 20 30 40 50

20 40 60 80 100 120

Diameter, cm

0 10 20 30 40 50

20 40 60 80 100 120 140

Diameter, cm

0 10 20 30 40 50

20 40 60 80 100 120 Density,(stems ha-1) *100

0 10 20 30 40 50

20 40 60 80 100 120 140 Density, (stems ha-1) *100

0 10 20 30 40 50 60

20 40 60 80 100 120 Basal area, m2ha-1

0 10 20 30 40 50

20 40 60 80 100 120 140 Basal area, m2ha-1

0 200 400 600 800

20 40 60 80 100 120 Volume, m3ha-1

0 100 200 300 400 500 600 700

20 40 60 80 100 120 140 Volume, m3ha-1

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Birch OMT Birch MT

Fig. 5 SIMA model calibration results for birch on OMT and MT sites. SIMA = dot line, GYT = solid line.

0 5 10 15 20 25 30 35

10 20 30 40 50 60 70 80

Height, m

0 5 10 15 20 25 30 35

10 20 30 40 50 60 70 80

Height, m

0 5 10 15 20 25 30

10 20 30 40 50 60 70 80

Diameter, cm

0 5 10 15 20 25 30

10 20 30 40 50 60 70 80

Diameter, cm

0 2 4 6 8 10 12

1 2 3 4 5 6 7 8

Density, (stems ha-1)*1000

0 2 4 6 8 10 12

10 20 30 40 50 60 70 80 Density, (stems ha-1)*1000

0 5 10 15 20 25 30 35

10 20 30 40 50 60 70 80 Basal area, m2ha-1

0 5 10 15 20 25 30 35

10 20 30 40 50 60 70 80 Basal area, m2ha-1

0 100 200 300 400

10 20 30 40 50 60 70 80 Volume, m3ha-1

0 100 200 300 400

10 20 30 40 50 60 70 80 Volume, m3ha-1

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3.2 Case study results 3.2.1 Timing of thinnings

For pine and spruce, 100 years rotation length was applied. The precommercial thinnings were done at stand age of 22 years. In Finnish management regime, 3 commercial thinnings were done, regardless of site fertility type. In Russian one, thinnings were less intensive, but more frequent, thus 4 commercial thinnings were applied on OMT site. However, on MT site Russian management was very similar to Finnish one (Table 4).

On Pine OMT site in Finnish management regime, commercial thinnings were done at stand ages of 31, 53 and 75 years. In Russian one, commercial thinnings were applied at stand ages of 32, 44, 60 and 80 years. On Pine MT site in Finnish management regime, thinnings were done at stand ages of 36, 58 and 78 years. In Russian one they were done at stand ages of 37, 52 and 72 years. On Spruce OMT site, commercial thinnings were done at stand ages of 32, 55 and 76 years in Finnish management regime, and at stand ages of 32, 44, 56 and 76 years in Russian one.

For birch, 70 years rotation length was applied. The precommercial thinnings were done at 12 years stand age. In Finnish management regime only 2 commercial thinnings were done, while Russian one used 4 less intensive commercial thinning, regardless of site fertility type (Table 4).

On Birch OMT site in Finnish management regime commercial thinnings were done at stand ages of 20 and 43 years. In Russian one commercial thinnings were applied at stand ages of 17, 25, 35 and 50 years. On less fertile MT site in Finnish management regime commercial thinnings were done at stand ages of 28 and 51 years. In Russian case commercial thinnings were applied at stand ages of 22, 30, 40 and 55 years.

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Table 4. Timing and intensity of commercial thinning.

Simulation case

1st commercial thinning

2nd commercial thinning

3rd commercial thinning

4th commercial thinning age intensity, % age intensity, % age intensity, % age intensity, % Pine OMT, 100 years rotation period

Fin 31 30 53 35 75 40

Rus 32 20 44 20 60 20 80 20

Pine MT, 100 years rotation period

Fin 36 35 58 35 78 40

Rus 37 15 52 15 72 10

Spruce OMT, 100 years rotation period

Fin 32 30 55 40 76 40

Rus 32 15 44 15 56 15 76 15

Birch OMT, 70 years rotation period

Fin 20 30 43 30

Rus 17 20 25 20 35 10 50 20

Birch MT, 70 years rotation period

Fin 28 30 51 30

Rus 22 10 30 15 40 20 55 15

3.2.2 Volume growth and mortality

The average stand volume over rotation was highest under No management regime, regardless of tree species and site fertility type, as well as the average volume growth and mortality (average volume of dead wood) (Table 5).

On Pine OMT site under No management regime the average volume and volume growth were 333 m³ha^-1 and 13,3 m³ha^-1year^-1 , and the average volume of dead wood 6,2 m³ ha^-1year^-1 over a rotation. The corresponding average volume and volume growth values were 18-19% and 8-9% smaller in Finnish and Russian management regimes, respectively. Mortality was 63% smaller in Finnish and 56% smaller in Russian one. On Pine MT site under No management regime the average volume and volume growth were 289 m³ha^-1 and 11,5 m³ha^-1year^-1 . Russian one gave almost same results and Finnish one showed 18% and 5% smaller values, respectively. However, the average volume of dead wood was 6,2 m³ha^-1year^-1 in No management regime, and 62% less in Finnish one and 36% less in Russian one. On Spruce OMT site under No management case the average volume and volume growth were 346 m³ha^-1 and 14,9 m³ha^-1year^-1 ,

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and the average volume of dead wood was 6,3 m³ha^-1year^-1. Finnish one resulted in 21%, 17% and 68% smaller values and Russian one 9%, 8% and 44% smaller values, respectively.

On Birch OMT site under No management regime the average volume and volume growth were 209 m³ha^-1 of and 13,3 m³ha^-1year^-1 and the average volume of dead wood was 5,4 m³ha^-1year^-1. Average volume was 11% smaller and average volume growth and mortality values were 16-10% and 56-48% smaller in Finnish and Russian management regimes, respectively. On Birch MT site the corresponding values were 156 m³ha^-1, 10,7 m³ha^-1year^-1 and 3,9 m³ha^-1year^-1 . Finnish and Russian regimes resulted in 16-11%, 17- 7% and 59-41% smaller values, the lowest range related to Russian case.

Table 5. Average values of stand volume, volume growth and volume of dead wood.

Simulation case

Average volume, m³ha^-1/over rotation

Average volume growth, m³ha^-1year^-1

Average volume of dead wood, m³ha^-1year^-1 Pine OMT

Fin 274.3 12.2 2.3

Rus 268.7 12.1 2.7

NM 333.3 13.3 6.2

Pine MT

Fin 236.3 10.9 1.9

Rus 287.4 11.4 3.2

NM 289.0 11.5 5.0

Spruce OMT

Fin 272.5 12.4 2.0

Rus 313.8 13.7 3.5

NM 346.3 14.9 6.3

Birch OMT

Fin 186.2 11.2 2.4

Rus 186.1 12.0 2.8

NM 209.3 13.3 5.4

Birch MT

Fin 131.5 8.9 1.6

Rus 138.6 9.9 2.3

NM 155.8 10.7 3.9

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3.2.3 Development of volume, height, diameter and stand density

Stand density was highest under No management regime regardless of site type and tree species, which led to lowest final tree height and diameter (see Appendix C). However, situation was different considering the final stand volume.

The highest stand volume at the end of rotation on Pine OMT site was observed with No management regime, while volumes from Russian and Finnish management regimes were quite close to each other. Situation with height and diameter was opposite. Russian and Finnish management regimes led to 17% higher trees and 29% larger diameter at the end of rotation compared to No management regime. On Pine MT and Spruce OMT sites the highest stem volumes at the end of rotation were observed with Russian and No management regimes. Oppositely, height and diameter at the end of rotation were biggest in Finnish case. On Pine MT site Russian case resulted in 8% shorter trees and 17% smaller diameter compared to Finnish case. No management regime showed 18%

and 31% lower values, respectively. On Spruce OMT site, Russian management regime led to 6% shorter trees and 14% smaller diameter. No management regime resulted in 12% shorter trees and 25% smaller diameter.

On Birch sites, No management scenario resulted in the highest volume at the end of rotation. On OMT site Finnish and Russian management regimes produced almost equal volume. Height and diameter were oppositely higher in Finnish and Russian cases compared to No management regime on OMT site. Russian and Finnish management regimes led to 9% higher trees and 17% bigger stem diameter at the end of rotation. On MT the lowest stem volume represented by Finnish management regime. Oppositely, height and diameter were highest in Finnish case. Russian management regime led to 3% shorter trees and smaller diameter, and No management regime resulted in 11%

shorter trees and 18% smaller diameter at the end of rotation.

3.2.4 Amount of harvested timber and NPV Harvested timber

Finnish management regime resulted in highest total volume of harvested timber for pine on OMT site (762 m³ha^-1) and on MT site (692 m³ha^-1). Russian one gave 5% less timber on OMT site (723 m³ha^-1) and 11% less on MT one (616m³ha^-1). No management regime gave 32-34% lower values on OMT and MT sites (516 and 459 m³ha^-1), respectively (Fig. 7). Finnish and Russian management regimes gave equal saw and

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pulpwood percentages of total volume of wood (80% of saw wood and 20% of pulp wood), regardless of site fertility type. In No management regime, total volume consists of 89% of saw wood and 11% of pulp wood on OMT site and of 85% of saw wood and 15% of pulp wood on MT site.

On OMT site, in Finnish management regime and in Russian one, 27% of all harvested timber is saw wood (206 m³ha^-1 and 196 m³ha^-1) and around 17% pulp wood (121 m³ha^-1 and 130 m³ha^-1) from thinnings. While final cut brings approximately 52% (403 m³ha^-1 and 372 m³ha^-1) saw wood and 4% (31 m³ha^-1 and 25 m³ha^-1) pulp wood in Finnish management regime and in Russian ones. In No management regime all wood comes from final cut (459 m³ha^-1 of saw wood and 57 m³ha^-1 of pulp wood) (Fig. 6).

On MT site total amount of harvested timber in Finnish one consists of 25% of saw wood (175 m³ha^-1) and 20% of pulp wood (135 m³ha^-1) from thinning, and 51% (354 m³ha^-1) saw wood and 4% (29 m³ha^-1) pulp wood from final cut. Russian management regime differs mostly in distribution of volumes between thinnings and final cut. Shares of saw and pulp wood from thinnings and final cut in total amount of harvested wood are 6% of saw wood (34 m³ha^-1) and 14% of pulp wood (88 m³ha^-1) from thinning and 73% (451 m³ha^-1) saw wood and 7% (44 m³ha^-1) pulp wood from final cut. No management regime resulted in 388 m³ha^-1 of saw wood and 71 m³ha^-1 of pulp wood (Fig. 6).

On Spruce OMT site, Finnish management regime resulted in highest total volume of harvested timber for spruce (690 m³ha^-1). Russian one gave 6% less (651 m³ha^-1) and No management regime 33% less (464 m³ha^-1) (Fig. 7). Again saw and pulpwood percentages were almost same in Finnish and Russian management regimes (approximately 75% of saw wood and 25% of pulp wood). In No management regime total volume consists of 87% of saw wood and 13% of pulp wood.

In Finnish management regime total amount of harvested wood includes 26% of saw wood (180 m³ha^-1) and 18% of pulp wood (126 m³ha^-1) from thinning and 51% (354 m³ha^-1) saw wood, 5% (31 m³ha^-1) pulp wood from final cut. Russian management regime differs mostly in amount of saw wood in thinnings and final cut. Shares of saw and pulp wood from thinnings and final cut in total amount of harvested wood are 11%

of saw wood (73 m³ha^-1) and 20% of pulp wood (128 m³ha^-1) from thinning and 61%

(399 m³ha^-1) saw wood and 8% (51 m³ha^-1) pulp wood from final cut. No management regime resulted in 402 m³ha^-1 of saw wood and 62 m³ha^-1 of pulp wood (Fig. 6).

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On OMT site, Finnish and Russian management regimes resulted in highest total volume of harvested timber for birch (415 m³ha^-1). No management regime gave 24% less (314 m³ha^-1) (Fig. 7). Shares of saw and pulpwood in total volume of wood are equal in Finnish and Russian management regimes (62% of saw wood and 38% of pulp wood).

In No management regime total volume consists of 65% of saw wood and 35% of pulp wood.

On OMT site 3% of all harvested timber is saw wood in Finnish management regime and 7% in Russian one (12 m³ha^-1 and 30 m³ha^-1) and around 25% of pulp wood in both regimes (108 m³ha^-1 and 104 m³ha^-1) come from thinnings. While final cut brings 59%

and 55% (244 m³ha^-1 and 227 m³ha^-1) saw wood and 13% (50 m³ha^-1 and 53 m³ha^-1) pulp wood. In No management regime all wood comes from final cut (206 m³ha^-1 of saw wood and 109 m³ha^-1 of pulp wood) (Fig. 6).

On MT site, Finnish management regime resulted in smaller volume of harvested timber (358 m³ha^-1), than Russian one. Russian management regime gave 3% more (367 m³ha^-

1) and No management regime gave 19% less (296 m³ha^-1) (Fig. 7). However, percentages of saw and pulpwood in total volume of harvested timber are the same in Finnish and Russian scenarios (51% of saw wood and 49% of pulp wood). In No management regime total volume consists of 44% of saw wood and 56% of pulp wood.

About 1% of all harvested timber is saw wood (3 m³ha^-1 and 4 m³ha^-1) in Finnish management regime and in Russian one and around 28% is pulp wood (106 m³ha^-1 and 98 m³ha^-1) from thinnings. While final cut brings approximately 50% (184 m³ha^-1 and 182 m³ha^-1) saw wood and 20% (66 m³ha^-1 and 83 m³ha^-1) pulp wood. In No management regime all wood comes from final cut (129 m³ha^-1 of saw wood and 167 m³ha^-1 of pulp wood) (Fig. 6).

The NPV

The general tendency was that Finnish management regime resulted in highest NPV in all stands, except birch on MT site (Fig. 7). Also the more fertile sites show higher NPV.

The highest NPV was found on Pine OMT site regardless of interest rate.

The second highest NPV was obtained in spruce on OMT site, and the least profitable was birch on MT site.

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On Pine OMT site under 4% interest rate NPV for Finnish management accounts 1279 euros ha^-1. Russian one gave 7% less (1194 euros ha^-1) and No management 61% less (504 euros ha^-1). Under 7,25% interest rate Finnish one resulted in 415 eurosha^-1, and Russian and No management regimes gave 10% (375 euros ha^-1) and 90% less (43 euros ha^-1), respectively. On Pine MT site Finnish regime under 4% interest rate, accounts 1051 euros ha^-1, and Russian one 24% less (801 euros ha^-1) and No management 58% less (445 euros ha^-1). Under 7,25% interest rate, on Pine MT site Finnish one gave 293 eurosha^-1, and Russian one 40% less (175 euros ha^-1) and No management 87% less (38 euros ha^-1).

On Spruce OMT site, Finnish management regime resulted in 1117 euros ha^-1 under 4%

interest rate. Russian management regime gave 15% less (950 euros ha^-1) and No management regime 60% less (451 euros ha^-1). Under 7,25% interest rate results are following: 343 euros ha^-1 in Finnish one, and 25% (258 euros ha^-1) and 89% less (39 euros ha^-1) in Russian and No management regimes.

On birch stands under 4% interest rate results for Finnish and Russian management regimes were very similar. On OMT site Finnish one gave 763 euros ha^-1, while Russian gave 2% less (748 euros ha^-1) and No management regime 34% less (505 euros ha^-1). On MT site Finnish and Russian ones gave 571 euros ha^-1. No management regime gave 28% less (412 euros ha^-1). Under 7,25% interest rate results are following: 205 euros ha^-1 in Finnish one, and 6% less (194 euros ha^-1) in Russian one and 61% less (80 euros ha^-1) in No management regime on OMT site. On MT site, corresponding values were: 127 euros ha^-1 in Finnish one, and 6% less (120 euros ha^-1) in Russian and 49%

less (65 euros ha^-1) in No management. These results are without considering cost of tending of seedlings, as it was considered to be done by the forest owner. If consider tending costs for seedlings in the calculations, then some NPV values in Finish and Russian management regimes would be negative.

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0 200 400 600 800

Fin Rus NM Timber, m3ha-1

Pine OMT Final cut saw wood

Final cut pulp wood

Thinning saw wood

Thinning pulp

wood 0

200 400 600 800

Fin Rus NM Timber, m3ha-1

Pine MT Final cut saw wood

Final cut pulp wood

Thinning saw wood

Thinning pulp wood

0 200 400 600 800

Spruce OMT Final cut saw wood

Final cut pulp wood

Thinning saw wood

Thinning pulp

wood 0

100 200 300 400 500

Fin Rus NM Timber, m3 ha-1

Birch OMT

Final cut saw wood

Final cut pulp wood

Thinning saw wood

Thinning pulp wood

0 100 200 300 400

Birch MT Final cut saw wood

Final cut pulp wood

Thinning saw wood

Thinning pulp wood

Fig. 6 Comparison of shares of saw and pulp wood in thinning and final cut in all simulation cases.

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Fig. 7 Comparison of NPV and amounts of harvested timber (pulp wood, saw wood and total amount) in different simulation cases.

0 200 400 600 800

FIN RUS NM Timber, m3ha-1

Pine OMT

Pulp wood Saw wood

Total 0

500 1000 1500

FIN RUS NM

NPV, €ha-1

Pine OMT

NPV (4%), €/ha NPV

(7.25%), €/ha

0 200 400 600 800

Pine MT

Pulp wood Saw wood

Total 0

500 1000 1500

FIN RUS NM

NPV, €ha-1

Pine MT

(7.25%), €/ha

0 200 400 600 800

Spruce OMT

Pulp wood Saw wood

Total 0

500 1000 1500

FIN RUS NM

NPV, €ha-1

Spruce OMT

NPV (4%), €/ha NPV (7.25%), €/ha

0 100 200 300 400 500

Birch OMT

Pulp wood Saw wood

Total 2000

400 600 800 1000

FIN RUS NM

NPV, €ha-1

Birch OMT

(7.25%), €/ha

0 100 200 300 400

Birch MT

Pulp wood Saw wood

Total 0

200 400 600

FIN RUS NM

NPV, €ha-1

Birch MT

NPV (4%), €/ha NPV (7.25%), €/ha

Effects of forest management on timber production in Scots pine, Norway spruce and Silver birch stands in Moscow area based on forest ecosystem model simulations

EFFECTS OF FOREST MANAGEMENT ON TIMBER PRODUCTION IN SCOTS PINE, NORWAY SPRUCE AND SILVER BIRCH STANDS IN MOSCOW AREA BASED ON FOREST ECOSYSTEM MODEL SIMULATIONS

CONTENTS