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Article

The Effect of Transition to Close-to-Nature Forestry on Growing Stock, Wood Increment and Harvest Possibilities of Forests in Slovakia

by
Martina Štěrbová
1,2,*,
Ivan Barka
1,
Ladislav Kulla
1 and
Joerg Roessiger
1
1
Forest Research Institute, National Forest Centre Zvolen, T. G. Masaryka 2175/22, 960 01 Zvolen, Slovakia
2
Faculty of Forestry, Technical University in Zvolen, T. G. Masaryka 24, 960 01 Zvolen, Slovakia
*
Author to whom correspondence should be addressed.
Land 2024, 13(10), 1714; https://doi.org/10.3390/land13101714
Submission received: 10 September 2024 / Revised: 16 October 2024 / Accepted: 17 October 2024 / Published: 19 October 2024
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Abstract

:
The aim of the study is to quantify the impacts of a possible transition to close-to-nature forestry in Slovakia and to compare the expected development of the total volume production, growing stock, merchantable wood increment and harvesting possibilities of forests in Slovakia with current conventional management using the FCarbon forest-growth model and available data from the Information System of Forest Management. The subject of the study was all forest stands available for wood supply (FAWS). The simulations were run in annual iterations using tree input data aggregated over 10-year-wide age classes. The calculation of wood increments was based on available growth models. In the business-as-usual (BAU) scenario, stock losses were based on the actual intensity of wood harvesting in the reference period 2013–2022. In the scenario of the transition to close-to-nature forest management, the losses were specifically modified from the usual harvesting regime at the beginning, to the target harvesting mode in selective forest at the end of the simulated period. With the modelling method used, a gradual increase in forest stocks occurred in both evaluated scenarios in the monitored period, namely by 10% in the case of BAU and by 23% in the case of close-to-nature forest management until 2050. In absolute mining volume, CTNF is by 5–10% lower than BAU management, with the difference gradually decreasing. The results show that the introduction of close-to-nature forest management will temporarily reduce the supply of wood to the market, but this reduction will not be significant and will be compensated by a higher total volume production, and thus also by increased carbon storage in forests.

1. Introduction

The rational use and consumption of wood plays a leading role in the circular bioeconomy and green economy [1], supports industry, maintains employment and contributes to rural development [2]. By storing carbon in forests and wood products and by replacing non-renewable raw materials, the forestry and timber industry (FTI) contributes significantly to mitigating the impacts of climate change [3].
The ongoing climate change negatively affects the stability of the primary production of wood in forests and its supply to the market. Warming increases the population dynamics and aggressiveness of biotic pests, and the intensity of extreme weather events, which synergistically causes extensive calamities in forests. For FTI, this means a threat to the stability of the supply of wood assortments, fluctuations and even a temporary collapse of the markets with some assortments—see the sales crisis with coniferous wood in 2019–2020, when its prices in Europe fell to half of the previous years. The impacts of disturbances represent an enormous economic loss for European forests [4]. Primary producers are confronted with increased costs for processing calamitous wood, with loss of production on calamitous clearings, and with increased costs for their reforestation. The reduction in value production of forests due to the expected changes in the representation of woody plants in favour of trees with less valuable wood will have long-term economic impacts. As a possible solution for stabilizing the volume and structure of wood production in the conditions of climate change, close-to-nature forestry (CTNF) is often mentioned in the literature, e.g., [5,6].
Slovakia is one of the countries with high proportion of forests (41% of the state territory) and an expected strong impact of climate change [7]. As a central European country with a high altitude range due to the presence of the Carpathian mountains, a wide range of climate change effects are expected, especially changes in forest growth and productivity due to increased frequency of weather extremes and pest and disease outbreaks, followed by changes in species distribution—elevational shifts in forest types driven by new climate conditions [8]. With respect to this, there is growing interest in the transition of actual forest management in the country, which still prefers even-aged stands managed by the shelter-wood system, to CTNF [6,9] as a measure to increase the adaptation potential of forest stands. However, the future effects of such a transition applied to substantial areas of Slovak forests on forest growth, productivity and a wide complex of ecosystem services and biodiversity are just to be evaluated [9,10,11].
According to the new legislation in Slovakia (§ 2 letter x) of Act no. 326/2005 Coll. on forests, CTNF mean cultivation and restoration procedures aimed at creating and growing forests with differentiated age, species, genetic and spatial structures as close as possible to those of a natural forest characteristic of the conditions of the given location; these procedures use natural processes to the maximum extent possible, especially the natural renewal of trees, the regenerative capacity of the forest ecosystem, the individual height and thickness growth of trees, the ability of self-reduction and the shape variability of forest trees.
This definition of CTNF is consistent with the principles of PRO SILVA, paying serious attention to (i.e., maintaining or restoring) the natural forest vegetation pattern, maintenance of soil productivity through continuous cover and through the maintenance of biomass in the forest (including dead wood), propagation of mixed forest with special attention to rare and endangered species, and restricting the use of exotics [12].
Multi-aged-forest management systems represent a promising approach for increasing resistance and resilience, thereby limiting major disruptions to timber production and other ecosystem services. Multi-aged stands inherently have greater resistance and resilience to disturbances because of the presence of several age classes and more potential pathways for post-disturbance management and recovery [13,14]. In one study, the economic recovery from disturbance in the continuous cover system was between 18.2 and 51.5% faster than in the clear fell system [15].
According to published foreign knowledge [16,17,18,19] and initial domestic experiences [20], CTNF is at least equivalent to conventional management in terms of wood production and profitability. By creating diversified, permanently diverse forests without clearcuts, it significantly reduces the risk of calamities and supports biodiversity and the fulfillment of other forest ecosystem services [21,22], as sources of future additional income for forestry. In a survey conducted in 2018 by Forest Europe, 11 out of 12 forestry-developed European countries indicated CTNF as a suitable measure for adapting forests to climate change [23].
Although they are in the minority, there are also studies stating that CTNF is not comparable in terms of production and economics to conventional management. These mainly come from Scandinavia, e.g., [24], but also from central European conditions, especially from the woodprocessing industry [25]. Currently, CTNF is carried out on approximately 4% of Slovakia’s forest area, i.e., on an area approaching 90 thousand ha within the pilot and demonstration objects of the ProSilva, especially on state lands. By 2030, the ambition is to fulfil the goal of the “Concept of close-to-nature forestry in Slovakia” [26] and to increase the area of CTNF to 25% of the area of Slovakia’s forests, i.e., to approx. 500 thousand ha. This is also in accordance with the objectives of the strategic document “National Forestry Program of the Slovak Republic for the period 2025–2030” (NFP SR) approved by the Slovak government [26].
Simulating the transition from traditional forest management to CTNF usually involves using forest growth models that account for complex, uneven-aged, mixed-species stands and natural regeneration processes [27]. Modelling is typically carried out on a single-stand level [28] or a set of representative stands [29]. These approaches require time-consuming and laborious simulations as well as an extrapolation of the model results to region or country level when the effects of transition at national scale are to be evaluated. To eliminate the above-mentioned steps, a modified forest growth model FCarbon, which utilizes summary data at national level for forest growth predictions, was aimed at evaluating possible effects of such a transition of forest management in central European conditions.
The aim of this study is as follows:
(1)
To use the FCarbon model and available data from the Information System of Forest Management to simulate the medium-term impacts of CTNF on the growing stock, wood increment and harvesting possibilities of forests in Slovakia;
(2)
To compare the impacts of transition to CTNF with the simulated medium-term impacts of current forest management and to verify the hypothesis of the equivalence of these two management systems in terms of wood production.

2. Materials and Methods

2.1. Study Design

The presented research was accomplished according to the design presented on Figure 1. Input data on forest cover from reference period 2013–2022 were stratified by tree species and age classes. On these data, the FCarbon model was calibrated and used to simulate future development of forests under actual management and CTNF. Results of the simulations were further processed, analysed and discussed.

2.2. Study Area

For modelling the development of forest characteristics, data for all forests available for wood supply (FAWS) in Slovakia, with a total area of 1,790,615 ha (91.6% of the total forest area of the country as of 31 December 2022), were used. These are all commercial forests, including some parts of forests for special purposes and protective forests, and excluding unmanaged forests of protected areas, forests in the protected zones of water resources, protective forests in extremely unfavourable habitats, and forest stands in the dwarf-pine zone at the upper timberline. The main tree species which were included in the simulation of CTNF and their share in Slovak forests (in %) are as follows: European beech (Fagus sylvatica L.) 35.4%, Norway spruce (Picea abies L.) 21.1%, oaks (especially Quercus robur L., Q. petraea Liebl. and Q. cerris L.) 12.9% and silver fir (Abies alba Mill.) 4.0%. Together, these species create 73.4% of Slovak forests. Other important tree species are Scots pine (Pinus sylvestris L.) with 6.4% and European hornbeam (Carpinus betulus L.) with 6%; however, they are not typical CTNF tree species. The share of other tree species like larch, maples, ash, elm or poplars does not exceed 3%.
This forest area is managed under Forest Management Plans (FMPs), and quantitative and qualitative information on individual forest stands (compartments) is stored in the Information System of Forest Management (ISFM), managed by the National Forest Centre (NFC).

2.3. Forest Growth Model

FCarbon, a relatively simple model simulating the future development of forests [30], was developed at NFC according to the methodology proposed by the Joint Research Center of the European Union [31,32], in connection with the application of EU Regulation 2018/841. The main reasons for the development of the FCarbon model, originally used for the prediction of the carbon sequestration in Slovak forests [33], were the requirements for consistency with the reporting of greenhouse gas emissions and the inclusion of age-related dynamic characteristics of the forest stands. The model is able to simulate on an annual basis the development of the forest age structure, changes in growing stocks through annual wood increments and harvesting rates (ratios of harvested wood to wood available for harvesting). The calculation of wood increment is based on yield tables [34,35], which determine increases in wood volume (in m3 of merchantable wood under bark) for the main tree species in Slovakia (spruce, fir, pine, beech, oak and hybrid poplar). The volumes of thinning and final harvesting are calculated using harvesting rates. The model is also able to simulate changes in the total forest area due to afforestation and deforestation, following the International Panel for Climate Change rules [36].
The FCarbon model requires the following input data, structured by tree species and 10-year-wide age classes: growing stock (m3), area (ha), yield class and harvesting rates of thinning, regeneration harvest and optionally sanitary felling (%).
When initializing the simulation, the model converts 10-year-wide age classes to 1-year-wide ones. While the area of the 10-year-wide age class is split equally among the 10 1-year-wide classes, the growing stock is divided according to the growing stock curve prescribed by yield tables. During each simulation step, the initial growing stock of the age class is increased by the calculated wood increment (value from the yield tables adjusted by stocking level) and decreased by harvested volume:
G S i = G S i − 1 + I i × L I M − G S i − 1 + I i × L I M × H r ¯ ,
where GSi is the growing stock of a 1-year-wide age class of a tree species in year i, GS(i−1) is the growing stock in the previous year, Ii is the current annual increment in year i (taken from yield tables), LIM is light increment modifier (see description of management scenarios) and Hr is the harvest ratio for the given age class of tree species; values of GS and I are in merchantable wood under bark.
The harvesting rate (mean harvest ratio) was calculated as the ratio of harvested volume from the wood volume available for harvesting (Equation (2)):
H r ¯ = ∑ i = 1 n H i G S i + H i n  
where H r ¯ is the mean annual harvest rate, Hi is the annual harvested volume in year i, GSi is the growing stock at the end of the year i, and n is the number of years in the reference period.
The harvested area in the case of the final harvest within the simulation step is transferred to the youngest age classes. In the case of thinning, the area of age class remains unchanged. If a ratio of natural regeneration is known (in case of the shelterwood regeneration system), the naturally regenerated area is transferred to the 5th age class (it is assumed that the rejuvenation in the stand during the final harvesting is 5 years old). The ratio of natural regeneration to artificial planting was summarized from the FMP data, where this ratio is estimated for each stand with planned regeneration harvesting in the next decade. The rest of the harvested area is used to create a new grade with an initial age of −1, which simulates the 2 years allowed for reforestation.
The FCarbon model is coded in Python 3.7 programming language with data stored in a SQLite database.

2.4. Model Calibration

For the purposes of this study, the model was calibrated using data for the 2013–2022 period. During this reference period, the total current annual increment of growing stock was approximately 11 × 106 m3 of merchantable wood under bark, and the annual harvests (total wood extraction) ranged from 7.6 × 106 to 10.2 × 106 m3. The aim of the calibration was to increase the accuracy of the simulation process and to reproduce FMP data as precisely as possible. The simulated increments of individual tree species were compared with the FMP data, and the ratios of the simulated values to the actual values were calculated. These ratios were used as multipliers for the volume increments determined from the yield tables. By calibration, the increment multipliers were adjusted to replicate the values of wood increment and harvests of simulated tree species. Trends of changes in tree species composition from reference period were applied during the whole simulation period. The harvested area was distributed among tree species according to these trends in each simulation step. Then, the calibrated model was used to simulate the development of the age structure of forests, growing stocks and harvests of wood in the period 2023–2050. The simulation itself started with data valid as of 31 December 2022.

2.5. Scenario Business as Usual (BAU)

The scenario with current management was based on the actual FMPs for all FAWS valid in the reference period 2013–2022. The main forest management system currently applied in Slovakia and simulated within the BAU scenario is the shelterwood system, with regeneration harvesting applied in strips with an area of up to 3.0 ha. Only in a minor extension in special cases is clearcut applied (poplar plantations, pine monocultures, damaged or low-quality forests under conversion).
For individual tree species and age classes, the annual harvesting rates were derived from the FMP data and forest management registry (FMR) from the reference period and applied to the entire projected period of 2023–2050.

2.6. Scenario Close-to-Nature Forestry (CTNF)

The CTNF scenario was simulated as a gradual transition from the initial state of BAU in 2022 to the target (quasi-equilibrium) state of CTNF for beech, oak, fir and spruce species after 50 years from the beginning of the simulation over the entire area overgrown by these tree species. The selective forest management system in general is simulated within the CTNF scenario, representing single-tree or group selection, as well as irregular small-scale shelterwood on areas of up to 0.2 ha.
For the simulations of the CTNF scenario, the basic harvest rate was set at the level of wood increment in the target (equilibrium) state of the uneven-aged forest for the basic (most represented) habitat/forest development type (FDT) of the given tree species for FAWS in Slovakia at an interest rate of 1% [11]. In order to approach the target growing stock and to the target tree species composition in the CTNF, harvest rates were further corrected: (1) in all age classes, by the coefficient of deviation of the average growing stock of tree species within an altitudinal zone determined by the national forest inventory [37] from the target growing stock of FDT with dominance of the given tree species [24]; for spruce [10]; and (2) in individual age classes, by the coefficient of deviation of the actual species composition (according to the Green Report 2023 [38]) from the prospective natural representation of tree species in the forests of Slovakia [39]. Since the transition to the CTNF target state will take 50 years on average, for a realistic forecast, the correction of harvest rates was spread over the entire simulated period by linear interpolation. The corrected harvest rates were applied from the age over 30 years, when the trees enter the growth phase of the stem. On the contrary to the BAU scenario, all harvests including thinning are treated like the final harvest, with the shift of harvested area to the youngest age class. For younger age classes, the simulation was identical to BAU management.
In the CTNF scenario, the increment of growing stock, taken from the yield tables, was increased by the so-called light increment, considered a benefit of CTNF and based on the stocking level of the stand [40] according to [41,42]. Mathematically, it was a multiplier (LIM in Equation (1)) with a value higher than 1. With an average stocking level of 0.7, the values of the light increment multiplier were 1.29 for oak, 1.32 for beech and 1.35 for fir and spruce. At lower stocking levels for beech, the values rose up to a value of 2.0; for other woods at both higher and lower stocking levels, the values gradually decreased to 1.0.
Other details of the simulations remained unchanged compared to the procedure of BAU management.

3. Results

Simulation outputs showed a gradual increase in growing stocks during the whole simulated period (Figure 2 and Table 1) in both evaluated scenarios, namely by 10% in the case of BAU and by up to 23% in the case of CTNF until 2050.
In the case of BAU, it is due to the continued increase in forest area, as well as lower wood removal compared to its increment. This is the continuation of the trend from the reference period. In the case of CTNF, the benefit of the light increment during the earlier and more intensive thinning of the stands during transition also plays a role, as well as an effort to approach the target growing stock of uneven-aged forest through management. Current growing stock, according to the above-mentioned sources, is lower than the target for most forest types (with the exception of oak forests).
The development of the wood increment (Figure 2) is interesting, as it is almost the same for both scenarios until 2040 and then diverges in favour of BAU. However, it reliably exceeds the value of 9 million m3 per year even in 2050 in both scenarios, which exceeds the level of current annual harvest.
Despite the increase in growing stocks, simulated harvests do not rise along with them. Especially in the case of BAU, harvest decrease is quite significant, in 2050 to the level of 90% from 2023. This is mainly related to the lower representation of age classes with harvest maturity compared to the initial state (Figure 3). For CTNF, this decrease is less significant, to the level of 96% from 2023 in 2050. However, in absolute volume of extraction, CTNF is slightly lower than BAU. With a stable development and an expected slight increase in production after the target growing stock is reached (the stock will no longer grow but will only be maintained), approximately the same volume of production can be expected in the more distant future after 2050 for both scenarios.
It can be seen from Figure 4 that the beech, as the most represented tree species, is the main cause of the difference in wood increments. The wood increment of beech grows faster after 2040 under BAU than under CTNF, which in turn can only be explained by the temporary constellation of a higher area of this species in age classes 4–6 with a culminating volume increment (Figure 5). In general, beech is a promising tree species for climate change with increasing growing stocks, increments and harvests under both scenarios.
The growing stock of oaks will also increase, especially in the CTNF scenario, due to its targeted support against other tree species of the oak zone (hornbeam, beech). The protection of oaks also explains its lower harvests at CTNF compared to BAU, but the difference is diminishing over time. We consider the decrease in growth in both scenarios to be temporary and related to today’s oak deficit in young stands (the relative lack of middle-aged classes with the most rapid growth).
Fir has relatively the highest growth rate and the fastest accumulation of stock in the CTNF scenario when compared to BAU of all analysed tree species. The wood increment is comparable or little bit higher in the CTNF scenario and tends to increase after 2030 for both scenarios. Logging is still lower as the stock grows, but will catch up with time and will very likely exceed the BAU scenario for this species later.
Spruce is the only tree species for which harvesting is higher in the CTNF than in the BAU scenario. This is due to its more intense elimination in CTNF in favour of other native species—fir and beech. Nevertheless, even under the CTNF scenario, spruce maintains a stock of 76% in 2050 and an increment of 60% of initial value in 2023.
A significant finding is that the total volume production, which represents the sum of growing stock and cumulated harvest in a given year within the simulation period, was for all main tree species higher in the CTNF scenario when compared to BAU. It shows the better carbon sequestration ability of CTNF. This difference increased over time, especially for fir, and to a lesser extent also for beech. On the contrary, the difference increased more slowly over time in the case of spruce and partially also for oak in the later periods (Figure 2).
The age structure of forests, represented by the area of age classes, changed during the simulation period in favour of the younger forests, while the area of the older forests decreased significantly in both scenarios (Figure 3). The main differences between the scenarios are in the young stands from the 1st to the 4th age class (i.e., forests younger than 40 years). While the BAU scenario tends to create a new peak of the 4th age class area, CTNF tends to create a more balanced age structure towards a downward curve of diameter distribution, typical for a steady state of an uneven aged forest.
Contributions of individual tree species to these changes are different, with the dominant impact being that of the most represented, beech (Figure 5). It should be emphasized that the trends in proportion of tree species in the youngest age stages are extrapolated from the calibration period without any additional active actions for the support of certain woody species, e.g., within adaptation to climate change. The proportion of beech in the youngest classes increases due to spontaneous natural regeneration. On the other hand, regeneration of oak and fir stagnates or declines due to damage caused by ungulate game. Spruce is a generally decreasing species.
A comparison with results of other studies (Figure 6) is discussed in the next section.

4. Discussion

4.1. Close-to-Nature Forestry and Wood Production

Long-lasting studies which compare CTNF with BAU under comparable conditions are rare but do exist. Regarding wood production, results with higher TVP of CTNF, similarly to this study, were provided by Pukkala et al. [43], who compared BAU in the form of even-aged rotation forest management (RFM) and CTNF (in the sense of single-tree selection) in two silvicultural experiments in southern Finland. The total volume production during a 75-year period was higher under CTNF compared to RFM, at 530 against 510 m3/ha in the Vessari experiment and 450 against 400 m3/ha in the Honkamäki experiment.
An example of a non-significant difference between CTNF and BAU was provided by a study from the Kreuzberg Municipal Forest (Germany, Eastern Bavaria). There, management units of RFM and CTNF are applied on comparable site conditions, both are dominated by nearly adult stands (mean age RFM 85 years, CTNF 77.5 years), and both are spruce/fir forests with 10% beech, but CTNF has a higher fir proportion. The growing stock in RFM is 387, in CTNF 378 m3/ha. The harvest in RFM is 4.90, in CTNF 4.58 m3/ha/year, but the harvest in CTNF consisted of trees with higher diameters and value [44].

4.2. Projections of Forest Management and Wood Production

There are also long-term projections comparing CTNF and RFM (BAU) managements. In Finland, there is a published [45] increase in stocking for even-aged BAU but decreasing productivity detected for CTNF in a projection until the year 2051.
A further 100-year simulation in Eastern Finland used Monsu simulation-optimisation software [46]. The timber drain in m3/ha is higher for CTNF compared to RFM by thinning from below, but comparable or slightly higher for RFM by thinning from above, for interest rates of 3% and 4%, but opposite for 2%. Carbon stock is increasing for CTNF and is decreasing for RFM compared to the initial state.
A further study [47] simulated stocking for 23 European countries with the European Forestry Dynamics Model. Despite its not presenting a scenario for CTNF, it expects stocking to peak or to stabilise between the years 2030 and 2040 in Slovakia. However, it assumes increased felling, although on a lower level (Vauhkonen 2019 [47] in Figure 6). The projections of stocking are higher compared to the starting value in the year 2023, and also higher than the other sources presented in Figure 6, because Vauhkonen et al. [47] as well as Šebeň [37] used national forest inventories including all forests, i.e., also forests on non-forest land.
Our simulations by the FCarbon model also lead to a continuously increasing growing stock curve, in contrast to extrapolated FMP time series by Moravčík (2009) [48], peaking in the year 2015. The main reason for this difference are the real harvest rates used in our study, and the planned harvest rate in the study [48]. The ratio of real harvest rates to planned harvest rates in the reference period was 0.92, i.e., 92% of the planned volume was harvested in reality.
Real growing stock in Slovakia up to the year 2022 is reported by official documents—green reports [38] peaked during the period 2018–2022 at 248.8 m3·ha−1 (Figure 6), and a decline is expected for the next several decades after a culminating surplus of older age classes. Nevertheless, the real development of harvests is volatile [37,38] and correlates closely with abiotic and biotic damage (the volume of realized harvests can vary in particular years by up to 15% from the average harvested volume during the reference period).
As Figure 6 illustrates, our projection of the development of growing stock in forests of Slovakia is quite optimistic, especially for the CTNF scenario, and of timber harvest generally more pessimistic than other available projections for the region. However, it is based on the real forest growth and real behaviour of forest managers in the calibration period, which may also reflect new trends in the support of ecosystem services, generally limiting timber harvesting to a certain extent.
While the European Climate Law aims at high-growing stocks to store carbon and the European Nature Restoration Law requires lower amounts of harvest to enable natural processes, the European Bioeconomy Strategy, in contrast, supports the use of wood to substitute energy-intensive non-renewable materials and thus implies higher harvest levels in order to achieve a considerable effect of such substitution. Therefore, our projections of increasing growing stock need to be interpreted with care. Projections using other harvest assumptions, models or data sources can lead to an opposite trend [26,35,36].

4.3. Species Specific Growth Dispositions for CTNF

Generally better growth of shade-tolerant tree species in uneven-aged, more layered stand structures is assumed, but relevant studies from the central European region empirically justifying this assumption are lacking. Our results are consistent with this assumption. Additionally, our results also correspond with findings of another study using the same harvest rate but based on Slovak National Forest Inventory data [49].
According to Roessiger et al. [49], in general, average tree growth is faster in CTNF more complex stand structures for the shade-tolerant tree species beech and fir. Contrarily, the light-demanding tree species spruce, oak and hornbeam grow faster in simple stand structures typical for BAU. The problem is more complex, and the increment of trees was, additionally to stand structure, significantly influenced by individual tree diameter and stand density.
Fir in CTNF grow strongly faster than BAU except at low stand density. In the case of the same stand density, oak and spruce grow faster in BAU, and beech grows faster in CTNF stand structure. The growth benefit of CTNF versus BAU generally increased with diameter for fir and beech, while this effect was not clear for light-demanding tree species [11].
The spruce harvest higher than increment (different from all other species) corresponds to high levels of damage by wind and bark beetles, with consequent unplanned salvage cuttings in Slovakia [38,50] similar to the wider region. Higher spruce harvest in CTNF compared to BAU resulted in the same spruce growing stock, representing a stabilisation of spruce by CTNF [49] and therewith avoiding the exclusion of spruce in the first age class in the year 2050 (Figure 5).

4.4. Close-to-Nature Forestry and Climate Change

An important advantage of CTNF is that it is based mostly on late-successional tree species which are expected to be “winners” of climate change [51]—diverse stands of silver fir, European beech with spruce admixture in mountains and oaks in lower elevations, while BAU relies on even-aged stands with more uniform species composition and higher portions of spruce and pine.
Yield tables [21,22], also used in our study, underestimate real growth. Pretzsch et al. [52] found stand productivity increased due to climate change, e.g., by 5–77% for beech compared to yield tables, suggesting that older yield tables currently recommend a too-low amount of thinning and improved increment caused by climate change. This justifies the need for calibration of FCarbon to make FMP data and projection based on them more realistic regarding climate change as well.
The results of our study are in line with other issues related to climate change. Promoting mixed stands is also important regarding the susceptibility of beech to drought [53,54,55] and other risks [56]. Natural regeneration and tree competition, as an integral part of CTNF, can contribute to preventing the risk of genetic maladaptation [57,58].

4.5. Close-to-Nature Forestry and Ecosystem Services

Last but not least, compared to BAU, CTNF produces better ecosystem services (e.g., the ability to store carbon, improve water quality), increases the safety of production [20,59,60,61] and ensures the protective functions of forests [62]. Many forest science studies conclude that CTNF may be the best way to compensate for natural disturbances and uncertainties [61,63,64,65]. As Pukkala [22] wrote, CTNF provides a higher amount of ecosystem services than BAU, namely carbon balance, berry yield, scenic beauty, nesting and feeding habitat area of the Siberian jay, and it is more efficient in the production of ecosystem services when optimizing financial outcomes. In addition, it seems evident that CTNF has a better carbon balance than even-aged forestry [66]. Pukkala [22] explains the better carbon balance of CNTF by the lower share of pulpwood in the harvested timber.

5. Conclusions

The results of our study indicate that the introduction of CTNF could cause an increase in the growing stock of 12% and in total wood production of 6% when compared with the BAU scenario until 2050. On the other hand, due to selective cutting and the tendency to approach the target growing stock, which is higher under CTNF than the mean growing stock under BAU, the absolute harvested volume will be 5–10% lower under CTNF in the same period, although the difference should decrease over time.
Fir and beech appear to be the most suitable wood species for CTNF in terms of wood production. While beech has good natural regeneration and ingrowth, essential for CTNF, the current management on which the model calibration was based has a problem with regeneration and ingrowth of fir. If fir is to be a key species of future forests under CTNF, it is necessary to solve the issue of its high damage by ungulate game and regeneration.
Results for oak are also promising, but more than for other species affected by unbalanced age structures with a surplus of old and a lack of young stands. The proportion of oak as a heat- and drought-tolerant species with valuable production in young stands will need to be increased under climate change, by supporting it by thinning and reducing wild boar populations.
The spruce is the only wood species with a decrease in growing stock, wood increment and harvests under both CTNF and BAU scenarios. The spruce is affected by high mortality rates in the reference period and this trend is expected to continue. Nevertheless, the CTNF scenario envisages the maintenance of an admixture of this species, which is interesting from the point of view of wood production in young stands.
The results show that the introduction of close-to-nature forest management might temporarily reduce the supply of wood to the market, but this reduction will not be substantial, and seems to be temporary. It should be compensated by more stable and more continuous forests with fewer calamities, higher total volume production, and thus also by increased carbon storage in forests as well as better assumptions for the fulfilment of other ecosystem services.

Author Contributions

Conceptualization: M.Š. and L.K.; methodology: L.K. and I.B.; software: I.B.; validation: L.K., I.B. and J.R.; formal analysis: I.B., L.K., J.R. and M.Š.; investigation: M.Š., I.B., L.K. and J.R.; resources: M.Š., L.K., I.B. and J.R.; data curation: I.B.; writing—original draft preparation: M.Š., L.K., I.B. and J.R.; writing—review and editing: M.Š., L.K., I.B. and J.R.; visualization: M.Š., I.B. and J.R.; supervision: M.Š.; project administration: M.Š., L.K. and I.B.; funding acquisition: M.Š., L.K. and I.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the following projects: research intention NFC 2022–2026 EPRIBLES within a contract between the National Forest Centre and the Ministry of Agriculture and Rural Development of the Slovak Republic and Slovak Research and Development Agency grants No. APVV-21-0290, APVV-20-0429, APVV-18-0195, APVV-20-0215.

Data Availability Statement

The datasets presented in this article are not readily available because the data are part of an ongoing study. Requests to access the datasets should be directed to the authors.

Acknowledgments

We are grateful for the financial support from the Ministry of Agriculture and Rural Development of the Slovak Republic and Slovak Research and Development Agency.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Hassegawa, M.; Van Brusselen, J.; Cramm, M.; Verkerk, P.J. Wood-based products in the circular bioeconomy: Status and opportunities towards environmental sustainability. Land 2022, 11, 2131. [Google Scholar] [CrossRef]
  2. Giurca, A.; Nichiforel, L.; Stăncioiu, P.T.; Drăgoi, M.; Dima, D.P. Unlocking Romania’s forest-based bioeconomy potential: Knowledge-aAction-aaps and the way forward. Land 2022, 11, 2001. [Google Scholar] [CrossRef]
  3. Cornelis van Kooten, G. The challenge of mitigating climate change through forestry activities: What are the rules of the game? Ecol. Econ. 2018, 146, 35–43. [Google Scholar] [CrossRef]
  4. Hanewinkel, M.; Cullmann, D.A.; Schelhaas, M.J.; Nabuurs, G.J.; Zimmermann, N.E. Climate change may cause severe loss in the economic value of European forest land. Nat. Clim. Chang. 2012, 3, 203–207. [Google Scholar] [CrossRef]
  5. Roitsch, D.; Abruscato, S.; Lovrić, M.; Lindner, M.; Orazio, C.; Winkel, G. Close-to-nature forestry and intensive forestry—Two response patterns of forestry professionals towards climate change adaptation. For. Policy Econ. 2023, 154, 103035. [Google Scholar] [CrossRef]
  6. Brang, P.; Spathelf, P.; Larsen, J.B.; Bauhus, J.; Boncina, A.; Chauvin, C.; Drössler, L.; García-Güemes, C.; Heiri, C.; Kerr, G.; et al. Suitability of close-to-nature silviculture for adapting temperate European forests to climate change. Forestry 2014, 87, 492–503. [Google Scholar] [CrossRef]
  7. Hlásny, T.; Trombik, J.; Dobor, L.; Barcza, Z.; Barka, I. Future climate of the Carpathians: Climate change hot-spots and implications for ecosystems. Reg. Environ. Chang. 2016, 16, 1495–1506. [Google Scholar] [CrossRef]
  8. Gubka, A.; Nikolov, C.; Gubka, K.; Galko, J.; Vakula, J.; Kunca, A.; Leontovyč, R. History, present and expected future of forests in Slovakia. Am. J. Plant Sci. 2013, 4, 711–716. [Google Scholar] [CrossRef]
  9. Báliková, K.; Šálka, J. Are silvicultural subsidies an effective payment for ecosystem services in Slovakia? Land Use Policy 2022, 116, 106056. [Google Scholar] [CrossRef]
  10. Renčo, M.; Gömöryová, E.; Čerevková, A. Close-to-nature forest management effects on soil nematodes and microbial activity in pine plantations on aeolian sands. Community Ecol. 2024, 1–12. [Google Scholar] [CrossRef]
  11. Roessiger, J.; Kulla, L.; Murgaš, V.; Sedliak, M.; Šebeň, V. Economically optimised target state of uneven-aged forest management for main forest types in Slovakia. Cent. Eur. For. J. 2023, 69, 233–247. [Google Scholar] [CrossRef]
  12. PRO SILVA Association. Pro Silva Principles; PRO SILVA Association: Zürich, Switzerland, 2012; 67p. [Google Scholar]
  13. O’Hara, K.L.; Ramage, B.S. Silviculture in an uncertain world: Utilizing multi-aged management systems to integrate disturbance. Forestry 2013, 86, 401–410. [Google Scholar] [CrossRef]
  14. Spiecker, H. Silvicultural management in maintaining biodiversity and resistance of forests in Europe—Temperate zone. J. Environ. Manag. 2003, 67, 55–65. [Google Scholar] [CrossRef] [PubMed]
  15. Knoke, T.; Paul, C.; Gosling, E.; Jarisch, I.; Mohr, J.; Seidl, R. Assessing the economic resilience of different management systems to severe forest disturbance. Environ. Resour. Econ. 2023, 84, 343–381. [Google Scholar] [CrossRef]
  16. Ammon, W. Das plenterprinzip in der schweizerischen forstwirtschaft; folgerungen aus 30 jahren bewirtschaftung von plenterwalden; Haupt: Bern, Switzerland, 1937; 108p. [Google Scholar]
  17. Knoke, T. The economics of continuous cover forestry. In Managing Forest Ecosystems—Continuous Cover Forestry, 2nd ed.; Pukkala, T., Gadow, K., Eds.; Springer: Dordrecht, The Netherlands, 2012; pp. 167–193. [Google Scholar]
  18. Schütz, J.P. Der Planterbetrieb; ETH: Zürich, Switzerland, 1989; 54p. [Google Scholar]
  19. Tarp, P.; Helles, F.; Holten-Andersen, P.; Bo Larsen, J.; Strange, N. Modelling near-natural silvicultural regimes for beech—An economic sensitivity analysis. For. Ecol. Manag. 2000, 130, 187–198. [Google Scholar] [CrossRef]
  20. Kulla, L.; Roessiger, J.; Kovalčík, M.; Murgaš, V.; Sedliak, M. Modelové porovnanie nákladov a výnosov prírode blízkeho a bežného hospodárenia v lesoch (Model comparison of costs and benefits of close-to-nature and conventional forest management). In Current Issues of the Economy and Forestry Policy of the Slovak Republic: Proceedings from the Scientific Conference; Šebeň, V., Sarvašová, Z., Eds.; National Forest Centre—Forest Research Institute: Zvolen, Slovakia, 2021; pp. 73–82. (In Slovak) [Google Scholar]
  21. O’Hara, K. What is close-to-nature silviculture in a changing world? For. Int. J. For. Res. 2016, 89, 1–6. [Google Scholar] [CrossRef]
  22. Pukkala, T. Which type of forest management provides most ecosystem services? For. Ecosyst. 2016, 3, 9. [Google Scholar] [CrossRef]
  23. NFC; MARD SR. Koncepcia Prírode Blízkeho Hospodárenia v Lesoch Slovenskej Republiky (The Concept of Close-to-Nature Management in the Forests of Slovakia); National Forest Centre: Zvolen, Slovakia, 2019; 31p. (In Slovak) [Google Scholar]
  24. Wikström, P. A solution method for uneven-aged management applied to norway spruce. For. Sci. 2000, 46, 452–463. [Google Scholar] [CrossRef]
  25. Václavík, J. Budoucnost lesnictví nemůže být postavená na mýtech (The future of forestry cannot be built on myths). Les-Letokruhy 2019, 75, 18–22. (In Czech) [Google Scholar]
  26. NFC; MARD SR. Národný Lesnícky Program Slovenskej Republiky pre Obdobie Rokov 2025–2030 (National Foresty Programme of the Slovak Republiky for the Period 2025–2030); National Forest Centre: Zvolen, Slovakia, 2024; 95p. (In Slovak) [Google Scholar]
  27. Bravo, F.; Fabrika, M.; Ammer, C.; Barreiro, S.; Bielak, K.; Coll, L.; Fonseca Fidalgo, T.; Kangur, A.; Löf, M.; Merganičová, K.; et al. Modelling approaches for mixed forests dynamics prognosis. Research gaps and opportunities. For. Syst. 2019, 28, eR002. [Google Scholar] [CrossRef]
  28. Schneider, R.; Franceschini, T.; Duchateau, E.; Bérubé-Deschênes, A.; Dupont-Leduc, L.; Proudfoot, S.; Power, H.; de Coligny, F. Influencing plantation stand structure through close-to-nature silviculture. Eur. J. For. Res. 2021, 140, 567–587. [Google Scholar] [CrossRef]
  29. Caicoya, A.T.; Poschenrieder, W.; Blattert, C.; Eyvindson, K.; Hartikainen, M.; Burgas, D.; Mönkkönen, M.; Uhl, E.; Vergarechea, M.; Pretzsch, H. Sectoral policies as drivers of forest management and ecosystems services: A case study in Bavaria, Germany. Land Use Policy 2023, 130, 106673. [Google Scholar] [CrossRef]
  30. Barka, I. FCarbon. 2024. Available online: https://web.nlcsk.org/?page_id=17445 (accessed on 20 August 2024).
  31. Grassi, G.; Pilli, R. Projecting Forest GHG Emissions and Removals Based on the “Continuation of Current Forest Management”: The JRC Method. EUR 28623 EN; Publications Office of the European Union: Luxembourg, 2017; 26p. [Google Scholar]
  32. Forsell, N.; Korosuo, A.; Federici, S.; Gusti, M.; Rincón-Cristóbal, J.J.; Rüter, S.; Sánchez-Jiménez, B.; Dore, C.; Brajterman, O.; Gardiner, J. Guidance on Developing and Reporting Forest Reference Levels in Accordance with Regulation (EU) 2018/841; European Commision: Brussels, Belgium, 2018; 120p. [Google Scholar]
  33. Barka, I.; Priwitzer, T.; Hušťáková, E. National Forestry Accounting Plan of the Slovak Republic Including a Proposed Forest Reference Level; Ministry of Agriculture and Rural Development: Bratislava, Slovakia, 2018; 97p.
  34. Halaj, J.; Petráš, R. Rastové Tabuľky Hlavných Drevín (Growth Charts of the Main Trees); Slovak Academic Press: Bratislava, Slovakia, 1998; 325p. (In Slovak) [Google Scholar]
  35. Petráš, R.; Mecko, J. Rastové Tabuľky Topoľových Klonov (Poplar Clone Growth Charts); Slovak Academic Press: Bratislava, Slovakia, 2005; 136p. (In Slovak) [Google Scholar]
  36. IPCC. IPCC Guidelines for National Greenhouse Gas Inventories; Eggleston, H.S., Buendia, L., Miwa, K., Ngara, T., Tanabe, K., Eds.; IGES: Hayama, Japan, 2006; Available online: https://www.ipcc-nggip.iges.or.jp/public/2006gl/ (accessed on 22 August 2024).
  37. Šebeň, V. Národná inventarizácia a monitoring lesov Slovenskej republiky 2015–2016. Informácie, metódy, výsledky (National Forest Inventory of the Slovak Republic 2015–2016. Information’s, Methods, Results); National Forest Centre: Zvolen, Slovakia, 2017; 255p. (In Slovak)
  38. MARD SR; NFC. Report on Forest Management in the Slovak Republic for the Year 2022 (Green Report); Ministry of Agriculture and Rural Development of the Slovak Republic: Bratislava, Slovakia, 2023; 66p. [Google Scholar]
  39. Vladovič, J. Oblastné Východiská a Princípy Hodnotenia Drevinového Zloženia a Ekologickej Stability Lesov Slovenska (Regional Starting Points and Principles of Assessment of Wood Species Composition and Ecological Stability of Forests in Slovakia); Príroda: Bratislava, Slovakia, 2003; 160p. (In Slovak) [Google Scholar]
  40. Machanský, M. Metodický Postup Kalkulácie Zásoba—Ťažba—Prírastok pre Alternatívne Spôsoby Obhospodarovania Lesov (Methodical Procedure for Calculating Stock—Extraction—Increase for Alternative Methods of Forest Management); National Forest Centre: Zvolen, Slovakia, 2017; 53p. (In Slovak) [Google Scholar]
  41. Šmelko, Š. Dendrometria (Dendrometry); Technical University in Zvolen: Zvolen, Slovakia, 2000; 399p. (In Slovak) [Google Scholar]
  42. Fabrika, M.; Pretzsch, H. Analýza a Modelovanie Lesných Ekosystémov (Analysis and Modeling of Forest Ecosystems); Technical University in Zvolen: Zvolen, Slovakia, 2011; 599p. (In Slovak) [Google Scholar]
  43. Pukkala, T.; Lähde, E.; Laiho, O. Continuous cover forestry in Finland—Recent research results. In Continuous Cover Forestry. Managing Forest Ecosystems; Pukkala, T., von Gadow, K., Eds.; Springer: Dordrecht, The Netherlands; Heidelberg, Germany; London, UK, 2012; pp. 85–128. [Google Scholar]
  44. Knoke, T. Ökonomische aspekte der holzproduktion in ungleichaltrigen Wäldern: Einführende untersuchungen zur forstbetriebsplanung im kreuzberger gemeindewald. Economic aspects on timber production in uneven-aged forests: Preliminary studies on forest management planning in the Kreuzberg Municipal Forest. Forstwiss. Cent. 1997, 116, 178–196. [Google Scholar]
  45. Räty, M.; Juutinen, A.; Korhonen, K.T.; Syrjänen, K.; Kärkkäinen, L. EU wood production vs. biodiversity goals—Possible reconciliation in Finland? Scand. J. For. Res. 2023, 38, 287–299. [Google Scholar] [CrossRef]
  46. Díaz-Yáńez, O.; Pukkala, T.; Packalen, P.; Peltola, H. Multifunctional comparison of different management strategies in boreal forests. Forestry 2019, 93, 84–95. [Google Scholar] [CrossRef]
  47. Vauhkonen, J.; Berger, A.; Gschwantner, T.; Schadauer, K.; Lejeune, P.; Perin, J.; Pitchugin, M.; Adolt, R.; Zeman, M.; Johannsen, V.K.; et al. Harmonised projections of future forest resources in Europe. Ann. For. Sci. 2019, 76, 79. [Google Scholar] [CrossRef]
  48. Moravčík, M.; Konôpka, J.; Tutka, J.; Burgan, K.; Zúbrik, M.; Balogh, P. Vízia, Prognóza a Stratégia Rozvoja Lesníctva na Slovensku (Vision, Prognosis and Strategy for the Development of Forestry in Slovakia); National Forest Centre: Zvolen, Slovakia, 2009; 172p, Available online: https://www.forestportal.sk/wp-content/uploads/2022/06/VIZIA-PROGNOZA-A-STRATEGIA-ROZVOJA-LESNICTVA-NA-SLOVENSKU.pdf (accessed on 19 August 2024). (In Slovak)
  49. Roessiger, J.; Kulla, L.; Murgaš, V.; Sedliak, M.; Šebeň, V.; Štefančík, I. Aké rozdiely medzi PBHL a bežným hospodárením ukazujú údaje? (What differences between near-natural and business-as-usual show the data? In Current Issues of the Economy and Forestry Policy of the Slovak Republic: Proceedings from the Scientific Conference; Šebeň, V., Sarvašová, Z., Eds.; National Forest Centre—Forest Research Institute: Zvolen, Slovakia, 2023; pp. 151–161. (In Slovak) [Google Scholar]
  50. Hlásny, T.; Zimová, S.; Merganičová, K.; Štěpánek, P.; Modlinger, R.; Turčáni, M. Devastating outbreak of bark beetles in the Czech Republic: Drivers, impacts, and management implications. For. Ecol. Manag. 2021, 490, 119075. [Google Scholar] [CrossRef]
  51. Dyderski, M.K.; Paź, S.; Frelich, L.E.; Jagodziński, A.M. How much does climate change threaten European forest tree species distributions? Glob. Chang. Biol. 2018, 24, 1150–1163. [Google Scholar] [CrossRef]
  52. Pretzsch, H.; Biber, P.; Schütze, G.; Uhl, E.; Rötzer, T. Forest stand growth dynamics in Central Europe have accelerated since 1870. Nat. Commun. 2014, 5, 4967. [Google Scholar] [CrossRef]
  53. Zimmermann, J.; Hauck, M.; Dulamsuren, C.; Leuschner, C. Climate warming-related growth decline affects Fagus sylvatica, but not other broad-leaved tree species in central European mixed forests. Ecosystems 2015, 18, 560–572. [Google Scholar] [CrossRef]
  54. Dulamsuren, C.; Hauck, M.; Kopp, G.; Ruff, M.; Leuschner, C. European beech responds to climate change with growth decline at lower, and growth increase at higher elevations in the center of its distribution range (SW Germany). Trees 2016, 31, 673–686. [Google Scholar] [CrossRef]
  55. Hlásny, T.; Mátyás, C.; Seidl, R.; Kulla, L.; Merganičová, K.; Trombik, J.; Dobor, L.; Barcza, Z.; Konôpka, B. Climate change increases the drought risk in Central European forests: What are the options for adaptation? Cent. Eur. For. J. 2014, 60, 5–18. [Google Scholar] [CrossRef]
  56. Hlásny, T.; Barka, I.; Roessiger, J.; Kulla, L.; Trombik, J.; Sarvašová, Z.; Bucha, T.; Kovalčík, M.; Čihák, T. Conversion of Norway spruce forests in the face of climate change: A case study in Central Europe. Eur. J. For. Res. 2017, 136, 1013–1028. [Google Scholar] [CrossRef]
  57. Frank, A.; Howe, G.T.; Sperisen, C.; Brang, P.; St. Clair, J.B.; Schmatz, D.R.; Heiri, C. Risk of genetic maladaptation due to climate change in three major European tree species. Glob. Chang. Biol. 2017, 23, 5358–5371. [Google Scholar] [CrossRef] [PubMed]
  58. Leites, L.; Benito Garzón, M. Forest tree species adaptation to climate across biomes: Building on the legacy of ecological genetics to anticipate responses to climate change. Glob. Chang. Biol. 2023, 29, 4711–4730. [Google Scholar] [CrossRef]
  59. Bruchánik, R. LESY SR, š.p. a ProSilva—Spoločne k Prírode Blízkemu Obhospodarovaniu Lesa; (LESY SR, š.p. and ProSilva—Together for Close-to-Nature Forestry); Lesy SR, š.p.: Banská Bystrica, Slovakia, 2019; 154p. (In Slovak) [Google Scholar]
  60. Pommerening, A. A review of the history, definitions and methods of continuous cover forestry with special attention to afforestation and restocking. Forestry 2004, 77, 27–44. [Google Scholar] [CrossRef]
  61. Schütz, J.P. Der Plenterwald und Weitere Formen Strukturierter und Gemischter Wälder; Parey: Berlin, Germany, 2001; 207p. [Google Scholar]
  62. Boncina, A. History, current status and future prospects of unevenaged forest management in the Dinaric region: An overview. Forestry 2011, 84, 467–478. [Google Scholar] [CrossRef]
  63. Ciancio, O.; Iovino, F.; Menguzzato, G.; Nicolaci, A.; Nocentini, S. Structure and growth of a small group selection forest of calabrian pine in Southern Italy: A hypothesis for continuous cover forestry based on traditional silviculture. For. Ecol. Manag. 2006, 224, 229–234. [Google Scholar] [CrossRef]
  64. Huth, F.; Wagner, S. Ökosystemleistungen von Dauerwäldern—Eine aktuelle Analyse des Waldbaus. Schweiz. Z. Forstwes. 2013, 164, 27–36. [Google Scholar] [CrossRef]
  65. Knoke, T. Zur finanziellen attraktivität von dauerwaldwirtschaft und überführung: Eine Literaturanalyse | On the financial attractiveness of continuous cover forest management and transformation: A review. Swiss For. J. 2009, 160, 152–161. [Google Scholar] [CrossRef]
  66. Pukkala, T. Does biofuel harvesting and continuous cover management increase carbon sequestration? For. Policy Econ. 2014, 43, 41–50. [Google Scholar] [CrossRef]
Land 13 01714 g001
Figure 1. Flowchart of the research design process.
Figure 1. Flowchart of the research design process.
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Figure 2. Simulated growing stock (GS), total volume production (TVP) and annual wood increment (Inc) and harvests (Har) of managed forests in Slovakia (FAWS) in close-to-nature forestry (CTNF) and business-as-usual (BAU) management scenarios.
Figure 2. Simulated growing stock (GS), total volume production (TVP) and annual wood increment (Inc) and harvests (Har) of managed forests in Slovakia (FAWS) in close-to-nature forestry (CTNF) and business-as-usual (BAU) management scenarios.
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Figure 3. Simulated age structure of managed forests in Slovakia (FAWS) represented by area of age classes in the beginning (initial) and at the end of simulation period for scenario BAU and CTNF (100% transition).
Figure 3. Simulated age structure of managed forests in Slovakia (FAWS) represented by area of age classes in the beginning (initial) and at the end of simulation period for scenario BAU and CTNF (100% transition).
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Figure 4. Simulated growing stock (GS), total volume production (TVP) and annual wood increment (Inc) and harvests (Har) for main tree species under close-to-nature forestry (CTNF) and business-as-usual (BAU) management scenarios within managed forests in Slovakia.
Figure 4. Simulated growing stock (GS), total volume production (TVP) and annual wood increment (Inc) and harvests (Har) for main tree species under close-to-nature forestry (CTNF) and business-as-usual (BAU) management scenarios within managed forests in Slovakia.
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Figure 5. Simulated age structure of main tree species within managed forests in Slovakia (FAWS) represented by area of age classes in the beginning (initial) and at the end of simulation period for scenario BAU and CTNF (100% transition).
Figure 5. Simulated age structure of main tree species within managed forests in Slovakia (FAWS) represented by area of age classes in the beginning (initial) and at the end of simulation period for scenario BAU and CTNF (100% transition).
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Figure 6. A comparison of growing stock and timber harvest projections as simulated for scenario BAU within this study with other relevant projections investigated by Šebeň 2017 [37], Green Report 2023 [38], Vauhkonen et al., 2019 [47], and Moravčík 2009 [48].
Figure 6. A comparison of growing stock and timber harvest projections as simulated for scenario BAU within this study with other relevant projections investigated by Šebeň 2017 [37], Green Report 2023 [38], Vauhkonen et al., 2019 [47], and Moravčík 2009 [48].
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Table 1. Total volume production, growing stock, annual increment and harvest under BAU, realistic transition (25% of forest area) and total transition (100% of forest area) to CTNF.
Table 1. Total volume production, growing stock, annual increment and harvest under BAU, realistic transition (25% of forest area) and total transition (100% of forest area) to CTNF.
ScenarioInitial StateBusiness as Usual (BAU)Realistic Transition
(25% CTNF)		Total Transition
(100% CTNF)
Year2023203520502035205020352050
Units103 m3
Total volume production448.8570.8733.4575.8743.9591.0775.3
Growing stock444.8457.2490462.0504.6476.4548.2
Annual increment10.29.59.89.59.79.59.3
Annual harvest8.38.27.58.07.47.47.2
Unitsm3·ha−1
Total volume production248.4314.6396.9317.4402.7325.7420.1
Growing stock248.4253.3265.8256.0273.8263.9297.7
Annual increment5.75.35.45.35.35.35.1
Annual harvest4.74.54.14.44.14.13.9
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Štěrbová, M.; Barka, I.; Kulla, L.; Roessiger, J. The Effect of Transition to Close-to-Nature Forestry on Growing Stock, Wood Increment and Harvest Possibilities of Forests in Slovakia. Land 2024, 13, 1714. https://doi.org/10.3390/land13101714

AMA Style

Štěrbová M, Barka I, Kulla L, Roessiger J. The Effect of Transition to Close-to-Nature Forestry on Growing Stock, Wood Increment and Harvest Possibilities of Forests in Slovakia. Land. 2024; 13(10):1714. https://doi.org/10.3390/land13101714

Chicago/Turabian Style

Štěrbová, Martina, Ivan Barka, Ladislav Kulla, and Joerg Roessiger. 2024. "The Effect of Transition to Close-to-Nature Forestry on Growing Stock, Wood Increment and Harvest Possibilities of Forests in Slovakia" Land 13, no. 10: 1714. https://doi.org/10.3390/land13101714

APA Style

Štěrbová, M., Barka, I., Kulla, L., & Roessiger, J. (2024). The Effect of Transition to Close-to-Nature Forestry on Growing Stock, Wood Increment and Harvest Possibilities of Forests in Slovakia. Land, 13(10), 1714. https://doi.org/10.3390/land13101714

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