Differentiation of the Forest Structure as the Mitigation Action of Adverse Effects of Climate Change

*Janusz Szmyt*

#### **Abstract**

For several decades, the attention of societies has been focused on potential environmental changes due to climate change. Although climate change is not a new phenomenon, in the recent two decades, there has been a growing interest of scientists trying to determine scenarios of trends and their potential impact on forest ecosystems and forestry. Despite the uncertainties of climate change and the response of forest ecosystem to change, the forest management must deal with these uncertainties. There is no single prescription on how to manage forest resources under climate change in order to fulfill all demands from society. Various strategies in forest management are developed to counteract the adverse effects of climate change on forests and forestry. The future forest management should implement the following three main strategies: create forests which are resistant to change, promote their greater resilience to change, and enable forests to respond to change. It is expected that the more the structured forest, the higher the adaptive capacity is expected. Experiment focused on the influence of different silvicultural procedures on the structure of Scots pine in Poland is presented. Achieved results indicated that the process of stand structure conversion is a long-term process and different structural elements can be modified to different extents.

**Keywords:** stand structure, adaptive management, stand diversity, adaptive silviculture, *Pinus sylvestris*

#### **1. Forests and forestry under climate change**

For several decades, the attention of societies has been focused on the information about potential changes in our environment due to the changing climate system. Although the climate change is not a new phenomenon, in the recent two decades, there has been a growing interest of scientists trying to determine trends in climate change and their potential impact on a number of areas of human life. The impact of these changes is also studied in the context of forest ecosystems and forestry [1–3].

As the Intergovernmental Panel on Climate Change (IPCC) reports indicate, one of the significant reasons for the observed climate change is the increasing content of greenhouse gases in the atmosphere and the human activity attributed to them. Apart from determining the causes of the increasing content of these gases and their origin, the increase in average air temperature, changes in precipitation regimes, and changes in the natural disturbance regimes observed in recent years raise concern among scientists dealing with forest ecosystems as well as foresters and forest owners [4].

In addition to the uncertainty of the scale and rate of climate change and the different nature of these impacts on forest ecosystems, the response of forest ecosystems to these changes is subject to high uncertainty too [1, 5]. This problem is not easy to solve because it must be underlined that the projections derived from global circulation (climatic) models and ecological models are not predictions of future climate conditions, but they are rather description of possible conditions resulting from certain scenarios [4, 5]. In other words, climate models represent the range of probable features of the future environmental conditions, and here we are dealing with uncertainties. Therefore, the forest management under climate change must deal with uncertainties. Up to now, there is no single prescription on how to manage forest resources under climate change in order to fulfill all the demands from society.

Due to the growing concern about the future of the forests around the world, various strategies in forest management are developed to counteract the adverse effects of climate change on forests and forestry [4, 6]. Novel environmental conditions resulting from climate change might result in changes of forest tree species distribution (change in natural ranges) through changes in forest productivity and the economic value of managed forests [3, 7].

Up to now, different paradigms of forest management are suggested as the potential solution, that is, close-to-nature forestry, adaptive forestry, systemic forestry [8–11]. It is expected that the future forest management should implement the following three main strategies [6, 12]:


Adaptive management can be defined as a systematic and iterative approach for improving forest resource management by learning from management outcomes. It can be done by exploring alternative ways to meet the management objectives [13].

Modern forest management, taking into account the multifunctionality of forests and uncertainty of future climate conditions, will then require the introduction of innovative ways of management to ensure all services provided by forest ecosystems under the future unpredictable environmental conditions. Different approaches of short-term and long-term strategies are assumed to be required [13].

Three ways of adaptation strategies concerning forests are indicated [14, 15]. The so-called *business as usual* (no intervention) relies on today's practices and management targets. It is based on the assumption that forests themselves can adapt to changing environmental conditions as they did in the past. The second strategy called *reactive adaptation* takes place in the moment just after the fact. This strategy takes in account salvage cutting, updated harvest scheduling, recalculating allowable cuttings, etc. The third strategy is called *planned adaptation*, and it involves redefining goals and practices in advance taking into consideration climate change risk and uncertainties. This strategy will require new thinking of foresters taking into account the considerations of the global implications of local operations. Of

**61**

*Differentiation of the Forest Structure as the Mitigation Action of Adverse Effects of Climate…*

course, the planned adaptation for climate change involves greater uncertainty and novel risk. Among the activities related to planned adaptation strategy, one can consider the planting of new provenances or species capable of growing in environment under projected climatic conditions, reaping the benefits of new products from forests (i.e., carbon sequestration). It is expected that the adaptation strategy will increase the resilience of the forests while simultaneously decreasing their vulnerability. Other operations within planned adaptation include the silviculture of mixed stands, use of clones better suited for novel conditions, modification of

Bolte et al. [12] indicated three other strategies in adaptation of forest ecosystems to change to meet the management goals: *conservation of forests structure*, *active adaptation*, and *passive adaptation*. The first can be treated as business as usual and it relies on the maintenance of the structural consistency independently on the pressure due to environmental change. Active adaptation means the use of silvicultural methods to change the structure of the stand in a way that the resulting forest is better adapted to a new climatic condition than it would happen by natural succession. Passive adaptation uses spontaneous adaptation processes in terms of

**2. Different silvicultural tools for increasing adaptive capacity of forests** 

To understand the importance of silviculture, it is worth to recall its goals. They are defined as related to creating and maintaining the forest that will best fulfill all objectives of both owner and society. As they stated, the wood production is neither the only nor necessarily the dominant goal. At present the benefits of the forest are manifold, and all of them, for example, recreation, esthetics, or habitat protection, must be taken into account in modern forestry. The biggest problem, however, in modern silviculture, is getting the owners and society to define the management objectives which should aim to ensure all services and functions provided by the

forest for a long time despite the impact of the potential climate change.

While the priority of timber production was clearly seen in the past, one can observe that the forest management focused mainly on the providing economic benefits is no longer possible. Ecological and cultural services seem to be more and more desirable by society even when their provision is mostly possible due to the timber harvesting. Therefore, it is obvious that protection and production functions of the forest are both important to society and the conflict between these two func-

The changing needs of society also require a change in forest management which must provide more services than wood production. In Europe, such management, called *continuous cover forestry* (CCF) is only one option for that, and it is now successfully implemented in practice in many countries [18–20]. The concept of CCF mostly relies on close-to-nature silviculture (CTNS) or natural silviculture [21, 22]. Different aspects of the implementation of CTNS to increase the stability of forest ecosystems can be recommended: avoidance or limitation of clear-fellings, promotion of highly structured forests, and promotion of native tree species and selective individual tree silviculture are among the most important. Two basic principles of CTNS should be implemented: (1) reducing silvicultural risk and (2) reducing its spreading. Both are extremely important to mitigate the potential adverse impact of climate change on forests and forestry as well. Under the first principle, the following

*DOI: http://dx.doi.org/10.5772/intechopen.93327*

natural succession and natural species migration.

tions must be avoided or, at least, mitigated [16, 17].

activities should be promoted [23, 24]:

thinning regimes, etc.

**to change**

*Differentiation of the Forest Structure as the Mitigation Action of Adverse Effects of Climate… DOI: http://dx.doi.org/10.5772/intechopen.93327*

course, the planned adaptation for climate change involves greater uncertainty and novel risk. Among the activities related to planned adaptation strategy, one can consider the planting of new provenances or species capable of growing in environment under projected climatic conditions, reaping the benefits of new products from forests (i.e., carbon sequestration). It is expected that the adaptation strategy will increase the resilience of the forests while simultaneously decreasing their vulnerability. Other operations within planned adaptation include the silviculture of mixed stands, use of clones better suited for novel conditions, modification of thinning regimes, etc.

Bolte et al. [12] indicated three other strategies in adaptation of forest ecosystems to change to meet the management goals: *conservation of forests structure*, *active adaptation*, and *passive adaptation*. The first can be treated as business as usual and it relies on the maintenance of the structural consistency independently on the pressure due to environmental change. Active adaptation means the use of silvicultural methods to change the structure of the stand in a way that the resulting forest is better adapted to a new climatic condition than it would happen by natural succession. Passive adaptation uses spontaneous adaptation processes in terms of natural succession and natural species migration.

#### **2. Different silvicultural tools for increasing adaptive capacity of forests to change**

To understand the importance of silviculture, it is worth to recall its goals. They are defined as related to creating and maintaining the forest that will best fulfill all objectives of both owner and society. As they stated, the wood production is neither the only nor necessarily the dominant goal. At present the benefits of the forest are manifold, and all of them, for example, recreation, esthetics, or habitat protection, must be taken into account in modern forestry. The biggest problem, however, in modern silviculture, is getting the owners and society to define the management objectives which should aim to ensure all services and functions provided by the forest for a long time despite the impact of the potential climate change.

While the priority of timber production was clearly seen in the past, one can observe that the forest management focused mainly on the providing economic benefits is no longer possible. Ecological and cultural services seem to be more and more desirable by society even when their provision is mostly possible due to the timber harvesting. Therefore, it is obvious that protection and production functions of the forest are both important to society and the conflict between these two functions must be avoided or, at least, mitigated [16, 17].

The changing needs of society also require a change in forest management which must provide more services than wood production. In Europe, such management, called *continuous cover forestry* (CCF) is only one option for that, and it is now successfully implemented in practice in many countries [18–20]. The concept of CCF mostly relies on close-to-nature silviculture (CTNS) or natural silviculture [21, 22]. Different aspects of the implementation of CTNS to increase the stability of forest ecosystems can be recommended: avoidance or limitation of clear-fellings, promotion of highly structured forests, and promotion of native tree species and selective individual tree silviculture are among the most important. Two basic principles of CTNS should be implemented: (1) reducing silvicultural risk and (2) reducing its spreading. Both are extremely important to mitigate the potential adverse impact of climate change on forests and forestry as well. Under the first principle, the following activities should be promoted [23, 24]:

*Silviculture*

and forest owners [4].

from society.

the economic value of managed forests [3, 7].

the following three main strategies [6, 12]:

• enable forests to respond to change.

• create forests which are resistant to change,

• promote their greater resilience to change, and

to them. Apart from determining the causes of the increasing content of these gases and their origin, the increase in average air temperature, changes in precipitation regimes, and changes in the natural disturbance regimes observed in recent years raise concern among scientists dealing with forest ecosystems as well as foresters

In addition to the uncertainty of the scale and rate of climate change and the different nature of these impacts on forest ecosystems, the response of forest ecosystems to these changes is subject to high uncertainty too [1, 5]. This problem is not easy to solve because it must be underlined that the projections derived from global circulation (climatic) models and ecological models are not predictions of future climate conditions, but they are rather description of possible conditions resulting from certain scenarios [4, 5]. In other words, climate models represent the range of probable features of the future environmental conditions, and here we are dealing with uncertainties. Therefore, the forest management under climate change must deal with uncertainties. Up to now, there is no single prescription on how to manage forest resources under climate change in order to fulfill all the demands

Due to the growing concern about the future of the forests around the world, various strategies in forest management are developed to counteract the adverse effects of climate change on forests and forestry [4, 6]. Novel environmental conditions resulting from climate change might result in changes of forest tree species distribution (change in natural ranges) through changes in forest productivity and

Up to now, different paradigms of forest management are suggested as the potential solution, that is, close-to-nature forestry, adaptive forestry, systemic forestry [8–11]. It is expected that the future forest management should implement

Adaptive management can be defined as a systematic and iterative approach for improving forest resource management by learning from management outcomes. It can be done by exploring alternative ways to meet the management objectives [13]. Modern forest management, taking into account the multifunctionality of forests and uncertainty of future climate conditions, will then require the introduction of innovative ways of management to ensure all services provided by forest ecosystems under the future unpredictable environmental conditions. Different approaches of short-term and long-term strategies are assumed to be required [13]. Three ways of adaptation strategies concerning forests are indicated [14, 15]. The so-called *business as usual* (no intervention) relies on today's practices and management targets. It is based on the assumption that forests themselves can adapt to changing environmental conditions as they did in the past. The second strategy called *reactive adaptation* takes place in the moment just after the fact. This strategy takes in account salvage cutting, updated harvest scheduling, recalculating allowable cuttings, etc. The third strategy is called *planned adaptation*, and it involves redefining goals and practices in advance taking into consideration climate change risk and uncertainties. This strategy will require new thinking of foresters taking into account the considerations of the global implications of local operations. Of

**60**


Among activities within the second principle, the most important is associated with the promotion (creation) of complex forest structure in terms of their species composition (mixed stands), vertical profiles (multilayered and multicohort stands), and horizontal patterns (patchy stands) [25, 26].

#### **3. Which adaptation strategy is better? A case study from Scots pine (***Pinus sylvestris* **L.) stands in Poland**

Why forest structure matters? Shortly—it is a key to the forest ecosystem, its function, and diversity [27, 28]. Understanding the forest structure dynamics allows us to better understand the history, functions, and future of the forest ecosystem. The stand structure of the stand can be described by lots of elements, for example, species composition, tree age, tree size, and dead wood amount. If we manage the forest structure, we will affect the forest functions. Potential benefits and limitations of different silvicultural regimes on the structuring forest stand are presented here on the base of the experiment in Scots pine (*Pinus sylvestris* L.) forests in Poland.

Forests cover in Poland ca. 9,200,000 hectares (29.6% of area) and *P. sylvestris* is the most economically important tree species in Poland. In Poland, this tree species has optimal climatic and site conditions within its Euro-Asiatic natural range. While conifers dominate the species structure of Polish forests, pine accounts for 58% of the area of forests. It also accounts for 56.5% in the volume structure of timber resources [29]. Most Scots pine forests in Poland are managed according to evenaged silviculture, and thus they represent rather structurally homogenous stands in terms of species composition, vertical and horizontal structures. The Department of Silviculture, Faculty of Forestry of the Poznan University of Life Sciences, has been involved for decades in research projects aiming at finding opportunities to change the even-aged silviculture of pine forest into more complex management, for example, shelterwood cuttings or selection cuttings [26]. One example of such studies is presented below.

#### **3.1 Methodological considerations of the experiment**

Experiment has been established in three stands where *P. sylvestris* shares 90% or more in abundance. Admixture tree species is silver birch (*Betula pendula* Roth.). Till the initialization of experiment in the 1980s of the last century, each stand (experimental object) has been managed according to even-aged silviculture and they could be characterized as monocultures, even-aged and single-layer stands. Three experimental objects, reflecting different status of silvicultural treatments, has been applied: control (C), experimental (Ex), and economic (E) of sizes 35.78, 37.88, and 41.01 ha, respectively. In the control object, no logging operation has been allowed and it represents *passive* adaptation strategy. In case of the

**63**

**Table 1.**

Aggregation index,

Mingling index,

Size differentiation

SM

*Differentiation of the Forest Structure as the Mitigation Action of Adverse Effects of Climate…*

experimental object, only selective thinning has been allowed and it can be treated as the *active* strategy of adaptation. In the economic object, a *business as usual* strategy has been planned and conducted according to the low thinning rules indicated

In 1988, the net of permanent circular measurement plots of size 0.05 ha each was laid out in the nodes of the rectangular grid of size 100 × 50 m. On each plot, the stem diameter at 1.3 m (dbh, cm), total tree height (*H*, m) of 2–3 trees, and polar coordinates (*x*, *y* calculated from the azimuth and distance to each tree from the plot center) were measured. Also, tree species and tree status (dead, live) were recorded. The first survey was done in 1988 and the second after 15 years, in 2003. The mean stand parameters (tree density, tree diameter, and basal area) coupled with spatially explicit structural indices (**Table 1**) describing different aspects of the stand structure were calculated. Tree diameter and basal area distributions were checked for their normality using Shapiro-Wilk test of normality. If the distributions were not significantly different from the normal distribution (α = 0.05), the analysis of variance (ANOVA) was applied to analyze the differences among treatment means in terms of both characteristics. If these differences are significant, the *post hoc* Tuckey's range test is

applied to find out which treatments differed significantly from each other.

**Index Formulation Explanations**

1

=

( )

*i j*

max size ,size

*r*

∑ *<sup>N</sup>*

0.5 0.0514 0.041

*r A PP N NN*

⋅ + ⋅+ ⋅

1/2

*<sup>i</sup> <sup>i</sup> <sup>A</sup>*

= =

1 1 <sup>=</sup> <sup>=</sup> ∑*<sup>k</sup> ij <sup>i</sup> SM v k*

> 3 1 1 <sup>1</sup> <sup>1</sup> = =

*i j*

= −

∑ ∑ min size ,size

*n i j*

*CE <sup>r</sup> <sup>N</sup>*

3/2

*r*A—Observed mean distances

) *N*—Total number of trees *P*—Circumference of the plot

*k*—Numbers of nearest

*v*ij = 1, if reference tree and neighbor are different species,

sizei—Diameter or height of

sizej—Diameter or height of

*n*—Number of neighbors

between trees *A*—Area (m2

neighbors

(*n* = 3)

*i*th tree

*j*th tree

(*n* = 3)

otherwise *v*ij = 0 *n*—Number of neighbors

from unity is estimated using the standard *z*-test value [30].

CE <sup>1</sup>

*E*

index, *<sup>T</sup>* ( )

*<sup>T</sup> <sup>n</sup>*

*Structural indices calculated in each of the objects analyzed.*

Spatial pattern of tree distribution was evaluated on the basis of the Clark-Evans (CE) index. For random distribution of trees, the index gets the value of 1.00. If CE < 1.00, trees are distributed in smaller or larger clumps while if CE > 1.00, they are more or less regularly dispersed in the stand. The significance of the departures

Size differentiation indices TD (for tree diameter) and TH (for tree height) are calculated for each tree in the plot in relation to three neighbors. The higher the value of the index, the more is the diversity in terms of tree size observed. Apart from the mean value of these indices, it is possible to analyze the distribution of them in five differentiation classes [30]: very low (<0.20), low (0.20–0.40), moderate (0.40–0.60), large (0.60–0.80), and very large (>0.80) differentiation among the nearest neighbors. Small value of size differentiation index means homogenous in size group of trees, and large value indicates heterogeneous groups of trees.

*DOI: http://dx.doi.org/10.5772/intechopen.93327*

in the management plan elaborated for this forestry district.

#### *Differentiation of the Forest Structure as the Mitigation Action of Adverse Effects of Climate… DOI: http://dx.doi.org/10.5772/intechopen.93327*

experimental object, only selective thinning has been allowed and it can be treated as the *active* strategy of adaptation. In the economic object, a *business as usual* strategy has been planned and conducted according to the low thinning rules indicated in the management plan elaborated for this forestry district.

In 1988, the net of permanent circular measurement plots of size 0.05 ha each was laid out in the nodes of the rectangular grid of size 100 × 50 m. On each plot, the stem diameter at 1.3 m (dbh, cm), total tree height (*H*, m) of 2–3 trees, and polar coordinates (*x*, *y* calculated from the azimuth and distance to each tree from the plot center) were measured. Also, tree species and tree status (dead, live) were recorded. The first survey was done in 1988 and the second after 15 years, in 2003. The mean stand parameters (tree density, tree diameter, and basal area) coupled with spatially explicit structural indices (**Table 1**) describing different aspects of the stand structure were calculated. Tree diameter and basal area distributions were checked for their normality using Shapiro-Wilk test of normality. If the distributions were not significantly different from the normal distribution (α = 0.05), the analysis of variance (ANOVA) was applied to analyze the differences among treatment means in terms of both characteristics. If these differences are significant, the *post hoc* Tuckey's range test is applied to find out which treatments differed significantly from each other.

Spatial pattern of tree distribution was evaluated on the basis of the Clark-Evans (CE) index. For random distribution of trees, the index gets the value of 1.00. If CE < 1.00, trees are distributed in smaller or larger clumps while if CE > 1.00, they are more or less regularly dispersed in the stand. The significance of the departures from unity is estimated using the standard *z*-test value [30].

Size differentiation indices TD (for tree diameter) and TH (for tree height) are calculated for each tree in the plot in relation to three neighbors. The higher the value of the index, the more is the diversity in terms of tree size observed. Apart from the mean value of these indices, it is possible to analyze the distribution of them in five differentiation classes [30]: very low (<0.20), low (0.20–0.40), moderate (0.40–0.60), large (0.60–0.80), and very large (>0.80) differentiation among the nearest neighbors. Small value of size differentiation index means homogenous in size group of trees, and large value indicates heterogeneous groups of trees.


#### **Table 1.**

*Structural indices calculated in each of the objects analyzed.*

*Silviculture*

Poland.

studies is presented below.

**3.1 Methodological considerations of the experiment**

promoted),

use of growing space, and

• forest site cultivation.

• full use of genetic diversity of forest tree species (natural regeneration

• tending treatments aiming at the increase of tree vitality and ensuring better

Among activities within the second principle, the most important is associated with the promotion (creation) of complex forest structure in terms of their species composition (mixed stands), vertical profiles (multilayered and multicohort

**3. Which adaptation strategy is better? A case study from Scots pine** 

Why forest structure matters? Shortly—it is a key to the forest ecosystem, its function, and diversity [27, 28]. Understanding the forest structure dynamics allows us to better understand the history, functions, and future of the forest ecosystem. The stand structure of the stand can be described by lots of elements, for example, species composition, tree age, tree size, and dead wood amount. If we manage the forest structure, we will affect the forest functions. Potential benefits and limitations of different silvicultural regimes on the structuring forest stand are presented here on the base of the experiment in Scots pine (*Pinus sylvestris* L.) forests in

Forests cover in Poland ca. 9,200,000 hectares (29.6% of area) and *P. sylvestris* is the most economically important tree species in Poland. In Poland, this tree species has optimal climatic and site conditions within its Euro-Asiatic natural range. While conifers dominate the species structure of Polish forests, pine accounts for 58% of the area of forests. It also accounts for 56.5% in the volume structure of timber resources [29]. Most Scots pine forests in Poland are managed according to evenaged silviculture, and thus they represent rather structurally homogenous stands in terms of species composition, vertical and horizontal structures. The Department of Silviculture, Faculty of Forestry of the Poznan University of Life Sciences, has been involved for decades in research projects aiming at finding opportunities to change the even-aged silviculture of pine forest into more complex management, for example, shelterwood cuttings or selection cuttings [26]. One example of such

Experiment has been established in three stands where *P. sylvestris* shares 90% or more in abundance. Admixture tree species is silver birch (*Betula pendula* Roth.). Till the initialization of experiment in the 1980s of the last century, each stand (experimental object) has been managed according to even-aged silviculture and they could be characterized as monocultures, even-aged and single-layer stands. Three experimental objects, reflecting different status of silvicultural treatments, has been applied: control (C), experimental (Ex), and economic (E) of sizes 35.78, 37.88, and 41.01 ha, respectively. In the control object, no logging operation has been allowed and it represents *passive* adaptation strategy. In case of the

• species composition adapted to the local site conditions,

stands), and horizontal patterns (patchy stands) [25, 26].

**(***Pinus sylvestris* **L.) stands in Poland**

**62**

Species mingling (SM) is calculated for each tree in the plot and their three nearest neighbors. The lower the value of the index, the more is the homogeneous group of trees in terms of their species. In the case of three neighbors, the index can take four values: 0.0, 0.3, 0.7, and 1.0, indicating no mingling, low mingling, large mingling, and full mingling, respectively [30].

All structural indices were calculated for plots with the number of trees ≥10.

Afterward, the change in stand structure over the 15-year period of the stand development was evaluated. To find out the differences in structural diversity between objects (C, Ex, and E), the Kruskal-Wallis nonparametric test followed by the Dunn's multi-comparison test were applied to test significant differences (α = 0.05).

Statistical calculations were done in R environment [31] and Siafor 1.0 software [32].

#### **3.2 Results**

#### *3.2.1 Stand parameters*

Scots pine was the dominant tree species in the stand independently on the experimental object. In 1988, the average number of trees per plot was for the control object 44.5 (SD = ±8.4), experimental—35.3 (SD = ±7.3), and economic—34.3 (SD = ±8.0). After 15 years, the density decreased in each object and, in 2015, the average number of trees per plot was reached in the control object 38.3 (SD = ±7.5), in experimental—23.8 (SD = ±4.7), and in the economic—21.0 (SD = ±6.1).

The highest number (on average) of individuals (per 1 ha) of this tree species was observed in the control object, where no logging was conducted. Experimental and economic objects showed similar number of trees of Scots pine. In case of birch, the highest number was observed in the economic object and the lowest in the control one (**Table 2**). Similar trend was observed in terms of basal area—Scots pine was the dominant tree species reaching the share by more than 90% in each of the object analyzed. In absolute numbers, however, the highest basal area was observed in 1988 in the economic object and the lowest in the control one (**Table 2**). In 2003, the highest basal area was obtained in the control object and the lowest in the economic one. The share of birch at the beginning of the experiment was the highest in the


#### **Table 2.**

*Average tree number (N ha−1) and basal area (BA ha−1) and the corresponding percentage (in brackets) of tree species present in the objects in 1988 and 2003.*

**65**

**Figure 2.**

**Figure 1.**

*Differentiation of the Forest Structure as the Mitigation Action of Adverse Effects of Climate…*

economic object and the lowest in the control one. After 15 years, this trend was still observed (**Table 2**). Other tree species, that is, Norway spruce (*Picea abies* (L.) Karst.) and black locust (*Robinia pseudoacacia* L.) were present only in the control

**Figure 1** shows that the mean diameter of living trees was the highest in the economic object, followed by the experimental one. The lowest dbh was observed in the case of the control object. Similar trend can be observed for the basal area.

was slightly higher in case of the economic object (25.7%) than in the others.

Coefficient of variation calculated for dbh was similar in all objects, however, it

The diameter distribution of living trees and their basal area were not different significantly from the normal distribution (data not shown). Analysis of variance, followed by Tukey's *post hoc* test, revealed that the mean tree diameter and basal area were significantly different in the analyzed objects in 1988 and 2003 (α = 0.05,

*Boxplots (mean, median, min, max, outliers, and first and third quartiles) for tree diameter (dbh) and basal* 

*Differences in the mean of dbh between objects in 1988 (a) and 2003 (b). If the confidence level does not include* 

*DOI: http://dx.doi.org/10.5772/intechopen.93327*

**Figure 2** for tree diameter).

*area (ba) in the objects in two inventories.*

*0 value, then two means are significantly different at α = 0.05.*

object and both are excluded from the further analysis.

*Differentiation of the Forest Structure as the Mitigation Action of Adverse Effects of Climate… DOI: http://dx.doi.org/10.5772/intechopen.93327*

economic object and the lowest in the control one. After 15 years, this trend was still observed (**Table 2**). Other tree species, that is, Norway spruce (*Picea abies* (L.) Karst.) and black locust (*Robinia pseudoacacia* L.) were present only in the control object and both are excluded from the further analysis.

**Figure 1** shows that the mean diameter of living trees was the highest in the economic object, followed by the experimental one. The lowest dbh was observed in the case of the control object. Similar trend can be observed for the basal area.

Coefficient of variation calculated for dbh was similar in all objects, however, it was slightly higher in case of the economic object (25.7%) than in the others.

The diameter distribution of living trees and their basal area were not different significantly from the normal distribution (data not shown). Analysis of variance, followed by Tukey's *post hoc* test, revealed that the mean tree diameter and basal area were significantly different in the analyzed objects in 1988 and 2003 (α = 0.05, **Figure 2** for tree diameter).

#### **Figure 1.**

*Silviculture*

(α = 0.05).

**3.2 Results**

software [32].

*3.2.1 Stand parameters*

Control object

Experimental object

Economic object

Species mingling (SM) is calculated for each tree in the plot and their three nearest neighbors. The lower the value of the index, the more is the homogeneous group of trees in terms of their species. In the case of three neighbors, the index can take four values: 0.0, 0.3, 0.7, and 1.0, indicating no mingling, low mingling, large

All structural indices were calculated for plots with the number of trees ≥10. Afterward, the change in stand structure over the 15-year period of the stand development was evaluated. To find out the differences in structural diversity between objects (C, Ex, and E), the Kruskal-Wallis nonparametric test followed by the Dunn's multi-comparison test were applied to test significant differences

Statistical calculations were done in R environment [31] and Siafor 1.0

Scots pine was the dominant tree species in the stand independently on the experimental object. In 1988, the average number of trees per plot was for the control object 44.5 (SD = ±8.4), experimental—35.3 (SD = ±7.3), and economic—34.3 (SD = ±8.0). After 15 years, the density decreased in each object and, in 2015, the average number of trees per plot was reached in the control object 38.3 (SD = ±7.5),

The highest number (on average) of individuals (per 1 ha) of this tree species was observed in the control object, where no logging was conducted. Experimental and economic objects showed similar number of trees of Scots pine. In case of birch, the highest number was observed in the economic object and the lowest in the control one (**Table 2**). Similar trend was observed in terms of basal area—Scots pine was the dominant tree species reaching the share by more than 90% in each of the object analyzed. In absolute numbers, however, the highest basal area was observed in 1988 in the economic object and the lowest in the control one (**Table 2**). In 2003, the highest basal area was obtained in the control object and the lowest in the economic one. The share of birch at the beginning of the experiment was the highest in the

**Year Pinus sylvestris Betula pendula Picea abies Robinia pseudoacacia N (%) BA (%) N (%) BA (%) N (%) BA (%) N (%) BA (%)**

1988 817 (92) 25.4 (91) 50.4 (6) 1.8 (6) 6.3 (1) 0.3 (1) 12.4 (1) 0.5 (2) 2003 709 (93) 35.3 (92) 41 (5) 2.1 (5) 6 (1) 0.4 (1) 5 (1) 0.4 (1)

*Average tree number (N ha−1) and basal area (BA ha−1) and the corresponding percentage (in brackets) of tree* 

1988 638 (90) 26.8 (91) 68 (10) 2.6 (9) —

1988 610 (89) 27.7 (92) 76 (11) 2.4 (8) —

2003 432 (92) 29.9 (92) 37 (8) 2.4 (8)

2003 383 (92) 28.8 (94) 33 (8) 2 (6)

*species present in the objects in 1988 and 2003.*

in experimental—23.8 (SD = ±4.7), and in the economic—21.0 (SD = ±6.1).

mingling, and full mingling, respectively [30].

**64**

**Table 2.**

*Boxplots (mean, median, min, max, outliers, and first and third quartiles) for tree diameter (dbh) and basal area (ba) in the objects in two inventories.*

#### **Figure 2.**

*Differences in the mean of dbh between objects in 1988 (a) and 2003 (b). If the confidence level does not include 0 value, then two means are significantly different at α = 0.05.*

#### *3.2.2 Structural parameters*

#### *3.2.2.1 Control object*

#### *3.2.2.1.1 Spatial distribution*

The average value of the CE index for the object at the beginning reached the value of 1.14 and was significantly different from the random expectation. The index ranged from 0.81 to 1.35 with its variation among plots at the level of 8% (**Figure 3**). There were 32 plots (51% of all plots) in the control object on which trees showed regular pattern of their distribution (CE > 1.0) and only on one plot in this index was significantly lower than CE < 1.0, indicating clumped distribution of trees. On the rest of the plots (48%), the deviations from the random expectation were not statistically proved and trees were randomly distributed. After 15 years in 2003—the mean value of CE index did not change (CE = 1.14). The value of this index varied among plots between 0.83 and 1.30. The number of plots on which the index was significantly higher than 1.0 indicating regular pattern decreased to 23 (36%). No plot indicating clumped distribution of trees was observed. Therefore, the random pattern was still dominant in 2003 and this type of tree distribution was observed on 40 plots (63%).

#### *3.2.2.1.2 Tree size diversity*

Just after the initialization of the experiment, in 1988, the mean value of diameter differentiation index, TD, in the case of the control object reached TD = 0.19. This index ranged from 0.00 to 0.54 depending on the plot (**Figure 3**). Coefficient of variation for TD index between plots was large (cv = 50%). The mean index showed that, in general, the variation in dbh among neighboring trees was low. This was confirmed by the distribution of the index in differentiation classes (**Table 3**). The dominant classes were these of very low and low diameter differentiation, which indicates that the diversity in diameter between the nearest neighbors was lower than 40%. After 15 years, the situation did not change much. The average TD index took the value of TD = 0.20 with much smaller range: 0.17–0.31 than in 1988. The variation of TD among plots clearly decreased to 20%. Again, the distribution of TD index in diameter differentiation classes confirmed that trees were mostly similar in their diameter at the small spatial scale (**Table 3**).

In the case of tree height differentiation, the mean value of the TH index was much smaller than for tree diameter and it reached TH = 0.10. The index ranged from 0.06–0.16 (**Figure 3**) depending on plot, and the coefficient of variation between plots was 28%. This indicates that neighboring trees were very similar in their height (**Table 3**). Homogenous groups of trees are indicated also by the share of trees belonging to the lowest differentiation class (92.9% of trees). After 15 years, the mean value of the TH index did not change (TH = 0.10), with the range varying between 0.06 and 0.15. The share of trees in the lowest differentiation class increased to more than 95% (**Table 3**).

#### *3.2.2.1.3 Species mingling*

In 1988, the species mingling index, SM, reached the mean value of SM = 0.08, indicating very homogenous conditions, on average, in terms of species diversity at the small spatial scale. The index ranged from 0.00 to 0.54 and its variation between plots was at very high level, cv = 103% (**Figure 3**). There were 17 plots (27%) in the control object on which the index was equal to 0.00, indicating the

**67**

single mixture.

**Figure 3.**

**Table 3.**

*objects in 1988 and 2003.*

*3.2.2.2 Experimental object*

*3.2.2.2.1 Spatial distribution*

*Differentiation of the Forest Structure as the Mitigation Action of Adverse Effects of Climate…*

lack of species mingling at all. On other plots, the mingling index varied from low to moderate. Analysis of the index distribution in mingling classes pointed out that the neighborhood of most trees in the control object was homogeneous (83% of trees). Only in the case of 9% of trees, their neighborhood was more heterogeneous in terms of species, meaning that 1–2 neighbors were different in species. In 2003, the mean SM index increased to SM = 0.12 and ranged from 0.00 to 0.55 depending on the plot. The coefficient of variation between plots in the control object decreased slightly after 15 years and got the value of 97%. On 19 plots (30%), the index value was equal to 0.00, indicating the lack of species mixture. Again, the dominance of very low mingling class can be observed on most plots in the object (84% trees). Both tree species showed the opposite behavior (**Figure 4**). Scots pine formed large homogenous groups of trees, while silver birch was present in the stand mostly as a

*Statistical characteristic (mean, median, max, min, outliers, and first and third quartiles) of structural indices describing spatial pattern (CE), tree size differentiation (TD), and species mingling (SM) in the experimental* 

TD 1988 45.5 48.5 5.8 0.18 0

TH 1988 92.9 6.98 0.04 0 0

*The share (%) of diameter (TD) and height (TH) differentiation classes in the control object.*

2003 44.8 48.0 6.6 0.46 0.12

2003 95.5 4.14 0.33 0 0

**Very low Low Moderate Large Very large**

**Index Year Differentiation classes**

In 1988, the mean CE index for the experimental object took the value of CE = 1.18, indicating regular pattern in tree distribution. The value of the index ranged from 0.65 to 1.28 and the coefficient of variation among plots reached the

*DOI: http://dx.doi.org/10.5772/intechopen.93327*

*Differentiation of the Forest Structure as the Mitigation Action of Adverse Effects of Climate… DOI: http://dx.doi.org/10.5772/intechopen.93327*

#### **Figure 3.**

*Silviculture*

*3.2.2 Structural parameters*

*3.2.2.1.1 Spatial distribution*

observed on 40 plots (63%).

their diameter at the small spatial scale (**Table 3**).

increased to more than 95% (**Table 3**).

*3.2.2.1.3 Species mingling*

*3.2.2.1.2 Tree size diversity*

The average value of the CE index for the object at the beginning reached the value of 1.14 and was significantly different from the random expectation. The index ranged from 0.81 to 1.35 with its variation among plots at the level of 8% (**Figure 3**). There were 32 plots (51% of all plots) in the control object on which trees showed regular pattern of their distribution (CE > 1.0) and only on one plot in this index was significantly lower than CE < 1.0, indicating clumped distribution of trees. On the rest of the plots (48%), the deviations from the random expectation were not statistically proved and trees were randomly distributed. After 15 years in 2003—the mean value of CE index did not change (CE = 1.14). The value of this index varied among plots between 0.83 and 1.30. The number of plots on which the index was significantly higher than 1.0 indicating regular pattern decreased to 23 (36%). No plot indicating clumped distribution of trees was observed. Therefore, the random pattern was still dominant in 2003 and this type of tree distribution was

Just after the initialization of the experiment, in 1988, the mean value of diameter differentiation index, TD, in the case of the control object reached TD = 0.19. This index ranged from 0.00 to 0.54 depending on the plot (**Figure 3**). Coefficient of variation for TD index between plots was large (cv = 50%). The mean index showed that, in general, the variation in dbh among neighboring trees was low. This was confirmed by the distribution of the index in differentiation classes (**Table 3**). The dominant classes were these of very low and low diameter differentiation, which indicates that the diversity in diameter between the nearest neighbors was lower than 40%. After 15 years, the situation did not change much. The average TD index took the value of TD = 0.20 with much smaller range: 0.17–0.31 than in 1988. The variation of TD among plots clearly decreased to 20%. Again, the distribution of TD index in diameter differentiation classes confirmed that trees were mostly similar in

In the case of tree height differentiation, the mean value of the TH index was much smaller than for tree diameter and it reached TH = 0.10. The index ranged from 0.06–0.16 (**Figure 3**) depending on plot, and the coefficient of variation between plots was 28%. This indicates that neighboring trees were very similar in their height (**Table 3**). Homogenous groups of trees are indicated also by the share of trees belonging to the lowest differentiation class (92.9% of trees). After 15 years, the mean value of the TH index did not change (TH = 0.10), with the range varying between 0.06 and 0.15. The share of trees in the lowest differentiation class

In 1988, the species mingling index, SM, reached the mean value of SM = 0.08, indicating very homogenous conditions, on average, in terms of species diversity at the small spatial scale. The index ranged from 0.00 to 0.54 and its variation between plots was at very high level, cv = 103% (**Figure 3**). There were 17 plots (27%) in the control object on which the index was equal to 0.00, indicating the

*3.2.2.1 Control object*

**66**

*Statistical characteristic (mean, median, max, min, outliers, and first and third quartiles) of structural indices describing spatial pattern (CE), tree size differentiation (TD), and species mingling (SM) in the experimental objects in 1988 and 2003.*


#### **Table 3.**

*The share (%) of diameter (TD) and height (TH) differentiation classes in the control object.*

lack of species mingling at all. On other plots, the mingling index varied from low to moderate. Analysis of the index distribution in mingling classes pointed out that the neighborhood of most trees in the control object was homogeneous (83% of trees). Only in the case of 9% of trees, their neighborhood was more heterogeneous in terms of species, meaning that 1–2 neighbors were different in species. In 2003, the mean SM index increased to SM = 0.12 and ranged from 0.00 to 0.55 depending on the plot. The coefficient of variation between plots in the control object decreased slightly after 15 years and got the value of 97%. On 19 plots (30%), the index value was equal to 0.00, indicating the lack of species mixture. Again, the dominance of very low mingling class can be observed on most plots in the object (84% trees). Both tree species showed the opposite behavior (**Figure 4**). Scots pine formed large homogenous groups of trees, while silver birch was present in the stand mostly as a single mixture.

#### *3.2.2.2 Experimental object*

#### *3.2.2.2.1 Spatial distribution*

In 1988, the mean CE index for the experimental object took the value of CE = 1.18, indicating regular pattern in tree distribution. The value of the index ranged from 0.65 to 1.28 and the coefficient of variation among plots reached the

#### **Figure 4.**

*Spatial mingling of Scots pine and silver birch in the control object in two inventories.*

level of 12% (**Figure 3**). In the case of 19 plots (25% of all plots), the value of the index was significantly larger than that for the random expectation and on the others the distribution pattern was random. After 15 years, in 2003, the mean value of the CE index increased to CE = 1.22 and it varied from 0.89 to 1.29 depending on the plot. The coefficient of variation among plots was at the level of 8%. In the case of 26 plots (35%), the value of this index was significantly different from the randomness, indicating clear regularity in the spatial distribution of trees.

#### *3.2.2.2.2 Tree size diversity*

In 1988, the mean value of the diameter differentiation index, TD, was TD = 0.23 which pointed to the low diversity in diameter of trees at small spatial scale. This index ranged in this object from 0.12 to 0.38, and the variation on it among all plots was at the level of 20% (**Figure 3**). Most trees in the experimental object could be characterized by very low and low differentiation (95% of all trees), which confirmed that trees were similar in their diameter at the nearest-neighbor spatial scale (**Table 4**). In 2003, the average value of the TD index decreased to TD = 0.19. The lowest value of the index was 0.14 and the largest was 0.31. Coefficient of variation of the index among plots decreased to the level of 15%. Up to 97% of trees were characterized by very low and low differentiation in diameter at small spatial scale (**Table 4**). At the beginning of the experiment, the mean index describing the differentiation of tree in terms of their height took the value of TH = 0.10. It pointed to a large similarity of trees in tree height. The index ranged from 0.04 to 0.21 with the coefficient of variation among plots at the level of 28%. Up to 96% of trees showed similarity in height with their nearest neighbors (**Table 4**). In 2003, the mean value of the TH index decreased to TH = 0.07, with the minimum value of 0.05 and maximum one of 0.15. Variation in the TH index among plots was at the level of 21%. The share of trees which showed large similarity in their total height with the nearest neighbors increased to 98% (**Table 4**).

#### *3.2.2.2.3 Species mingling*

In 1988, the mean value of the species mingling index got SM = 0.13, indicating rather low species mixture in the experimental object. The value of this index varied

**69**

both these classes.

object.

*Differentiation of the Forest Structure as the Mitigation Action of Adverse Effects of Climate…*

TD 1988 43 52 5 0.3 0

TH 1988 96 3.5 0.3 0 0

2003 60 37 2 0.4 0

2003 98 1.5 0.5 0 0

**Very low Low Moderate Large Very large**

**Index Year Differentiation classes**

among plots from 0.00 to 0.60 (**Figure 3**) and the coefficient of variation was very high, cv = 78%. Species homogenous neighborhood, expressed by the index SM = 0.00, was observed in the case of 28 plots (39%), and the others showed higher mingling level. After 15 years, the mean value of the index was almost the same like in 1988— SM = 0.12. The minimum value of SM was 0.00 and the highest one was 0.55, with variation among plots reaching the level of 89%. The number of plots with the index SM = 0.00 decreased in 2003–2018 (25%), that is a 34% decrease. Similar to the control object, Scots pine and silver birch showed opposite behavior (**Figure 5**). Homogenous neighborhood was observed in the case of Scots pine, while birch was present most

*The share (%) of diameter (TD) and height (TH) differentiation classes in the experimental object.*

At the beginning of the experiment, the mean value of CE describing the spatial pattern of living trees in the economic object took the value CE = 1.21, pointing to a clear regular pattern. The lowest value of the index was CE = 0.74 and the highest was CE = 1.35 (**Figure 3**). The coefficient of variation between plots for this index was low—10%. The index differed significantly from randomness (CE = 1.00) in the case of 33 plots (43%). The dominant spatial pattern of living trees was therefore a random pattern. No clumping was observed on any plot. After 15 years, in 2003, the mean value of the index increased to CE = 1.27. In the object, the index ranged from 0.84 to 1.40 depending on the plot, with the coefficient of variation at the level of 11%. The number of plots with the CE index significantly larger than 1.0 was 34 (48%). The dominance of the random pattern was still observed in this

In 1988, the mean value of diameter differentiation index reached TD = 0.23, indicating rather low diversity in tree diameter among the nearest neighbors. The index ranged from 0.09 to 0.47 (**Figure 3**), with the coefficient of variation among plots at the level of 22%. Most trees showed very low or low diameter differentiation at the small spatial scale (92% of trees) and only few (7.8%) showed larger variation in diameter (**Table 5**). In 2003, the mean value of the index decreased to TD = 0.19, varying between 0.10 and 0.34 depending on the plot. The variation of the index among plot was at the level of 23%. After 15 years of stand development, the number of trees in the lowest two classes of diameter differentiation clearly increased (**Table 5**). As much as 97% of trees belonged to

*DOI: http://dx.doi.org/10.5772/intechopen.93327*

often as a single mixture.

**Table 4.**

*3.2.2.3 Economic object*

*3.2.2.3.1 Spatial distribution*

*3.2.2.3.2 Tree size diversity*

*Differentiation of the Forest Structure as the Mitigation Action of Adverse Effects of Climate… DOI: http://dx.doi.org/10.5772/intechopen.93327*


**Table 4.**

*Silviculture*

**Figure 4.**

level of 12% (**Figure 3**). In the case of 19 plots (25% of all plots), the value of the index was significantly larger than that for the random expectation and on the others the distribution pattern was random. After 15 years, in 2003, the mean value of the CE index increased to CE = 1.22 and it varied from 0.89 to 1.29 depending on the plot. The coefficient of variation among plots was at the level of 8%. In the case of 26 plots (35%), the value of this index was significantly different from the random-

In 1988, the mean value of the diameter differentiation index, TD, was TD = 0.23 which pointed to the low diversity in diameter of trees at small spatial scale. This index ranged in this object from 0.12 to 0.38, and the variation on it among all plots was at the level of 20% (**Figure 3**). Most trees in the experimental object could be characterized by very low and low differentiation (95% of all trees), which confirmed that trees were similar in their diameter at the nearest-neighbor spatial scale (**Table 4**). In 2003, the average value of the TD index decreased to TD = 0.19. The lowest value of the index was 0.14 and the largest was 0.31. Coefficient of variation of the index among plots decreased to the level of 15%. Up to 97% of trees were characterized by very low and low differentiation in diameter at small spatial scale (**Table 4**). At the beginning of the experiment, the mean index describing the differentiation of tree in terms of their height took the value of TH = 0.10. It pointed to a large similarity of trees in tree height. The index ranged from 0.04 to 0.21 with the coefficient of variation among plots at the level of 28%. Up to 96% of trees showed similarity in height with their nearest neighbors (**Table 4**). In 2003, the mean value of the TH index decreased to TH = 0.07, with the minimum value of 0.05 and maximum one of 0.15. Variation in the TH index among plots was at the level of 21%. The share of trees which showed large similarity in their total height with the nearest neighbors increased to 98% (**Table 4**).

In 1988, the mean value of the species mingling index got SM = 0.13, indicating rather low species mixture in the experimental object. The value of this index varied

ness, indicating clear regularity in the spatial distribution of trees.

*Spatial mingling of Scots pine and silver birch in the control object in two inventories.*

*3.2.2.2.2 Tree size diversity*

*3.2.2.2.3 Species mingling*

**68**

*The share (%) of diameter (TD) and height (TH) differentiation classes in the experimental object.*

among plots from 0.00 to 0.60 (**Figure 3**) and the coefficient of variation was very high, cv = 78%. Species homogenous neighborhood, expressed by the index SM = 0.00, was observed in the case of 28 plots (39%), and the others showed higher mingling level. After 15 years, the mean value of the index was almost the same like in 1988— SM = 0.12. The minimum value of SM was 0.00 and the highest one was 0.55, with variation among plots reaching the level of 89%. The number of plots with the index SM = 0.00 decreased in 2003–2018 (25%), that is a 34% decrease. Similar to the control object, Scots pine and silver birch showed opposite behavior (**Figure 5**). Homogenous neighborhood was observed in the case of Scots pine, while birch was present most often as a single mixture.

#### *3.2.2.3 Economic object*

#### *3.2.2.3.1 Spatial distribution*

At the beginning of the experiment, the mean value of CE describing the spatial pattern of living trees in the economic object took the value CE = 1.21, pointing to a clear regular pattern. The lowest value of the index was CE = 0.74 and the highest was CE = 1.35 (**Figure 3**). The coefficient of variation between plots for this index was low—10%. The index differed significantly from randomness (CE = 1.00) in the case of 33 plots (43%). The dominant spatial pattern of living trees was therefore a random pattern. No clumping was observed on any plot. After 15 years, in 2003, the mean value of the index increased to CE = 1.27. In the object, the index ranged from 0.84 to 1.40 depending on the plot, with the coefficient of variation at the level of 11%. The number of plots with the CE index significantly larger than 1.0 was 34 (48%). The dominance of the random pattern was still observed in this object.

#### *3.2.2.3.2 Tree size diversity*

In 1988, the mean value of diameter differentiation index reached TD = 0.23, indicating rather low diversity in tree diameter among the nearest neighbors. The index ranged from 0.09 to 0.47 (**Figure 3**), with the coefficient of variation among plots at the level of 22%. Most trees showed very low or low diameter differentiation at the small spatial scale (92% of trees) and only few (7.8%) showed larger variation in diameter (**Table 5**). In 2003, the mean value of the index decreased to TD = 0.19, varying between 0.10 and 0.34 depending on the plot. The variation of the index among plot was at the level of 23%. After 15 years of stand development, the number of trees in the lowest two classes of diameter differentiation clearly increased (**Table 5**). As much as 97% of trees belonged to both these classes.

#### **Figure 5.**

*Spatial mingling of Scots pine and silver birch in the experimental object in two inventories.*


#### **Table 5.**

*The share (%) of diameter (TD) and height (TH) differentiation classes in the economic object.*

The height differentiation of trees in the economic object was clearly lower than the diameter. The mean value of the index, TH, was 0.10 and it ranged from 0.03 to 0.25, with cv = 35%. Up 93% of trees showed very low differentiation in height among their neighbors (**Table 5**). In 2003, the TH index reached the same mean value as in 1988 (TH = 0.10). The index varied from 0.05 to 0.23 depending on plot, and the coefficient of variation of TH index among plots was at the level of 32%. Again, the most abundance class was the one indicating very low height differentiation (**Table 5**).

#### *3.2.2.3.3 Species mingling*

In 1988, the spatial mingling index, SM, reached the mean value of 0.17, with its range from 0.00 to 0.46 (**Figure 3**). The coefficient of variation for the index was at the level of 74%. The relative low mean value of the index pointed to rather homogenous neighborhoods in terms of tree species. In the case of eight plots (10%), the index showed no mingling and in the case of the others, the species diversity was slightly higher. The low species mingling in the economic object was also confirmed by the distribution of this index in the mingling classes. Trees belonging to the lowest mingling class accounted for 68.8%, but 6.4% belonged to the mingling class showing very large mingling. In 2003, the mean value of SM index dropped to 0.13, varying between 0.00 and 0.51 among plots (cv = 97%). The abundance of very low mingling class increased to 75.8% at the expense of the classes of higher species

**71**

**Figure 6.**

*Differentiation of the Forest Structure as the Mitigation Action of Adverse Effects of Climate…*

mingling. Both tree species showed more complex situation in terms of spatial mingling comparing to the other objects (**Figure 6**). However, Scots pine formed large homogenous groups of trees, while silver birch was mixed in the form of groups or a

At the beginning of the experiment, the analyzed stands in the control, experimental, and economic objects showed significant differences in terms of spatial distribution of trees (α = 0.05). It was indicated by the Kruskal-Wallis test (KS)

= 5.8092, *P* = 0.05).

= 87.6834, *P* = 0.00). They have

 = 27.6787, *P* = 0.00). The Dunn's test, applied to find out which objects differed, showed that such significant differences were observed between the control and experimental objects (*P* = 0.00) as well as between the experimental and economic ones (*P* = 0.00). No significant difference in terms of spatial pattern was observed between the control and economic objects (*P* = 0.57). In 2003, the KS test

However, the Dunn's test indicated the only significant differences between the control object and economic one (*P* = 0.02). No differences have been observed

In 1988, the differences in the diameter differentiation index between the objects

= 52.4553, *P =* 0.00) and they were observed in the case of the same pairs of

been observed in the case of economic and control objects (*P* = 0.00) as well as the experimental and the control ones (*P* = 0.00). The experimental and economic objects were not different in terms of diameter differentiation of trees at the neighborhood spatial scale (*P* = 0.96). After 15 years, these differences were still signifi-

*3.2.3 Difference in structural diversity of the stand between objects*

confirmed the significant differences between the objects (χ<sup>2</sup>

*Spatial mingling of Scots pine and silver birch in the economic object in two inventories.*

*DOI: http://dx.doi.org/10.5772/intechopen.93327*

single mixture.

(χ2

*3.2.3.1 Spatial distribution*

between other pairs of the objects.

have been statistically proven by the KS test (χ<sup>2</sup>

*3.2.3.2 Tree size diversity*

cant (χ<sup>2</sup>

*Differentiation of the Forest Structure as the Mitigation Action of Adverse Effects of Climate… DOI: http://dx.doi.org/10.5772/intechopen.93327*

mingling. Both tree species showed more complex situation in terms of spatial mingling comparing to the other objects (**Figure 6**). However, Scots pine formed large homogenous groups of trees, while silver birch was mixed in the form of groups or a single mixture.

#### *3.2.3 Difference in structural diversity of the stand between objects*

#### *3.2.3.1 Spatial distribution*

*Silviculture*

**Figure 5.**

**Table 5.**

**70**

tion (**Table 5**).

*3.2.2.3.3 Species mingling*

The height differentiation of trees in the economic object was clearly lower than the diameter. The mean value of the index, TH, was 0.10 and it ranged from 0.03 to 0.25, with cv = 35%. Up 93% of trees showed very low differentiation in height among their neighbors (**Table 5**). In 2003, the TH index reached the same mean value as in 1988 (TH = 0.10). The index varied from 0.05 to 0.23 depending on plot, and the coefficient of variation of TH index among plots was at the level of 32%. Again, the most abundance class was the one indicating very low height differentia-

TD 1988 45.6 46.4 7.0 0.8 0

TH 1988 93.2 6.2 0.7 0 0

*The share (%) of diameter (TD) and height (TH) differentiation classes in the economic object.*

2003 63.9 32.9 2.6 0.6 0

2003 93.9 4.9 1.1 0.1 0

**Very low Low Moderate Large Very large**

*Spatial mingling of Scots pine and silver birch in the experimental object in two inventories.*

**Index Year Differentiation classes**

In 1988, the spatial mingling index, SM, reached the mean value of 0.17, with its range from 0.00 to 0.46 (**Figure 3**). The coefficient of variation for the index was at the level of 74%. The relative low mean value of the index pointed to rather homogenous neighborhoods in terms of tree species. In the case of eight plots (10%), the index showed no mingling and in the case of the others, the species diversity was slightly higher. The low species mingling in the economic object was also confirmed by the distribution of this index in the mingling classes. Trees belonging to the lowest mingling class accounted for 68.8%, but 6.4% belonged to the mingling class showing very large mingling. In 2003, the mean value of SM index dropped to 0.13, varying between 0.00 and 0.51 among plots (cv = 97%). The abundance of very low mingling class increased to 75.8% at the expense of the classes of higher species

At the beginning of the experiment, the analyzed stands in the control, experimental, and economic objects showed significant differences in terms of spatial distribution of trees (α = 0.05). It was indicated by the Kruskal-Wallis test (KS) (χ2 = 27.6787, *P* = 0.00). The Dunn's test, applied to find out which objects differed, showed that such significant differences were observed between the control and experimental objects (*P* = 0.00) as well as between the experimental and economic ones (*P* = 0.00). No significant difference in terms of spatial pattern was observed between the control and economic objects (*P* = 0.57). In 2003, the KS test confirmed the significant differences between the objects (χ<sup>2</sup> = 5.8092, *P* = 0.05). However, the Dunn's test indicated the only significant differences between the control object and economic one (*P* = 0.02). No differences have been observed between other pairs of the objects.

#### *3.2.3.2 Tree size diversity*

In 1988, the differences in the diameter differentiation index between the objects have been statistically proven by the KS test (χ<sup>2</sup> = 87.6834, *P* = 0.00). They have been observed in the case of economic and control objects (*P* = 0.00) as well as the experimental and the control ones (*P* = 0.00). The experimental and economic objects were not different in terms of diameter differentiation of trees at the neighborhood spatial scale (*P* = 0.96). After 15 years, these differences were still significant (χ<sup>2</sup> = 52.4553, *P =* 0.00) and they were observed in the case of the same pairs of

objects. In 1988, the differences in tree height differentiation between objects were statistically significant (χ<sup>2</sup> = 20.5312, *P* = 0.00). Dunn's test proved the significance of the differences between the control and economic objects (*P* = 0.00) as well as for control and experimental objects (*P* = 0.001). Trees in the economic object and experimental one were not significantly different in terms of total tree height (*P* = 0.81) at the beginning of the experiment. While the KS test showed significant differences between the objects after 15 years, the pairs of them for which Dunn's test pointed out the significant differences were different. No significant differences in tree height at the small spatial scale were confirmed for the economic and control objects (*P* = 0.66), but in the case of the other pairs of objects they were significant (*P* = 0.00).

#### *3.2.3.3 Species mingling*

Species diversity expressed in the form of species mingling index showed that the objects differed significantly (χ<sup>2</sup> = 28.6449, *P* = 0.00) but only at the beginning of the experiment (in 1988). The Dunn's test showed that such differences could be observed between the control and economic objects (*P* = 0.004) and between the economic and experimental ones (*P* = 0.01).

#### **4. Conclusion**

The structure of Scots pine stands has been shaped by the historical management system, that is, even-aged silviculture. This system results in the homogenous stand structure what is confirmed by the analysis of the stand structure based on different structural metrics. Just after the initialization of the experiment with different silvicultural strategies and their impact on the stand structure, the common stand parameters (dbh, basal area) were quite similar in each of the objects being analyzed. Fifteen years after, these parameters changed clearly, and the objects differed significantly. The highest mean tree diameter was reached in the economic object followed by the experimental one. The lowest was in the case of the control object. The total stand basal area was the highest in the control object.

While the spatial pattern of tree distribution was regular, on average, the silvicultural strategies influenced clearly in the number of plots for which the regularity was statistically proved. Active strategy led to the increase of regularity and passive strategy favored the random pattern occurrence in the stand.

The previous even-aged silviculture favored low diameter differentiation of trees in each of the object. Fifteen years of the experiment, passive and active silvicultural strategies resulted in more differentiation between objects. Each of the strategies led to a lower tree diameter diversity, but business as usual strategy favored diameter homogeneity to much more extent than other strategies. Passive strategy supported higher diversity of tree diameter. In the case of tree height diversity, all strategies considered here were associated with decreasing of tree height diversity. There was no clear impact of any strategy on creating tree height diversity in Scots pine stands.

The dominance of Scots pine in the stands was confirmed by the structural metrics in each of the object. Species homogenous plots were favored by two strategies: passive and business as usual. The share of homogeneous plots decreased after 15 years of experiment only in case of the experimental object.

**73**

**Author details**

Janusz Szmyt

Poznan, Poland

*Differentiation of the Forest Structure as the Mitigation Action of Adverse Effects of Climate…*

Department of Silviculture, Faculty of Forestry, Poznan University of Life Sciences,

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

\*Address all correspondence to: janusz.szmyt@up.poznan.pl

provided the original work is properly cited.

*DOI: http://dx.doi.org/10.5772/intechopen.93327*

*Differentiation of the Forest Structure as the Mitigation Action of Adverse Effects of Climate… DOI: http://dx.doi.org/10.5772/intechopen.93327*

### **Author details**

*Silviculture*

(*P* = 0.00).

*3.2.3.3 Species mingling*

**4. Conclusion**

the objects differed significantly (χ<sup>2</sup>

economic and experimental ones (*P* = 0.01).

statistically significant (χ<sup>2</sup>

objects. In 1988, the differences in tree height differentiation between objects were

Species diversity expressed in the form of species mingling index showed that

of the experiment (in 1988). The Dunn's test showed that such differences could be observed between the control and economic objects (*P* = 0.004) and between the

The structure of Scots pine stands has been shaped by the historical management system, that is, even-aged silviculture. This system results in the homogenous stand structure what is confirmed by the analysis of the stand structure based on different structural metrics. Just after the initialization of the experiment with different silvicultural strategies and their impact on the stand structure, the common stand parameters (dbh, basal area) were quite similar in each of the objects being analyzed. Fifteen years after, these parameters changed clearly, and the objects differed significantly. The highest mean tree diameter was reached in the economic object followed by the experimental one. The lowest was in the case of the control

While the spatial pattern of tree distribution was regular, on average, the silvicultural strategies influenced clearly in the number of plots for which the regularity was statistically proved. Active strategy led to the increase of regularity and passive

The previous even-aged silviculture favored low diameter differentiation of trees in each of the object. Fifteen years of the experiment, passive and active silvicultural strategies resulted in more differentiation between objects. Each of the strategies led to a lower tree diameter diversity, but business as usual strategy favored diameter homogeneity to much more extent than other strategies. Passive strategy supported higher diversity of tree diameter. In the case of tree height diversity, all strategies considered here were associated with decreasing of tree height diversity. There was no clear impact of any strategy on creating tree height diversity

The dominance of Scots pine in the stands was confirmed by the structural metrics in each of the object. Species homogenous plots were favored by two strategies: passive and business as usual. The share of homogeneous plots decreased after

object. The total stand basal area was the highest in the control object.

strategy favored the random pattern occurrence in the stand.

15 years of experiment only in case of the experimental object.

of the differences between the control and economic objects (*P* = 0.00) as well as for control and experimental objects (*P* = 0.001). Trees in the economic object and experimental one were not significantly different in terms of total tree height (*P* = 0.81) at the beginning of the experiment. While the KS test showed significant differences between the objects after 15 years, the pairs of them for which Dunn's test pointed out the significant differences were different. No significant differences in tree height at the small spatial scale were confirmed for the economic and control objects (*P* = 0.66), but in the case of the other pairs of objects they were significant

= 20.5312, *P* = 0.00). Dunn's test proved the significance

= 28.6449, *P* = 0.00) but only at the beginning

**72**

in Scots pine stands.

Janusz Szmyt Department of Silviculture, Faculty of Forestry, Poznan University of Life Sciences, Poznan, Poland

\*Address all correspondence to: janusz.szmyt@up.poznan.pl

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

### **References**

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[2] Bonan GB. Forests and climate change: Forcings, feedbacks, and the climate benefits of forests. Science. 2008;**320**:1444-1449

[3] Hanewinkel M, Cullmann DA, Schelhaas MJ, Nabuurs GJ, Zimmermann NE. Climate change may cause severe loss in the economic value of European forest land. Nature Climate Change. 2013;**3**:203-207

[4] Lindner M, Maroschek M, Netherer S, Kremer A, Barbati A, Garcia-Gonzalo J, et al. Climate change impacts, adaptive capacity, and vulnerability of European forest ecosystems. Forest Ecology and Management. 2010;**259**:698-709

[5] Harris RMB, Grose MR, Lee G, Bindoff NL, Porfirio LL, Fox-Hughes P. Climate projections for ecologists. Wiley Interdisciplinary Reviews: Climate Change. 2014;**5**:621-637

[6] Millar CI, Stephenson NL, Stephens SL. Climate change and forests of the future: Managing in the face of uncertainty. Ecological Applications. 2007;**17**:2145-2151

[7] Dyderski MK, Paź S, Frelich LE, Jagodziński AM. How much does climate change threaten European forest tree species distributions? Global Change Biology. 2018;**24**:1150-1163

[8] Corona P, Scotti R. Systemic silviculture, adaptive management and forest monitoring perspectives. L'Italia For. e Mont. 2011:219-224

[9] Messier C, Puettmann K, Chazdon R, Andersson KP, Angers VA, Brotons L, et al. From management to stewardship: Viewing forests as complex adaptive systems in an Uncertain World. Conservation Letters. 2015;**8**:368-377

[10] Puettmann KJ. Silvicultural challenges and options in the context of global change: "Simple" fixes and opportunities for new management approaches. Journal of Forestry. 2011;**31**:855-856 discussion 856

[11] Puettmann KJ, Wilson SMG, Baker SC, Donoso PJ, Drössler L, Amente G, et al. Silvicultural alternatives to conventional even-aged forest management—what limits global adoption? Forest Ecosystem. 2015;**2**:8. DOI: 10.1186/s40663-015-0031-x

[12] Bolte A, Ammer C, Löf M, Madsen P, Nabuurs GJ, Schall P, et al. Adaptive forest management in Central Europe: Climate change impacts, strategies and integrative concept. Scandinavian Journal of Forest Research. 2009;**24**:473-482

[13] Nocentini S, Buttoud G, Ciancio O, Corona P. Managing forests in a changing world: The need for a systemic approach. A review. Forest Systems. 2017;**26**:1-15

[14] Sousa-Silva R, Ponette Q, Verheyen K, Van Herzele A, Muys B. Adaptation of forest management to climate change as perceived by forest owners and managers in Belgium. Forest Ecosystem. 2016;**3**:22. DOI: 10.1186/ s40663-016-0082-7

[15] Lexer MJ, Jandl R, Nabernegg S, Bednar-Friedl B. Forestry IPCC Assess Rep. 2015. pp. 147-167

[16] Temperli C, Bugmann H, Elkin C. Adaptive management for competing forest goods and services under climate

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hodowla lasu (w kotekście zasady wielofunkcyjnej lasu). Stud. i Mater. Cent. Edukac. Przyr. 2008;**19**:41-54

[27] Franklin JF, Spies TA, Van Pelt R, Carey AB, Thornburgh DA, Berg DR, et al. Disturbances and structural development of natural forest ecosystems with silvicultural implications, using Douglas-fir forests as an example. Forest Ecology and Management. 2002;**155**:399-423

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ISBN: 9788371609312

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[29] Rozkrut D. Statistical Yearbook of Forestry. Zakład Wydawnictw Statystycznych Warszawa; 2018. ISSN

Nachtergale L, Geudens G, Lust N. Spatial methods for quantifying forest stand structure development: A comparison between nearest-neighbor indices and Variogram analysis. Forest

[31] R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2019

[32] Kint V. SIAFOR 1.0—User Guide. Laboratory of Forestry, Ghent

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change. Ecological Applications.

[17] D'Amato AW, Bradford JB, Fraver S, Palik BJ. Forest management for mitigation and adaptation to climate

change: Insights from long-term

[18] Pommerening A, Murphy ST. A review of the history, definitions and methods of continuous cover forestry with special attention to afforestation and restocking. Forestry. 2004;**77**:27-44

silviculture experiments. Forest Ecology and Management. 2011;**262**:803-816

[19] Puettmann K, Coates KD, Messier C. A Critique of Silviculture. Managing for Complexity. Washington-Covelo-London: Island Press; 2009. ISBN:

[20] Pukkala T, Lähde E, Laiho O, Salo K, Hotanen JP. A multifunctional comparison of even-aged and unevenaged forest management in a boreal region. Canadian Journal of Forest

[21] Brang P, Spathelf P, Larsen JB, Bauhus J, Bončína A, Chauvin C, et al. Suitability of close-to-nature silviculture for adapting temperate European forests to climate change.

[22] O'Hara KL. What is close-to-nature silviculture in a changing world?

[23] Bernadzki E. Gospodarka leśna w obliczu zmian klimatu. Sylwan.

[24] Bernadzki E. Cele hodowli lasu wczoraj i dziś. Sylwan. 1997;**4**:23-31

silviculture: Is this concept compatible with species diversity? Forestry.

[25] Schütz J. Close-to-nature

[26] Brzeziecki B. Podejście ekosystemowe i półnaturalna

Research. 2011;**41**:851-862

Forestry. 2014;**87**:492-503

Forestry. 2016;**89**:1-6

1995;**1**:19-32

1999;**72**:359-366

2012;**22**:2065-2077

978-1-59726-145-6

*Differentiation of the Forest Structure as the Mitigation Action of Adverse Effects of Climate… DOI: http://dx.doi.org/10.5772/intechopen.93327*

change. Ecological Applications. 2012;**22**:2065-2077

[17] D'Amato AW, Bradford JB, Fraver S, Palik BJ. Forest management for mitigation and adaptation to climate change: Insights from long-term silviculture experiments. Forest Ecology and Management. 2011;**262**:803-816

[18] Pommerening A, Murphy ST. A review of the history, definitions and methods of continuous cover forestry with special attention to afforestation and restocking. Forestry. 2004;**77**:27-44

[19] Puettmann K, Coates KD, Messier C. A Critique of Silviculture. Managing for Complexity. Washington-Covelo-London: Island Press; 2009. ISBN: 978-1-59726-145-6

[20] Pukkala T, Lähde E, Laiho O, Salo K, Hotanen JP. A multifunctional comparison of even-aged and unevenaged forest management in a boreal region. Canadian Journal of Forest Research. 2011;**41**:851-862

[21] Brang P, Spathelf P, Larsen JB, Bauhus J, Bončína A, Chauvin C, et al. Suitability of close-to-nature silviculture for adapting temperate European forests to climate change. Forestry. 2014;**87**:492-503

[22] O'Hara KL. What is close-to-nature silviculture in a changing world? Forestry. 2016;**89**:1-6

[23] Bernadzki E. Gospodarka leśna w obliczu zmian klimatu. Sylwan. 1995;**1**:19-32

[24] Bernadzki E. Cele hodowli lasu wczoraj i dziś. Sylwan. 1997;**4**:23-31

[25] Schütz J. Close-to-nature silviculture: Is this concept compatible with species diversity? Forestry. 1999;**72**:359-366

[26] Brzeziecki B. Podejście ekosystemowe i półnaturalna hodowla lasu (w kotekście zasady wielofunkcyjnej lasu). Stud. i Mater. Cent. Edukac. Przyr. 2008;**19**:41-54

[27] Franklin JF, Spies TA, Van Pelt R, Carey AB, Thornburgh DA, Berg DR, et al. Disturbances and structural development of natural forest ecosystems with silvicultural implications, using Douglas-fir forests as an example. Forest Ecology and Management. 2002;**155**:399-423

[28] Szmyt J. Ocena Dynamiki Struktury Drzewostanów Sosnowych (*Pinus sylvestris* L.) Objętych Różnym Postępowaniem Hodowlanym. Wydawnictwo Uniwersytetu Przyrodniczego w Poznaniu; 2019. ISBN: 9788371609312

[29] Rozkrut D. Statistical Yearbook of Forestry. Zakład Wydawnictw Statystycznych Warszawa; 2018. ISSN 2657-3199

[30] Kint V, Meirvenne MV, Nachtergale L, Geudens G, Lust N. Spatial methods for quantifying forest stand structure development: A comparison between nearest-neighbor indices and Variogram analysis. Forest Science. 2003;**49**:36-49

[31] R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2019

[32] Kint V. SIAFOR 1.0—User Guide. Laboratory of Forestry, Ghent University; 2004

**74**

*Silviculture*

**References**

[1] Lindner M, Fitzgerald JB,

Management. 2014;**146**:69-83

2008;**320**:1444-1449

Change. 2013;**3**:203-207

[4] Lindner M, Maroschek M, Netherer S, Kremer A, Barbati A, Garcia-Gonzalo J, et al. Climate change

impacts, adaptive capacity, and vulnerability of European forest ecosystems. Forest Ecology and Management. 2010;**259**:698-709

[5] Harris RMB, Grose MR, Lee G, Bindoff NL, Porfirio LL, Fox-Hughes P. Climate projections for ecologists. Wiley Interdisciplinary Reviews: Climate

Change. 2014;**5**:621-637

2007;**17**:2145-2151

[6] Millar CI, Stephenson NL,

Stephens SL. Climate change and forests of the future: Managing in the face of uncertainty. Ecological Applications.

[7] Dyderski MK, Paź S, Frelich LE, Jagodziński AM. How much does climate change threaten European forest tree species distributions? Global Change Biology. 2018;**24**:1150-1163

[8] Corona P, Scotti R. Systemic

For. e Mont. 2011:219-224

silviculture, adaptive management and forest monitoring perspectives. L'Italia

[2] Bonan GB. Forests and climate change: Forcings, feedbacks, and the climate benefits of forests. Science.

[3] Hanewinkel M, Cullmann DA, Schelhaas MJ, Nabuurs GJ,

Zimmermann NE. Climate change may cause severe loss in the economic value of European forest land. Nature Climate

Zimmermann NE, Reyer C, Delzon S, van der Maaten E, et al. Climate change and European forests: What do we know, what are the uncertainties, and what are the implications for forest management? Journal of Environmental [9] Messier C, Puettmann K, Chazdon R, Andersson KP, Angers VA, Brotons L, et al. From management to stewardship: Viewing forests as complex adaptive systems in an Uncertain World. Conservation Letters. 2015;**8**:368-377

[10] Puettmann KJ. Silvicultural challenges and options in the context of global change: "Simple" fixes and opportunities for new management approaches. Journal of Forestry. 2011;**31**:855-856 discussion 856

[11] Puettmann KJ, Wilson SMG, Baker SC, Donoso PJ, Drössler L, Amente G, et al. Silvicultural

[12] Bolte A, Ammer C, Löf M, Madsen P, Nabuurs GJ, Schall P, et al. Adaptive forest management in Central Europe: Climate change impacts, strategies and integrative concept. Scandinavian Journal of Forest

Research. 2009;**24**:473-482

[13] Nocentini S, Buttoud G,

[14] Sousa-Silva R, Ponette Q, Verheyen K, Van Herzele A, Muys B. Adaptation of forest management to climate change as perceived by forest owners and managers in Belgium. Forest Ecosystem. 2016;**3**:22. DOI: 10.1186/

[15] Lexer MJ, Jandl R, Nabernegg S, Bednar-Friedl B. Forestry IPCC Assess

[16] Temperli C, Bugmann H, Elkin C. Adaptive management for competing forest goods and services under climate

Systems. 2017;**26**:1-15

s40663-016-0082-7

Rep. 2015. pp. 147-167

Ciancio O, Corona P. Managing forests in a changing world: The need for a systemic approach. A review. Forest

alternatives to conventional even-aged forest management—what limits global adoption? Forest Ecosystem. 2015;**2**:8. DOI: 10.1186/s40663-015-0031-x

**77**

cultivation objectives.

**Chapter 5**

**Abstract**

**1. Introduction**

Afforestation

*Jie Duan and Dilnur Abduwali*

Basic Theory and Methods of

Afforestation is an important practice in silviculture. This chapter outlines the forest site, site preparation, selection of afforestation materials in the process of afforestation. The life cycle of forests is very long, and it is difficult to change them once afforested. Therefore, the forest site must be analyzed in depth before afforestation to maintain the success of afforestation and the healthy growth of forests later. Forest sites are mainly affected by environmental and human activities. To facilitate afforestation, it is necessary to evaluate and classify the forest site factors and achieve a suitable species planted on the right site. Site preparation is also based on site classification. It is usually carried out after determining the type of afforestation land, divided into mechanical land preparation and chemical methods. An essential task of site preparation is to maintain soil moisture and promote seedlings' survival and growth. Afforestation materials are mainly divided into three categories: seed, seedling, and cutting. The choice of these three types of afforestation

materials and methods is related to site conditions, tree species, and age.

**Keywords:** afforestation, forest site, site preparation, afforestation material

One of the most important afforestation principles is to adapt the trees to the site [1, 2]. In a narrow sense, a forest site refers to afforestation land. In a broad sense, it refers to all factors that affect forest growth, including natural factors such as climate, soil, vegetation, and human activities. These factors constitute the forest site factor. From an ecological point of view, these factors interact with the forest and will change over time. From this perspective, forest factors affect the survival rate of afforestation and affect the forest's entire life cycle. The systematic study of forest sites has a history of over 200 years and is still continuing. Most forest site research objects are mountain forests. With the continuous development of urban forestry, urban forest site research also appears [3]. The forest site conditions of mountain forests (**Figure 1**) are entirely different from urban forests (**Figure 2**). Forest site factors can have many combinations, each of which determines the corresponding suitable tree species and its afforestation methods, and even subsequent management methods. Therefore, the site factors of the forest should be scrutinized and analyzed before afforestation to avoid afforestation failure. After the type of afforestation land is devised, afforestation tree species and afforestation methods suitable for the type are selected according to the

#### **Chapter 5**

## Basic Theory and Methods of Afforestation

*Jie Duan and Dilnur Abduwali*

#### **Abstract**

Afforestation is an important practice in silviculture. This chapter outlines the forest site, site preparation, selection of afforestation materials in the process of afforestation. The life cycle of forests is very long, and it is difficult to change them once afforested. Therefore, the forest site must be analyzed in depth before afforestation to maintain the success of afforestation and the healthy growth of forests later. Forest sites are mainly affected by environmental and human activities. To facilitate afforestation, it is necessary to evaluate and classify the forest site factors and achieve a suitable species planted on the right site. Site preparation is also based on site classification. It is usually carried out after determining the type of afforestation land, divided into mechanical land preparation and chemical methods. An essential task of site preparation is to maintain soil moisture and promote seedlings' survival and growth. Afforestation materials are mainly divided into three categories: seed, seedling, and cutting. The choice of these three types of afforestation materials and methods is related to site conditions, tree species, and age.

**Keywords:** afforestation, forest site, site preparation, afforestation material

#### **1. Introduction**

One of the most important afforestation principles is to adapt the trees to the site [1, 2]. In a narrow sense, a forest site refers to afforestation land. In a broad sense, it refers to all factors that affect forest growth, including natural factors such as climate, soil, vegetation, and human activities. These factors constitute the forest site factor. From an ecological point of view, these factors interact with the forest and will change over time. From this perspective, forest factors affect the survival rate of afforestation and affect the forest's entire life cycle. The systematic study of forest sites has a history of over 200 years and is still continuing. Most forest site research objects are mountain forests. With the continuous development of urban forestry, urban forest site research also appears [3]. The forest site conditions of mountain forests (**Figure 1**) are entirely different from urban forests (**Figure 2**).

Forest site factors can have many combinations, each of which determines the corresponding suitable tree species and its afforestation methods, and even subsequent management methods. Therefore, the site factors of the forest should be scrutinized and analyzed before afforestation to avoid afforestation failure. After the type of afforestation land is devised, afforestation tree species and afforestation methods suitable for the type are selected according to the cultivation objectives.

**Figure 1.** Pinus tabulaeformis *forest in Song Mountain, Beijing.*

**Figure 2.** Pinus tabulaeformis *forest in city plain area, Beijing.*

Another critical factor affecting the success or failure of afforestation is the healthy growth of the root system. Whether it is a seed or a seedling, only rooting or rooting after transplanting can form a forest [4]. Site preparation promotes and ensures that the root system can be closely integrated with the soil through different methods. Furthermore, promote the root system to absorb enough water.

Common afforestation materials include seeds, seedlings, and cuttings. Each material has its advantages and disadvantages. The selection of suitable afforestation materials should fully consider the characteristics of the tree species. Many studies have shown that the age of planting materials, planting season and time, and methods all affect the survival rate [5–7].

In summary, this chapter mainly introduces the concept of forest site, analyzes different site factors, and summarizes forest site evaluation and classification.

**79**

*Basic Theory and Methods of Afforestation DOI: http://dx.doi.org/10.5772/intechopen.96164*

planting characteristics.

**2.1 Concept of forest site**

ered first in afforestation activities.

**2.2 Forest site factors**

productivity [21, 22].

*2.2.1 Environmental factor*

(earth-based) or phytocentric (plant-based) methods.

**2. Forest site**

Next, the types of afforestation land and standard land preparation methods are introduced. Finally, we outline the three different afforestation materials and their

Forest is an important part of the ecosystem, which means silviculture is ecosystem management. Afforestation must be carried out from an ecosystem perspective, specifically light, water, carbon dioxide, and various nutrients. In the traditional sense, forest site refers to the overall environment of an area [8]. Generally speaking, the forest site has two meanings. First, it refers to geographic location; second, it refers to integrating environmental conditions (biology, soil, and climate) in a particular location [1]. The forest site remains unchanged for a certain period, especially climate conditions, and irrelevant with the tree species growing on it. Meantime, some experts refer to the forest site potential and productivity are not constant but change over time [9, 10]. Forest site and its quality should be consid-

Forest Site research in various countries around the world mainly focuses on on-site classification and site productivity evaluation. In the late 18th century, European silviculturists tried to classify forestry's productivity by compiling stand yield tables [11, 12]. In 1946, multifactor forest site classification developed into a comprehensive multifactor classification based on climate, geography, soil, and vegetation, namely the Baden-Württemberg forest site classification [13]. Since the 1950s, multifactor site classification methods have been widely used in Canada and the United States [14, 15]. Skovsgaadr and Vanclay's [16] review paper mentioned that there are two methods to assess forest site productivity include geocentric

With the intervention of mathematical methods such as remote sensing, geographic information system, computer technology, and multivariate statistical analysis, forest site classification has gradually moved from qualitative to quantitative or a combination, from the single-factor to the ecological multifactor

Environmental factors include climate, topography, soil, and hydrology factors. Climate factors determine the water and heat conditions that plants depend on, thus forming vegetation types. Meteorologists divided climate into macroclimate and microclimate base on the ecological scale. The macroclimate has often been referred to as that climate resulting from air masses' passage [18]. The microclimate is the suite of climatic conditions measured in the localized areas near the earth's surface [19]. Macroclimate mainly affects tree distribution [20]. For afforestation, the foresters pay more attention to microclimate factors. Light, temperature, rainfall, solar radiation, wind speed, and other factors affect tree growth and forest

The topography factors include elevation, aspect, slope, position, slope type, etc. Elevation and aspect appeared to be fundamental variables in the assessment of forest site quality [20]. In mountainous areas, tree height and forest productivity

classification for multi-purpose forest resource management [17].

Next, the types of afforestation land and standard land preparation methods are introduced. Finally, we outline the three different afforestation materials and their planting characteristics.

#### **2. Forest site**

*Silviculture*

**Figure 1.**

**Figure 2.**

Pinus tabulaeformis *forest in Song Mountain, Beijing.*

**78**

Another critical factor affecting the success or failure of afforestation is the healthy growth of the root system. Whether it is a seed or a seedling, only rooting or rooting after transplanting can form a forest [4]. Site preparation promotes and ensures that the root system can be closely integrated with the soil through different

Common afforestation materials include seeds, seedlings, and cuttings. Each material has its advantages and disadvantages. The selection of suitable afforestation materials should fully consider the characteristics of the tree species. Many studies have shown that the age of planting materials, planting season and time,

In summary, this chapter mainly introduces the concept of forest site, analyzes different site factors, and summarizes forest site evaluation and classification.

methods. Furthermore, promote the root system to absorb enough water.

and methods all affect the survival rate [5–7].

Pinus tabulaeformis *forest in city plain area, Beijing.*

#### **2.1 Concept of forest site**

Forest is an important part of the ecosystem, which means silviculture is ecosystem management. Afforestation must be carried out from an ecosystem perspective, specifically light, water, carbon dioxide, and various nutrients. In the traditional sense, forest site refers to the overall environment of an area [8]. Generally speaking, the forest site has two meanings. First, it refers to geographic location; second, it refers to integrating environmental conditions (biology, soil, and climate) in a particular location [1]. The forest site remains unchanged for a certain period, especially climate conditions, and irrelevant with the tree species growing on it. Meantime, some experts refer to the forest site potential and productivity are not constant but change over time [9, 10]. Forest site and its quality should be considered first in afforestation activities.

Forest Site research in various countries around the world mainly focuses on on-site classification and site productivity evaluation. In the late 18th century, European silviculturists tried to classify forestry's productivity by compiling stand yield tables [11, 12]. In 1946, multifactor forest site classification developed into a comprehensive multifactor classification based on climate, geography, soil, and vegetation, namely the Baden-Württemberg forest site classification [13]. Since the 1950s, multifactor site classification methods have been widely used in Canada and the United States [14, 15]. Skovsgaadr and Vanclay's [16] review paper mentioned that there are two methods to assess forest site productivity include geocentric (earth-based) or phytocentric (plant-based) methods.

With the intervention of mathematical methods such as remote sensing, geographic information system, computer technology, and multivariate statistical analysis, forest site classification has gradually moved from qualitative to quantitative or a combination, from the single-factor to the ecological multifactor classification for multi-purpose forest resource management [17].

#### **2.2 Forest site factors**

#### *2.2.1 Environmental factor*

Environmental factors include climate, topography, soil, and hydrology factors. Climate factors determine the water and heat conditions that plants depend on, thus forming vegetation types. Meteorologists divided climate into macroclimate and microclimate base on the ecological scale. The macroclimate has often been referred to as that climate resulting from air masses' passage [18]. The microclimate is the suite of climatic conditions measured in the localized areas near the earth's surface [19]. Macroclimate mainly affects tree distribution [20]. For afforestation, the foresters pay more attention to microclimate factors. Light, temperature, rainfall, solar radiation, wind speed, and other factors affect tree growth and forest productivity [21, 22].

The topography factors include elevation, aspect, slope, position, slope type, etc. Elevation and aspect appeared to be fundamental variables in the assessment of forest site quality [20]. In mountainous areas, tree height and forest productivity decreased as elevation increased [23, 24]. The increase in elevation within a specific area can reduce temperature, decrease evaporation, shorten the frost-free period, increase precipitation and atmospheric and soil moisture, increase soil fertility, dense vegetation, or change vegetation types [23–25]. The light of different aspect is usually different, which can indirectly influence the soil moisture content; the south has more soil moisture than the north [14]. Furthermore, sites with lower slopes have better soil quality and higher nutrients and soil moisture than sites with steep slopes [14].

Soil is the substrate for tree growth and the forest site's essential factor, and it can influence root distribution and the ability to take up water and nutrients exchange [26]. Before afforestation, it is essential to assess the soil factors, mainly soil type, soil layer depth, soil texture, soil structure, soil nutrients, pH, and soil erosion [27]. South African forest site classification system has six soil variables (parent material, soil classification, effective soil depth, depth limiting material, topsoil organic matter, and topsoil texture), and these variables are dynamically changing [28]. It is necessary to collect many soil samples to represent the actual site situation despite this is very expensive.

Hydrology factors include groundwater depth and seasonal changes, groundwater salinity and salt composition, the presence or absence of seasonal stagnant water, and its duration. For some forests in plain areas, hydrology plays a significant role. Such as Ningxia, China, has a high groundwater level and heavy soil salinization [29]. Controlling the rise of the groundwater level is the key to forest site improvement in the irrigation area. When afforestation in mountainous areas, generally does not consider the groundwater level because it is difficult for the tree roots to reach the groundwater layer, and more consideration is streamflow. Moisture tends to increase with elevation and gets wetter on the northern aspects [30].

At present, there are many monitoring instruments that can monitor forest meteorological factors, including radiation, temperature, humidity, wind speed, wind direction, etc. (**Figure 3**). It provides an important data source for afforestation and future forest management.

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**Figure 4.**

*Reforestation site after clearcutting.*

*Basic Theory and Methods of Afforestation DOI: http://dx.doi.org/10.5772/intechopen.96164*

Forest vegetation types and the distribution comprehensively reflect different site conditions, especially for soil conditions. Many studies indicated that vegetation factors could refect soil fertility, soil moisture, soil nutrient, and indirectly site quality [31, 32]. In the cold-temperate forests of Russia, Northern Europe, Canada, plant species or plant communities are widely used to evaluate sites [33–35]. Zhang Wanru [36] researched the use of vegetation types as the basis for forest site classification systems in China. However, in China, many plantations are damaged, and

Human activity can affect the forest; some are negative, such as removing litter

Many factors affect the forest site and tree growth; see 2.2.1, 2.2.2, and 2.2.3. However, some factors have little effect on the growth and development of trees, and some factors play a decisive role. These decisive factors are called dominant factors. Generally, the climate is the dominant factor at the regional scale, and landform and soil are the dominant factors at the management unit scale [39]. There are two methods to determine the dominant factors. One is to analyze the relationship between each environmental factor and the essential living factors (light, heat, air,

from forest land and mining groundwater seriously, which will deteriorate the site, cause soil erosion, and lower groundwater levels. Some human activities can severely impact forests, such as the destruction of forests caused by the slashing and burning of agricultural activities in Europe [37]. From an ecological perspective, forest management is also a disturbance to forest growth, but most of them are positive effects, such as afforestation and reforestation [38] (**Figure 4**). Human activity factors are generally analyzed in forest site assessment as one of the driving forces for forming or changing other site factors, not as a constituent factor of site

it is not easy to use indicator plants to evaluate these forest sites.

*2.2.2 Vegetation factor*

*2.2.3 Human activity factor*

condition types.

*2.2.4 Forest site dominant factor*

**Figure 3.** *A weather station that can monitor a variety of climate factors.*

#### *2.2.2 Vegetation factor*

*Silviculture*

steep slopes [14].

aspects [30].

situation despite this is very expensive.

tion and future forest management.

*A weather station that can monitor a variety of climate factors.*

decreased as elevation increased [23, 24]. The increase in elevation within a specific area can reduce temperature, decrease evaporation, shorten the frost-free period, increase precipitation and atmospheric and soil moisture, increase soil fertility, dense vegetation, or change vegetation types [23–25]. The light of different aspect is usually different, which can indirectly influence the soil moisture content; the south has more soil moisture than the north [14]. Furthermore, sites with lower slopes have better soil quality and higher nutrients and soil moisture than sites with

Soil is the substrate for tree growth and the forest site's essential factor, and it can influence root distribution and the ability to take up water and nutrients exchange [26]. Before afforestation, it is essential to assess the soil factors, mainly soil type, soil layer depth, soil texture, soil structure, soil nutrients, pH, and soil erosion [27]. South African forest site classification system has six soil variables (parent material, soil classification, effective soil depth, depth limiting material, topsoil organic matter, and topsoil texture), and these variables are dynamically changing [28]. It is necessary to collect many soil samples to represent the actual site

Hydrology factors include groundwater depth and seasonal changes, groundwater salinity and salt composition, the presence or absence of seasonal stagnant water, and its duration. For some forests in plain areas, hydrology plays a significant role. Such as Ningxia, China, has a high groundwater level and heavy soil salinization [29]. Controlling the rise of the groundwater level is the key to forest site improvement in the irrigation area. When afforestation in mountainous areas, generally does not consider the groundwater level because it is difficult for the tree roots to reach the groundwater layer, and more consideration is streamflow. Moisture tends to increase with elevation and gets wetter on the northern

At present, there are many monitoring instruments that can monitor forest meteorological factors, including radiation, temperature, humidity, wind speed, wind direction, etc. (**Figure 3**). It provides an important data source for afforesta-

**80**

**Figure 3.**

Forest vegetation types and the distribution comprehensively reflect different site conditions, especially for soil conditions. Many studies indicated that vegetation factors could refect soil fertility, soil moisture, soil nutrient, and indirectly site quality [31, 32]. In the cold-temperate forests of Russia, Northern Europe, Canada, plant species or plant communities are widely used to evaluate sites [33–35]. Zhang Wanru [36] researched the use of vegetation types as the basis for forest site classification systems in China. However, in China, many plantations are damaged, and it is not easy to use indicator plants to evaluate these forest sites.

#### *2.2.3 Human activity factor*

Human activity can affect the forest; some are negative, such as removing litter from forest land and mining groundwater seriously, which will deteriorate the site, cause soil erosion, and lower groundwater levels. Some human activities can severely impact forests, such as the destruction of forests caused by the slashing and burning of agricultural activities in Europe [37]. From an ecological perspective, forest management is also a disturbance to forest growth, but most of them are positive effects, such as afforestation and reforestation [38] (**Figure 4**). Human activity factors are generally analyzed in forest site assessment as one of the driving forces for forming or changing other site factors, not as a constituent factor of site condition types.

#### *2.2.4 Forest site dominant factor*

Many factors affect the forest site and tree growth; see 2.2.1, 2.2.2, and 2.2.3. However, some factors have little effect on the growth and development of trees, and some factors play a decisive role. These decisive factors are called dominant factors. Generally, the climate is the dominant factor at the regional scale, and landform and soil are the dominant factors at the management unit scale [39]. There are two methods to determine the dominant factors. One is to analyze the relationship between each environmental factor and the essential living factors (light, heat, air,

**Figure 4.** *Reforestation site after clearcutting.*

water, and nutrition) of trees to determine the most significant impact on living factors. On the other hand, it is to find out those environmental factors in extreme conditions and restrict plant growth [1]. Generally, the most restrictive factors play a leading role, such as drought, severe cold, strong wind, and extreme weather. For example, in Saihanba Forest farm, some afforestation area has very thin soil; the dominant factor is water (**Figure 5**).

#### **2.3 Forest site classification**

The forest site classification refers to a traditionally used method to determine the suited tree species in the right site and perform macro-classification and microclassification [1, 40]. The system generally consists of multiple (level) taxa depend on the scale. The climate is the primary effect factor at the landscape and regional scale, whereas topography and soil at the local scale [27]. Usually, afforestation always tends to pay more attention to micro-forest sites because it directly relates them to the tree survival rate and have similar management properties. As climate change is concerned, afforestation is increasingly considering the macro-regional scales and the forest life cycle.

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*Basic Theory and Methods of Afforestation DOI: http://dx.doi.org/10.5772/intechopen.96164*

Canada [31, 33, 34, 45].

soil and vegetation [48].

type groups, and 4463 site types.

**2.4 Forest site quality and assessment**

Site classification methods are divided into two groups, single factor and multifactor methods [41]. Single factors classification systems depend on one factor to express a forest site, such as soil, indicator plant, or climate. The soil classification system was wildly used in many countries, like the United States and Ireland, to quantify site quality and determine its suitability for afforestation or timber yields [42–44]. Indicator plants or plant communities could also indicate a forest site's fertility and moisture status, especially in some humid climate regions or coastal countries, such as Scotland, Britain, Ireland, Finland, and British Columbia in

Recently, with the rise of sustainable forest management, multifactor forest site classifications system has developed rapidly. The biogeoclimatic ecosystem classification in British Columbia has combined the climate, vegetation, and soil factors to assess the site productivity and guide the afforestation and forest management [46, 47]. The Finland upland forest site classification system consists of six clusters depend on the vegetation types and the site water conditions [34]. In Germany, the Baden-Wurttemberg silviculture forest site classification has three levels, landscape level, regional level, and local level. The landscape level contains subunit called growth districts, divided into smaller areas at the regional level depending on the climate and topographic; the basic ecological units are called site units according to

In China, the research group of "China Forest Site Classification," headed by Zhan Zhaoning, proposed a site classification system in 1989 [49]. Zhang Wanru formally established a site classification system based on timber forests [36]. The Chinese Forest Site Classification and Chinese Forest Site are national classification systems. Forest Site Classification system in China can be divided into six levels [49]: site area, site region, site sub-region, site type district, group of site type, and site type. According to this classification system, China divides forest sites into 8 site regions, 50 site areas, 166 site sub-areas, 494 site type communities, 1716 site

Site quality refers to a given forest's production potential on a forest site or forest land's ability to grow trees [1, 42]. Site quality impact factors include climate factors, soil factors, and biological factors, determining forest growth quality and quantity. Generally, forest sites' potential productivity should be predicted and evaluated before afforestation, and the same or similar forest sites should be classified.

Forest site quality evaluation methods can be simplified into direct and indirect

methods. The direct evaluation method refers to using the forest's harvest and growth data to evaluate site quality, such as volume, tree height, site index. In 1881, the German forest scientist Von Baur used the stand average tree height to indicate site class; Assmann recommended using top tree height instead later in 1961 [50, 51]. In the United States, from about 1910 to 1925, there were three different site evaluation methods: some people strongly agreed to express by volume; another group of people favored using the "forest site type system", which is based on the plant to indicate site types; the third part support the use of site index [42]. The indirect evaluation method refers to assess site quality with the characteristics of physio-

Site index (SI) is the most commonly used, relatively density-independent quantitative indicator of site productivity [53]. It was defined as the top height of the trees at a specified (index) age [54]. Many countries used site index to evaluate the site quality among different species, like *Picea abies* in Germany, *Quercus suber* in Portugal, *Eucalyptus Grandis* in South Africa, *Pinus tabuliformis* in China [55–58]. Site

graphic, climate, edaphic variables, and understory [52].

#### *Basic Theory and Methods of Afforestation DOI: http://dx.doi.org/10.5772/intechopen.96164*

*Silviculture*

dominant factor is water (**Figure 5**).

**2.3 Forest site classification**

scales and the forest life cycle.

water, and nutrition) of trees to determine the most significant impact on living factors. On the other hand, it is to find out those environmental factors in extreme conditions and restrict plant growth [1]. Generally, the most restrictive factors play a leading role, such as drought, severe cold, strong wind, and extreme weather. For example, in Saihanba Forest farm, some afforestation area has very thin soil; the

The forest site classification refers to a traditionally used method to determine the suited tree species in the right site and perform macro-classification and microclassification [1, 40]. The system generally consists of multiple (level) taxa depend on the scale. The climate is the primary effect factor at the landscape and regional scale, whereas topography and soil at the local scale [27]. Usually, afforestation always tends to pay more attention to micro-forest sites because it directly relates them to the tree survival rate and have similar management properties. As climate change is concerned, afforestation is increasingly considering the macro-regional

**82**

**Figure 5.**

*Young* Pinus sylvestris *forest of Saihanba Forest farm is planted on a thin site.*

Site classification methods are divided into two groups, single factor and multifactor methods [41]. Single factors classification systems depend on one factor to express a forest site, such as soil, indicator plant, or climate. The soil classification system was wildly used in many countries, like the United States and Ireland, to quantify site quality and determine its suitability for afforestation or timber yields [42–44]. Indicator plants or plant communities could also indicate a forest site's fertility and moisture status, especially in some humid climate regions or coastal countries, such as Scotland, Britain, Ireland, Finland, and British Columbia in Canada [31, 33, 34, 45].

Recently, with the rise of sustainable forest management, multifactor forest site classifications system has developed rapidly. The biogeoclimatic ecosystem classification in British Columbia has combined the climate, vegetation, and soil factors to assess the site productivity and guide the afforestation and forest management [46, 47]. The Finland upland forest site classification system consists of six clusters depend on the vegetation types and the site water conditions [34]. In Germany, the Baden-Wurttemberg silviculture forest site classification has three levels, landscape level, regional level, and local level. The landscape level contains subunit called growth districts, divided into smaller areas at the regional level depending on the climate and topographic; the basic ecological units are called site units according to soil and vegetation [48].

In China, the research group of "China Forest Site Classification," headed by Zhan Zhaoning, proposed a site classification system in 1989 [49]. Zhang Wanru formally established a site classification system based on timber forests [36]. The Chinese Forest Site Classification and Chinese Forest Site are national classification systems. Forest Site Classification system in China can be divided into six levels [49]: site area, site region, site sub-region, site type district, group of site type, and site type. According to this classification system, China divides forest sites into 8 site regions, 50 site areas, 166 site sub-areas, 494 site type communities, 1716 site type groups, and 4463 site types.

#### **2.4 Forest site quality and assessment**

Site quality refers to a given forest's production potential on a forest site or forest land's ability to grow trees [1, 42]. Site quality impact factors include climate factors, soil factors, and biological factors, determining forest growth quality and quantity. Generally, forest sites' potential productivity should be predicted and evaluated before afforestation, and the same or similar forest sites should be classified.

Forest site quality evaluation methods can be simplified into direct and indirect methods. The direct evaluation method refers to using the forest's harvest and growth data to evaluate site quality, such as volume, tree height, site index. In 1881, the German forest scientist Von Baur used the stand average tree height to indicate site class; Assmann recommended using top tree height instead later in 1961 [50, 51]. In the United States, from about 1910 to 1925, there were three different site evaluation methods: some people strongly agreed to express by volume; another group of people favored using the "forest site type system", which is based on the plant to indicate site types; the third part support the use of site index [42]. The indirect evaluation method refers to assess site quality with the characteristics of physiographic, climate, edaphic variables, and understory [52].

Site index (SI) is the most commonly used, relatively density-independent quantitative indicator of site productivity [53]. It was defined as the top height of the trees at a specified (index) age [54]. Many countries used site index to evaluate the site quality among different species, like *Picea abies* in Germany, *Quercus suber* in Portugal, *Eucalyptus Grandis* in South Africa, *Pinus tabuliformis* in China [55–58]. Site index established by the multiple regression analysis methods indicates the relationship between the average height of the dominant tree or tallest trees (also called the upper canopy height). We can clearly see the highest height from the site index table that Chinese pine can grow on different sites and at different ages [57] (**Table 1**).

Some scientists used edaphic or physiographic variables in site quality models [59, 60]. In contrast, some scientists have combined the site index with climate data to establish a stable site index that evaluates site quality under climate change [61] (**Table 2**). Using a site index to test the site quality of uneven-aged-mixed forest stands has low accuracy. McNab et al. [62] used the indicator species method combined with the site index to evaluate the hardwood stands' site productivity in Western North Carolina and pointed out that the good quality site's predicting accuracy is higher (85 percent accuracy) than the poorer site (60 percent accuracy).


#### **Table 1.**

*Site index table of* Pinus tabulaeformis *Carriese plantation.*


#### **Table 2.**

*Characterization of the environmental variables for spruce and beech plots used for site index model fitting.*

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*Basic Theory and Methods of Afforestation DOI: http://dx.doi.org/10.5772/intechopen.96164*

Site preparation is a crucial activity affecting the survival rate of afforestation. It is the step after forest classification and before planting. Site preparation is various among afforestation land types. The treatment applied to slash, ground story vegetation, forest floor, and soil to exclude or reduce competing vegetation, pests, fire, and make the site suitable for afforestation or natural regeneration [2]. Site preparation usually includes mechanical and chemical methods, include mounding, scalping, trenching, bedding, chopping, herbicide,

The types of afforestation land are different in each country. Some countries have diverse terrains, such as China and the United States, and some countries have a few terrain types, such as some European countries [1]. This is the primary reason that affects the type of afforestation land. There are five types of afforestation land in china, namely barren mountains and wasteland, farmland, logging, and burning land, and secondary forest land [1]. The site quality of farmland is high, and the site quality of other afforestation sites is poor. In Ireland, the country's afforestable land was divided into four types based on biophysical factors, biological factors, national and EU designations and policies, and potential afforestation, respectively [63]. Kadam et al. [17] used the Land Suitability Analysis (LSA) method to divide the afforestation land types of Western Ghat in India into four classes (highly, moderately, marginally, and not suitable); the main dividing factors are topographical

The primary purpose of mechanical methods is to remove undesirable plants, reduce their growth, protect the surface soils, and improve site quality [2]. Mechanical methods can redistribute the dead vegetation, like slashing or chopping; they also can reshape the soil surface, like bedding, plowing, and

Mechanical site preparation can influence the species diversity, quantity, composition of underground vegetation. Sebesta et al's research showed that mechanical site preparation decreased the species richness of the understorey and increased the number of non-native species coursed by soil disturbance [64]. Newmaster et al. revealed no differences in the frequency of native species and composition in

Proper site preparation methods, either mechanical or chemical methods, can improve both conifers and hardwoods' survival rate and growth [66–68]. However, in coniferous and broad-leaved mixed forests, different site preparation methods lead to different effects. Cain et al's research showed that mechanical or chemical site preparation methods reduced the density and stocking of Oak in a pine-hardwood mixed forest [69]. Mohler et al's study also mentioned that red Oak trees had benefited most from the larger gaps without site preparation [70]. Therefore, mechanical methods should be applied carefully and

The cost of mechanical site preparation should be a consideration. Such as slash can be expensive or course diseases, but it can reduce forest fires' risk, protect the seedlings, and provide organic and inorganic nutrients [71–73]. The equipment and

**3. Site preparation**

prescribed burning, et al.

**3.2 Mechanical methods**

mechanical site preparation [65].

adapted to site conditions.

the labor cost is expensive for afforestation (**Figure 6**).

mounding.

**3.1 Types of afforestation lands**

factors, soil factors, and meteorological factors.

#### **3. Site preparation**

*Silviculture*

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**Table 2.**

**Table 1.**

**Variables Subvariables**

*Site index table of* Pinus tabulaeformis *Carriese plantation.*

**Tree age/a Site index**

Summer/annual temperature Mean annual temperature (°C)

Winter temperature Mean January temperature (°C)

Precipitation Annual precipitation sum (mm)

Continentality Continentality index

Elevation Elevation (m)

Mean temperature warmest quarter (°C) Mean temperature May to Sept. (°C) Max. temperature warmest month (°C)

Min. temperature coldest month (°C)

Precipitation sum warmest quarter (mm) Precipitation sum May to Sept. (mm)

Mean July temperature (°C)

**4 5 6 7 8 9 10**

 2.2 ∼ 2.9 3.0 ∼ 3.6 3.7 ∼ 4.3 4.4 ∼ 5.0 5.1 ∼ 5.7 5.8 ∼ 6.5 6.6 ∼ 7.2 3.0 ∼ 4.0 4.1 ∼ 5.0 5.1 ∼ 6.0 6.1 ∼ 7.0 7.1 ∼ 8.0 8.1 ∼ 9.0 9.1 ∼ 10.0 3.5 ∼ 4.7 4.8 ∼ 5.9 6.0 ∼ 7.0 7.1 ∼ 8.2 8.3 ∼ 9.4 9.5 ∼ 10.5 10.6 ∼ 11.7 3.9 ∼ 5.1 5.2 ∼ 6.4 6.5 ∼ 7.7 7.8 ∼ 9.0 9.1 ∼ 10.3 10.4 ∼ 11.5 11.6 ∼ 12.9 4.1 ∼ 5.5 5.6 ∼ 6.8 6.9 ∼ 8.2 8.3 ∼ 9.6 9.7 ∼ 10.9 11.0 ∼ 12.3 12.4 ∼ 13.7 4.3 ∼ 5.7 5.8 ∼ 7.1 7.2 ∼ 8.6 8.7 ∼ 10.0 10.1 ∼ 11.4 11.5 ∼ 12.8 12.9 ∼ 14.3 4.4 ∼ 5.9 6.0 ∼ 7.4 7.5 ∼ 8.8 8.9 ∼ 10.3 10.4 ∼ 11.8 11.9 ∼ 13.3 13.4 ∼ 14.7 4.5 ∼ 6.0 6.1 ∼ 7.6 7.7 ∼ 9.1 9.2 ∼ 10.6 10.7 ∼ 12.1 12.2 ∼ 13.6 13.7 ∼ 15.1 4.6 ∼ 6.2 6.3 ∼ 7.7 7.8 ∼ 9.3 9.4 ∼ 10.8 10.9 ∼ 12.3 12.4 ∼ 13.9 14.0 ∼ 15.4

index established by the multiple regression analysis methods indicates the relationship between the average height of the dominant tree or tallest trees (also called the upper canopy height). We can clearly see the highest height from the site index table that Chinese pine can grow on different sites and at different ages [57] (**Table 1**). Some scientists used edaphic or physiographic variables in site quality models [59, 60]. In contrast, some scientists have combined the site index with climate data to establish a stable site index that evaluates site quality under climate change [61] (**Table 2**). Using a site index to test the site quality of uneven-aged-mixed forest stands has low accuracy. McNab et al. [62] used the indicator species method combined with the site index to evaluate the hardwood stands' site productivity in Western North Carolina and pointed out that the good quality site's predicting accuracy is higher (85 percent accuracy) than the poorer site (60 percent accuracy).

Tmax\_wm-Tmin\_cm (°C)

T\_wq-T1 (°C)

*Characterization of the environmental variables for spruce and beech plots used for site index model fitting.*

Site preparation is a crucial activity affecting the survival rate of afforestation. It is the step after forest classification and before planting. Site preparation is various among afforestation land types. The treatment applied to slash, ground story vegetation, forest floor, and soil to exclude or reduce competing vegetation, pests, fire, and make the site suitable for afforestation or natural regeneration [2]. Site preparation usually includes mechanical and chemical methods, include mounding, scalping, trenching, bedding, chopping, herbicide, prescribed burning, et al.

#### **3.1 Types of afforestation lands**

The types of afforestation land are different in each country. Some countries have diverse terrains, such as China and the United States, and some countries have a few terrain types, such as some European countries [1]. This is the primary reason that affects the type of afforestation land. There are five types of afforestation land in china, namely barren mountains and wasteland, farmland, logging, and burning land, and secondary forest land [1]. The site quality of farmland is high, and the site quality of other afforestation sites is poor. In Ireland, the country's afforestable land was divided into four types based on biophysical factors, biological factors, national and EU designations and policies, and potential afforestation, respectively [63]. Kadam et al. [17] used the Land Suitability Analysis (LSA) method to divide the afforestation land types of Western Ghat in India into four classes (highly, moderately, marginally, and not suitable); the main dividing factors are topographical factors, soil factors, and meteorological factors.

#### **3.2 Mechanical methods**

The primary purpose of mechanical methods is to remove undesirable plants, reduce their growth, protect the surface soils, and improve site quality [2]. Mechanical methods can redistribute the dead vegetation, like slashing or chopping; they also can reshape the soil surface, like bedding, plowing, and mounding.

Mechanical site preparation can influence the species diversity, quantity, composition of underground vegetation. Sebesta et al's research showed that mechanical site preparation decreased the species richness of the understorey and increased the number of non-native species coursed by soil disturbance [64]. Newmaster et al. revealed no differences in the frequency of native species and composition in mechanical site preparation [65].

Proper site preparation methods, either mechanical or chemical methods, can improve both conifers and hardwoods' survival rate and growth [66–68]. However, in coniferous and broad-leaved mixed forests, different site preparation methods lead to different effects. Cain et al's research showed that mechanical or chemical site preparation methods reduced the density and stocking of Oak in a pine-hardwood mixed forest [69]. Mohler et al's study also mentioned that red Oak trees had benefited most from the larger gaps without site preparation [70]. Therefore, mechanical methods should be applied carefully and adapted to site conditions.

The cost of mechanical site preparation should be a consideration. Such as slash can be expensive or course diseases, but it can reduce forest fires' risk, protect the seedlings, and provide organic and inorganic nutrients [71–73]. The equipment and the labor cost is expensive for afforestation (**Figure 6**).

**Figure 6.** *Use a tractor for site preparation.*

#### **3.3 Chemical methods**

Chemical methods usually refer to herbicides, pesticides, and fertilization. Chemical site preparation, like herbicides or pesticides, are both harmful and beneficial to site quality. The herbicide can promote trees' early survival rate and have a long-term effect on maintaining forest growth. However, people are also concerned about the environmental effect and cost [74]. It should be noted that herbicide use is related to the length of time of different research. Much long-term research of longleaf pine showed that herbicide applied for site preparation increased seedling growth and had a lasting improvement effect. However, some short time studies reported that the longleaf pine seedling survival was unaffected or reduced by herbicide [68, 75, 76]. Compared with the mechanical methods, herbicides' cost looks more efficient; it was wildly used in South American, especially in pine plantations [66]. Callaghan et al's study showed that herbicide could reduce the hardwood competition and improve the *Pinus taeda* growth, as it costs less [77].

Fertilization can supplement the nutrient loss of the soil caused by logging and increase the seedlings' survival rate. Nitrogen, phosphorus, and potassium (NPK) are the main nutrient elements in fertilizers [78]. Although the fertilization costs a lot, it can improve either hardwood or conifer seedling growth [79, 80]. Fertilization is also used for larger trees. A fertilization study for conifers in Finland showed that after ten years of fertilization if harvested as sawlogs or pulpwood, the additional volume increment was 25% and 75%, respectively, higher than non-fertilized forests [81].

#### **3.4 Site water management**

Site water management also plays an essential role in site preparation in certain areas. If drainage is not considered in some low-lying afforestation lands, the afforestation will fail (**Figure 7**). Flooding is a treatment that use channel or dikes to guide the water from afforestation site with high moisture, like coastal and riparian lands, shrimp ponds, swamps. Flooding or irrigation also can reduce the salt and alkali content of saline soil [82]. Irrigation is an essential way in improving site water conditions in water-deficient areas, such as the Middle East, South Africa, China, India, especially for the cultivation of timber forests [83–85].

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**4. Afforestation materials**

**Figure 7.**

**4.1 Afforestation with seeds**

management and protection.

competitive vegetation, and seed predation [87–90].

soil thickness is appropriate (except for broadcast sowing) [1].

Direct seeding is a widely used afforestation method. Compared with the seedling method, it has the merits of simple operation, high efficiency, low cost, and can be used over hard to reach areas. Direct seeding was considered the 'best practice' for producing seedlings, regeneration, and afforestation [8]. Sowing seeds directly to forest land without lifting seedlings, packaging, transportation, and planting, the root system of seedlings will not be damaged. Therefore, direct seeding is more "close-to-nature." It can keep intact natural distribution and expansion of the root system, especially of the pivot root the tree species. The seeds that germinate and grow on the forested land are better adapted to the climate and soil conditions [86]. However, it has strict requirements on the water, heat, and vegetation conditions. Compared with seedling afforestation, seedlings formed by direct seeding grow slowly at the initial stage, so it takes a longer time to reach crown closure [86]. Sometimes, the seeds after sowing are easily damaged by birds and animals, trampled by livestock, and human destruction, so it is necessary to strengthen the

*After a rain, the accumulation of water in afforestation land caused the death of seedlings.*

Generally, before sowing, the seeds should be disinfected, soaked, sprouted, dressed, coating, and gluing [1]. The purpose of pre-sowing treatment is to shorten the time of seeds in the soil before germination, ensure the emergence of seedlings orderly, and prevent the harm of birds, mammals, and diseases. The germination rate after sowing is related to seed size and weight. Moreover, the establishment rate is accord to the timing of seeding, planting practices, microsite environment,

Seed afforestation methods include seed burial, spot, and broadcast [91–93]. Seed burial refers to put the seeds under the soil to store water, preserve moisture, create conditions for germination, and protect seeds. Some seeding experiments conducted in the nursery show that the suitable spot seeding depth is between one and two times the seed width [94, 95]. Broadcast seeding has the advantages of small workload, simple construction, great flexibility in site selection, and is widely used for barren mountains and wasteland (including desert) and cutting and burning slash site [1]. No matter which seeding method, it is required that the covering

**Figure 7.**

*Silviculture*

**3.3 Chemical methods**

*Use a tractor for site preparation.*

**Figure 6.**

**3.4 Site water management**

Chemical methods usually refer to herbicides, pesticides, and fertilization. Chemical site preparation, like herbicides or pesticides, are both harmful and beneficial to site quality. The herbicide can promote trees' early survival rate and have a long-term effect on maintaining forest growth. However, people are also concerned about the environmental effect and cost [74]. It should be noted that herbicide use is related to the length of time of different research. Much long-term research of longleaf pine showed that herbicide applied for site preparation increased seedling growth and had a lasting improvement effect. However, some short time studies reported that the longleaf pine seedling survival was unaffected or reduced by herbicide [68, 75, 76]. Compared with the mechanical methods, herbicides' cost looks more efficient; it was wildly used in South American, especially in pine plantations [66]. Callaghan et al's study showed that herbicide could reduce the hardwood

competition and improve the *Pinus taeda* growth, as it costs less [77].

Fertilization can supplement the nutrient loss of the soil caused by logging and increase the seedlings' survival rate. Nitrogen, phosphorus, and potassium (NPK) are the main nutrient elements in fertilizers [78]. Although the fertilization costs a lot, it can improve either hardwood or conifer seedling growth [79, 80]. Fertilization is also used for larger trees. A fertilization study for conifers in Finland showed that after ten years of fertilization if harvested as sawlogs or pulpwood, the additional volume increment was 25% and 75%, respectively, higher than non-fertilized forests [81].

Site water management also plays an essential role in site preparation in certain

areas. If drainage is not considered in some low-lying afforestation lands, the afforestation will fail (**Figure 7**). Flooding is a treatment that use channel or dikes to guide the water from afforestation site with high moisture, like coastal and riparian lands, shrimp ponds, swamps. Flooding or irrigation also can reduce the salt and alkali content of saline soil [82]. Irrigation is an essential way in improving site water conditions in water-deficient areas, such as the Middle East, South Africa,

China, India, especially for the cultivation of timber forests [83–85].

**86**

*After a rain, the accumulation of water in afforestation land caused the death of seedlings.*

#### **4. Afforestation materials**

#### **4.1 Afforestation with seeds**

Direct seeding is a widely used afforestation method. Compared with the seedling method, it has the merits of simple operation, high efficiency, low cost, and can be used over hard to reach areas. Direct seeding was considered the 'best practice' for producing seedlings, regeneration, and afforestation [8]. Sowing seeds directly to forest land without lifting seedlings, packaging, transportation, and planting, the root system of seedlings will not be damaged. Therefore, direct seeding is more "close-to-nature." It can keep intact natural distribution and expansion of the root system, especially of the pivot root the tree species. The seeds that germinate and grow on the forested land are better adapted to the climate and soil conditions [86]. However, it has strict requirements on the water, heat, and vegetation conditions. Compared with seedling afforestation, seedlings formed by direct seeding grow slowly at the initial stage, so it takes a longer time to reach crown closure [86]. Sometimes, the seeds after sowing are easily damaged by birds and animals, trampled by livestock, and human destruction, so it is necessary to strengthen the management and protection.

Generally, before sowing, the seeds should be disinfected, soaked, sprouted, dressed, coating, and gluing [1]. The purpose of pre-sowing treatment is to shorten the time of seeds in the soil before germination, ensure the emergence of seedlings orderly, and prevent the harm of birds, mammals, and diseases. The germination rate after sowing is related to seed size and weight. Moreover, the establishment rate is accord to the timing of seeding, planting practices, microsite environment, competitive vegetation, and seed predation [87–90].

Seed afforestation methods include seed burial, spot, and broadcast [91–93]. Seed burial refers to put the seeds under the soil to store water, preserve moisture, create conditions for germination, and protect seeds. Some seeding experiments conducted in the nursery show that the suitable spot seeding depth is between one and two times the seed width [94, 95]. Broadcast seeding has the advantages of small workload, simple construction, great flexibility in site selection, and is widely used for barren mountains and wasteland (including desert) and cutting and burning slash site [1]. No matter which seeding method, it is required that the covering soil thickness is appropriate (except for broadcast sowing) [1].

Different sowing season can affect the seed germination rate, which should be determined according to the tree species' characteristics and environmental conditions [96]. Many studies were conducted about the suitable sowing season, such as some pine species in southern US and Finnland (spring seeding), *Pinus palustris* in the US (fall seeding), *Fraxinus excelsior,* and *Acer pseudoplatanus* in the UK (winter seeding) [5, 96, 97]. Some species can be sowed in multiple seasons, like temperate hardwoods in the US [98].

#### **4.2 Afforestation with seedlings**

There are two types of seedlings, bare root and containerized. Compared with the direct seeding, seedlings have a complete or partial root system. It can be planted in almost all suitable sites, and site conditions requirements are not high. In general, container seedlings are used for afforestation under difficult site conditions. Furthermore, seedlings are usually grown in nurseries or a controlled greenhouse environment, transplantation more or less damaged the root system, and both bare root seedlings and container seedlings can be produced all year round [6, 99].

Bareroot seedlings have always been promoted for reforestation projects because it can be easily hand-carried by forester and less expensive than containerized seedlings [100]. The survival ratio of bare root seedlings is affected by the seedling vitality, planting time, or season, especially the soil moisture and temperature [101]. The water content in the seedling is the most critical factor affecting the seedling vitality. To maintain the seedlings' water balance, appropriate treatment measures should be taken before planting, such as pruning and partial-root cutting, which can remove most of the seedling leaves, branches, trunk, and roots, reducing water evaporation [102, 103].

Compared with seeding afforestation, container seedlings show better environmental adaptability and stress resistance because of their protected root systems (**Figure 8**). Container seedlings have increased survival rates of or more than other transplant types and show improved growth on adverse sites, though they cost more than bare-root seedlings [104]. Under droughty conditions, container seedlings survived and grew better than bare root seedlings [105]. Furthermore, some researchers mentioned no difference between bare root seedlings and container seedlings when soil moisture was adequate at the planting time [106].

**89**

**Figure 9.**

*Use excavators to dig a hole for the afforestation.*

*Basic Theory and Methods of Afforestation DOI: http://dx.doi.org/10.5772/intechopen.96164*

a process of root restoration and adaptation.

**4.3 Afforestation with cutting**

Afforestation with seedlings requires a series of practices include lifting, storage, transport to the site, and planting. All of these operations can affect seedling performance [107]. The protection of seedling roots during those operations is critical to maintaining the water content and seedlings' vitality. To this end, it is advantageous to shorten the operation time of each process; grading and packaging should be carried out in a shady, wet, and cold environment. Some studies have shown that exposing the seedlings' roots to air can limit their growth [108, 109]. The seedling's roots should be closely contacted with the surrounding soil during planting. The planting depth of each seedling should be the same in the nursery; sometimes, a little deeper is more favorable. After planting, the bare root seedlings generally have

Hole planting is a common method that is suitable for all kinds of bare root seedlings. Digging tools can be large machinery, shovel, mattock, spade (**Figure 9**). The depth and width of the hole are determined according to the seedling root's length and width [110]. Generally, the planting depth should be about 3 cm above the original soil seal at the seedling's ground path [1]. The planting method can also be divided into a single plant and cluster plant according to one or more plants per hole. Recently, seedlings with root-ball were widely used in afforestation, especially in urban afforestation; it can maintain a relatively complete root system, and the planting survival rate

To ensure planting seedlings, it is necessary to select the appropriate season and time according to the climate and soil conditions. Bareroot seedlings and container seedlings are produced in one to four growing seasons or one to two years [6, 111]. Theoretically, the appropriate planting time should be when the physiological activity of the aboveground part of the seedling is weak (deciduous broadleaf tree species are in the deciduous stage), and the physiological activity of the root is vital, so the root healing ability is strong [112]. Generally, hardwood seedlings must be planted in late winter or early spring, when the seedlings are dormant and the ground has thawed [113].

Seeds and seedlings are sexual afforestation method, and many trees also have asexual reproduction ability. Cutting is a piece of a plant that can be used in

is high, but the weight is massive, so the afforestation cost is relatively high.

**Figure 8.** *Container seedlings of* Larix principis*-rupprechtii in a greenhouse.*

#### *Basic Theory and Methods of Afforestation DOI: http://dx.doi.org/10.5772/intechopen.96164*

*Silviculture*

hardwoods in the US [98].

**4.2 Afforestation with seedlings**

Different sowing season can affect the seed germination rate, which should be determined according to the tree species' characteristics and environmental conditions [96]. Many studies were conducted about the suitable sowing season, such as some pine species in southern US and Finnland (spring seeding), *Pinus palustris* in the US (fall seeding), *Fraxinus excelsior,* and *Acer pseudoplatanus* in the UK (winter seeding) [5, 96, 97]. Some species can be sowed in multiple seasons, like temperate

There are two types of seedlings, bare root and containerized. Compared with the direct seeding, seedlings have a complete or partial root system. It can be planted in almost all suitable sites, and site conditions requirements are not high. In general, container seedlings are used for afforestation under difficult site conditions. Furthermore, seedlings are usually grown in nurseries or a controlled greenhouse environment, transplantation more or less damaged the root system, and both bare root seedlings and container seedlings can be produced all year round [6, 99].

Bareroot seedlings have always been promoted for reforestation projects because it can be easily hand-carried by forester and less expensive than containerized seedlings [100]. The survival ratio of bare root seedlings is affected by the seedling vitality, planting time, or season, especially the soil moisture and temperature [101]. The water content in the seedling is the most critical factor affecting the seedling vitality. To maintain the seedlings' water balance, appropriate treatment measures should be taken before planting, such as pruning and partial-root cutting, which can remove most of the seedling leaves, branches, trunk, and roots, reducing water evaporation [102, 103]. Compared with seeding afforestation, container seedlings show better environmental adaptability and stress resistance because of their protected root systems (**Figure 8**). Container seedlings have increased survival rates of or more than other transplant types and show improved growth on adverse sites, though they cost more than bare-root seedlings [104]. Under droughty conditions, container seedlings survived and grew better than bare root seedlings [105]. Furthermore, some researchers mentioned no difference between bare root seedlings and container seedlings

when soil moisture was adequate at the planting time [106].

*Container seedlings of* Larix principis*-rupprechtii in a greenhouse.*

**88**

**Figure 8.**

Afforestation with seedlings requires a series of practices include lifting, storage, transport to the site, and planting. All of these operations can affect seedling performance [107]. The protection of seedling roots during those operations is critical to maintaining the water content and seedlings' vitality. To this end, it is advantageous to shorten the operation time of each process; grading and packaging should be carried out in a shady, wet, and cold environment. Some studies have shown that exposing the seedlings' roots to air can limit their growth [108, 109]. The seedling's roots should be closely contacted with the surrounding soil during planting. The planting depth of each seedling should be the same in the nursery; sometimes, a little deeper is more favorable. After planting, the bare root seedlings generally have a process of root restoration and adaptation.

Hole planting is a common method that is suitable for all kinds of bare root seedlings. Digging tools can be large machinery, shovel, mattock, spade (**Figure 9**). The depth and width of the hole are determined according to the seedling root's length and width [110]. Generally, the planting depth should be about 3 cm above the original soil seal at the seedling's ground path [1]. The planting method can also be divided into a single plant and cluster plant according to one or more plants per hole. Recently, seedlings with root-ball were widely used in afforestation, especially in urban afforestation; it can maintain a relatively complete root system, and the planting survival rate is high, but the weight is massive, so the afforestation cost is relatively high.

To ensure planting seedlings, it is necessary to select the appropriate season and time according to the climate and soil conditions. Bareroot seedlings and container seedlings are produced in one to four growing seasons or one to two years [6, 111]. Theoretically, the appropriate planting time should be when the physiological activity of the aboveground part of the seedling is weak (deciduous broadleaf tree species are in the deciduous stage), and the physiological activity of the root is vital, so the root healing ability is strong [112]. Generally, hardwood seedlings must be planted in late winter or early spring, when the seedlings are dormant and the ground has thawed [113].

#### **4.3 Afforestation with cutting**

Seeds and seedlings are sexual afforestation method, and many trees also have asexual reproduction ability. Cutting is a piece of a plant that can be used in

**Figure 9.** *Use excavators to dig a hole for the afforestation.*

**Figure 10.** *Root sprouting of black locust.*

afforestation. It can be taken from stems, branches, leaves, roots and directly plant on forest land. Cuttings can maintain the parent tree's target characteristics, such as high yield, fast growth, and stress resistance ability [114, 115]. Some research showed that cuttings' behavior varies with age, genotypes, the parent plant's physiological status, cutting position, and temperature [116, 117]. Besides, sprouting is another afforestation method that can produce a new forest. Sprouts are more resistant to disturbance than seed-origin seedlings and grow fast [7, 118]. Compared with the plant with seed and seedling, cutting afforestation is labor-saving, time-saving, and low-cost.

Stump or root adventitious sprouts are commonly used sprouting materials, and it can rapidly produce many adventitious roots with strong water-absorbing ability, such species include *Populus*, *Robinia pseudoacacia*, *Salix*, *Cunninghamia lanceolata*, *Ziziphus jujuba*, *Paulownia tomentosa*, and *Toxicodendron vernicifluum* [119–121] (**Figure 10**). It is believed that the sprouting ability is related to species, stem size, age, management intensity [7, 122–125]. It was reported that different damage treatments on beech roots could cause different sprouting results [126]. Some sproutings come from the stump after cutting, like *Cercidiphyllum japonicum* and some tropical species [127, 128]. Recently, sprouting has been widely used in coppice forest cultivation. In China, Europe, and the Americas, this method is used to develop short rotation energy forests to get biomass raw materials [129, 130].

The cutting plant or sprouting harvest season varies with the tree species and region. Generally, the most suitable time is the same as planting seedlings, like fall, winter, and early spring [127, 131]. However, some studies showed that harvest season had no effect on sprout number but can affect the dominant sprout height in the first year [132, 133].

#### **5. Conclusion**

Afforestation and reforestation activities must be considered systematically and integrally. More and more studies have shown that making afforestation plans from the perspective of forest ecosystems is the future trend. Using multi-factor methods to analyze forest site characteristics will become the primary site evaluation and classification method. Although the cost is high, with the continuous advancement

**91**

**Author details**

Jie Duan\* and Dilnur Abduwali

Beijing Forestry University, Beijing, China

provided the original work is properly cited.

\*Address all correspondence to: duanjie@bjfu.edu.cn

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

*Basic Theory and Methods of Afforestation DOI: http://dx.doi.org/10.5772/intechopen.96164*

**Acknowledgements**

of technology, afforestation machinery will be popularized in the afforestation process, such as land preparation and planting. At the same time, many future afforestation activities will fully consider climate change dynamics on the forest site and the forest itself and determine afforestation tree species, materials and methods.

We sincerely thank all the experts and editors who reviewed this chapter, and

their valuable suggestions greatly help us improve the content.

#### *Basic Theory and Methods of Afforestation DOI: http://dx.doi.org/10.5772/intechopen.96164*

of technology, afforestation machinery will be popularized in the afforestation process, such as land preparation and planting. At the same time, many future afforestation activities will fully consider climate change dynamics on the forest site and the forest itself and determine afforestation tree species, materials and methods.

### **Acknowledgements**

*Silviculture*

**Figure 10.**

*Root sprouting of black locust.*

afforestation. It can be taken from stems, branches, leaves, roots and directly plant on forest land. Cuttings can maintain the parent tree's target characteristics, such as high yield, fast growth, and stress resistance ability [114, 115]. Some research showed that cuttings' behavior varies with age, genotypes, the parent plant's physiological status, cutting position, and temperature [116, 117]. Besides, sprouting is another afforestation method that can produce a new forest. Sprouts are more resistant to disturbance than seed-origin seedlings and grow fast [7, 118]. Compared with the plant with seed and seedling, cutting afforestation is labor-saving, time-saving, and low-cost.

Stump or root adventitious sprouts are commonly used sprouting materials, and it can rapidly produce many adventitious roots with strong water-absorbing ability, such species include *Populus*, *Robinia pseudoacacia*, *Salix*, *Cunninghamia lanceolata*, *Ziziphus jujuba*, *Paulownia tomentosa*, and *Toxicodendron vernicifluum* [119–121] (**Figure 10**). It is believed that the sprouting ability is related to species, stem size, age, management intensity [7, 122–125]. It was reported that different damage treatments on beech roots could cause different sprouting results [126]. Some sproutings come from the stump after cutting, like *Cercidiphyllum japonicum* and some tropical species [127, 128]. Recently, sprouting has been widely used in coppice forest cultivation. In China, Europe, and the Americas, this method is used to develop

The cutting plant or sprouting harvest season varies with the tree species and region. Generally, the most suitable time is the same as planting seedlings, like fall, winter, and early spring [127, 131]. However, some studies showed that harvest season had no effect on sprout number but can affect the dominant sprout height in

Afforestation and reforestation activities must be considered systematically and integrally. More and more studies have shown that making afforestation plans from the perspective of forest ecosystems is the future trend. Using multi-factor methods to analyze forest site characteristics will become the primary site evaluation and classification method. Although the cost is high, with the continuous advancement

short rotation energy forests to get biomass raw materials [129, 130].

**90**

the first year [132, 133].

**5. Conclusion**

We sincerely thank all the experts and editors who reviewed this chapter, and their valuable suggestions greatly help us improve the content.

#### **Author details**

Jie Duan\* and Dilnur Abduwali Beijing Forestry University, Beijing, China

\*Address all correspondence to: duanjie@bjfu.edu.cn

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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[22] 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(1):1-10.

[23] Worrell R, Malcolm DC. Productivity of sitka spruce in northern britain 1. The effects of elevation and climate. Forestry. 1990;63(2):105-118.

[24] Moser G, Leuschner C, Hertel D, Graefe S, Soethe N, Iost S. Elevation effects on the carbon budget of tropical mountain forests (S Ecuador): The role of the belowground compartment. Glob Chang Biol. 2011;17(6):2211-2226.

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[27] Quichimbo P, Jiménez L, Veintimilla D, Tischer A, Günter S, Mosandl R, et al. Forest site classification in the Southern Andean region of ecuador: A case study of pine plantations to collect a base of soil attributes. Forests. 2017;8(12):473.

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preparation on floristic diversity in a central Ontario clearcut. For Ecol Manage. 2007;246(2-3):196-207.

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[105] Grossnickle SC, El-Kassaby YA. Bareroot versus container stocktypes: a performance comparison. Vol. 47, New Forests. 2016. p. 1-51.

[106] South DB, Barnett JP. Herbicides and planting date affect early performance of container-grown and bare-root loblolly pine seedlings in Alabama. New For. 1986;1(1):17-27.

[107] Parker B. Ecophysiology of Northern Spruce Species: The Performance of Planted Seedlings. Tree Physiol. 2001;21(7):415-416.

[108] Deans JD, Lundberg C, Tabbush PM, Cannell MGR, Sheppard LJ, Murray MB. The influence of desiccation, rough handling and cold storage on the quality and establishment of sitka spruce planting stock. Forestry. 1990;63(2):129-141.

[109] McKay HM. Frost hardiness and cold-storage tolerance of the root system of picea sitchensis, pseudotsuga menziesii, larix kaempferi and pinus sylvestris bare-root seedlings. Scand J For Res. 1994;9(1-4):203-213.

[110] Region USFSS. A Guide to the Care and Planting of Southern Pine Seedlings [Internet]. USDA Forest Service, Southern Region; 1996. (Management bulletin R8). Available from: https://books.google.com.sg/ books?id=pgpFKH-zN9AC

[111] Landis TD, Tinus RW, McDonald SE, Barnett JP. Containers and Growing Media, Vol. 2. Contain Tree Nurs Manual Agric Handb 674. 1990;88.

[112] Margolis HA, Brand DG. An ecophysiological basis for understanding plantation establishment. Can J For Res. 1990;20(4):375-390.

[113] Teskey RO, Grier CC, Hinckley TM. Change in photosynthesis and water relations with age and season in Abies amabilis. Can J For Res. 1984;14(1):77-84.

[114] Cox DA. Hartmann and Kester's Plant Propagation Principles and Practices, 9th Edition. HortScience. 2018;53(5):741.

[115] Kauffman JB. Survival by Sprouting Following Fire in Tropical Forests of the Eastern Amazon. Biotropica. 1991;23(3):219.

[116] Henning RK. The Jatropha Booklet A Guide to the Jatropha System and its Dissemination in Africa. Bagani Gbr. 2003;37.

[117] Hansen J. Influence of cutting position and temperature during rooting on adventitious root formation and axillary bud break of Stephanotis floribunda. Sci Hortic (Amsterdam). 1989;40(4):345-354.

[118] Bellingham PJ, Sparrow AD. Resprouting as a life history strategy in woody plant communities. Vol. 89, Oikos. 2000. p. 409-416.

[119] Isikawa H. Generation of Adventitious Plant Organs by Tissue Culture Methods in Forest Trees. Bull For For Prod Res Inst. 1987;(343):119-154.

[120] Morgenson G. Vegetative Propagation of Poplar and Willow 1. Growth (Lakeland). 1992;2-4.

[121] Wan Gaiping (Nanjing Forestry Univ. J (China)). Nitrogen nutrition in sprouting buds from chinese fir stamps. Vol. 1995, Journal of Zhejiang Forestry College. 1995. p. 133-138.

[122] Lust N, Mohammady M. Regeneration of coppice. Silva Gandav. 1973;39.

[123] Zimmerman JK, III EME, Waide RB, Lodge DJ, Taylor CM, Brokaw NVL. Responses of Tree Species to Hurricane Winds in Subtropical Wet Forest in Puerto Rico: Implications for Tropical Tree Life Histories. J Ecol. 1994;82(4):911.

**99**

*Basic Theory and Methods of Afforestation DOI: http://dx.doi.org/10.5772/intechopen.96164*

[124] Ito S, Gyokusen K. Analysis of the multi-stem clump structure of Litsea japonica Juss. growing in a coastal dwarf forest. Ecol Res. 1996;11(1):17-22.

[133] Xue Y, Zhang W, Zhou J, Ma C, Ma L. Effects of stump diameter, stump height, and cutting season on Quercus variabilis stump sprouting. Scand J For

Res. 2013;28(3):223-231.

[125] Keeley JE. Recruitment of seedlings and vegetative sprouts in unburned chaparral. Ecology.

[126] Jones RH, Raynal DJ. Root sprouting in American beech (Fagus grandifolia): Effects of root injury, root exposure, and season. For Ecol Manage.

[127] Kubo M, Sakio H, Shimano K, Ohno K. Age structure and dynamics of Cercidiphyllum japonicum sprouts based on growth ring analysis. For Ecol Manage. 2005;213(1-3):253-260.

[128] Negreros-Castillo P, B. Hall R. Sprouting capability of 17 tropical tree species after overstory removal in Quintana Roo, Mexico. For Ecol Manage. 2000;126(3):399-403.

[129] Dickmann DI. Silviculture and biology of short-rotation woody crops in temperate regions: Then and now. Biomass and Bioenergy.

[130] Meifang Y, Lu W, Honghui R, Xinshi Z. Biomass production and carbon sequestration of a shortrotation forest with different poplar clones in northwest China. Sci Total Environ [Internet]. 2017;586:1135- 1140. Available from: http://dx.doi. org/10.1016/j.scitotenv.2017.02.103

[131] Pyttel PL, Fischer UF, Suchomel C, Gärtner SM, Bauhus J. The effect of harvesting on stump mortality and re-sprouting in aged oak coppice forests.

For Ecol Manage. 2013;289:18-27.

to felling time. For Ecol Manage.

1992;53(1-4):283-296.

[132] Johansson T. Sprouting of 10- to 50-year-old Betula pubescens in relation

2006;30(8-9):696-705.

1992;73(4):1194-1208.

1988;25(2):79-90.

*Basic Theory and Methods of Afforestation DOI: http://dx.doi.org/10.5772/intechopen.96164*

*Silviculture*

Forests. 2016. p. 1-51.

[105] Grossnickle SC, El-Kassaby YA. Bareroot versus container stocktypes: a performance comparison. Vol. 47, New

[114] Cox DA. Hartmann and Kester's Plant Propagation Principles and Practices, 9th Edition. HortScience.

[115] Kauffman JB. Survival by Sprouting Following Fire in Tropical Forests of the Eastern Amazon. Biotropica.

[116] Henning RK. The Jatropha Booklet A Guide to the Jatropha System and its Dissemination in Africa. Bagani Gbr.

[117] Hansen J. Influence of cutting position and temperature during rooting on adventitious root formation and axillary bud break of Stephanotis floribunda. Sci Hortic (Amsterdam).

[118] Bellingham PJ, Sparrow AD. Resprouting as a life history strategy in woody plant communities. Vol. 89,

2018;53(5):741.

1991;23(3):219.

1989;40(4):345-354.

Oikos. 2000. p. 409-416.

[119] Isikawa H. Generation of Adventitious Plant Organs by Tissue Culture Methods in Forest Trees. Bull For For Prod Res Inst. 1987;(343):119-154.

[120] Morgenson G. Vegetative Propagation of Poplar and Willow 1. Growth (Lakeland). 1992;2-4.

College. 1995. p. 133-138.

1973;39.

1994;82(4):911.

[122] Lust N, Mohammady M.

[123] Zimmerman JK, III EME, Waide RB, Lodge DJ, Taylor CM, Brokaw NVL. Responses of Tree Species to Hurricane Winds in Subtropical Wet Forest in Puerto Rico: Implications for Tropical Tree Life Histories. J Ecol.

[121] Wan Gaiping (Nanjing Forestry Univ. J (China)). Nitrogen nutrition in sprouting buds from chinese fir stamps. Vol. 1995, Journal of Zhejiang Forestry

Regeneration of coppice. Silva Gandav.

2003;37.

[106] South DB, Barnett JP. Herbicides

performance of container-grown and bare-root loblolly pine seedlings in Alabama. New For. 1986;1(1):17-27.

Performance of Planted Seedlings. Tree

Sheppard LJ, Murray MB. The influence of desiccation, rough handling and cold storage on the quality and establishment of sitka spruce planting stock. Forestry.

[109] McKay HM. Frost hardiness and cold-storage tolerance of the root system of picea sitchensis, pseudotsuga menziesii, larix kaempferi and pinus sylvestris bare-root seedlings. Scand J

For Res. 1994;9(1-4):203-213.

books?id=pgpFKH-zN9AC

[111] Landis TD, Tinus RW,

1990;20(4):375-390.

1984;14(1):77-84.

McDonald SE, Barnett JP. Containers and Growing Media, Vol. 2. Contain Tree Nurs Manual Agric Handb 674. 1990;88.

[112] Margolis HA, Brand DG. An ecophysiological basis for understanding plantation establishment. Can J For Res.

Change in photosynthesis and water relations with age and season in Abies amabilis. Can J For Res.

[113] Teskey RO, Grier CC, Hinckley TM.

[110] Region USFSS. A Guide to the Care and Planting of Southern Pine Seedlings [Internet]. USDA Forest Service, Southern Region; 1996. (Management bulletin R8). Available from: https://books.google.com.sg/

and planting date affect early

[107] Parker B. Ecophysiology of Northern Spruce Species: The

Physiol. 2001;21(7):415-416.

[108] Deans JD, Lundberg C, Tabbush PM, Cannell MGR,

1990;63(2):129-141.

**98**

[124] Ito S, Gyokusen K. Analysis of the multi-stem clump structure of Litsea japonica Juss. growing in a coastal dwarf forest. Ecol Res. 1996;11(1):17-22.

[125] Keeley JE. Recruitment of seedlings and vegetative sprouts in unburned chaparral. Ecology. 1992;73(4):1194-1208.

[126] Jones RH, Raynal DJ. Root sprouting in American beech (Fagus grandifolia): Effects of root injury, root exposure, and season. For Ecol Manage. 1988;25(2):79-90.

[127] Kubo M, Sakio H, Shimano K, Ohno K. Age structure and dynamics of Cercidiphyllum japonicum sprouts based on growth ring analysis. For Ecol Manage. 2005;213(1-3):253-260.

[128] Negreros-Castillo P, B. Hall R. Sprouting capability of 17 tropical tree species after overstory removal in Quintana Roo, Mexico. For Ecol Manage. 2000;126(3):399-403.

[129] Dickmann DI. Silviculture and biology of short-rotation woody crops in temperate regions: Then and now. Biomass and Bioenergy. 2006;30(8-9):696-705.

[130] Meifang Y, Lu W, Honghui R, Xinshi Z. Biomass production and carbon sequestration of a shortrotation forest with different poplar clones in northwest China. Sci Total Environ [Internet]. 2017;586:1135- 1140. Available from: http://dx.doi. org/10.1016/j.scitotenv.2017.02.103

[131] Pyttel PL, Fischer UF, Suchomel C, Gärtner SM, Bauhus J. The effect of harvesting on stump mortality and re-sprouting in aged oak coppice forests. For Ecol Manage. 2013;289:18-27.

[132] Johansson T. Sprouting of 10- to 50-year-old Betula pubescens in relation to felling time. For Ecol Manage. 1992;53(1-4):283-296.

[133] Xue Y, Zhang W, Zhou J, Ma C, Ma L. Effects of stump diameter, stump height, and cutting season on Quercus variabilis stump sprouting. Scand J For Res. 2013;28(3):223-231.

**101**

**Chapter 6**

**Abstract**

**1. Introduction**

*and Shuangshuang Chen*

Afforestation in Karst Area

*Ninghua Zhu, Hai Shang, Liling Liu, Xiaowei Yang, Fei Liu* 

In order to study the afforestation technology in rocky desertification area and provide guidance for the cultivation and management of artificial forest in the later stage, an experimental study was carried out on the artificial forest in National long term scientific research base for comprehensive control of rocky desertification in Wuling Mountain, Western Hunan Province. The experiences of afforestation, land preparation and forest management in this area were summarized. The result show that: 1. Through appropriate afforestation land preparation and forest management measures, the forest in rocky desertification area can be successfully restored. 2. Vegetation restoration in rocky desertification area has formed relatively healthy and stable multi tree species and multi-level forest communities. 3. The biological yield of each afforestation tree species was significantly different with different tree species. 4. The diversity index and evenness index of undergrowth plants in different stands were significantly different. 5. Young trees of dominant species dominated the undergrowth vegetation of different stands, and the natural regeneration of each stand has been stabilized. 6. There are some differences in soil chemical properties under different stands. There were significant differences in SOM, TN, NO3-N, NH4-N and AP contents in the soil of the eight stands.

**Keywords:** karst area, afforestation, site preparation, growth pattern, biodiversity

Rocky desertification land is one of the difficult forestation areas faced by human beings. 12% of the world's land is facing the problem of rocky desertification. The area of rocky desertification in China is 50 million ha. From Sinian to Triassic, the underlying strata deposited thick carbonate rocks, which laid the material foundation for the formation of rocky desertification in this area. Early studies have shown that the species diversity of vegetation communities will gradually increase with the improvement of environmental conditions and the development of succession stages and the community structure will become better and better (see [1]). The karst area has strong spatial heterogeneity, poor anti-interference ability, low ecosystem function, and very fragile environment. In addition, it is affected by backward productivity and unreasonable human activities. Vegetation is gradually degraded, vegetation coverage is reduced, and the ability of soil to retain water and soil is reduced. It restricts the growth of plants, makes soil erosion present a vicious circle, and slows down the process of ecological civilization construction in karst areas (see [2, 3]).

Xiangxi Autonomous Prefecture is located in the hinterland of Wuling Mountain,

with a forest area of 633,200 hectares and a forest coverage rate of 61%. The territory is rich in biological species resources, with many rare species, which can

## **Chapter 6** Afforestation in Karst Area

*Ninghua Zhu, Hai Shang, Liling Liu, Xiaowei Yang, Fei Liu and Shuangshuang Chen*

#### **Abstract**

In order to study the afforestation technology in rocky desertification area and provide guidance for the cultivation and management of artificial forest in the later stage, an experimental study was carried out on the artificial forest in National long term scientific research base for comprehensive control of rocky desertification in Wuling Mountain, Western Hunan Province. The experiences of afforestation, land preparation and forest management in this area were summarized. The result show that: 1. Through appropriate afforestation land preparation and forest management measures, the forest in rocky desertification area can be successfully restored. 2. Vegetation restoration in rocky desertification area has formed relatively healthy and stable multi tree species and multi-level forest communities. 3. The biological yield of each afforestation tree species was significantly different with different tree species. 4. The diversity index and evenness index of undergrowth plants in different stands were significantly different. 5. Young trees of dominant species dominated the undergrowth vegetation of different stands, and the natural regeneration of each stand has been stabilized. 6. There are some differences in soil chemical properties under different stands. There were significant differences in SOM, TN, NO3-N, NH4-N and AP contents in the soil of the eight stands.

**Keywords:** karst area, afforestation, site preparation, growth pattern, biodiversity

#### **1. Introduction**

Rocky desertification land is one of the difficult forestation areas faced by human beings. 12% of the world's land is facing the problem of rocky desertification. The area of rocky desertification in China is 50 million ha. From Sinian to Triassic, the underlying strata deposited thick carbonate rocks, which laid the material foundation for the formation of rocky desertification in this area. Early studies have shown that the species diversity of vegetation communities will gradually increase with the improvement of environmental conditions and the development of succession stages and the community structure will become better and better (see [1]). The karst area has strong spatial heterogeneity, poor anti-interference ability, low ecosystem function, and very fragile environment. In addition, it is affected by backward productivity and unreasonable human activities. Vegetation is gradually degraded, vegetation coverage is reduced, and the ability of soil to retain water and soil is reduced. It restricts the growth of plants, makes soil erosion present a vicious circle, and slows down the process of ecological civilization construction in karst areas (see [2, 3]).

Xiangxi Autonomous Prefecture is located in the hinterland of Wuling Mountain, with a forest area of 633,200 hectares and a forest coverage rate of 61%. The territory is rich in biological species resources, with many rare species, which can

be called a natural treasure house of wild animal and plant resources and a gene bank of biological species. 19 species of world-famous relict plants such as *Cyclops*, *Metasequoia*, *Davidia involucrata*, *Ginkgo biloba*, *Gastrodia*, *camphor*, and *turmeric* are preserved; more than 230 species of oily plants with seed oil content greater than 10%; 216 ornamental plants in 91 families 383 species; There are more than 60 kinds of vitamin plants; 12 kinds of pigment plants. It is the main producing area of *Tung Oil, Camellia oleifera, lacquer* and Chinese medicinal materials, especially in the prefecture, there is the most complete and largest low-altitude evergreen broad-leaved primary secondary forest in the subtropical zone. In the past thousand years, the forests in this area were cut down and the hillsides were used for farming. As a result, the soil erosion in this area was accelerated and the rocky desertification was intensified. Vegetation restoration is the key to ecological reconstruction, and the restoration of plant diversity is an important part of vegetation restoration. The zonality and succession of vegetation should be followed by the selection of suitable economic tree species, and the optimal allocation of forest should have configured shrub and grass. Therefore, it is of great significance to explore the technology of forest vegetation restoration in rocky desertification area, and Vigorously promote the use of rocky desertification management models based on locally suitable native tree species, continue to increase comprehensive conservation efforts, and ultimately create a near-natural growth community environment for vegetation growth, so as to achieve the expected results of rocky desertification vegetation restoration.

The purpose of this study is to restore the near natural forest ecosystem with multi tree species and multi canopy in the rocky desertification area with serious vegetation degradation through silviculture. This experimental study preliminarily achieved the goal, improved the soil production capacity, reduced soil erosion, improved the microclimate of afforestation in rocky desertification area, produced a certain amount of wood, and it has improved the living environment, also increases the income of the people in the area. This effort caused the social production activities into a sustainable virtuous circle.

#### **2. Materials and methods**

#### **2.1 Overview of the study area**

Under the influence of subtropical monsoon and mountain control, the national long-term scientific research base of Wuling Mountain has obvious Subtropical monsoon climate characteristics. The four seasons are distinct, the precipitation is abundant. The annual average sunshine hours are 1240-1440 h, the annual average temperature is 15.8–16.9°C, the annual active accumulated temperature is 4835–5200°C, the frost free period is 269–292 days, and the annual average rainfall is 1300–1500 mm.

Since 1964, the local forestry department has carried out the artificial afforestation movement on the mountain with serious rocky desertification. After 55 years of hard work, 126 native tree species of 39 families and more than 10 exotic tree species have been successfully used to carry out forest vegetation restoration test on 386.7hm<sup>2</sup> of serious rocky desertification mountain. Here, from the past chaotic rock slope with overgrown weeds, it has become today's lush and green mountains Linhai has formed a modern forestry construction demonstration base integrating forest management and forestry scientific research in rocky desertification areas.

The national long-term scientific research base for comprehensive management of rocky desertification in Wuling Mountain is selected as the research object. The research base is located in Qingping Town, Yongshun County, Xiangxi Autonomous Prefecture, Hunan Province, 110° 13′40.296 "E, 29 ° 3'21.59"N, belonging to the

**103**

**Figure 2.**

*carried out land cave-shaped soil preparation in 1973.*

*Afforestation in Karst Area*

exposure.

**Figure 1.**

*DOI: http://dx.doi.org/10.5772/intechopen.95294*

lowest altitude is 320 meters (**Figures 1** and **2**).

There are three main methods of land preparation in this area:

*Research location; a. Research location in China b. The plot distribution map.*

*Land preparation; A. The level artificial trench B. The level artificial bund C. The local forestry workers* 

central area of Wuling Mountain Area. The highest altitude is 820 meters and the

a.The artificial trench is suitable for sites with more than 90% rock exposure.

b.The artificial bund is suitable for slope land with less than 90% rock

c.Cave shaped site preparation is suitable for the site of stone bud pile.

The parent rock is limestone, which belongs to severe rocky desertification area.

#### *Afforestation in Karst Area DOI: http://dx.doi.org/10.5772/intechopen.95294*

*Silviculture*

be called a natural treasure house of wild animal and plant resources and a gene bank of biological species. 19 species of world-famous relict plants such as *Cyclops*, *Metasequoia*, *Davidia involucrata*, *Ginkgo biloba*, *Gastrodia*, *camphor*, and *turmeric* are preserved; more than 230 species of oily plants with seed oil content greater than 10%; 216 ornamental plants in 91 families 383 species; There are more than 60 kinds of vitamin plants; 12 kinds of pigment plants. It is the main producing area of *Tung Oil, Camellia oleifera, lacquer* and Chinese medicinal materials, especially in the prefecture, there is the most complete and largest low-altitude evergreen broad-leaved primary secondary forest in the subtropical zone. In the past thousand years, the forests in this area were cut down and the hillsides were used for farming. As a result, the soil erosion in this area was accelerated and the rocky desertification was intensified. Vegetation restoration is the key to ecological reconstruction, and the restoration of plant diversity is an important part of vegetation restoration. The zonality and succession of vegetation should be followed by the selection of suitable economic tree species, and the optimal allocation of forest should have configured shrub and grass. Therefore, it is of great significance to explore the technology of forest vegetation restoration in rocky desertification area, and Vigorously promote the use of rocky desertification management models based on locally suitable native tree species, continue to increase comprehensive conservation efforts, and ultimately create a near-natural growth community environment for vegetation growth, so as to

achieve the expected results of rocky desertification vegetation restoration.

tion activities into a sustainable virtuous circle.

**2. Materials and methods**

**2.1 Overview of the study area**

The purpose of this study is to restore the near natural forest ecosystem with multi tree species and multi canopy in the rocky desertification area with serious vegetation degradation through silviculture. This experimental study preliminarily achieved the goal, improved the soil production capacity, reduced soil erosion, improved the microclimate of afforestation in rocky desertification area, produced a certain amount of wood, and it has improved the living environment, also increases the income of the people in the area. This effort caused the social produc-

Under the influence of subtropical monsoon and mountain control, the national long-term scientific research base of Wuling Mountain has obvious Subtropical monsoon climate characteristics. The four seasons are distinct, the precipitation is abundant. The annual average sunshine hours are 1240-1440 h, the annual average temperature is 15.8–16.9°C, the annual active accumulated temperature is 4835–5200°C, the frost free

Since 1964, the local forestry department has carried out the artificial afforestation movement on the mountain with serious rocky desertification. After 55 years of hard work, 126 native tree species of 39 families and more than 10 exotic tree species have been successfully used to carry out forest vegetation restoration test

rock slope with overgrown weeds, it has become today's lush and green mountains Linhai has formed a modern forestry construction demonstration base integrating forest management and forestry scientific research in rocky desertification areas. The national long-term scientific research base for comprehensive management of rocky desertification in Wuling Mountain is selected as the research object. The research base is located in Qingping Town, Yongshun County, Xiangxi Autonomous Prefecture, Hunan Province, 110° 13′40.296 "E, 29 ° 3'21.59"N, belonging to the

of serious rocky desertification mountain. Here, from the past chaotic

period is 269–292 days, and the annual average rainfall is 1300–1500 mm.

**102**

on 386.7hm<sup>2</sup>

central area of Wuling Mountain Area. The highest altitude is 820 meters and the lowest altitude is 320 meters (**Figures 1** and **2**).

The parent rock is limestone, which belongs to severe rocky desertification area. There are three main methods of land preparation in this area:


**Figure 2.**

*Land preparation; A. The level artificial trench B. The level artificial bund C. The local forestry workers carried out land cave-shaped soil preparation in 1973.*


#### **Table 1.**

*The basic situation of monitoring sample plots.*

In terms of tree species selection, afforestation mode and stand tending management, the selection principle of tree species follows the principle of local tree species and suitable tree species, and In order to increase local species resources and land biodiversity, a small number of exotic species are introduced. For example, *Liriodendron chinense* (Hemsl.) Sarg. All the afforestation methods are seedling planting. Young forest tending combined with crop interplanting was used to loosen soil and weed. In the early stage of afforestation, Corn was the main crop in early interplanting, After the stand was closed, crop interplanting was stopped, and the stand density was adjusted by artificial pruning and thinning.

#### **2.2 Experimental design**

The fixed standard plot survey method was adopted in January 2019. In the study area, eight representative native precious tree species were selected: *Toona sinensis* (Juss.), *Choerospondias axillaris* (Roxb.), *Corylus chinensis* (Franch.), *Taiwania cryptomerioides* (Hayata), *Cupressus lusitanica* (Mill.), *Nyssa sinensis* (Oliver.) and *Liriodendron chinensis* (Sarg.). One is unplanted shrub and grassland as a control plot, Three20 m \* 30 m sample plots were set up for each stand. The DBH, tree height, height of the beginning of the crown, crown diameter and stem straightness were recorded, and the average tree height and DBH were calculated. Calculation of average DBH and average tree height of sample plot: the average DBH of sample plot is the DBH corresponding to the average cross-sectional area, so the cross-sectional area of each tree should be calculated, and then the average cross-sectional area should be calculated to calculate the average DBH. The average tree height is to find out the corresponding tree height with the average DBH on the basis of the DBH tree height curve. The basic conditions of the monitored plots are shown in **Table 1**.

#### **3. Research contents**

Eight artificial forests were selected for the study. The main research contents are as follows:

**105**

*Afforestation in Karst Area*

*DOI: http://dx.doi.org/10.5772/intechopen.95294*

Each tree was investigated in the sample plot, and one standard tree was selected

Three 2 m \* 2 m shrub plots were set up in 8 fixed sample plots of *Toona sinensis* (Juss.), *Choerospondias axillaris* (Roxb.), *Corylus chinensis* (Franch.), *Taiwania cryptomerioides* (Hayata), *Cupressus lusitanica* (Mill.), *Nyssa sinensis* (Oliver.) and *Liriodendron chinensis* (Sarg.). Three 1 m × 1 m small plots were set up to investigate

The biomass of standard wood was measured by stratified harvest method. 500 g samples were taken from the upper, middle and lower layers of branches and stem. The underground part was excavated in three layers of 0–20 cm, 20–40 cm and 40–60 cm within the radius range of 1 m of sample tree, and were divided into coarse roots (d > 5) three levels of roots (5 cm > d > 1 cm), medium root (5 cm > d > 1 cm) and fine root (d < 1 cm) were placed by classification, and 500 g samples of their fresh weights were weighed. The fresh weights of leaves, stem, bark and roots were measured, and then dried in 85°C oven to constant weight. The water content of each part and the biomass of standard tree were calculated, and the biomass of the whole tree layer was calculated. Calculate the dry mass of each component, calculate the dry mass of the sample wood, and then convert the dry mass per unit area and stand biomass.

The ground diameter, DBH, tree height, crown diameter and stem straightness of all young trees in the plot were recorded, and the average tree height and DBH

The soil physical properties were mainly measured for *Taiwania cryptomerioides* (Hayata.), *Liriodendron chinensis* (Sarg.), and *Taiwania cryptomerioides*-*Liriodendron chinensis* mixed forest, and soil nutrients were measured for eight forests. Three 20 m\* 20 m sample plots were selected as the sample plots in the fixed sample plots, and the soil samples were randomly selected from three points in each sample plot. The visible animal and plant residues and small stones were carefully removed, and then were mixed evenly through a 2 mm sieve. The samples were taken back to the laboratory for analysis. The rocky desertification unforested shrub grassland was taken as the research sample plot, and each sample was determined three times. Soil samples were dried by natural air to remove impurities. 5-10 g samples were screened by 2 mm soil sieve to determine the contents of C, N and P in soil. Soil C was determined by potassium dichromate external heating sulfuric acid oxidation method (LY / T 1237–1999), while soil N and P were determined by semi micro Kjeldahl method (LY / T 1228–1999) and molybdenum antimony resistance

for stem analysis in each standard plot. Through the measurement of DBH, tree height and volume growth process, the measured data were obtained, and the total growth, annual growth and average growth curve of each tree species were drawn to

**3.1 Growth patterns of plantation**

analyze their growth pattern.

**3.2 Biodiversity of plantation**

the shrub and grass diversity under the forest.

**3.3 Biomass survey of tree layer in plantation**

**3.4 Regeneration patterns of plantation**

Colorimetry (LY/ T 1232–1999) (see [4]).

**3.5 Soil sampling and analysis**

were calculated.

#### **3.1 Growth patterns of plantation**

*Silviculture*

(Mill.)

(Hayata.)

(Sarg.)

(Roxb.)

**Table 1.**

*Cupressus lusitanica*

*Quercus acutissima* (Carruth.)

*Corylus chinensis* (Franch.)

*Liriodendron chinensis*

*Choerospondias axillaris*

*The basic situation of monitoring sample plots.*

*Taiwania cryptomerioides*

**Plot name Afforestation**

**patterns**

**Plot area/m2 Canopy** 

**closure**

Pure forest 20 m\*30 m 0.68 25 3%

Pure forest 20 m\*30 m 0.7 40 5%

Pure forest 20 m\*30 m 0.69 20 4%

Pure forest 20 m\*30 m 0.8 42 2%

Pure forest 20 m\*30 m 0.67 37 8%

Pure forest 20 m\*30 m 0.77 35 5%

**Stand age/a**

**Percentage of total forest area/%**

In terms of tree species selection, afforestation mode and stand tending management, the selection principle of tree species follows the principle of local tree species and suitable tree species, and In order to increase local species resources and land biodiversity, a small number of exotic species are introduced. For example, *Liriodendron chinense* (Hemsl.) Sarg. All the afforestation methods are seedling planting. Young forest tending combined with crop interplanting was used to loosen soil and weed. In the early stage of afforestation, Corn was the main crop in early interplanting, After the stand was closed, crop interplanting was stopped, and the

*Toona sinensis* (Juss.) Pure forest 20 m\*30 m 0.82 40 5% *Nyssa sinensis* (Oliver.) Pure forest 20 m\*30 m 0.65 40 3%

The fixed standard plot survey method was adopted in January 2019. In the study area, eight representative native precious tree species were selected: *Toona sinensis* (Juss.), *Choerospondias axillaris* (Roxb.), *Corylus chinensis* (Franch.), *Taiwania cryptomerioides* (Hayata), *Cupressus lusitanica* (Mill.), *Nyssa sinensis* (Oliver.) and *Liriodendron chinensis* (Sarg.). One is unplanted shrub and grassland as a control plot, Three20 m \* 30 m sample plots were set up for each stand. The DBH, tree height, height of the beginning of the crown, crown diameter and stem straightness were recorded, and the average tree height and DBH were calculated. Calculation of average DBH and average tree height of sample plot: the average DBH of sample plot is the DBH corresponding to the average cross-sectional area, so the cross-sectional area of each tree should be calculated, and then the average cross-sectional area should be calculated to calculate the average DBH. The average tree height is to find out the corresponding tree height with the average DBH on the basis of the DBH tree height

stand density was adjusted by artificial pruning and thinning.

curve. The basic conditions of the monitored plots are shown in **Table 1**.

Eight artificial forests were selected for the study. The main research contents

**2.2 Experimental design**

**3. Research contents**

are as follows:

**104**

Each tree was investigated in the sample plot, and one standard tree was selected for stem analysis in each standard plot. Through the measurement of DBH, tree height and volume growth process, the measured data were obtained, and the total growth, annual growth and average growth curve of each tree species were drawn to analyze their growth pattern.

#### **3.2 Biodiversity of plantation**

Three 2 m \* 2 m shrub plots were set up in 8 fixed sample plots of *Toona sinensis* (Juss.), *Choerospondias axillaris* (Roxb.), *Corylus chinensis* (Franch.), *Taiwania cryptomerioides* (Hayata), *Cupressus lusitanica* (Mill.), *Nyssa sinensis* (Oliver.) and *Liriodendron chinensis* (Sarg.). Three 1 m × 1 m small plots were set up to investigate the shrub and grass diversity under the forest.

#### **3.3 Biomass survey of tree layer in plantation**

The biomass of standard wood was measured by stratified harvest method. 500 g samples were taken from the upper, middle and lower layers of branches and stem. The underground part was excavated in three layers of 0–20 cm, 20–40 cm and 40–60 cm within the radius range of 1 m of sample tree, and were divided into coarse roots (d > 5) three levels of roots (5 cm > d > 1 cm), medium root (5 cm > d > 1 cm) and fine root (d < 1 cm) were placed by classification, and 500 g samples of their fresh weights were weighed. The fresh weights of leaves, stem, bark and roots were measured, and then dried in 85°C oven to constant weight. The water content of each part and the biomass of standard tree were calculated, and the biomass of the whole tree layer was calculated. Calculate the dry mass of each component, calculate the dry mass of the sample wood, and then convert the dry mass per unit area and stand biomass.

#### **3.4 Regeneration patterns of plantation**

The ground diameter, DBH, tree height, crown diameter and stem straightness of all young trees in the plot were recorded, and the average tree height and DBH were calculated.

#### **3.5 Soil sampling and analysis**

The soil physical properties were mainly measured for *Taiwania cryptomerioides* (Hayata.), *Liriodendron chinensis* (Sarg.), and *Taiwania cryptomerioides*-*Liriodendron chinensis* mixed forest, and soil nutrients were measured for eight forests. Three 20 m\* 20 m sample plots were selected as the sample plots in the fixed sample plots, and the soil samples were randomly selected from three points in each sample plot. The visible animal and plant residues and small stones were carefully removed, and then were mixed evenly through a 2 mm sieve. The samples were taken back to the laboratory for analysis. The rocky desertification unforested shrub grassland was taken as the research sample plot, and each sample was determined three times.

Soil samples were dried by natural air to remove impurities. 5-10 g samples were screened by 2 mm soil sieve to determine the contents of C, N and P in soil. Soil C was determined by potassium dichromate external heating sulfuric acid oxidation method (LY / T 1237–1999), while soil N and P were determined by semi micro Kjeldahl method (LY / T 1228–1999) and molybdenum antimony resistance Colorimetry (LY/ T 1232–1999) (see [4]).

#### **3.6 Statistical analyses**

*3.6.1 Species diversity calculation method*

1.The calculation formula of species importance value is as follows:

Important value = (relative density + relative dominance + relative frequency)/3 × 100% (see [5]).

2.Species diversity calculation method

Berger Parker index : d Nmax / N − =

Simpson index : D 1 = −∑ − − {*ni ni* ( 1/ NN 1 ) ( ) }

*s i pi pi* = − = −∑1 Shannon wiener index : H ln

$$\text{Pieluu index}: \text{P} = \frac{H}{\ln \text{S}}$$

Note: In the formula, Nmax is the number of individuals of the most dominant species; N is the total number of individuals; ni is the number of individuals of the i-th species. Pi is the ratio of the number of individuals of the i-th species to the number of individuals of all species in the community; S is the total number of species in the community (see [6]).

#### *3.6.2 Calculation method of stand average DBH*

1.Quadratic mean diameter at breast height

The quadratic mean diameter at breast height of the stand is calculated based on the section area of the stand height at breast height, as follows: *<sup>n</sup> g i <sup>i</sup> D d <sup>n</sup>* <sup>=</sup> <sup>=</sup> ∑ <sup>2</sup> 1 1

Dg——Stand quadratic mean diameter at breast height

di——Diameter at breast height of the i-th tree

n——Total number of trees in the plot

2.Average stand height

The average height of forest stands adopts the weighted average height of section area, and the calculation formula is: *k i i i k i i h G H G* = = = ∑ ∑ 1 1

*H* —— Average stand height

*hi* —— The arithmetic average height of the i-th diameter tree in the forest stand

**107**

**Figure 3.**

*Height growth curve.*

*Afforestation in Karst Area*

3.Volume per plant

fc——Average form factor

h——Average tree height

*3.6.3 Data analysis*

**4. Results and analysis**

*DOI: http://dx.doi.org/10.5772/intechopen.95294*

calculation formula is as follows:*V gh f average* = +×× 1.3 ( 3 667 ) *<sup>c</sup>*

Vaverage——Average forest accumulation per m2

g1.3——Average wood breast height section area

soil nutrients; origin8.0 was used for mapping.

**4.1 Growth patterns of different plantations**

*4.1.1 The growth pattern of plantation tree height*

The volume per plant is calculated using the average stem profile. The specific

Use Excel to calculate the standard tree's height (H(t)), diameter at breast height(D(t)), and volume per plant(V(t)), volume average growth (Vθ(t)), volume annual growth (VZ(t)), etc. Statistical analysis was performed with SPSS25.0 (see [7]), single-factor analysis of variance was used to test the significant differences in soil physical and chemical properties of different forest stands, and Pearson correlation was used to study the correlation between plant community diversity and

According to the survey data of fixed sample plots, the age variation curve of tree height was drawn. The height and growth of each tree species increase with age (**Figure 3**), but the rapid growth period of each tree species is different. The specific performance is as follows: *Taiwania cryptomerioides* 1 ~ 15 years is the fast-growth period, and the growth rate gradually slows down after 15 years, and the tree height growth reaches 19.7 m at 40 years; *Quercus acutissima* 1 ~ 2 years is the fast-growth period, and the growth rate gradually slows down after 2 years. The height growth

*Gi* —— The cross-sectional area of the breast height of the i-th diameter forest tree in the stand

k——Number of stand diameter steps

#### *Afforestation in Karst Area DOI: http://dx.doi.org/10.5772/intechopen.95294*

3.Volume per plant

*Silviculture*

**3.6 Statistical analyses**

*3.6.1 Species diversity calculation method*

2.Species diversity calculation method

frequency)/3 × 100% (see [5]).

species in the community (see [6]).

*3.6.2 Calculation method of stand average DBH*

1.Quadratic mean diameter at breast height

1.The calculation formula of species importance value is as follows:

Important value = (relative density + relative dominance + relative

Berger Parker index : d Nmax / N − =

Simpson index : D 1 = −∑ − − {*ni ni* ( 1/ NN 1 )

− = −∑

Shannon wiener index : H ln

Pielou index : P <sup>=</sup> ln

Note: In the formula, Nmax is the number of individuals of the most dominant species; N is the total number of individuals; ni is the number of individuals of the i-th species. Pi is the ratio of the number of individuals of the i-th species to the number of individuals of all species in the community; S is the total number of

The quadratic mean diameter at breast height of the stand is calculated based on

The average height of forest stands adopts the weighted average height of section

*k i i i k i i*

*hi* —— The arithmetic average height of the i-th diameter tree in the forest stand *Gi* —— The cross-sectional area of the breast height of the i-th diameter forest

= ∑ ∑ 1 1

*h G*

*G* = =

the section area of the stand height at breast height, as follows: *<sup>n</sup>*

*H*

Dg——Stand quadratic mean diameter at breast height

di——Diameter at breast height of the i-th tree

n——Total number of trees in the plot

2.Average stand height

area, and the calculation formula is:

*H* —— Average stand height

k——Number of stand diameter steps

( ) }

*pi pi*

*g i <sup>i</sup> D d <sup>n</sup>* <sup>=</sup> <sup>=</sup> ∑ <sup>2</sup> 1

1

*s*

*i*

*H S* =

1

**106**

tree in the stand

The volume per plant is calculated using the average stem profile. The specific calculation formula is as follows:*V gh f average* = +×× 1.3 ( 3 667 ) *<sup>c</sup>*

Vaverage——Average forest accumulation per m2 fc——Average form factor g1.3——Average wood breast height section area h——Average tree height

#### *3.6.3 Data analysis*

Use Excel to calculate the standard tree's height (H(t)), diameter at breast height(D(t)), and volume per plant(V(t)), volume average growth (Vθ(t)), volume annual growth (VZ(t)), etc. Statistical analysis was performed with SPSS25.0 (see [7]), single-factor analysis of variance was used to test the significant differences in soil physical and chemical properties of different forest stands, and Pearson correlation was used to study the correlation between plant community diversity and soil nutrients; origin8.0 was used for mapping.

#### **4. Results and analysis**

#### **4.1 Growth patterns of different plantations**

#### *4.1.1 The growth pattern of plantation tree height*

According to the survey data of fixed sample plots, the age variation curve of tree height was drawn. The height and growth of each tree species increase with age (**Figure 3**), but the rapid growth period of each tree species is different. The specific performance is as follows: *Taiwania cryptomerioides* 1 ~ 15 years is the fast-growth period, and the growth rate gradually slows down after 15 years, and the tree height growth reaches 19.7 m at 40 years; *Quercus acutissima* 1 ~ 2 years is the fast-growth period, and the growth rate gradually slows down after 2 years. The height growth

**Figure 3.** *Height growth curve.*

reaches 14.9 m; *Cupressus lusitanica* 1 ~ 4a is the fast growth period, the growth rate gradually slows down after 15 years, and the tree height growth reaches 16.8 m at 24 years (**Figure 4**). *Corylus chinensis* 1 ~ 27 years is the fast-growing period. After 27 years, the growth rate gradually slows down. After 37 years, the height of the tree grows extremely slowly, and the height of the tree reaches 22.3 m at 42 years. For *Choerospondias axillaris*,1-10a is the fast-growing period, and the growth rate gradually slows down after 10a, and the tree height grows to 18.5 m at 35 years. *Toona sinensis* 1 ~ 35 years is the fast-growing period. After 35 years, the growth rate gradually slows down, and the tree height grows to 23.5 m at 40 years. *Nyssa sinensis* 1–30 years is the fast-growing period, the growth rate gradually slows down after 30a, and the tree height growth reaches 20.9 m at 40 years. *Liriodendron chinensis* 1 to 27 years is a fast-growing period, after 27 years, the growth rate gradually slows down, and the tree height grows to 22.1 m at 37 years.

#### *4.1.2 Growth pattern of diameter at breast height of plantation*

The growth of diameter at breast height of each tree species increases with age. Specifically, it shows that: the first 1–5 years after planting of *Taiwania cryptomerioides* grows slowly, the growth enters the fast growth period after 5 years, the growth of diameter at breast height begins to slow down after 15 years, and the growth of diameter at breast height reaches 23 cm at the 40th year; the rapid growth period after the plantation of *Cupressus lusitanica*, The growth slowed down after 14th year, and the breast diameter growth reached 27 cm at 24th year. After afforestation, *Corylus chinensis* entered the fast-growing period, and the growth slowed down after 17 years, and the diameter at breast height reached 17.9 cm at 42 years; *Choerospondias axillaris* quickly entered the fast-growing period after afforestation, the growth slowed down after 15 years, and the diameter at breast height reached 16.8 cm at 35 years. *Toona sinensis* grows slowly in the first 5 years after afforestation. After a slow growth period, it enters the fast-growth period after 5 years and slows down after 25 years. The diameter at breast height reaches 17.2 cm at 40th year. *Nyssa sinensis* grow slowly in the first 5 years after afforestation. After a slow growth period, they enter the fast-growth period after 5 years, and the growth

**109**

**Figure 5.**

*growth volume VQ(t) of plantation forest.*

*Afforestation in Karst Area*

reached 0.2004 m3

maximum value of 0.01344 m3

mum value of 0.0264 m3

0.0120 m3

of 0.0061m3

maximum value of 0.0156 m3

the maximum value of 0.00542 m3

the volume growth reached 0.0814m3

*DOI: http://dx.doi.org/10.5772/intechopen.95294*

*4.1.3 Volume growth pattern of plantation forest*

10 years, and the volume growth reaches 0.1476m3

growth VZ(t) reached its maximum value of 0.0037m3

growth VZ(t) reached the maximum value of 0.0083 m3

age growth volume reached the maximum value of 0.0048 m3

growth period after 4 years. The volume growth reached 0.3749 m3

reached quantitative maturity and theoretically the optimal cutting age.

growing stage after five years. The volume growth reached 0.1868 m3

slows down after 25 years. The diameter at breast height reaches 13.8 cm at 40th year. After afforestation, the first 2 years of *Liriodendron chinensis* grows slowly. After the slow growth stage, it enters the fast growth stage after 2 years. The growth slows down after 32 years. The diameter at breast height reaches 15.5 cm at 37 years.

It can be seen from **Figure 5** that the volume growth of each tree species increases

. The continuous annual growth of the volume VZ(t) reached the

at 24th year, and the average growth volume reached the

at VQ(t) at 24th year; none of the four tree species has

at 32th year, and the average growth volume reached

at VQ(t) at 37th year*. Quercus acutissima* was in a

at 17th year. The volume of continuous annual

at 40th year. Its volume annual

at VQ(t) at 40th year. In the

at 17th year, and the aver-

at VQ(t) at 17th year.

at 24th year. The

in 35 years.

.

with age. Specifically, it shows that *Nyssa sinensis* go through the first 10 years of slow growth period after afforestation, and then enter the fast growth period after

first 2 years of *Liriodendron chinensis*, the forest was in a slow growth period, and after 2 years, it entered the rapid growth period. At 37th year, the volume growth

slow growth period 5 years ago, and entered a fast growth period 5 years later, and

*Cupressus lusitanica* was in the slow growth period 4 years ago, and entered the fast

continuous annual growth volume VZ(t) of *Cupressus lusitanica* reached the maxi-

After afforestation, the growth of *Toona sinensis* growth slowly in the first 10 years, then growth accelerated to 40 years of volume growth reached 0.2540 m3

at 25th year, and the average growth VQ(t) reached the maximum value

at 40th year. VZ (t) and VQ(t) intersected at the 39th year, when

The annual volume growth of *Toona sinensis* reached the maximum value of

*Toona sinensis* reached the best cutting age. After afforestation, the growth rate of *Choerospondias axillaris* was slow in the first five years, and entered the fast-

The annual volume growth of *Choerospondias axillaris* reached the maximum

*Volume growth curve; a. The continuous annual growth volume VZ(t) of plantation forest; b. The average* 

#### *Afforestation in Karst Area DOI: http://dx.doi.org/10.5772/intechopen.95294*

*Silviculture*

**Figure 4.** *DBH growth curve.*

reaches 14.9 m; *Cupressus lusitanica* 1 ~ 4a is the fast growth period, the growth rate gradually slows down after 15 years, and the tree height growth reaches 16.8 m at 24 years (**Figure 4**). *Corylus chinensis* 1 ~ 27 years is the fast-growing period. After 27 years, the growth rate gradually slows down. After 37 years, the height of the tree grows extremely slowly, and the height of the tree reaches 22.3 m at 42 years. For *Choerospondias axillaris*,1-10a is the fast-growing period, and the growth rate gradually slows down after 10a, and the tree height grows to 18.5 m at 35 years. *Toona sinensis* 1 ~ 35 years is the fast-growing period. After 35 years, the growth rate gradually slows down, and the tree height grows to 23.5 m at 40 years. *Nyssa sinensis* 1–30 years is the fast-growing period, the growth rate gradually slows down after 30a, and the tree height growth reaches 20.9 m at 40 years. *Liriodendron chinensis* 1 to 27 years is a fast-growing period, after 27 years, the growth rate gradually slows

The growth of diameter at breast height of each tree species increases with age. Specifically, it shows that: the first 1–5 years after planting of *Taiwania cryptomerioides* grows slowly, the growth enters the fast growth period after 5 years, the growth of diameter at breast height begins to slow down after 15 years, and the growth of diameter at breast height reaches 23 cm at the 40th year; the rapid growth period after the plantation of *Cupressus lusitanica*, The growth slowed down after 14th year, and the breast diameter growth reached 27 cm at 24th year. After afforestation, *Corylus chinensis* entered the fast-growing period, and the growth slowed down after 17 years, and the diameter at breast height reached 17.9 cm at 42 years; *Choerospondias axillaris* quickly entered the fast-growing period after afforestation, the growth slowed down after 15 years, and the diameter at breast height reached 16.8 cm at 35 years. *Toona sinensis* grows slowly in the first 5 years after afforestation. After a slow growth period, it enters the fast-growth period after 5 years and slows down after 25 years. The diameter at breast height reaches 17.2 cm at 40th year. *Nyssa sinensis* grow slowly in the first 5 years after afforestation. After a slow growth period, they enter the fast-growth period after 5 years, and the growth

down, and the tree height grows to 22.1 m at 37 years.

*4.1.2 Growth pattern of diameter at breast height of plantation*

**108**

slows down after 25 years. The diameter at breast height reaches 13.8 cm at 40th year. After afforestation, the first 2 years of *Liriodendron chinensis* grows slowly. After the slow growth stage, it enters the fast growth stage after 2 years. The growth slows down after 32 years. The diameter at breast height reaches 15.5 cm at 37 years.

#### *4.1.3 Volume growth pattern of plantation forest*

It can be seen from **Figure 5** that the volume growth of each tree species increases with age. Specifically, it shows that *Nyssa sinensis* go through the first 10 years of slow growth period after afforestation, and then enter the fast growth period after 10 years, and the volume growth reaches 0.1476m3 at 40th year. Its volume annual growth VZ(t) reached its maximum value of 0.0037m3 at VQ(t) at 40th year. In the first 2 years of *Liriodendron chinensis*, the forest was in a slow growth period, and after 2 years, it entered the rapid growth period. At 37th year, the volume growth reached 0.2004 m3 . The continuous annual growth of the volume VZ(t) reached the maximum value of 0.01344 m3 at 32th year, and the average growth volume reached the maximum value of 0.00542 m3 at VQ(t) at 37th year*. Quercus acutissima* was in a slow growth period 5 years ago, and entered a fast growth period 5 years later, and the volume growth reached 0.0814m3 at 17th year. The volume of continuous annual growth VZ(t) reached the maximum value of 0.0083 m3 at 17th year, and the average growth volume reached the maximum value of 0.0048 m3 at VQ(t) at 17th year. *Cupressus lusitanica* was in the slow growth period 4 years ago, and entered the fast growth period after 4 years. The volume growth reached 0.3749 m3 at 24th year. The continuous annual growth volume VZ(t) of *Cupressus lusitanica* reached the maximum value of 0.0264 m3 at 24th year, and the average growth volume reached the maximum value of 0.0156 m3 at VQ(t) at 24th year; none of the four tree species has reached quantitative maturity and theoretically the optimal cutting age.

After afforestation, the growth of *Toona sinensis* growth slowly in the first 10 years, then growth accelerated to 40 years of volume growth reached 0.2540 m3 . The annual volume growth of *Toona sinensis* reached the maximum value of 0.0120 m3 at 25th year, and the average growth VQ(t) reached the maximum value of 0.0061m3 at 40th year. VZ (t) and VQ(t) intersected at the 39th year, when *Toona sinensis* reached the best cutting age. After afforestation, the growth rate of *Choerospondias axillaris* was slow in the first five years, and entered the fastgrowing stage after five years. The volume growth reached 0.1868 m3 in 35 years. The annual volume growth of *Choerospondias axillaris* reached the maximum

#### **Figure 5.**

*Volume growth curve; a. The continuous annual growth volume VZ(t) of plantation forest; b. The average growth volume VQ(t) of plantation forest.*

value of 0.0097 m3 at 20 years, and the average growth reached the maximum value of 0.0057 m3 when VQ(t) was 25 years. VZ(t) and VQ(t) intersect at 27th year, which is the best cutting age of *Choerospondias axillaris*. After afforestation, the growth of *Corylus chinensis* was slow in the first seven years, and entered the fast-growing stage after seven years. The volume growth reached 0.2783m3 at 42th year. The annual volume growth of *Corylus chinensis* reached the maximum value of 0.0112 m3 at 37 years, and the average growth reached the maximum value of 0.0068 m3 when VQ(t) was 42 years. VZ(t) and VQ(t) intersect, and the best cutting age at 42th year. After afforestation, *Taiwania cryptomenoides* experienced slow growth period in the first 10 years, and entered the fast-growing stage after 10 years, and the volume growth reached 0.3959 m3 at 40 years. VZ (t) reaches the maximum value of 0.0194 m3 at 25th year and 0.0099m3 at VQ(t) 40th year. VZ(t) and VQ(t) do not intersect before 40th year. Therefore, the best cutting age of *Taiwania cryptomenoides* is at least 40th year.

#### **4.2 Biomass per tree and its distribution**

The biomass of individual tree was significantly different with different tree species (**Table 2**). The order of biomass per plant of eight tree species was as follows: *Cupressus lusitanica* (382.483 kg/plant) > *Taiwania cryptomenoides* (239.907 kg/plant) > *Corylus chinensis* (205.245 kg /plant) > *Toona sinensis* (167.054 kg/ plant) > *Quercus acutissima* (149.734 kg/ plant) > *Choerospondias axillaris* (126.345 kg/ plant) > *Nyssa sinensis* (124.824 kg/ plant) > *Liriodendron chinensis* (117.456 kg/ plant). The results showed that the biomass of each component of tree species was as follows: stem > branches> roots of *Cupressus lusitanica* and *Taiwania cryptomenoides*; the biomass of *Corylus chinensis*, *Toona sinensis*, *Quercus acutissima*, *Choerospondias axillaris*, *Nyssa sinensis* and *Liriodendron chinensis* shows:Stem > roots> branches.


**111**

**Table 3.**

*Afforestation in Karst Area*

*DOI: http://dx.doi.org/10.5772/intechopen.95294*

**4.3 Stand biomass and its distribution pattern**

analyzed under the condition of similar forest age.

*sinensis* > *Quercus acutissima* > *Nyssa sinensis.*

*sis > Choerospondias axillaris.*

*sima* > *Choerospondias axillaris*.

*chinensis* > *Choerospondias axillaris.*

**forest /a**

**Tree species Age of** 

*Taiwania cryptomenoides*

*Liriodendron chinensis*

*Choerospondias axillaris*

*Stand biomass of different tree species.*

5.Tree layer > litter layer > understory vegetation layer

The biomass of individual tree is converted into stand biomass as shown

(210.264 t·ha−1) > *Taiwania cryptomenoides* (186.601 t·ha−1) > *Toona sinensis* (18 5.386 t·ha−1) > *Choerospondias axillaris* (181.875 t·ha−1) > *Liriodendron chinensis* (161.548 t·ha−1) > Nyssa sinensis (158.32 t·ha−1).The biomass of tree layer, understory vegetation layer and litter layer of different stands were compared and

1.Tree layer: *Cupressus lusitanica > Quercus acutissima > Corylus chinensis > Toona sinensis > Taiwania cryptomenoides > Nyssa sinensis > Liriodendron chinen-*

2.Undergrowth vegetation layer: *Taiwania cryptomenoides* > Choerospondias axillaris > *Cupressus lusitanica* > *Corylus chinensis* > *Liriodendron chinensis* > *Toona* 

3.Litter layer: *Corylus chinensis* > Nyssa sinensis > *Cupressus lusitanica* > *Taiwania cryptomenoides* > *Liriodendron chinensis* > *Toona sinensis* > *Quercus acutis-*

4.Total biomass:*Cupressus lusitanica* > *Quercus acutissima* > *Coryl*us chinensis > *Taiwania cryptomenoides* > *Toona sinensis* > *Nyssa sinensis* > *Liriodendron* 

The results showed that: the total biomass of *Cupressus lusitanica* forest was the largest, the biomass of understory vegetation layer and litter layer was also higher than that of other forests, and the growth trend was better than that of other tree species. It can be seen that there are some problems in the regeneration of evergreen

**Stem Branch Tree** 

*Cupressus lusitanica* 25 201.79 62.34 48.23 2.65 4.16 319.17

*Quercus acutissima* 20 207.02 31.72 38.27 1.71 2.48 281.20 *Corylus chinensis* 42 162.01 4.60 33.51 2.06 8.09 210.26 *Toona sinensis* 40 152.70 6.20 22.08 1.81 2.60 185.39 *Nyssa sinensis* 40 138.43 13.24 25.16 0.76 4.28 181.88

**Stand biomass /t·ha−1**

**Undergrowth vegetation**

**Litter Total**

**root**

40 116.01 37.10 26.82 2.98 3.69 186.60

37 124.02 5.84 26.75 1.94 3.00 161.55

35 119.09 8.20 26.43 2.79 1.81 158.32

in **Table 3**. The biomass of each stand is as follows: *Cupressus lusitanica* (319.171 t·ha−1) > *Quercus acutissima* (281.197 t·ha−1) > *Corylus chinensis*

#### **Table 2.**

*Biomass comparison of different tree species.*

*Silviculture*

value of 0.0097 m3

value of 0.0057 m3

maximum value of 0.0194 m3

of 0.0112 m3

roots> branches.

0.0068 m3

at 20 years, and the average growth reached the maximum

when VQ(t) was 25 years. VZ(t) and VQ(t) intersect at 27th

at 37 years, and the average growth reached the maximum value of

when VQ(t) was 42 years. VZ(t) and VQ(t) intersect, and the best cut-

at 25th year and 0.0099m3

at 42th

at 40 years. VZ (t) reaches the

**Stem Branches Tree root Total**

at VQ(t) 40th year. VZ(t)

year, which is the best cutting age of *Choerospondias axillaris*. After afforestation, the growth of *Corylus chinensis* was slow in the first seven years, and entered the fast-growing stage after seven years. The volume growth reached 0.2783m3

year. The annual volume growth of *Corylus chinensis* reached the maximum value

ting age at 42th year. After afforestation, *Taiwania cryptomenoides* experienced slow growth period in the first 10 years, and entered the fast-growing stage after

and VQ(t) do not intersect before 40th year. Therefore, the best cutting age of

The biomass of individual tree was significantly different with different tree species (**Table 2**). The order of biomass per plant of eight tree species was as follows: *Cupressus lusitanica* (382.483 kg/plant) > *Taiwania cryptomenoides* (239.907 kg/plant) > *Corylus chinensis* (205.245 kg /plant) > *Toona sinensis*

(167.054 kg/ plant) > *Quercus acutissima* (149.734 kg/ plant) > *Choerospondias axillaris* (126.345 kg/ plant) > *Nyssa sinensis* (124.824 kg/ plant) > *Liriodendron chinensis* (117.456 kg/ plant). The results showed that the biomass of each component of tree species was as follows: stem > branches> roots of *Cupressus lusitanica* and *Taiwania cryptomenoides*; the biomass of *Corylus chinensis*, *Toona sinensis*, *Quercus acutissima*, *Choerospondias axillaris*, *Nyssa sinensis* and *Liriodendron chinensis* shows:Stem >

*Cupressus lusitanica* 247.09 76.34 59.06 382.48 *%* 64.60 20.00 15.40 100.00 *Taiwania cryptomenoides* 154.68 49.47 35.75 239.91 *%* 64.50 20.60 14.90 100.00 *Corylus chinensis* 166.16 4.72 34.37 205.25 *%* 81.00 2.30 16.70 100.00 *Toona sinensis* 140.95 5.73 20.38 167.05 *%* 84.40 3.40 12.20 100.00 *Quercus acutissima* 111.9 17.15 20.69 149.73 *%* 74.70 11.50 13.80 100.00 *Choerospondias axillaris* 97.88 6.74 21.72 126.35 *%* 77.50 5.30 17.20 100.00 *Nyssa sinensis* 97.72 9.35 17.76 124.82 *%* 78.30 7.50 14.20 100.00 *Liriodendron chinensis* 93.02 4.38 20.06 117.46 *%* 79.20 3.70 17.10 100.00

**Tree species Biomass per plant /(kg/plant)**

10 years, and the volume growth reached 0.3959 m3

*Taiwania cryptomenoides* is at least 40th year.

**4.2 Biomass per tree and its distribution**

**110**

**Table 2.**

*Biomass comparison of different tree species.*

#### **4.3 Stand biomass and its distribution pattern**

The biomass of individual tree is converted into stand biomass as shown in **Table 3**. The biomass of each stand is as follows: *Cupressus lusitanica* (319.171 t·ha−1) > *Quercus acutissima* (281.197 t·ha−1) > *Corylus chinensis* (210.264 t·ha−1) > *Taiwania cryptomenoides* (186.601 t·ha−1) > *Toona sinensis* (18 5.386 t·ha−1) > *Choerospondias axillaris* (181.875 t·ha−1) > *Liriodendron chinensis* (161.548 t·ha−1) > Nyssa sinensis (158.32 t·ha−1).The biomass of tree layer, understory vegetation layer and litter layer of different stands were compared and analyzed under the condition of similar forest age.


The results showed that: the total biomass of *Cupressus lusitanica* forest was the largest, the biomass of understory vegetation layer and litter layer was also higher than that of other forests, and the growth trend was better than that of other tree species. It can be seen that there are some problems in the regeneration of evergreen


#### **Table 3.**

*Stand biomass of different tree species.*

broad-leaved trees, which need to be paid attention to. It is not our ultimate goal to build artificial pure forest. We need to use artificial afforestation technology to restore its ecological function, carry out natural regeneration, and finally form a complex and stable ecological community structure.

#### **4.4 Differences of undergrowth plant diversity in different stands**

Habitat heterogeneity and plant biological characteristics are the main factors affecting the diversity of understory plants (see [8]). The diversity index and evenness index of different plantations were significantly different (**Figure 6**), indicating that there were differences in the diversity level of understory plants in different plantations. The Berger Parker index of shrub layer in different stands was the largest in *Liriodendron chinensis*. The Simpson index of is the largest of *Corylus chinensis* forest, and that of *Cupressus lusitanica* is the smallest among 8 stands. For Shannon Wiener index, *Cupressus lusitanica* is the largest, *Liriodendron chinensis* is the smallest. The Berger Parker index of herbaceous layer in different stands was the largest in *Toona sinensis* forest and the smallest in *Taiwania cryptomenoides* forest; Simpson index of eight stands was the largest in *Taiwania cryptomenoides* forest and the smallest in *Quercus acutissima* forest. For Shannon Wiener index*, Nyssa sinensis* forest is the largest, *Taiwania cryptomenoides* forest is the smallest. The analysis of variance of undergrowth shrub and herb diversity in each stand shows that the diversity of undergrowth plants is significant, but there is no significant difference in Pielou index among the eight stands, indicating that the evenness of plants in the eight stands is basically similar. Secondly, the Shannon Wiener index of undergrowth shrub in *Cupressus lusitanica* was higher than that in other forest stands, indicating that the plant diversity under *Cupressus lusitanica* was richer than that in other stands, but the diversity index of shrub grassland without afforestation was higher than that of other stands (P < 0.05).

#### **4.5 The regeneration difference of young trees in different stands**

The horizontal spatial distribution of seedlings and young trees is often reflected by the spatial distribution pattern, which will change with the biological characteristics of plants and the comprehensive influence of environmental conditions (see [9]). There are many factors that affect the spatial distribution of seedlings and saplings, and the main factors are seed dispersal and different habitats (see [10]). It can be seen from **Table 4** that saplings of dominant species dominate the undergrowth vegetation of different stands. The regeneration of saplings under *Taiwania cryptomenoides* and *Toona sinensis* stands has gradually appeared other tree

**113**

species, and the natural regeneration of each stand has become stable. The growth of undergrowth plants is closely related to the growth of trees. The composition and structural characteristics of understory plants are closely related to the internal environmental conditions of plantations. On the one hand, because the growth and management of plantation affect the soil, water, light intensity, temperature and humidity and other micro environmental conditions, the growth and development of understory plants is limited, which directly affects the species, coverage,

*Relationship between tree growth and natural regeneration of young forest under the forest.*

*Nyssa sinensis Nyssa sinensis* 1.95 ± 0.82 2.07 ± 1.11 0.46 ± 0.28

*Toona sinensis Acer davidii* 4.25 ± 1.07 2.82 ± 0.52 2.88 ± 1.02

*Afforestation in Karst Area*

*Choerospondias axillaris*

*Cupressus lusitanica*

*Liriodendron chinensis*

*Taiwania cryptomenoides*

*Quercus acutissima*

**Table 4.**

*Note: Mean ± standard error.*

*DOI: http://dx.doi.org/10.5772/intechopen.95294*

*Choerospondias axillaris*

*Cinnamomum camphora*

*Cinnamomum camphora*

*Zanthoxylum bungeanum*

*Cinnamomum camphora*

*Liriodendron chinensis*

*Liriodendron chinensis*

*Cinnamomum camphora*

*Taiwania cryptomenoides*

*Taiwania cryptomenoides*

*Cinnamomum camphora*

**Sample plot Seedling species Tree height /m DBH/cm Average crown** 

*Corylus chinensis Corylus chinensis* 1.73 ± 0.11 2.90 ± 0.28 36.5 ± 2.12

*Lindera communis* 2.87 ± 0.06 1.93 ± 0.55 2.74 ± 0.61

*Camellia japonica* 1.10 ± 0.83 18.22 ± 52.38 0.41 ± 0.65 *Cupressus lusitanica* 2.40 ± 0.28 3.65 ± 0.21 0.09 ± 0.11 *Eriobotrya japonica* 1.25 ± 0.35 0.65 ± 0.21 0.04 ± 0.00 *Rhus chinensis* 1.55 ± 0.66 2.18 ± 2.07 0.09 ± 0.08

*Vernicia fordii* 3.25 ± 0.21 9.95 ± 7.14 0.16 ± 0.00

*Acer davidii* 6.68 ± 1.72 3.05 ± 0.56 2.77 ± 2.67

*Vernicia fordii* 4.81 ± 0.57 3.15 ± 0.49 3.24 ± 1.19

*Phoebe bournei* 1.48 ± 0.82 1.61 ± 0.82 0.37 ± 0.27 *Camellia japonica* 1.15 ± 0.48 1.71 ± 0.66 0.56 ± 0.25

*Quercus acutissima* 7.37 ± 3.32 3.63 ± 1.12 3.41 ± 1.84

**diameter /m**

3.47 ± 1.21 3.15 ± 0.88 3.47 ± 2.81

4.32 ± 1.17 3.32 ± 0.75 4.35 ± 2.02

1.40 ± 0.14 2.11 ± 0.43 28.6 ± 20.67

1.17 ± 0.32 33.2 ± 55.95 0.14 ± 0.19

1.02 ± 0.6 1.43 ± 0.15 0.11 ± 0.08

1.27 ± 0.43 3.16 ± 1.81 0.03 ± 0.01

6.21 ± 2.82 3.22 ± 1.10 0.97 ± 0.36

5.22 ± 2.36 3.02 ± 0.90 0.92 ± 0.32

3.77 ± 1.10 3.19 ± 0.75 2.51 ± 0.89

2.51 ± 1.13 2.41 ± 1.43 0.75 ± 0.74

4.33 ± 1.73 3.35 ± 1.17 3.65 ± 3.61

**Figure 6.** *Diversity index of understory plants; a. The shrub diversity index; b. The herb diversity index.*


#### *Afforestation in Karst Area DOI: http://dx.doi.org/10.5772/intechopen.95294*

*Silviculture*

broad-leaved trees, which need to be paid attention to. It is not our ultimate goal to build artificial pure forest. We need to use artificial afforestation technology to restore its ecological function, carry out natural regeneration, and finally form a

Habitat heterogeneity and plant biological characteristics are the main factors affecting the diversity of understory plants (see [8]). The diversity index and evenness index of different plantations were significantly different (**Figure 6**), indicating that there were differences in the diversity level of understory plants in different plantations. The Berger Parker index of shrub layer in different stands was the largest in *Liriodendron chinensis*. The Simpson index of is the largest of *Corylus chinensis* forest, and that of *Cupressus lusitanica* is the smallest among 8 stands. For Shannon Wiener index, *Cupressus lusitanica* is the largest, *Liriodendron chinensis* is the smallest. The Berger Parker index of herbaceous layer in different stands was the largest in *Toona sinensis* forest and the smallest in *Taiwania cryptomenoides* forest; Simpson index of eight stands was the largest in *Taiwania cryptomenoides* forest and the smallest in *Quercus acutissima* forest. For Shannon Wiener index*, Nyssa sinensis* forest is the largest, *Taiwania cryptomenoides* forest is the smallest. The analysis of variance of undergrowth shrub and herb diversity in each stand shows that the diversity of undergrowth plants is significant, but there is no significant difference in Pielou index among the eight stands, indicating that the evenness of plants in the eight stands is basically similar. Secondly, the Shannon Wiener index of undergrowth shrub in *Cupressus lusitanica* was higher than that in other forest stands, indicating that the plant diversity under *Cupressus lusitanica* was richer than that in other stands, but the diversity index of shrub grassland without afforestation was

**4.4 Differences of undergrowth plant diversity in different stands**

complex and stable ecological community structure.

higher than that of other stands (P < 0.05).

**4.5 The regeneration difference of young trees in different stands**

*Diversity index of understory plants; a. The shrub diversity index; b. The herb diversity index.*

The horizontal spatial distribution of seedlings and young trees is often reflected

by the spatial distribution pattern, which will change with the biological characteristics of plants and the comprehensive influence of environmental conditions (see [9]). There are many factors that affect the spatial distribution of seedlings and saplings, and the main factors are seed dispersal and different habitats (see [10]). It can be seen from **Table 4** that saplings of dominant species dominate the undergrowth vegetation of different stands. The regeneration of saplings under *Taiwania cryptomenoides* and *Toona sinensis* stands has gradually appeared other tree

**112**

**Figure 6.**

#### **Table 4.**

*Relationship between tree growth and natural regeneration of young forest under the forest.*

species, and the natural regeneration of each stand has become stable. The growth of undergrowth plants is closely related to the growth of trees. The composition and structural characteristics of understory plants are closely related to the internal environmental conditions of plantations. On the one hand, because the growth and management of plantation affect the soil, water, light intensity, temperature and humidity and other micro environmental conditions, the growth and development of understory plants is limited, which directly affects the species, coverage,

biomass and diversity of understory plants. On the other hand, after nearly 40 years of development, the plantations in the study area have formed a relatively stable understory environment. The pattern of understory plants is mainly formed by natural competition, which fully reflects the advantages and disadvantages of internal environmental conditions of different artificial forests and the intermediate relationship of understory niche. Therefore, most of the plantations are shade tolerant plants with strong adaptability.

#### **4.6 The difference of soil physical and chemical properties among different stands**

Soil density and total porosity are not only the basic physical characteristics of forest soil, but also important indicators of soil and water conservation, which affect the growth and development of understory plants (see [11]). The soil physical properties of typical *Taiwania cryptomenoides* (coniferous forest), *Liriodendron chinensis* (broad-leaved forest) and *Taiwania cryptomenoides*-*Liriodendron chinensis* mixed forest (coniferous and broad-leaved forest) were determined (**Table 5**). The average soil density in 0 ~ 30 cm depth soil layer was as follows: *Taiwania cryptomenoides* > *Liriodendron chinensis* > mixed forest > shrub grassland (CK)Plot.

In the depth of 0 ~ 30 cm, the average soil porosity was mixed forest (*Taiwania cryptomenoides- Liriodendron chinensis*) > shrub grassland > *Taiwania cryptomenoides* pure forest >*Liriodendron chinensis* pure forest. The soil density in 0–15 cm and 15–30 cm soil layers of the shrub grassland was significantly lower than that of the pure *Taiwania cryptomenoides* forest (P ≤ 0.05), but there was no significant difference in soil density between the mixed forest of *Taiwania cryptomenoides* and pure forest. The soil density of each stand decreased with the increase of soil depth. The soil total porosity of different stands increased with the increase of soil depth, and with the increase of soil layer, the soil porosity of different stands showed significant difference. On the whole, the soil water holding capacity of the mixed forest of *Taiwania cryptomenoides* and *Liriodendron chinensis* was higher than that of pure *Taiwania cryptomenoides* and pure *Liriodendron chinensis*. Compared with pure forest, the maximum water holding capacity of *Taiwania cryptomenoides- Liriodendron chinensis* mixed forest was significantly increased, and the field water holding capacity of 0 ~ 15 cm soil layer in different stands did not reach significant difference. Except for *Taiwania cryptomenoides*, the maximum water holding capacity and field water holding capacity of other stands increased with the increase of soil depth. Among them, the maximum water holding capacity and field water holding capacity of 15 ~ 30 cm soil layer of *Taiwania cryptomenoides* -*Liriodendron chinensis* mixed forest and *Liriodendron chinensis* mixed forest were significantly higher than those of *Taiwania cryptomenoides* pure forest and Liriodendron chinensis pure forest (P ≤ 0.05), but there was no significant difference between 0 ~ 15 cm soil layer.

Soil is the matrix of plant growth, and its physical and chemical characteristics determine the distribution of plant community types. At the same time, the plant community reacts on the soil to improve its habitat conditions and make the community develop. Through the analysis of soil chemical properties under different stands, the results show that there are some differences in soil properties under different stands (**Table 6**). Among the eight stands, the contents of TP, SOM and TN in the soil of *Choerospondias axillaris* forest were the highest, the contents of NH4-N and NO3-N in *Quercus acutissima* forest were higher than those in other stands, the AP content of *Taiwania cryptomenoides* was the highest, and the SOM content of shrub grassland was significantly lower than that of plantation. There were significant differences in SOM, TN, NO3-N, NH4-N and AP contents in the soil of the eight stands.

**115**

**Thickness of soil layer (cm)**

0-15 cm

*Liriodendron chinensis*

*Taiwania cryptomenoides- Liriodendron* 

*chinensis*

*Taiwania cryptomenoides*

shrub grassland

*Liriodendron chinensis*

*Taiwania cryptomenoides- Liriodendron* 

*chinensis*

*Taiwania cryptomenoides*

shrub grassland

*Note: Mean ± standard error; the same letter means no significant difference; no same letter means significant difference.*

**Table 5.**

*Soil physical properties of different stands.*

0.17 ± 0.01b 0.29 ± 0.05a

1.13 ± 0.11b

1.39 ± 0.06a

0.24 ± 0.02b 0.40 ± 0.06a

0.20 ± 0.01b 0.33 ± 0.05a

0.38 ± 0.01c

0.43 ± 0.02b

15-30 cm

0.18 ± 0.01b 0.23 ± 0.03ab

0.16 ± 0.01b 0.33 ± 0.01a

1.22 ± 0.02ab

1.38 ± 0.04a

1.21 ± 0.13b

1.47 ± 0.01a

0.24 ± 0.01a 0.35 ± 0.07a 0.28 ± 0.01b 0.40 ± 0.02a

0.22 ± 0.01a 0.28 ± 0.04a 0.23 ± 0.01b 0.34 ± 0.02a

0.36 ± 0.01b

0.41 ± 0.04a

0.34 ± 0.01d

0.49 ± 0.01a

0.15 ± 0.05b 0.32 ± 0.03a

1.29 ± 0.03ab

1.39 ± 0.02ab

**Stand type**

**Moisture content (100%)**

**Bulk density (g/cm3**

**Maximum water holding** 

**Minimum water holding** 

**capacity**

**(100%)**

0.22 ± 0.03a 0.33 ± 0.03a

**capacity**

**(100%)**

0.24 ± 0.02a 0.36 ± 0.02a

**)**

*Afforestation in Karst Area*

*DOI: http://dx.doi.org/10.5772/intechopen.95294*

**porosity**

**(100%)**

0.33 ± 0.03b

0.46 ± 0.02a

**Total** 

