Growth and quality of 16-year-old sessile oak ( Quercus petraea (Matt.) Liebl.) planted in traditional and alternative row planting patterns

Abstract

Traditional oak silviculture is costly due to high initial planting density required to obtain management goals of producing high quality timber. New methods therefore reduce the initial planting density and use a planting pattern that allows the inclusion of naturally regenerated trees of other species. The study presents the results of a 13-year experiment on the growth and quality of sessile oak planted in traditional and three alternative planting patterns. We hypothesized that increasing of initial spacing and use of natural regeneration to compensate for lower intra-specific competition does not deteriorate growth and quality parameters of oak. Breast height diameter, height, slenderness, height of the first live branch, diameter of the thickest branch, stem shape and potential future crop oak trees (PFCT) were measured and analysed. The local competition of admixture species was estimated on the basis of the relationship between the height of the oak and the height of admixture trees in its close proximity. The type of planting pattern had no significant effect on oak diameter, but oak height was greater in the traditional pattern than in alternative patterns. Height of the first live branch was the only qualitative trait that differed significantly between the compared planting patterns. The probability of an oak being selected as a PFCT was similar in traditional and alternative patterns, but PFCT absolute number was increasing with an increase of oak planting density. Analysis of competition of admixture species in alternative methods shows that it can reduce height, diameter and stability of oaks. On the other hand, the probability of trees being selected as a PFCT increases significantly with increasing interspecific competition. We conclude that, with controlled competition and at least moderate natural regeneration, the alternative planting patterns can produce oaks with similar growth and quality to those in the traditional pattern.

Full text

Handling editor: Dr. Dominik Thom
Received: June 1, 2022. Revised: October 30, 2023. Accepted: November 2, 2023
© The Author(s) 2023. Published by Oxford University Press on behalf of Institute of Chartered Foresters.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which
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Forestry: An International Journal of Forest Research, 2023, 1–16
https://doi.org/10.1093/forestry/cpad059
Original Article
Growth and quality of 16-year-old sessile oak (Quercus
petraea (Matt.) Liebl.) planted in traditional and
alternative row planting patterns
Tadeusz Andrzejczyk1,Mateusz Liziniewicz2, Leszek Bolibok 1,*
1Department of Silviculture, Institute of Forest Sciences University of Life Sciences, Warsaw, Nowoursynowska 159, 34, 02-776, Poland
2Skogforsk, The Forest Research Institute of Sweden, Ekebo 2250, SE-268 90 Svalöv, Sweden
*Corresponding author: Tel: +48 225938106; Fax: +48 225938101; Email: leszek_bolibok@sggw.edu.pl
Abstract
Traditional oak silviculture is costly due to high initial planting density required to obtain management goals of producing high quality
timber. New methods therefore reduce the initial planting density and use a planting pattern that allows the inclusion of naturally
regenerated trees of other species. The study presents the results of a 13-year experiment on the growth and quality of sessile oak
planted in traditional and three alternative planting patterns. We hypothesized that increasing of initial spacing and use of natural
regeneration to compensate for lower intra-specific competition does not deteriorate growth and quality parameters of oak. Breast
height diameter, height, slenderness, height of the first live branch, diameter of the thickest branch, stem shape and potential future
crop oak trees (PFCT) were measured and analysed. The local competition of admixture species was estimated on the basis of the
relationship between the height of the oak and the height of admixture trees in its close proximity. The type of planting pattern had
no significant effect on oak diameter, but oak height was greater in the traditional pattern than in alternative patterns. Height of the
first live branch was the only qualitative trait that differed significantly between the compared planting patterns. The probability of
an oak being selected as a PFCT was similar in traditional and alternative patterns, but PFCT absolute number was increasing with
an increase of oak planting density. Analysis of competition of admixture species in alternative methods shows that it can reduce
height, diameter and stability of oaks. On the other hand, the probability of trees being selected as a PFCT increases significantly with
increasing interspecific competition. We conclude that, with controlled competition and at least moderate natural regeneration, the
alternative planting patterns can produce oaks with similar growth and quality to those in the traditional pattern.
Keywords: wide spacings; interspecific competition; natural regeneration; forest adaptation; oak silviculture
Introduction
Pedunculate oak (Quercus robur L.) and sessile oak (Quercus petraea
(Matt.) Liebl.) are among the most important species of decid-
uous trees in Central Europe from the point of view of ecology
and forest management (Johnson et al. 2009;Löf et al. 2016;
Leuschner and Ellenberg 2017). They are components of natural
forests on moderately fertile and fertile sites (Matuszkiewicz 2001;
Andrzejczyk and Sewerniak 2016;Eaton et al. 2016;Leuschner and
Ellenberg 2017) and can host substantial numbers of organisms
that increase the biodiversity of forest ecosystems (Löf et al. 2016).
They also have high economic value, providing raw material for
diverse wood products (Bartelheimer 1991;Praciak 2013;Eaton
et al. 2016). However, in the 19th century and first half of the
20th century, oak forests in the region were often transformed into
monocultures of Scots pine (Pinus sylvestris L.) and Norway spruce
(Picea abies L. Karst) (Bernadzki 1993;Spiecker 2003;Matuszkiewicz
et al. 2007). Thus, the abundance of oak is relatively low due to pre-
vious forest management practices. For example, in Poland, oak
stands currently occupy 8 per cent and other deciduous species
about 22 per cent of the forest area (Rozkrut 2021). According to
Mayer (1984), in the period before intensive forest management,
deciduous species covered about 2/3 of the forest area in Central
Europe.
In recent decades, a reversal of this transformation, from conif-
erous monocultures to mixed or deciduous stands, has started
in Central Europe (Bernadzki 1993;Zerbe 2002;Spiecker 2003;
Leder and Hanke 2005;Knoke et al. 2008;Noack 2011;Vrˇ
ska
et al. 2016). The rationale is that this will enhance the stands’
adaptation ability to anticipated site and habitat conditions under
ongoing and predicted climate change. Several models used to
predict species’ ranges under climate change indicate that the
abundance of Scots pine and Norway spruce in Central European
forests will decrease in the coming decades (Hanewinkel et al.
2013;Sáenz-Romero et al. 2017;Dyderski et al. 2018;Buras and
Menzel 2019). Driving factors for this predicted trend include
increases in frequencies of forest fires, pest outbreaks, storms,
and recurrent droughts during the growing season (Bellassen and
Luyssaert 2014;Jaime et al. 2019;Sierota et al. 2019). In many
models, sessile and pedunculate oaks are predicted to be the
most important broadleaf species in the future potential natural
vegetation (Bolte et al. 2009;Brang et al. 2014;Lindner et al. 2014;
Schelhaas et al. 2015). Natural ranges of both oak species are
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2|Andrzejczyk et al.
predicted to shift towards the north and north-east in response
to ongoing and predicted changes in European climate (Bolte et al.
2009;Hanewinkel et al. 2013;Dyderski et al. 2018;Takolander et al.
2019). The promotion of sessile oak and pedunculate oak seems
advisable due to their superior resistance to drought (Leuschner
et al. 2001,Friedrichs et al. 2009;Pretzsch et al. 2013;Perkins et al.
2018)andtostorms(Burschel and Huss 2003;Joyce et al. 1998).
Both oak species enable the formation of mixed stands that are
more stable and productive than monocultures in the conditions
of climate change (Pretzsch et al. 2020;Steckel et al. 2019,2020).
The high value of oak wood and the accelerated growth and
development of oak stands in recent decades (Pretzsch et al.
2014) are further arguments for the importance of oak for Central
European forestry.
However, there are substantial obstacles to increasing the
abundance of oak in European landscapes, including high costs of
planting, protection from browsing and other tending measures
in early stages (Annighöfer et al. 2015;Mölder et al. 2019;Löf et al.
2021;Bolibok et al. 2021;Dobrowolska et al. 2020). Increasing
salaries and limited numbers of employees interested to work
in the silvicultural sector might also exacerbate the problem.
Therefore, cheaper methods consistent with principles of nature-
based forest management have been proposed (Pommerening and
Murphy 2004;Dobrowolska et al. 2021). These include use of lower
than traditional oak planting densities, with complementary
admixtures of tree species from natural regeneration to obtain
overall tree densities that provide comparable growth and quality
(Gockel 1995;Gockel et al. 2001;Dong et al. 2007;Andrzejczyk et al.
2015;Saha et al. 2017). Proposed alternative planting patterns for
oak include planting in various group arrangements or in widely
spaced rows.
In the traditional methods, 6.000 to 8.000 oak seedlings are
usually planted per hectare with a 0.8–1.0 ×1,5 m spacing (Joyce
et al. 1998;Burschel and Huss 2003;Andrzejczyk 2009). In alterna-
tive group planting treatments, groups of 80 to 100 oak seedlings
are planted per hectare with 17–29 oaks and 1 ×1 m spacing per
group (Gockel 1995;Gockel et al. 2001;Saha et al. 2017). In the
alternative row planting method, oak seedlings are planted in
modules consisting of 1 to 3 rows with 0.8×1.5 m spacing within
rows separated by 3–7 m wide unplanted strips (Andrzejczyk 2011;
Andrzejczyk et al. 2015;Paluch and Gil 2018). The density of
planted seedlings is 2000 to 4000 seedlings ha−1:50to70percent
lower than in the conventional method (Joyce et al. 1998;Burschel
and Huss 2003). The areas between groups or rows are left for
natural regeneration, which is expected to provide overall stand
densities and associated conditions that promote the oak trees’
growth and quality development (Gockel et al. 2001;Guericke
et al. 2008;Saha et al. 2012,2014;Andrzejczyk et al. 2015;Bolibok
et al. 2021). Naturally regenerated species enable the formation
of mixed stands and support the development of many economic
and ecosystem services. The advantages of mixed stands are
increase in quantitative (Jactel et al. 2018;Liang et al. 2016;Pret-
zsch et al. 2017) or qualitative wood production (Spiecker 1991;
von Lüpke 1998), increase of the resistance to biotic or abiotic
disturbances (Jactel and Brockerhof 2007;Bauhus et al. 2017),
stabilisation or improvement of soil fertility (Rothe and Binkley
2001) and increase of biodiversity (Bauhus and Schmerbeck 2010;
Saha et al. 2013;Sławska and Sławski 2018).
A number of studies has investigated the growth and quality
of oaks planted in groups, both in the first years of growth
(Gockel 1995;Gockel et al. 2001;Rock et al. 2004;Ehring and
Keller 2006;Harari and Brang 2008), and at an older age, in
the thicket and early polewood stages (Saha et al. 2012,2013,
2014,2017;Skiadaresis et al. 2016). These studies have showed
that oak trees planted in groups have comparable quality and
growth to those planted traditionally in rows (Skiadaresis et al.
2016;Saha et al. 2017). However, results concerning the growth
and quality of oaks planted with wide initial spacing between
rows are limited to a few growing seasons after planting, when
competition between stems has not yet started (Andrzejczyk et al.
2015;Bolibok et al. 2021). This is an important caveat as the onset
of competition is an important factor in the process of natural
pruning of the main oak stems, which influences the quality of
individual stems and improves stands’ total value (Spiecker 1991;
Attocchi 2015). In oak stands planted in rows (i.e. the traditional
method) young oaks compete mainly with other oaks, whereas in
stands planted in an alternative approach (groups or wide spaced
rows) they compete also with other, naturally regenerated, tree
species. However, when oak is planted in groups, interspecific
competition mainly concerns oaks growing at edges of groups
(Rock et al. 2004;Saha et al. 2014), while almost all oaks are
subject to interspecific competition, if planted in rows. The effect
of interspecific competition varies and depends on the stocking,
species composition, their ecological features, such as whether
tree species are shade-tolerant or not, growth of natural regen-
eration and silvicultural measures, e.g. pre-commercial thinning
(Leder 1996;Wagner and Röker 2000;Rock et al. 2004;Liziniewicz
et al. 2016;Jensen and Löf 2017;Bolibok et al. 2021). High stem
densities of naturally regenerated, fast-growing broadleaf species
have been shown to reduce oak trees’ growth and increase their
mortality (Liziniewicz et al. 2016;Jensen and Löf 2017). However,
low densities are unfavourable for oaks’ future quality as they
reduce self-pruning and, thus, timber quality.
The density and distribution of oak seedlings planted with row
methods may significantly affect possibilities for selecting evenly
spaced crop oaks in the future, especially in oak populations with
unfavourable forms of trees. Preliminary assessments of stands’
silvicultural value are possible at the thicket stage, based on the
selection of potential future crop trees (PFCTs) (Kuehne et al.
2013;Skiadaresis et al. 2016;Saha et al. 2017). Saha et al. (2017)
showed that the group method resulted in greater numbers of
PFCT trees than the traditional method, but there have been
no such comparisons of alternative methods with widely spaced
rows and traditional methods with closely spaced rows.
To address this research gap, we have studied growth and
quality of 16-year-old oaks (Q. petraea) grown under the traditional
planting pattern (high oak densities) without interspecific compe-
tition and under three alternative planting patterns (low oak den-
sities) with inter-specific competition from naturally regenerated
trees. In alternative methods, we analysed the effect of different
level of intra- and interspecific competition on oak growth and
quality. We also assessed the effects of planting patterns and com-
petition on the possibilities for selection of potential future crop
oak trees. The research was carried out in a 13-year experiment,
located in central Poland. We tested the following hypotheses. (I)
First, the planting pattern has no effect on the growth and quality
of 16-year-old oaks. This would mean that more economical alter-
native planting patterns can produce oaks with similar growth
and quality to those in the traditional pattern. (II) Second, in
alternative patterns, oak trees’ growth decreases with increases in
competition in their neighbourhood, but their quality parameters
improve. This would indicate that in alternative methods there is
an optimal level of interspecific competition, which ensures both
good growth and quality of oaks. Maintaining this balanced level
of competition is the goal of pre-commercial thinning. (III) Third,
the probabilities of trees being selected as PFCTs are similar in
traditional and alternative methods of oak stand establishment
and their numbers are proportional to the density of planted
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Growth and quality of 16-year-old sessile oak (Quercus petraea (Matt.) Liebl.) planted |3
Figure 1. Distribution of experimental blocks and planting patterns plots within compartment 147 in Rogów Forests.
seedlings. This would be an additional confirmation of the similar
quality of oak in traditional and alternative methods. In addition,
it would provide insight for determining the optimal number of
PFCTs in the analysed alternative methods.
Methods
Study area
The data examined in this study was collected in an experi-
ment established at Rogów Forest Research Station (compartment
147b), central Poland (51◦82’N, 19◦91E, alt. 215 m a.s.l.). The
experiment was established and planted in the spring of 2007
with 3-year-old oak (Q. petraea) seedlings in a randomized block
design with five blocks and 3 or 4 plots per block (Fig. 1). The
oaks were planted in artificial gaps, ranging between 0.20 and
0.5 hectare in size, cut in the 110-year-old pine stand, as part
of the combined patch-clear cutting system. In one gap there
was one, exceptionally two blocks of the experiment. The area
of the experimental plots varied between 0.05 and 0.09 ha and
the area of blocks ranged from 0.18 ha (blocks 3 and 5) to 0.30 ha
(block 4). The stand around the gaps was cut down 6 years after
the oak was planted. The site is moderately fertile with haplic
luvisol soil.
The oaks were planted with four initial spacing patterns
(Figure 2), hereafter planting patterns (PPs). These were a control
pattern (T -traditional) with 1.5 ×0.85 m spacing, representing
the traditional spacing for oak planting in Poland) and three
(designated row PPs) with higher spacings: 4.5 ×0.85 m (1R_4.5),
3.0 ×0.85 m (1R_3.0), and double rows with 1.5×0.85 m spacing
and 3 m between the pairs of rows (2R). The initial densities in
these planting patterns were 7800, 2600, 3900 and 5200 seedlings
ha−1, respectively. The planting patterns 1R_3.0, 2R and T were
repeated on five plots, and the pattern 1R_4.5 on three plots within
the experiment (Fig. 1).
In the period 2008–2011 a weed control treatment, mainly
involving removal of raspberries (Rubus idaeus L. and Rubus nessen-
sis Hall) in close proximity of oak rows (up to 0.75 m) was applied
once or twice a year. Weed control also resulted in almost com-
plete removal of natural regeneration of admixture tree in the T
plots, and only partial reduction in the alternative PPs plots. In
addition, dominant trees of admixture species were removed or
topped twice (in June 2013 and March 2017) in 1R_3.0, 1R_4.5 and
2R plots, and once (in March 2017) in the T plots.
Field measurements
The data reported here were collected in the spring of 2020,
13 years after planting, by measuring oaks in one sample plot
within each experimental plot. To avoid edge effect, oaks growing
closer than 4.5 m from the planting plots border were excluded.
For this reason, the experimental plots were smaller than the
planting plots and had dimensions of 13.5–18 m x 20–25 m. Due to
much higher planting density in the T variant, only the six most
inner rows were measured in this variant.
We measured the diameter at breast height (1.3 m) (DBH) of all
trees in each sample plot, and the height (H) and height to the
first living branch (HFLB) of 66 per cent of the trees (successively
measuring two trees in a row then ignoring the next). We also
calculated the H:DBH ratio (slenderness) of every tree for which
we acquired height and diameter measurements,and both quality
and competition indicators for oaks in upper and middle layers of
the stands (defined as those with heights greater than 35 per cent
percent of the height of dominant trees in their stands). One was
diameter of the thickest branch (THB) of each of these trees. The
diameter of the branches was not measured directly, because they
were difficult to measure in upper parts of the crowns, but in each
case it was assigned to one of the following three classes: B1, 0–
25 mm; B2, 25–35 mm; B3, >35 mm. Stem shape (SSHP) was also
assessed, according to a three-class scale: 1, monopodial or with
steeply angled branches tending toward a monopodial shape; 2,
monopodial with forking tendency in the upper part; and 3, single,
double or multiple forks (Figure 3).
The assessment of competition intensity from trees in the
neighbourhood was based on the relative height difference
between the oak and other trees growing within a 2.5 m radius.
In situations where there was no other tree on one side of an oak
row we assigned a lack of competition. If the height of the other
tree was less than or equal to 50 per cent of the height of the oak
tree, this was evaluated as weak competition. If the neighbour
was 50–100 per cent of the oak height, the influence was rated
as moderate, and if the neighbour was taller than the oak, it was
rated as strong. To account for competition from both sides of
the planting row, four classes of neighbourhood competition were
used and described in the neighbourhood competition index (NCI)
illustrated in Figure 4: 1, no or weak competition from both sides
of the planting row; 2, no or weak competition from one side of
the oak row and strong or moderate competition from the other
side; 3, moderate competition from both sides of the oak row; 4,
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4|Andrzejczyk et al.
Figure 2. Designs of the applied planting patterns: two variants of one-row (1R_4.5 and 1R_3.0), double-row (2R) and traditional (T) planting, with 2600,
3900, 5200 and 7800 planted oaks per hectare, respectively. The distance between planted oaks in each row was 0.85 m.
Figure 3. Illustration of the three morphological classes of stem shape
(SSHP): 1, monopodial or fork with monopodial tendency; 2, monopodial
with forking tendency in the upper part of crown; 3, single, double or
multiple forks.
strong competition from both sides of the oak row or moderate
competition from one side and strong competition from the other
side.
Potential future crop trees (PFCTs) were identified in each
experimental plot during the inventory based on five criteria.
These were: height-based classification as predominant, domi-
nant or co-dominant tree (Kraft 1884), vigorous vitality, monopo-
dial stem or stem with steeply angled branches tending toward
monopodial shape (SSHP—class 1) and relatively straight stem
with relatively thin branches.
Natural regeneration in the areas between oak rows was mea-
sured in circular (5 m2) sample plots, 15 in each alternative PPs
plot (1R_3.0, 1R_4.5 and 2R). Sample plots were spaced 6 x 5 m
(1R_3.0) or 4.5 x 5 m (1R_4.5, 2R) across the plot. The species, DBH,
and H of all admixture trees in each sample plot were recorded
in 2020. In the traditional PP plots, natural regeneration was not
measured as it was removed by tending treatments and an oak
monoculture is expected there.
Statistical analysis
The effects of planting pattern (PP) and neighbourhood competi-
tion index (NCI) on H, DBH, slenderness and HFLB were evaluated
using a linear mixed effects model, with PP and NCI as fixed
effects, while experimental block and plot within block were
added as random effects to account for the nested structure of
the experiment. The statistical unit in the design models was an
observation on a single tree. The general algebraic expression of
the fitted models was:
Yij =β0+β1×PPij +β2×NCIij +γi+γij +εij (1)
where Y represents the quantitative variable of interest. The
fixed effects parameters are β0—β2, while γi,andγij are random
effects on block and plot level respectively γi∼N(0,τ00 Block2),
γi∼N(0,τ00 Plot.Block2). Finally, εij stands for independent and iden-
tically distributed errors, εij ∼N(0,σ2). A more detailed model
description is provided in the supplementary materials (Supple-
ment 1).
The effects of PP and NCI on the qualitative traits were evalu-
ated using generalized linear mixed effects models with binomial
distribution. The probability that a tree has desired qualitative
feature (p) was modelled with logit-link function g (p)=log(p/(1-
p)). If the dependent qualitative variable has more than two levels
it was transformed to the binomial form with a selected level set
to 1 and other levels recoded to 0. Thegeneral algebraic expression
of these models was:
g(Y)ij =β0+β1×PPij+β2×NCIij +β3×X1+β4×X2+β5×X3+γi+εij (2)
where Y represents the qualitative variable of interest and X1-
X3represent additional quantitate variables (e.g. DBH) added to
some models. The fixed effects parameters are β0—β5, while
γi,andγij are random effects on block and plot level respec-
tively γi∼N(0,τ00 Block2), γi∼N(0,τ00 Plot.Block2). Finally, εij stands for
independent and identically distributed errors, εij ∼N(0,σ2). More
detailed descriptions of the models is given in the supplementary
material (Supplement 1).
If the model did not converge when parameterized with both
levels of random effect, a simpler model with only one or no
random effect was chosen, following Pasch et al. (2013).
As the constructed models did not meet normality of dis-
tribution and homoscedasticity of residuals requirements, the
dependent variables were Box-Cox transformed. In the statistical
summary of the models the transformed values of dependent
variables are presented, but in other tables and figures back-
transformed values are presented. The estimated marginal means
for the analysed planting patterns and NCI classes were calcu-
lated using functions in the emmeans package (Lenth, 2016)and
compared using functions from the multicomp package (Hothorn
et al., 2008) in multiple comparisons procedure.
The qualitative and quantitative traits of oaks grown in dif-
ferent PPs were compared in two steps, since the competitive
influence of other species (NCI) was evaluated only for those
grown in row PPs. In the first step, only the influence of the
planting pattern on oaks traits was analysed. If PP proved to be
a statistically important variable in the fitted model, the multiple
comparison procedure with a Tukey-adjusted p-value was applied
to evaluate the statistical significance of the differences between
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Growth and quality of 16-year-old sessile oak (Quercus petraea (Matt.) Liebl.) planted |5
Figure 4. Illustration of the four classes of the neighbourhood competition index (NCI) based on the heights of oaks relative to neighbouring trees: 1,
no or weak competition (height of neighbouring trees ≤50 per cent of oak height); 2, no or weak competition from one side of an oak row; and strong or
moderate competition from the other side (height of neighbouring trees >oak height or 50–100 per cent of oak height respectively); 3, moderate
competition from both sides of an oak row; 4, strong competition from both sides of the oak row or moderate competition from one side and strong
competition from the other side.
experiment variants. Thiscomparison indicates whether there is a
significant difference between oaks grown in traditional and other
PPs. In the second step, only oaks grown in the row spacing PPs
were included in the comparison, but other variables were also
added to the models, particularly NCI. The second step was con-
ducted mainly to determine if the effect of local NCI significantly
influences the growth and quality of oaks in alternative PPs. Three
PPs and four levels of NCI results in twelve distinct combination
(levels) of growing conditions which potentially can influence
oak features in different manner. The statistically significant
differences between such levels were also assessed in the multiple
comparison procedure described above.
One of the potential independent variables used in the models
was the DBH of oak. The relationship between oak DBH and
probability of occurrence of a particular qualitative trait could
be nonlinear, thus three models were created and compared. In
the first, a linear relationship was assumed, in the second a
quadratic term for DBH was included as a covariate, and in the
third model the DBH was log-transformed. The final model was
chosen based on Akaike Criterion (AIC) values of the fitted models
(Akaike 1973). The models were fitted with the glmmTMB library
(Brooks et al. 2017) and the sjPlot library was used (Lüdecke 2020)
to visualise results. R software (R Core Team 2019) was used for
all calculations.
Results
The average oak density 13 years after planting varied between
1600 and 4100 trees ha−1, that is, between 60 and 62 per cent of
the planted trees were left in the row planting patterns and ca.53
per cent in the traditional pattern (T). Both relative and absolute
mortality were highest in the T plots (Table 1). The number of
PFCTs ranged from about 360 ha−1in 1R_4.5 plots to 941 ha−1
in the T plots, but their abundance as a proportion of all trees
was similar (21–23 per cent), regardless of the planting pattern
(Table 1).
Natural regeneration in the areas between oak rows was
composed mainly of hornbeam (Carpinus betulus L.), willow (Salix
caprea L.), aspen (Populus tremula L.) and hazel (Corylus avellana L.),
with sporadic birch (Betula pendula Roth), rowan (Sorbus aucuparia
L.) and cherry (Prunus avium L.) trees. The average density of
natural regeneration, regardless of planting pattern, was 8700
trees ha−1. Based on the analysis of variance, it was found that
density and height of natural regeneration was spatially diverse
and significantly differed between the experimental blocks (P-
value <0.0001 in both cases) but not between planting patterns
(summary tables of analysis of variance are not presented).Willow
and aspen trees had higher average height than the oak while
other inventoried species had lower average height (Table 2).
Tab le 1. Stand characteristics of oaks 13 years after planting
(PFCT: potential future crop tree)
Tra it Planting pattern
1R_4.5 1R_3.0 2R T
Initial density (trees ha−1)2600 3900 5200 7800
Stocking (trees ha−1)1600 2410 3110 4100
Survival rate (%) 61.5 61.8 59.8 52.6
PFCTs’ stocking (trees ha−1)360 536 658 941
PFCTs share (% of all trees) 22.5 22.3 21.2 22.9
Effects of establishment methods on oak
quantitative traits
The average height of oak trees in the T planting pattern was
7.2 m, while oaks planted in all row patterns were significantly
(ca. 0.6 m) lower (Fig. 5). Average heights of oaks in the three row
PPs did not significantly differ and they formed a homogeneous
group in multiple comparisons. Similarly, the average height to
the first living branch (HFLB) was significantly (60–86 cm) higher
in T plots than in the other plots, and there were no significant
differences in this respect between oaks grown in the three row
patterns (Fig. 5). No significant difference in the DBH of oak was
detected between the tested planting patterns (Fig. 5). In terms
of slenderness, oaks in T plots only significantly differed from
those in the 2R plots, and there were no significant differences
in slenderness between oaks in the three row patterns (Fig. 5).
Statistical summaries of mixed effect models used to estimate
mentioned above mean values could be found in supplementary
materials (Supplement 2,Table S1).
Effects of establishment methods on quality
traits of oaks
PP had no significant effect on any of the quality variables. All four
establishment methods resulted in similar likelihoods of an oak
having the thickest branch diameter less than 25 mm (THB25),the
most desirable stem shape (SSHP), and high overall tree quality
that met the criteria for classification as a potential future crop
tree (PCFT) (Table 3).
Effects of competition on quantitative traits of
oaks in row planting patterns
The growth conditions of oaks in specific row PPs are related
to the spatial relationships between the planted oaks (designed
at the beginning of the experiment) and spatial relationships
between oaks and spontaneously occurring natural regeneration
between planted rows. The spatial arrangement and size of the
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6|Andrzejczyk et al.
Tab le 2. Stocking and average height of naturally regenerated trees by species in the row planting patterns. The statistical significance
of differences between blocks were proven by ANOVA procedure (p-value <0.0001). Homogenous groups are depicted with Latin letters
Species Mean stocking and standard error (x103trees ha−1)inblock Average
height [m]
12345Average
Hornbeam 0.43 2.51 4.00 11.38 2.45 4.15 3.8
±0.17 ±0.49 ±0.90 ±1.41 ±0.59 ±0.45 ±0.10
Willow 1.19 1.86 0.52 0.76 0.84 1.03 7.0
±0.24 ±0.28 ±0.16 ±0.21 ±0.24 ±0.11 ±0.15
Aspen 0.19 0.65 0.26 0.10 0.19 0.28 7.5
±0.09 ±0.18 ±0.15 ±0.07 ±0.11 ±0.06 ±0.32
Hazel 1.52 3.72 3.74 1.71 4.06 2.95 4.4
±0.57 ±1.01 ±1.09 ±0.56 ±0.89 ±0.38 ±0.06
Other 0.19 0.37 0.26 0.57 0.25 0.33 4.5
±0.09 ±0.16 ±0.11 ±0.25 ±0.12 ±0.07 ±0.41
Total 3.52a9.11b8.78b14.53c7.80b8.74 4.5
±0.70 ±1.17 ±1.25 ±1.53 ±1.16 ±0.60 ±0.07
Mean height of all species (m) 5.8c3.7a4.6b4.1a5.0bc 4.5
±0.30 ±0.20 ±0.12 ±0.11 ±0.15 ±0.07
Figure 5. Comparison of quantitative tritest of oaks planted according to different planting patterns. Bars show averaged values for a given planting
pattern estimated from mixed effects models. The whiskers show 95 per cent confidence intervals of the mean estimates and the same letters code
variant groups whose means do not differ in statistically significant way.
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Growth and quality of 16-year-old sessile oak (Quercus petraea (Matt.) Liebl.) planted |7
Tab le 3. Parameter estimates and statistics of the nonlinear mixed effect models for the probability that the thickest branch of oaks
will have a diameter less than 25 mm (THB25) and that the oaks will have a stem of the best shape (SSHP) and probability that the oaks
will be classified as potential future crop tree (PFCT). All results are presented in relation to the traditional planting pattern (T)
THB25 SSHP PFCT
Predictors Odds
Ratios
std. Error pOdds
Ratios
std. Error pOdds
Ratios
std. Error p
(Intercept) 1.16 0.19 0.360 1.30 0.16 0.031 3.03 0.40 <0.001
1R_4.5 0.81 0.20 0.408 1.20 0.23 0.336 0.77 0.17 0.241
1R_3.0 0.77 0.15 0.189 1.09 0.15 0.546 0.88 0.16 0.494
2R 0.84 0.16 0.351 1.02 0.14 0.865 0.88 0.16 0.486
Random Effects
σ2
τ00 Block
τ00 Plot.Block
ICC
NBlock
NPlot.Block
Observations
Marginal R2
Conditional R2
3.29
0.04
0.05
0.03
5
18
1457
0.003
0.030
3.29
0.03
5
1464
0.001
0.010
1058
0.001
admixture trees in relation to the oaks significantly affected the
oaks’ quantitative traits. According to our models, the NCI level
significantly affected the oaks’ H, DBH, slenderness (H:DBH ratio)
and HFLB, while PP only affected HFLB. Statistical summaries of
mixed effect models used to estimate mentioned above mean
values can be found in supplementary materials (Supplement 2,
Table S2).
The influence of competition depended on the trait considered.
Homogenous groups obtained by applying the multiple compar-
isons procedure, after simplification by removing insignificant
variables referring to PPs (Fig. 6) differ for particular traits. The
oaks growing in the neighbourhood with the least interspecies
competition (NCI class 1) did not significantly differ in height from
those growing in neighbourhoods with more intense competition
(NCI classes 2 and 3). They had significantly greater DBH than
oaks growing in NCI class 3 and 4 conditions, and they were also
much less slender than oaks growing in the most competitive
neighbourhoods (NCI class 4).
In the model describing HFLB, both planting pattern and NCI
were important. The multiple comparisons procedure created six
homogenous groups composed of oaks in 12 combinations of
planting patterns and NCI class (not tabulated here). The groups
with the smallest HFLB values were mostly composed of oaks
in combinations with the 1R_4.5 PP (e.g. 1R_4.5 and NCI class
1), where initial oak density was the lowest. The group with the
largest HFLB values were mainly oaks in combinations containing
with the 2R pattern (e.g. 2R & NCI class 4),where initial oak density
was highest among row PPs. Moreover, in the mentioned homoge-
nous groups the average HFLB value increased with increases in
NCI value.
Effects of competition and growth parameters on
quality traits of oaks in row planting patterns
The probability that the thickest oak branch was less than 25 mm
(THB25) was not dependent on the planting pattern or local
competition (NCI) (Tabl e 4). The only variable that significantly
affected this probability was DBH. The relationship was not linear,
and the probability of having this trait decreased fairly rapidly for
oaks with DBH greater than 40 mm and was close to 0 for oaks
thicker than 90 mm (Figure 7).
The probability of oaks having a monopodial stem was signifi-
cantly dependent on their NCI and DBH, but not on the establish-
ment method (Table 4 and Figure 8). High interspecific competi-
tion (NCI class 4) significantly reduced this probability, which was
the highest for trees with the smallest diameter and decreased
steadily with increasing diameter (Figure 8).
The probability that an oak tree would be of the highest quality
and classified as a PFCT significantly depended on its level of com-
petition (NCI), stem shape and height, but not the PPs (Table 4 and
Figure 9). It increased with increasing intensity of competition,
but the differences were only significant between the lowest and
highest levels (NCI classes 1 and 4). The probability of selecting
trees as PFCTs was highest for trees with monopodial stems,lower
for those with monopodial stems and a tendency to fork, and
lowest for those with a forked stem. The probability of finding
a PFCT also increased with tree height up to approx. 9 m, after
which the probability decreased.
Discussion
This study presents the results of a 13-year experiment on the
growth and quality of sessile oaks planted in a traditional PP with
a high initial density (7.8 thousand seedlings ha−1) and in three
alternative PPs with increased spacing between oak rows (up to
3.0 and 4.5 m) and reduced initial seedling densities of 5.2, 3.9,
and 2.6 thousand ha−1, respectively. In alternative PPs, naturally
regenerated trees of admixture species were expected to compen-
sate for the lower oak density and provide favourable growth and
quality conditions for oak. However, in this experiment, strong
interspecific competition was systematically controlled. In our
study, we first wanted to know whether alternative PPs under
controlled interspecific competition provided similar growth
and quality of oak as the traditional PP. We then analysed the
effects of interspecific competition on oak growth and quality in
alternative PPs. The overall density of natural regeneration in the
experiment considered here was low to moderate (8700 stems
ha−1), with high spatial variability. In other reported experiments
with oak the density of natural regeneration has ranged from
2000–4000 ha−1(Saha et al. 2013) to 35 000–63 000 ha−1(Bolibok
et al. 2021) so our oaks have grown under low or moderate
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8|Andrzejczyk et al.
Figure 6. Influence of NIC on quantitative tritest of oaks planted according to experimental row planting patterns. Because planting pattern was not
statistically significant variable for majority of traits only influence of NCI was shown. Bars show averaged values for a given NIC level estimated from
mixed effects models. The whiskers show 95 per cent confidence intervals of the mean estimates and the same letters code variant groups whose
means do not differ in statistically significant way.
interspecific competition. In addition, trees and shrubs of the
most competitive species (aspen, willow and hazel), which
restricted upper and lateral space available for the oaks, were
topped and removed in successive treatments. Thus, we studied
the growth and quality of oaks in different planting patterns
under conditions of controlled interspecific competition, which
should reduce the negative effects of admixing species of trees
and shrubs on growth while maintaining their positive effects on
oak quality.
Growth traits of oak
The planting pattern had no significant effect on the oaks’ diam-
eter, but the traditional PP resulted in higher average height than
the alternative PPs, where they were subject to mixed intra- and
inter-specific competition. Many studies have shown that strong
and long-term interspecific competition inhibits increments in
DBH before height increments (von Lüpke 1991,1998;Wagner
and Röker 2000;Petersen et al. 2009;Liziniewicz et al. 2016;
Andrzejczyk and Brzeziecki 2018;Andrzejczyk and Milewski 2019).
Thus, the lack of differences in diameter between traditional and
alternative PPs indicates that the effect of intraspecific competi-
tion between oaks could be comparable to the combined effects
of intra and interspecific competition in row PPs. It should be
emphasized, however, that this result was achieved thanks to
systematic tending treatments that limited the level of interspe-
cific competition in the alternative planting patterns. Otherwise,
oak growth would be significantly weakened, which is also clear
from our analysis of the impact of interspecific competition. If
so, intraspecific competition can be successfully replaced only by
controlled interspecific competition. The between-method differ-
ences in average oak height is not easy to explain, but their growth
may have slowed in response to reductions in competition for light
after the tending and lower thicket canopy closure associated
with the alternative planting methods. In such conditions, the
allocation of assimilates is likely to shift from elongation of the
main shoot to expansion of the crown and DBH growth. For
example, Pretzsch and Rais (2016) found that unshaded deciduous
trees tend to allocate more resources to diameter growth and less
to height growth than shaded conspecifics. This phenomenon has
been found in thinning experiments with oak (Kerr 1996), beech
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Growth and quality of 16-year-old sessile oak (Quercus petraea (Matt.) Liebl.) planted |9
Tab le 4. Parameter estimates and statistics of the nonlinear mixed effect models for probabilities that the thickest branch of oaks will
have a diameter less than 25 mm (THB25), the oaks will have a stem of the best shape (SSHP) and be classified as potential future crop
trees (PFCTs). All results are presented in relation to values for oaks in the 1R_3.0 row planting pattern and lowest level of
neighbourhood competition (NCI class 1)
THB25 SSHP PFCT
Predictors Odds
Ratios
std. Error POdds
Ratios
std. Error pOdds
Ratios
std. Error p
(Intercept) 3.0 x 1091.98 x 1010 0.001 1.88 0.47 0.012 0.30 0.10 <0.001
1R_4.5 0.84 0.23 0.536 1.36 0.29 0.150 1.00 0.27 0.995
2R 0.80 0.16 0.298 0.83 0.13 0.216 1.11 0.23 0.610
NCI [2] 0.85 0.29 0.636 0.86 0.20 0.527 1.79 0.59 0.079
NCI [3] 1.25 0.37 0.453 0.99 0.22 0.951 1.67 0.51 0.093
NCI [4] 1.23 0.38 0.498 0.56 0.13 0.011 2.25 0.71 0.011
log(DBH) <0.00 <0.00 <0.001 0.60 0.04 <0.001
SSHP2 0.69 0.13 0.047
SSHP3 0.03 0.02 <0.001
log(H) 9.01 4.91 <0.001
(H)∧20.25 0.12 0.004
Random Effects
σ2
τ00 Block
ICC
NBlock
Observations
Marginal R2
Conditional R2
986
0.49
3,29
0.12
0.04
5
990
0.076
0.109
3.29
0.12
0.04
5
744
0.374
0.397
Figure 7. Dependence of the probability of oaks’ thickest branch
diameter being <25 mm on DBH, grey band shows 95 per cent
confidence intervals.
(Reventlow et al. 2019) and pine (Mäkinen and Isomäki 2004). In all
cases, the average height was significantly reduced by heavy thin-
ning that increased the space available for lateral crown devel-
opment. In the cited experiments, reductions in average stand
height were accompanied by increases in diameter.However, such
reactions were not detected in our experiment, possibly due to
the continuing competition from admixture species, which were
not permanently removed but topped during tending treatments.
After topping, they usually regenerated and formed new shoots.
Figure 8. The probability of oaks having a monopodial stem 13 years
after planting in relation to diameter at breast height (DBH) and
neighbourhood competition class (NCI). Grey bands show 95 per cent
confidence intervals.
Wright et al. (2000) found that light-demanding species’ growth
potential significantly declined after release from a period of
growth under heavy competition, and as sessile oak is a light-
demanding species (Krahl-Urban 1959) it should respond in a
similar manner. Although we made every effort in our studies
to exclude the influence of environmental variability (between
blocks and plots), it may be that significant differences between
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10 |Andrzejczyk et al.
Figure 9. Probabilities of an oak being classified as a potential future crop tree in relation to height 13 years after planting, competition (NCI) and three
stem shapes (1, monopodial; 2, monopodial with tendency to forking; 3, forked or multiple forks). Grey bands show 95 per cent confidence intervals.
T and row PPs in oak height could be attributed in some extent
to small differences in site conditions between plots within the
block. Soils in the Rogów Forests are generally very fertile, but
exhibit small-scale variability due to differences in the genesis of
soil-forming sediments (Konecka-Betley et al. 1993).
Analysis of the effects of interspecific competition (NCI) on
growth traits shows clear differentiation of young oaks traits’
reactions to competitive pressure. Of the traits studied here, oak
DBH is the most sensitive and oak height is the least sensitive.
Thus, our experiment confirmed the results of other studies (von
Lüpke 1991,1998;Ammer and Dingel 1997;Wagner and Röker
2000;Petersen et al. 2009;Liziniewicz et al. 2016;Wallraf and Wag-
ner 2019) that oaks respond to increasing competitive pressure
with an earlier and greater reduction in their DBH and a later
and smaller reduction in their height growth. Thus, the increase in
competitive intensity led to an increase in the slenderness index
and a decrease in oak stability.
Quality traits of oak
Of the analysed quality traits (HFLB, branch thickness and
stem shape), the PP only significantly affected HFLB, which was
significantly higher in traditionally planted plots than the others.
The initial oak density in these plots (7800 seedlings per hectare)
was close to the optimal density, which promotes early onset of
natural self-pruning of main stems and hence relatively long
branch-free stems, an important parameter of oak quality
(Schmaltz et al. 1997;Nagel and Rumpf 2010;Kuehne et al.
2013). The model that only considered effects of the three row
methods revealed significant differences between them in HFLB,
which increased with increases in initial densities. The results
indicated that the wide-spaced rows planting patterns resulted in
less natural self-pruning than the traditional scheme. The lower
oak density associated with the row methods was not sufficiently
compensated by admixture species, which did not exert sufficient
lateral pressure on the oak stems to compensate for the lower
intraspecific competition. High interspecific competition has
been found to accelerate the self-pruning process in young
oak stands and increase crown base height (Zenner et al. 2012;
Liziniewicz et al. 2016;Jensen and Löf 2017;Wallraf and Wagner
2019). Similarly, in this study, alternative patterns, with medium
and strong tree competition (NCI classes 3 and 4) increased the
average HFBL value more than the pattern that provided the
weakest competition (NCI class 1) or competition only from one
side of the oak rows (NCI class 2). Thus, the PP- and NCI-related
results clearly suggest that the HFLB depends on the competitive
pressure exerted on oaks by both other oaks and other competing
trees (as dictated by the PP and NCI, respectively).
The tending treatments could also have contributed to the
relatively low level of interspecific competition, in accordance
with findings by Küster (2000) and Nörr and Mößmer (2004).
The likelihood of occurrence of thin-branched trees was not
related to PP or local competition, but decreased significantly with
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Growth and quality of 16-year-old sessile oak (Quercus petraea (Matt.) Liebl.) planted |11
increasing DBH. Generally, an increase in initial oak density
decreases the thickest branch diameter (Schmaltz et al. 1997;
Malinauskas 2007;Kuehne et al. 2013). However, we did not
observe this in our experiment, despite large differences in
initial oak spacing between treatments, due to the presence of
admixture trees species in the row planting patterns. In many
studies, competition from admixture species such as birch,
beech, hornbeam and hazel has been found to greatly reduce
oak branch thickness (Wagner and Röker 2000;Otto et al. 2009;
Petersen et al. 2009;Nagel and Rumpf 2010;Liziniewicz et al. 2016;
Wallraf and Wagner 2019). However, we found that the presence
of admixture species resulted in an equalization of branch
thickness between planting treatments. As for the previously
considered traits, control of admixture competition by tending
treatments influenced the results. Without competition control
and with greater density of admixture species the row planting
methods would presumably lead to higher local competition
and hence higher proportions of thin-branched trees than the
traditional method. Reductions in the probability of trees having
thin branches with increases in DBH reflect the general positive
relationship between branch diameter and DBH (Kint et al. 2010).
We detected no significant effect of PP on the occurrence of
monopodial trees, which are the most valuable for production
of high-value logs, although many studies have found positive
relationships between frequencies of monopodial trees and both
initial oak density and intraspecific competition (Gaul and Stüber
1996;Schmaltz et al. 1997;Fischer 2000;Kuehne et al. 2013;
Prévosto et al. 2016;Jensen and Löf 2017;Wallraf and Wagner
2019).
Our results indicate that the admixture species in the row
planting patterns sufficiently compensated for the lower oak
density and counteracted the reduction in the oak stems’ quality.
However, our analysis of effects of local competitive intensity
(NCI) on oaks in the alternative PPs did not confirm this hypoth-
esis. The probabilities of occurrence of oaks being uniaxial under
low competition, medium competition, and competition exerted
from one side of the oak rows were similar and significantly higher
than under strong competition (NCI class 4). This indicates that
the deterioration of oak stems in the presence of strong inter-
specific competition might be a result of mechanical damage to
the uppermost oak buds by overgrowing competitors (Leibundgut
1976).
The probability of monopodial trees’ occurrence decreased sig-
nificantly with increases in DBH. Considering the close relation-
ship between DBH and crown size (Dubravac et al. 2009;Attocchi
and Skovsgaard 2015), our results indirectly suggest that uniaxial
trees had smaller crowns and were less competitive than forked
trees. Therefore, to promote their occurrence, tending treatments
involving the selection and promotion of potentially promising
trees should be applied as early as the thicket stage.
The relatively weak and ambiguous response of oak quality
traits to different levels of competition intensity may be
due to two reasons. The first was the way competition was
quantified, and the second could be the temporal aspect. We
quantified competition categorically, distinguishing four levels
of competition intensity. It is likely that the use of continuous
measures of competition, such as the Hegyi index or similar
(Hegyi 1974;Ammer and Dingel 1997), would allow a more precise
determination of the analysed relationships. The temporal aspect
was related to the generally low temporal stability of the level of
interspecific competition in alternative PPs. This was the result of
systematic tending treatments, repeated every 3 years, which
particularly reduced competition from fast-growing species
(willow, aspen) and hazel clumps. In such a tending scheme, the
effects of competition could not be strong enough, and some oak
traits did not respond at all (diameter of thickest branch, shape
of the stem) or responded relatively weakly (height of first living
branch). Thus, under conditions of uncontrolled competition, the
results would certainly be different, primarily a reduced growth
and survival of oak (Jensen and Löf 2017;Mölder et al. 2019)
should be expected. Without competition control, the intensity
of interspecific competition may depend, among other things, on
the biological and ecological characteristics of the admixed trees.
Shade-tolerant species, especially European beech, may exert
a greater competitive influence on oak than light-demanding
species (Leder 1996;von Lüpke 1998;Ammer et al. 2005;Saha
et al. 2013).
Potential future crop trees (PFCT)
The probability of a particular tree meeting criteria for a PFCT
in thickets established by the traditional and row planting meth-
ods was similar, showing that the admixture species effectively
compensated for the lower density of oak in the latter. Moreover,
increases in the intensity of interspecific competition conferred
by the alternative methods increased this probability. Accordingly,
increases in interspecific competition have been found to reduce
branch thickness and accelerate the natural self-pruning process
of oak (Wagner and Röker 2000;Rock et al. 2004;Saha et al. 2014;
Liziniewicz et al. 2016;Wallraf and Wagner 2019). An important
criterion for PFCT selection was a highly competitive and bio-
social class (Kraft 1884); only predominant, dominant, and co-
dominant trees (corresponding to Kraft tree classes 1, 2, and 3,
respectively), should be selected. We found that the probability
of a specific tree meeting the PFCT criteria increased with tree
height up to approx. 9 m, after which the probability decreased.
This change seems to be related to the deterioration of the quality
of the highest predominant trees, mainly due to their wide crown
and thick branches (Kint et al. 2010), and the gradual decrease in
the number of trees in the largest height classes. The monopodial
trees were selected most often as PFCTs, followed by monopodial
trees with a forking tendency in the upper part of stem. Due to
the low stability of stem form in young oaks (Mosandl et al. 1991),
the choices of PFCTs may differ in the future, as some trees may
improve and others may deteriorate (Dong et al. 2007). However,
these changes seem to be similar across planting patterns.
It should be emphasized that although in the compared plant-
ing patterns both the probabilities and proportions of PFCTs rela-
tive to the total tree populations were similar, their actual density
per hectare varied considerably (from 941 in the T pattern to
360 in the R_4.5 pattern). The recommended number of crop trees
(CT) in a mature oak stand at the rotation age of 120–160 years is
between 80 and 100 individuals ha−1(Evans 1984;Spiecker 1991;
Hochbichler 1993;Gockel 1995). Assuming that there should be at
least four times more PFCTs than CTs in the thicket stage, their
minimum density should be 320–400 individuals ha−1.Inslightly
older stands (in the middle of the pole stage), a minimum of 250–
350 PFCT ha−1is recommended (Mosandl and Paulus 2002;Röhrig
et al. 2006;Dong et al. 2007). All planting patterns applied in this
study met these thresholds. However, in the R_4.5 pattern, where
oak planting density was reduced to 2600 seedlings ha−1, PFCT
density was alarmingly close to the minimum, assuming that 100
CT might be not selected in the future. Thus, when alternative
row methods are used, reducing plantingdensity to less than 3000
seedlings/ha may make it difficult to select sufficient numbers of
high-quality PFCTs. However, group regeneration methods with
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12 |Andrzejczyk et al.
even greater reductions in oak planting density have report-
edly resulted in higher frequencies of PFCTs than the traditional
method (Skiadaresis et al. 2016;Saha et al. 2017). This suggests
that the presence of admixture, trainer species (which moderately
shadows sides of oaks crowns) together with their appropriate
placement (at group edges), significantly favours the formation of
high-quality oak. In addition, the group method allows selection
of PFCTs from admixture species (Skiadaresis et al. 2016). If the
density of PFCTs is too small, it is still possible to improve the
quality of some trees by pruning (Kerr 1996;Beinhofer 2010).
However, pruning is a costly operation that requiring an economic
analysis (Beinhofer 2010).
Conclusion
Our results have confirmed all of the hypotheses raised. First,the
planting method has generally been shown to have small effects
on oaks’ growth and quality at the thicket stage. A difference of
about 0.5 m in average height between oaks in traditional and
alternative planting patterns (as observed in this study) is not
highly important neither silviculturally nor financially. Therefore,
it can be assumed that under conditions of controlled competition
and at least moderate density of natural regeneration, alternative
planting patterns should generally provide similar growth and
quality of oak as the traditional method.
Intensification of interspecific competition near oak plants
in stands generated by the alternative methods significantly
affected growth (height and DBH), slenderness (H:DBH ratios)
and height of the first living branch (HFLB) of oak. Greater
competition decreased the growth and stability (H:DBH ratio) but
increased quality characteristics (HFLB). We also found that the
probability of trees meeting PFCT criteria significantly increased
with increasing intensity of interspecific competition. These
results confirm the second hypothesis. However, no significant
effect of competition intensity was found on either branch
thickness or the form of the tree stems. This was probably because
such responses require more persistent and intense competition
than our treatments provided, due to the periodic control reducing
its intensity and changing the environmental conditions.
The third hypothesis was also confirmed, according to which
the row planting methods provided similar probabilities of trees
meeting PFCT criteria to the conventional method, but their fre-
quencies were proportional to the initial planting density. To
ensure a high probability that there will be at least the minimum
recommended frequencies of PFCTs, the initial planting density
of oaks in row planting patterns should generally be at least 3000
trees/ha. It may be lower if the planting material has high, proven
genetic quality, but higher if risks of browsing damages are high
and no measures are taken to reduce them. A final conclusion
is that regulation of interspecific competition of admixture trees
during tending operations in young oak stands established in
sparsely planted rows is crucial for optimising the oaks’ growth
and quality.
Acknowledgements
The authors would like to thank our colleagues Kamil Bielak for
support in research work and Henryk Szeligowski and Michał
Dzwonkowski for help in collecting data. We are thankful to
General Directorate of the State Forests for funding this project.
We are indebted to two anonymous reviewers for suggestions and
comments that have greatly improved the paper. Furthermore, we
are grateful to Dr. Fabian Fassnacht, Editor-in-Chief, Dr. Dominik
Thom, Section Editor,for the insightful and helpful comments and
linguistic corrections an earlier version of this article.
CRediT statement
Tadeusz Andrzejczyk (Conceptualization, Funding acquisition,
Investigation, Resources, Writing—original draft), Mateusz
Liziniewicz (Investigation, Methodology, Writing—review &
editing), Leszek Bolibok (Conceptualization, Formal analysis,
Investigation, Methodology, Writing—original draft).
Supplementary data
Supplementary data are available at Forestry online.
Conflict of interest statement: None declared.
Funding
This work was supported by the State Forests National Forest
Holding, General Directorate of the State Forests [grant: Alterna-
tive methods of oak stand establishment].
Data Availability
Data available on request.
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