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Southern Cross UniversityePublications@SCU
Theses
2007
The effect of competition on stand, tree, and woodgrowth and structure in subtropical Eucalyptusgrandis plantationsFelicity HarrisSouthern Cross University
ePublications@SCU is an electronic repository administered by Southern Cross University Library. Its goal is to capture and preserve the intellectualoutput of Southern Cross University authors and researchers, and to increase visibility and impact through open access to researchers around theworld. For further information please contact [email protected].
Publication detailsHarris, F 2007, 'The effect of competition on stand, tree, and wood growth and structure in subtropical Eucalyptus grandisplantations', PhD thesis, Southern Cross University, Lismore, NSW.Copyright F Harris 2007
The Effect of Competition on
Stand, Tree, and Wood Growth and Structure in
Subtropical Eucalyptus grandis Plantations
Felicity Catherine Harris
Bachelor of Science (Forestry) with Honours (1st Class), ANU
Bachelor of Economics, ANU
School of Environmental Science and Management
Southern Cross University
A thesis submitted for the Degree of Doctor of Philosophy
within Southern Cross University.
January 2007
i
STATEMENT OF SOURCES
The work presented in this thesis is my own. Specific contributions made by others are
referred to in the text and acknowledgements.
The material of this thesis has not been submitted either in whole, or in part, for a degree at
this or any other University.
Felicity Harris
ii
ABSTRACT
In this study, the effect of competition on stand, tree, and wood structure was examined in the
context of the viability of high-density eucalyptus plantations for the production of quality
timber. The study was based on a 4-year-old Eucalyptus grandis trial planted in south-eastern
Queensland in March 1999 by Greenfield Resource Options P/L. Stocking densities of 250,
1,000, 5,000 and 10,000 stems/ha allowed growth traits in extreme stockings to be compared.
High stocking densities led to greater stand growth and also greater inequality in tree size.
The largest trees in high stocking densities were smaller than those in low stocking densities,
however high stocking densities had more trees in the largest cohort and they were more
uniform in size. Consequently the total stem volume of the largest 1,000 st/ha was similar for
all stocking densities, and the total stem volume of the remaining trees increased with
stocking density.
The allocation of tree biomass skewed away from crown production and towards stem
production as stocking density (general competition) increased and as stem diameter
(competitive status) decreased. Dominant trees in high stocking densities had similar
aboveground biomass accumulation per unit leaf area compared to dominant trees in low
stocking densities, but had a larger proportion of biomass allocated to the stem when
compared to the crown. Dominant trees in high stocking densities therefore had similar tree
growth efficiency but better stem growth efficiency than dominant trees in low stocking
densities. Increased competition appeared to restrict the growth of dominant trees by
restricting resource capture rather than by reducing the efficiency of growth, since the tree
growth efficiency of dominant trees was not affected by competition.
Examination of the stem wood structure revealed that the largest trees in high stocking
densities exhibited more desirable wood properties including more uniform wood density, less
variability in wood anatomy, and better branch-shed (hence low knot content) than the largest
trees in the low stocking densities. This suggests that densely stocked plantations could be of
better value for timber production than lightly stocked plantations.
The results illustrate the importance of including stand structure in forest research since a
failure to do so will underestimate the productivity of the largest trees in densely stocked
stands and does not adequately account for the structural benefits of high stocking density.
iii
The findings are based on a young plantation, however they indicate that densely stocked
plantations could be used to provide an early cash return from a harvested biomass crop with
no detrimental effect on a retained solid hardwood crop of the largest 1,000 st/ha. The results
of this study indicate that the perception that densely stocked plantations cannot produce an
equivalent volume of sawlogs of similar quality wood to that produced from lightly stocked
plantations is incorrect.
iv
ACKNOWLEDGEMENTS
My thanks to Southern Cross University, the Commonwealth Government of Australia, and
industry sponsors Southern Pacific Petroleum Pty/Ltd and Greenfield Resource Options
Pty/Ltd, without whose support through providing scholarships, grants, study material and
equipment this Ph..D. study would not have been possible.
My thanks to my supervisors A. Prof. Alison Specht and Prof. Jerry Vanclay of Southern
Cross University and Dr Nigel Turvey of Greenfield Resource Options, who have provided
constant guidance, support and encouragement throughout the thesis.
My thanks to technical and administrative staff at Southern Cross University, especially
Maxine Dawes, Paul Kelly and Delva Smith, who provided efficient and friendly support
whenever needed.
My thanks to family members who came from Canberra (ACT) to Gin Gin (QLD) to provide
invaluable help with field measurements, including my sisters Cecilia (who came twice) and
Bridget (who came whilst enduring morning sickness), and my father John.
My thanks to my parents, Ruth and John Harris, who provided a laptop computer for my
studies as well as their interest and support.
Finally my thanks to my husband, Gareth Wooler, who has gracefully accepted and
encouraged all the time and effort spent I have spent on the Ph.D.
v
TABLE OF CONTENTS
STATEMENT OF SOURCES...............................................................................................................................I
ABSTRACT .......................................................................................................................................................... II
ACKNOWLEDGEMENTS ................................................................................................................................IV
TABLE OF CONTENTS ..................................................................................................................................... V
1. INTRODUCTION ....................................................................................................................................... 1
1.1 BACKGROUND TO THE STUDY ............................................................................................................... 1
1.2 STRUCTURE OF THE THESIS ................................................................................................................... 4
2. THE SPACING TRIAL .............................................................................................................................. 5
2.1 STUDY SITE........................................................................................................................................... 5
2.2 EXPERIMENTAL DESIGN ........................................................................................................................ 7
2.3 ESTABLISHMENT ................................................................................................................................... 9
2.4 SAMPLE SELECTION ............................................................................................................................ 12
2.4.1 3 Year Old Sample Selection ......................................................................................................... 12
2.4.2 4 Year Old Sample Selection ......................................................................................................... 12
3. STAND GROWTH AND STRUCTURE ................................................................................................. 14
3.1 STATE OF KNOWLEDGE ....................................................................................................................... 14
3.1.1 Stand Growth................................................................................................................................. 14
3.1.2 Stand Structure .............................................................................................................................. 16
3.2 EXPERIMENTAL RATIONALE ............................................................................................................... 20
3.3 METHODOLOGY .................................................................................................................................. 21
3.3.1 Sample Age and Size...................................................................................................................... 21
3.3.2 Data Collection and Calculation................................................................................................... 21
3.4 RESULTS AND DISCUSSION.................................................................................................................. 25
3.4.1 Stand Growth................................................................................................................................. 25
3.4.2 Stand Structure .............................................................................................................................. 28
3.5 SUMMARY ........................................................................................................................................... 39
4. TREE GROWTH AND STRUCTURE.................................................................................................... 40
4.1 STATE OF KNOWLEDGE ....................................................................................................................... 40
4.1.1 Tree Growth................................................................................................................................... 40
4.1.2 Tree Structure ................................................................................................................................ 46
4.2 EXPERIMENTAL RATIONALE ............................................................................................................... 55
4.3 METHODOLOGY .................................................................................................................................. 56
4.3.1 Sample Age and Size...................................................................................................................... 56
4.3.2 Data Collection and Calculation................................................................................................... 56
vi
4.3.3 Data Analysis................................................................................................................................. 62
4.4 RESULTS AND DISCUSSION.................................................................................................................. 64
4.4.1 Tree Growth................................................................................................................................... 65
4.4.2 Tree Structure ................................................................................................................................ 74
4.4.3 Implications for Stand Growth and Structure................................................................................ 90
4.5 SUMMARY ........................................................................................................................................... 94
5. WOOD GROWTH AND STRUCTURE ................................................................................................. 97
5.1 STATE OF KNOWLEDGE ....................................................................................................................... 97
5.1.1 Wood Growth................................................................................................................................. 97
5.1.2 Wood Structure .............................................................................................................................. 99
5.1.3 Wood Types ................................................................................................................................. 104
5.1.4 Wood Properties .......................................................................................................................... 107
5.2 EXPERIMENTAL RATIONALE ............................................................................................................. 120
5.3 METHODOLOGY ................................................................................................................................ 122
5.3.1 Sample Age, Size and Preparation .............................................................................................. 122
5.3.2 Data Collection and Calculation................................................................................................. 124
5.3.3 Data Analysis............................................................................................................................... 129
5.4 RESULTS AND DISCUSSION................................................................................................................ 131
5.4.1 Sapwood ...................................................................................................................................... 131
5.4.2 Wood Anatomy............................................................................................................................. 133
5.4.3 Stemwood Basic Density.............................................................................................................. 147
5.4.4 Branching Habits......................................................................................................................... 151
5.5 SUMMARY ......................................................................................................................................... 160
6. CONCLUSION ........................................................................................................................................ 164
6.1 SYNOPSIS .......................................................................................................................................... 164
6.2 MAJOR DISCOVERY........................................................................................................................... 168
6.3 MANAGEMENT APPLICATIONS .......................................................................................................... 169
6.4 FURTHER RESEARCH REQUIREMENTS ............................................................................................... 170
REFERENCES .................................................................................................................................................. 172
APPENDICES ................................................................................................................................................... 185
APPENDIX 1: STEM VOLUME MODELS............................................................................................................. 185
Chapter 1 Introduction Page 1
1. INTRODUCTION
1.1 Background to the Study
The vast majority of eucalyptus plantations in Australia are fast-growing plantations managed
to produce pulpwood for paper production (Ferguson et al. 2002; Turner et al. 2004). There is
increasing demand, however, to source additional wood products from hardwood plantations.
Such products include sawn solid hardwood for construction and furniture timber, in which
there is a national trading deficit (Turner et al. 2004), and raw wood biomass for renewable
bio-fuel, in which there is increasing demand due to the global drive for greenhouse gas
reduction (Stucley et al. 2004). The future ability to source such products from eucalyptus
plantations will require either establishing new plantations or converting existing pulpwood
plantations to other uses.
The establishment of new eucalyptus plantations for solid hardwood production is forecast to
increase up to eight-fold over the next 40 years, yet the increased production is not expected
to meet demands for solid hardwood products (Ferguson et al. 2002). Additional new
eucalyptus plantations are clearly required if Australia is to reduce the trade deficit in solid
hardwood products, however there exist substantial impediments to achieving this aim. These
include a lack of expertise in managing eucalyptus plantations for solid wood production and
the long-term nature of the investment. An alternative to establishing new solid hardwood
plantations is to source solid hardwood from current pulpwood plantations. This option,
however, is often discounted as the relatively high stocking rates used in pulpwood
plantations (1,200-1,500 st/ha) are thought to restrict growth of the final sawlog crop and are
considered unlikely to produce good quality solid hardwood (Turner et al. 2004).
Raw wood biomass is readily available from current and future eucalyptus plantations, but is
generally restricted to harvesting residues (Bugg et al. 2002). Few eucalyptus plantations in
Australia are dedicated to producing biomass since it is not a profitable commodity. There is
substantial government and industry funding for research into renewable fuels in Australia
due to the global drive for greenhouse gas reduction, indicating that it is only a matter of time
before bio-fuel markets emerge (Stucley et al. 2004). Biomass may then become a profitable
commodity, providing a return on forest residues and possibly stimulating the establishment
of eucalyptus plantations for biomass production alone.
Chapter 1 Introduction Page 2
It is generally accepted that pulpwood plantations cannot produce good quality solid
hardwood within a profitable time period due to their relatively high stocking rates, creating
an impediment to converting pulpwood plantations into solid hardwood plantations. This
perception, however, is challenged by stand development in fast-growing native eucalyptus
forests, in which prolific seedling regeneration results in very highly stocked stands that go on
to produce good quality solid hardwood (Florence 1996). Furthermore, evidence suggests that
increased competition due to higher stocking does not greatly impede the growth of the
largest trees in the stand (Bredenkamp and Burkhart 1990b; Battaglia 2001; Franc 2001;
Binkley et al. 2002), indicating that the time required to produce large trees suitable for
sawing into solid hardwood products may not be increased by high stocking rates.
The above evidence provides impetus for closer examination of the effect of stocking density
(competition) on eucalyptus plantation development, not only at the stand and tree level, but
also at the wood level. The broad objective of this study is to determine the effect of
competition on stand, tree, and wood growth and structure in fast-growing sub-tropical
Eucalyptus plantations. The intention is to take a holistic approach so it could be shown how
competitive interactions between trees shape the stand, and the extent to which stand and tree
development affect wood quality. Throughout the investigation a primary focus is on the
largest trees in the stand, as these trees are representative of the solid hardwood (sawlog) crop.
By structuring the investigation from the stand level through to the wood level, the pathways
through which competitive mechanisms affect stand, tree, and wood characteristics could be
investigated (Figure 1.1).
Chapter 1 Introduction Page 3
Figure 1.1: A model of tree growth (absolute size) and structure (relative size) in which current structural characteristics (bold arrows) shape the future growth and structure of the stand, tree, and wood. The sum of growth and structure of all trees in the stand determine stand growth and structure (a). The position of the individual tree within the stand structure (b) affects individual tree resource capture (competitive ability) (c) and growth partitioning (maximises future resource capture) (d). The structure of individual trees (e) affects growth partitioning (maintenance of structural requirements) (f) and wood structure (maintenance of structural, food storage and transpiration requirements) (g).
(a)
(b)
(c)
(d)
(e)
(g)
(f)
Chapter 1 Introduction Page 4
1.2 Structure of the Thesis
The thesis is broken into chapters, each of which focuses on aspects of the investigation of the
effect of competition on stand, tree, and wood growth and structure in sub-tropical eucalyptus
plantations;
• The Spacing Trial: provides the rationale for the location, silviculture and design of the
spacing trial, as well as establishment details and subsequent growth conditions.
• Stand Growth and Structure: focus on identifying competition intensity in the whole stand
and the competitive status of individual trees within the stand.
• Tree Growth and Structure: focus on comparison between dominant trees in different
planting densities, and between dominant and suppressed trees within planting densities.
• Wood Growth and Structure: focus on comparison between dominant trees in different
planting densities, and between dominant and suppressed trees within planting densities.
Patterns similar to those for tree growth and structure are considered as potential
indicators of functional changes in wood growth and structure.
• Conclusion: closes the study by providing a synopsis and discussing significant
discoveries, management applications, and future research requirements.
Chapter 2 The Spacing Trial Page 5
2. THE SPACING TRIAL
The spacing trial was established in March 1999 and managed by Greenfield Resource
Options P/L (GRO) on behalf of Southern Pacific Petroleum NL/Central Pacific Minerals NL
(SPP). The spacing trial is a small component of wide ranging reforestation trials designed by
GRO to determine the most appropriate species, establishment techniques and silvicultural
systems for eucalyptus plantations established to capture and sequester atmospheric carbon in
south-east Queensland (Turvey1 pers. comm. 2001).
2.1 Study Site
LOCATION
The spacing trial was established on the property ‘Lucy’, which is located approximately 20
km north of Gin Gin (latitude 24.91°S, longitude 151.85°E) in south-east Queensland,
Australia (Figure 2.1). It was located at a site on the property where the fastest possible
growth could occur in order to expedite the onset of competitive effects between trees.
Figure 2.1: Map showing the location of the ‘Lucy’ property in relation to the state of Queensland, Australia.
1 Dr N. Turvey, Managing Director, Greenfield Resource Options (Forestry Project Management & Consulting)
Chapter 2 The Spacing Trial Page 6
GEOLOGY AND LANDSCAPE
The property ‘Lucy’ is situated in the Burnett River catchment, which has a great diversity of
soils due to its geological complexity. The region is comprised mostly of hilly lands of
metamorphic and granite rocks, sediments and basalt (Hubble and Isbell 1983). The soils local
to ‘Lucy’ reflect the underlying granite geology.
The landscape local to ‘Lucy’ comprised of low, undulating hills with seasonal drainage. On
‘Lucy’ the spacing trial was located on a creek-flat bordered by two creeks in the midst of low
undulating hills, and much of the water moving through the surrounding landscape was
expected to filter through the site with the result that ground water would be readily available.
SOILS
Most soils on ‘Lucy’ were classified as brown or grey chromosols (Isbell 1996), which are
texture contrast soils of medium depth and fertility, with sandy loam surfaces overlying
medium brown or grey clays. The brown chromosols on ‘Lucy’ were characterised by low
erodibility and were widespread on the property, whereas the grey chromosols were of
moderate erodibility and were limited to steeper slopes.
In contrast, the creek-flat on which the spacing trial was situated had very deep (> 2 m),
alluvial fertile soil. The surface soil was sandy-loam textured grading to clay loams at depth
and was classified as a grey ferrosol (Isbell 1996). The deep soil profile was expected allow
trees unobstructed access to ground water.
CLIMATE
The area in which ‘Lucy’ is located is described by the Australian Bureau of Meteorology
(BOM) as having a hot, humid summer climate with a long term mean annual rainfall of 1050
mm (BOM 2005), and is characterised by wet, hot summers and dry, mild winters (Figure
2.2). Light frosts between 0 - 3°C occur annually from late May to early August, whereas
heavy frosts between -3 - 0°C occur from June to July but may only occur in 50 – 80% of
years.
Chapter 2 The Spacing Trial Page 7
0
50
100
150
200
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
Rain
fall (
mm
)
0
5
10
15
20
25
30
35
Tem
pera
ture
(°C)
Mean Monthly Rainfall Range
Mean Monthly Maximum Temperature Range
Mean Monthly Minimum Temperature Range
Figure 2.2: The mean monthly rainfall range and mean monthly minimum and maximum temperature range for the area in which ‘Lucy’ is located, based on standard year records (1961-1990) (BOM 2005).
2.2 Experimental Design
PLANTING DENSITY TREATMENT
The role of the spacing trial as established by GRO on behalf of SPP was to demonstrate the
effects of intra-specific competition on the accumulation and distribution of plant biomass and
carbon in stands and trees at competition intensities ranging from very low to very high levels
of competition (Turvey2 pers. comm. 2001). Planting density was an appropriate mechanism
to create different competition intensities between stands. The planting densities used were
250, 1,000, 5,000 and 10,000 stems per hectare (st/ha), which allowed a comparison between
very low competition in 250 st/ha and very high competition in 10,000 st/ha.
SPECIES SELECTION
A number of sub-tropical eucalypt species were under consideration by GRO for plantations
in south-east Queensland. Of these Eucalyptus grandis had exhibited excellent performance in
plantations both in Australia and overseas and had proven its potential to sequester carbon
quickly owing to its rapid early growth habit. E. grandis was therefore selected for the
spacing trial. In its native habitat mature E. grandis is a tall tree reaching 45-55 m in height
and exhibiting excellent form. Its main area of occurrence is coastal regions from Newcastle
2 Dr N. Turvey, Managing Director, Greenfield Resource Options (Forestry Project Management & Consulting)
Chapter 2 The Spacing Trial Page 8
to Bundaberg (latitude 25-33°S) on the east coast of Australia, and scattered populations
extend as far north as Townsville and Bloomfield (latitude 16-19°S) (Boland et al. 1992).
Whilst the research was targeted on fast-growing E. grandis, the pattern of results were
expected to apply to all fast-growing eucalyptus species exhibiting similar characteristics in
stand development, including similar height growth in dominant trees regardless of planting
density, the rapid differentiation of the stand into dominance cohorts due to asymmetric
competition, and a significant loss of photosynthetic capacity in shaded leaves.
DESIGN LAYOUT
The spacing trial contained four replicates of each planting density treatment, resulting in a
total of sixteen plots (four treatments multiplied by four replicates). The size and layout of the
trial was designed to maximise the use of the 1.6 ha area available. The spacing trial was
established in 24 x 24 m plots in a randomised block design (Figure 2.3). Within each plot,
trees were established in a square grid pattern, whereby the spacing within and between rows
was equal. This resulted in a square spacing of 6.20 m for 250 st/ha, 3.15 m for 1,000 st/ha,
1.41 m for 5,000 st/ha and 1.00 m for 10,000 st/ha. The outermost row of each plot was
excluded from measurements since it was considered to be a buffer row absorbing edge-
effects of each plot. The trial has a strong statistical design that allows differences in growth
between planting densities to be tested under the appropriate statistical assumptions.
Plot Layout Plot
Replicate
Treatment (st/ha) NORTH
1 2 5 6 7 8 9
1 1 2 2 2 2 3
10,000 5,000 250 10,000 5,000 1,000 250
4 3 14 13 12 11 10
1 1 4 4 3 3 3
1,000 250 1,000 250 5,000 1,000 10,000
15 16
4 4
10,000 5,000
Figure 2.3: The layout of the spacing trial.
Chapter 2 The Spacing Trial Page 9
2.3 Establishment
SEEDLING SOURCE
Seed of E. grandis was sourced from the Coffs Harbour Orchard, NSW, having been collected
by and purchased from the Australian Tree Seed Centre (ATSC) (Seedlot 18146). Trees
grown in the Coffs Harbour Orchard were originally sourced from native forest trees known
as ‘plus trees’ for their exceptional growth and form, and consequently the seed sourced from
the Coffs Harbour Orchard were biased towards competitive genotypes. This was of benefit in
the spacing trial as it provided a relatively even distribution of genotypes across the trial, with
the result that differences between individuals were more likely to be due to the process of
competition than the genetic ability of individuals to grow at different rates.
Seed was supplied to Minyon Forest Nursery, who was contracted to grow and deliver the
seedlings. Following germination seedlings were transferred to V-93 Hiko trays, which are
plastic trays consisting of 40 root plug ‘cavities’, each of 93 ml volume and with vertical root
training ribs. Seedlings were grown to a height of 25-30 cm and were required to have healthy
leaves and a well developed root system. Seedlings were hardened-off in the nursery prior to
delivery, and watered in the field prior to planting.
SITE PREPARATION
In order to provide uniform site preparation for all planting density treatments, the area of the
spacing trial was cultivated completely using a trailed 12 disc plough-harrow to a depth of 15
to 20 cm (Figure 2.4(a-b)).
Pre-plant weed control was used to ensure that seedlings did not experience significant
competition or competition induced mortality from weeds during their establishment. In order
to ensure that all weeds were eradicated, weeds were initially left to germinate and grow
following cultivation, and were treated only when weeds were more than 50 cm tall and
growing vigorously. Weeds were then treated using Roundup 360 (9 l/ha) together with LI700
(300 ml/100 l) as a penetrant and surfactant, and water (150 l/ha) to penetrate the dense grass
and thoroughly wet the vegetation cover. Weedicide was applied by an 8 m wide boom with
nozzles every 50 cm, at an operating pressure set at 2 Bar. The spray rig and storage tanks
were mounted on a 4WD Case 4230 80 HP tractor, and a standard operating speed of 8 km/hr
was used.
Chapter 2 The Spacing Trial Page 10
(a) (b)
(c) (d)
Figure 2.4: Images of the establishment of the spacing trial on the ‘Lucy’ property; (a) the creek-flat on which the spacing trial was established with the spacing trial cultivation visible in the lower half of the image; (b) broad-scale cultivation of the spacing trial; (c) the spacing trial at 3-months-old with a 250 st/ha plot in the foreground and a 10,000 st/ha plot in the background; (d) a 10,000 st/ha plot at 1 year old.
PLANTING
The spacing trial was planted by hand in March 1999. The exact planting location of each tree
was measured out and marked with non-toxic spray paint prior to planting to ensure the
correct spacing. A 20-25 cm deep hole was created using a spade and root plugs were placed
vertically in the hole, which was then in filled and firmed down by treading lightly on each
side of the seedling.
Chapter 2 The Spacing Trial Page 11
SILVICULTURE
Following planting, the spacing trial was fertilised with di-ammonium phosphate (Nitrogen
18%, Phosphorous 20%, Sulphur 1.7%) at a rate of 300 kg/ha, or 1,200 g/tree in 250 st/ha,
300 g/tree in 1,000 st/ha, 60 g/tree in 5,000 st/ha, and 30 g/tree in 10,000 st/ha. Care was
taken to distribute the fertiliser evenly around the root zone of the seedling so as not to bias
root growth in any direction. Post-planting weed control was carried out in June 1999,
approximately 2 months after planting. The weedicide consisted of a mixed of Lontrel at 0.9
l/ha, Verdict at 1.6 l/ha, Simazine flowable at 5 l/ha and LI-700 at 0.45 l/ha. The application
was broadcast by a tractor mounted rig with a computerised delivery system.
The establishment of the spacing trial was highly successful, with low mortality rates of 6.2%
for 250 st/ha, 6.6% for 1,000 st/ha, 6.2% for 5,000 st/ha and 3.1% for 10,000 st/ha in the first
year of growth. The eradication of weeds during early establishment was successful as
seedlings in the trial did not experience significant competition from weeds during early
establishment (Figure 2.4(c)). In later development grassy weeds did establish in the 250 and
1,000 st/ha planting densities, however the canopies had developed sufficiently that grass
growth did not cause any light competition. Canopy closure in the 5,000 and 10,000 st/ha
planting densities was very rapid, occurring between 6-12 months, and consequently grassy
weeds were unable to establish (Figure 2.4(d)).
RAINFALL
The year following planting had 1,077 mm annual rainfall, just exceeding the long term mean
annual rainfall of 1,050 mm (Figure 2.5), which aided the successful establishment of the
spacing trial. Annual rainfall then fell below the long term mean for the remainder of the
measurement period, and was lowest in the third year of growth which was a drought year.
0
200
400
600
800
1000
1200
1 2 3 4 5
Age (yrs)
An
nu
al
Ra
infa
ll (
mm
)
Mean Annual Rainfall Current Annual Rainfall
Figure 2.5: Mean annual rainfall and current annual rainfall recorded at Gin Gin, QLD (BOM 2005).
Chapter 2 The Spacing Trial Page 12
2.4 Sample Selection
Annual stem measurements were made on the whole spacing trial, excluding those trees in
buffer rows, and as a result 16 stems were measured annually in 250 st/ha plots, as compared
to 1,936 stems in 10,000 st/ha plots (Table 2.1). Detailed measurements were collected at ages
3 and 4 years.
Table 2.1: The number of stems in the spacing trial for each planting density; by plot and by treatment.
Planting Density No. Stems per Plot No. Stems per Treatment
250 st/ha 4 16
1,000 st/ha 30 120
5,000 st/ha 225 900
10,000 st/ha 484 1,936
2.4.1 3 Year Old Sample Selection
The primary purpose of non-destructive measurements at 3 years was to investigate branching
patterns. At 3 years, 2 trees per plot were selected using a systematic sequence of selection in
each plot, with the result that 8 trees per planting density were selected.
2.4.2 4 Year Old Sample Selection
The primary purpose of destructive measurements at 4 years was to investigate tree allometry
and wood quality. At 4 years, 2 trees per plot in 250 st/ha and 5 trees per plot in 1,000, 5,000
and 10,000 st/ha were selected using a stratified random sampling method. The selection was
made by dividing the range of tree sizes (as determined by stem diameter at breast height
(DBH)) into two equal size classes for 250 st/ha and five equal size classes for 1,000, 5,000
and 10,000 st/ha, and randomly selecting one tree per size class per plot. The stratified
random sampling method was necessary to ensure that the whole range of tree sizes in each
planting density was selected, since a true random sample was likely to result in a bias
towards the selection of smaller trees due to positive skewness in the range of tree sizes
(Figure 2.6).
Chapter 2 The Spacing Trial Page 13
0
15
30
Perc
en
tag
e F
req
uen
cy (
%)
0
15
30
0
15
30
0
15
30
0-1 2-3 4-5 6-7 8-9 10-11 12-13 14-15 16-17 18-19 20-21 22-23 24-25 26-27
Diameter Class (cm)
(a)
(b)
(c)
(d)
Figure 2.6: Histograms of the range in tree sizes of 4 year old E. grandis planted at; (a) 250 st/ha, (b) 1,000 st/ha, (c) 5,000 st/ha, and (d) 10,000 st/ha.
Chapter 3 Stand Growth and Structure Page 14
3. STAND GROWTH AND STRUCTURE
Stand growth and structural development is a dynamic process in which factors including the
genetic characteristics of the species present, the climate and resource availability, and the
rate of onset and intensity of competition, combine to influence the rate at which stand growth
occurs and stand structure changes. A comprehensive understanding of both stand growth and
stand structure is essential to understanding stand development, since each influences the
other.
3.1 State of Knowledge
3.1.1 Stand Growth
Stand size is typically measured at the stand level in terms of mean tree size or aggregate
stand size, and stand growth occurs when trees within a stand increase in mass and size.
During stand growth trees increasingly utilise gaps available in the stand until such time as the
entire site is accessed and occupied. The absolute limit of stand growth is determined by the
maximum biomass carrying-capacity of the site, and as stands approach this threshold stand
density (biomass per unit of space) increases. Stand density is historically used as a
management tool as it can provide an indication of the mean stem size and expected growth
rate of stands (Curtis 1970). Measures of stand density typically compare measures of stand
size such as stem number, stem diameter, stem volume and/or stem height, with the space
occupied by the stand (Bredenkamp and Burkhart 1990a). Some better known examples
include basal area, relative spacing (Hart 1928), the stand density index (Reineke 1933), the
S-curve (O'Connor 1935) and the -3/2 power law (Yoda et al. 1963).
During stand development, changes in the rate of stand growth occur. Changes in stand
growth rate are commonly measured in terms of the current annual increment (CAI) in mean
tree or stand size over one year of growth, and the mean annual increment (MAI) in mean tree
or stand size from initial establishment to a specified age (Brack and Wood 1998). The
general pattern of growth is well known (Gower et al. 1996); CAI increases at an increasing
rate in the early stages of stand establishment, following which it increases at a decreasing
rate until it peaks and begins a process of decline (Sprugel 1984; Ryan and Waring 1992).
MAI follows a similar (but delayed) sequence, and peaks at the point at which it crosses the
CAI schedule (Figure 3.1).
Chapter 3 Stand Growth and Structure Page 15
Figure 3.1: The general pattern of change in current annual increment (CAI) and mean annual increment (MAI) of forest stands over the course of development (Brack and Wood 1998).
The study of stand development in plantation eucalypts to date has emphasised stand growth
and neglected stand structure. A classic example of this is the correlated curve trend (CCT)
spacing trials established with E. grandis in South Africa (Laar and Bredenkamp 1979), in
which the response of stand development to different spacing and thinning treatments is
measured by mean stem diameter and stand basal area, thereby providing good information
about stand growth but generalised and incidental information about stand structure. The vast
majority of research on eucalypts is similar, with changes in stand growth and structure due to
treatments including planting density, thinning, fertilising, weeding, irrigation and pruning,
being reported in terms of mean tree size and/or aggregate stand size only.
Research in eucalyptus plantation stand growth has been extensive, and as a result the
productivity of eucalyptus plantations has been greatly enhanced, and modern models (both
empirical and process based) can be used to make reasonably accurate predictions of the
patterns of change in stand growth over a range of commercial plantation species and sites.
Yet whilst research allows more accurate definition of stand growth patterns, much of it does
little to provide an explanation as to why such stand growth patterns should occur. The reason
why specific stand growth patterns occur, and particularly why stand growth declines, has
emerged as a contentious issue since this information could aid further increases in plantation
productivity by enabling silviculturalists to delay the point at which stand growth rate
declines, and more universally it could aid the modelling of growth in tree species and forest
ecosystems that have not been intensively studied (such as is required for global carbon
budgeting).
Whilst the cause of declining stand growth is not known, there is a great deal of speculation
on what could be causing it. Possible explanations include one, or more likely a combination
Chapter 3 Stand Growth and Structure Page 16
of (Berger et al. 2004), the following; a change in the balance between photosynthesis and
respiration towards respiration as trees age (Kira and Shidei 1967), subsequently refuted in
several studies (Ryan and Waring 1992; Yoder et al. 1994; Ryan et al. 2004); a change in the
allocation of carbon from stem wood to other components such as leaves, branches, roots, and
reproductive components (Yoder et al. 1994; Ryan et al. 2004); a loss of carbon from the
stand ‘budget’ due to mortality (Ryan et al. 1997); an increase in the hydraulic constraints of
the canopy as trees grow larger causing a decrease in stomatal conductance and
photosynthetic capacity (Yoder et al. 1994; Gower et al. 1996; Hubbard et al. 2001),
subsequently refuted in several studies (Hubbard et al. 2002; McDowell et al. 2002; Barnard
and Ryan 2003; Ryan et al. 2004); a shortage in nutrient availability as the growing forest
captures the resources available (Binkley et al. 1995; Gower et al. 1996), subsequently
refuted in several studies (Ryan et al. 1997; Ryan et al. 2004); and a loss of resource use
efficiency in sub-dominant and suppressed stems (thereby reducing overall stand
productivity) (Binkley et al. 2002), previously refuted by studies on sub-alpine forests
(Kaufmann and Ryan 1986).
Changes in tree or stand structure are implicit in many of these explanations and a knowledge
of stand structure would be advantageous to prove or disprove the hypotheses, yet most
published research lacks information about stand structure. This is largely due to the emphasis
on stand growth and the subsequent amalgamation and/or summarization of raw data into
stand level values. Several authors have consequently called for more study into the changes
in forest structure that accompany stand growth, particularly declining stand growth (Gower
et al. 1996; Binkley et al. 2002; Berger et al. 2004).
3.1.2 Stand Structure
Stand structure describes the manner in which stand growth is distributed within the stand and
is typically described by a number of parameters as no one measure provides an adequate
description of stand structure. Parameters of stand structure include the stocking rate, the size
distribution of stems, the size variability of stems, the spatial distribution of stems, and the
phenology of and variability in tree morphology.
As a general rule, plantation stands are established with as uniform a structure as possible, and
if stand structure remained uniform then stand level growth measures like mean tree size and
Chapter 3 Stand Growth and Structure Page 17
aggregate stand size would provide an adequate description of stand structure. Plantation
stands, however, do not maintain structural uniformity, but rather structural diversity develops
as a range of tree sizes and shapes emerge over time, presumably due to a combination of
factors including genetic diversity, variation between micro-sites and competition between
individuals (Jacobs 1955; Harper 1967; Ford 1975; Opie et al. 1978; Evans 1982).
Competition in plant communities occurs when individuals use a resource or resources that
would otherwise have been used by their neighbour had they not been present (Donald 1963),
and the onset of competition is hastened by greater initial plant population densities and, in
the case of light competition, by greater stand growth rates due to better site quality (Weiner
1985). The development of structural variation within even-aged plant populations is
considered to be due to a ‘hierarchy of exploitation’ following the onset of intra-specific
competition (Harper 1967; Weiner 1986), whereby larger plants are able to capture a
relatively greater ratio of resources than smaller plants, resulting in greater relative growth
rates for larger plants (Ford 1975; Weiner 1986). Several authors suggest that the dominants
in tree stands are able to capture as many resources as they need, with the result that their
growth rate is largely unaffected by the level of competition in the stand (Bredenkamp and
Burkhart 1990b; Battaglia 2001; Franc 2001; Binkley et al. 2002).
Changes in relative growth rates between plants lead to changes in population structure, and
because these changes are the result of competition, the rate and extent of their occurrence are
considered indicative of the onset and intensity of competition in the population (Weiner
1986). Typical changes in stand structure as a result of increased competition are outlined in
the following paragraphs.
As competition increases, the stocking rate is reduced as the most suppressed stems die.
Mortality due to competition is termed density-dependant mortality or self-thinning, and has
been found to approximate a -3/2 gradient between mean plant mass and stocking density for
many plant species (particularly shade intolerant species) (Yoda et al. 1963) (Figure 3.2).
Whether this particular gradient applies to all species is a topic that has undergone
considerable debate (White and Harper 1970; Drew and Flewelling 1977; Westoby 1977;
Lonsdale and Watkinson 1982; Lonsdale 1983; Westoby 1984; Osawa and Sugita 1989;
Lonsdale 1990; Weller 1990; Zeide 1991; Bi 2001), however there is no doubt that a negative
relationship exists and for many species -3/2 is a reasonable approximation of its slope.
Chapter 3 Stand Growth and Structure Page 18
Figure 3.2: The -3/2 gradient between mean plant mass and tree density in natural single-species stands of Abies sachalinensis in Hokkaido, Japan. Source: Yoda et al. 1963.
Increased competition causes changes in the size frequency distribution of tree stands. Size
frequency distributions show the frequency of trees in consecutive size classes within the
stand, and are typically analysed by comparison to a normal (symmetric bell-shaped)
distribution. Single-species, even-aged tree stands are typically established with a normal size
distribution due to natural variation in seed size and germination rates. As competition
increases a positive skewness develops in the size distribution (Ford 1975; Kohyama and Hara
1989; Oliver and Larson 1996) due to asymmetric competition between large and small
individuals (Weiner and Thomas 1986), resulting in a small number of trees much greater
than the mean and a large number of trees slightly smaller than the mean. Furthermore, where
normal size distributions are uni-modal (single peaked), increased competition has been found
to cause bimodality (double peaks) in the size distribution of many plant populations (Ford
1975; Weiner 1986; Franc 2001).
Increased competition causes changes in the size variability within stands. Size variability is
the relative difference in size between individuals in the stand, and evidence shows that that
increased competition results in greater inequality in relative growth rates between large and
small individuals and therefore increased size variability (Weiner 1985; Weiner and Thomas
1986; Weiner et al. 1990b). Size variability can be measured using a standard statistical
measure of relative variation, the coefficient of variation (CV) (Weiner and Thomas 1986),
Chapter 3 Stand Growth and Structure Page 19
and by using other tools including the Lorenze curve and the Gini coefficient (Weiner and
Thomas 1986; Taylor 1998), which were developed by economists to study the concept of
inequality. Lorenze curves are constructed by ranking size from smallest to largest and
plotting the cumulative increase in total size with the addition of consecutive individuals. The
plotted Lorenze curve is compared to the Lorenze curve for equality (assumes all individuals
are equal in size), and the extent to which the plotted Lorenze curve diverges from the
equality Lorenze curve provides an indication of the inequality. The gini-coefficient is a
numerical measure of the difference between the Lorenze curves, whereby the plotted
Lorenze curve is calculated as a percentage of the equality Lorenze curve. A reduction in the
gini-coefficient indicates greater inequality and therefore greater size variability.
Increased competition also causes changes in the spatial distribution of tree sizes. The spatial
distribution of large and small tree sizes is fairly random at the point of natural and plantation
stand establishment. Increased competition creates pressure for space with the result that trees
of the same size, particularly dominants, tend to develop at equal distances apart rather than in
clusters, the distance apart being reflective of their zone of influence (Ford 1975; Batista and
Maguire 1998). Finally increased competition has been shown to cause alterations in the
morphology of trees due to the plasticity of their responses to changing growing conditions
(Donald 1963; Harper 1967; Mohler et al. 1978; Marks et al. 1986; Weiner and Fishman
1994; Yokozawa and Hara 1995). Typical responses to increased competition include
increased crown lift, greater stem height to diameter ratios and greater aboveground to
belowground biomass ratios.
The information available on stand structure indicates that the patterns of change in stand
structure are well known. Where knowledge is lacking is in the ability to predict the actual
rate and extent of change in stand structure, and fundamental to this is the failure thus far to
fully investigate the effect of competition on both stand growth and stand structure
simultaneously, since competition is essentially the nature in which stand growth and stand
structure interact. Without such knowledge it is difficult to ensure that forest management
practices are optimised for maximum productivity and/or value. Furthermore, a better
knowledge of competition in stand development would enable more accurate modelling not
only of commercial forests, but also of natural forests and global carbon budgets, and might
provide evidence for the mechanism(s) behind declining stand productivity.
Chapter 3 Stand Growth and Structure Page 20
3.2 Experimental Rationale
The initial investigation of stand development in the spacing trial examines traditional
measures of stand growth and stand structure. This is an important first step providing
preliminary insight into the impact of the initial population (planting density) on biomass
accumulation and the onset and intensity of competition. It also provides a benchmark for
comparison between the spacing trial and other plantations.
In the early stages of stand development during which the spacing trial was measured it is
expected that increased planting density will lead to increased stand growth due to greater site
occupancy, and increased stand structural variation due to more rapid onset of and intensity in
competition (Table 3.1).
Table 3.1: Hypotheses of the effect of increased planting density on variables of stand growth and stand structure during early stages of stand development in sub-tropical E. grandis plantations.
Stand Variable Hypothesis
Stand Growth
Stand Total Stem Volume Increment
Increased planting density will result in increased stand total stem volume increment since the rate of increment in stand total stem volume is increased by greater site occupancy.
Stand Total Stem Volume
Increased planting density will result in increased stand total stem volume since stand total stem volume is the result of increment in stand total stem volume.
Stand Mean Stem Volume
Increased planting density will result in decreased stand mean stem volume since competition causes suppression in the size of a number stems within stands.
Stand Structure
Stand Mortality Increased planting density will result in increased stand mortality since competition causes density dependent mortality.
Stand Stem Volume Size
Distribution
Increased planting density will result in increased positive skewness and bimodality in the size distribution of stand stem volumes since competition causes suppression in the size of a number stems within stands.
Stand Stem Volume Size
Inequality
Increased planting density will result in increased stand stem volume size inequality since competition causes suppression in the size of a number stems within stands.
Stand Dominance Classes
Increased planting density will have no effect on the size of stems in the largest dominance class since competition does not cause suppression of all stems within stands.
Chapter 3 Stand Growth and Structure Page 21
3.3 Methodology
To test the hypotheses a number of data were collected from the spacing trial, and in some
cases collected data were used to calculate estimated values of additional tree and stand
variables. The information collected on stand growth and structure was investigated using
graphs and descriptive statistics.
3.3.1 Sample Age and Size
Some data were collected annually for every live tree in the whole trial, whereas other data
were collected at 3 and 4 years from a smaller sample size using various selection methods
(Table 3.2).
Table 3.2: The age and sample size of variables for which data was collected from the spacing trial.
Number of Trees Sampled Tree Variable
Age (yrs) 250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
Annual Measurements
1 15 111 843 1873 2 13 109 829 1706 3 13 109 80 80 4 8 20 20 20
Stem Height
5 n/a(a)
32 32 32
1 15 111 843 1873 Stem Base Diameter
2 13 109 829 1706
2 13 109 829 1706 3 13 109 792 1588 4 13 105 742 1491
Stem Diameter at Breast Height
5 n/a(a)
84 674 1372
3 Year Old Measurements(b)
Stem Diameter at 1 m Height Intervals 3 8 8 8 8
4 Year Old Measurements(b)
Stem Diameter at 1 m Height Intervals 4 8 20 20 20
(a) Due to whole tree destructive sampling at 4 years there were insufficient trees to adequately sample the 250 st/ha planting density treatment at 5 years.
(b) A full description of the sample selection methods for 3 and 4 year old measurements are provided in Chapter 2 – The Spacing Trial, sub-sections 2.4.1 and 2.4.2 respectively.
3.3.2 Data Collection and Calculation
Location – the spatial location of the tree within the trial. Tree locations were defined by plot
and tree number, which were identified on maps of the trial.
Chapter 3 Stand Growth and Structure Page 22
Stem Height – the vertical distance from the base of the stem at ground level to the apex of
the stem at the highest growing tip. Stem height was measured directly with a height stick at
1-2 years. At 3 and 5 years stem height was measured indirectly with a Lasertech
Hypsometer. At 4 years stem height was measured directly with a measuring tape. For stems
not measured, stem height was predicted using a stem height model (Table 4.4, p68).
Stem Base Diameter – the diameter of the cross-sectional area of the whole stem at 0.1 m
stem height. Stem diameter at base was measured directly with callipers at 1-2 years. Stem
diameter at base was calculated at 3-4 years using stem diameter at 1 and 2 m height in the
following formula (based on re-arranging the straight line formula y = mx + b):
stem diameter at base = ((y – b)
/m) + stem diameter at 2 m height
where y = 1 (1 m height)
b = 2 (2 m height)
m = (2 m height – 1 m height) / ( stem diameter at 2 m height – stem diameter at 2 m height)
Stem Base Basal Area – the cross-sectional area of the whole stem at 0.1 m height. Stem
basal area at base was calculated for every stem using stem diameter at base and assuming a
circular stem cross-section.
Stem Diameter at Breast Height (DBH) – the diameter of the cross-sectional area of the whole
stem at 1.3 m stem height. DBH was calculated for every stem at 1 year old using stem height
and stem diameter at base and assuming stems were conical. DBH was measured directly with
callipers at 2 years and with a diameter tape at 3-5 years.
Stem Basal Area at Breast Height – the cross-sectional area of the whole stem at 1.3 m
height. Stem basal area at breast height was calculated for every stem using stem diameter at
breast height and assuming a circular stem cross-section.
Stem Diameter at 1 m Stem Height Intervals – the diameter of the cross-sectional area of the
whole stem at 1 m stem height intervals. At 3 years access to the stem was gained using a
ladder and stem diameter at 1 m height intervals was measured directly with a measuring tape
to a height of 6 m. At 4 years access to the stem was gained by felling the stem and stem
diameter was measured directly with a measuring tape to the tip of the stem. Where branch
swellings obstructed measurements, they were moved up to the nearest clear stem section.
Chapter 3 Stand Growth and Structure Page 23
Stem Volume – the volume of the 3-dimensional shape of the whole stem, excluding the 0.1
m stump, at a given age. At 1-2 years stem volume was calculated using stem height, stem
basal area at base and stem basal area at breast height. The calculations assumed that the stem
section between the base and breast height is a frustum of a second degree paraboloid, and
that the stem section above breast height is a cone:
2nd
Degree Paraboloid Frustum = height* ((base basal area + top basal area) / 2)
Cone = height * base basal area * ⅓
At 3-4 years stem volume was calculated using stem diameter at 1 m stem height intervals and
stem height. Stem volume was calculated as the sum of the conical frustum volumes formed
between each stem diameter (with the top frustum forming a cone). The frustum volumes
were calculated using the formula:
Conical Frustum = ⅓ * π * (base radius2 + top radius
2 + (base radius*top radius)) * height
For stems at 3-4 years that were not measured for stem diameter at 1 m stem height intervals,
stem volume was predicted using stem volume models developed from stems that were
measured for stem diameter at 1 m stem height intervals (Appendix 1). At 5 years stem
volume was predicted using the 4 year old stem volume model (Appendix 1).
Stem Volume Increment – the increment in stem volume in the year up to the subscripted
age. Stem volume increment was calculated for each tree at each age by subtracting the
previous year’s stem volume from the current year’s stem volume.
Stand Total Stem Volume – the total stem volume per unit area in the stand. Stand total stem
volume was calculated at each age for each planting density as the sum of the stem volume
(m3) of all trees measured divided by the sum area (ha) occupied by all trees measured.
Stand Total Stem Volume Increment – the current annual increment (CAI) in stand total stem
volume per unit area over a year of growth and the mean annual increment (MAI) in stand
total stem volume per unit area from initial establishment to a specified age.
Stand Mean Stem Volume – the mean stem volume of each planting density. Stand mean
stem volume was calculated at each age for each planting density as stand total stem volume
divided by the number of trees in the trial.
Chapter 3 Stand Growth and Structure Page 24
Stand Mortality – the number of stems in the stand that have died. Stand mortality was
calculated from 1-4 years and for each planting density as the number of stems established in
the trial minus the current number of live stems in the trial. Percentage stand mortality was
calculated from 1-4 years and for each planting density as stand mortality divided by the
number of stems established in the trial.
Stand Stem Volume Size Distribution – the nature by which stem volumes range in size and
number in the stand. Size frequency histograms and skewness values of stand stem volumes
were generated at each age and for each planting density.
Stand Stem Volume Size Inequality – the inequality between stem sizes in the stand. Lorenze
curves, gini-coefficient values and coefficient of variation values of stand stem volumes were
generated at each age and for each planting density.
Stand Dominance Classes – dominance classes are groups of stems ranked according to their
relative performance in the stand, whereby greater stem volume indicated better performance.
In each planting density stem volumes were divided into 250 stem cohorts and 1,000 stem
cohorts: for example the 1,000 st/ha planting density had four groups in 250 stem cohorts
(1,000/250 = 4) and one group in 1,000 stem cohorts (1,000/1,000 = 1), and so on for other
planting densities. The dominance classes were constructed at each age and for each planting
density by ranking stems from largest to smallest and grouping stems in 250 stem cohorts or
1,000 stem cohorts in order of diminishing dominance (Table 3.3). The mean stem volume
was then calculated for each dominance class.
Table 3.3: The calculation of the number of stems in 250 stem cohorts and 1,000 stem cohorts in each planting density.
PLANTING
DENSITY
(ST/HA)
NO. STEMS
IN TRIAL STEM RATIO IN 250
STEM COHORTS
NO. STEMS IN
250 STEM
COHORTS (a)
STEM RATIO IN
1,000 STEM
COHORTS
NO. STEMS IN
1,000 STEM
COHORTS (a)
250 16 250
/250 = 1 16 * 1 = 16 n/a(b)
n/a(b)
1,000 120 250
/1,000 = 0.25 120 * 0.25 = 30 1,000
/1,000 = 1 120 * 1 = 120
5,000 900 250
/5,000 = 0.05 900 * 0.05 = 45 1,000
/5,000 = 0.2 900 * 0.2 = 180
10,000 1936 250
/10,000 = 0.025 1936 * 0.025 = 48 1,000
/10,000 = 0.1 1936 * 0.1 = 194
(a)Results were rounded to the nearest whole number.
(b)The 250 st/ha planting density did not allow a meaningful comparison of properties in the 1,000 stem
cohorts as there were not enough stems to fill the cohorts.
Chapter 3 Stand Growth and Structure Page 25
3.4 Results and Discussion
3.4.1 Stand Growth
STAND TOTAL STEM VOLUME
The results confirm the hypothesis that stand total stem volume will increase in response to
increased planting density, as at any given age stand total stem volume increased as planting
density increased (Figure 3.3). The result indicated that at the early stage of development in
which the trial was measured, any loss of biomass due to competition induced mortality in
higher planting densities was compensated by greater total growth in higher planting
densities, presumably due to greater site occupancy.
0
50
100
150
200
250
300
350
0 1 2 3 4 5
Age (yrs)
Sta
nd
To
tal S
tem
Vo
lum
e (
m3h
a-1
)
250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
Figure 3.3: The stand total stem volume of E. grandis at planting density 250 st/ha from 1-4 years, and planting densities 1,000 st/ha, 5,000 st/ha and 10,000 st/ha from 1-5 years.
STAND TOTAL STEM VOLUME INCREMENT
The results confirm the hypothesis that stand total stem volume increment will increase in
response to increased planting density, as at any given age stand total stem volume mean
annual increment (MAI) increased as planting density increased (Figure 3.4). This result was
similar to the above result for stand total stem volume, which was expected since total stem
volume is the result of stem volume increment.
Chapter 3 Stand Growth and Structure Page 26
0
10
20
30
40
50
60
70
0 1 2 3 4 5
Age (yrs)
Sta
nd
To
tal
Ste
m V
olu
me
MA
I (m
3h
a-1
yr-1
)250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
Figure 3.4: The mean annual increment (MAI) in stand total stem volume in E. grandis at planting density 250 st/ha from 1-4 years, and planting densities 1,000 st/ha, 5,000 st/ha and 10,000 st/ha from 1-5 years.
At 5 years stand total stem volume increment was still increasing in productivity (Figure 3.4),
which is typical of early stand development (Figures 3.1). The exception to increasing
productivity can be seen in the dip in volume increment between 3 and 4 years (Figure 3.4),
which was probably due to low rainfall from 2-4 years (Figure 2.5). Even with low rainfall,
the MAI in stand total stem volume of 27 m3/ha at 5 years for 1,000 st/ha was comparative to
plantation stands of similar stocking on good quality sites in Australia (Ipsen3 pers. comm.
2005). The MAI in stand total stem volume of 55 m3/ha for 5,000 st/ha and 68 m
3/ha for
10,000 st/ha at 5 years was very high compared to standard stocking rates around 1,000 st/ha.
STAND MEAN STEM VOLUME
The results confirm the hypothesis that stand mean stem volume will decrease in response to
increased planting density, as at any given age stand mean stem volume decreased as planting
density increased (Figure 3.5). It was not clear at this point whether reduced stand mean stem
volume in higher planting densities was the result of a reduction in the stem volume of all
trees, or the inclusion of a greater number of smaller trees, or a combination of the two.
3 Mr J. Ipsen, Forester, Integrated Tree Cropping (Hardwood Plantation Forestry Management)
Chapter 3 Stand Growth and Structure Page 27
0.00
0.05
0.10
0.15
0.20
0 1 2 3 4 5
Age (yrs)
Sta
nd
Me
an
Ste
m V
olu
me
(m
3)
250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
Figure 3.5: The stand mean stem volume of E. grandis at planting density 250 st/ha from 1-4 years, and planting densities 1,000 st/ha, 5,000 st/ha and 10,000 st/ha from 1-5 years.
Overall, stand growth in the 250 and 1,000 st/ha planting densities compared well to
eucalyptus plantations of similar planting densities on good site quality, whereas stand growth
in the 5,000 and 10,000 st/ha planting densities was very high, showing that eucalypts have
the potential to capture carbon very quickly. The point at which MAI begins to decrease,
disregarding the drought between 3-4 years, had not yet occurred in the spacing trial.
Based on the above growth data, the best information available on stand structure from 0-5
years was that increased planting density resulted in increased total stem volume (Figure 3.3),
but decreased mean stem volume (Figure 3.5). This pattern of stand development is well
known and sawlog plantations are rarely established with high stocking densities since the
reduction in mean stem volume as stocking density increases is thought to be due in part to
slowed growth in the ‘final crop’ stems. The dismissal of higher stocking densities in sawlog
plantations is a sensible management outcome if stand growth patterns provide an accurate
description of slower growth in final crop trees, yet evidence suggests that this is not the case.
Several authors propose that dominant stems may continue to grow largely unrestricted
regardless of stocking density (Bredenkamp and Burkhart 1990b; Battaglia 2001; Franc 2001;
Binkley et al. 2002), with the result that the growth of final crop trees in highly stocked stands
might be unaffected by stocking density.
Chapter 3 Stand Growth and Structure Page 28
Interestingly, pulpwood plantations in Australia are not established at high planting densities,
despite that reduced mean stem size does not affect pulpwood value and that high planting
densities have the potential to reduce rotation length by increasing the rate of stand growth.
The major reasons cited are establishment and harvesting costs, which under current
technologies are significantly increased by increased planting density, and wood quality,
which is considered inferior below 10 years old due to low wood density, thereby rendering
the early thinning required in high density systems as non-commercial (Ipsen4 pers. comm.
2005). As such there is currently no economic benefit to be gained from increasing planting
density to shorten rotation lengths in pulpwood plantations.
3.4.2 Stand Structure
STAND MORTALITY
The results confirm the hypothesis that stand mortality will increase in response to increased
planting density, since at any given age absolute stand mortality increased as planting density
increased (Figure 3.6(a)). The trend was not as strong in percentage stand mortality since
mortality in 250 st/ha was higher than that for 5,000 st/ha and 1,000 st/ha (Figure 3.6(b)).
0
50
100
150
200
250
300
350
400
450
0 1 2 3 4Age (yrs)
Sta
nd
Mo
rta
lity
(N
o.
ste
ms
)
250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
0
5
10
15
20
25
0 1 2 3 4Age (yrs)
Pere
cn
tag
e S
tan
d M
ort
ali
ty (
%)
(b)(a)
Figure 3.6: Stand mortality in E. grandis from 1-4 years for planting densities 250 st/ha, 1,000 st/ha, 5,000 st/ha and 10,000 st/ha by (a) absolute mortality (No. stems) and (b) percentage mortality (%).
4 Mr J. Ipsen, Forester, Integrated Tree Cropping (Hardwood Plantation Forestry Management)
Chapter 3 Stand Growth and Structure Page 29
Whilst the results generally align with the hypothesis, the difference of only 10% in
percentage stand mortality between planting densities 1,000 st/ha and 10,000 st/ha seemed
small given the large difference in population pressure. As such it could not be concluded that
the increased mortality in high planting densities was the result of increased competition since
it was possible that all planting densities had experienced approximately 18% incidental
mortality by 4 years. Further investigation of stand mortality was therefore required.
When the calculated values of stand mean stem volume and stand stocking (planting density
minus stand mortality) were plotted against each other in the fashion of Yoda (1963) for each
age and planting density (Figure 3.7), the results show little difference in gradient between
planting densities. In all planting densities but 250 st/ha the relationship between plant size
and population density is showing signs of approaching a negative ceiling like the -3/2 self-
thinning line defined by Yoda (1963) (Figure 3.2), and notably the 5,000 st/ha and 10,000
st/ha planting densities do not exhibit a steeper negative slope than 1,000 st/ha. Based on
these results the hypothesis that higher planting densities will exhibit greater competition
induced mortality during early stages of stand development must be refuted.
0.00
0.01
0.10
1.00
1 100 10,000
ln S
tan
d M
ea
n S
tem
Vo
lum
e (
m3)
250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
ln Stocking (st/ha)
Figure 3.7: The natural logarithm of stand mean stem volume plotted against the natural logarithm of stocking of E. grandis for planting density 250 st/ha from 1-4 years, and for planting densities 1,000 st/ha, 5,000 st/ha and 10,000 st/ha from 1-5 years.
Chapter 3 Stand Growth and Structure Page 30
The above results are surprising given the difference in population pressure between planting
densities; however it is possible that the results do not provide an accurate indication of stand
dynamics. If a ‘dead’ stem is defined as one that contributes no growth to the stand, then
‘mortality’ in the higher planting densities may be underestimated. Eucalypts are known for
the persistence of highly suppressed stems in the stand, which may live for several years
before dieing (Jacobs 1955). Such stems exhibit negligible growth, and if classed as ‘dead’ it
is likely that ‘mortality’ would increase by a greater amount in higher planting densities.
STAND STEM VOLUME SIZE DISTRIBUTION
The hypothesis for stand stem volume size distribution was that positive skewness and
bimodality in the size distribution of stand stem volumes would increase in response to
increased planting density. Size frequency histograms of stem volume were generated and
skewness calculated at each age for each planting density (Figure 3.8). These results should
not be compared directly due to the different sample number and size classes used between
ages and planting densities, however they do provide an indication of the pattern of change in
skewness.
Initial perusal of the frequency histograms and their skewness indicated that the stands
generally had a positive skewness regardless of their planting density and/or age, and that
skewness tended to increase during stand development (over time). Closer examination
showed that the skewness in the distribution of stem sizes in 250 st/ha was not significant at
any point (as indicated by skewness/standard deviation < 2), and that the skewness in the
distribution of stem sizes in 1,000 st/ha was significant at all ages, but was generally low and
even diminished at later ages. These results indicate that competition was not particularly
strong in planting densities 250 st/ha and 1,000 st/ha during the period of measurement.
In contrast, the skewness in the distribution of stem sizes in 5,000 st/ha and 10,000 st/ha was
significant at all ages, and showed a strong trend of increasing during stand development,
particularly for 10,000 st/ha. This indicates that strong competition occurred in planting
densities 5,000 st/ha and 10,000 st/ha during the period of measurement. Overall the results
confirmed the hypothesis that positive skewness in the size distribution of stand stem volumes
will increase in response to increased planting density.
Chapter 3 Stand Growth and Structure Page 31
(a) 250 st/ha (b) 1,000 st/ha (c) 5,000 st/ha (d) 10,000 st/ha
Age 1Age 1Age 1Age 1:::: Skew (-0.400/0.580) = -0.7 Age 1:Age 1:Age 1:Age 1: Skew (0.685/0.229) = 3.0 Age 1:Age 1:Age 1:Age 1: Skew (0.994/0.084) = 11.8 Age 1:Age 1:Age 1:Age 1: Skew (0.781/0.057) = 13.7
SV
.0087.0075.0062.0050.0037.0025.00120.0000
6
5
4
3
2
1
0
SV
.0080
.0075
.0070
.0065
.0060
.0055
.0050
.0045
.0040
.0035
.0030
.0025
.0020
.0015
.0010
.0005
0.0000
P: 1000 AGE: 1
20
10
0
Std. Dev = .00
Mean = .0027
N = 111.00
SV
.0125
.0112
.0100
.0087
.0075
.0062
.0050
.0037
.0025
.0012
0.0000
P: 5000 AGE: 1
140
120
100
80
60
40
20
0
Std. Dev = .00
Mean = .0031
N = 843.00
SV
.0095
.0090
.0085
.0080
.0075
.0070
.0065
.0060
.0055
.0050
.0045
.0040
.0035
.0030
.0025
.0020
.0015
.0010
.0005
0.0000
P: 10000 AGE: 1
400
300
200
100
0
Age Age Age Age 2222:::: Skew (0.486/0.616) = 0.8 Age Age Age Age 2222:::: Skew (0.816/0.231) = 3.5 Age Age Age Age 2222:::: Skew (0.949/0.085) = 11.2 Age Age Age Age 2222:::: Skew (1.493/0.059) = 25.3
SV
.063.056.050.044.038.031.025.019.013.006
P: 250 AGE: 2
5
4
3
2
1
0
Std. Dev = .02
Mean = .033
N = 13.00
SV
.0600
.0550
.0500
.0450
.0400
.0350
.0300
.0250
.0200
.0150
.0100
.0050
0.0000
P: 1000 AGE: 2
14
12
10
8
6
4
2
0
Std. Dev = .01
Mean = .0218
N = 109.00
SV
.0450
.0425
.0400
.0375
.0350
.0325
.0300
.0275
.0250
.0225
.0200
.0175
.0150
.0125
.0100
.0075
.0050
.0025
0.0000
P: 5000 AGE: 2
140
120
100
80
60
40
20
0
SV
.0475
.0450
.0425
.0400
.0375
.0350
.0325
.0300
.0275
.0250
.0225
.0200
.0175
.0150
.0125
.0100
.0075
.0050
.0025
0.0000
P: 10000 AGE: 2
500
400
300
200
100
0
Age Age Age Age 3333:::: Skew (0.799/0.616) = 1.3 Age Age Age Age 3333:::: Skew (0.934/0.231) = 4.0 Age Age Age Age 3333:::: Skew (0.954/0.087) = 11.0 Age Age Age Age 3333:::: Skew (1.457/0.061) = 23.9
SV
.250.225.200.175.150.125.100.075.050
3.5
3.0
2.5
2.0
1.5
1.0
.5
0.0
Std. Dev = .08
Mean = .125
N = 13.00
SV
.225
.213
.200
.188
.175
.163
.150
.138
.125
.113
.100
.088
.075
.063
.050
.038
.025
.013
P: 1000 AGE: 3
20
10
0
Std. Dev = .04
Mean = .078
N = 109.00
SV
.113
.106
.100
.094
.088
.081
.075
.069
.063
.056
.050
.044
.038
.031
.025
.019
.013
.006
0.000
P: 5000 AGE: 3
120
100
80
60
40
20
0
Std. Dev = .02
Mean = .033
N = 792.00
SV
.125.113
.100.088
.075.063
.050.038
.025.013
0.000
P: 10000 AGE: 3
500
400
300
200
100
0
Age Age Age Age 4444:::: Skew (0.801/0.616) = 1.3 Age Age Age Age 4444:::: Skew (0.877/0.236) = 3.7 Age Age Age Age 4444:::: Skew (1.038/0.090) = 11.5 Age Age Age Age 4444:::: Skew (1.645/0.063) = 26.1
SV
.38.31.25.19.13.06
P: 250 AGE: 4
6
5
4
3
2
1
0
Std. Dev = .11
Mean = .17
N = 13.00
SV
.288.263
.238.213
.188.163
.138.113
.088.063
.038.013
P: 1000 AGE: 4
20
10
0
Std. Dev = .06
Mean = .105
N = 105.00
SV
.188.175
.163.150
.138.125
.113.100
.088.075
.063.050
.038.025
.0130.000
P: 5000 AGE: 4
120
100
80
60
40
20
0
Std. Dev = .04
Mean = .044
N = 742.00
SV
.225
.213
.200
.188
.175
.163
.150
.138
.125
.113
.100
.088
.075
.063
.050
.038
.025
.013
0.000
P: 10000 AGE: 4
500
400
300
200
100
0
Age Age Age Age 5555:::: Skew (0.556/0.263) = 2.1 Age Age Age Age 5555:::: Skew (1.365/0.094) = 14.5 Age Age Age Age 5555:::: Skew (2.054/0.066) = 31.1
SV
.500
.475
.450
.425
.400
.375
.350
.325
.300
.275
.250
.225
.200
.175
.150
.125
.100
.075
.050
.025
P: 1000 AGE: 5
14
12
10
8
6
4
2
0
Std. Dev = .10
Mean = .192
N = 84.00
SV
.400.375
.350.325
.300.275
.250.225
.200.175
.150.125
.100.075
.050.025
0.000
P: 5000 AGE: 5
140
120
100
80
60
40
20
0
SV
.400.375
.350.325
.300.275
.250.225
.200.175
.150.125
.100.075
.050.025
0.000
P: 10000 AGE: 5
400
300
200
100
0
Figure 3.8: Size frequency histograms and skewness values for stand stem volumes of E. grandis from 1-4 years for planting density (a) 250 st/ha, and from 1-5 years for planting densities (b) 1,000 st/ha, (c) 5,000 st/ha, and (d) 10,000 st/ha. Skewness was significant where (skewness/standard deviation) > 2. Arrows indicate possible bimodality in the size distribution.
In bimodality there was some indication that a double peak in the size distribution was
beginning to occur at 4-5 years in 5,000 st/ha and at 5 years in 10,000 st/ha (Figure 3.8). The
evidence, however, was not strong, and therefore the hypothesis that bimodality in the size
distribution of stand stem volumes will increase in response to increased planting density
could not be confirmed.
Chapter 3 Stand Growth and Structure Page 32
STAND STEM VOLUME SIZE INEQUALITY
Lorenz curves provide a measure of inequality and were constructed by ranking stem volumes
from smallest to largest and then plotting the cumulative increase in percentage stand stem
volume against the cumulative increase in percentage stand stem count. If trees in the stand
were perfectly uniform the curve would follow the Lorenz curve for equality, all trees being
of equal size. The extent to which the stand curve digressed from the equality curve was an
indication of the inequality in stem size between stems in the stand. Lorenz curves of stand
stem volume were generated at each age for each planting density (Figure 3.9).
0.0
0.5
1.0
0.0 0.5 1.0Cu
mu
lati
ve P
rop
ort
ion
of
Sta
nd
Ste
m V
olu
me
Lorenz Curve for Equality Lorenz Curve for 250 st/ha Lorenz Curve for 1,000 st/ha Lorenz Curve for 5,000 st/ha Lorenz Curve for 10,000 st/ha
0.0 0.5 1.0
5 years old
0.0 0.5 1.0
3 years old
0.0 0.5 1.0
4 years old
0.0 0.5 1.0
2 years old1 year old
Cumulative Proportion of Stand Stem Count
Figure 3.9: Lorenz curves for stand stem volumes of E. grandis from 1-4 years for planting density 250 st/ha, and from 1-5 years for planting densities 1,000 st/ha, 5,000 st/ha and 10,000 st/ha.
Increased departure from a uniform distribution is also indicated by a reduction in the gini-
coefficient (area under the stand Lorenze Curve/area under the equality Lorenze Curve) and
an increase in the coefficient of variation. The gini-coefficients and coefficients of variation
were calculated at each age and for each planting density (Table 3.4).
Table 3.4: Gini-coefficients and coefficients of variation for stand stem volumes of E. grandis from 1-4 years for planting density 250 st/ha, and from 1-5 years for planting densities 1,000 st/ha, 5,000 st/ha and 10,000 st/ha.
PLANTING DENSITY
ST/HA AGE 1 AGE 2 AGE 3 AGE 4 AGE 5
250 0.733 0.728 0.714 0.705 n/a(a)
1,000 0.677 0.720 0.709 0.708 0.717
5,000 0.651 0.617 0.615 0.534 0.489
GINI
COEFFICIENT
10,000 0.686 0.566 0.532 0.446 0.408
250 53.9% 55.8% 60.6% 61.6% n/a(a)
1,000 59.5% 52.8% 54.4% 53.9% 51.5%
5,000 65.1% 70.9% 70.3% 85.8% 97.7%
COEFFICIENT
OF VARIATION
10,000 58.6% 83.9% 89.8% 109.7% 124.4%
(a) Due to whole tree destructive sampling at 4 years, there were insufficient trees to adequately sample planting density 250 st/ha at 5 years.
Chapter 3 Stand Growth and Structure Page 33
The results show that all stands had a degree of inequality (gini-coefficient > 0), and this was
no surprise since some variability in size within each population was anticipated given the
expectation of a normal distribution. A pattern similar to that of skewness (Figure 3.8) was
evident since size inequality tended to increase with age and planting density. Planting
densities 250 st/ha and 1,000 st/ha had similar levels of size inequality which did not change
much over the period of measurement. In contrast planting densities 5,000 st/ha and 10,000
st/ha started with similar inequality to the low planting densities, but then developed much
higher inequality over the period of measurement. Again this was indicative of little
competition in the low planting densities graduating to intense competition in the high
planting densities, and it confirmed the hypothesis that stand stem volume size inequality
would increase in response to increased planting density.
Overall the results for stand structure reveal evidence of stronger competition occurring in the
higher planting densities, as shown by increased skewness and increased size inequality. This
information, however, did not reveal how the most dominant stems compare across planting
densities or if the structural differences between planting densities was reflected in different
structures in terms of the number of dominance classes. Further investigation of stand
structure was required.
STAND DOMINANCE CLASSES
It was hypothesised that the size of stems in the primary dominance class (dominant stems)
would not change in response to competition intensity (planting density). This hypothesis was
investigated by ranking stem volumes and dividing them into 250 and 1,000 stem cohorts,
with the result that the 1,000 st/ha planting density had four groups in the 250 stem cohorts
(1,000/250 = 4) and one group in the 1,000 stem cohorts (1,000/1,000 = 1), and so on for
other planting densities. Mean stem volume for the whole stand and for the largest (dominant)
250 and 1,000 stem cohorts were plotted against age for each planting density (Figure 3.10).
The results show that mean stem volume of the largest (dominant) cohort increased as the
number of stems included in the cohort was reduced from 1,000 to 250 (Figure 3.10 (a-c)).
Mean stem volume in planting density 250 st/ha did not change between cohorts since each
included all stems in the 250 st/ha planting density. Similarly, mean stem volume in planting
density 1,000 st/ha did not change between the whole stand and the dominant 1,000 stem
cohort since both cohorts included all stems in the 1,000 st/ha planting density.
Chapter 3 Stand Growth and Structure Page 34
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0 1 2 3 4 5
Me
an
Ste
m V
olu
me (
m3/h
a)
250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
0 1 2 3 4 5Age (yrs)
(b) Dominant 1,000 Stem Cohort
0 1 2 3 4 5
(c) Dominant 250 Stem Cohort(a) Whole Stand Cohort
Figure 3.10: Mean stem volume of E. grandis from 1-4 years for planting density 250 st/ha, and from 1-5 years for planting densities 1,000 st/ha, 5,000 st/ha and 10,000 st/ha for (a) the whole stand cohort, (b) the dominant 1,000 stem cohort, and (c) the dominant 250 stem cohort.
It is of interest that there was little difference in the mean stem volume of the dominant 1,000
stem cohort between planting densities 1,000 st/ha and 10,000 st/ha (Figure 3.10(b)). If one
considers the dominant 1,000 stem cohort to be representative of dominant stems during the
period of measurement (which assumes that all stems in planting density 1,000 st/ha had a
dominant status), then the above shows that the primary dominance class does not change in
response to planting density. It is possible, however, that competition was starting to have a
restrictive effect on the higher planting densities since the dominant 1,000 stem cohort in
planting density 1,000 st/ha was beginning to increase in size compared to planting densities
5,000-10,000 st/ha. Nevertheless the difference in the mean stem size of dominants between
planting densities was remarkably small given the extreme levels of competition that were
present in the higher planting densities (Figures 3.8-3.9), providing strong evidence that
competition is asymmetric in that dominant stems tend to capture the amount of resources
required leaving intermediate and suppressed stems to cope with resource restrictions.
In comparison to the dominant 1,000 stem cohorts (Figure 3.10(b)), the dominant 250 stem
cohorts (Figure 3.10(c)) show that planting density 1,000 st/ha was of greater similarity to 250
st/ha than 5,000-10,000 st/ha. This was the more expected result since planting densities 250-
1,000 st/ha had similar measures of competition intensity; however it did raise the question as
Chapter 3 Stand Growth and Structure Page 35
to why this did not also occur in the dominant 1,000 stem cohorts (Figure 3.10(b)). The most
obvious answer was that size variability within the dominant 1,000 stem cohorts was greater
in planting density 1,000 st/ha than in 5,000 or 10,000 st/ha, so when the smallest 750 st/ha
were removed from the dominant 1,000 st/ha cohort to create the dominant 250 stem cohort,
there was a greater increase in mean stem size for 1,000 st/ha than for 5,000 st/ha or 10,000
st/ha (Figure 3.10(b,c)). This suggested that planting density 1,000 st/ha had multiple
dominance classes, despite relatively low measures of skewness and size inequality.
The above findings revealed that dominance classes changed between planting densities, and
their definition therefore required a physiologically meaningful division between classes
rather than the application of absolute numbers. A closer examination was consequently made
of the 250 stem cohorts to determine if stand structure could be better defined into dominance
classes. The examination was made on stem volume increment since rate of growth was
considered a flexible indicator of dominance and suppression over time (Figure 3.11).
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0 1 2 3 4 5
Me
an
Ste
m V
olu
me
MA
I (m
3y
r-1)
1,000 st/ha (1)1,000 st/ha (2)1,000 st/ha (3)1,000 st/ha (4)
0 1 2 3 4 5
Age (yrs)
5,000 st/ha (1)5,000 st/ha (2)5,000 st/ha (3)5,000 st/ha (4)5,000 st/ha (5)5,000 st/ha (6)5,000 st/ha (7)5,000 st/ha (8)5,000 st/ha (9)5,000 st/ha (10)5,000 st/ha (11)5,000 st/ha (12)5,000 st/ha (13)5,000 st/ha (14)5,000 st/ha (15)5,000 st/ha (16)5,000 st/ha (17)5,000 st/ha (18)5,000 st/ha (19)5,000 st/ha (20)
(b) 5,000 st/ha
37%
0 1 2 3 4 5
10,000 st/ha (1)10,000 st/ha (2)10,000 st/ha (3)10,000 st/ha (4)10,000 st/ha (5)10,000 st/ha (6)10,000 st/ha (7)10,000 st/ha (8)10,000 st/ha (9)10,000 st/ha (10)10,000 st/ha (11)10,000 st/ha (12)10,000 st/ha (13)10,000 st/ha (14)10,000 st/ha (15)10,000 st/ha (16)10,000 st/ha (17)10,000 st/ha (18)10,000 st/ha (19)10,000 st/ha (20)10,000 st/ha (21)10,000 st/ha (22)10,000 st/ha (23)10,000 st/ha (24)10,000 st/ha (25)10,000 st/ha (26)10,000 st/ha (27)10,000 st/ha (28)10,000 st/ha (29)10,000 st/ha (30)10,000 st/ha (31)10,000 st/ha (32)10,000 st/ha (33)10,000 st/ha (34)10,000 st/ha (35)10,000 st/ha (36)10,000 st/ha (37)10,000 st/ha (38)10,000 st/ha (39)10,000 st/ha (40)
(c) 10,000 st/ha
32%
(a) 1,000 st/ha
38%
Figure 3.11: The mean annual increment (MAI) in mean stem volume of 250 stem cohorts of E. grandis from 1-5 years for planting densities (a) 1,000 st/ha, (b) 5,000 st/ha, and (c) 10,000 st/ha. Red lines indicate 250 stem cohorts declining in mean stem volume MAI between 4-5 years (the end of the measurement period), noting that MAI decline between 3-4 years was ignored in this classification since this was a drought year. At 3 years (prior to the drought) arrows mark the stem cohort below which mean stem volume MAI has been shown to decline by 5 years, and the percentage growth rate compared to the dominant 250 stem cohort is indicated. Stars indicate 250 stem cohorts which cease to exist due to mortality, and the age by which they perish.
Chapter 3 Stand Growth and Structure Page 36
A striking aspect of the above results was that some cohorts in planting density 1,000 st/ha
were declining in productivity (Figure 3.11(a)), suggesting that these trees were experiencing
competitive effects despite previous indications that competition intensity was low in planting
density 1,000 st/ha. Also striking was the similarity between planting densities in the cohorts
which were declining in productivity by 5 years, as most of these began their decline in MAI
at 3 years (Figure 3.11) coinciding with the drought at 3 years (Figure 2.5). The benchmark
for declining MAI was similar for all planting densities in that any cohort with a growth rate
of approximately 35% or less of the largest cohort at 3 years would decline in MAI from 3
years onwards (Figure 3.11), suggesting a structural significance to the point at which stems
began to decline in productivity. This may explain why the slowest growing cohorts in
planting density 1,000 st/ha were declining in MAI by 4-5 years, despite lower evidence of
competition in that stand.
It was noteworthy that the few cohorts that declined in productivity during the drought
recovered increasing MAI in the following year. This provided evidence that stems find it
difficult to recover having lost their competitive edge, thereby corroborating the theory that
asymmetric competition generally causes trees to maintain or decrease in dominance status
rather than increase in dominance status.
The similarities between planting densities in the pattern of change in cohort productivity over
time provided evidence that every stand had multiple dominance classes, including planting
density 1,000 st/ha which had shown little previous evidence of competition. The meaningful
definition of these dominances classes, however, required relative rather than absolute
definition so that the number of cohorts in dominance classes could change between planting
densities and over time. In this case differences between cohorts in the rate of change in MAI,
as indicated by differences in the slope of the MAI line from one year to the next (Figure
3.11), were used to define cohorts into dominance classes. For example the dominant class
was defined as the dominant cohort, plus any cohort increasing in MAI within 90% of the
dominant cohort. The co-dominant class was defined as cohorts increasing in MAI at 50% to
90% of the rate of the dominant class, the suppressed class as cohorts increasing in MAI at
10% or below the rate of the dominance class, leaving an intermediate class defined as
increasing in MAI at 10% to 50% of the rate of the dominant class (Figure 3.12).
Chapter 3 Stand Growth and Structure Page 37
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0 1 2 3 4 5
Me
an
Ste
m V
olu
me M
AI (m
3y
r-1)
0 1 2 3 4 5
Age (yrs)
(b) 5,000 st/ha
0 1 2 3 4 5
(c) 10,000 st/ha(a) 1,000 st/ha
Dominant Co-Dominant Intermediate Suppressed
Figure 3.12: The mean annual increment (MAI) in the mean stem volume of 250 stem cohorts of E. grandis from ages 1-5 years for planting densities (a) 1,000 st/ha, (b) 5,000 st/ha, and (c) 10,000 st/ha. The rate of change in mean stem volume MAI is indicated by the slope of the MAI line from one year to the next. Dominance classes are shown at 3 years and 5 years and consist of stems increasing in MAI at 100-90% (dominant), 90-50% (co-dominant), 50-10% (intermediate) and <10% (suppressed) of the rate of the largest 250 stem cohort during the previous year.
The above definition illustrates the traditional idea of dominance classes, and again showed
that cohorts tended to maintain or decline in dominance rather than rise (Figure 3.12). In
addition, higher planting densities had a propensity for more stems within each class,
including the higher dominance classes. It is possible that higher planting densities had
greater site occupancy, collected more resources and could therefore ‘afford’ to have a greater
number of larger stems, or alternatively trees in higher planting densities might have used
resources more efficiently, possibly triggered by higher competition intensity, allowing them
to grow larger. A third scenario is simply that higher planting densities had a greater number
of stems with ‘dominant’ genes since they had a greater initial population, thereby resulting in
a greater number of larger trees. A comparison of the difference in tree morphology between
planting densities might provide an indication of which is the case.
In addition to providing insight to stand dominance dynamics, the above results provide
evidence that declining stand productivity could be attributed to stand structural changes.
Clearly a large ratio of stems declining in MAI could cause total stand MAI to decline, even if
the dominant cohort were still increasing in MAI. It has been noted that the decline in the
Chapter 3 Stand Growth and Structure Page 38
productivity of smaller stems could be due to reduced resource use efficiency compared to
larger stems; however it may just be a case of reduced resource capture due to competition. A
comparison of the difference in tree morphology between dominant and suppressed stems
may shed light on the mechanism controlling declining productivity in trees.
Overall, the examination of productivity in the 250 stem cohorts illustrates the feedback
effects of stand growth and structure upon each other under the theory of asymmetric
competition. We start with stand structure; a certain number of stems in approximately normal
size distribution. Growth then occurs according to the capacity of the site and the level of site
occupancy, but is captured by stems dependent on their relative size due to asymmetric
competition, with the largest stems generally avoiding resource restrictions. This then changes
the structure towards positive skewness and increased inequality in the size distribution,
which increases the effects of asymmetric competition causing greater suppression in the
smaller stems and possibly declining mean growth in the whole stand due to slowed growth in
smaller stems. This effect may be exacerbated if the smallest stems are persistent (live but not
growing), as suggested by the results for stand mortality, as this has the effect of
mathematically reducing mean growth.
Chapter 3 Stand Growth and Structure Page 39
3.5 Summary
The examination of stand growth has shown that eucalypt plantations established at high
planting densities (5,000-10,000 st/ha) have the potential to capture carbon in their stems
twice as quickly as those planted at the standard 1,000 st/ha. Mean and total stand growth
measures, however, did not provide good information about stand structure.
Closer examination of stand structure showed evidence of strong competition occurring in the
higher planting densities as shown by increased skewness in size distribution and increased
size inequality. Dominant stems were shown to be remarkably similar in size across planting
densities despite a very large difference in the competition intensity apparent in the stands,
and higher density stands tended to have more stems in all dominance classes, not just the
smaller ones, suggesting either a greater capacity to ‘afford’ dominant stems through
increased site occupancy and/or increased resource use efficiency, or simply a greater number
of ‘dominant’ genes due to a greater initial population. A comparison of the difference in tree
growth and structure between dominant stems in different planting densities might provide an
indication as to which explanation is most likely.
Investigation of the growth rate of 250 stem cohorts revealed that a drought at 3 years
triggered declining growth rates in stem cohorts growing at less than 35% of the rate of the
dominant cohort, and that few stem cohorts recovered increased growth rates after the initial
drought-triggered decline. It had been noted that the decline in the productivity of smaller
stems could be due to reduced resource use efficiency compared to larger stems; however the
drought triggered decline suggests it may just be a case of reduced resource capture due to
competition. A comparison of the difference in tree growth and structure between dominant
and suppressed stems may shed light on the mechanism controlling declining productivity in
trees and stands.
The examination of stand growth and structure has shown that a more detailed investigation is
required of individual tree growth and structure, particularly in terms of comparing dominants
across planting densities and size inequality within planting densities, in order to shed light on
the mechanisms by which stand structural components affect stand growth and vice versa.
Chapter 4 Tree Growth and Structure Page 40
4. TREE GROWTH AND STRUCTURE
Stands comprise individual trees, and stand growth and structure is defined by the sum of
growth and structure of individual trees. Within the stand individual trees interact through
asymmetric competition, whereby larger trees capture a greater pool of resources relative to
their size and thereby grow at a greater relative growth rate compared to smaller trees.
Asymmetric competition causes stand growth and stand structure to change dynamically over
time since the relative growth rates of individual trees and the size difference between trees is
constantly changing (Watkinson et al. 1983; Specht 1985; Brand and Magnussen 1988;
Schwinning and Weiner 1998).
In addition to affecting relative growth rate, asymmetric competition affects tree structure.
Plants are thought to partition current growth amongst components to maximise future capture
of their scarcest resource (Weiner et al. 1990a), and trees experiencing a different balance of
resource capture will have different strategies for partitioning growth, and therefore a
different tree structure.
The comparison of tree growth and structure between planting densities will show how the
largest (dominant) trees differ due to different competitive stress between stands whereas the
comparison of tree growth and structure within planting densities will show how the largest
(dominant) trees differ to the smallest (suppressed) trees due to asymmetric competition.
4.1 State of Knowledge
4.1.1 Tree Growth
Tree growth is an increase in tree size, and is typically measured by increases in tree
dimensions, such as height, diameter and volume, and/or by increases in tree mass. Site
quality is one of the primary determinants of tree growth (productivity) since this determines
resource availability. On the vast majority of sites trees are subject to some sort of resource
deficiency resulting in reduced growth potential rather than any ill-health (Dell et al. 1995).
Site quality also affects tree productivity through soil quality (permeability for root expansion,
micro-organism presence for nutrient cycling, aeration for root respiratory gas exchange and
soil nutrient oxidation, and water retention for extending water availability) and climatic
conditions (affecting the rate of photosynthesis). With all else equal, improved site quality
will accelerate the rate of tree growth and therefore improve productivity.
Chapter 4 Tree Growth and Structure Page 41
Within the limits defined by site quality and genetic ability, tree growth follows a basic
process. The first step is primary growth, which is the lengthening of stem and branch shoots
as a result of cell division in specialised zones called meristems. Meristems are located at the
tips of all terminal shoots (apical meristems) and in the axil at which leaves join the stem
(axillary meristems) and consist of a rounded dome in which leaf primordia are located. As
the leaf primordia expand to form leaves, the dome expands upwards/outwards and
simultaneously forms new leaf primordia whilst lengthening the shoot (Wilson and White
1986; Salisbury and Ross 1992).
The most common measurement of primary growth is stem height. Many species exhibit slow
height growth in the preliminary establishment years (Oliver and Larson 1996), but eucalypts
are capable of very rapid height growth at an early age and the majority of plantation
eucalypts achieve their largest annual height increment before 5 years (Jacobs 1955; Opie et
al. 1978). The capacity of eucalypts to grow rapidly at an early age is due to their unique
naked bud system, which allows rapid crown expansion whenever environmental conditions
are suitable (Jacobs 1955; Florence 1996). Where other genera form resting buds at the end of
the growing season, in which a complete annual shoot is contained in embryonic form,
eucalypts form a naked bud which is capable of rapid development as soon as the parent leaf
unfolds and may form an indefinite number of leaves and shoots within any growth period. In
this way stem height growth may continue indefinitely whilst conditions remain favourable,
although in reality rapid expansion generally occurs in growth spurts (Jacobs 1955; Specht
1985; Florence 1996).
Shoot elongation from primary growth may also be measured by crown width, as measured by
the diameter of the widest part of the crown. As with stem height, crown width has the
capacity to expand rapidly due to the naked bud system, yet naked buds also have the effect of
restricting crown width due to a property known as crown shyness, whereby new shoots do
not form where crowns ‘brush’ together because naked buds are easily damaged by abrasion
(Jacobs 1955; Opie et al. 1978). Crown width in eucalypt species is therefore strongly
restricted by growing space (Opie et al. 1978; Laar and Bredenkamp 1979; Cameron et al.
1989; Zeide 1991).
Leaf formation from primary growth is usually measured by leaf mass (oven-dry) and leaf
area, which is the surface area of the upper side of leaves. Leaf mass provides information
Chapter 4 Tree Growth and Structure Page 42
about the amount of biomass dedicated to the photosynthate-producing component of the tree,
whereas leaf area provides information about the amount of light radiation being intercepted
for photosynthate production. The relationship between the two is positive but not constant
since leaves may vary in thickness in response to environmental conditions. Leaf area is
considered fundamental to understanding productivity since intercepted radiation is a primary
determinant of photosynthate production in individual trees and tree stands (Linder 1985;
Landsberg and Hingston 1996).
In contrast to primary growth, secondary growth is the lateral expansion (diameter growth) of
the shoots. Secondary growth occurs in the lateral meristem, which forms a thin and
uninterrupted layer between the wood and bark of stems. The lateral meristem consists of two
layers; the vascular cambium which is adjacent to the wood and the cork cambium which is
adjacent to the bark.
Wood growth initiates with the division of cambial initials in the vascular cambium. Cambial
initials adjacent to the wood core elongate and expand into wood cells, resulting in increased
diameter growth of the wood core, whilst cambial initials adjacent to the cork cambium
continue to divide, allowing the vascular cambium to ‘stretch’ around the wood growth
(Wilson and White 1986; Salisbury and Ross 1992). Bark growth follows a similar process in
the cork cambium, however, where new wood cells build upon previous wood layers, new
bark cells form under previous bark layers and must ‘stretch’ the outermost layers in order to
accommodate their own growth as well as the increased diameter of the wood core (Wilson
and White 1986; Salisbury and Ross 1992). Eucalypt species have distinct methods to
‘stretch’ the outermost bark layers, including fibrous bark that expands, bark that splits, and
bark that sheds to remove the outermost layer altogether (Jacobs 1955). The combined effects
of bark ‘stretch’ and weathering on bark ensure that whilst both wood and bark grow for the
life of the tree, the bark layer exhibits a relatively constant width compared to the growing
width of the wood core.
Stem diameter is the most common measure of secondary growth, and indeed the most
common of any measure made on trees. Stem diameter is usually measured as stem diameter
at breast height (DBH), which is the diameter of the whole stem at 1.3 m stem height, although
it is also common for the measurement to be made of the wood only.
Chapter 4 Tree Growth and Structure Page 43
Common measurements of the combined affects of primary and secondary growth (expansion
in length and width) include stem volume, stem mass and branch mass. Stem volume is
usually measured when the stem is green and is calculated from a number of stem diameter
measurements and stem height, since direct measurement of stem volume is difficult. Stem
volume has a positive relationship with both stem diameter and stem height; however the
relationship is not constant due to changes in stem shape. Mass is usually assessed by oven-
dry mass to calculate the amount of biomass (not including water) that is included in the
component under investigation. The mass of the stem is often partitioned into that of
stemwood and stembark, whereas branch mass is almost always measured as the combined
mass of branchwood and branchbark due to the mechanical difficulty of separating the two.
Stem mass shares a positive relationship with stem volume since larger stems are usually
heavier, however the relationship is not constant as wood density changes according to
species, age and growth rate.
EFFECT OF COMPETITION ON TREE GROWTH
Competition has the effect of limiting resource availability, and the productivity of a tree in a
competitive environment is dependent on its ability to maintain access to the resources
required to produce photosynthate. Resources like rainwater, nutrients, and carbon are
relatively evenly distributed between neighbouring trees, and the ability to capture these
resources is likely to be in some sort of ratio to the size of the organ collecting the resource. In
comparison, saturated light has an asymmetric distribution (radiating down from above) and
may be largely intercepted by a more dominant tree with greater crown height. Similarly,
water table moisture may be considered to have an asymmetric distribution (permeating up
from below) and may be largely intercepted by a more dominant tree with greater root depth.
For resources with asymmetric distributions, the position of individual trees in relation to
neighbouring trees is of vital importance since this affects relative resource capture and
subsequent photosynthate production. The effect of tree position on relative resource capture
(and relative growth rate) is amplified as competition for resources with asymmetric
distributions increases (Kuppers 1989; Oliver and Larson 1996) particularly in shade-
intolerant genera like Eucalyptus.
Studies of the effects of competition on primary growth show that mean stem height is
reduced by increased stocking density (Laar and Bredenkamp 1979; Bredenkamp 1987;
Cameron et al. 1989; Coetzee 1995; Coetzee et al. 1996; Bernardo et al. 1998; Coetzee and
Chapter 4 Tree Growth and Structure Page 44
Naicker 1998a; Coetzee and Naicker 1998b; Coetzee 1999), and the ratio of mean stem height
to mean stem diameter is increased by increased stocking density (Opie et al. 1978; Drew and
Flewelling 1979; Cameron et al. 1989; Schonau and Coetzee 1989; Coetzee 1995; Bi and
Turvey 1996; Pinkard and Neilsen 2003), as stems essentially become more elongated or less
tapered. Increased competition due to increased stand density also results in decreased mean
crown width (Opie et al. 1978; Laar and Bredenkamp 1979; Cameron et al. 1989; Zeide 1991)
and decreased mean leaf mass (Henskens et al. 2001; Pinkard and Neilsen 2003) due to crown
shyness in eucalypt trees. Correspondingly increased competition due to stocking density
results in reduced mean leaf area, as has been found in E. nitens (Medhurst and Beadle 2001;
Pinkard and Neilsen 2003), E. globulus (Henskens et al. 2001) and E. grandis (Leite et al.
1997).
A striking feature of stem height growth in even-aged eucalypt stands is that the height of
dominant trees will increase at approximately the same rate regardless of the level of
competition, provided the sites have similar resource availability (Pinkard and Neilsen 2003).
As a result of this relationship the stem height of the dominant trees in even-aged stands is
used as a reliable measure of site quality regardless of stocking density (Coetzee et al. 1996;
Oliver and Larson 1996; Coetzee and Naicker 1998a; Coetzee and Naicker 1998b), with the
restriction that stem height must be measured at a reasonably advanced age (typically 5 years)
to reduce errors attributable to short term climatic fluctuations affecting height growth, such
as drought or unseasonably cold temperatures. These findings indicate that the commonly
reported reduction in mean stem height with increased stocking density is probably due to a
greater number of shorter trees rather than a reduction in stem height growth in all trees.
The properties of primary growth are often reflected in secondary growth since the rate of
secondary growth in the stem and branches is dependent on the productivity and structural
requirements of the crown. This is the case for stem diameter, for which investigations of the
effect of competition on stem diameter show that increased stocking density results in
decreased mean stem diameter (Hart 1928; Reineke 1933; O'Connor 1935; Jacobs 1955; Yoda
et al. 1963; White and Harper 1970; Bowersox and Ward 1976; Belanger and Pepper 1978;
Opie et al. 1978; Laar and Bredenkamp 1979; Geyer 1981; Laar 1982; Baker and Attiwell
1984; Weiner 1985; Bredenkamp 1987; Brand and Magnussen 1988; Kohyama and Hara
1989; Bredenkamp and Burkhart 1990b; Ralph 1990; Coetzee 1995; Merriam et al. 1995; Bi
and Turvey 1996; Lee 1996; Bouvet 1997; Gerrand et al. 1997; Leite et al. 1997; Bernardo et
Chapter 4 Tree Growth and Structure Page 45
al. 1998; Pinkard and Neilsen 2003). Whilst universally reported, the knowledge that
increased competition decreases mean stem diameter is not always useful, as it is a gross
summary of stand structure that leaves many unanswered questions. For example, is the
reduction in mean stem diameter with increased competition caused by a reduction in stem
diameter in all trees or is it caused by a greater quantity of smaller trees or a combination of
the two? To what extent in self-thinning stands is mean stem diameter increment due to the
death of the smallest trees (thereby mathematically increasing mean stem diameter) and to
what extent is it due to growth in surviving stems? Such questions are pertinent to
understanding how trees and stands survive and mitigate the effects of competition, yet they
are rarely addressed.
Some studies begin to answer the above questions, showing that the diameter growth of the
largest (dominant) stems continues despite intense competition (Bredenkamp and Burkhart
1990b), and that where stem diameter growth of dominant trees is reduced by competition, the
effects of competition are asymmetric, in that dominant trees are less affected by competition
than suppressed trees (Weiner 1985; Brand and Magnussen 1988; Battaglia 2001). Further
information on how increased competition affects dominance classes, particularly the most
dominant class, is certainly pertinent to understanding the development of stand structure.
As with stem diameter, increased competition due to stocking density results in decreased
stand mean stem volume (Opie et al. 1978; Drew and Flewelling 1979; Cameron et al. 1989;
Schonau and Coetzee 1989; Coetzee 1995; Bi and Turvey 1996; Pinkard and Neilsen 2003).
One can also expect mean stem mass (Pinkard and Neilsen 2003) and mean live branch mass
(Henskens et al. 2001; Pinkard and Neilsen 2003) to decrease as competition increases due to
stocking density.
It can be concluded that the general effect of increased competition due to stocking density is
to reduce mean tree growth and therefore mean tree size. As previously emphasized, this
finding provides little information about size differences between dominants growing in
different levels of stand competition intensity, or about size differences between dominants
and suppressed trees growing in the same stand competition intensity.
Chapter 4 Tree Growth and Structure Page 46
4.1.2 Tree Structure
Like the vast majority of terrestrial plants, trees possess leaves, roots and stems. Unlike a
large proportion of plants, trees possess a woody stem and a mature height of at least 9
metres. Yet within these parameters trees have developed many different structures, the
variety of which forms the basis for taxonomic division.
Tree structure may be regarded as the general shape or form of the tree, and the size of
various tree components in relation to each other. Tree growth affords the opportunity for tree
structure to change, and tree productivity will determine the rate of change in tree structure.
Just as tree growth is capable of dynamic responses to growing conditions, tree structural
development is capable of plastic responses to growing conditions. The extent of structural
development in eucalypts will vary widely due to the physiological responses of trees to
changes in their environment, particularly to changes in competition due to stocking density
(Opie et al. 1978). Many of the physiological responses of trees to changes in their
environment are known as growth habits, and were first comprehensively described for
eucalypts by Jacobs (1955).
The tree component most affecting crown structure is the branches. Branch formation (the
number of branches formed per unit of shoot elongation) has been found to be under strong
genetic control (Pinkard and Neilsen 2003), suggesting that it is possible to select trees that
form fewer branches to reduce the negative effects of branches on wood quality, and/or
minimise the costs of removing branches. The potential danger of this approach, however, is
that trees with more branches might have more leaves and greater growth rates, and selection
for fewer branches might therefore result in a loss of productivity. Examination of branch
formation in trees with different growth rates would provide evidence of a relationship
between branch formation and productivity.
Crown structure subsequent to branch formation is principally measured by the vertical
distribution of branches, which is strongly affected by shade tolerance and branch shedding.
Shade tolerance is the ability of trees to survive and grow in low light. Eucalypts are
predominantly shade-intolerant compared to other genera (Opie et al. 1978; Montagu et al.
2003), as indicated by a large drop in the photosynthetic rate of eucalypt leaves when they
lose light saturation (Leuning et al. 1991b; Leuning et al. 1991a; Pereira et al. 1992; Sands
1996). Individuals will rapidly become suppressed when subjected to shade (Schonau and
Chapter 4 Tree Growth and Structure Page 47
Coetzee 1989), and when leaves are unable produce sufficient photosynthate for their own use
from the available light (the light compensation point) the leaves and ultimately the branch
dies (Givnish 1988).
Branch shedding is the process by which lower branches die and are ejected from the stem
(Jacobs 1955). Branch shed is prevalent in dense stands since it is driven by shade intolerance
(Opie et al. 1978; Givnish 1988), but it may also be accelerated by competition for other
resources (Pereira et al. 1989; Cromer et al. 1993). In this case limited nutrients and/or water
are preferentially allocated and/or reallocated to light saturated leaves in the upper crown
(Field and Mooney 1983; Pereira 1990; Sands 1995) in response to their higher
photosynthetic rates (Leuning et al. 1991b; Leuning et al. 1991a; Pereira et al. 1992; Sands
1996). Lower leaves may be above the light compensation point but there are insufficient
resources to allocate to the lower branches once the upper branches have been supplied, with
the result that lower branches are shed.
Vertical distribution in crown structure is most commonly measured by stem and crown
height. Stem height measures the height to which the crown apex has grown and crown
height, the distance between the stem base and the start of the live crown, measures the height
to which branches have shed. Crown depth is the length of the crown (stem height minus
crown height), and the crown depth ratio (ratio of crown depth to stem height) is indicative of
relative crown retention. Crown structure may also be measured by crown form, which is the
general geometric shape of the crown. Eucalypt crowns which increase rapidly in height are
generally conical, whereas eucalypt crowns which increase slowly in height have a more
rounded appearance (Jacobs 1955; Florence 1996).
Another common measure of crown structure is leaf specific area, which is the ratio between
leaf area and leaf mass, and is a measure of the ‘thinness’ of leaves. High leaf specific area is
indicative of ‘thin’ leaves and may be considered a more efficient allocation of biomass since
a greater leaf area is created for a given amount of biomass. Low leaf specific area is
indicative of ‘thick’ leaves and is the result of natural selection in species originating from dry
and/or harsh habitats that minimises desiccation and damage to leaves at the expense of
reduced efficiency in biomass allocation (Specht and Specht 1999; Sefton et al. 2002).
Chapter 4 Tree Growth and Structure Page 48
Leaf nutrient content, the concentration of nutrients within the leaf, provides an indication of
the amount of nutrients captured by the tree, and is of interest as the photosynthetic capacity
of leaves is improved by high nutrient concentrations (Leuning et al. 1991a). Studies of leaf
nutrient content generally focus on the macronutrients nitrogen (N), phosphorus (P) and
potassium (K) as these nutrients are most likely to be in deficit and therefore of interest in
terms of ensuring their adequate supply for optimal growth. A study pooling the results of
three South African experiments measuring leaf nutrient concentrations in E. grandis
concluded that the optimum foliar nutrient concentrations for maximised growth are 2.8% N,
0.15% P, and 0.75% K (Herbert 1992), the values of which fall between the adequate ranges
reported for plantation eucalypts in Australia (Dell et al. 1995).
Nutrient concentrations may vary seasonally and with location in the crown (Grove et al.
1996). In young E. grandis, N and P were most concentrated towards the top of the crown and
then towards the sides (Leuning et al. 1991b), and in 4 year old Eucalyptus deglupta N, P and
K were most concentrated in the outer crown (Lamb 1976). These data suggest that mobile
nutrients are preferentially allocated to light-saturated leaves in the outer crown, particularly
towards the top. In this way carbon assimilation is maximised since the enhanced
photosynthetic capacity of nutrient rich leaves coincides with light saturation, when
photosynthesis is most efficient (Leuning et al. 1991b; Sefton et al. 2002; Macfarlane et al.
2004).
Whilst improved leaf nutrient content leads to increased photosynthetic capacity (Kirschbaum
and Tompkins 1990; Leuning et al. 1991b; Sheriff and Nambiar 1991; Kirschbaum et al.
1992; Sands et al. 1992; Misra et al. 1998), it is important to note that this is not the only
strategy by which an improved nutrient status may be used to increase carbon assimilation.
An increased nutrient supply can also cause an increased ratio of biomass allocated to the
crown (Cromer et al. 1984; Cromer and Jarvis 1990; Leuning et al. 1991b; Sheriff and
Nambiar 1991; Herbert 1992; Kirschbaum et al. 1992; Sands et al. 1992; Misra et al. 1998)
and increased leaf specific area (Cromer and Jarvis 1990; Kirschbaum and Tompkins 1990;
Kirschbaum et al. 1992; Sands et al. 1992), thereby increasing carbon assimilation through
increasing leaf area rather than by increasing photosynthetic capacity.
Like crown structure, stem structure also changes as the tree grows. On a macro-level, stem
structure is fairly simple, the stem essentially consisting of an elongated cylindrical cone of
Chapter 4 Tree Growth and Structure Page 49
wood surrounded by a layer of bark. Fast growing eucalypts usually exhibit a single, straight
stem as strong apical dominance results in a single, dominant, growth tip (Jacobs 1955; Opie
et al. 1978), however disturbance to the dominant growth tip, such as fire, physical breakage,
herbivory, or disease, can cause another shoot or shoots to establish dominance, which may
affect stem structure by causing bends, sweep and/or double leaders.
Stem form provides an indication of the shape of the stem. Stem form may be measured under
or over the bark and it is determined by the manner in which stem diameter changes with
height up the stem, also known as stem taper (West 2004). Increased stem taper results in a
more conical stem form, whilst decreased stem taper results in a more cylindrical stem form.
Increased stem taper is thought to be a response to increased need for mechanical support
against the bending stresses caused by wind since greater wind exposure results in increased
stem taper (Jacobs 1955; Valinger 1992; Osler et al. 1996). For a given large-end diameter,
logs with decreased taper exhibit higher conversion efficiencies.
Another structural attribute of stems is the proportion of the stem comprised of bark (bark
ratio), either in terms of mass or volume. In general, the bark ratio diminishes as stem size
increases (Schonau and Boden 1982; Negi et al. 1984), and it is an important consideration
when making stem measurements as ignoring it can result in significant errors in estimation,
particularly if the whole stem is assumed to consist of wood. Stemwood structure is also an
important consideration in the investigation of stem structure given the commercial
importance of the wood component of the tree and potential for the cellular structure of wood
to impact on wood properties. Due to the complicated nature of wood structure this aspect of
tree structure is addressed separately in Chapter 5 – Wood Growth and Structure.
The information available about root growth is the most limited of all the tree components due
to the difficulty and expense of measuring roots intensively. The few studies done on
plantation eucalypts show that if resource availability is reduced by decreased site quality,
then the proportion of biomass allocated to root biomass rather than aboveground biomass
(root:shoot ratio) increases (Reis et al. 1985; Misra et al. 1998), which mirrors findings in
temperate forest species (Vogt et al. 1997). If resource availability is restricted by increased
stocking density, the proportion of biomass allocated to roots generally decreases (Eastham
and Rose 1990; Bargali et al. 1992; Fabiao et al. 1995; Bernardo et al. 1998; Leles et al.
2001; Saint-André et al. 2005) (Figure 4.1).
Chapter 4 Tree Growth and Structure Page 50
0.0
0.2
0.4
0.6
0.8
1.0
0 500 1000 1500 2000 2500
Planting Density (st/ha)
Ro
ot:
Sh
oo
t R
ati
oE. camaldulensis; 3.4 yrs; SE Brazil (Bernardo et al. 1998) E. camaldulensis; 4.3 yrs; Brazil (Leles et al. 2001)
E. pellita; 3.4 yrs; SE Brazil (Bernardo et al. 1998) E. pellita; 4.3 yrs; Brazil (Leles et al. 2001)
E. urophylla; 3.4 yrs; SE Brazil (Bernardo et al. 1998) E. grandis; 2.5 yrs; SE QLD Australia (Eastham et al. 1990)
Figure 4.1: The effect of planting density on the root:shoot ratio in young eucalyptus plantations.
Most of the structural properties outlined for tree structure are allometric relationships.
Allometric relationships define tree structure by comparing the growth of different
components within one tree or group of trees. This removes the effect of absolute size in the
comparison between different trees (Ryan et al. 1997). It is well known that the relative
amount of biomass allocated to crown, stem and roots changes with factors such as age, site
quality and competition (Pereira et al. 1997). Many studies of plantation eucalypt growth
involve some sort of examination of allometric relationships, the reason for which may range
from the need to apply biomass findings to other trees based on a convenient measure like
stem diameter (Attiwill 1979), to an examination of the effect of silvicultural treatments on
growth and biomass distribution (Birk and Turner 1992; Bennett et al. 1997; Bernardo et al.
1998; Reed and Tomé 1998), or to find some sort of diagnostic relationship (Turner 1986;
Bargali et al. 1992) such as the relationship between stand net primary productivity and
projected leaf area and site quality. In most studies, however, the primary focus is mean tree
growth and little attention is paid to how structure differs between trees.
Resource use efficiency, the efficiency with which resources are used to sequester a given unit
of carbon, is another measure which removes the effect of absolute size. It is typically
investigated at the leaf level in terms of individual resources such as water, nutrients and light.
Studies of resource use efficiency at the leaf level show that it tends to increase as resource
Chapter 4 Tree Growth and Structure Page 51
availability decreases. Water use efficiency (photosynthesis/stomatal conductance of H2O) is
increased as water availability decreases (Farquhar et al. 1982; Farquhar et al. 1989; Ares and
Fownes 1999, 2000; Binkley et al. 2004; Wildy et al. 2004), and light use efficiency
(photosynthesis/units of photosynthetically active radiation) is increased as light quality is
reduced (Hoad and Leakey 1994; Binkley et al. 2004). The effect on nutrient use efficiency as
nutrient availability decreases is less clear. Decreased nutrient availability results in decreased
total leaf area and increased leaf specific area (Cromer and Jarvis 1990; Kirschbaum and
Tompkins 1990; Kirschbaum et al. 1992; McDonald et al. 1992; Wendler et al. 1995; Dewar
1996; Harrington et al. 2001; Binkley et al. 2004) so that leaf nutrient concentrations remain
relatively constant on a weight basis, however it is unclear how this effects nutrient use
efficiency (photosynthesis/nutrient concentration) due to changes in leaf specific area.
Whilst there is evidence to support the theory that resource use efficiency is inversely
proportional to resource availability for individual resources, it is unclear what the effect of
improved resource use efficiency in one resource has on the sum of the resource use
efficiency of all resources (Sefton et al. 2002). Evidence shows that water scarcity will cause
improved water use efficiency by increasing stomatal closure, yet this action must have the
inverse effect of reducing light use efficiency since photosynthesis is restricted whilst light is
still available, thus ‘wasting’ photosynthetically active radiation. At the leaf level it is
therefore difficult to determine whether the benefits of improved resource use efficiency in
one resource will be ‘cancelled’ by reduced resource use efficiency in a different resource.
Tree growth efficiency is the total resource use efficiency at the tree level, rather than
individual resource use efficiency at the leaf level. Tree growth efficiency is a measure of tree
vigour whereby the total amount of resources captured by the tree are compared to the total
amount of tree growth. Leaf area is generally used as the measure of resource capture as it is
indicative of light interception, water transpiration and nutrient availability (Stoneman and
Whitford 1995). The few studies that have specifically analysed tree growth efficiency
indicate that compared to trees with lesser resource capture, trees with greater resource
capture have either greater relative growth, and therefore better tree growth efficiency
(Stoneman and Whitford 1995; Binkley et al. 2002) or similar relative growth and therefore
similar tree growth efficiency (Kaufmann and Ryan 1986). These results suggest that the sum
of resource use efficiency tends to be reduced by restrictions in the supply of individual
resources.
Chapter 4 Tree Growth and Structure Page 52
EFFECT OF COMPETITION ON TREE STRUCTURE
Branch formation (the number of branches formed per unit of shoot elongation) is unaffected
by competition, as shown in a study of E. nitens in which increased competition due to
planting density had no effect on the number of branches per unit of crown length (Pinkard
and Neilsen 2003). This finding is noteworthy as it shows that trees start off with a base
crown structure dependent on genetic properties, and that subsequent differences in crown
structure are due to branch growth and branch shed rather than branch formation.
Increased competition for light generally results in greater branch shed and therefore higher
mean crown height in eucalypts (Cameron et al. 1989; Henskens et al. 2001; Pinkard and
Neilsen 2003). There is some suggestion that crown height in dominant trees may be less
affected by competition than crown height in sub-dominant trees. Studies of 15 to 25 year old
E. delegatensis and E. regnans established in spacings of 200 to 2000 stems ha-1
(Hastings
and Opie 1974), and 11.5 year old E. pilularis established from 121 to 1250 stems ha-1
(Opie
et al. 1978), indicate that the crown height (log length) of dominants only increases up to
planting density of 400-500 st/ha, whereas mean crown height continues to increase
indefinitely with competition intensity. These findings imply that competition for resources,
rather than the light compensation point, is driving branch shed, since crown height would be
equal for all trees based on ambient light conditions in the canopy if the light compensation
point were driving branch shed.
Increased competition due to stocking density causes a decrease in the mean crown depth
ratio, as shown in E. grandis (Laar and Bredenkamp 1979), E. nitens (Medhurst and Beadle
2001), E. globulus (Pinkard and Neilsen 2003) and E. marginata (Jarrah) (Stoneman and
Whitford 1995). As mean crown depth ratio is considered a strong indicator of the
competitive stress experienced by stands, it has been suggested to use it as an indicator for
scheduling thinning operations (Hughes 2000). Within a given competition intensity,
dominant trees exhibit a greater crown depth ratio than suppressed trees (Stoneman and
Whitford 1995; Medhurst et al. 1999).
Increased competition skews mean foliage distribution upwards on the stem, as found in
investigations of E. nitens (Medhurst and Beadle 2001; Pinkard and Neilsen 2003), indicating
a less conical shape. The extent of this skewness is greater for suppressed individuals than
Chapter 4 Tree Growth and Structure Page 53
dominant individuals (Pinkard and Neilsen 2003), which is consistent with the general
consensus that crowns growing slower in height are less conical.
For leaf specific area increased competition due to planting density was found to have no
effect on leaves developing in the upper crown in 6 year old E. globulus (Macfarlane and
Adams 1998) and 7 year old E. nitens (Pinkard and Neilsen 2003), which is the expected
result for leaves of the same species forming in the same evaporative climate. A study of E.
nitens plantations found that leaves formed in the upper crown zone exhibited a lower leaf
specific area than leaves formed in the lower crown zone (Medhurst et al. 1999), and similar
results were found in separate studies including eucalypt species (Poorter and Evans 1998;
Evans and Poorter 2001), indicating that leaf specific area is reduced by increased exposure at
the time of leaf formation. In consequence, increased competition is expected to result in
increased leaf specific area in the mid and lower canopy due to increased ‘crowding’ and
shading of leaves in the canopy.
There are few studies specifically investigating the effect of competition on leaf nutrient
content in eucalypts, although some idea can be drawn from existing knowledge. In general,
increased stocking density will lead to greater competition for site resources and therefore
reduced average nutrient availability per stem. Previous findings have shown that tree
canopies respond to reduced nutrient availability in one of three ways: (i) decreased leaf
nutrient concentration, (ii) decreased total leaf biomass and/or (iii) decreased leaf specific
area. It is not always clear which is strongest or has the greatest affect on plant growth. One
study of nitrogen in E. grandis seedlings concluded that changes in total leaf biomass and leaf
specific area, or crown structure, correlated more strongly with plant growth than changes in
leaf nutrient concentration and photosynthetic capacity (Cromer and Jarvis 1990).
Mean stem taper has been found to decrease with increased competition due to stocking (Opie
et al. 1978; Cameron et al. 1989; Schonau and Coetzee 1989; Coetzee 1995; Bi and Turvey
1996; Coetzee and Naicker 1998b). This is probably a consequence of increased competition
resulting in reduced mean crown size (less crown for wind to catch) and more shelter from
neighbours, and therefore less requirement for mechanical support against bending stresses
caused by wind. This finding suggests that crown size relative to stem height has some affect
on stem taper. Studies of the effects of pruning (essentially reducing the crown size relative to
stem height) on stem taper partially support this idea, in that pruning was found to reduce
Chapter 4 Tree Growth and Structure Page 54
stem taper in conifers by causing an upward shift in relative stem growth (Pinkard and Beadle
2000). Pruning 50% of the live crown in E. nitens, however, had little effect on stem taper
(Pinkard et al. 1998).
There are no studies specifically investigating the effect of competition on bark ratio in
eucalypts, however studies of American sycamore (Platanus occidentalis L.) show that bark
ratio (for a given stem size) was unaffected by increased competition intensity due to planting
density (Saucier et al. 1972; Wittwer et al. 1978), implying a strong relationship between
stem size and bark ratio. It might therefore be expected that increased competition due to
planting density will result in a greater mean bark ratio (due to the presence of a greater
number of smaller stems), but no difference in bark ratio between dominant stems of a similar
stem size.
In summary, the existing understanding of tree structure shows that some components of tree
structure change considerably in response to competition, and others do not appear to respond
greatly to competition. The degree of confidence in these findings also varies according to the
amount of information available and how conflicting it is. Competition appears to have no
affect on branch formation, leaf specific area or leaf nutrient content in exposed leaves,
however increased competition results in decreased branch growth, increased branch shed,
decreased crown depth ratio, a more cylindrical crown form, increased mean leaf specific area
in the mid and lower canopy and possibly decreased leaf nutrient concentrations in the mid
and lower canopy. Competition has no effect on the proportion of wood or bark for a given
stem size, however increased competition does increase the stand mean bark ratio due to
increasing the ratio of smaller trees, and it encourages straight stem form and more cylindrical
stem form. Overall increased competition causes tree mass to be skewed away from the crown
(and probably the roots), and towards the stem. Whilst this pattern of change is generally well
known, the exact manner in which individual tree structure, coupled with relative size and
position in the stand, affects individual tree growth, is not clear.
Chapter 4 Tree Growth and Structure Page 55
4.2 Experimental Rationale
The effect of competition on tree growth and tree structure provides insight into interactions
between growth and structure and how the development of individual trees might collectively
affect stand development. Planting density is used to approximate the general level of
competition in the stand, and stem diameter is used to approximate the level of competitive
pressure experienced by individuals in the stand.
For a given stem diameter, increased planting density is expected to result in increased stem
growth and decreased crown growth, and therefore a skewing of tree structure towards the
stem rather than the crown (Table 4.1). The relative effect of planting density is expected to
decrease as stem diameter increases.
Table 4.1: Hypotheses of the effect of increased planting density on variables of tree growth and tree structure during early stages of stand development in sub-tropical E. grandis plantations.
Tree Variable Hypothesis
Tree Growth
Stem Increased planting density will result in increased stem growth (stem height, stem volume, stem mass) for a given stem diameter.
Crown Increased planting density will result in decreased crown growth (crown width, crown leaf area, crown mass) for a given stem diameter.
Tree Structure
Branch Number Increased planting density will have no effect on branch number.
Branch Shed Increased planting density will result in increased branch shed (increased crown height and decreased crown depth ratio).
Tree Form Increased planting density will result in decreased conicity of tree form (stem form and crown form).
Tree Mass Ratios Increased planting density will result in tree mass ratios skewed towards the stem.
Leaf Specific Area Increased planting density will have no effect on the leaf specific area of dominant trees, but will result in increased leaf specific area in suppressed trees.
Leaf Nutrient Content Increased planting density will result in decreased leaf nutrient content.
Chapter 4 Tree Growth and Structure Page 56
4.3 Methodology
To test the hypotheses a number of data were collected from the spacing trial, and in some
cases collected data were used to calculate estimated values of additional tree and stand
variables. The hypotheses for tree growth and structure were tested by statistical analysis of
collected and calculated data.
4.3.1 Sample Age and Size
In addition to data that were collected annually for every live tree in the whole trial (Table
3.2), data were collected from the spacing trial at 3 and 4 years from smaller samples (Table
4.2).
Table 4.2: The age and sample size of variables for which tree data were collected from the spacing trial.
Number of Trees Sampled Tree Variable
Age (yr) 250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
3 Year Old Measurements(a)
Stem Diameter at 1 m Height IntervalsBranch Formation
Crown Height3 8 8 8 8
4 Year Old Measurements(a)
Stem Diameter at 1 m Height IntervalsCrown Height
Crown DiameterTree Green Mass
Tree Sample Green MassStem Sample Diameter
Tree Sample Oven-Dry Mass Leaf Specific Area
4 8 20 20 20
Leaf Nutrient Content 4 8 8 8 8 (a)
A full description of the sample selection methods for 3 and 4 year old measurements are provided in Chapter 2 – The Spacing Trial, sub-sections 2.4.1 and 2.4.2 respectively.
4.3.2 Data Collection and Calculation
The method of collection or calculation for each tree variable outlined in Table 4.2 is
explained in the following paragraphs.
Stem Diameter at 1 m Stem Height Intervals – the diameter of the cross-sectional area of the
whole stem at 1 m stem height intervals. At 3 years access to the stem was gained using a
ladder and stem diameter at 1 m height intervals was measured directly with a measuring tape
Chapter 4 Tree Growth and Structure Page 57
to a height of 6 m. At 4 years access to the stem was gained by felling the stem and stem
diameter was measured directly with a measuring tape to the tip of the stem. Where branch
swellings obstructed measurements, they were moved up to the nearest clear stem section.
Branch Formation - the number of branches that have left evidence of having formed on the
stem, where evidence of the formation of a branch is shown either by the presence of a branch
or by a branch scar in the bark. Branch number was measured directly by counting branches
and scars at 3 years (stem accessed by ladder), and the height from the base of the stem to
each branch formed was measured directly with a measuring tape.
Crown Height – the height from the base of the stem to the lowest live branch of the crown.
Crown height was measured directly with a measuring tape at 3 years (crown accessed by
ladder) and at 4 years (crown accessed by felling trees for destructive sampling).
Crown Depth Ratio – the ratio of the vertical space occupied by the live crown (crown depth)
to stem height. Crown depth ratio was calculated as:
(stem height – crown height) / stem height
Crown Diameter – the diameter of the cross-section of the crown at a given height from the
base of the stem. Crown diameter was measured directly with a measuring tape at 1 m height
intervals at 4 years (crown accessed by felling trees for destructive sampling). The single
measurement of crown diameter at each interval (rather than two or more diameter
measurements) assumes that the crown cross-section is circular, which is a reasonable
assumption for young healthy trees as they tend to have a strong central stem and conoid
shape (Philip 1994). These measurements of crown diameter are likely to be underestimates,
since the branches were not subject to gravity pulling them down and out as measurements
were made on the felled tree.
Crown Width – the crown diameter at the widest point of the crown. Crown width was
determined as the largest crown diameter.
Crown Volume – the volume of the crown 3-dimensional shape. Crown volume is usually
modelled as the volume of a cone (Philip 1994), however since crown diameter was measured
at 1 metre intervals, crown volume was more accurately calculated by summing the volumes
Chapter 4 Tree Growth and Structure Page 58
of the conical frustums formed between each crown diameter measurement (with the top
frustum forming a cone).
Crown Form Factor – the ratio of the crown volume to that of a cylinder with the same width
and depth as the crown (Philip 1994). Given form factors suggest the following general
shapes: 0.25 neiloid; 0.33 conoid; 0.50 quadratic paraboloid; 0.60 cubic paraboloid; 1.00
cylinder.
The above data were compiled from measurements made on whole trees (either standing or
having been felled). The following data were compiled from measurements which required
that felled trees be broken down into various components.
Tree Green Mass – the fresh mass of the aboveground tree. Following dimensional
measurements of trees felled for whole tree destructive sampling at 4 years, the trees were
broken down into their stem, leaf and branch components. For each tree the stem was pruned
of all branches and weighed with 50 kg scales to the nearest gram. Leaves were stripped by
hand from the pruned branches over a tarpaulin, placed in garbage bins and weighed with 50
kg scales to the nearest gram. Stripped branches were divided into live and dead branches,
stacked into garbage bins and weighed with 50 kg scales to the nearest gram. The whole
process was conducted as quickly as possible and in the shade to minimise evaporation,
particularly from the leaves. The sum of the green mass of tree components (excluding dead
branches) then provided tree green mass.
Tree Sample Green Mass – the fresh mass of samples taken from the stem, leaf and branch
components of destructively sampled trees. The mass of every sample was weighed on
scientific scales to the nearest milligram as soon as possible after collection to minimise
evaporation from the samples, and all samples were then stored in labelled paper bags. Stem
samples consisted of 50 mm thick disks that were taken at breast height (1.3 m) and at 25%,
50% and 75% of stem height. Stem samples were weighed whole, following which the
stembark was removed and kept as stembark samples, and the stemwood was weighed and
kept as stemwood samples. Leaf samples consisted of two samples of several leaves from
each tree; one of fully formed new leaves from the top of the canopy and one of mature leaves
from the middle of the canopy. Branch samples consisted of several 50 mm branch sections of
live and dead branches from random positions in the canopy.
Chapter 4 Tree Growth and Structure Page 59
Stem Sample Diameter – the diameter of the cross-section of stem samples. Stem samples
consisted of 50 mm thick disks that were taken at breast height (1.3 m) and at 25%, 50% and
75% of stem height. The diameter of each sample was measured twice (on perpendicular
angles) using callipers, and the two diameter measurements were averaged to provide a single
diameter measurement. The process was repeated on each stem sample for the stemwood
diameter, which excludes bark from diameter measurement.
A number of whole stem calculations required the use of data from the stem. For this purpose
the stem was divided into sections relating to the closest stem sample by placing the ‘break’
between stem sections midway between the stem sample locations (Figure 4.2).
Figure 4.2: Diagram showing the division of the stem into four sections based on the location of the stem samples.
Stem Volume – the volume of the stem, the stemwood and the stembark. The volume of each
stem section (Figure 4.2) was calculated using the formula for a conical frustum:
Conical Frustum = ⅓ * π * (base radius2 + top radius
2 + (base radius*top radius)) * height
The radius at the base and apex of each stem section frustum was determined as the average
radius of the two closest stem samples, since the division between each stem section was
equi-distant from the stem sample on either side. The volumes of the stem section frustums
were then summed to determine stem volume. The same method was used to calculate
stemwood volume using stemwood diameter. Stembark volume was determined as the
difference between stem volume and stemwood volume.
Stemwood Green Mass– the fresh mass of the stemwood. Stemwood green mass was not
measured directly due to the difficulty of debarking whole stems, therefore a number of
Chapter 4 Tree Growth and Structure Page 60
calculations were required to provide an estimate of stemwood green mass. The ideal method
to estimate stemwood green mass is to multiply the green mass of each stem section by the
proportion of stemwood green mass in each stem sample, with the sum of the stem sections
providing stemwood green mass. This method was not possible, however, since the green
mass of the stem was weighed whole rather than in the stem sections.
The alternative was then to divide stem green mass into stem sections equivalent to those
determined for stem volume. The proportion of stem volume in each stem section was
calculated as the stem section volume divided by stem volume. Stem green mass was then
divided into the same proportions and multiplied by the proportion of stemwood green mass
in the equivalent stem sample, the sum of which determined stemwood green mass. This
method makes the assumption that the ratio of stem volume to stem mass (stem density) does
not change within the tree: however it was considered the best method to use with the
available data. Stembark green mass was then determined as the difference between stem
green mass and stemwood green mass.
Bark Volume Ratio – the volume of stembark relative to stem volume. Stembark ratio was
calculated by dividing stembark volume by stem volume.
Stem Form Factor – the ratio of stem volume to that of a cylinder with the same diameter and
height as the stem (Philip 1994), whereby the diameter of the stem cylinder is taken as stem
diameter at breast height. Given form factors suggest the following general shapes: 0.25
neiloid; 0.33 conoid; 0.50 quadratic paraboloid; 0.60 cubic paraboloid; 1.00 cylinder.
Tree Sample Oven-Dry Mass – the mass of samples taken from the stem, leaf and branch
components of each tree having had liquid water removed from cellular cavities. The wet
samples were placed in a scientific oven and dried at 80°C until their mass had stabilised for
one week (indicating that no further water would evaporate from the sample). The oven-dry
mass of samples was weighed using scientific scales to the nearest milligram.
Tree Oven-Dry Mass – the mass of the aboveground tree having had liquid water removed
from cellular cavities. The oven-dry mass of the aboveground tree was calculated as the sum
of the oven-dry mass of the tree components (excluding dead branches). The oven-dry mass
of the stem components were calculated for each stem section by multiplying the green mass
Chapter 4 Tree Growth and Structure Page 61
of the stemwood or stembark by the proportion of oven-dry mass in the equivalent stemwood
or stembark sample. The sum of the stem sections then provided stemwood or stembark oven-
dry mass. The oven-dry mass of the leaf component was calculated by multiplying the green
mass of the leaves by the average proportion of oven-dry mass in the two leaf samples. The
oven-dry mass of the branch component was calculated by multiplying the green mass of the
branches by the proportion of oven-dry mass in the branch samples.
Leaf Specific Area – the upper leaf surface area per unit of leaf oven-dry mass, the inverse of
which is leaf specific weight (Specht and Specht 1999; West 2004). A 10 mm hole punch was
used to punch 2 discs from 5 leaves in every leaf sample (care was taken to avoid the leaf
mid-rib), resulting in 10 leaf disks per leaf sample. The leaf disks were dried as per previous
tree samples and the oven-dry mass of the leaf disks were weighed using scientific scales to
the nearest milligram. Leaf specific area was then determined by dividing the surface area of
the leaf disks by the oven-dry mass of the leaf disks.
Crown Leaf Area – the total upper leaf surface area of leaves in the tree crown. Crown leaf
area was calculated as:
crown leaf oven-dry mass * leaf specific area
Leaf Nutrient Content – the percentage concentration of nitrogen, phosphorus and potassium
in the leaves (%). Of the 68 destructively sampled trees, a sub-sample of 32 trees (the largest
and smallest tree from each plot) were selected for leaf nutrient analysis. In preparation for
analysis, leaf samples from the top and middle of each tree canopy were oven-dried and
ground to a fine powder. The leaf nitrogen content was determined by analysis with a LECO
carbon/nitrogen/sulphur analyser (CNS 2000). Ground leaf samples were prepared for further
analysis by creating a microwave digest solution. The leaf phosphorus and potassium solute
content (mg/L) were then determined by analysis with a Perkin Elmer ELAN 6000
Inductively Coupled Plasma - Mass Spectrometer (ICP-MS).
Tree Mass Ratio – the oven-dry mass of tree components relative to tree oven-dry mass. Tree
mass ratios were calculated for the stem, live branch and leaf components of the tree by
dividing the oven-dry mass of the component by tree oven-dry mass.
Chapter 4 Tree Growth and Structure Page 62
Growth Efficiency Ratios – the amount of growth produced in the tree and tree components
relative to the amount of resources used, whereby the amount of resources used were
approximated by crown leaf area. Growth efficiency ratios were calculated for tree mass, stem
mass and stem volume by dividing the mass or volume of the component by crown leaf area.
4.3.3 Data Analysis
Raw data were entered into a Microsoft Excel spreadsheet and copied to MLwiN and SPSS to
facilitate an examination for recording errors (unusually high or low values and missing
values) using histograms and residual distributions. Where unusual values were identified the
original field data sheets were cross-checked for recording errors and mistakes were
corrected. Where unusual values were also present in the original field data sheets they were
included in the data set, with the exception of those which were impossible.
Due to the positive skewness in the size distribution of trees in the spacing trial (Figure 2.6)
the data set analysed consisted of a stratified random sample of the population. The data are
not therefore a true random sample of the population and the confidence intervals determined
for relationships between dependent variables and the factors affecting them are not
applicable to the population. In fact the confidence intervals determined in the analyses are
likely to be greater than those that would have been determined from a random sample,
particularly at the lower end of the DBH range.
Since these data formed a hierarchical structure with a minimum two levels of plot and tree, it
was appropriate to use multilevel modelling to analyse the data5 (Snijders and Bosker 1999).
As the most extensive multilevel package (Snijders and Bosker 1999), the MLwiN software
(Rasbash et al. 2003) was used for this purpose. The dependent variables modelled were
generally scale numbers greater than or equal to zero, with the assumption that these variables
had continuous distributions above zero, and that their residuals had normal distributions at all
levels. In these cases a hierarchical linear model identifying random variation around the
intercept coefficient (random intercept model) was used to analyse data and define the model
which reduced random variation by the greatest significant amount (Snijders and Bosker
1999; Rasbash et al. 2003). An hierarchical linear model is capable of identifying random
variation around slope coefficients (random slope model). Random variation around slope
5 Dr L. Brooks, Student Advisor, Research Methodology Unit, Southern Cross University.
Chapter 4 Tree Growth and Structure Page 63
coefficients was identified only if doing so reduced total random variation by a significant
amount.
In several cases it was necessary to transform the dependent variable raw data in order to
build a more realistic model. In the case that the raw data of the dependent variable displayed
a heteroscedastic distribution (fanning out) against predictive factors, then a natural logarithm
transformation was employed on both the dependent variable and predictive variable data in
order to reduce heteroscedasticity during analysis. Where the raw data of the dependent
variable approached zero at the lower end of its range, then a natural logarithm transformation
was employed on the dependent variable data so that the dependent variable could approach
zero but not become negative in the model. Where the dependent variable was a scale number
with a finite distribution between zero and one (such as a ratio) and approached its range
limits, then a sine transformation was employed so that the dependent variable could approach
its limits but not cross them.
Some dependant variables had discrete rather than continuous distributions, and residuals did
not exhibit normal distributions. In these cases specialised models assuming a non-normal
residual distribution (i.e. Poisson, binomial, multinomial) were required (Snijders and Bosker
1999; Rasbash et al. 2003).
At this stage of the thesis the main objective was to investigate the effects of competition on
the tree properties measured. As such the primary factors tested are planting density (P) as a
measure of the general level of competition in the stand, and stem diameter at breast height
(DBH) as a measure of the level of competition pressure experienced by individuals in the
stand. Given the potential for spatial variation in tree properties, sample position was
occasionally tested as a factor in order to detect any patterns in spatial variation. Age was also
included as a factor where applicable, since it was likely that relationships between tree
properties, P and DBH would change over time.
Chapter 4 Tree Growth and Structure Page 64
4.4 Results and Discussion
Immediately prior to examining the results, it is useful to examine the properties of the two
factors used to explore the effect of competition; planting density and stem diameter at breast
height. Planting density is a measure of population size and is used to approximate the general
level of competition in the stand. The planting densities used had a mean growth space per
tree of 38.44 m2 for 250 st/ha, 9.92 m
2 for 1,000 st/ha, 1.99 m
2 for 5,000 st/ha and 1.00 m
2 for
10,000 st/ha. It is noteworthy that mean growth space per tree becomes more equitable as
planting density increases, with the result that 5,000 st/ha and 10,000 st/ha have the least
difference in mean growth space, despite having a difference of 5,000 st/ha between them.
Stem diameter at breast height (DBH) is a measure of tree size indicating the competitive status
of trees in the stand. The trees selected for analysis of tree growth and structure included trees
from the range of dominance classes identified in each planting density, ensuring that trees of
all levels of competitive status were included in the analyses (Figure 4.3).
0.00
0.04
0.08
0.12
0.16
0.20
0.24
0.28
Ste
m D
iam
ete
r a
t B
reas
t H
eig
ht
(m)
(b) 1,000 st/ha(a) 250 st/ha (d) 10,000 st/ha(c) 5,000 st/ha
Selected TreesDBH Range
Dominant Co-Dominant
Intermediate Suppressed
Dominance Status
250 st/ha 1,000 st/ha
5,000 st/ha 10,000 st/ha
Figure 4.3: The stem diameter at breast height (DBH) of 4 year old E. grandis trees selected for analysis of tree growth and structure in planting densities (a) 250 st/ha, (b)1,000 st/ha, (c) 5,000 st/ha, and (d) 10,000 st/ha. The DBH of select trees (off-set points) in each planting density are shown in comparison to the DBH range and the dominance classes identified in each planting density. The division of each planting density into dominance classes is based on stem volume mean annual increment (MAI) in 250 stem cohorts (Figure 3.12), whereby 250 stem cohorts increasing in stem volume MAI at 100 to 90% of the largest 250 stem cohort are defined as dominant, 90 to 50% are co-dominant, 50 to 10% are intermediate and <10% are suppressed.
Chapter 4 Tree Growth and Structure Page 65
An examination of the select trees in each planting density indicates that the largest select
trees in low planting densities are larger than the largest select trees in high planting densities,
partly due to being larger in general and partly due to individuals being selected, by chance,
from the upper end of the dominant range (the shaded in blue in Figure 4.3). Despite the
difference in size between the largest select trees, however, the average size of the top five
select trees (dominant trees) is remarkably similar given the large difference in the
competition intensity between planting densities. It should be noted that within each planting
density the largest trees selected are defined as dominant trees and the smallest trees selected
are defined as suppressed trees, in order to allow comparison of dominant tree properties
across planting densities, and dominant and suppressed tree properties within planting
densities.
The following results are the product of testing the hypotheses of the effects of increased
competition on tree growth and structure (Table 4.1). For each variable the results are
presented in tables and figures. The tables show the statistically significant regression
coefficients of the model developed for each variable. Where an interaction between two
predictive factors is significant at 95% significance (p < 0.05), the singular effect of both
factors must also be included in the model regardless of their significance due to the
significance of the interaction. The figures show the differences in each variable along the
selected DBH range of each planting density, and overlapping of the 95% confidence intervals
indicates no significant difference between planting densities and/or stem diameters.
4.4.1 Tree Growth
STEM DIAMETER
Mean stem diameter at breast height (DBH) decreased as planting density increased, for the
whole stand (Table 4.3(a)), and for the largest (dominant) 250 stem cohort (Table 4.3(b))
(Figure 4.4). The results show that whilst the largest 250 stem cohort mean DBH was reduced
by increased planting density (Figure 4.4(b)), the absolute difference in the mean DBH of the
largest 250 stem cohort between planting densities equated to about 1 years growth by 5 years
old. This was small given the severe competition that was evident in high planting densities,
and shows that to some extent dominant stems were able to capture resources regardless of
competition.
Chapter 4 Tree Growth and Structure Page 66
Table 4.3: The fixed-effect regression coefficients of the random intercept model of the effects of planting density (P) (st/ha) and age (A) (yrs) on (a) stand mean diameter at breast height (m) and (b) the largest 250 stem cohort mean diameter at breast height (m).
VARIABLE COEFFICIENT S.E. P - VALUE INTERACTION COEFFICIENT S.E. P - VALUE
INTERCEPT 0.0216 0.0176 p = 0.220 lnP*A -0.0082 0.0006 p < 0.001
lnP 0.0011 0.0020 p = 0.582
A 0.0832 0.0057 p < 0.001
VARIABLE COEFFICIENT S.E. P - VALUE INTERACTION COEFFICIENT S.E. P - VALUE
INTERCEPT 0.0281 0.0256 p = 0.272 lnP*A -0.0037 0.0009 p < 0.001
lnP 0.0014 0.0029 p = 0.629
A 0.0578 0.0081 p < 0.001
0.00
0.05
0.10
0.15
0.20
0.25
0 1 2 3 4 5
Age (yrs)
Me
an
Ste
m D
iam
ete
r a
t B
reas
t H
eig
ht
(m)
(a) Stand
0 1 2 3 4 5
Age (yrs)
(b) Largest 250 Stem Cohort
250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
250 95% CI 1,000 95% CI 5,000 95% CI 10,000 95% CI
Figure 4.4: The relationship between the dependent variables stand mean diameter at breast height (DBH) (m) and largest 250 stem cohort mean DBH (m) and the factors planting density (st/ha) and age (yrs). The predicted values of (a) stand mean DBH and (b) largest 250 stem cohort mean DBH (with 95% confidence intervals) are plotted against age and identified by planting density.
STEM HEIGHT
For a given stem diameter, stem height increased as planting density increased. Within
planting densities, stem height had a positive relationship with stem diameter (Table 4.4)
(Figure 4.5). The results for stem height indicate that increased competitive pressure (planting
density) stimulated faster growth in stem height for a given stem diameter, though increased
shelter from close neighbours may also have aided faster height growth. The positive
(a)
(b)
Chapter 4 Tree Growth and Structure Page 67
correlation between stem height and stem diameter was expected as trees with greater stem
height tend also to have greater diameter.
Table 4.4: The fixed-effect regression coefficients in the random slope model of the effects of stem diameter (DBH) (m), planting density (P) (st/ha) and age (A) (yrs) on stem height (m).
VARIABLE COEFFICIENT S.E. P - VALUE INTERACTION COEFFICIENT S.E. P - VALUE
INTERCEPT -4.916 2.327 p = 0.035 √DBH*lnP -3.778 0.421 p < 0.001
√DBH 9.544 3.370 p = 0.005 lnP*√A -1.111 0.153 p < 0.001
lnP 1.710 0.229 p < 0.001 √DBH*lnP*√A 5.440 0.187 p < 0.001
√A 1.881 1.490 p = 0.207
0
10
20
30
DBH (m)
Ste
m H
eig
ht
(m)
(a) 1 year
(c) 3 years
0
10
20
30
0.0 0.1 0.2 0.3
(d) 4 years
0.0 0.1 0.2 0.3
(e) 5 years
0
10
20
30(b) 2 years
0102030
250 st/ha 1,000 st/ha
5,000 st/ha 10,000 st/ha
250 95% C.I. 1,000 95% C.I.
5,000 95% C.I. 10,000 95% C.I.
DBH (m)
Figure 4.5: The relationship between the dependent variable stem height (m) and the factors DBH (m), planting density (st/ha), and age (yrs). The predicted value of stem height (with 95% confidence intervals) is plotted against DBH and identified by planting density for ages (a) 1 year, (b) 2 years, (c) 3 years, (d) 4 years, and (e) 5 years.
Consistent with other studies of stem height in even-aged eucalypt monocultures (Pinkard and
Neilsen 2003), increased planting density had no effect on the stem height of dominant trees
from 3 to 5 years (Figure 4.5). A difference in dominant stem height between planting
densities was apparent from 1 to 2 years since the largest trees in high planting densities had
significantly greater stem height than those in low planting densities. The greater early height
growth of dominant trees in high planting densities was likely due to stimulus by earlier onset
of competition and/or greater shelter provided by closer proximity of neighbours. It begs the
question, however, how did dominants in low planting densities then catch up in height
Chapter 4 Tree Growth and Structure Page 68
growth, rather than lag behind indefinitely? A possible explanation is that trees reached a
height at around 2 years at which shading, particularly of slanted light in the morning and
evening, has stimulated dominant trees in low planting densities to catch up in height growth.
STEM VOLUME
For a given stem diameter, stem volume and stemwood volume increased as planting density
increased. Within planting densities, stem volume and stemwood volume had a positive
relationship with stem diameter (Table 4.5) (Figure 4.6).
Table 4.5: The fixed-effect regression coefficients of the random slope model of the effects of stem diameter (DBH) (m), planting density (P) (st/ha) and age (A) (yrs) on (a) the natural logarithm of stem volume (m
3) and (b) the natural logarithm of stemwood volume (m
3)(a)
.
(a) VARIABLE COEFFICIENT S.E. P - VALUE (b) VARIABLE COEFFICIENT S.E. P - VALUE
INTERCEPT 1.082 0.094 p < 0.001 INTERCEPT 1.4330 0.1070 p < 0.001
lnDBH 2.265 0.031 p < 0.001 lnDBH 2.3080 0.0450 p < 0.001
lnP 0.076 0.009 p < 0.001 lnP 0.1050 0.0140 p < 0.001
A 0.167 0.019 p < 0.001
(a)Stemwood volume was only measured at one age (4 years) hence age had no effect.
0.0
0.1
0.2
0.3
0.4
0.0 0.1 0.2 0.3DBH (m)
Ste
m V
olu
me
(m
3)
0.0 0.1 0.2 0.3DBH (m)
(b) Stem (4 years)(a) Stem (3 years)
0.0 0.1 0.2 0.3DBH (m)
(c) Stemwood (4 years)
Raw Data 250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
250 95% CI 1,000 95% CI 5,000 95% CI 10,000 95% CI
Figure 4.6: The relationship between the dependent variables stem and stemwood volume (m
3) and
the factors DBH (m), planting density (st/ha) and age (yrs). The predicted values of stem volume (m3) at
ages (a) 3 years and (b) 4 years, and stemwood volume (m3) at age (c) 4 years (with 95% confidence
intervals) is plotted against DBH and identified by planting density for each tree measured. Windows enlarge the 95% confidence intervals.
Chapter 4 Tree Growth and Structure Page 69
The results for stem volume show that increased competitive pressure (planting density)
resulted in increased stem growth for a given stem diameter, which was likely due to
increased stem height for a given stem diameter. The relatively small difference in volume
between planting densities 5,000 and 10,000 st/ha compared to other planting densities was
expected as they had the least difference in mean growth space per tree. Overlapping 95%
confidence intervals between 5,000 st/ha and 10,000 st/ha (Figure 4.6) show that there was no
significant difference in stem volume between the two high planting densities.
The results for stemwood volume (Figure 4.6(c)) followed the same pattern as stem volume
(Figure 4.6(b)), the difference between the two being indicative of stembark volume. The
results indicate that competitive pressure had a large affect on stemwood volume growth, with
dominant trees in 5,000 and 10,000 st/ha only three fifths the size of dominant trees in 1,000
st/ha, and less than half the size of relatively free-growing trees in 250 st/ha. Clearly, the
relatively small difference in stem diameter that was apparent between dominant trees (Figure
4.4) has been compounded in the estimates of stem volume.
STEM MASS
For a given stem diameter, stem and stemwood oven-dry mass increased as planting density
increased. Within planting densities, stem and stemwood oven-dry mass had a positive
relationship with stem diameter (Table 4.6) (Figure 4.7).
Table 4.6: The fixed-effect regression coefficients of the random intercept model of the effects of stem diameter (DBH) (m) and planting density (P) (st/ha) on the natural logarithm of (a) stem oven-dry mass (kg) and (b) stemwood oven-dry mass (kg).
(a) VARIABLE COEFFICIENT S.E. P - VALUE (b) VARIABLE COEFFICIENT S.E. P - VALUE
INTERCEPT 8.030 0.107 p < 0.001 INTERCEPT 7.814 0.110 p < 0.001
lnDBH 2.240 0.045 p < 0.001 lnDBH 2.268 0.046 p < 0.001
lnP 0.035 0.014 p = 0.012 lnP 0.054 0.015 p < 0.001
Whilst the results for stem mass show a similar pattern to those for stem height and volume in
that increased competitive pressure (planting density) resulted in increased stem growth for a
given stem diameter, the effect of planting density on stem mass does not appear to be as
great as it was on stem height and volume. At 4 years old there was a significant increase in
stem height (Figure 4.5(d)), stem volume (Figure 4.6(b)) and stemwood volume (Figure
4.6(c)) for a given stem diameter as planting density increased from low to high planting
densities (1,000 st/ha to 5,000 st/ha). In contrast, the increase in stem and stemwood mass
Chapter 4 Tree Growth and Structure Page 70
from low to high planting densities (1,000 st/ha to 5,000 st/ha) was not significant, as
indicated by overlapping 95% confidence intervals between 1,000 st/ha and 5,000 st/ha
(Figure 4.7).
0
40
80
120
160
200
0.0 0.1 0.2 0.3DBH (m)
Ste
m O
ven
-Dry
Mas
s (
kg
)
(a) Stem
0.0 0.1 0.2 0.3DBH (m)
(b) Stemwood
250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
250 95% CI 1,000 95% CI 5,000 95% CI 10,000 95% CI
Raw Data
Figure 4.7: The relationships between the dependent variables stem and stemwood oven-dry mass (kg) and the factors DBH (m) and planting density (st/ha). The predicted values of (a) stem and (b) stemwood oven-dry mass (with 95% confidence intervals) is plotted against DBH and identified by planting density for each tree measured. Windows enlarge the 95% confidence intervals.
The weaker effect of planting density on stem mass compared to stem volume suggests two
possibilities. The first possibility is that there was greater variation in the stem mass data than
the stem volume data. The second possibility is that increased planting density had a negative
effect on stem density; mass is the product of volume and density, and the positive effect of
planting density on stem volume would be reduced in stem mass if planting density had a
negative effect on stem density.
CROWN WIDTH
For a given stem diameter, crown width decreased as planting density increased. Within
planting densities, crown width had a positive relationship with stem diameter (Table 4.7)
(Figure 4.8).
Chapter 4 Tree Growth and Structure Page 71
Table 4.7: The fixed-effect regression coefficients in the random intercept model of the effects of stem diameter (DBH) (m) and planting density (P) (st/ha) on the natural logarithm of crown width (m).
VARIABLE COEFFICIENT S.E. P - VALUE
INTERCEPT -0.263 0.215 p = 0.221
√DBH 5.328 0.319 p < 0.001
lnP -0.086 0.018 p < 0.001
0
1
2
3
4
5
6
7
8
0.0 0.1 0.2 0.3DBH (m)
Cro
wn
Wid
th (
m)
250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
250 95% C.I. 1,000 95% C.I. 5,000 95% C.I. 10,000 95% C.I.
Raw Data
10,000 st/ha
250 st/ha
1,000 st/ha
5,000 st/ha
Me
an
Gro
wth
Sp
ace
Be
tween
Tre
es
(m)
Figure 4.8: The relationship between the dependent variable crown width (m) and the factors DBH (m) and planting density (st/ha). The predicted value of crown width (with 95% confidence intervals) is plotted against DBH and identified by planting density for each tree measured. The mean growth space between trees is shown as a horizontal line for each planting density.
The results for crown width showed no significant difference between planting densities 250
to 1,000 st/ha or 5,000 to 10,000 st/ha. This was unexpected for 250 to 1,000 st/ha since trees
in 250 st/ha had twice the mean growth space compared to trees in 1,000 st/ha (Figure 4.8),
yet all trees in planting density 250 st/ha used less space than the mean growth space available
such that canopy closure had not occurred. This suggested that branch shed would be poor in
250 st/ha as more light would filter into the lower canopy, and it also showed that crown
width in 1,000 st/ha was not significantly restricted compared to relatively free-growing trees.
In high planting densities horizontal crown expansion was probably restricted by increased
competitive pressure. The overlapping of crowns in the high planting densities, shown by the
width of most crowns in 5,000 to 10,000 st/ha exceeding the mean growth space (Figure 4.8),
indicates that crowns were crowded. Crown overlapping in eucalypts is usually due to crowns
Chapter 4 Tree Growth and Structure Page 72
growing directly under or over neighbouring crowns rather than intertwining, although some
intertwining is not uncommon in young, dense stands (Jacobs 1955; Florence 1996).
CROWN LEAF AREA
For a given stem diameter, the leaf area of the crown decreased as planting density increased.
Within planting densities, crown leaf area had a positive relationship with stem diameter
(Table 4.8) (Figure 4.9).
Table 4.8: The fixed-effect regression coefficients in the random intercept model of the effects of stem diameter (DBH) (m) and planting density (P) (st/ha) on the natural logarithm of crown leaf area (m
2).
VARIABLE COEFFICIENT S.E. P - VALUE
INTERCEPT 4.016 0.359 p < 0.001
lnDBH 1.844 0.133 p < 0.001
lnP -0.247 0.042 p < 0.001
0.0
0.5
1.0
1.5
0.0 0.1 0.2 0.3DBH (m)
Cro
wn
Le
af
Are
a (
m2)
250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
250 95% C.I. 1,000 95% C.I. 5,000 95% C.I. 10,000 95% C.I.
Raw Data
Figure 4.9: The relationship between the dependent variable crown leaf area (m
2) and the factors DBH
(m) and planting density (st/ha). The predicted value of crown leaf area (with 95% confidence intervals) is plotted against DBH and identified by planting density for each tree measured.
The results for crown leaf area were similar to crown width, except that the slope of the
relationship between stem diameter and crown size was more convex (exhibits greater
upwards curvature) for crown leaf area than for crown width. This suggests that increased
crown width provided a fairly constant increase in growth potential, whereas increased crown
Chapter 4 Tree Growth and Structure Page 73
leaf area provided a diminishing increase in growth potential. The result probably reflects
changes in saturated light capture, whereby increased crown width was more likely to
increase saturated light capture than increased crown leaf area.
CROWN MASS
For a given stem diameter, crown leaf and branch oven-dry mass per tree decreased as
planting density increased. Within planting densities, crown leaf and branch oven-dry mass
had a positive relationship with stem diameter (Table 4.9) (Figure 4.10).
Table 4.9: The fixed-effect regression coefficients in the random slope model of the effects of stem diameter (DBH) (m) and planting density (P) (st/ha) on the natural logarithm of (a) crown leaf oven-dry mass (kg) and (b) crown branch oven-dry mass (kg).
(a) VARIABLE COEFFICIENT S.E. P - VALUE (b) VARIABLE COEFFICIENT S.E. P - VALUE
INTERCEPT 5.795 1.087 p < 0.001 INTERCEPT 10.233 0.402 p < 0.001
lnDBH 1.457 0.568 p = 0.010 lnDBH 2.789 0.130 p < 0.001
lnP 0.182 0.141 p = 0.197 lnP -0.339 0.054 p < 0.001
INTERACTION COEFFICIENT S.E. P - VALUE
lnDBH*lnP 0.166 0.072 p = 0.021
0
10
20
30
40
0.0 0.1 0.2 0.3DBH (m)
Cro
wn
Ov
en
-Dry
Ma
ss
(k
g)
250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
250 95% C.I. 1,000 95% C.I. 5,000 95% C.I. 10,000 95% C.I.
Raw Data
0
20
40
60
80
100
120
0.0 0.1 0.2 0.3DBH (m)
Cro
wn
Ove
n-D
ry M
ass
(kg
)
(a) Crown Leaf Oven-Dry Mass (b) Crown Branch Oven-Dry Mass
Figure 4.10: The relationship between the dependent variables crown leaf and branch oven-dry mass (kg) and the factors DBH (m) and planting density (st/ha). The predicted values of (a) crown leaf oven-dry mass and (b) crown branch oven-dry mass (with 95% confidence intervals) are plotted against DBH and identified by planting density for each tree measured.
Chapter 4 Tree Growth and Structure Page 74
A comparison between crown leaf and branch oven-dry mass (Figure 4.10) shows that as
planting density increased for a given stem diameter, the relative decrease in branch mass was
greater than the relative decrease in leaf mass, so that increased planting density resulted in
less branch mass per unit of leaf mass for a given stem diameter. This was probably due to
decreased crown width (shorter branches) in higher planting densities (Figure 4.8). The
convex shape of the positive slope between stem diameter and crown mass (Figure 4.10)
again suggested diminishing stem growth returns to increased crown size.
In summary, the tree growth data showed that in all cases stem diameter had a positive
relationship with tree growth variables, and this was expected since trees with greater
competitive status (stem diameter) generally have greater growth in all tree components.
Increased planting density appeared to alter the relationship between crown size and stem
growth, so that trees in high planting densities appeared to exhibit greater stem growth returns
by producing more stem biomass per unit of crown biomass.
4.4.2 Tree Structure
TREE FORM
Stem and crown form were estimated using form factor, where decreased form factor was
indicative of increased conicity. For a given stem diameter, stem form factor was unaffected
by increased planting density and crown form factor increased as planting density increased.
Within planting densities, stem form factor had a negative relationship with stem diameter
and crown form factor was unaffected by stem diameter (Table 4.10) (Figure 4.11).
Table 4.10: The fixed-effect regression coefficients of the random intercept model of the effects of stem diameter (DBH) (m) and planting density (P) (st/ha) on (a) stem form factor and (b) the arcsine of crown form factor.
(a) VARIABLE COEFFICIENT S.E. P - VALUE (b) VARIABLE COEFFICIENT S.E. P - VALUE
INTERCEPT 0.702 0.024 p < 0.001 INTERCEPT 0.261 0.086 p = 0.002
√DBH -0.703 0.062 p < 0.001 lnP 0.032 0.011 p = 0.003
Past evidence has shown that tree stems requiring increased mechanical support (due to
increased tree size or reduced shelter from wind sway) achieve support through developing a
conical stem shape (Jacobs 1955; Valinger 1992; Osler et al. 1996). These results confirmed
the positive relationship between increased stem size and a more conical stem shape (reduced
form factor) (Figure 4.11(a)). In contrast, increased planting density (increased shelter) had no
Chapter 4 Tree Growth and Structure Page 75
effect on stem form (p = 0.95), suggesting that either (i) there was no difference in shelter
between planting densities (high planting density plots may have provided shelter to
neighbouring low planting density plots), (ii) ambient wind levels were low, so shelter had
little effect on stem form, and/or (iii) wind sway did not affect stem form in young E. grandis.
0.2
0.3
0.4
0.5
0.6
0.7
0.0 0.1 0.2 0.3DBH (m)
Fo
rm F
ac
tor
(a) Stem
Conoid
Quadratic
Paraboloid
Cubic
Paraboloid
0.0 0.1 0.2 0.3DBH (m)
(b) Crown
Raw Data All Planting Densities
All Planting Densities 95% CI
250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
250 95% CI 1,000 95% CI 5,000 95% CI 10,000 95% CI
Neiloid
Figure 4.11: The relationships between the dependent variables stem form factor and crown form factor and the factors DBH (m) and planting density (st/ha). The predicted values of (a) stem form factor and (b) crown form factor (with 95% confidence intervals) are plotted against DBH and identified by planting density (where applicable) for each tree measured.
Crowns in fast-growing young eucalypts are generally conical (Jacobs 1955; Florence 1996),
but increased competition can result in the foliage distribution being skewed upwards
(Medhurst and Beadle 2001; Pinkard and Neilsen 2003) and the extent of upward skew is
greater for suppressed individuals than dominant individuals (Pinkard and Neilsen 2003). The
above results confirm that increased competition resulted in foliage being skewed upwards
(less conical crown shape) (Figure 4.11(b)), however the extent of upward skew was not
greater for suppressed individuals since DBH did not effect crown form factor (p = 0.51). The
crown form factors of all planting densities were above the form factor representing a conoid
shape, even for crowns in 250 st/ha which are essentially ‘free-growing’ trees experiencing
very little competition. This result shows that the assumption of a conoid shape in young
eucalyptus crowns is not correct for E. grandis.
Chapter 4 Tree Growth and Structure Page 76
BARK RATIO
Bark ratio is the proportion of the stem comprised of bark. For a given stem diameter, bark
ratio by volume and by oven-dry mass decreased as planting density increased. Within
planting densities, bark ratio by volume and by oven-dry mass had a negative relationship
with stem diameter (Table 4.11) (Figure 4.12).
Table 4.11: The fixed-effect regression coefficients of the random slope model of the effects of stem diameter (DBH) (m) and planting density (P) (st/ha) on (a) bark ratio by volume (m
3 m
-3) and (b) bark
ratio by oven-dry mass (kg kg-1
).
(a) VARIABLE COEFFICIENT S.E. P - VALUE (b) VARIABLE COEFFICIENT S.E. P - VALUE
INTERCEPT 0.2160 0.0300 p < 0.001 INTERCEPT 0.2810 0.0210 p < 0.001
lnP -0.0160 0.0030 p < 0.001 lnP -0.0170 0.0020 p < 0.001
lnDBH -0.0290 0.0120 p = 0.016 DBH -0.2290 0.0550 p < 0.001
0.0
0.1
0.2
0.3
0.0 0.1 0.2 0.3DBH (m)
Bark
Rati
o
(a) By Volume
0.0 0.1 0.2 0.3DBH (m)
(b) By Oven-Dry Mass
250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
250 95% CI 1,000 95% CI 5,000 95% CI 10,000 95% CI
Raw Data
Figure 4.12: The relationship between the dependent variables bark ratio by volume and bark ratio by oven-dry mass and the factors DBH (m) and planting density (st/ha). The predicted value of (a) bark ratio by volume and (b) bark ratio by oven-dry mass (with 95% confidence intervals) are plotted against DBH and identified by planting density for each tree measured.
The negative effect of planting density on bark ratio was similar to the negative effect of
planting density on crown leaf area and mass (Figures 4.9, 4.10). This suggests a positive
relationship between crown size and bark ratio, which is possible given that the crown
produces photosynthate and bark is the avenue by which photosynthate is translocated down
Chapter 4 Tree Growth and Structure Page 77
the stem. The negative correlation between stem diameter and bark ratio was expected since
larger stems are known to have a lower proportion of bark due to bark shed (Schonau and
Boden 1982; Negi et al. 1984).
BRANCH FORMATION
Branch formation is the number of branches originally formed on the stem. For a given stem
diameter, branch formation (between 0 and 6 m stem height) was unaffected by increased
planting density (p = 0.062). Within planting densities, branch formation had a positive
relationship with stem diameter (Table 4.12) (Figure 4.13).
Table 4.12: The fixed-effect regression coefficients in the Poisson model of the effect of stem diameter (DBH) (m) on the natural logarithm of branch formation (between 0 and 6 m stem height) (count).
VARIABLE COEFFICIENT S.E. P - VALUE
INTERCEPT 4.296 0.020 p < 0.001
DBH2 2.357 0.615 p < 0.001
60
70
80
90
100
0.00 0.05 0.10 0.15 0.20
DBH (m)
Branch Number from 0-6 m Stem Height (count)
Raw Data All Planting Densities All Planting Densities 95% C.I.
Figure 4.13: The relationship between the dependent variable branch formation (Branch Number from 0-6 m Stem Height) (count) and the factor DBH (m). The predicted value of branch formation (with 95% confidence intervals) is plotted against DBH for each tree measured.
For practical purposes the positive correlation between stem diameter and branch formation
was small as there was only a 7% increase in branch formation from the smallest to the largest
tree measured compared to a 375% increase in stem diameter. Even so, the relationship is
Chapter 4 Tree Growth and Structure Page 78
feasible since a genetic trait for a greater number of branches could result in a larger crown
and greater growth. Alternatively, fewer branches in smaller trees could be due to
suppression, in which case all trees would have the same branch formation at the base of the
stem, correlating to a period when tree size was relatively equal, but suppressed trees would
have fewer branches at higher points up the stem, correlating to the onset of competition.
An investigation was therefore made of how branch formation changes with stem height. The
0 to 6 m stem section was divided into twelve 0.5 m stem sections, and a comparison was
made of the number of branches formed in each stem section (Figure 4.14). Examination of
the raw data showed little evidence that small trees distributed their branches differently from
large trees; therefore the number of branches formed does not appear to be affected by
competition. Given the strong positive relationship between stem diameter and crown size
(Figure 4.8, 4.9, 4.10) and evidence that increased stem diameter resulted in only a small
increase in branch formation (Figure 4.13), it is apparent that crown size is primarily
dependent on what happens to branches subsequent to formation (such as the extent of size
growth and/or branch shed) rather than the number of branches formed.
0
5
10
15
20
25
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0
0.5 m Stem Sections
Branch Number per 0.5 m Stem Section (count)
250 st/ha DBH: 0.191 m 1,000 st/ha DBH: 0.168 m 5,000 st/ha DBH: 0.120 m 10,000 st/ha DBH: 0.130 m
250 st/ha DBH: 0.103 m 1,000 st/ha DBH: 0.071 m 5,000 st/ha DBH: 0.077 m 10,000 st/ha DBH: 0.038 m
DBH
DBH
DBH
DBH
DBHDBH
DBHDBH
Figure 4.14: The measured branch formation (Branch Number per 0.5 m Stem Section (count) from 0 to 6 m stem height of the largest and smallest select tree from each planting density. Each tree is identified by planting density, and the stem diameter (DBH) is shown in legend. The data of each tree are slightly offset so that data points are less obscured by overlapping.
Chapter 4 Tree Growth and Structure Page 79
BRANCH SHED
For a given stem diameter, crown height (the height to which branches have shed) increased
as planting density increased at 3 and 4 years old. Within planting densities, crown height had
a positive relationship with stem diameter (Table 4.13) (Figure 4.15).
Table 4.13: The fixed-effect regression coefficients in the random intercept model of the effects of stem diameter (DBH) (m), planting density (P) (st/ha) and age (A) (yrs) on the natural logarithm of crown height (m).
VARIABLE COEFFICIENT S.E. P - VALUE INTERACTION COEFFICIENT S.E. P - VALUE
INTERCEPT -11.418 1.605 p < 0.001 lnP*A -0.350 0.082 p < 0.001
DBH 2.478 0.652 p < 0.001
lnP 1.396 0.209 p < 0.001
A 3.517 0.628 p < 0.001
0
5
10
15
20
0.0 0.1 0.2 0.3
DBH (m)
Cro
wn
He
igh
t (m
)
(a) 3 years
0.0 0.1 0.2 0.3
DBH (m)
(b) 4 years
250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
250 95% C.I. 1,000 95% C.I. 5,000 95% C.I. 10,000 95% C.I.
Raw Data
Figure 4.15: The relationship between the dependent variable crown height (m) and the factors DBH (m), planting density (st/ha) and age (yrs). The predicted value of crown height (with 95% confidence intervals) at ages (a) 3 years and (b) 4 years is plotted against DBH and identified by planting density for each tree measured.
Branch shed is usually thought to be driven by light competition, whereby trees shed branches
below the light compensation point resulting in a uniform crown height (branch shed) within
the stand since the stand is subject to relatively uniform ambient light conditions in the lower
canopy. Branch shed may be hastened, however, by competition for water and/or nutrients,
Chapter 4 Tree Growth and Structure Page 80
whereby trees have insufficient resources to increase the upper crown (essential for continued
access to saturated light) and also maintain the lower crown, and therefore the lowest
branches are shed in order to re-allocate resources to higher branches. In the latter case it is
possible that dominant and suppressed trees will develop different branch height due to
different relative resource capture.
The results for crown height show that increased competition resulted in increased branch
shed. The large change in crown height between planting densities for a given stem diameter
(similar resource capture) (Figure 4.15) suggests that light competition was the factor most
affecting branch shed. There was evidence, however, that branch shed was in part hastened by
competition for water and/or nutrients since increased dominance status (stem diameter)
resulted in increased branch shed, and the positive relationship between dominance status and
branch shed became stronger as stand competition (planting density) increased (Figure 4.15).
Age had a positive relationship with branch shed (Table 4.13) (Figure 4.15). This was
expected as branch shed is an ongoing process, and the increase in crown height over time is
indicative of the responsiveness of the crown to growing conditions.
A second parameter relevant to branch shed is the crown depth ratio, which is the ratio of
crown depth (vertical space occupied by the crown) to stem height. It is often used as a
measure of relative crown retention and tree vigour (Hughes 2000) and it is indicative of
dominance status within the stand (Stoneman and Whitford 1995; Medhurst et al. 1999). For a
given stem diameter, crown depth ratio was found to decrease as planting density increased.
Within planting densities, crown depth ratio had a positive relationship with stem diameter
(Table 4.14) (Figure 4.16).
Table 4.14: The fixed-effect regression coefficients in the random intercept model of the effects of stem diameter (DBH) (m), planting density (P) (st/ha) and age (A) (yrs) on the arcsine of crown depth ratio.
VARIABLE COEFFICIENT S.E. P - VALUE INTERACTION COEFFICIENT S.E. P - VALUE
INTERCEPT 4.481 0.544 p < 0.001 lnP*A 0.079 0.027 p = 0.003
lnDBH 0.158 0.030 p < 0.001
lnP -0.362 0.070 p < 0.001
A -0.907 0.210 p < 0.001
Chapter 4 Tree Growth and Structure Page 81
0.0
0.2
0.4
0.6
0.8
1.0
0.0 0.1 0.2 0.3
DBH (m)
Cro
wn
Dep
th R
ati
o
(a) 3 years
0.0 0.1 0.2 0.3
DBH (m)
(b) 4 years
250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
250 95% C.I. 1,000 95% C.I. 5,000 95% C.I. 10,000 95% C.I.
Raw Data
Figure 4.16: The relationship between the dependent variable crown depth ratio and the factors DBH (m), planting density (st/ha), and age (yrs). The predicted value of crown depth ratio (with 95% confidence intervals) at ages (a) 3 years and (b) 4 years is plotted against DBH and identified by planting density for each tree measured.
The results for crown depth ratio show that increased competition resulted in decreased crown
depth ratio. It appears, however, that crown depth ratio was not necessarily a good indication
of tree vigour, since for a given stem diameter the higher planting densities had a lower crown
depth ratio (less crown retention) yet greater stem volume and mass (Figures 4.6, 4.7). Within
planting densities, crown depth ratio was a good indicator of vigour and dominance status,
since it shares a positive relationship with stem diameter (Figure 4.16). This finding
corroborates evidence that dominant trees exhibit a greater crown depth ratio than suppressed
trees (Stoneman and Whitford 1995; Medhurst et al. 1999).
Age was shown to have a negative relationship with crown depth ratio (Table 4.14) (Figure
4.16), indicating that increases in branch shed were greater than increases in stem height
between 3 and 4 years, since there was a decrease in relative crown retention over that time.
This cannot happen indefinitely since it would result in trees with no crown, however it is
likely to happen in times of stress, and the incidence of this occurring from 3 to 4 years was
likely to be related to the drought that occurred over this period of time (Figure 2.5).
Chapter 4 Tree Growth and Structure Page 82
LEAF SPECIFIC AREA
Leaf specific area is the ratio of leaf area to leaf oven-dry mass, and is a measure of leaf
‘thinness’. Reduced leaf specific area indicates ‘thicker’ leaves; a growth response of leaves
forming under greater exposure to desiccating factors like extreme temperatures and low
humidity (Specht 1985; Salisbury and Ross 1992; Specht and Specht 1999). For a given stem
diameter, leaf specific area decreased as planting density increased. Within planting densities,
leaf specific area had a positive relationship with stem diameter (Table 4.15) (Figure 4.17).
Table 4.15: The fixed-effect regression coefficients in the random intercept model of the effects of stem diameter (DBH) (m), planting density (P) (st/ha) and crown location (CL) on leaf specific area (cm
2g
-1).
VARIABLE COEFFICIENT S.E. P - VALUE INTERACTION COEFFICIENT S.E. P - VALUE
INTERCEPT -0.065 0.024 p = 0.006 lnDBH*CL 0.019 0.006 p = 0.001
lnDBH -0.078 0.010 p < 0.001
lnP -0.006 0.002 p = 0.003
CL 0.038 0.012 p = 0.001
0.0
0.4
0.8
1.2
0.0 0.1 0.2 0.3
Le
af
Sp
ec
ific
Are
a (
cm
2 g
-1)
(a) Crown Location - Upper
0.0 0.1 0.2 0.3DBH (m)
(b) Crown Location - Middle
250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
250 95% C.I. 1,000 95% C.I. 5,000 95% C.I. 10,000 95% C.I.
Raw Data
DBH (m)
Figure 4.17: The relationship between the dependent variable leaf specific area (cm2 g
-1) and the
factors DBH (m), planting density (st/ha) and crown location. The predicted value of leaf specific area (with 95% confidence intervals) at crown locations (a) upper and (b) middle is plotted against DBH and identified by planting density for each tree measured.
Chapter 4 Tree Growth and Structure Page 83
Within species, the greatest influence on leaf specific area is the exposure of leaves to
desiccating elements during formation. As leaves mostly form in the upper crown (noting that
leaves in the middle crown formed in a past upper crown), the location of the upper crown
within the whole canopy determines the exposure of developing leaves. Increased stem
diameter was therefore expected to correlate with decreased leaf specific area due to increased
exposure of the upper crown as a result of increased stem height (Figure 4.5). The results for
leaf specific area confirmed that this was the case since stem diameter was shown to share a
strong negative relationship with leaf specific area that was significant within all planting
densities, as there was no overlap of the 95% confidence intervals between the largest and
smallest DBH within each planting density (Figure 4.17). Clearly the level of exposure of
leaves during formation did have a strong negative influence on leaf specific area.
In comparing between planting densities it was apparent that there was no significant
difference in leaf specific area between the largest trees in each planting density, as indicated
by overlap of the 95% confidence intervals (Figure 4.17). Given that there was also no
significant difference in stem height between the largest trees in each planting density (Figure
4.5), then the above result was expected since the leaves of the largest trees in each planting
density all formed at the highest level of exposure in the upper canopy. Also apparent was that
for a given stem diameter increased planting density resulted in decreased leaf specific area.
This was unexpected since greater shading (less exposure) in high planting densities was
thought to result in higher leaf specific area, however if stem height is again considered
(Figure 4.5) it is apparent that higher planting densities had taller stems for a given stem
diameter, and may therefore be expected to develop lower leaf specific area for a given stem
diameter due to greater exposure for a given stem diameter.
The results for leaf specific area reflect the history of canopy development. We can see that
the leaf specific area of the largest trees was lowest, indicating that their upper crowns formed
in the highest exposure in the upper canopy, and that the leaf specific area of the largest trees
remained constant from the middle to the upper crown, indicating the constant presence of
their upper crowns in the upper canopy. In comparison the leaf specific area of the smallest
trees was highest, indicating that their upper crowns formed in lower exposure within the
canopy, and the leaf specific area of the smallest trees increased from the middle to the upper
crown, indicating that their upper crowns have been subject to progressively less exposure
(more shading) as crowns developed and the canopy became more crowded.
Chapter 4 Tree Growth and Structure Page 84
LEAF NUTRIENT CONTENT
Leaf nutrient content was measured as a percentage of leaf dry mass. For a given stem
diameter, the leaf nitrogen content decreased as planting density increased, whereas leaf
phosphorus content and leaf potassium content were unaffected by planting density. All leaf
nutrient contents had a negative relationship with stem diameter (Table 4.16) (Figure 4.18).
Table 4.16: The fixed-effect regression coefficients in the random intercept model of the effects of stem diameter (DBH) (m), planting density (P) (st/ha) and crown location (CL) on (a) leaf nitrogen content (%), (b) leaf phosphorus content (mg kg
-1) and (c) leaf potassium content (mg kg
-1).
(a) VARIABLE COEFFICIENT S.E. P - VALUE (b) VARIABLE COEFFICIENT S.E. P - VALUE
INTERCEPT 3.470 0.282 p < 0.001 INTERCEPT 2148.113 163.295 p < 0.001
√DBH -3.047 0.674 p < 0.001 √DBH -3548.535 1108.661 p = 0.001
P -0.000033 0.000014 p = 0.018
CL -0.265 0.076 p < 0.001
(c) VARIABLE COEFFICIENT S.E. P - VALUE
INTERCEPT 5557.280 560.232 p < 0.001
√DBH 176.587 47.465 p < 0.001
1.0
1.5
2.0
2.5
3.0
0.0 0.1 0.2 0.3
Leaf
Nu
trie
nt
Co
nte
nt
(%)
(a) Nitrogen
Crown Location - Upper
1.0
1.5
2.0
2.5
3.0
0.0 0.1 0.2 0.3
DBH (m)
(b) Nitrogen
Crown Location - Middle
250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
250 95% CI 1,000 95% CI 5,000 95% CI 10,000 95% CI
Raw Data
0.0
0.2
0.4
0.6
0.8
1.0
1.2
0.0 0.1 0.2 0.3
DBH (m)
(d) Potassium
Crown Location - Whole
0.0
0.2
0.4
0.6
0.8
1.0
1.2
0.0 0.1 0.2 0.3
DBH (m)
(c) Phosphorus
Crown Location - Whole
All Planting Densities
All Planting Densities 95% CI
DBH (m)
Figure 4.18: The relationship between the dependent variables leaf nitrogen, phosphorus and potassium content (% dry mass) and the factors DBH (m), planting density (st/ha) and crown location. The predicted values of (a) leaf nitrogen content in the upper crown, (b) leaf nitrogen content in the middle crown, (c) leaf phosphorus content in the whole crown, and (d) leaf potassium content in the whole crown (with 95% confidence intervals) are plotted against DBH and identified by planting density where applicable.
Chapter 4 Tree Growth and Structure Page 85
Neither planting density nor stem diameter were expected to affect leaf nutrient content since
it was thought that trees would accommodate nutrient shortages by reducing total leaf mass or
leaf specific area. The results for leaf nutrient content disprove this hypothesis since both
planting density and stem diameter were found to affect leaf nutrient content.
The results show that increased planting density correlated with decreased leaf nitrogen
content, but apparently not phosphorus or potassium content. Possibly nitrogen was the
nutrient in greatest demand (shortest supply) relative to the available nutrient supply, and
therefore limitations in nitrogen supply became apparent before limitations in other nutrients,
and were most apparent in the higher density stands that were likely to use more nutrients.
Leaf nitrogen content in the spacing trial was within previously reported ranges (Table 4.17),
but generally below the optimum level of 2.8%. Leaf phosphorus and potassium content were
also within previously reported ranges, but approximated the optimum levels of 0.15% and
0.75% respectively, rather than below. This comparison confirms that nitrogen was the
nutrient in greatest relative demand (shortest relative supply) since leaf nitrogen content was
generally low, especially in higher planting densities. Furthermore, leaf nitrogen content was
greater in the upper crown than the middle crown (Figure 4.18), indicating that nitrogen was
being mobilised from the middle to the upper crown due to excess demand for nitrogen.
Table 4.17: Published leaf nutrient contents arising from studies on E. grandis plantations compared to results for the current study.
Leaf Nutrient Content (%) Study Details
Nitrogen Phosphorus Potassium
Typical range in young E. grandis plantations (Judd et al. 1996).
1.25 - 2.75 0.075 - 0.200 0.60 - 1.15
Mean content in 9.25 year old E. grandis in NSW, Australia (Birk and Turner 1992)
1.56 0.084 0.60
Encountered range in E. grandis plantations in South Africa (Herbert 1992).
1.25 - 3.35 0.100 - 0.350 0.36 - 1.19
Optimum content in E. grandis plantations in South Africa (Herbert 1992).
2.80 0.150 0.75
Current Study 1.4 - 2.4 0.12 – 0.20 0.65 - 0.95
The negative relationship between stem diameter and leaf nutrient content (Figure 4.18) was
similar to the negative relationship between stem diameter and leaf specific area (Figure
4.17), indicating that shaded leaves had increased leaf specific area and increased leaf nutrient
content. This correlation has been observed previously (Cromer and Jarvis 1990; Stewart et
al. 1990; Kirschbaum et al. 1992), and it is typically attributed to more photosynthetically-
Chapter 4 Tree Growth and Structure Page 86
active components per unit of leaf area (Salisbury and Ross 1992; Kriedemann and Cromer
1996). In shaded leaves this maximises light use-efficiency by allowing use of all the limited
light incidentally hitting the leaf (Salisbury and Ross 1992), however it probably results in a
reduction in nutrient use-efficiency due to high nutrient requirements. In consequence,
smaller, more shaded trees appear to maximise light use-efficiency at the expense of reduced
nutrient use-efficiency, and conversely it is possible that larger, less shaded trees maximise
water and/or nutrient use-efficiency at the expense of reduced light use-efficiency.
TREE MASS RATIOS
Tree mass ratios are the oven-dry mass of the stem, branch and leaf components of the tree
relative to tree oven-dry mass. For a given stem diameter, stem mass ratio was found to
increase as planting density increased, whereas branch and leaf mass ratios were found to
decrease as planting density increased. Within planting densities, stem mass ratio had a
negative relationship with stem diameter, whereas branch and leaf mass ratios had a positive
relationship with stem diameter (Table 4.18) (Figure 4.19).
Table 4.18: The fixed-effect regression coefficients of the random intercept model of the effects of stem diameter (DBH) (m) and planting density (P) (st/ha) on (a) stem to tree mass ratio, (b) branch to tree mass ratio and (c) leaf to tree mass ratio.
(a) VARIABLE COEFFICIENT S.E. P - VALUE (b) VARIABLE COEFFICIENT S.E. P - VALUE
INTERCEPT 0.1350 0.0710 p = 0.057 INTERCEPT 0.6106 0.0553 p < 0.001
lnDBH -0.0640 0.0160 p < 0.001 lnDBH 0.0303 0.0150 p = 0.043
lnP 0.0640 0.0090 p < 0.001 lnP -0.0521 0.0073 p < 0.001
(c) VARIABLE COEFFICIENT S.E. P - VALUE
INTERCEPT 0.2429 0.0220 p < 0.001
lnDBH 0.0327 0.0061 p < 0.001
lnP -0.0110 0.0029 p < 0.001
The results for tree mass ratios indicate that increased planting density resulted in tree mass
ratios skewed towards the stem rather than the crown (Figure 4.19). The results further
indicate that skewness of tree structure towards the stem was greater for smaller trees, since
stem diameter had a negative relationship with stem mass ratio (Figure 4.19(a)) and a positive
relationship with branch and leaf mass ratio (Figure 4.19(b-c)). Overall, the greater the level
of competition pressure experienced by individual stems, at the general stand level (increased
planting density) and within the stand (decreased stem diameter), the greater the ratio of tree
mass was contained in the stem rather than in the branches or leaves.
Chapter 4 Tree Growth and Structure Page 87
0.5
0.6
0.7
0.8
0.9
1.0
0.0 0.1 0.2 0.3
DBH (m)
Mas
s R
ati
o (
kg
kg
-1)
(a) Stem to Tree
0.0
0.1
0.2
0.3
0.4
0.5
0.0 0.1 0.2 0.3
DBH (m)
(b) Branch to Tree
0.0 0.1 0.2 0.3
DBH (m)
(c) Leaf to Tree
250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
250 95% C.I. 1,000 95% C.I. 5,000 95% C.I. 10,000 95% C.I.
Raw Data
Mas
s R
ati
o (
kg
kg
-1)
Figure 4.19: The relationship between the dependent variables stem, branch and leaf mass ratio and the factors DBH (m) and planting density (st/ha). The predicted value of the ratio of (a) stem, (b) branch and (c) leaf mass to tree mass (with 95% confidence intervals) is plotted against DBH and identified by planting density for each tree measured.
The above findings raise the question of growth efficiency; the structural comparison of the
total amount of resources captured, as approximated by leaf area, to the amount of growth
produced. In terms of stem growth efficiency (the efficiency of producing a marketable
product), it appears that trees experiencing greater competitive pressure (high planting
density/low stem diameter) were more efficient since they produced the greatest relative stem
mass (Figure 4.19(a)) from the smallest relative crown size (Figure 4.19(b-c)). This logic,
however, is not in concert with previous findings that stem cohorts experiencing greater
competitive pressure (high planting density/low stem volume increment) were decreasing in
productivity (Figure 3.11). Closer examination of growth efficiency will provide insight into
whether trees under the greatest competitive pressure (small, shaded trees) with declining
productivity, were in fact most growth efficient.
GROWTH EFFICIENCY
Growth efficiency compares the total amount of resource capture to the amount of growth
produced. Sophisticated methods for estimating light capture and water transpiration were not
at the disposal of the current study, and crown leaf area was therefore used as rough measure
of the potential light capture and water transpiration of individual trees. The growth
Chapter 4 Tree Growth and Structure Page 88
efficiencies of tree mass, stem mass and stem volume were calculated as the ratio of each
variable to crown leaf area. For a given stem diameter, growth efficiency was found to
increase as planting density increased. Within planting densities, growth efficiency had a
positive relationship with stem diameter (Table 4.19) (Figure 4.20).
Table 4.19: The fixed-effect regression coefficients of the random intercept model of the effects of stem diameter (DBH) (m) and planting density (P) (st/ha) on (a) tree mass growth efficiency (kg m
-2), (b)
stem mass growth efficiency (kg m-2
) and (c) stem volume growth efficiency (m3 m
-2).
(a) VARIABLE COEFFICIENT S.E. P - VALUE (b) VARIABLE COEFFICIENT S.E. P - VALUE
INTERCEPT 202.886 106.325 p = 0.056 INTERCEPT 22.295 93.672 p = 0.813
lnDBH 157.756 39.932 p < 0.001 lnDBH 110.178 33.894 p < 0.001
lnP 53.284 11.040 p < 0.001 lnP 57.028 9.738 p < 0.001
(c) VARIABLE COEFFICIENT S.E. P - VALUE
INTERCEPT -0.353 0.190 p = 0.062
1/DBH 0.022 0.009 p = 0.015
lnP 0.142 0.025 p < 0.001
0
200
400
600
0.0 0.1 0.2 0.3DBH (m)
(a) Tree Mass to Leaf Area
0.0
0.3
0.6
0.9
1.2
0.0 0.1 0.2 0.3DBH (m)
(c) Stem Volume to Leaf Area
0.0 0.1 0.2 0.3DBH (m)
(b) Stem Mass to Leaf Area
250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
250 95% C.I. 1,000 95% C.I. 5,000 95% C.I. 10,000 95% C.I.
Raw DataV
olu
me
pe
r U
nit
Le
af
Are
a (
m3 m
-2)
Mas
s p
er
Un
it L
eaf
Are
a (
kg
m-2
)
Figure 4.20: The relationship between the growth efficiency (mass or volume per unit leaf area) of the dependent variables tree mass, stem mass and stem volume, and the factors DBH (m) and planting density (st/ha). The predicted value of the growth efficiency of (a) tree mass, (b) stem mass and (c) stem volume (with 95% confidence intervals) are plotted against DBH and identified by planting density for each tree measured.
Chapter 4 Tree Growth and Structure Page 89
The results for tree growth efficiency indicate that planting density had no significant affect
on the tree growth efficiency of dominant trees since the 95% confidence intervals overlap
between the largest trees in all planting densities (Figure 4.20(a)). Within planting densities
increased dominance status (stem diameter) resulted in increased tree growth efficiency
(Figure 4.20(a)). It is noteworthy, however, that tree growth efficiency measurements do not
include the roots, and it is therefore expedient to consider whether the inclusion of root mass
would affect the above results.
Evidence from previous studies shows that the proportion of biomass allocated to roots
generally increases as stocking density decreases (Eastham and Rose 1990; Bargali et al.
1992; Fabiao et al. 1995; Bernardo et al. 1998; Leles et al. 2001; Saint-André et al. 2005)
(Figure 4.1). In consequence, the underestimation of tree mass caused by not including root
mass was likely to be greater for trees in low planting densities, and the difference in tree
mass growth efficiency between dominants in low and high planting densities would have
been reduced if root mass were included in the tree mass measurement. The finding that
planting density did not affect tree growth efficiency in dominant trees is therefore
corroborated by considering the inclusion of root mass in tree mass.
Within planting densities it is likely that competition for ground water is similar to
competition for light in that it is asymmetric; deeper roots having access to the most saturated
soil, and shallower roots having to make do with what remaining water is able to seep past
deeper roots. A previous study shows that roots extend to a greater soil depth in higher
planting densities (given no physical obstructions) (Eastham and Rose 1990), presumably
stimulated by increased water deficits closer to the surface as a result of greater competition
for water. Just as stem height extends higher for dominant trees, it is likely that root depth
extends lower, with the result that relative root mass is similar between dominant and
suppressed trees and tree growth efficiency would not be significantly affected by including
root mass in the tree mass measurement. The finding that increased dominance status (stem
diameter) resulted in increased tree growth efficiency is therefore corroborated by considering
the inclusion of root mass in tree mass.
The results for stem growth efficiency followed the same pattern to those for tree growth
efficiency; however significant differences occurred between dominant (largest) trees in high
and low planting densities (Figure 4.20(b)). Dominant trees in high planting densities had
Chapter 4 Tree Growth and Structure Page 90
greater stem growth efficiency since they allocated a higher proportion of tree mass to the
stem per unit of leaf area. This result was implied in the previous tree mass ratio results
(Figure 4.19) since dominant trees in all planting densities had similar relative leaf mass, yet
increased planting density resulted in a reduced proportion of mass allocated to branches and
an increased proportion of mass allocated to the stem in dominant trees. Overall it appears
that regardless of planting density, dominant trees had similar photosynthate production per
unit leaf area, as evidenced by similarities in leaf specific area (Figure 4.17) and leaf nutrient
content (Figure 4.18), and similar tree growth efficiency. Stem growth efficiency, however,
increased in dominant trees as planting density increased due to a change in growth portioning
strategy away from the branches and towards the stem.
The finding that dominant trees had greater stem growth efficiency than suppressed trees was
not in concert with tree mass ratio results since on a mass basis, suppressed trees had the
greatest stem mass per unit leaf mass (Figure 4.19). Given that growth efficiency is calculated
using leaf area, the likely explanation for this disparity is leaf morphology. Suppressed trees
were shown to have greater leaf specific area than dominant trees within planting densities
(Figure 4.17), indicating that leaf area has been maximised by ‘spreading’ a given leaf mass
over a larger area. So despite suppressed trees having the greatest stem mass per unit leaf
mass (Figure 4.19), the corresponding increase in leaf specific area is sufficient to cause
suppressed trees to exhibit reduced stem growth efficiency (stem mass per unit leaf area)
compared to dominant trees.
The results for stem volume growth efficiency (Figure 4.20(c)) follow a similar pattern to the
previous growth efficiency measurements; however there is a greater magnitude of difference
between dominant trees in different planting densities, suggesting that dominant trees in low
planting densities have greater wood density (i.e. less volume for a given mass). The issue of
wood density is pertinent to wood quality and is addressed in greater detail in Chapter 5 –
Wood Growth and Structure.
4.4.3 Implications for Stand Growth and Structure
Examination of stand structure in the previous chapter showed that the mean size of the top
(dominant) 250 and 1,000 stem cohorts were remarkably similar in size, despite very strong
competition occurring in the high planting densities. The results for tree growth and structure
Chapter 4 Tree Growth and Structure Page 91
showed that whilst dominant trees in high planting densities had greater stem growth
efficiency than dominant trees in low planting densities due to different carbon partitioning
strategies, they were in fact significantly smaller than dominant trees in low planting densities
despite similar tree growth efficiencies. The similarity in size of the top 250 and 1,000 stem
cohorts between planting densities was therefore not due to a similarity in the size of the very
largest trees, but rather due to high planting densities having modest size and greater size
uniformity in the top cohort (i.e. no smaller trees in the top cohort dragging the average size
of the cohort down).
Examination of the 250 stem cohorts in the previous chapter indicated that high planting
densities had a greater representation of dominant and co-dominant trees in the top four 250
stem cohorts (Figure 3.12). A closer examination of the top four 250 stem cohorts (top 1,000
stem cohort) shows that planting densities 5,000-10,000 st/ha had less size inequality in the
top 1,000 stem cohort than 1,000 st/ha (Figure 4.21), despite having greater size inequality in
the whole stand (Figure 3.9). This indicates that trees in the top 1,000 stem cohort in high
planting densities were of similar high dominance in the stand, whereas trees in the top 1,000
stem cohort in 1,000 st/ha included dominant, intermediate and suppressed trees. As a result
the mean stem volume in the dominant 1,000 stem cohort is similar between planting
densities, despite the most dominant trees in high planting densities being smaller than the
most dominant trees in low planting densities.
The above discussion shows that high planting densities exhibited more dominant and co-
dominant trees per unit area than low planting densities. It is possible that high planting
densities had greater site occupancy, and were therefore able to ‘fit’ more dominant and co-
dominant trees into the stand matrix. Alternatively high density stands may force trees to
perform to their maximum potential in order to avoid suppression and death. The fierce
competition for dominance from the seedling stage renders it unlikely that any trees would
‘dawdle’ in growth, hence there was greater uniformity amongst the top ranked stems.
Finally, it may simply have been the case that all planting densities had a similar proportion of
dominant genotypes present in the stand, and consequently high density stands had a greater
absolute number of trees with dominant genotypes present in the stand by virtue of having a
greater population size.
Chapter 4 Tree Growth and Structure Page 92
0.0
0.5
1.0
0.0 0.5 1.0
Cu
mu
lati
ve P
rop
ort
ion
of
Ste
m V
olu
me
in t
he T
op
1,0
00 s
t/h
a S
ize C
lass
Equality
1,000 st/ha
5,000 st/ha
10,000 st/ha
1,000 st/ha
GC = 0.707
CV = 53.9%
MSV = 0.105 m3
Mortality = 12.5%
TSV = 11.00 m3
Cumulative Proportion of Stem Count
in the Top 1,000 st/ha Size Class
5,000 st/ha
GC = 0.857
CV = 26.7%
MSV = 0.099 m3
Mortality = 0.0%
TSV = 17.88 m3
10,000 st/ha
GC = 0.859
CV = 27.1%
MSV = 0.093 m3
Mortality = 0.0%
TSV = 17.95 m3
Lorenze Curves
Figure 4.21: Lorenz curves and the gini-coefficient (GC), coefficient of variation (CV), mean stem volume (MSV), mortality and total stem volume (TSV) values for stem volume in the dominant 1,000 stem cohort of E. grandis at age 4 years for planting densities 1,000 st/ha, 5,000 st/ha, and 10,000 st/ha.
A separate finding in the previous examination of stand structure was that several of the
bottom 250 stem cohorts in planting densities 1,000-10,000 st/ha were declining in
productivity (Figure 3.11) despite that total and mean stand productivity were increasing
(Figures 3.4, 3.5). A separate study suggested that declining stand productivity could be due
to reduced growth efficiency in sub-dominant and suppressed trees (Binkley et al. 2002). The
results for tree growth and structure support this argument since they indicate that suppressed
trees had lower growth efficiency than dominant trees, possibly due to leaf morphology and
reduced nutrient use-efficiency during photosynthesis in more shaded leaves.
The evidence that increased dominance status resulted in increased growth efficiency is of
relevance to total resource use-efficiency and the debate on declining stand productivity. One
could argue that suppressed trees should have greater total resource use-efficiency given that
studies at the leaf level show that resource use-efficiency increases in response to resource
restrictions, and suppressed trees clearly experience resource restrictions. This theory,
however, implies that suppressed trees have the ability to resist decreasing growth rates by
being more efficient, whereas the analysis of stand growth implied that trees tend to move
down dominance classes and were unable to resist decreased growth rates once suppressed. It
Chapter 4 Tree Growth and Structure Page 93
therefore seems probable that dominant trees have superior total resource use-efficiency
leading to greater tree growth efficiency, and that declining stand productivity is in part
caused by reduced resource use-efficiency in suppressed trees rather than dominant trees. The
issue of total resource use-efficiency, which is essentially the balance between trading off
improved resource use-efficiency in one resource against reduced resource use-efficiency in
other resources, therefore appears to be of importance in understanding the mechanisms
causing suppression in trees and declining stand productivity.
If one regards the top 1,000 st/ha as the final solid wood crop, keeping in mind that it would
probably be harvested in one or two commercial thinning operations in addition to the final
harvest operation, then the results for tree growth and structure show that high planting
density could be a practical management tool. High planting density had the effect of
increasing size uniformity in the top 1,000 st/ha at the expense of a small reduction in mean
stem diameter. Furthermore high planting density provides an ‘insurance policy’ against
mortality, such that total stem volume in the top 1,000 st/ha is almost certainly increased due
to zero mortality. High planting densities also have the potential to provide a substantial
biomass harvest and financial return early in the life of the plantation, and possibly multiple
times if biomass harvest stems coppice. That there was no significant difference between
5,000 st/ha and 10,000 st/ha in every result indicates that at most a 5,000 st/ha planting
density would be dense enough to achieve the above results.
The potential gains of using high planting density as a management tool are worth pursuing
only if such management is not detrimental to the final product, which is usually solid wood
or wood fibre. Therefore it is appropriate to examine the effect of competition on wood
growth and structure before further considering high planting density as a management tool.
Chapter 4 Tree Growth and Structure Page 94
4.5 Summary
In comparing dominant trees between planting densities (Table 4.20), dominant trees in high
planting densities were significantly smaller than dominant trees in low planting densities in
all aspects of tree growth other than stem height. Tree structure variables indicate that
compared to dominant trees in low planting densities, dominant trees in high planting
densities had a larger proportion of biomass allocated to the stem rather than the crown but
similar rates of photosynthesis per unit leaf area, and consequently dominant trees in high
planting densities had better stem growth efficiency than dominant trees in low planting
densities. Dominant trees in high planting densities had similar tree growth efficiency to
dominant trees in low planting densities.
Table 4.20: A summary of the results for tree growth and structure comparing dominant trees in low (250-1,000 st/ha) and high (5,000-10,000 st/ha) planting densities.
Planting Density Result Location Stand Variable
Low High Figure Page
Tree Growth
Stem Diameter high low Figure 4.4 66
Stem Height similar similar Figure 4.5 67
Stem Volume high low Figure 4.6 68
Stem Mass high low Figure 4.7 70
Crown Width high low Figure 4.7 71
Crown Leaf Area high low Figure 4.9 72
Crown Mass - Leaf high low Figure 4.10 (a) 73
Crown Mass - Branch high low Figure 4.10 (b) 73
Tree Structure
Tree Form (cylindrical : conical) - Stem similar similar Figure 4.11 (a) 75
Tree Form (cylindrical : conical) - Crown low high Figure 4.11 (b) 75
Bark Ratio high low Figure 4.12 76
Branch Formation similar similar Figure 4.13 77
Branch Shed - Crown Height low high Figure 4.15 79
Branch Shed - Crown Depth Ratio high low Figure 4.16 81
Leaf Specific Area similar similar Figure 4.17 82
Leaf Nutrient Content similar similar Figure 4.18 84
Tree Mass Ratio - Stem : Tree low high Figure 4.19 (a) 87
Tree Mass Ratio - Branch : Tree high low Figure 4.19 (b) 87
Tree Mass Ratio - Leaf : Tree high low Figure 4.19 (c) 87
Growth Efficiency - Tree low high Figure 4.20 (a) 88
Growth Efficiency - Stem similar similar Figure 4.20 (b-c) 88
In comparing suppressed and dominant trees within planting densities (Table 4.35),
suppressed trees were significantly smaller than dominant trees in all aspects of tree growth.
Where differences between suppressed and dominant trees occurred in tree structure,
Chapter 4 Tree Growth and Structure Page 95
suppressed trees exhibited greater skewness of the crown towards the crown apex and mass
towards the stem, and these trends were usually stronger in high planting densities. Despite
greater stem mass per unit leaf mass, suppressed trees exhibited lower stem growth efficiency
than dominant trees, probably due to greater leaf specific area. Suppressed trees exhibited
lower tree growth efficiency than dominant trees, possibly due to the shaded leaves of
suppressed trees having lower total resource use-efficiency.
Table 4.21: A summary of the results for tree growth and structure comparing suppressed and dominant trees (as determined by small and large stem diameter) within planting densities. Planting densities are marked with a � when the trend between suppressed and dominant trees was significant (95% confidence intervals overlap), and a � when the trend between suppressed and dominant trees was insignificant (95% confidence intervals do not overlap).
Dominance Status Significance within
Planting Density (st/ha) Stand Variable
Suppressed Dominant 250 1,000 5,000 10,000
Tree Growth
Stem Height less more � � � �
Stem Volume less more � � � �
Stem Mass less more � � � �
Crown Width less more � � � �
Crown Leaf Area less more � � � �
Crown Mass - Leaf less more � � � �
Crown Mass - Branch less more � � � �
Tree Structure Tree Form (cylindrical : conical) - Stem more less � � � �
Tree Form (cylindrical : conical) - Crown same same � � � �
Bark Ratio - By Volume more less � � � �
Bark Ratio - By Mass more less � � � �
Branch Formation less more � � � �
Branch Shed - Crown Height less more � � � �
Branch Shed - Crown Depth Ratio less more � � � �
Leaf Specific Area more less � � � �
Leaf Nutrient Content more less � � � �
Tree Mass Ratio - Stem : Tree more less � � � �
Tree Mass Ratio - Branch : Tree less more � � � �
Tree Mass Ratio - Leaf : Tree less more � � � �
Growth Efficiency - Tree less more � � � �
Growth Efficiency - Stem less more � � � �
Overall the results for tree growth and structure show that increased competition had the
effect of reducing tree size and skewing mass distribution towards the stem. The major
strategy for mitigating the effects of competition appeared to be to shed the least efficient
mass, which in the crown was the lower leaves and branches, and in the stem was butt-swell
(thickening at the base of the stem causing a more conical stem shape), and in the roots could
Chapter 4 Tree Growth and Structure Page 96
possibly be auxiliary tap roots. Growth could then be concentrated into maximising the
chance of improving resource capture by increasing stem height and probably increasing tap
root depth. It is likely that in young, competitive stands stem diameter only increases insofar
as to provide the minimum support requirement for the crown.
In terms of stand structure, the similarity in mean stem diameter in the top (dominant) 250 and
1,000 stem cohorts between planting densities was due to a combination of better stem growth
efficiency in dominant trees in high planting densities, and a greater number of dominant
and/or co-dominant trees in the dominant 1,000 stem cohort in high density stands. Within the
dominant 1,000 stem cohort, high planting densities exhibited low size inequality and
therefore similar dominance between trees, whereas low planting densities exhibited greater
size inequality and therefore variable dominance between trees, providing evidence that high
planting densities had more dominant and co-dominant trees per unit area than low planting
densities. This resulted in comparable stand mean stem volume in the top (dominant) 250 and
1,000 stem cohorts between planting densities, and greater total stem volume in the dominant
1,000 stem cohort in high planting densities due to zero mortality compared to low planting
densities.
The results for tree growth and structure support the argument that declining stand
productivity could be due to reduced resource use efficiency in co-dominant and suppressed
trees. The lower tree growth efficiency exhibited by suppressed trees may be due to a reduced
resource use-efficiency balance during photosynthesis in shaded leaves.
The results for tree growth and structure indicated that high planting density could be a
practical management tool since in the top 1,000 st/ha it increased size uniformity, eliminated
mortality and increased total stem volume, despite a reduction in mean stem diameter. The
use of high planting density as a management tool is worth pursuing only if not detrimental to
the final wood product. Therefore it is appropriate to examine the effect of competition on
wood growth and structure before considering the use of high planting density as a
management tool.
Chapter 5 Wood Growth and Structure Page 97
5. WOOD GROWTH AND STRUCTURE
Wood growth and structure is an important aspect of plantation management as wood
products are the primary produce currently emerging from hardwood plantations in Australia
(Turner et al. 2004). The vast majority of hardwood plantations in Australia are fast-growing
eucalyptus plantations that are managed for pulpwood production (Turner et al. 2004), and the
majority of research on eucalyptus plantations has historically focused on improving growth
in order to maximise pulpwood production, with less effort expended in research on the
potential of hardwood eucalyptus plantations to produce solid clearwood products.
In concert with the development of the pulpwood industry, there has been building pressure to
increase plantation production of solid hardwood products given that Australia has a trade
deficit in sawnwood and is decreasing production of native hardwood sawnwood due to
increased conservation of native forests (Turner et al. 2004). The development of a solid
hardwood plantation industry, however, is hampered by a lack of knowledge and experience
in growing eucalypts for sawlogs or veneer logs. The potential for current eucalyptus
pulpwood plantations to produce sawnwood products has been recognised, but is generally
discounted since highly stocked hardwood pulpwood plantations are considered unlikely to
produce quality sawlogs (Turner et al. 2004). Within this environment there is impetus for
increased research into the effect of plantation growth on wood structure, and whether high
stocking rates are detrimental to wood quality.
5.1 State of Knowledge
5.1.1 Wood Growth
In all tree species, wood is a heterogenous substance composed of cells originating from a
thin outer layer called the vascular cambium, which forms an uninterrupted cone around the
stemwood surface (Jane 1970; Wilson and White 1986; Zobel and Buitjtenen 1989). The
vascular cambium consists of fusiform initials and ray initials, collectively known as cambial
initials (Jane 1970; Wilson and White 1986).
Cambial initials have thin primary walls, and are joined to adjacent cells by a thin layer called
the middle lamella (Jane 1970; Wilson and White 1986). Wood growth begins with the
division of cambial initials to form two ‘daughter’ cells (Figure 5.1(a)), whereby the
outermost cell remains a cambial initial and the innermost cell differentiates to form one of
Chapter 5 Wood Growth and Structure Page 98
the many cell types found in the wood (Jane 1970; Wilson and White 1986). The mechanism
determining what cell type each initial forms is unknown, however it is thought that the
proximity of other cell types may have some influence. It is known that plant hormones like
auxins trigger the process of differentiation, however it is not known whether they cause the
initial division of cambial initials, or whether cambial initials divide regardless and hormones
cause the differentiation of daughter cells (McCann 1997). Once differentiation has been
initiated hormones are not thought to be required to maintain the process (Stacey et al. 1995).
Figure 5.1: The process of wood growth including (a) the division of cambial initials, (b) cell elongation and expansion, (c) deposition of the secondary wall and (d) lignin impregnation.
The process of differentiation from cambial initial to wood cell has three stages (Wardrop
1965). The first stage is a change in cell shape and an increase in cell size from the cambial
initial (Figure 5.1(b)) (Wardrop 1965; Jane 1970). These changes are possible as the primary
wall and middle lamella of cambial initials are extensible, allowing the cell to grow (Jane
1970). The growth of cells during differentiation is thought to be driven by turgor pressure
within the tree; whereby filaments in primary walls are stretched like a spring by turgor
pressure, and additional materials are laid down to fill in the gaps. In this way cells are able to
grow faster when there is greater water availability due to a greater turgor pressure (amongst
other factors) (Salisbury and Ross 1992). The growth of cells causes them to push against
other cells, and with no room to move inwards due to existing wood, growing cells expand
outwards creating diameter growth in the stem (Wilson and White 1986).
The second stage of differentiation is the deposition of a secondary wall onto the primary
wall, which may begin in the middle of the cell before the primary wall has finished (Jane
1970) expanding at the tips (Figure 5.1(c)) (Wardrop 1965; Jane 1970; Panshin and De Zeeuw
(a) (b) (c) (d)
Chapter 5 Wood Growth and Structure Page 99
1980; Wilson and White 1986). The secondary wall consists of three layers, which together
are much thicker than the primary wall (Jane 1970; Wilson and White 1986). Unlike the
primary wall, the secondary wall is inflexible and therefore prevents any further expansion of
the growing cell (Salisbury and Ross 1992).
The final stage of differentiation is lignin impregnation between the filaments constructing the
cell walls (Figure 5.1(d)) (Jane 1970; Panshin and De Zeeuw 1980; Wilson and White 1986).
Lignification of the middle lamella and primary wall usually begins during the deposition of
the secondary wall, whilst lignification of the secondary wall begins once the secondary wall
is complete (Wardrop 1964, 1965; Panshin and De Zeeuw 1980; Downes and Ward 1993;
Donaldson 2001). Lignin deposition usually begins first in the cell wall corners, from where it
spreads across the remaining cell wall (Donaldson 2001), and in normal wood most lignin is
located in the middle lamella and primary wall (Wardrop 1964, 1965). Following
differentiation the now mature wood cell dies, and its organelles may be dissolved or
deposited on the inner face of the cell wall before the cell opens up for fluid translocation
(Panshin and De Zeeuw 1980).
5.1.2 Wood Structure
The ultimate structure of wood is the structure of the material wood is made from, including
the nature of the composite materials and their arrangement (Jane 1970; Wilson and White
1986). The wood of all trees generally contains 65-80% holocellulose, 20-35% lignin, and 1%
extraneous materials (Jane 1970; Panshin and De Zeeuw 1980; Wilson and White 1986).
Holocellulose consists of linear polymers, including pure cellulose, which are crystalline in
nature and chain together to form long filaments called microfibrils (Wardrop 1964; Panshin
and De Zeeuw 1980; Wilson and White 1986). In comparison, lignin consists of three-
dimensional and amorphous polymers (Wardrop 1964; Jane 1970; Panshin and De Zeeuw
1980; Wilson and White 1986; Donaldson 2001). Extraneous materials are mineral and
organic deposits, and their 1% ratio increases up to around 20% in heartwood due to their
deposition into wood cells during conversion from sapwood to heartwood (Panshin and De
Zeeuw 1980; Wilson and White 1986). Extractives are thought to form ‘in-situ’, or very close
to, the cells they are deposited in (Hillis 1971).
Chapter 5 Wood Growth and Structure Page 100
Wood cell walls are composite structures including the middle lamella, the primary wall and
the secondary wall (Figure 5.2) (Jane 1970; Panshin and De Zeeuw 1980; Wilson and White
1986). The middle lamella consists mainly of extraneous pectic substances which have been
heavily lignified, and contains no cellulose (Jane 1970; Wilson and White 1986). The primary
wall is thin and consists of microfibrils (cellulose) embedded in lignin. The microfibrils are
scattered in flat helices around the wall, with some vertically orientated at the corners of the
cell (Wardrop 1964; Jane 1970; Panshin and De Zeeuw 1980; Wilson and White 1986).
Evidence suggests microfibrils in the primary wall have been pulled apart, creating a ‘latticed’
effect, which is thought to have facilitated cell wall expansion during cell formation (Boyd
and Foster 1975).
Figure 5.2: The ultimate structure of a typical wood cell (adapted from Jane 1970).
The secondary wall has a higher ratio of cellulose than in the primary wall, and consists of
three layers called the S1, S2 and S3 layers (Wardrop 1964; Jane 1970; Panshin and De Zeeuw
1980; Wilson and White 1986). Microfibril arrangement varies between the three S layers
(Figure 5.2) due to changes in the number of lamellae (sheets of microfibrils) (Wardrop 1964;
Panshin and De Zeeuw 1980; Wilson and White 1986), the orientation of microfibrils within
each lamellae (left to right helix (S) or right to left helix (Z)) (Wardrop 1964; Panshin and De
Zeeuw 1980; Wilson and White 1986), and the average angle of each lamella from the
vertical (Wardrop 1964; Panshin and De Zeeuw 1980; Wilson and White 1986). Of the S
layers the S2 layer is the thickest, and therefore the most important in determining many
physical properties of wood (Wilkins 1986). Again there is evidence to suggest that ‘latticing’
S3 microfibrils form 0-12 lamellae, with either S
or Z helices, with angles between 50-70°.
S2 microfibrils form 30-40 lamellae, all with Z
helices and with an angle between 10-30°.
S1 microfibrils form 4-6 lamellae, with either S or
Z helices, with angles between 50-70°.
Primary Wall
Middle Lamella
Chapter 5 Wood Growth and Structure Page 101
occurs in order to facilitate cell wall expansion, although the extent of this in the secondary
wall is less than in the primary wall (Boyd and Foster 1975).
The basic ultrastructure of wood cells has been established for many years, yet there is still a
great deal that is unknown. The chemical composition of lignin is only partly described, as is
that of the many extractive compounds found in wood, and the description of these substances
is complicated by the variety of lignins and extractives found in different species (Hillis 1971;
Wilson and White 1986), and different wood cell types (Donaldson 2001). The cell organelles
involved in synthesising and moving cell wall components have generally been isolated,
however the mechanisms by which holocellulose (microfibrils) and lignin are created,
deposited, and oriented are in doubt (Muhlethaler 1965; Panshin and De Zeeuw 1980;
McCann 1997; Donaldson 2001).
Together wood cells form a solid cellular structure, which in hardwood genera such as
Eucalyptus, is relatively complex. The structure is comprised of five cell types, these being
fibres, parenchyma, vessel elements, tracheids and fibre-tracheids (Figure 5.3). All five
elements form elongated structures, which are arranged axially (parallel) to the stem, branch
or root they occur in, with the exception of parenchyma which may also be arranged radially
(Wardrop 1964; Panshin and De Zeeuw 1980; Wilson and White 1986). An additional feature
of the wood anatomy of eucalypts is the openings, or pits, that occur between cells and allow
fluids to move from cell to cell (Wardrop 1964; Panshin and De Zeeuw 1980; Wilson and
White 1986).
Figure 5.3: 3D image of hardwood cellular structure featuring (a) fibres, (b) ray parenchyma and (c) vessel elements (Meylan and Butterfield 1972).
Chapter 5 Wood Growth and Structure Page 102
Fibres – Compared to cambial initials, fibres are 4-6 times longer (Wardrop 1965), although
in plantation grown E. globulus fibres were found to be only twice the size of cambial initials
(Ridoutt and Sands 1993, 1994). Fibres may be mature in the middle whilst the tips are still
juvenile and elongating (Wardrop 1965). In consequence, fibres are long narrow cells, often
with forked or serrated tips where the expanding tip has divided around some obstruction,
such as a ray (Wardrop 1965). Fibres typically have thick cell walls that become heavily
lignified, and their simple pits are small with an elongated, slit-like shape, resembling a
flattened funnel in the thick cell wall (Panshin and De Zeeuw 1980). Following cell wall
development, most fibres are left senescent after their protoplasts are dissolved and replaced
by sap.
Fibres primarily function for mechanical strength and occur in large groups within the wood
structure (Panshin and De Zeeuw 1980), constituting up to 70% of wood in eucalypts
(Wardrop 1964). Due to their intrusive growth, resulting in high surface area to volume ratios,
fibres have high intercellular cohesion. It is this intercellular cohesion, in addition to thick cell
walls, which produce the generally superior mechanical properties of hardwoods. Fibres may
also undergo food storage capacities in specialised septate fibres, which form a longitudinal
column of cells used for food storage. Septate fibres are prominent in tropical species and
species in which longitudinal parenchyma (specialised food storage cells) are not abundant.
They may be a flexible response mechanism for storing short-term oversupplies of food
production.
Parenchyma - Parenchyma develop from a single cambial initial into a ‘ribbon’ of multiple
parenchyma cells, which is typically 1-3 cells wide and multiple cells high. Parenchyma may
occur on the axial and radial axis, and radial parenchyma are called rays because they form
radial bands through the wood in the direction of pith to bark. Rays are generally straight, but
may become deflected by the developmental expansion of adjacent cells. The rays of
hardwoods are diverse, varying in length, height and width (Panshin and De Zeeuw 1980).
Parenchyma primarily function for food storage within the tree, and contain metabolic
products like starch grains, or other specialised substances like oils and salt crystals (Panshin
and De Zeeuw 1980). Parenchyma remain alive and functional for the duration of the
sapwood, after which their contents are mobilised and used and they are converted to
heartwood (Panshin and De Zeeuw 1980).
Chapter 5 Wood Growth and Structure Page 103
Vessel Elements - Vessel elements are cells that align in a vertical column to form a ‘tube’,
and they are characteristic of hardwoods rather than softwoods, although they are not found in
all hardwood species (Panshin and De Zeeuw 1980; Wilson and White 1986). Each vessel
element is derived from a cambial initial, and during differentiation the cell walls become
thickened with highly pitted secondary walls, except at the ends where adjacent vessel
elements align to form a column (Panshin and De Zeeuw 1980; Wilson and White 1986).
Rather than thicken these end-points dissolve, leading to the formation of an open tube
(Panshin and De Zeeuw 1980; Wilson and White 1986). The mature vessel is then left as an
inert transpiration pipe filled with sap (Wilson and White 1986).
Vessels primarily function for water conduction (Panshin and De Zeeuw 1980; Wilson and
White 1986), and in terms of mechanical strength, they are comparatively weak (Wilson and
White 1986). Evidence suggests that in comparison to tree height, the majority of vessels are
short at up to 20cm in length (Wilson and White 1986). They form a continuous transpiration
pathway by sharing pits (openings) with other vessels and cell types (Panshin and De Zeeuw
1980; Wilson and White 1986).
Vasicentric-Tracheids - Tracheids are not present in all hardwoods, however they are present
in Eucalyptus species in the form of vasicentric-tracheids, which are characteristically found
adjacent to vessels (Panshin and De Zeeuw 1980). Compared to fibres, vasicentric-tracheids
have rounded tips, thin walls, and are not greatly elongated (Panshin and De Zeeuw 1980).
They are usually distorted in shape due to the expansion of the adjacent vessel elements.
Vasicentric-tracheids have round pits, called bordered pits (Panshin and De Zeeuw 1980),
many of which join to vessel element pits. Like vessels, vasicentric-tracheids are primarily
water conducting structures that are mechanically weak.
Fibre-Tracheids - Fibre-tracheids are cells that largely resemble fibres, except that they have
bordered pits similar to tracheids (Panshin and De Zeeuw 1980). In consequence they are
defined fibre-tracheids, however for all intensive purposes they have the same behaviour and
function as fibres, and are usually referred to as fibres (Panshin and De Zeeuw 1980).
Chapter 5 Wood Growth and Structure Page 104
5.1.3 Wood Types
The description of wood to this point has described normal wood, which is also known as
mature sapwood. There are, however, a number of other wood types found in tree stems that
are important sources of variability in wood (Zobel and Buitjtenen 1989).
Juvenile and Mature Wood - As trees develop, their wood cells change between consecutive
growth layers. These changes are rapid during early development but become more gradual
over time, and they affect wood properties, principally because fibres become longer and
wider, with thicker cell walls. Juvenile and mature wood refer to the rate of change in wood
cells between consecutive growth layers. Juvenile wood is characterised by rapid changes in
wood cells and is restricted to the stem core, whereas mature wood is characterised by gradual
changes in wood cells and encases the juvenile core (Wilson and White 1986; Zobel and
Buitjtenen 1989). In practice there is no sharp distinction between juvenile and mature wood
(Wilson and White 1986), the change between the two occurs as the tree reaches maturity and
the rate of change in wood cells between consecutive growth layers diminishes (Zobel and
Buitjtenen 1989). In young trees all the wood is juvenile, and this changes as mature wood
begins to form at around 10-20 years in eucalypts (Wilson and White 1986). From this point
onwards the juvenile/mature wood ratio declines as the ratio of mature wood increases.
The ratio of juvenile to mature wood is an important consideration for plantation managers
because the properties of these wood types have significant implications for the quality of
product produced. In eucalypts, producers of reconstituted wood products generally prefer a
high juvenile/mature wood ratio because of superior pulping and gluing qualities of juvenile
wood (Zobel and Buitjtenen 1989), whereas producers of solid wood and bio-fuel products
prefer a low juvenile/mature wood ratio due to the greater strength, stability, durability and
energy content of mature wood (Bootle 1983; Groves and Chivuya 1989). This raises the
question of whether the juvenile/mature wood ratio can be manipulated other than by waiting
for the mature wood to grow.
It is difficult to answer the above question when little is known about the triggers for causing
changes in wood cells or the function of changes in wood cells. One line of thought is that the
age of the vascular cambium triggers the characteristics of wood cells formed, and the rate of
change from juvenile to mature wood is genetically predetermined (Wilkes 1988). Since the
change to mature wood results in wood that provides more structural support, it is thought that
Chapter 5 Wood Growth and Structure Page 105
the function of the change in wood cells is to provide greater structural support for the larger
tree. On reflection, however, the hypothesis that the age of the vascular cambium triggers the
characteristics of wood cells formed is inconsistent with the hypothesis that the function of
the change in wood cells is to provide greater structural support for the larger tree, since the
correlation between age and size is by no means constant but depends on the growing
conditions. If the function of the change in wood cells is to provide adequate structural
support for the tree, this then suggests that tree size rather than age should trigger changes in
wood cells.
A separate hypothesis suggests that changes in wood cells are triggered by a reduced
concentration of crown hormones, whereby the onset of mature wood formation occurs as
crown height lifts and the influence of the crown decreases (Zobel and Buitjtenen 1989). This
hypothesis has merit given that crown hormones move slowly and require metabolic energy to
travel, and are therefore less likely to exert influence on stemwood if the distance between the
two is greater.
Dependent on which hypothesis is true, then what might be the effect of increased
competition (planting density) on the juvenile/mature wood ratio? In terms of growth pattern,
increased planting density is likely to result in similar size growth prior to onset of
competition but restricted size growth subsequent to the onset of competition, and it is also
likely to result in an increased rate of crown lift due to more rapid onset of and intensity in
competition. If age and/or tree size is the trigger for mature wood production, then increased
planting density would result in an increased juvenile/mature wood ratio due to similar early
growth but restricted later growth. On the other hand, if crown influence is the trigger for
mature wood production, then increased planting density would result in a decreased
juvenile/mature wood ratio due to an increased rate of crown lift. These deliberations, whilst
simplistic, illustrate the potential to manipulate wood properties by managing stand growing
conditions.
Sapwood and Heartwood - When wood is first formed it is physiologically active in both
water transpiration and food storage, and is referred to as sapwood since it is the wood in
which sap-flow occurs. After some time the food content in the sapwood is mobilised and
used, and extractives are deposited into the wood cell cavities, essentially blocking them up
(Rudman 1966; Bamber 1985). This conversion results in the loss of the physiological
Chapter 5 Wood Growth and Structure Page 106
functions that characterize sapwood. The resulting inert wood is called heartwood since it
resides in the centre of the stem.
The extractives found in heartwood are thought to form adjacent to the cells they are
deposited in (Hillis 1971), an hypothesis that is supported by evidence that there is a
substantial increase in the respiration rate of sapwood in the zone adjacent to the heartwood
(Bamber 1976). During the process of conversion, extractive content increases from around
1% in sapwood to around 20% in heartwood (Panshin and De Zeeuw 1980; Wilson and White
1986), and the increased extractive content contributes to increased wood density (Groves and
Chivuya 1989).
Once initiated, heartwood continues to expand throughout the life of the tree, whilst a band of
sapwood is maintained around the heartwood due to continuing wood growth (Panshin and De
Zeeuw 1980). In eucalypts heartwood formation may begin at the relatively early age of 4 to 5
years (Bamber 1985), although one study indicates that E. grandis begins earlier than this
(Bhat et al. 1988). The radial width of the heartwood then expands with age, whereas the
width of the sapwood remains relatively constant, with the result that the proportion of
stemwood comprised of heartwood gradually increases with age (Bamber 1985). In E. grandis
the proportion of heartwood in the stemwood has been found to increase from 36.8% at 3
years to 66.4% at 9 years. The proportion of heartwood decreases with increasing height
within the stem, indicating that heartwood formation either begins at a later stage and/or
proceeds at a slower rate, as height within the stem increases (Bhat et al. 1988; Taylor et al.
2002).
The trigger for heartwood formation is unclear. It may be a way of mitigating the negative
effects of damage in sapwood caused by natural aging, air embolisms, and/or pathogens
(Panshin and De Zeeuw 1980; Bamber 1985). Alternatively, heartwood formation may be an
active physiological process, the function of which is to optimise the sapwood conducting
area based on the physiological requirements of the tree crown (Rudman 1966; Bamber
1976). Whilst the first theory may apply in isolated incidences of damage, in practice it is
likely that the latter theory is prevalent given the strong relationship between conducting
sapwood area and leaf area. The latter theory is supported by the finding that the ratio of
heartwood decreases with increased height within the stem, since this is the logical result of a
constant sapwood conducting area but decreasing stem diameter.
Chapter 5 Wood Growth and Structure Page 107
Reaction Wood - Reaction wood is a specialised wood that forms in tree stems when they
grow on a lean or in response to other factors like wind sway or fast growth. In hardwoods
reaction wood forms on the upper side of leaning trees and tends to pull the tree up. It is also
referred to as tension wood given that it is under tension. In contrast, the reaction wood of
softwoods forms under compression on the lower side of leaning trees and pushes trees up,
and is known as compression wood (Zobel and Buitjtenen 1989). The properties of reaction
wood differ considerably from normal wood, and it is undesirable for most end products
(Zobel and Buitjtenen 1989). Reaction wood is minimised by using silvicultural techniques to
reduce wind sway and grow trees as straight and as vertical as possible (Zobel and Buitjtenen
1989).
5.1.4 Wood Properties
High wood quality has the potential to increase the value of plantations by improving
conversion efficiencies, increasing potential product ranges, and producing greater
percentages of high grade/high value products (Malan et al. 1997). The improvement of
plantation wood quality is commonly addressed through selection and breeding programs
since the use of silviculture to influence wood quality is generally regarded as ineffective
(Zobel and Buitjtenen 1989). Wood quality is rarely considered in economic analyses of
silvicultural management options (Downes and Raymond 1997), yet the potential of wood
quality to effect plantation value behoves managers to improve knowledge on links between
silviculture and wood quality.
Wood growth in trees varies in response to genetic control, tree development and
environmental changes (Zobel and Buitjtenen 1989), of which the latter two may be affected
by silviculture. Wood quality is almost always improved by reduced variability in wood
(Zobel and Buitjtenen 1989; Malan et al. 1997), and the potential for growing conditions to
effect wood variability is an important silvicultural consideration. Examination of wood
properties will facilitate investigation of variability in wood (Zobel and Buitjtenen 1989), and
should indicate whether silvicultural techniques are beneficial for both wood volume and
wood quality, or otherwise allow trade-offs between improving either wood volume or wood
quality to be accounted for.
Chapter 5 Wood Growth and Structure Page 108
Wood Anatomy – The most commonly measured anatomical features in eucalypts are the
wood cells; fibres, vessels and parenchyma. Changes in anatomical diversity are generally
measured from the stem centre (pith) to the stem surface (bark), and from the stem base (base)
to the stem tip (apex).
Most eucalypt species exhibit similar patterns of anatomical diversity within the stem. From
pith to bark the length, diameter and wall thickness of fibres tend to increase (Bamber and
Curtin 1974; Taylor 1984; Bamber 1985; McKimm and Ilic 1987; Wilkes 1988; Bhat et al.
1990). The proportion of fibrewall material in fibres generally increases from pith to bark
(Wilkes 1988), although this is not always the case, particularly in the first few years of
growth (McKimm and Ilic 1987). Vessel diameter increases and vessel frequency decreases
from pith to bark (Bamber and Curtin 1974; Bamber 1985; McKimm and Ilic 1987; Wilkes
1988). The proportion of space occupied by each cell type generally remains constant at
>60% for fibres, (Wilkes 1988)10-20% for vessels and 20-30% for rays (Wilkes 1988),
however rapid changes in growth rate can result in fluctuations (Bamber 1985).
From base to apex, the reports of changes in wood anatomy are conflicting. In E. grandis fibre
length has been found to gradually decrease with height (Bhat et al. 1990), whereas for
eucalypts in general fibre length increases to a point well up the bole, then decreases at higher
levels (Wilkes 1988). Decreases in fibre length at higher levels in the stem may be the result
of more rapid division and differentiation of cambial initials due to greater proximity to the
crown (Wilkes 1988), whereby cambial initials and fibres do not have time to reach full
length before division and differentiation occur. The greater expression of this phenomenon in
E. grandis (Bhat et al. 1990) may be due to a greater apical influence over the tree in general.
There is little information pertaining to changes in other cell properties on the vertical axis
and this would appear to be a substantial knowledge gap.
Wood Density - Density is a measure of mass per unit of volume, and wood density is the
amount of wood substance present in a harvested (green) volume (Zobel and Buitjtenen
1989). Wood density is most commonly measured as basic density, the mass of oven-dry
wood per unit of green volume (g/cm3 or kg/m
3), and specific gravity, the ratio of the oven-
dry mass of a given volume of wood to the mass of an equal volume of water at 4°C (Zobel
and Buitjtenen 1989). Other wood density measures include green density, air-dry density and
Chapter 5 Wood Growth and Structure Page 109
oven-dry density, for which both the mass and volume parameters are measured at the said
moisture content (Groves and Chivuya 1989).
Wood density is related to many wood properties and is widely considered to be the most
important property of wood affecting utilisation and conversion (Hillis and Brown 1984;
Malan 1989; Zobel and Buitjtenen 1989; Haslett and Young 1990). In solid wood products
high wood density increases strength and toughness properties (Bootle 1983; Yang and
Waugh 1996b) and in fuel the energy yield of wood is improved by higher wood density
(Gough et al. 1989; Groves and Chivuya 1989). In reconstituted wood products like paper,
high wood density reduces burst strength, tensile strength and folding endurance, but
increases tear strength, whereas in chipboard and medium density fibreboard, high wood
density reduces flake and fibre bonding areas and board strength (Zobel and Buitjtenen 1989).
Given the universal significance of wood density, plantation producers increasingly include
wood density as a priority consideration in selection and breeding programs (Dickinson et al.
2001).
Changes in wood density are caused by changes in the frequency, dimension and chemistry of
wood cells (Malan 1989; Zobel and Buitjtenen 1989; Ilic et al. 2000). In hardwoods increased
fibrewall thickness has the greatest positive influence on wood density (Malan and Gerischer
1987), however reductions in fibre and vessel diameter and vessel frequency may also
increase wood density (Zobel and Buitjtenen 1989). Wood density is therefore an average
measurement that does not fully reveal the distribution of wood cell types (Zobel and
Buitjtenen 1989). In practice wood density is relatively simple to measure, however the
changes in wood anatomy that create the changes in wood density are more difficult to
observe, and generally require microscopy (Downes and Raymond 1997).
The basic wood density of most commercial eucalypts ranges between 450 - 900 kg/m3
(Bootle 1983), whilst the basic wood density of E. grandis ranges between 400 - 670 kg/m3
(Bootle 1983; Downes and Raymond 1997; Ilic et al. 2000). Within annual growth layers
eucalypts are characterised by low variation in wood density on the horizontal axis (Zobel and
Buitjtenen 1989), with the result that visible annual growth rings (caused by wood density
increasing from summer to winter) are absent from species like E. grandis and E. globulus
(Downes and Raymond 1997). Between successive annual growth layers the wood density of
eucalypts generally increases from pith to bark (Downes and Raymond 1997; Ilic et al. 2000).
Chapter 5 Wood Growth and Structure Page 110
This pattern has been confirmed for several plantation eucalypts including E. grandis
(Bamber and Humphreys 1963; Bamber et al. 1969; Hans et al. 1972; Taylor 1973a, b;
Schonau 1974; Hans 1976; Bamber et al. 1982; Taylor 1984; Malan and Gerischer 1987;
Malan 1988; Wilkins 1990; Malan 1991; Wilkins and Horne 1991; Malan and Hoon 1992), E.
nitens (Nicholls and Pederick 1979; McKimm 1985; Yang and Waugh 1996a), E. globulus
(Yang and Waugh 1996b), E. regnans (Nicholls and Griffin 1978; Frederick et al. 1982; Yang
and Waugh 1996a), E. obliqua (Nicholls and Griffin 1978) and E. pilularis (Bamber and
Curtin 1974). A notable deviation from the above pattern is occasionally found in E. grandis,
for which wood density may exhibit an initial decrease between growth layers before
increasing, a trend that becomes stronger with height and appears to be related to slower
growth (Taylor 1973a; Wilkins 1989, 1990; Wilkins and Horne 1991). Initial decreases in
wood density from pith to bark have also been found in a study of fast-grown E. nitens
(McKimm and Ilic 1987).
From base to apex, wood density within annual growth layers in E. grandis has been found to
remain constant with height (Malan 1988) and decrease with height (Bamber et al. 1969;
Wilkins and Horne 1991), whereas the average wood density of all growth layers generally
increases with increased height within the stem (Downes and Raymond 1997; Ilic et al. 2000),
as has been found for E. grandis (Taylor 1973a; Malan 1988; Coetzee et al. 1996), E.
globulus (Downes and Raymond 1997), E. nitens (Purnell 1988; Yang and Waugh 1996a)
and E. regnans (Dargavel 1968; Chafe 1981; Frederick et al. 1982; Yang and Waugh 1996a).
In deviation to the above results, a number of studies have found that average wood density of
plantation eucalypts may initially decrease with increased height within the stem before
increasing (Downes and Raymond 1997), as was found in E. grandis (Bamber et al. 1969;
Taylor 1973b; Vital and Della Lucia 1987; Wilkes 1988; Bhat et al. 1990; Wilkins 1990;
Wilkins and Horne 1991), E. globulus (Beadle et al. 1996; Raymond and MacDonald 1998),
E. nitens (Purnell 1988; Lausberg et al. 1995; Beadle et al. 1996; Raymond and MacDonald
1998), and E. regnans (Frederick et al. 1982). Two studies found that average wood density
remained constant with height in E. grandis (Hans 1976) and E. globulus (Yang and Waugh
1996b). In essence it is apparent that different patterns of changes in wood density within and
between growth layers may result in average wood density increasing, decreasing or
remaining constant with increased height within the stem (Figure 5.4).
Chapter 5 Wood Growth and Structure Page 111
Average wood density of the whole stem increases with successive growth layers, and the
average wood density of young plantation timber is comparable to native forest timber if
compared at similar ages (Haslett et al. 1990; Yang and Waugh 1996a, b). Increases in
average density over time may be affected by many factors, however the dominant controlling
factor appears to be age (Bamber et al. 1969; Wilkes 1988), hence the understanding that
wood property comparisons between plantations must take age into account (Zobel and
Buitjtenen 1989). Increases in average stem wood density are most rapid during the juvenile
stage of growth, slowing down as the cambium reaches maturity at 10-20 years (Wilkes
1988). Trees with high wood density at an early age are generally found to produce wood with
high wood density in subsequent growth (Taylor 1984), which has positive implications for
selecting for wood density at an early age.
Figure 5.4: Stylised depictions of changes in wood density within the stem (average wood density shown by vertical bars). The base of each stem is the same, with wood density increasing in successive growth layers. Within growth layers wood density may (a) remain constant with increased height, or (b,c) decrease with increased height. As a result of the above patterns, average wood density may (a) increase with increased height, (b) decrease and then increase with increased height, or (c) remain constant with increased height.
Knot Content - knots consist of the woody base and/or scar tissue of branches within the stem
(Wilson and White 1986). The primary effect of knots is to reduce wood strength as a result
of the deflection of normal wood tissue around the knot (Bootle 1983; Hillis 1984). Both the
number and size of knots are important considerations since a given increase in knot content
results in a comparatively greater loss of mechanical strength in wood (Yang and Waugh
1996b). Other serious defects associated with knots include kino veins (Jacobs 1955; Bootle
1983; Gerrand et al. 1997), and decay entry (Hillis 1984; Glass and McKenzie 1989). The
occurrence of knots is not common in wood sourced from native forests (Bootle 1983),
however a high incidence of knots in plantation-grown tropical timbers is common (Haslett et
(a) (b) (c)
Chapter 5 Wood Growth and Structure Page 112
al. 1990; Montagu et al. 2003), and several studies of plantation grown eucalypt species have
shown that knots are the leading cause of defect and downgrade in solid wood produced in
plantations (Waugh and Rozsa 1991; Borough and Humphreys 1996; Yang and Waugh
1996a, b). The opportunity to minimise knot content (maximise clear wood) clearly represents
an effective mechanism by which to improve the value of eucalypt plantations (Montagu et al.
2003).
Branching habits, including the number and size of branches, the angle branches make with
the stem, the rate of branch shed, and the persistence of dead branches, all affect the number
and size of knots (Hillis and Brown 1984). Of the branching habits, branch number appears to
be inherited (Hillis and Brown 1984), whereas branch size, branch angle and the rate of
branch shed are strongly influenced by growing conditions in the stand (Florence 1996).
Silvicultural techniques that stimulate growth, such as thinning and fertilising, have the
potential to increase knot content by stimulating branch growth and persistence (Hillis 1984).
Silvicultural techniques that encourage branch shed, such as close spacing, have the potential
to decrease knot content.
Genera and species differ widely in the efficiency of branch shed, but most eucalypts shed
branches quickly when grown in stands (Florence 1996). The branch ejection mechanism is
primarily responsible for efficient branch shed in eucalypts since dead branches are
effectively removed from the stem, whereas in other species dead branches may persist for
long periods. The process of branch shed in eucalypts starts as the crown grows upwards and
the lower branches become moribund and die. A layer of tannins, latex or resins usually
develops between the branch base and the stemwood during the moribund period, essentially
separating the branch from the stemwood. At this point a number of scenarios may occur, and
in the best case scenario the whole branch is ejected from the wood and bark of the stem. This
is a favourable growth habit of eucalypts since it removes the branch down to the solid wood,
minimising the knotty core and allowing rapid occlusion of the branch scar.
In many cases, however, the dead branches become brittle and break, leaving a stub in the
bark. As with whole branches, stubs may be ejected from the wood and bark of the stem
(Figure 5.5(a)), and this is said to occur for nearly all branches up to 2 cm in diameter (Jacobs
1955). Where stubs have not ejected they may in fact be ejected from the wood but become
caught in the bark (Figure 5.5(b)). The bark then drags the stub through the wood layers as the
Chapter 5 Wood Growth and Structure Page 113
stem grows outwards, creating a kino vein behind the stub. Kino is a gum-like substance that
hardens to a brittle mass on exposure to air, causing a structural hole in the wood. Stubs that
are not ejected from the wood remain stuck, and are eventually occluded by the diameter
growth of the tree with the result that the dead stub is enclosed in the stem (Figure 5.5(c)).
Figure 5.5: The nature in which branch shed affects the development of the knotty core, the defect core and clearwood. (a) An ejected stub causing a small knotty core, a small defect core and moderate clearwood growth, (b) an ejected stub held in the bark causing a small knotty core, a large defect core and no clearwood growth, and (c) an un-ejected stub causing a moderate knotty core, a moderate defect core and small clearwood growth.
In each scenario openings in the stem bark caused by dead branches or stubs are potential
sites for decay entry. Some protection from infection is obtained by the layer of tannins, latex
or resins between the branch/stub and stemwood; however this protective layer can fail. A
longer occlusion period caused by larger scars or persistent stubs will increase the chance of
decay entry. When infection does occur it generally travels from the point of entry to the pith
of the stem, with the result that decay is generally contained in the defect core (Glass and
McKenzie 1989).
Due to the problems associated with poor branch shed, including reduced clearwood
production and decay entry, plantation growers usually elect to minimise the knotty core by
pruning branches. In Australia pruning methods were developed in pine plantations, where
dead branches tend to persist for long periods before finally decaying to the stage when they
snap off. In order to hasten occlusion and maximise clear wood production, dead branches
were pruned off. The effectiveness of pruning depends on branch diameter, the method of
cutting branches and the length of remaining stubs. Due to its high cost pruning is generally
KNOTTY CORE DEFECT CORE CLEARWOOD
(a) (c) (b)
Chapter 5 Wood Growth and Structure Page 114
restricted to the lowest sawlog (butt log) of the final stocking trees only (Hillis and Brown
1984).
Pruning dead branches has been known to be problematic when applied to eucalypt
plantations. It has been found in E. nitens that pruning dead branches can interrupt the
sequence of branch shed, resulting in the stub getting caught in the bark (Figure 5.5(b)) and
causing a kino vein through what was intended to be clearwood (Gerrand et al. 1997). It was
concluded that branches should be pruned when they are live to avoid this kind of defect;
however pruning live branches introduces further problems, one of which is infection. Live
branches are more susceptible to decay entry as they have not yet developed a protective layer
between branch stubs and stemwood. In pruned E. regnans infection incidence increased as
pruned live branch stub diameter increased (Glass and McKenzie 1989), presumably due to an
increased occlusion period. Decay from infection was found to extend inwards (including
upwards and downwards) from the initial entry point, but the extent of decay was not related
to branch diameter. Decay was not detected in wood laid down after infection occurred, and
was therefore thought to be restricted to the defect core. Despite the finding that decay
appeared to be limited to a part of the stem that is otherwise useless, the authors supported the
guideline to prune branches before reaching 2.5 cm branch diameter in order to minimise
decay entry.
The extent and timing of pruning is also of concern because live branches are part of the
productive crown and it is uncertain how much of the crown can be removed before growth
rate is negatively impacted. Studies show that eucalypts are quite flexible, as up to 50% of the
crown can be removed with no significant loss in growth rate (Pinkard and Beadle 1998a;
Pinkard and Beadle 1998b), however if branches need to be pruned before reaching 2.5 cm
diameter (Glass and McKenzie 1989), then pruning may be required on a number of occasions
in order to remove branches before they get to big without removing too much of the crown.
Given that each pruning event adds to operational costs, it is debatable that the expense could
be justified when eucalypts tend to effectively shed branches to a diameter of 2 cm (Jacobs
1955). This may be the case since a study of E. grandis showed that pruning caused only a
small increase in the yield of clearwood at 25 years age (Bredenkamp et al. 1980).
Chapter 5 Wood Growth and Structure Page 115
THE EFFECT OF COMPETITION ON WOOD PROPERTIES
The general consensus remains that age and genotype appear to be the critical factors
governing the rate of anatomical change within stems, whereas environmental changes have
only minimal impact (Bamber 1985; Wilkes 1988). Despite this, rate of growth is known to
affect several wood properties, suggesting that competition-induced changes in growing
conditions will also affect wood properties.
Wood Anatomy – Findings on the effect of growth rate and competition on wood anatomy are
conflicting. Studies of E. grandis found that increased growth rate due to dominance within
the stand had no effect on average fibre length (Bamber et al. 1982; Wilkes and Abbot 1983;
Taylor 1984), fibre diameter (Bamber et al. 1982; Wilkes and Abbot 1983), or fibre wall
thickness (Bamber et al. 1982). Similarly, increased growth rate due to silvicultural treatments
like initial spacing, fertilising, and thinning had no effect on fibre length (Wilkins and
Kitahara 1991; Malan and Hoon 1992).
In contrast, studies of 8.5 yr old E. grandis progenies (Malan 1991) and E. grandis grown in
different parts of South Africa (Taylor 1984) revealed a correlation between an increased rate
of height growth and decreased average fibre length, consistent with the hypothesis that faster
growth causes more rapid division of cambial initials resulting in decreased fibre length. A
study of 27 to 34 year old regrowth E. regnans (Higgs and Rudman 1973), found that
increased growth rate due to fertilisation resulted in decreased average fibre length, whereas
increased growth rate due to thinning increased the average fibre length. It was thought that
fertilising alleviated nutrient shortages only, hence favouring sporadic and rapid height
growth increases when water was available, whereas thinning alleviated light, water and
nutrient stresses and space restrictions, hence favouring sustained growth increases both in
height and diameter.
In 40 year old eucalypt dry open-forest species, increased growth rate did not affect fibre
diameter but it did result in greater average fibre wall thickness (Wilkes and Abbot 1983).
The same finding was implied in E. grandis thinning trials (Malan and Hoon 1992) due to the
correlation between wood density and fibre wall thickness. Growth rate does not appear to
affect the ratio of fibres present (Wilkes and Abbot 1983; Malan and Hoon 1992).
Chapter 5 Wood Growth and Structure Page 116
Trees with fast growth rates exhibit gradual changes in fibre properties from pith to bark,
whilst suppressed trees exhibit rapid changes in fibre properties from pith to bark. When
expressed in terms of relative distance from pith, however, growth rate has little effect on the
rate of change in fibre properties (Malan and Hoon 1992). This suggests that increased growth
rate has no effect on fibre dimensions at the time of differentiation, but it does reduce within-
tree variability by spreading the same amount of change in fibre dimensions over a greater
area.
In response to increased growth rate, vessel frequency has been found to decrease in eucalypts
(Wilkes and Abbot 1983), particularly E. grandis (Bamber et al. 1982; Malan and Gerischer
1987). Vessel diameter has been found to increase (Wilkes and Abbot 1983; Malan 1991) and
decrease (Bamber et al. 1982) with increased growth rate, and the proportion of stemwood
comprised of vessels has been found to increase (Wilkes and Abbot 1983), decrease (Bamber
et al. 1982), and remain constant (Malan 1991; Malan and Hoon 1992) with increased growth
rate.
In response to increased growth rate, the proportion of stemwood comprised of parenchyma
(rays) has been shown to increase (Bamber et al. 1982; Malan and Gerischer 1987), and
remain constant (Wilkes and Abbot 1983; Malan and Hoon 1992). It is possible that the
volume of parenchyma in stemwood may increase in response to increased growth rate due to
a greater requirement for lateral conduction (through ray parenchyma) and/or excess food
storage in fast grown trees (Bamber et al. 1982).
Anatomical variation between trees due to different environments appears to be minimal, and
often of no practical significance, particularly in relation to pulp and bio-fuel products (Malan
and Gerischer 1987; Malan 1991; Malan and Hoon 1992). In contrast, anatomical variation
between trees in similar environments is often high (McKimm and Ilic 1987), suggesting that
wood anatomy is strongly controlled by genotype (Wilkes 1988). Wood cells most affected by
faster growth are the physiologically active vessels and parenchyma (Bamber et al. 1982),
however any conclusion as to the physiological function of these changes is complicated by
conflicting results, particularly for vessels. Further research is required to improve knowledge
of the physiology of wood formation in eucalypts (Wilkes 1988). In addition, the effect of
growth rate on within-tree variability, and the associated consequences for solid wood
products, is deserving of further research.
Chapter 5 Wood Growth and Structure Page 117
Wood Density - Investigation of the effect of competition on wood density is complicated by
two issues. The first is that wood density is known to have high genetic variation (De Villiers
1968; Wang et al. 1984; McKimm 1985; Malan and Gerischer 1987; Malan 1988), and this
has to be accounted for before variation due to other factors can be identified. Some authors
specifically note that possible relationships between wood density and growth rate may have
been masked by large variation (Wilkins and Horne 1991), however more often one reads that
a trend was apparent, however it was not significant. The second issue is the interpretation of
the effect of growth rate on wood density, which is complicated by the investigation of
average wood density with little focus on the changes in wood density within the stem. For
example, take two identical stands of the same age which have grown at the same rate up until
100mm radius (Figure 5.6).
0.3
0.4
0.5
0.6
0.7
0.8
0 100 200 300
Distance from the pith (mm)
Air
-dry
wo
od
de
ns
ity
(g
/cm
3)
Stand A Stand B
Mean Density B = 0.591
Mean Density A = 0.677
Figure 5.6: A stylised example of the effect of increased competition (reduced growth rate) on wood density where wood density is determined by age and continues to increase at the same rate over time. Two stands of the same age, A and B, have the same growth rate up to a 100 mm radius. At this point Stand A is thinned and the remaining stems continue to grow at a similar rate, whereas Stand B is not thinned, resulting in the development of competition and a reduced mean growth rate (adapted from Malan and Hoon 1992).
At the point of 100mm radius, this point Stand A is thinned and the remaining stems continue
to grow at a similar rate, whereas Stand B is not thinned, resulting in the development of
competition and a reduced mean growth rate. In both stands wood density is determined by
age and continues to increase at the same rate over time. As the result of increased
competitive pressure in stand B there is an increase in the gradient of change in wood density,
which is caused by the reduction in growth rate rather than an increase in the rate of change in
wood density per se. Furthermore, mean wood density is reduced in stand B compared to
stand A, which must be the case if wood density increases at the same rate over time, but
Chapter 5 Wood Growth and Structure Page 118
growth is slowed down at some point (Bamber and Humphreys 1963). This analysis shows
that if age is the primary determinant of wood density, then faster growth at a later age must
result in increased mean wood density. Any deviation from this trend raises the hypothesis
that some factor other than age is affecting the level of wood density at the time of formation.
Several eucalypt studies therefore support the hypothesis that wood density at the time of
formation is unaffected by growth rate, but that increased average growth rates generally
result in increased average wood density. This was found to be the case where increased
average growth rate was due to intensive silviculture such as fertilising and weeding (Bamber
et al. 1982; Wilkins 1989, 1990; Wilkins and Horne 1991; Beadle et al. 1996; Cromer et al.
1998), reduced stocking (Higgs and Rudman 1973; Wilkins 1989; Malan and Hoon 1992;
Coetzee et al. 1996; Coetzee and Naicker 1998b; De Bell et al. 2001), and greater dominance
(Bamber and Humphreys 1963; Bamber et al. 1969; Wilkes 1984).
In contrast, other studies indicate that increased growth rate in eucalypts results in unchanged
or decreased average wood density. This was found where increased growth rate was due to
intensive silviculture (Higgs and Rudman 1973; Raymond and Muneri 2000), better site
quality (Muneri and Raymond 2000), decreased stocking (Schonau 1974; Vital and Della
Lucia 1987), and greater dominance (Taylor 1984; Malan 1991). These results suggest that
factors other than age and genotype affect wood density, and more specifically they imply that
increased growth rate results in decreased wood density at the time of formation.
Whatever the effect of growth rate on wood density at the time of formation, growth rate does
affect the rate of density variation from pith to bark. An increased growth rate corresponds to
an increase in the size of successive growth layers, with the result that a similar increase in
wood density over time is spread through a larger volume of wood, and the gradient of change
from pith to bark is reduced (Malan and Hoon 1992; De Bell et al. 2001). That increased
growth rate may result in reduced variability in wood has implications for plantation
management. Fast grown logs are commonly regarded as exhibiting poor conversion
efficiency, with problems such as end-splitting appearing more pronounced. Malan and Hoon
(1992), however, found that when the degree of end-splitting in E. grandis was adjusted for
log-size there was little difference in end-splitting between logs of different growth rates. This
indicates that the inherently greater stress in larger logs was compensated for by lower within-
Chapter 5 Wood Growth and Structure Page 119
stem variation, and suggests no loss in conversion efficiency from managing plantations for
fast growth.
In general the effect of growth rate on wood density is difficult to interpret, given the many
causes of differences in growth rate, the various methods of measuring wood density, and the
nature of the comparison between trees. Further investigation needs to determine the age of
wood at the time of formation in order to clarify the issue, and wood density should be
considered in comparison to other parameters of the tree. Of particular interest is the positive
relationship between wood density and tree height (Bamber and Humphreys 1963; Bhat and
Bhat 1984; Coetzee et al. 1996), as this suggests tree structure affects wood density.
Knot Content - Competition is well recognised as a tool by which to manipulate branching
habits and knot content. As competition increases knots become smaller since the additional
shade created by the denser upper canopy accelerates natural pruning and restricts branch
growth to smaller diameters (Hillis and Brown 1984). The level of stocking required,
however, to create the desired branch shed without sacrificing growth is uncertain. Studies
comparing plantation grown E. globulus (Yang and Waugh 1996b), E. nitens (McKimm 1985;
Yang and Waugh 1996a) and E. regnans (Yang and Waugh 1996a) with their native forest
counterparts have shown plantation grown timber had a higher incidence of knots and larger
knots. The evidence shows that compared to the ‘wheat-field’ regeneration exhibited by
native forests, the stocking rates in the plantations were not high enough to stimulate similar
branch shed.
Chapter 5 Wood Growth and Structure Page 120
5.2 Experimental Rationale
The effect of competition on wood variables is examined to determine how tree development
impacts wood quality. Planting density is used to approximate the general level of competitive
pressure in the stand, and stem diameter is used to approximate the level of competition
pressure experienced by individuals in the stand. Increased competition, due to increased
planting density and decreased stem diameter, is expected to effect wood variables in different
ways (Table 5.1).
Table 5.1: Hypotheses of the effects of increased planting density and decreased stem diameter on wood variables during early stages of stand development in sub-tropical E. grandis plantations.
Wood Variable Hypothesis
Sapwood
Sapwood Basal Area Sapwood basal area will decrease as a result of increased planting density and decreased stem diameter.
Stemwood Sapwood Ratio
Stemwood sapwood ratio will decrease as a result of increased planting density and decreased stem diameter.
Wood Anatomy
Stemwood Ray Ratio Stemwood ray ratio will decrease as a result of increased planting density and decreased stem diameter.
Ray Height Ray height will decrease as a result of increased planting density and decreased stem diameter.
Stemwood Vessel Ratio Stemwood vessel ratio will not be affected by increased planting density or decreased stem diameter.
Vessel Diameter Vessel diameter will not be affected by increased planting density or decreased stem diameter.
Stemwood Fibre Ratio Stemwood fibre ratio will increase as a result of increased planting density and decreased stem diameter.
Fibre Diameter Fibre diameter will not be affected by increased planting density or decreased stem diameter.
Fibrelumen Diameter Fibrelumen diameter will increase as a result of increased planting density and decreased stem diameter.
Fibrewall Ratio Fibrewall ratio will decrease as a result of increased planting density and decreased stem diameter.
Stemwood Fibrewall Ratio
Stemwood fibrewall ratio will decrease as a result of increased planting density and will not be affected by decreased stem diameter.
Wood Density
Stemwood Basic Density
Stemwood basic density will decrease as a result of increased planting density and will not be affected by decreased stem diameter.
Table Continued...
Chapter 5 Wood Growth and Structure Page 121
Wood Variable Hypothesis
Branching Habits
Branch Diameter Branch diameter will decrease as a result of increased planting density and decreased stem diameter.
Branch Diameters > 2cm
Branch diameters > 2 cm will decrease as a result of increased planting density and decreased stem diameter.
Branch Angle Branch angle will decrease as a result of increased planting density and decreased stem diameter.
Branch Mortality Branch mortality will increase as a result of increased planting density and decreased stem diameter.
Branch Form Branch form will skew towards scars and stubs rather than branches as a result of increased planting density and decreased stem diameter.
Chapter 5 Wood Growth and Structure Page 122
5.3 Methodology
The spacing trial was sampled for wood variable measurements on two occasions, at which
time data were collected or otherwise calculated using collected data. The hypotheses for
wood growth and structure were tested by statistical analysis of collected and calculated data.
5.3.1 Sample Age, Size and Preparation
Trees were sampled for wood variable measurements when the trees were 3 and 4 years old
(Table 5.2). At 3 years old wood variables were measured in situ, whereas at 4 years old trees
were destroyed and wood variables were measured on extracted samples. Using a chainsaw,
wood disks were extracted from heights of 1.3 m stem height and 25%, 50% and 75% stem
height, totalling 272 stemwood disk samples. Using a bandsaw, small wood blocks were taken
from the stemwood disk samples at radii of 25%, 50%, 75% and 100% of stem radius,
totalling 1,088 stemwood block sub-samples (Figure 5.7).
Table 5.2: The age and sample size of wood variables for which data were collected from the spacing trial.
Number of Trees Sampled Wood Variable
250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
3 Year Old Measurements(a)
Branching Habits 8 8 8 8
4 Year Old Measurements(a)
Sapwood Wood Anatomy
Wood Density 8 20 20 20
(a) A full description of the sample selection methods for 3 and 4 year old measurements are provided in Chapter 2 – The Spacing Trial, sub-sections 2.4.1 and 2.4.2 respectively.
Figure 5.7: The location of stemwood disk samples and block sub-samples taken from tree stems.
75% stem height
50% stem height
25% stem height
1.3 m height
Stemwood
Disk Samples
Stemwood
Block Sub-Samples
50% stem radius
25% stem radius
100% stem radius
75% stem radius
Chapter 5 Wood Growth and Structure Page 123
Most wood variables could be observed and measured with the naked eye, with the exception
of wood anatomy variables which required microscopy. Of the microscopic methods available
for observing wood anatomy, scanning electron microscopy (SEM) imaging is excellent as it
provides the ability to observe wood anatomy in high resolution detail. SEM imaging,
however, requires the investment of substantial time into the preparation and measurement of
specimens, and the scope of this study did not provide sufficient time and financial resources
to include all the 1088 wood blocks in SEM analysis. In order to reduce the number of
samples, yet capture spatial variation in wood anatomy, the wood blocks were reduced to
those sourced from 1.3m and 50% stem height, and 50% and 100% stem diameter, resulting
in a total of 272 wood blocks for wood anatomy analysis. Both the horizontal surface (HS)
and tangential longitudinal surface (TLS) were prepared for SEM analysis, as these planes
enabled optimal observation of the features under study.
Several steps were required to convert wood blocks into SEM specimens (Heady 2000), and
these included (i) size reduction, (ii) softening, (iii) cutting, (iv) dehydration, (v) mounting,
and (vi) coating. Each step is explained in the following paragraphs.
(i) Size Reduction The wood blocks were too large to be used directly in SEM, so smaller
sized pieces were sawn from the outer edge of the wood blocks using a bandsaw. These pieces
resembled short match sticks measuring approximately 3 mm by 3 mm at the ends and 15 mm
in length. To simplify subsequent preparation, three longitudinally orientated specimens and
three horizontal-radially orientated specimens were cut from each wood block. Specimens
were placed in labelled specimen jars (one per wood block).
(ii) Softening Specimens were soaked in distilled water in labelled 5 mL glass vials until they
became saturated and sank to the bottom, a process which took up to three days. Three drops
of ethanol were added to each vial to minimise microbial colonisation of the specimens and
covers were placed over the vials to avoid contamination by dust and fungal spores. Once
saturated, specimens became soft enough to obtain a non-frayed cut of the specimen surface.
(iii) Cutting Each specimen was clamped firmly in a small vice, with the 3 mm2 end
uppermost and horizontal to the bench. The vice was stuck to the base of a stereo-microscope
(through which the cutting action was observed) with heavy duty double sided tape. Cutting
was carried out using a hand-held, hard-backed single-edged microtome blade (Kucera 1986).
Chapter 5 Wood Growth and Structure Page 124
Initial cuts to expose ends were made with used blades. The final end surface was prepared by
slicing a thin section off the exposed surface using a new blade. A thin section was preferred
as it minimised distortion to the underlying surface from compression caused by the blade,
resulting in a flat and true plane. Hand-held cutting was preferred to using a microtome since
the microtome available could not take thin enough slices, which resulted in chattering and
jags across the cut surface. The lower portion of each specimen was then removed, creating a
3 mm3 specimen block with one face prepared. Longitudinally orientated specimens produced
a horizontal surface (HS) on the prepared face, while horizontal-radially orientated specimens
produced a tangential longitudinal surface (TLS) on the prepared face.
(iv) Dehydration The strong vacuum of the SEM necessitated drying the specimen blocks
beforehand, as rapid and violent evaporation of moisture from specimens into the vacuum
could cause damage to the prepared surface and distortion of the specimen image. Freshly cut
3 mm3 specimen blocks were placed with the prepared surface uppermost in 5 mL Petri dishes
within a desiccator containing silica gel, and allowed to dry at atmospheric pressure and room
temperature. Specimen blocks were dried for at least two days before mounting.
(v) Mounting The dehydrated specimen block was glued to a 12 mm aluminium SEM
specimen stub, with its prepared surface uppermost, using finger-nail varnish as adhesive.
There was space for up to four specimen blocks on each SEM stub, therefore two HS and two
TLS specimen blocks from each wood block were placed on each stub. Specimen blocks from
different wood blocks were never placed on the same stub in order to avoid incorrect
identification. Specimen stubs were labelled on top and bottom with indelible pen.
(vi) Coating Wood is a poor conductor of electricity, and therefore specimen blocks had to be
rendered fully conductive by coating them with a thin film of metal in order to prevent
‘charging’ distortions of the SEM image. A 10 nm coating of pure gold was applied to all
specimen stubs in an argon gas sputter coating unit using a 20 mA current for three minutes.
5.3.2 Data Collection and Calculation
Data were collected for sapwood, wood anatomy, wood density and branching habit variables.
The method of collection or calculation of each variable is explained in the following
paragraphs.
Chapter 5 Wood Growth and Structure Page 125
SAPWOOD
Stem Heartwood Diameter – heartwood was identified by spraying the cross-sectional surface
of the main stem, adjacent to the points from which disk samples had been removed, with a
dye solution containing 5% iodine. Within a few minutes dye in contact with sapwood was
drawn into the stem with the sap, whereas dye in contact with heartwood remained on the
surface, delineating the heartwood as a darker stained area. Heartwood was measured on
orthogonal diameters using callipers, and stem heartwood diameter was calculated as the
average of the two orthogonal measurements.
Stem Sapwood Basal Area – stem sapwood basal area was calculated as stem basal area
minus stem heartwood basal area. Stem basal area and stem heartwood basal area were
determined using the stem diameter and stem heartwood diameter of disk samples and
assuming a circular shape.
Stem Sapwood Ratio – the stem sapwood basal area divided by stem basal area.
WOOD ANATOMY
Certain procedures were adopted for the collection of wood anatomy data by SEM. For the
study of a given anatomical feature, imaging distortions were held constant by maintaining
the same working distance (the physical distance between the final lens and the specimen) and
electron beam current settings. These procedures ensured that imaging distortions were
uniform across all measurements of a given feature, thereby allowing accurate comparison
between specimens. The settings for each anatomical feature were determined during
preliminary exploratory viewing based on the order of magnification required. Anatomical
features were measured using a point to point measuring facility built into the SEM. In
addition, SEM images were converted into Tagged Image File Format (TIFF) files, which
were then analysed using the ImageJ V1.30 program (Rasband 2003).
Wood Cell Ratios – the ratio of the cross-sectional area of ray parenchyma (rays), vessels and
fibres to stemwood cross-sectional area. The cross-sections of rays were observed on the
tangential longitudinal surface (TLS) using the SEM. This was the best surface to make a
Chapter 5 Wood Growth and Structure Page 126
measure of ray area as it provided an image of the height and the width of the rays6. The
cross-sections of vessels were observed on the horizontal surface (HS) using the SEM. This
was the best surface to make a measure of vessel area because it provided an image of the
radial and tangential width of the vessels7. TIFF images of rays and vessels were recorded for
each wood sample and analysed using ‘ImageJ V1.30’ (Rasband 2003). Image analysis
involved highlighting rays (Figure 5.8(a)) and vessels (Figure 5.8(b)) in red as regions of
interest (ROI’s). Once all rays or vessels in an image were identified as ROI’s, a pixel count
was done of the whole image and then of the ROI’s, essentially providing a measure of the
area of each, and stemwood ray and vessel ratios were calculated for each wood sample as the
pixel count of the ROI divided by the pixel count of the whole image.
(a) (b) (c)
(d) (e) (f)
Figure 5.8: Images of measurements of the wood cell anatomy of E. grandis. (a) A tangential longitudinal surface view showing one ray highlighted as a region of interest. (b) A horizontal surface view showing one vessel highlighted as a region of interest. (c) A tangential longitudinal surface view showing the height of one ray being measured as the distance between the two crosshairs. (d) A horizontal surface view showing the radial diameter of one vessel being measured as the distance between the two crosshairs. (e) A horizontal surface view showing the radial diameter of one fibre being measured as the distance between the two crosshairs. (f) A horizontal surface view showing the radial diameter of one fibrelumen being measured as the distance between the two crosshairs.
6 Ray area on the TLS is a good measure of ray volume since rays extend for a considerable distance in the radial
direction, as shown by the narrow double line extending through SEM images (Figures 5.8(b,d))
7 Vessel area on the HS is good measure of vessel volume since vessels extend for a considerable distance in the
longitudinal direction.
Chapter 5 Wood Growth and Structure Page 127
Fibre ratio was then calculated as the proportion of stemwood not occupied by rays or vessels
under the assumption that anything not constituting a ray or a vessel could be designated as a
fibre.
Ray Height – the cross-sectional distance from the top to the bottom of the ray. A line
transect was placed on the SEM image, and from left to right every ray crossing the line
transect was measured until 20 rays had been measured. Ray height was measured directly in
nanometres using a point to point measuring facility built into the SEM (Figure 5.8(c)).
Vessel Diameter – the cross-sectional distance from edge to edge of the vessel. It was
necessary to measure vessel diameter in the radial and tangential directions since vessels were
usually elliptical in the radial direction (Figure 5.8(d)), and a single measurement in either the
radial or tangential direction would result in an overestimate or underestimate of vessel
diameter. For every whole vessel visible in the SEM image the radial and tangential diameter
was measured, resulting in the measurement of 6-10 vessels. Vessel diameter was measured
directly in nanometres using a point to point measuring facility built into the SEM (Figure
5.8(d)).
Fibre Diameter – the cross-sectional distance from edge to edge of the fibre. A line transect
was placed on the SEM image and working from left to right every fibre crossing the line
transect was measured until 20 fibres had been measured. Fibres were measured alternatively
in the radial and tangential direction to reduce bias due to any elliptical orientation of the
fibres in either direction. Fibre diameter was measured directly in nanometers using a point to
point measuring facility built into the SEM (Figure 5.8(e).
Fibrelumen Diameter – the cross-sectional distance from edge to edge of the cavity inside the
fibre. Fibrelumen diameter was measured on every fibre for which fibre diameter had been
measured, in the same direction as fibre diameter was measured, and in the same fashion fibre
diameter had been measured (Figure 5.8(f)).
Fibrewall Ratio – the ratio of fibrewall basal area to fibre basal area. Fibre basal area and
fibrelumen basal area were determined using fibre diameter and fibrelumen diameter and
assuming a circular shape. Fibrewall ratio was then calculated as fibre basal area minus
fibrelumen basal area.
Chapter 5 Wood Growth and Structure Page 128
Stemwood Fibrewall Ratio – the ratio of total fibrewall basal area to total stemwood basal
area. Stemwood fibrewall ratio was calculated for each wood sample using the formula:
Fibrewall Ratio **** Fibre Ratio
WOOD DENSITY
Stemwood Disk Sample Volume – stemwood disk sample volume was measured in the field
by water displacement. Green disks were submerged in a known volume of water in a
container on a level surface, and the volume of the combined disk and water was measured
directly to the nearest 5 mL. Stemwood disk sample volume was then calculated as the
combined volume minus the known water volume.
Stemwood Disk Sample Oven-Dry Mass – the stemwood disk samples were placed in a
scientific oven and dried at 80°C for one week. When their mass had stabilised the oven-dry
mass of the samples was measured to the nearest milligram.
Stemwood Disk Sample Basic Density – the oven-dry mass per unit of green volume, usually
measured in kg m-3
, for each stemwood disk sample. In order to convert the data to kg m-3
, the
following formula was used on the sample data that had been recorded in grams and
millilitres:
(stem disk sample oven-dry mass / 1,000) //// (stem disk sample volume /1,000,000)
BRANCHING HABITS
Branch Height – the distance from the base of the stem at ground level to the base of the
branch at stem intercept. Access to the branches was obtained by leaning a 6 m ladder against
the stem and using ropes to tie the ladder off to a minimum of three anchor trees around the
select tree. It was necessary to use anchor trees since in many cases the stem of the tree under
investigation was not large enough to support the mass of the ladder and person leaning
against it. Branches were located either by their presence or by a scar in the bark. Branch
height was measured directly with a measuring tape for every located branch.
Branch Aspect – the compass direction at which the branch has formed on the stem. Branch
aspect was estimated based on the known layout of the trial, and rounded to the nearest
Chapter 5 Wood Growth and Structure Page 129
multiple of 45° (i.e. north, northeast, east, southeast, south, southwest, west and northwest)
for every located branch.
Branch Diameter – the diameter of the base of the branch in a cross-section perpendicular to
the branch axis. Branch diameter was measured directly with callipers for every located
branch.
Branch Angle – the angle from which the branch diverges from ‘pointing’ to the ground.
Branch angle was measured directly with a protractor, and rounded to the nearest multiple of
5° for every located branch.
Branch Status – the life stage of the branch (summarised into the categories alive and dead).
Branch status was judged as alive if there were green leaves on the branch and dead if there
were brown desiccated leaves or no leaves on the branch for every located branch.
Branch Form – the stage of branch shed (summarised into the categories un-shed, part-shed
and full-shed). Branch form was judged as un-shed if the branch remained fully intact on the
stem, part-shed if the stub or base of the branch remained intact on the stem and full-shed if
only the scar of a branch remained on the stem for every located branch.
5.3.3 Data Analysis
The methodology of data analysis described for tree growth and structure was applied to the
analysis of wood growth and structure, with the exception of wood anatomy. Wood
anatomical properties are known to exhibit a high degree of genetic variability, a
characteristic that can reduce the chance of identifying variability caused by environmental
conditions. In order to allow a higher degree of ‘noise’ into the analysis whilst still identifying
trends in variability due to environmental conditions, the threshold of significance was
reduced from 95% to 90% confidence for the analysis of wood anatomical properties. It will
be noted that despite the reduction in significance to 90% confidence, the p value of most
variables and their interactions remained below 0.05, indicating that these variables would be
accepted at the 95% confidence level.
Chapter 5 Wood Growth and Structure Page 130
The main objective of the data analysis of wood growth and structure was to investigate the
effects of planting density (P) and stem diameter (DBH) on wood variables. Since wood is
known to be affected by position in the stem, the effects of sample positions (where
applicable) on wood variables were also investigated. Sample positions included sample
height at breast height (SHBH) and at 25%, 50% and 75% of stem height (SH%); sample
diameter at 50% and 100% of stem diameter (SD%); branch height (BH); and branch aspect
(BAS
X).
Chapter 5 Wood Growth and Structure Page 131
5.4 Results and Discussion
5.4.1 Sapwood
In addition to testing the effects of planting density and stem diameter on sapwood, the effect
of sample height (SHX) was also tested. The sample heights tested were breast height (1.3 m)
and 25%, 50% and 75% of stem height.
SAPWOOD BASAL AREA
Increased competition had positive and negative effects on sapwood basal area (Table 5.3).
Sapwood basal area increased as planting density increased, although the effect was not
significant at sample heights SHBH and SH25%, whereas it decreased as stem diameter
decreased (Figure 5.9).
Table 5.3: The fixed-effect regression coefficients in the random intercept model of the effects of stem diameter (DBH) (m), planting density (P) (st/ha) and sample height (SH%) on sapwood basal area (m
2).
VARIABLE COEFFICIENT S.E. P - VALUE INTERACTION COEFFICIENT S.E. P - VALUE
INTERCEPT -0.0027 0.0010 p = 0.007 SH%*DBH2 -1.0092 0.0470 p < 0.001
SH% 0.0047 0.0011 p < 0.001 SH%*lnP -0.0006 0.0001 p < 0.001
DBH2 0.6421 0.0404 p < 0.001 DBH
2*lnP -0.0209 0.0059 p < 0.001
lnP 0.0004 0.0001 P = 0.003 SH%*DBH2*lnP 0.0610 0.0069 p < 0.001
0.00
0.01
0.02
0.03
0.0 0.1 0.2 0.3DBH (m)
Sa
pw
oo
d B
as
al
Are
a (
m2)
0.0 0.1 0.2 0.3
DBH (m)
(d) SH75%
0.0 0.1 0.2 0.3DBH (m)
(c) SH50%
0.0 0.1 0.2 0.3DBH (m)
(b) SH25%
250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
250 95% C.I. 1,000 95% C.I. 5,000 95% C.I. 10,000 95% C.I.
Raw Data
(a) SHBH
Figure 5.9: The relationship between the dependent variable sapwood basal area (m
2) and the factors
DBH (m), P (st/ha) and SH (%) at sample positions (a) SHBH, (b) SH25%, (c) SH50% and (d) SH75%. The predicted values of sapwood basal area (with 95% confidence intervals) are plotted against DBH and identified by P.
Chapter 5 Wood Growth and Structure Page 132
Sample height had a negative effect on sapwood basal area, since for a given planting density
and stem diameter sapwood basal area decreased as stem height increased (Figure 5.9(a-d)).
This result was expected since stems are known to taper as stem height increases. Overall the
results show that larger stems have a greater area of sapwood, however it was unclear whether
the relative amount of sapwood in stems changed due to competition.
SAPWOOD RATIO
Increased competition had no effect on sapwood ratio (Table 5.4); although there was a trend
for high planting densities to exhibit greater sapwood ratio as sample height increased (Figure
5.10).
Table 5.4: The fixed-effect regression coefficients in the random intercept model of the effects of planting density (P) (st/ha) and sample height (SH%) on sapwood ratio.
VARIABLE COEFFICIENT S.E. P - VALUE INTERACTION COEFFICIENT S.E. P - VALUE
INTERCEPT 0.6697 0.0214 p < 0.001 SH%*lnP 0.0000124 0.0000028 p < 0.001
SH% 0.3263 0.0168 p < 0.001
lnP -0.0000023 0.0000036 p = 0.523
0.4
0.6
0.8
1.0
0.0 0.1 0.2 0.3DBH (m)
Sap
wo
od
Rati
o (
%)
0.0 0.1 0.2 0.3
DBH (m)
(d) SH75%
0.0 0.1 0.2 0.3DBH (m)
(c) SH50%
0.0 0.1 0.2 0.3DBH (m)
(b) SH25%
250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
250 95% C.I. 1,000 95% C.I. 5,000 95% C.I. 10,000 95% C.I.
Raw Data
(a) SHBH
Figure 5.10: The relationship between the dependent variable sapwood ratio and the factors P (st/ha) and SH% at sample positions (a) SHBH, (b) SH25%, (c) SH50% and (d) SH75%. The predicted values of sapwood ratio (with 95% confidence intervals) are plotted against DBH and identified by P.
Chapter 5 Wood Growth and Structure Page 133
The factor most affecting sapwood ratio is sample height, since the sapwood ratio decreases
(heartwood ratio increases) as sample height decreases (Figure 5.10(a-d); which is to be
expected given that heartwood formation starts at the base of the stem.
Given that the sapwood ratio is similar between all trees and that the ratio of stem size (mass
and volume) to leaf area is greater in high planting densities (Figure 4.20(b,e)), the above
findings indicate that increased competition intensity results in increased stemwood water-
flow capacity per unit crown size. This relationship could be advantageous to trees by
allowing water stressed trees in high planting densities to take rapid advantage of water when
it does become available by increasing water-flow capacity. Alternatively increased water-
flow capacity could be due to an inability of trees in high planting densities to form
heartwood. Evidence shows that heartwood formation requires substantial energy input
(Panshin and De Zeeuw 1980; Wilson and White 1986), and decreased heartwood ratio
(increased sapwood ratio) relative to crown size in trees in high planting densities could be
due to a shortage of energy for forming heartwood. In this case increased stemwood water-
flow capacity per unit crown size may be disadvantageous to the tree, possibly by requiring a
greater proportion of captured water to remain in the stem (to maintain turgor pressure) rather
than transfer to the crown.
5.4.2 Wood Anatomy
In addition to testing the effects of planting density and stem diameter on wood anatomy, the
effects of sample height (SHX) and sample diameter (SDX) were also tested. The sample
heights tested were breast height (1.3 m) and 50% of stem height and the sample diameters
tested were 50% and 100% of stem diameter.
STEMWOOD RAY RATIO
Rays are physiologically active wood cells with the primary role of food storage (Panshin and
De Zeeuw 1980), and it is likely that the ratio of rays in stemwood is to some degree
indicative of the capacity of the tree to produce food requiring storage. Stemwood ray ratio
was found to decrease as competition increases (Table 5.5), as shown by the decrease in
stemwood ray ratio as planting density increases at SD50% and SD100% (Figure 5.11(a,b)), and
as shown by the decrease in stemwood ray ratio as stem diameter decreases at SD100% (Figure
5.11(b)). Stemwood ray ratio was unaffected by stem height sample position.
Chapter 5 Wood Growth and Structure Page 134
Table 5.5: The fixed-effect regression coefficients in the 90% confidence interval random intercept model of the effects of stem diameter (DBH) (m), planting density (P) (st/ha) and sample diameter (SD%) on stemwood ray ratio.
VARIABLE COEFFICIENT S.E. P - VALUE INTERACTION COEFFICIENT S.E. P - VALUE
INTERCEPT 0.246 0.028 p < 0.001 SD%*DBH 0.638 0.171 p < 0.001
lnP -0.013 0.002 p < 0.001
SD% -0.090 0.021 p < 0.001
DBH -0.356 0.143 p = 0.012
0.00
0.05
0.10
0.15
0.20
0.0 0.1 0.2 0.3DBH (m)
Ste
mw
oo
d R
ay R
ati
o
0.0 0.1 0.2 0.3DBH (m)
(b) SD100% (a) SD50%
250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
250 90% C.I. 1,000 90% C.I. 5,000 90% C.I. 10,000 90% C.I.
Raw Data
Figure 5.11: The relationship between the dependent variable stemwood ray ratio and the factors P (st/ha), DBH (m) and SD% at sample positions (a) SD50% and (b) SD100%. The predicted values of stemwood ray ratio (with 90% confidence intervals) are plotted against DBH and identified by P.
In the smallest trees (low end of DBH range) stemwood ray ratio decreased from SD50% to
SD100% in planting densities 1,000-10,000 st/ha (Figure 5.11), whereas in the largest trees
(high end of DBH range) stemwood ray ratio increased from SD50% to SD100% in planting
densities 250-1,000 st/ha (Figure 5.11). In both cases it is feasible that this correlates to
relative resource availability and excess food storage in the trees. In the case of the smallest
(suppressed) trees from 1,000-10,000 st/ha, it is likely that when the inner stemwood formed
at an earlier point in time, competition and relative resource capture were more uniform
between trees. As competition intensified, however, the relative resource capture would have
fallen by a greater amount for suppressed trees than for dominant trees, with the result that
recently formed stemwood in suppressed trees has a reduced stemwood ray ratio due to a
reduced stimulus for excess food storage. In the case of the largest (dominant) trees from 250-
Chapter 5 Wood Growth and Structure Page 135
1,000 st/ha, it is possible that these stands had not achieved full site occupancy at the point in
time at which the inner stemwood formed. In consequence there was opportunity for
dominant trees to increase relative resource capture, resulting in increased stemwood ray ratio
in recently formed wood due to an increased stimulus for excess food storage.
Between the two heights sampled stemwood ray ratio was unaffected by stem height,
suggesting that the stimulus affecting stemwood ray ratio is equal along the length of the
stem. This finding strengthens the above hypothesis that ray formation is stimulated by
relative resource capture and excess food production, since sugar concentrations in the sap are
likely to be reasonably uniform throughout the stem.
The results for stemwood ray ratio provide evidence that ray formation is a physiologically
responsive characteristic (Bamber et al. 1982). Since rays serve the purpose of storing excess
food it is probable that trees with a greater stemwood ray ratio have greater relative resource
capture and a greater propensity to ‘insure’ against resource loss by producing and storing
excess food. The results indicate that suppressed trees are most likely to die during periods of
resource shortage since they have a reduced food storage capacity. In terms of the physiology
of wood formation, it is of interest to determine whether stemwood ray ratio changed due to a
change in ray size and/or ray frequency.
RAY HEIGHT
Exploratory observations revealed that rays in E. grandis exhibit greater variability in height
than in width since they are almost universally uniserate (one cell wide), but ranged in height
from a few cells high to over 20 cells high. Ray height was therefore considered the best
measure of ray size, and any change in stemwood ray ratio not correlating with ray height
could then be attributed to a change in ray frequency.
Ray height was found to decrease as competition increased (Table 5.6), as shown by the
decrease in ray height as stem diameter decreases at all sample positions (Figure 5.12(a-d)).
Ray height showed a trend to decrease as planting density increased; however this was
significant only in interaction with radial and vertical variation in ray height, whereby the
trend for ray height to decrease as planting density increased became stronger with increased
sample height and increased sample diameter (Figure 5.12(a-d)). The results for ray height
indicate that ray size is positively correlated with the rate of stem growth.
Chapter 5 Wood Growth and Structure Page 136
Table 5.6: The fixed-effect regression coefficients in the 90% confidence interval random intercept model of the effects of stem diameter (DBH) (m), planting density (P) (st/ha), sample height (SH%) and sample diameter (SD%) on mean ray height (µm).
VARIABLE COEFFICIENT S.E. P - VALUE INTERACTION COEFFICIENT S.E. P - VALUE
INTERCEPT 151.44 27.40 p < 0.001 lnP* SD%* SH% -7.49 3.17 p = 0.018
DBH 255.74 69.17 p < 0.001
lnP -1.52 2.50 p = 0.543
SD% 13.05 9.43 p = 0.166
SH% 44.00 20.08 p = 0.028
100
150
200
250
300
0.0 0.1 0.2 0.3DBH (m)
Me
an
Ray
Heig
ht
(µm
)
0.0 0.1 0.2 0.3DBH (m)
(d) SHBH SD100%
100
150
200
250
300
Me
an
Ra
y H
eig
ht
(µm
) (a) SH50% SD50% (b) SH50% SD100%
(c) SHBH SD50%
250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
250 90% C.I. 1,000 90% C.I. 5,000 90% C.I. 10,000 90% C.I.
Raw Data
Figure 5.12: The relationship between the dependent variable mean ray height (µm) and the factors P (st/ha), DBH (m), SH% and SD% at sample positions (a) SH50% SD50%, (b) SH50% SD100%, (c) SHBH SD50% and (d) SHBH SD100%. The predicted values of mean ray height (with 90% confidence intervals) are plotted against DBH and identified by P.
The decrease in stemwood ray ratio due to increased planting density (Figure 5.11) was
probably the result of decreased ray frequency rather than decreased ray height, since ray
height has been shown to be largely unaffected by planting density (Figure 5.12). Altogether,
the examination of ray morphology suggests that ray frequency is negatively correlated with
planting density whereas ray size is positively correlated with stem growth rate, with the
result that stemwood ray ratio decreases as competitive pressure (increased planting density
and decreased stem diameter) increases.
Chapter 5 Wood Growth and Structure Page 137
STEMWOOD VESSEL RATIO
Vessels are physiologically active wood cells with the primary role of water translocation
(Panshin and De Zeeuw 1980; Wilson and White 1986), and the ratio of vessels in stemwood
may therefore be indicative of the water translocation capacity of the tree. Stemwood vessel
ratio was found to be unaffected by increased competition, except in the most suppressed
stems (Table 5.7, Figure 5.13).
Table 5.7: The fixed-effect regression coefficients in the 90% confidence interval random intercept model of the effects of stem diameter (DBH) (m), sample height (SH%) and sample diameter (SD%) on stemwood vessel ratio.
VARIABLE COEFFICIENT S.E. P - VALUE INTERACTION COEFFICIENT S.E. P - VALUE
INTERCEPT 0.2084 0.0138 p < 0.001 DBH-1*SH%
2 -0.0169 0.0065 p = 0.009
DBH-1 -0.0013 0.0009 p = 0.149 DBH
-1* SD%*SH%
2 0.0237 0.0064 p < 0.001
SD% -0.0419 0.0124 p = 0.001
SH%2 0.0060 0.0475 p = 0.899
0.05
0.10
0.15
0.20
0.25
0.0 0.1 0.2 0.3DBH (m)
Ste
mw
oo
d V
es
sel
Ra
tio
0.0 0.1 0.2 0.3DBH (m)
(d) SHBH SD100%
0.05
0.10
0.15
0.20
0.25
Ste
mw
oo
d V
esse
l R
ati
o (a) SH50% SD50% (b) SH50% SD100%
(c) SHBH SD50%
Raw Data Predicted Value Predicted Value 90%C.I.
Figure 5.13: The relationship between the dependent variable stemwood vessel ratio and the factors DBH (m), SH% and SD% at sample positions (a) SH50% SD50%, (b) SH50% SD100%, (c) SHBH SD50% and (d) SHBH SD100%. The predicted values of stemwood vessel ratio are plotted against DBH (with 90% confidence intervals).
Stemwood vessel ratio was unaffected by increased planting density and was largely
unaffected by stem diameter, except in the smallest stems in which it was marginally
decreased (Figure 5.13(a-d)). In practical terms average stemwood vessel ratio was constant at
16-18% between the points sampled, corresponding to previous findings that vessel ratio is
Chapter 5 Wood Growth and Structure Page 138
constant at between 10-20% of wood volume (Wilkes 1988). In relation to water translocation
through the sapwood, the above results indicate that the average water translocation capacity
(vessel content) of the sapwood is similar between planting densities and between dominance
classes, except for the most suppressed trees in which the average water translocation capacity
is reduced.
VESSEL DIAMETER
Exploratory observations revealed that the horizontal cross-section of vessels was elliptical,
and that the longest cross-sectional diameter was in the radial direction, suggesting that
vessels were ‘squashed’ in the radial direction during formation. It was therefore thought
appropriate to analyse vessel tangential diameter and vessel radial diameter separately, rather
than analyse vessel diameter as the average of the two.
Increased competition had positive and negative effects on vessel tangential and radial
diameter (Table 5.8(a,b)). Vessel tangential diameter increased in response to increased
planting density, and decreased in the most suppressed (smallest) stems (Figure 5.14). Vessel
radial diameter followed a similar pattern, however the effect of planting density was reduced
(planting density had no significant effect at individual sample positions), whereas the effect
of stem diameter was increased (there was a more pronounced slope between stem diameter
and vessel radial diameter) (Figure 5.15).
Table 5.8: The fixed-effect regression coefficients in the 90% confidence interval random intercept model of the effects of stem diameter (DBH) (m), planting density (P) (st/ha), sample height (SH%) and sample diameter (SD%) on (a) vessel tangential diameter (µm) and (b) vessel radial diameter (µm).
VARIABLE COEFFICIENT S.E. P - VALUE INTERACTION COEFFICIENT S.E. P - VALUE
INTERCEPT 59.71 9.21 p < 0.001 lnP*SH%2 -16.32 7.88 p = 0.038
SD% 28.75 4.50 p < 0.001 SD%*lnP*SH%2 23.23 6.00 p < 0.001
DBH-1 -1.89 0.30 p < 0.001 DBH
-1*lnP*SH%
2 0.75 0.39 p = 0.034
lnP 3.46 1.18 p = 0.003 SD%*DBH-1*lnP*SH%
2 -1.37 0.46 p = 0.003
SH%2 1.52 43.92 p = 0.972
VARIABLE COEFFICIENT S.E. P - VALUE INTERACTION COEFFICIENT S.E. P - VALUE
INTERCEPT 126.04 15.52 p < 0.001 DBH-1*SH%
2 -57.41 25.29 p = 0.023
SD% 21.94 7.01 p = 0.002 SH%2*lnP -54.52 20.02 p = 0.006
DBH-1 -3.58 0.51 p < 0.001 SD%*DBH
-1*SH%
2 56.44 23.33 p = 0.016
lnP 3.63 2.02 p = 0.072 SD%*SH%2*lnP 43.25 10.00 p < 0.001
SH%2 159.73 151.60 p = 0.292 DBH
-1*SH%
2*lnP 8.25 2.84 p = 0.004
SD%*DBH-1*SH%
2*lnP -8.86 2.51 p < 0.001
(a)
(b)
Chapter 5 Wood Growth and Structure Page 139
0
50
100
150
200
0.0 0.1 0.2 0.3DBH (m)
Me
an
Ve
ss
el
Ta
ng
en
tial
Dia
me
ter
(µm
)
0.0 0.1 0.2 0.3DBH (m)
(d) SHBH SD100%
0
50
100
150
200(a) SH50% SD50% (b) SH50% SD100%
(c) SHBH SD50%
250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
250 90% C.I. 1,000 90% C.I. 5,000 90% C.I. 10,000 90% C.I.
Raw Data
Figure 5.14: The relationship between the dependent variable mean vessel tangential diameter (µm) and the factors DBH (m), P (st/ha), SH% and SD% at sample positions (a) SH50% SD50%, (b) SH50% SD100%, (c) SHBH SD50% and (d) SHBH SD100%. The predicted values of mean vessel tangential diameter (with 90% confidence intervals) are plotted against DBH and identified by P.
0
50
100
150
200
0.0 0.1 0.2 0.3DBH (m)
Me
an
Ve
ss
el
Ra
dia
l D
iam
ete
r (µ
m)
0.0 0.1 0.2 0.3DBH (m)
(d) SHBH SD100%
0
50
100
150
200(a) SH50% SD50% (b) SH50% SD100%
(c) SHBH SD50%
250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
250 90% C.I. 1,000 90% C.I. 5,000 90% C.I. 10,000 90% C.I.
Raw Data
Figure 5.15: The relationship between the dependent variable mean vessel radial diameter (µm) and the factors DBH (m), P (st/ha), SH% and SD% at sample positions (a) SH50% SD50%, (b) SH50% SD100%, (c) SHBH SD50% and (d) SHBH SD100%. The predicted values of mean vessel radial diameter (with 90% confidence intervals) are plotted against DBH and identified by P.
Chapter 5 Wood Growth and Structure Page 140
Vessel tangential and radial diameter increased as sample diameter position increased (from
SD50% to SD100%), however vessel tangential and radial diameter were little affected by
increased sample height. Initial observations that vessel radial diameter exceeds vessel
tangential diameter are visible in a comparison of the results (Figures 5.14, 5.15), and it is
apparent that the difference between the two increases as stem diameter increases.
The hypothesis that vessels are ‘squashed’ in the radial direction during formation suggests
that vessel radial diameter is more affected by growth rate than is vessel tangential diameter.
The above results support this hypothesis since vessel radial diameter was more affected by
stem diameter (growth rate) than was vessel tangential diameter. It is likely that in growing
vessels the least resistance to expansion is towards the bark, hence their elliptical shape in the
radial direction, particularly in rapidly expanding vessels in faster growing trees. The pressure
to ‘find’ space would apply to all expanding wood cells, yet vessels in particular were
observed to have an elliptical shape. Their more plastic response could be due to having
relatively thin, and therefore more elastic, cell walls.
The relationship between vessel diameter (radial and tangential), stem diameter and planting
density indicates that there was little difference in vessel diameter between the dominant
(largest) trees in each planting density (Figures 5.14, 5.15). This is similar to the finding that
there was little difference in stem height between the dominant trees in each planting density
(Figure 4.3), suggesting that vessel diameter is controlled either by stem height or the same
stimuli as stem height. A comparison between stem height and stem diameter as the primary
factor explaining variation in vessel diameter (Table 5.9) showed that stem height explained
more variation in vessel diameter than stem diameter, particularly for vessel tangential
diameter.
Table 5.9: A comparison between stem diameter and stem height as the primary predictive variable for (a) vessel tangential diameter (µm) and (b) vessel radial diameter (µm). The comparison is made through the deviance reduction (maximum likelihood) whereby the greater the reduction in deviance, the better the fit between the dependent and predictive variables.
(a) MODEL DEVIANCE DEVIANCE REDUCTION (b) MODEL DEVIANCE DEVIANCE REDUCTION
INTERCEPT 15194.07 Empty Model INTERCEPT 16464.26 Empty Model
INTERCEPT + DBH 15168.55 15194.07 – 15168.55 = 25.52 INTERCEPT + DBH 16426.66 16464.26 – 16426.66 = 37.60
INTERCEPT + SH 15151.92 15194.07 – 15151.92 = 42.15 INTERCEPT + SH 16424.72 16464.26 – 16424.72 = 39.54
Overall the results indicate that the best predictor of vessel diameter was stem height,
however stem diameter was also an important predictor of vessel diameter as vessel radial
Chapter 5 Wood Growth and Structure Page 141
diameter was positively affected by stem radial growth rate. Since stemwood vessel ratio was
constant regardless of competition or sample position, vessel frequency may be assumed to
follow the opposite pattern to vessel diameter, so that stemwood vessel ratio remains constant.
STEMWOOD FIBRE RATIO
Fibres are physiologically inactive wood cells with the primary role of structural support.
Fibre properties affect wood quality since increased fibre content results in stronger, more
durable wood and decreased spatial variation in fibre properties results in more stable wood
(Zobel and Buitjtenen 1989; Malan et al. 1997). Stemwood fibre ratio was found to increase
as competition increased (Table 5.10), as shown by the increase in stemwood fibre ratio as
planting density increased, particularly at SD50% (Figure 5.16(a,c)), and by the increase in
stemwood fibre ratio as stem diameter decreased, particularly at SD100% (Figure 5.16(b,d)).
Table 5.10: The fixed-effect regression coefficients in the 90% confidence interval random intercept model of the effects of stem diameter (DBH) (m), planting density (P) (st/ha), sample height (SH%) and sample diameter (SD%) on stemwood fibre ratio.
VARIABLE COEFFICIENT S.E. P - VALUE INTERACTION COEFFICIENT S.E. P - VALUE
INTERCEPT 0.4949 0.0951 p < 0.001 DBH*SD% -0.6774 0.3176 p = 0.033
DBH 0.2215 0.2541 p = 0.383 SD%*lnP -0.0182 0.0110 p = 0.098
SD% 0.2780 0.1137 p = 0.014 SD%*SH%2 -0.2252 0.0932 p = 0.016
lnP 0.0237 0.0095 p = 0.013
SH%2 0.1451 0.0736 p = 0.049
The results show that the stemwood fibre ratio of dominant (large) trees in each planting
density remained relatively constant regardless of sample position. Given that fibres occupy
the space in wood that is unoccupied by physiologically active cells, the above finding
indicates that dominant trees have maintained their ratio of physiologically active wood cells
over the measurement period. In contrast, at SHBH the stemwood fibre ratio of suppressed
(small) trees increased from earlier to later formed wood (from SD50% to SD100%) (Figure
5.16), indicating a reduction in the ratio of physiologically active wood cells over time. These
results show that the ratio of physiologically active wood cells decreases as relative resource
capture decreases.
Given that fibres provide the primary structural support for the stem and tree, the finding that
larger trees had a smaller stemwood fibre ratio suggests that faster grown trees will be less
dense than slower growing trees. Previous studies of wood properties, however, do not
consistently report such a correlation, especially in relation to eucalyptus wood density. It is
Chapter 5 Wood Growth and Structure Page 142
therefore appropriate to investigate fibre morphology to examine whether stems may
compensate for a reduced stemwood fibre ratio by increasing individual fibre strength.
0.6
0.7
0.8
0.9
1.0
0.0 0.1 0.2 0.3DBH (m)
Ste
mw
oo
d F
ibre
Rati
o
0.0 0.1 0.2 0.3DBH (m)
(d) SHBH SD100%
0.6
0.7
0.8
0.9
1.0
Ste
mw
oo
d F
ibre
Rati
o (a) SH50% SD50% (b) SH50% SD100%
(c) SHBH SD50%
250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
250 90% C.I. 1,000 90% C.I. 5,000 90% C.I. 10,000 90% C.I.
Raw Data
Figure 5.16: The relationship between the dependent variable stemwood fibre ratio and the factors DBH
(m), P (st/ha), SH% and SD% at sample positions (a) SH50% SD50%, (b) SH50% SD100%, (c) SHBH SD50% and (d) SHBH SD100%. The predicted values of stemwood fibre ratio (with 90% confidence intervals) are plotted against DBH and identified by P.
FIBRE AND FIBRELUMEN DIAMETER
Fibre and fibrelumen diameter were found to increase as competition increased (Table
5.11(a,b)), as shown by the increase in mean fibre diameter (Figure 5.17(a,b)) and the increase
in mean fibrelumen diameter (Figure 5.17(c,d)) as planting density increased. The effect of
increased competition on fibre and fibrelumen diameter was reduced compared to the effect of
increased competition on other cell types, since mean fibre diameter and mean fibrelumen
diameter were unaffected by decreased stem diameter.
Table 5.11: The fixed-effect regression coefficients in the 90% confidence interval random intercept model of the effects of planting density (P) (st/ha), sample height (SH%) and sample diameter (SD%) on (a) fibre diameter (µm) and (b) fibrelumen diameter (µm).
VARIABLE COEFFICIENT S.E. P - VALUE INTERACTION COEFFICIENT S.E. P - VALUE
INTERCEPT 8.0350 0.9130 p < 0.001 SH%-1*SD% -0.2070 0.1160 p = 0.074
SH%-1 0.0670 0.0440 p = 0.128 SH%
-1*SD%*lnP 0.0300 0.0130 p = 0.021
SD% 1.2790 0.4860 p = 0.008
lnP 0.2670 0.1050 p = 0.011
(a)
Chapter 5 Wood Growth and Structure Page 143
VARIABLE COEFFICIENT S.E. P - VALUE INTERACTION COEFFICIENT S.E. P - VALUE
INTERCEPT 5.0540 1.0420 p < 0.001 SH%-1*SD% -0.3630 0.1320 p = 0.006
SH%-1
0.0650 0.0500 p = 0.194 SH%-1*SD%*lnP 0.0460 0.0150 p = 0.002
SD% 1.2290 0.5570 p = 0.027
lnP
0.3350 0.1190 p = 0.005
6
9
12
15
Me
an
Dia
mete
r (µ
m)
6
9
12
15
0 10 20 30 40 50 60SH%
Me
an
Dia
mete
r (µ
m) (c) Fibrelumen SD50%
(b) Fibre SD100% (a) Fibre SD50%
250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
250 90% C.I. 1,000 90% C.I. 5,000 90% C.I. 10,000 90% C.I.
Raw Data
0 10 20 30 40 50 60SH%
(d) Fibrelumen SD100%
Figure 5.17: The relationship between the dependent variables mean fibre diameter (µm) and mean fibrelumen diameter (µm) and the factors P (st/ha), SH% and SD%. The predicted values of mean fibre diameter (with 90% confidence intervals) are plotted against SH% and identified by P at sample positions (a) SD50% and (b) SD100%. The predicted values of mean fibrelumen diameter (with 90% confidence intervals) are plotted against SH% and identified by P at sample positions (c) SD50% and (d) SD100%. The 90% confidence intervals at SH50% are offset so as not to obscure each other.
That mean fibre and fibrelumen diameter were unaffected by growth rate (stem diameter)
provides evidence for the general consensus that fibre dimensions do not exhibit a plastic
response to environmental conditions. The finding, however, that increased planting density
resulted in increased mean fibre and fibrelumen diameter shows that environmental conditions
can affect fibre dimensions. The results for fibre and fibrelumen diameter are similar to those
of crown height (Figure 4.13) as both were increased by increased planting density, but
unaffected by stem diameter. This indicates a positive correlation between crown height and
fibre and fibrelumen diameter, suggesting that increased crown height (increased mean
distance of the crown from stemwood formation) may have an effect on mean fibre and
fibrelumen diameter.
(b)
Chapter 5 Wood Growth and Structure Page 144
The pattern of change in mean fibre and fibrelumen diameter with sample height supports the
above hypothesis, since lower stem sample height (SH%), or increased distance from the
crown, resulted in increased mean fibre and fibrelumen diameter (Figure 5.17). Furthermore,
the above trend was strongest in high planting densities which have greater crown height
(Figure 4.13) and for which the reduction in sample position from SH50% to SHBH could result
in a greater increase in the relative distance from the crown. These findings are similar to a
trend found in fibre length (Wilkes 1988) whereby fibre length increases as crown proximity
decreases. In that case it was suggested that increased fibre size at lower levels in the stem
may be due to reduced apical influence and less rapid wood formation, with the result that
fibres have more time to reach full size before the secondary wall is laid down.
The results corroborate previous findings that fibre diameter increases with radial growth of
the stem, since mean fibre diameter increases as sample diameter increases, especially in high
planting densities. The results also show that suppressed stems tend to have a greater change
in wood properties with radial growth of the stem, since the mean fibre and fibrelumen
diameters of high planting densities change by a greater degree from SD50% to SD100% than the
mean fibre and fibrelumen diameters of low planting densities.
Overall the results for mean fibre and fibrelumen diameter generally confirm previous
findings, with the exception that increased planting density had a positive effect on mean fibre
and fibrelumen diameter, possibly by reducing crown influence on wood at the time of
formation. In combination, mean fibre diameter and mean fibrelumen diameter follow similar
patterns and it is difficult to determine whether fibrewall thickness is changed. It is therefore
appropriate to examine fibrewalls directly.
FIBREWALL RATIO
Fibrewall ratio was found to decrease as competition increased (Table 5.12), as shown by the
decrease in fibrewall ratio as planting density increased, particularly at SHBH (Figure
5.18(c,d)), and by the decrease in fibrewall ratio as stem diameter decreased, particularly at
SH50% (Figure 5.18(a,b)).
Chapter 5 Wood Growth and Structure Page 145
Table 5.12: The fixed-effect regression coefficients in the 90% confidence interval random intercept model of the effects of stem diameter (DBH) (m), planting density (P) (st/ha), sample height (SH%) and sample diameter (SD%) on fibrewall ratio.
VARIABLE COEFFICIENT S.E. P - VALUE INTERACTION COEFFICIENT S.E. P - VALUE
INTERCEPT 0.6146 0.1051 p < 0.001 lnP* SD% -0.0263 0.0157 p = 0.094
lnP -0.0256 0.0125 p = 0.041 SH%2* SD% -1.3581 0.3594 p < 0.001
DBH2 -0.3242 0.7955 p = 0.684 lnP* DBH
2* SH%
2 1.2050 0.5745 p = 0.036
SH%2 -0.0523 0.1373 p = 0.703 lnP* SH%
2* SD% 0.1547 0.0412 p < 0.001
SD% 0.2194 0.1267 p = 0.083
0.2
0.4
0.6
0.8
0.0 0.1 0.2 0.3DBH (m)
Me
an
Fib
rew
all R
ati
o
0.0 0.1 0.2 0.3DBH (m)
(d) SHBH SD100%
0.2
0.4
0.6
0.8
Mea
n F
ibre
wall
Rati
o
(a) SH50% SD50% (b) SH50% SD100%
(c) SHBH SD50%
250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
250 90% C.I. 1,000 90% C.I. 5,000 90% C.I. 10,000 90% C.I.
Raw Data
Figure 5.18: The relationship between the dependent variable mean fibrewall ratio and the factors DBH (m), P (st/ha), SH% and SD% at sample positions (a) SH50% SD50%, (b) SH50% SD100%, (c) SHBH SD50% and (d) SHBH SD100%. The predicted values of fibrewall ratio (with 90% confidence intervals) are plotted against DBH and identified by P.
Comparison between the results for stemwood fibre ratio (Figure 5.16) and fibrewall ratio
(Figure 5.18) reveal that the pattern of the results are generally opposing. Where stemwood
fibre ratio had a positive correlation with planting density, fibrewall ratio had a negative
correlation with planting density, and where stemwood fibre ratio had a negative correlation
with stem diameter, fibrewall ratio had a positive correlation with stem diameter, both of
which indicate that stems compensate for a smaller ratio of fibres by increasing relative
fibrewall thickness; thereby maintaining adequate mechanical support. Overall it was unclear
whether total fibrewall material in the stemwood was increased or decreased by increased
competition due to the compensation effect between stemwood fibre ratio and fibrewall ratio.
Chapter 5 Wood Growth and Structure Page 146
STEMWOOD FIBREWALL RATIO
Stemwood fibrewall ratio is the ratio of stemwood containing fibrewall material (rather than
the ratio of fibre containing fibrewall material). Stemwood fibrewall ratio was found to
decrease as competition increased (Table 5.13), as shown by the decrease in stemwood
fibrewall ratio as planting density increased (Figure 5.19). In practical terms, however, the
effect of competition was not strong as the difference in stemwood fibrewall ratio due to
planting density was small at an approximate 6% mean difference between 250 st/ha and
10,000 st/ha, and stemwood fibrewall ratio was unaffected by stem diameter.
Table 5.13: The fixed-effect regression coefficients in the 90% confidence interval random intercept model of the effects of planting density (P) (st/ha), sample height (SH%) and sample diameter (SD%) on stemwood fibrewall ratio.
VARIABLE COEFFICIENT S.E. P - VALUE INTERACTION COEFFICIENT S.E. P - VALUE
INTERCEPT 0.2073 0.1082 p = 0.055 lnP* SH%2 -0.1692 0.0757 p = 0.025
lnP 0.0100 0.0135 p = 0.459 lnP* SD% -0.0456 0.0169 p = 0.007
SH%2 1.4652 0.6052 p = 0.015 SH%
2* SD% -2.5830 0.7655 p = 0.001
SD% 0.3916 0.1354 p = 0.004 lnP* SH%2* SD% 0.3016 0.0958 p = 0.002
0.1
0.2
0.3
0.4
0.5
0.0 0.1 0.2 0.3DBH (m)
Ste
mw
oo
d F
ibre
wa
ll R
ati
o
0.0 0.1 0.2 0.3DBH (m)
(d) SHBH SD100%
0.1
0.2
0.3
0.4
0.5
Ste
mw
oo
d F
ibre
wa
ll R
ati
o
(a) SH50% SD50% (b) SH50% SD100%
(c) SHBH SD50%
250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
250 90% C.I. 1,000 90% C.I. 5,000 90% C.I. 10,000 90% C.I.
Raw Data
Figure 5.19: The relationship between the dependent variable stemwood fibrewall ratio and the factors P (st/ha), SH% and SD% at sample positions (a) SH50% SD50%, (b) SH50% SD100%, (c) SHBH SD50% and (d) SHBH SD100%. The predicted values of stemwood fibrewall ratio (with 90% confidence intervals) are plotted against DBH and identified by P.
The results show that stemwood fibrewall ratio in planting densities 5,000-10,000 st/ha has
low spatial variability as it is unaffected by stem position. Stemwood fibrewall ratio is also
Chapter 5 Wood Growth and Structure Page 147
unaffected by sample position in planting densities 250-1,000 st/ha, except at sample height
SHBH and sample diameter SD100% (Figure 5.19(d)), where stemwood fibrewall ratio is
significantly greater than at most other sample positions. This finding coincides with high
fibrewall ratio in planting densities 250-1,000 st/ha at the same sample position (Figure
5.18(d)), indicating that the increase in stemwood fibrewall ratio in planting densities 250-
1,000 st/ha was due to increased fibrewall ratio.
Overall the results for stemwood fibre ratio and stemwood fibrewall ratio indicate that despite
trees in high planting densities exhibiting a higher stemwood fibre ratio, they actually
distribute less biomass to fibre production within a given volume of stemwood than if they
were at low planting densities. It was hypothesised that trees in high planting densities have
lower concentrations of photosynthate in the tree than trees in low planting densities, since
they exhibited a lower stemwood ray ratio (Figure 5.11) indicating a lower tendency to
produce excess food requiring storage. The results for stemwood fibre ratio provide evidence
for this hypothesis since a lower concentration of photosynthate in trees in higher planting
densities could trigger a more frugal use of photosynthate during fibre differentiation, leading
to relatively thin fibrewalls and less fibrewall material in the stemwood. Alternatively, trees in
high planting densities may have a reduced stemwood fibrewall ratio due to having smaller
crowns and more shelter from neighbouring trees and therefore less requirements for support,
however if this were the case one might expect no difference in the proportion of
physiologically active cells in the stemwood.
The consequence of the results for stemwood fibrewall ratio is that trees grown in high
planting densities are likely to have reduced variation in wood density, but lower wood
density overall than trees in lower planting densities. It is of interest to investigate wood
density to determine if results for wood anatomy correlate with and/or provide a physiological
explanation for changes in wood density.
5.4.3 Stemwood Basic Density
In addition to testing the effects of planting density and stem diameter on stemwood basic
density, the effect of sample height (SHX) was also tested. The sample heights tested were
breast height (1.3 m) and 25%, 50% and 75% of stem height.
Chapter 5 Wood Growth and Structure Page 148
Increased competition had positive and negative effects on stemwood basic density (Table
5.14). Stemwood basic density was found to decrease as planting density increased,
particularly at lower sample heights (Figure 5.20(a-b)), though the effect was not significant
at individual sample heights since the 95% confidence intervals overlapped between all
planting densities. In contrast, stemwood basic density increased in response to decreased
stem diameter, particularly in the most suppressed (smallest) trees (Figure 5.20). In addition,
stemwood basic density increased as sample height increased, though the effect was not
significant for the largest stems in 250 st/ha.
Table 5.14: The fixed-effect regression coefficients in the random intercept model of the effects of stem diameter (DBH) (m), planting density (P) (st/ha) and sample height (SH%) on stemwood basic density (kg m
-3).
VARIABLE COEFFICIENT S.E. P - VALUE INTERACTION COEFFICIENT S.E. P - VALUE
INTERCEPT 490.951 51.013 p < 0.001 SH%2*DBH
-1 85.569 19.418 p < 0.001
SH%2 -558.986 153.174 p < 0.001 SH%
2*lnP 85.255 18.635 p < 0.001
DBH-1 4.507 2.041 p = 0.027 SH%
2*DBH
-1*lnP -9.004 2.257 p < 0.001
lnP -16.519 7.180 p = 0.021
300
400
500
600
700
0.0 0.1 0.2 0.3DBH (m)
Ste
mw
oo
d B
asic
Den
sit
y (
kg
m-3
)
0.0 0.1 0.2 0.3DBH (m)
(d) SH75%
0.0 0.1 0.2 0.3DBH (m)
(c) SH50%
0.0 0.1 0.2 0.3DBH (m)
(b) SH25%
250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
250 95% C.I. 1,000 95% C.I. 5,000 95% C.I. 10,000 95% C.I.
Raw Data
(a) SHBH
Figure 5.20: The relationship between the dependent variable stemwood basic density (kg m
-3) and
the factors DBH (m), P (st/ha) and SH% at sample positions (a) SHBH, (b) SH25%, (c) SH50% and (d) SH75%. The predicted values of stemwood basic density (with 95% confidence intervals) are plotted against DBH and identified by P. Sample positions for which corresponding wood anatomy measurements have been analysed are highlighted with bold black borders.
Chapter 5 Wood Growth and Structure Page 149
In the previous examination of stemwood fibrewall ratio (Figure 5.19) it was hypothesised
that wood density would follow the same pattern as stemwood fibrewall ratio, whereby trees
in high planting densities would have reduced variation in wood density and lower overall
wood density than trees in low planting densities. In comparing results between the two,
however, it is important to note that different sampling techniques were used. Stemwood
fibrewall ratio was sampled at two points along the disk radius, whereas stemwood basic
density made measurement of the entire disk. Consequently the stemwood fibrewall ratio
results may not fully coincide with the stemwood basic density measurements. It is worth
noting that the stemwood fibrewall ratio measurements at SD100% are more likely to be
representative of stemwood basic density since the measurement taken at the outer
circumference of the disk is representative of a greater volume of the disk.
The results for stemwood basic density confirm that for a given stem diameter trees in high
planting densities tend to have lower wood density, but indicate that there is no significant
difference in wood density between the largest (dominant) trees in different planting densities
(Figure 5.20). As discussed previously, the results for stemwood basic density tend to
correlate more closely with the stemwood fibrewall ratio results at SD100% than those at
SD50%. This is apparent in the similar pattern of change between dominant trees in different
planting densities in both stemwood fibrewall ratio and stemwood basic density at sample
height SHBH (Figures 5.19(d), 5.20(a)) and sample height SH50% (Figures 5.19(b), 5.20(a)).
In contrast, the hypothesis that trees in high planting densities would have reduced variation
in wood density is refuted, since higher planting densities tended to show greater variation in
wood density as stem height increases, particularly in suppressed trees. It is noteworthy that
whatever the planting density, dominant trees vary little in basic density, particularly in the
lowest 0-6 m stem section (Figure 5.21). This indicates that dominant trees have similar wood
quality in the primary sawlog section of the stem regardless of planting density.
Chapter 5 Wood Growth and Structure Page 150
300
400
500
600
700
0 2 4 6 8 10 12 14 16
Sample Height (m)
Bas
ic D
en
sit
y (
kg
m-3
)250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
Most Dominant Tree:
Most Suppressed Tree:
Figure 5.21: Variation in predicted stemwood basic density with increased stem height of the most dominant and most suppressed tree measured in each planting density.
The finding that high planting densities exhibited less spatial variation in stemwood fibrewall
ratio, but more spatial variation in stemwood basic density, compared to lower planting
densities, suggests that factors other than the proportion of stemwood comprised of fibrewall
material affect wood density. One such factor may be the density of the fibrewall material.
The secondary wall in fibre cells is the primary determinant of wood density (Wilkins 1986).
The secondary wall is comprised of linear, crystalline polymer strands called microfibrils,
which are laid down in latticed layers to form the cell wall (Figure 5.2). Following cell wall
formation lignin, a three-dimensional and amorphous polymer, is impregnated into spaces
between the microfibrils, essentially cementing them into place and strengthening the cell
wall structure. Theoretically the density of the fibrewall material could be increased with
improved lignin impregnation, since this would result in fewer ‘gaps’ left between
microfibrils. It is possible that patterns of lignin impregnation vary with planting density,
leading to greater variation in stemwood basic density in high planting densities than
suggested by the variation in stemwood fibrewall ratio.
Overall the results for stemwood basic density indicate no significant difference in the wood
quality of the primary sawlog section of the stem between dominant trees in different planting
densities. Suppressed trees have been shown to exhibit higher wood density, which could
Chapter 5 Wood Growth and Structure Page 151
prove advantageous for biomass quality by increasing the carbon content of wood; and higher
variability in wood density, which would not affect biomass quality.
5.4.4 Branching Habits
Branching habits are included in the analysis of wood growth and structure as they are
indicative of knot content in the wood structure. Conditions that stimulate persistent, live and
growing branches result in wood with increased knot content, which is detrimental to wood
quality for most end-uses.
The effects of branch height (BH) and branch aspect (BAS) on branching habits were tested in
addition to the effects of planting density and stem diameter. Specialised models assuming a
non-normal residual distribution (binomial and multinomial models) were used to analyse
branching habit data that had discrete rather than continuos distributions (i.e. data for branch
mortality were either dead (1) or alive (0) rather than a scale number) (Snijders and Bosker
1999; Rasbash et al. 2003). These models return the probability that branches will have the
characteristic under analysis (i.e. the probability that a branch will be dead), rather than a
predicted value.
BRANCH DIAMETER
Branch diameter decreased as competition increased (Table 5.15), as shown by the decrease in
branch diameter as planting density increased and stem diameter decreased (Figure 5.22(a)).
Table 5.15: The fixed-effect regression coefficients of the random intercept model of the effects of stem diameter (DBH) (m), planting density (P) (st/ha), branch height (BH) (m) and branch aspect (BA
S)
(º) on branch diameter (m).
VARIABLE COEFFICIENT S.E. P - VALUE INTERACTION COEFFICIENT S.E. P - VALUE
INTERCEPT 0.01826 0.00211 p < 0.001 lnBH* DBH2 0.10926 0.02011 p < 0.001
lnBH 0.00038 0.00032 p = 0.230 sin(BAS*
PI/180)*DBH
2 0.29903 0.07544 p < 0.001
sin(BAS*
PI/180) 0.00096 0.00048 p = 0.045 sin(BA
S*
PI/180)*lnP*DBH
2 -0.03679 0.01313 p = 0.005
lnP -0.00149 0.00023 p < 0.001
DBH2 0.11237 0.04386 p = 0.009
Chapter 5 Wood Growth and Structure Page 152
0.00
0.01
0.02
0.03
0.04
0.05
0.0 0.1 0.2
DBH (m)
Branch Diameter (m)
Raw Data 250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
0 1 2 3 4 5 6
Branch Height (m)
(b)
0 90 180 270 360
Branch Aspect (°)
(c)(a)
Figure 5.22: The relationship between the dependent variable branch diameter (m) and the factors DBH (m), BH (m), P (st/ha) and BA
S (°). The predicted values of branch diameter are plotted against (a)
DBH, (b) BH and (c) BAS and identified by P. Branch diameter of 2 cm is highlighted by a bold black
line.
The results for branch diameter also show that increased branch height and an easterly aspect
(45-135°) resulted in increased branch diameter (Figure 5.22(b-c)), indicating that branches
receiving more incidental and/or morning light grow to a greater diameter. Light may not be
considered a limiting resource in eucalypt forests given their relatively open crown structure,
however the photosynthetic capacity of eucalyptus leaves is greatly enhanced by saturated
light with the result that limited nutrient and/or water resources are preferentially allocated to
leaves receiving more saturated light at the optimum time of day for photosynthesis
(mornings). Canopy light dynamics have a significant affect on branch diameter since branch
diameter growth is stimulated by the ability of its leaves to capture light for photosynthesis,
which is in turn affected by branch position. The results for branch diameter provide strong
evidence that light patterns and aboveground competition for light impact on eucalyptus
crown structure and influence growth dynamics and stand structure in eucalyptus plantations.
Observation of eucalyptus branch shed has found that branches up to 2 cm diameter shed well
(Jacobs 1955), whereas branches over 2 cm diameter may require pruning to reduce wood
knot content (Montagu et al. 2003). The model for branch diameter predicts that few branches
exceed 2 cm diameter (predicted values in Figure 5.22), whereas the raw values show that
many branches exceed 2 cm diameter. This underestimation of branch diameter by the model
Chapter 5 Wood Growth and Structure Page 153
is of concern as the model implies that pruning is not required to reduce knot content, whereas
the raw values suggest that pruning is required to reduce knot content. It is therefore
appropriate to investigate the occurrence of branches greater than (>) 2 cm diameter to
provide a better indication of whether pruning is required to minimise knot content.
BRANCH DIAMETERS > 2 CM
The occurrence of branch diameters > 2 cm was found to decrease as competition increased
(Table 5.16), as shown by the decrease in the probability of branch diameters > 2 cm as
planting density increases and stem diameter decreases (Figure 5.23(a)).
Table 5.16: The fixed-effect regression coefficients in the binomial model of the effects of stem diameter (DBH) (m), planting density (P) (st/ha), branch height (BH) (m) and branch aspect (BA
S) (º) on
the probability of branch diameter > 2 cm (BDx2
) where; BD>2
= 1, BD<2
= 0,
BD>2
model output = ln(#BD>2
/#BD<2)
BD>2
= exp(#BD>2
/#BD<2) / (1 + exp(#BD>2
/#BD<2))
BD<2
= 1 – BD>2
VARIABLE COEFFICIENT S.E. P - VALUE INTERACTION COEFFICIENT S.E. P - VALUE
INTERCEPT -1.765 4.957 p = 0.722 lnP* sin(BAS*
PI/180) 0.535 0.236 p < 0.023
lnP 0.105 0.821 p = 0.898 lnP*DBH-1 -0.222 0.094 p < 0.018
lnBH -2.197 1.379 p = 0.111 lnBH* sin(BAS*
PI/180) -0.478 0.233 p = 0.040
sin(BAS*
PI/180) 1.398 0.318 p < 0.001
DBH-1 1.130 0.579 p = 0.051
0.0
0.2
0.4
0.6
0.8
1.0
0.0 0.1 0.2
DBH (m)
Probability of Branch Diameter > 2 cm
250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
0 1 2 3 4 5 6
Branch Height (m)
(b)
0 90 180 270 360
Branch Aspect (°)
(c)(a)
Figure 5.23: The relationship between the probability of branch diameter > 2 cm (BD>2
X) and the factors DBH (m), BH (m), P (st/ha) and BA
S (°). The predicted values of the probability of branch
diameter > 2 cm are plotted against (a) DBH, (b) BH and (c) BAS and identified by P.
Chapter 5 Wood Growth and Structure Page 154
The results for branch diameters > 2 cm show that increased branch height and an easterly
aspect (45-135°) result in an increased probability of branch diameters > 2 cm (Figure 5.23(b-
c)), confirming that branches in more light saturated situations are likely to grow larger. The
concern that the model of branch diameter underestimated branch diameters > 2 cm was
legitimate, since all planting densities are shown to have a chance of branch diameters > 2 cm
whereas the branch diameter model predicted that only the 250 st/ha planting density would
have branches > 2 cm (Figure 5.22). Overall the results suggest that high planting density
stands self-prune effectively, whereas low density stands would probably benefit from
pruning to reduce knot content. In consequence, the additional costs of establishing higher
density stands might be offset by a reduced requirement for pruning.
BRANCH ANGLE
Branch angle provides an indication of the progress of branch shed, with branches exhibiting
a low branch angle (angled towards the ground rather than the sky) being likely to shed more
rapidly. Branch angle was found to decrease as competition increased (Table 5.17(a)), since
branch angle decreased as planting density increased and stem diameter decreased. The effect
of increased competition, however, was clouded by interactions with branch height and
branch aspect, and the predicted values of branch angle do not provide a clear indication that
increased planting density and decreased stem diameter result in decreased branch angle
(Figure 5.24(a)). The effects of branch height and branch aspect on branch angle were
somewhat clearer; the results indicating that increased branch height and an easterly aspect
(45-135°) generally result in increased branch angle (Figure 5.24(b-c)).
Table 5.17: The fixed-effect regression coefficients in the random slope model of the effects of (a) stem diameter (DBH) (m), planting density (P) (st/ha), branch height (BH) (m) and branch aspect (BA
S)
(º) on branch angle (°); and (b) branch diameter (BD) (m) on branch angle (°).
VARIABLE COEFFICIENT S.E. P - VALUE INTERACTION COEFFICIENT S.E. P - VALUE
INTERCEPT 107.074 6.662 p < 0.001 BH* sin(BAS*
PI/180) 1.888 0.438 p < 0.001
BH -8.269 3.433 p = 0.016 BH*DBH-1 1.632 0.388 p < 0.001
sin(BAS*
PI/180) 4.559 0.661 p < 0.001 BH*lnP*DBH
-1 -0.195 0.045 p < 0.001
lnP -3.530 1.017 p = 0.001
DBH-1 1.160 0.367 p = 0.002
VARIABLE COEFFICIENT S.E. P - VALUE INTERACTION COEFFICIENT S.E. P - VALUE
INTERCEPT 62.145 2.440 p < 0.001
√BD 529.926 16.688 p < 0.001
(b)
(a)
Chapter 5 Wood Growth and Structure Page 155
40
80
120
160
200
0.0 0.1 0.2
DBH (m)
Branch Angle (°)
250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
0 1 2 3 4 5 6
Branch Height (m)
(b)
0 90 180 270 360Branch Aspect (°)
(c)
0.00 0.03 0.06
Branch Diameter (m)
Raw Data Predicted Values
(d)
Deviance Reduction:
14408.3 - 13631.6 = 776.7
Deviance Reduction:
14408.3 - 13725.6 = 682.7
(a)
Figure 5.24: The relationship between the dependent variable branch angle (m) and the factors DBH (m), BH (m), P (st/ha) and BA
S (°), with the predicted values plotted against (a) DBH, (b) BH and (c)
BAS and identified by P; and the relationship between the dependent variable branch angle (m) and
the factor BD (m), with the predicted values plotted against (d) BD (with 95% confidence intervals) and identified by P. A comparison between the two relationships is supplied by the deviance reduction (maximum likelihood) whereby the greater the reduction in deviance, the better the fit between the dependent variable and the factors.
Given the large spread in the predicted values of branch angle in relation to planting density
and stem diameter, branch angle might be better fitted directly to another branch characteristic
that is affected by competition. This possibility was investigated with branch diameter (Table
5.17(b)), and the results indicate that the fit between branch angle and branch diameter was
better than the fit between branch angle and the factors (planting density, stem diameter,
branch height and branch aspect), as evidenced by a greater reduction in the (maximum
likelihood) deviance of the model (Figure 5.24). The strong positive relationship between
branch angle and branch diameter (Figure 5.24(d)) indicates that increased competition
(increased planting density and decreased stem diameter) will have similar effects on both
characteristics. In consequence it is confirmed that branch angle decreases as competition
increases, providing evidence that increased competition will result in more advanced branch
shed and therefore decreased knot content.
Chapter 5 Wood Growth and Structure Page 156
BRANCH MORTALITY
Branch mortality was found to increase as competition increased (Table 5.18), as shown by
the increase in the probability of branch mortality as planting density increased (Figure 5.25).
Branch mortality was unaffected by stem diameter.
Table 5.18: The fixed-effect regression coefficients in the binomial model of the effects of planting density (P) (st/ha), branch height (BH) (m) and branch aspect (BA
S) (º) on the probability of branch
mortality from 0-6 m stem height (BMX) where; dead = 1, live = 0,
dead model output = ln(#dead
/#live),
BMDEAD = exp(#dead
/#live) / (1 + exp(#dead
/#live))
BMLIVE = 1 – BMDEAD.
VARIABLE COEFFICIENT S.E. P - VALUE INTERACTION COEFFICIENT S.E. P - VALUE
INTERCEPT -5.862 1.152 p < 0.001 lnBH*sin(BAS*
PI/180) 0.537 0.198 p = 0.007
lnP 1.403 0.168 p < 0.001
lnBH -1.149 0.144 p < 0.001
sin(BAS*
PI/180) -1.265 0.266 p < 0.001
0.0
0.2
0.4
0.6
0.8
1.0
0 1 2 3 4 5 6Branch Height (m)
Probability of Branch Mortality
250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
0 45 90 135 180 225 270 315 360Branch Aspect (°)
(b)(a)
Figure 5.25: The relationship between the dependent variable branch mortality (BMX) and the factors P (st/ha), BH (m) and BA
S (°). The predicted values of the probability of branch mortality are plotted
against (a) branch height and (b) branch aspect and identified by P.
The finding that increased planting density resulted in a greater probability of branch
mortality was expected since increased planting density leads to greater shading, thereby
stimulating an increased rate of branch senescence. In consequence, branches in planting
densities 5,000 st/ha and 10,000 st/ha had almost a 100% chance of mortality from 0-6 m
Chapter 5 Wood Growth and Structure Page 157
stem height (Figure 5.25). Branches in 1,000 st/ha may approach a similar probability of
mortality as branch senescence and crown lift continues, however branches in 250 st/ha may
never reach the same probability of mortality due to lack of shading in these stands. That stem
diameter had no effect on the probability of branch mortality indicates that branch mortality is
unaffected by growth rate; an hypothesis which is corroborated by the result that stem
diameter had no effect on crown height (Figure 4.13). These results provide further evidence
that stems in high planting densities have a reduced knot content compared to stems in low
planting densities since the process of branch shed is more advanced in high planting densities
due to the higher probability of branch mortality.
Further results for branch mortality show that the probability of mortality decreased as branch
height increased (Figure 5.25(a)). This is logical since high branches have a lower instance of
shading from overhead branches than low branches, and therefore a reduced chance of
mortality. Branch mortality was also affected by aspect, with branches occurring on easterly
aspects (45-135°) having a reduced probability of mortality (Figure 5.25(b)), suggesting that
morning (easterly) light is of greater importance to photosynthate production than afternoon
(westerly) light since branches with an easterly aspect are more persistent. This result seems
probable given that the photosynthetic capacity of leaves has been found to be highest in the
mornings, particularly on cloudless days (Kuppers et al. 1986; Whitehead and Beadle 2004).
This provides further evidence that light conditions are an important factor affecting crown
dynamics, even when light is apparently abundant as is the case for the 250 st/ha planting
density.
BRANCH FORM
Branch form is the stage of branch shed of individual branches, and the possible stages consist
of scars, stubs and branches (full-shed, part-shed and un-shed branches). Branch form skewed
towards scars and stubs rather than branches as competition increased (Table 5.19(a,b)), as
shown by the increased probability of scars and stubs (Figure 5.26(a-b)) and the decreased
probability of branches (Figure 5.26(c)) as planting density increased and stem diameter
decreased.
Chapter 5 Wood Growth and Structure Page 158
Table 5.19: The fixed-effect regression coefficients in the multinomial model(a)
of the effects of stem diameter (DBH) (m), planting density (P) (st/ha) and branch height (BH) (m) on the probability of branch form (BFX) where; scar = 0, stub = 1, branch = 2
scar model output = ln(#sc
/#br)
stub model output = ln(#st
/#br)
BFSCAR = exp(#sc
/#bn) / (1 + exp (#sc
/#br) + exp(#st
/#br))
BFSTUB = exp(#st
/#br) / (1 + exp(#sb
/#br) + exp(#sc
/#br))
BFBRANCH = 1 - (BFSCAR + BFSTUB)
VARIABLE COEFFICIENT S.E. P - VALUE INTERACTION COEFFICIENT S.E. P - VALUE
INTERCEPT 1.381 0.545 p = 0.011 √DBH*P 0.000021 0.000011 p = 0.056(a)
√DBH -6.770 1.567 p < 0.001 BH-1*P -0.000383 0.000240 p = 0.110
(a)
BH-1 -0.031 0.062 p = 0.617
(a)
P 0.000166 0.000076 p = 0.029
VARIABLE COEFFICIENT S.E. P - VALUE INTERACTION COEFFICIENT S.E. P - VALUE
INTERCEPT 1.037 0.668 p = 0.121 √DBH*P 0.000074 0.000023 p = 0.001
√DBH -5.611 1.897 p = 0.003 BH-1*P -0.001055 0.000317 p = 0.001
BH-1 -0.690 0.166 p < 0.001
P 0.000220 0.000097 p = 0.023
(a) Each table represents either the scar section or the stub section of a single multinomial model of branch form,
and consequently variables that are insignificant in one section must be included both sections if they are found significant in the other section, and vice versa.
0.0
0.2
0.4
0.6
0.8
1.0
0.0 0.1 0.2DBH (m)
Probability
250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha
Branch Form Scar
0.0 0.1 0.2
DBH (m)
(b)
0.0 0.1 0.2
DBH (m)
(c)
Branch Form Stub Branch Form Branch
0 1 2 3 4 5 6
Branch Height (m)
(f)
0 1 2 3 4 5 6
Branch Height (m)
(e)
0.0
0.2
0.4
0.6
0.8
1.0
0 1 2 3 4 5 6
Branch Height (m)
Probability
(d)
(a)
Figure 5.26: The relationship between the dependent variable branch form (BFX) and the factors DBH (m), BH (m) and P (st/ha). The predicted values of (a,d) the probability of scars (BFSc), (b,e) the probability of stubs (BFSt) and (c,f) the probability of branches (BFBr) are plotted against DBH and BH and identified by P.
(b)
(a)
Chapter 5 Wood Growth and Structure Page 159
The findings show that increased competition resulted in more advanced branch shed since
increased planting density and decreased stem diameter (Figure 5.26(a-c)) correlated with a
greater probability of advanced branch shed (scars and stubs), and a greater probability of
full-shed branches (scars) compared to part-shed branches (stubs). As with previous results,
these results further suggest that increased competition will result in reduced knot content in
the stemwood by stimulating more advanced branch shed.
The results for branch form also show that the higher the branch the greater the probability of
more advanced branch shed (Figure 5.26(d-f)). This result is surprising as lower branches
have a greater probability of branch mortality (Figure 5.25), and might therefore be expected
to exhibit more advanced branch shed. Observations in the field do show that very small
branches at the base of the stem can persist for a remarkably long time. It is possible that these
branches die at such a tiny size that when they are ejected from the wood layer they are not
heavy enough to apply the downward pressure required to break free of the bark layer, and
therefore remain persistent as tiny branches embedded in the bark.
Chapter 5 Wood Growth and Structure Page 160
5.5 Summary
Knowledge of wood growth and structure is an important aspect of plantation management
since wood is the primary product of plantations in Australia. The development of a solid
hardwood plantation industry is hampered by a lack of knowledge and experience in growing
eucalypts in plantations for sawlogs or veneer logs. The potential for current eucalyptus
pulpwood plantations to produce solid hardwood products is discounted since highly stocked
pulpwood plantations are considered unlikely to produce quality sawlogs (Turner et al. 2004).
This provides impetus to determine whether high stocking rates are detrimental to the wood
quality of final sawlog crop trees (dominant trees) in eucalyptus plantations.
In this study planting density (competition) had little effect on sapwood area or sapwood ratio
for a given stem diameter. Sapwood area increased with increased stem diameter, but
sapwood ratio did not change. Sapwood area decreased with height up the stem, and sapwood
ratio increased with height up the stem. Given that the sapwood ratio was similar between all
trees and that the ratio of stem mass and volume to leaf area was greater in high planting
densities, the findings indicate that increased competition resulted in increased stemwood
water-flow capacity per unit crown size. It was unclear whether an increased water-flow
capacity per unit crown size would be of advantage or disadvantage to the tree.
Dominant trees in high planting densities had no change in stemwood ray ratio or ray height
within the stem, and therefore no change in ray frequency. In contrast dominant trees in low
planting densities increased in stemwood ray ratio radially within the stem, and this was due
to increased ray frequency since ray height did not change within the stem. Dominant trees in
high planting densities exhibited a 5-10% lower stemwood ray ratio within the stem compared
to dominant trees in low planting densities, and this was in part due to having a shorter ray
height. The results suggest that dominant stems in high planting densities were not able to
produce as much excess food for storage as dominant trees in low planting densities, and may
therefore be less resilient to stressful events such as drought or defoliation.
Stemwood vessel ratio was unaffected by planting density, and in dominant trees the
stemwood vessel ratio was constant at approximately 17% within the stem. In contrast, vessel
diameter increased as planting density increased; however there was no significant difference
in vessel diameter between dominant trees in different planting densities. The effect of
Chapter 5 Wood Growth and Structure Page 161
planting density on vessel diameter was similar to the effect of planting density on stem
height, and stem height may therefore influence vessel diameter.
Stemwood fibre ratio increased with increased planting density, particularly in the inner stem,
and dominant trees in high planting densities had stemwood fibre ratios in the order of 6%
greater than dominant trees in low planting densities. Stemwood fibre ratio increased with
decreased stem diameter, particularly in the outer stem. Stemwood fibre ratio followed the
opposite pattern to stemwood ray ratio, indicating that higher fibre ratios were balanced by
lower ray ratios, and vice versa. In consequence, the relative constancy of stemwood fibre
ratio within the stem in dominant trees showed that the ratio of physiologically active wood
cells in dominant trees was maintained over time, possibly due to no change in relative
resource capture. In contrast, the stemwood fibre ratio in suppressed trees increased in more
recently formed wood, suggesting a reduction in the ratio of physiologically active wood cells
over time, possibly due to reduction in relative resource capture.
Both fibre and fibrelumen diameter increased as planting density increased, and as they were
unaffected by stem diameter, dominant trees in high planting densities exhibited greater fibre
and fibrelumen diameter than dominant trees in low planting densities. Increased crown
height, or decreased crown proximity to wood during wood formation, may stimulate
increased fibre and fibrelumen diameter, since crown height shares similar relationships with
planting density and stem diameter as do fibre and fibrelumen diameter. The effect of height
within the stem on fibre and fibrelumen diameter supports this hypothesis since decreased
height within the stem (decreased proximity to the crown) resulted in increased fibre and
fibrelumen diameter. Furthermore, the above trend was strongest in high planting densities
which had greater crown height and for which increased height within the stem could result in
a greater change in the relative proximity of the crown. In combination, the results did not
provide clear indication whether decreased stemwood fibre ratios were compensated by
increased fibrewall ratios as it was difficult to determine whether fibrewall thickness was
changed.
Direct investigation of fibrewall ratio showed that the fibrewall ratio decreased in response to
increased planting density and decreased stem diameter. The pattern of the results for
stemwood fibre ratio and fibrewall ratio were opposing, indicating that stems compensate for
a smaller ratio of fibres by increasing relative fibrewall thickness. It was unclear whether total
Chapter 5 Wood Growth and Structure Page 162
fibrewall material in the stemwood was increased or decreased by increased competition due
to the compensation effect between stemwood fibre ratio and fibrewall ratio.
Stemwood fibrewall ratio decreased as planting density increased, however the effect was not
significant throughout the whole stem. Stemwood fibrewall ratio did not change within the
stem in planting densities 5,000-10,000 st/ha, whereas in planting densities 250-1,000 st/ha
the stemwood fibrewall ratio increased in the lower, outer part of the stem, which coincided
with a high fibrewall ratio in planting densities 250-1,000 st/ha in the same stem section.
Stemwood fibrewall ratio was unaffected by stem diameter. The results indicate that trees in
high planting densities distribute less biomass to fibre production within a given volume of
stemwood than low planting densities. This could be due to several factors including low
concentrations of photosynthate could trigger a more frugal use of photosynthate during fibre
differentiation, resulting in a relatively thin fibrewall. Overall the results indicate that trees
grown in high planting densities are likely to have less variation in wood density, but lower
wood density overall than trees in low planting densities.
Increased competition had conflicting effects on stemwood basic density. Stemwood basic
density was found to decrease as planting density increased, though the effect was only
significant between 250 st/ha and 10,000 st/ha. In contrast, stemwood basic density was found
to increase as stem diameter decreased, particularly in suppressed trees. The wood anatomy
data did not indicate a plausible link between this relationship and the wood anatomy
variables measured. It is possible that anatomical variables other than those measured, such as
fibrewall density, also effect wood density. Height within the stem had a positive effect on
wood density, and trees in high planting densities exhibited greater increases in wood density
with increased height within the stem than trees in low planting densities, particularly in
suppressed trees. Overall there was little difference in wood density variation in the lower
stems (0-6 m) between dominant trees in high and low planting densities, indicating little
difference in the wood quality of the primary sawlog section of the stem between dominant
trees in different planting densities. Suppressed trees were shown to exhibit higher wood
density, which could prove advantageous for biomass quality by increasing the carbon content
of wood, and higher variability in wood density, which would not affect biomass quality.
Branching habits indicate that trees in high planting densities are likely to have lower knot
content than tree in low planting densities as they exhibited smaller branch diameters, lower
Chapter 5 Wood Growth and Structure Page 163
branch angles, a greater probability of branch mortality and more advanced branch shed. The
effects of branch height and branch aspect on most branch characteristics provide clear
evidence that aboveground competition for light has significant implications for growth
dynamics and stand structure in eucalyptus plantations. It was significant that high planting
densities had a much reduced probability of the presence of branches exceeding 2 cm
diameter, as this implies that high planting density stands self-prune effectively, whereas low
density stands would probably require pruning to reduce knot content. In consequence, the
additional costs of establishing higher density stands might be offset by a reduced requirement
for pruning.
Chapter 6 Conclusion Page 164
6. CONCLUSION
6.1 Synopsis
The majority of eucalyptus plantations in Australia are managed for pulpwood production, the
silviculture for which includes establishment with relatively high planting densities (over
1,000 st/ha). Due to diminishing supply from native forests, there is increasing pressure to
establish plantations for solid hardwood products. This process could be fast-tracked by
converting current eucalyptus pulpwood plantations to solid hardwood plantations. This
option, however, is generally discounted since higher stocked pulpwood plantations are
thought to restrict growth of the final sawlog crop and are considered unlikely to produce
solid hardwood with low wood variability (to prevent splitting and warping of sawn wood)
and high wood density (for strength and durability).
The above perception was investigated in this study by examining the effect of planting
density on stand, tree, and wood structure, particularly of the largest trees which represent the
likeliest source of solid hardwood products. This allowed a detailed comparison of tree
growth and structure between dominant trees from low and high planting densities, and
between dominant and suppressed trees within low and high planting densities. A wide range
of planting densities were used (250-10,000 st/ha) and in that a large number of trees were
destructively sampled from every dominance class in all planting densities. The investigation
of properties from the stand and tree levels through to the wood level facilitated a unique
insight into whole tree growth and the pathways by which trees respond to interaction due to
competition (Figure 1.1).
The examination of stand growth and structure in E. grandis at 4 years of age revealed that
eucalypt plantations established at high planting densities (5,000-10,000 st/ha) had the
potential to capture carbon in their stems twice as quickly as those planted at the more
common density of 1,000 st/ha or less. Stand structure showed evidence of strong competition
occurring in the high planting densities, and the largest trees in high planting densities were
smaller than the largest trees in low planting densities. Yet high density stands had a greater
number of co-dominant and intermediate trees compared to low density stands, with the result
that the combined stem volume of the largest 1,000 stem cohort in each planting density was
similar from planting densities 1,000-10,000 st/ha (Figure 6.1).
Chapter 6 Conclusion Page 165
99 93
83127
92
0
50
100
150
200
250
1,000 5,000 10,000
Planting Density (st/ha)
Ste
m V
olu
me
(m
3h
a-1
)
Largest 1,000 st/ha (Solid Hardwood Crop)
Remaining Stems (Biomass Crop)
Figure 6.1: The stem volume of the largest 1,000 st/ha and the stem volume of the remaining stems in 4 year old E. grandis in planting densities 1,000 st/ha, 5,000 st/ha and 10,000 st/ha.
Investigation of the growth rate of 250 stem cohorts revealed that a drought in year 3
triggered declining growth rates in stem cohorts growing at less than 35% of the rate of the
dominant 250 stem cohort, and few stem cohorts recovered increased growth rates once
growth rate had started to decline It was unclear whether the decline in the productivity of
smaller stem cohorts was due to reduced relative resource capture or due to reduced growth
efficiency, nevertheless the results show that declining stand productivity could be due to
reduced productivity in smaller trees rather than larger trees.
The examination of tree growth and structure showed that dominant trees in high planting
densities were significantly smaller than dominant trees in low planting densities in all aspects
of tree growth other than stem height. Dominant trees in high planting densities had similar
tree oven-dry mass accumulation per unit leaf area to dominant trees in low planting densities,
but had a larger proportion of tree oven-dry mass allocated to the stem as opposed to the
crown. In consequence dominant trees in high planting densities had similar tree growth
efficiency but better stem growth efficiency than dominant trees in low planting densities.
Increased competition was therefore shown to restrict the growth of dominant trees by
restricting resource capture rather than by reducing the efficiency of growth, since the tree
growth efficiency of dominant trees was not significantly affected by competition. Dominant
Chapter 6 Conclusion Page 166
trees in high planting densities compensated to some degree for restricted growth rates by
partitioning a greater proportion of tree growth to the stem.
Compared to dominant trees, suppressed trees had lower tree growth efficiency and lower
stem growth efficiency, and the hypothesis that declining stand productivity is due to reduced
tree growth efficiency in suppressed and intermediate trees was therefore supported. An
hypothesis was proposed that trees under greater shading (suppressed trees) had reduced tree
growth efficiency due to an unfavourable resource use-efficiency balance, however it was not
possible to further test this hypothesis in the course of the current study.
The examination of wood growth and structure showed that all trees had a similar sapwood
ratio regardless of dominance or planting density, however higher planting densities had
greater stemwood water-flow capacity per unit crown size since the ratio of stem volume to
leaf area was greater in higher planting densities. It was unclear whether this would be an
advantage to the tree.
In wood anatomy, dominant trees in high planting densities had stemwood ray ratios of
approximately 7.5%, which was 5-10% less than the ray ratio of dominant trees in low
planting densities. Since rays have the physiological function of storing excess food, the result
suggests that dominant trees in high planting densities did not produce as much excess food
for storage as dominant trees in low planting densities, and in comparison may be less
resilient to stressful events such as drought or defoliation. Dominant trees in high planting
densities had no significant change in ray ratio or ray height between sample positions,
suggesting constant relative resource capture, whereas dominant trees in low planting
densities had increased ray ratio as sample diameter increased (due to increased ray frequency
since ray height did not change), suggesting increased relative resource capture.
Dominant trees in high and low planting densities had similar stemwood vessel ratios of
approximately 17%. For all dominant trees the vessel ratio was constant between sample
positions, whereas vessel diameter increased as sample diameter increased, suggesting that
vessel frequency decreased as sample diameter increased since there was no significant
change in vessel ratio between sample positions. The effect of planting density on vessel
diameter followed a similar pattern to the effect of planting density on stem height, in that
there was no difference in vessel diameter between dominant trees in each planting density,
Chapter 6 Conclusion Page 167
however for a given stem diameter vessel diameter increased as planting density increased
and vessel diameter decreased as stem diameter decreased. Stem height may therefore
influence vessel diameter.
Dominant trees in high planting densities had a stemwood fibre ratio of approximately 75%,
which was 5-10% greater than the fibre ratio of dominant trees in low planting densities. For
all dominant trees the fibre ratio was constant between sample positions, whereas the fibre
ratio of suppressed trees tended to increase as sample diameter increased, suggesting a
reduction in the ratio of physiologically active wood cells over time, possibly due to a
reduction in relative resource capture. Dominant trees in high planting densities had increased
fibre and fibrelumen diameter compared to dominant trees in low planting densities. For
dominant trees in low planting densities the fibre and fibrelumen diameters were constant
between sample positions, whereas for dominant trees in high planting densities the fibre and
fibrelumen diameters increased as sample diameter increased and decreased as sample height
increased, resulting in the hypothesis that decreased crown proximity to wood during wood
formation stimulates increased fibre and fibrelumen diameter. Dominant trees in high planting
densities had reduced relative fibrewall thickness compared to dominant trees in low planting
densities, and there was no significant difference in relative fibrewall thickness between
sample positions for all dominant trees. The results indicate that trees compensate for a
reduced stemwood fibre ratio by increasing relative fibrewall thickness, and due to this
compensatory effect it was unclear whether there was a change in the total fibrewall material
in the stemwood of dominant trees.
Direct investigation of stemwood fibrewall ratio showed that dominant stems in high planting
densities had decreased stemwood fibrewall ratio compared to dominant trees in low planting
densities, however at most sample positions there was no significant difference between
planting densities 1,000-5,000 st/ha. For dominant trees in high planting densities the
stemwood fibrewall ratio was constant at approximately 30% between sample positions,
whereas the stemwood fibrewall ratio of dominant stems in low planting densities tended to
increase as sample diameter increased. The results indicate that dominant trees in high
planting densities partition 5-10% less biomass to fibre production within a given volume of
stemwood compared to dominant trees in low planting densities, suggesting a more frugal use
of photosynthate during fibre differentiation, possibly due to lower concentrations of
photosynthate. In consequence dominant trees in high planting densities are likely to have
Chapter 6 Conclusion Page 168
lower wood density than trees in low planting densities, yet they may also have lower
variation in wood density.
Investigation of wood density showed that whilst dominant trees in high planting densities
showed a tendency for lower wood density and greater wood density variation, there was no
significant difference in wood density or wood density variation in the primary sawlog section
of the stem between dominant trees from all planting densities. Suppressed trees generally
exhibited increased wood density, which could prove advantageous for biomass quality by
increasing the energy content of wood. It is probable that variables other than those measured,
such as intercellular spaces, also effect wood density since the results for wood density did
not entirely correlate with those for wood anatomy.
Investigation of branching characteristics showed that dominant trees in high planting
densities were likely to have lower knot content than dominant trees in low planting densities
as they exhibited a greater probability of branch mortality, more advanced branch shed,
decreased branch diameter and stub length, and lower branch angle. The much reduced
probability of the presence of branches exceeding 2 cm diameter in high planting densities
implies high planting density stands self-prune effectively, whereas low density stands would
probably require pruning to produce knot-free timber.
In summary it is concluded that the perception that high planting density plantations are
incapable of producing an equivalent volume of sawlogs of similar quality compared to low
planting density plantations is refuted by the evidence found in this study.
6.2 Major Discovery
The major discovery to be gained from this study is the utmost importance of accounting for
stand structure in competition research and forest modelling. Recent research on competition
has tended to focus on mean stand growth, whereas the results from this study indicate this is
an oversimplification which underestimates the productivity of the largest trees in higher
density stands and does not adequately account for the structural benefits of high planting
densities (i.e. increased size uniformity in the top 1,000 st/ha, reduced branching). This
clearly has the potential to result in management decisions which do not achieve the optimum
Chapter 6 Conclusion Page 169
desired result since they do not account for all the factors which may affect productivity and
quality.
The strong relationship of most tree growth and structural attributes to general population
pressure (planting density) and competitive status in the stand (stem diameter) indicates the
importance of placing tree growth and structure into the context of the stand in which the tree
is growing. Clearly trees do not grow as individuals, but are highly sensitive in many ways to
stand growing conditions and their position within it (Figure 1.1). This is strongly illustrated
by the exceptional consistency of the pattern of results in this study, including the constant
lack of significant difference between 5,000 st/ha and 10,000 st/ha (which were most similar
in mean space per tree), from the stand level right down to the wood level.
Placing tree growth and structure into the context of the stand should take a ‘top down’
approach due to asymmetric competition (whereby larger trees capture relatively more
resources). The first step is to assess the extent to which general population pressure (stand
density) affects dominant trees. In this case population pressure does not affect height growth;
however it does suppress diameter growth of the crown and encourage branch shed, which in
turn reduces total tree growth. Once the dominant cohort has been assessed and defined, the
remaining trees may be defined relative to the dominant cohort. The relative size of individual
trees compared to the dominant cohort determines the strength of asymmetric competition and
the extent of growth suppression experienced by individual trees. The process of asymmetric
competition is so reliable that, all remaining undisturbed, non-dominant trees are virtually
certain of diminishing in dominance over time (Figure 3.12).
6.3 Management Applications
The salient management application of this study was that high planting density had no
detrimental effect on the sawlog quality of dominant trees compared to low planting density.
Rather, the sawlog quality of dominant trees in high planting density plantations may be
improved compared to dominant trees in low planting densities due to lower knot content.
This research has immediate management applications since it shows that eucalyptus
pulpwood plantations may be converted to solid wood plantations without any loss to solid
wood quality as a result of establishment with relatively high planting densities.
Chapter 6 Conclusion Page 170
High planting densities have shown potential to improve plantation productivity compared to
the ‘standard’ planting density of 1,000 st/ha by producing a biomass crop (or crops) in
addition to a more uniform sawlog crop (Figure 6.1). In current markets this may not improve
profitability since biomass is not a valuable commodity; however increasing fuel prices are
likely to improve the future profitability of biomass crops by stimulating the development of
bio-fuel markets. In consequence, high planting densities could prove more profitable than the
‘standard’ 1,000 st/ha in the future, especially considering that biomass crops would provide
an early return cash crop on the plantation investment. A further useful finding was that there
was no practical difference between the results for 5,000-10,000 st/ha, so the benefits of high
density plantations can be achieved at planting densities of 5,000 st/ha, or possibly less.
The conclusions reached in this study were based on the study of E. grandis, however the
qualitative result pattern is likely to apply to all fast-growing eucalyptus species exhibiting
similar characteristics in stand development to E. grandis, including similar height growth in
dominant trees regardless of planting density, the rapid differentiation of the stand into
dominance due to asymmetric competition, and a significant loss of photosynthetic capacity in
the shaded leaves.
6.4 Further Research Requirements
A major research requirement emerging from this study is the need to apply the
growth/structure relationships defined to a process based model and test their validity as the
stand matures. The above investigation should also focus on the extent to which declining
stand productivity is due to structural changes in the stand and its trees.
There remain practical management issues to overcome before high planting density
plantations could be implemented on a commercial basis. Given the overall potential for high
planting density to improve plantation productivity and profitability compared to ‘standard’
planting densities, further research on practical management issues may be considered a
constructive investment in the plantation industry.
A major issue is that of high harvest costs per stem under modern harvesting technologies,
which are unlikely to be cost efficient for biomass crops due to the low value of individual
stems. The harvest of early biomass crops is more likely to be cost efficient if it involved the
Chapter 6 Conclusion Page 171
harvest of complete rows by a continuous pass machine, similar to cane or grain harvesters,
such as those used in Europe for woody biomass crops. This option, however, suggests that
rows need to be alternatively stocked with sawlog trees and biomass trees so that sawlog trees
are not harvested in biomass crops. A number of planting configurations and silvicultural
techniques could be used to achieve this outcome; however research is required to identify the
combination that will maximise the value of high planting density plantations.
The removal of multiple biomass crops in addition to the final sawlog crop increases the
potential for harvesting to reduce site nutrient balances and increase soil compaction in the
plantation. Research is required on technologies that will ensure that site nutrients are
maintained and compaction minimised, examples of which include returning biomass ash
from bio-energy plants back to the plantation (as is done in Sweden), and developing biomass
harvest machinery that deposits nutrient rich tree components (leaves and branches) on site in
the process of harvesting and spreads its weight over a larger ground contact area. The
removal of biomass crops also increases the potential for epicormic shooting in retained
sawlog trees, which is detrimental to wood quality, and research is required to ensure that the
likelihood of epicormic shooting is minimised.
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Appendices Page 185
APPENDICES
Appendix 1: Stem Volume Models
3 YEAR OLD STEM VOLUME MODEL
An investigation was made of the effects of diameter at breast height (DBH), stem height (SH)
and planting density (P) on stem volume at 3 years (Table A1.1). Note that interactions
including DBH and SH are significant and therefore DBH and SH must be included in the
model despite not being significant individually.
Table A1.1: The fixed-effect regression coefficients of the random intercept model of the effects of DBH (m), SH (m) and P (st/ha) on stem volume (m
3) at 3 years.
VARIABLE COEFFICIENT S.E. P - VALUE INTERACTION COEFFICIENT S.E. P - VALUE
INTERCEPT 0.0267 0.0105 p = 0.011 DBH2*SH
2 0.0319 0.0076 p < 0.001
DBH2 -3.4788 1.9607 p = 0.076 DBH
2*lnP 0.8233 0.2854 p = 0.004
SH2 0.000323 0.000027 p = 0.237 DBH
2*SH
2*lnP -0.0032 0.0011 p = 0.004
lnP -0.0032 0.0012 p = 0.009
4 YEAR OLD STEM VOLUME MODEL
An investigation was made of the effects of diameter at breast height (DBH), stem height (SH)
and planting density (P) on stem volume at 4 years (Table A1.2).
Table A1.2: The fixed-effect regression coefficients of the random intercept model of the effects of DBH (m) and SH (m) on stem volume (m
3) at 4 years.
VARIABLE COEFFICIENT S.E. P - VALUE INTERACTION COEFFICIENT S.E. P - VALUE
INTERCEPT -0.0115 0.0038 p = 0.003 DBH2*SH
2 0.0058 0.0009 p < 0.001
DBH2 3.2608 0.3036 p < 0.001
SH2 0.00010 0.00002 p < 0.001