Plant and Soil 231: 11–19, 2001.
© 2001 Kluwer Academic Publishers. Printed in the Netherlands.
11
Depth distribution of cherry (Prunus avium L.) tree roots as influenced by
grass root competition
L.A. Dawson1,5 , E.I. Duff2 , C.D. Campbell3 & D.J. Hirst2,4
1 Plant
Science Group, Macaulay Land Use Research Institute, Craigiebuckler, Aberdeen AB15 8QH, Scotland,
UK. 2 Biomathematics and Statistics Scotland, Macaulay Land Use Research Institute, Craigiebuckler, Aberdeen
AB15 8QH, Scotland, UK. 3 Soil Science Group, Macaulay Land Use Research Institute, Craigiebuckler, Aberdeen
AB15 8QH, Scotland, UK. 4 Currently at Norsk Regnesentral, P.O. Box 114, Blindern, N-0314, Oslo, Norway.
5 Corresponding author∗
Received 5 June 2000. Accepted in revised form 28 November 2000
Key words: competition, herbicide, minirhizotron, pasture, Prunus avium, root
Abstract
We investigated the effect of competition from grass roots (as controlled by herbicide application) on the depth
distribution of white roots in cherry trees, grown with varying rates and frequency of application of N in an
agroforestry system. Statistical summaries of distribution, namely mean and skewness, produced a concise and
interpretable analysis of the data.
There was a large increase in tree root numbers in the surface horizons after the herbicide had reduced grass
root competition. Where the surrounding grass had not been reduced by herbicide, the average depth of tree roots
increased with time, contrasting with a marked shift in the mode of distribution of root numbers to shallow depths
when grass competition was removed. These findings are important in the understanding of plant root competition
and for prescribing best practise for tree establishment in agroforestry systems.
Introduction
“Agroforestry is a collective name for land-use systems and technologies, where woody perennials (trees,
shrubs, palms, bamboos, etc) are deliberately used on
the same land management unit as agricultural crops
and/or animals, either in some form of spatial arrangement or temporal sequence” (ICRAF, 1993). One of
the main types of agroforestry of relevance in the UK
is a silvopastoral system in which trees are planted
directly into grazed pasture. These systems, therefore, force two plant species to compete for the same
resources particularly when root distributions often
overlap.
The presence of roots of competing vegetation,
such as grass, can reduce resource availability for tree
growth. Roots of one plant may influence the growth
and distribution of roots of neighbouring plants. For
∗ FAX No: 01224-311556. E-mail: [email protected]
example, root growth of Loblolly pine (Pinus taeda
L.) was reduced due to competition by crab grass
(Digitaria spp.), irrespective of nitrogen or soil water status (Ludovici and Morris, 1997). Also, the
presence of Eucalyptus (Eucalyptus grandis) trees reduced pasture root length densities in Queensland,
Australia (Eastham and Rose, 1990). In orchard systems, grasses can compete more effectively than trees
for water and nutrients, often as a result of higher
root densities particularly at the soil surface (Atkinson,
1977). Also, higher application rates of N fertilizer
have been shown to exacerbate competition between
cherry trees and pasture in a dry year (Campbell et al.,
1994b). Atkinson (1977) and Khatamian et al. (1984)
showed that increasing nutrient supply could stimulate
competition between grasses and trees, especially for
water. On the other hand, the greater depth to which
tree roots can extend, compared to grass roots, (Eastham and Rose, 1990) can allow exploitation of water
and reduce interspecific competition.
12
One way to reduce belowground competition in
the tree establishment phase is by maintenance of an
area of reduced weed/grass around the tree (Davies,
1987; Eason et al., 1991, 1992). Work has been done
on the use of herbicides in the establishment of radiata pine (P. radiata) and results demonstrate the
significant effect of tree-pasture competition on water relationships and on tree growth (Connor et al.,
1989), although little has been done to investigate the
associated below-ground responses.
In this study, we examined how the removal
of grass and weed competition from around cherry
(Prunus avium) influenced the depth distribution of the
tree roots. Minirhizotron methods (Mackie-Dawson
and Atkinson, 1991), which have the advantage of being non-destructive, were used to examine changes in
the vertical distributions of ‘white’ tree roots. The application of herbicide, 4 weeks after monitoring began,
to half of the trees in the study created an environment
for investigating the dynamics of vertical distribution
of tree roots both in the presence and absence of grass
roots. This experiment, therefore, provided an opportunity to monitor the depth distribution of tree roots
in a temporal fashion and to test the hypothesis that
the tree root distribution is unaffected by competition
from grass roots.
Materials and methods
The methods used to count points of root contact
with minirhizotrons have been described elsewhere
(Buckland et al., 1993). Similarly, the general site
description and experimental design have been previously described (Campbell et al., 1994b). Therefore,
only brief descriptions of both are given here. Twoyear-old cherry (Prunus avium L.) whips were planted
in 1987 at a 4 × 4 m spacing in two blocks (216
trees, excluding buffer zones). Each block was divided into four plots (0.12–0.15 ha) and within each
plot there were three subplots of nine trees used for
measurements. Treatments consisted of two levels of
nitrogen (high, 144 kg ha−1 yr−1 and low, 72 kg ha−1
yr−1 application) in two frequencies of split annual
applications (4 or 12) as detailed in Campbell et al.
(1994b). Actual amounts of nitrogen per application
were, therefore, 36 kg ha−1 (high) and 18 kg ha−1
(low) when 4 applications were used; and 12 kg ha−1
(high) and 6 kg ha−1 (low) when 12 applications were
administered. In addition, a 1m diameter circle of bare
ground was maintained around each tree by treating
the pasture with herbicide (glyphosate at 2 kg ha−1 )
since 1987. In 1990, however, the herbicide was applied between 24 and 29 May to the pasture around
half of the trees in each treatment, on a subplot basis.
The presence of clover was minor and was eliminated
in 1987 by spraying with a selective herbicide. The
resulting sward consisted primarily of Lolium perenne.
For this study, within each subplot, two of the nine
trees from each group were selected at random and
minirhizotrons installed.
Minirhizotron tubes were inserted vertically due to
the stony and indurated nature of the soil (Campbell et
al., 1994b), 3 months prior to start of measurements.
They were installed at a distance of 40 cm from the
base of the tree, to a depth of approximately 30 cm.
The location of the tube was chosen at random to be
either due North, South, East or West of the tree. A
clear plastic insert, scribed with four ladders of 40
counting grids (0.7 × 0.7 cm) at 90◦ intervals, was
fixed inside each tube, and the tube was placed in
the soil with one ladder directly facing the tree. The
method used to count the presence of tree and grass
roots was that of counting first and last points of contact with the tube (including root tips) (Buckland et al.,
1993). Counts were recorded for each grid square (0.7
× 0.7 cm) throughout the depth of the minirhizotrons.
These detailed data, collected from each minirhizotron, were then used to derive statistics summarising
the distribution of tree roots within the 30 cm zone
of soil below the soil surface. At the time of recording, roots were separated into type. White roots of
cherry were easily distinguished from grass roots by
their generally thicker diameter, opacity, more succulent appearance and lighter coloration. Since we
were primarily interested in how the distribution of
tree roots might change over time, we considered it
appropriate to focus on roots recorded as white as they
are the best indicator of new growth (Mackie-Dawson
et al., 1995).
Vertical locations of tree roots, from the 48 cherry
trees with minirhizotrons installed, were recorded approximately weekly throughout May and June and less
frequently thereafter until the end of August in 1990.
Given that tree root growth effectively ceased by the
end of June (Campbell et al., 1994a), data from the
sampling times during July and August were not used
in the analysis. Depth distribution data were cumulated over time for each of the months May and June,
respectively, resulting in depth distribution data for
each of two distinct periods; time 1, the 4 monitoring
times before herbicide application and time 2, the 5
13
40 cm from the tree, the same distance from the tree
as the minirhizotron tubes, and at a random orientation. Cores of 7 cm internal diameter were taken from
48 trees on the 28 May and 3 July, and total root
length was measured at four depth intervals (0–5 cm,
5–15 cm, 15–25 cm and 25–35 cm) for both tree and
pasture roots as described in Buckland et al. (1993).
Calculation of summary statistics
For the minirhizotron data, the distributions of cherry
roots through depth were summarised by deriving
two statistics: mean and skewness of depth of roots.
Both summary statistics were derived from the pooled
distributions (e.g. Figure 1), resulting in one value
for before and one value for after the time of herbicide application for each of the 48 trees in the study.
Skewness is defined as follows:
P
(xi − m)3
Skewness = i
(n − 1)s 3
Figure 1. Distribution of ‘white’ tree roots through the soil profile
for a single minirhizotron tube.
monitoring times after herbicide application. Because
of the need to retain information on the location of
roots, this involved summing counts at the level of individual grid squares. As an example, Figure 1 shows
the root distribution through the soil profile for one
tree (G7), a low N, high frequency treatment, before
and after application of herbicide.
Soil cores were also collected from the site, using a power driven system, and washed free of soil
with a hydropneumatic elutriation method (MackieDawson and Atkinson, 1991). Trees were selected at
random from all treatment plots, with the constraint
that no cores were taken from the trees monitored by
minirhizotrons, to avoid affecting subsequent growth.
A single core was taken from each sampled tree at
where xi is the depth at which a root is recorded, m
is mean depth of roots in the sample, s is standard
deviation of depth distribution of roots in the sample
and n is number of roots in the sample. Negative
skewness indicates that the majority of roots were recorded at lower depths with few recorded towards the
surface, whilst positive skewness indicates that most
roots were recorded nearer the surface with few recorded at greater depths. For the two cases where
there were less than three roots observed, skewness
was treated as missing (as was mean depth) and estimated in the subsequent statistical analysis. In addition
to the statistics summarising the distribution of tree
roots through depth, counts of white roots (before and
after herbicide) were summed over the entire depth
of the minirhizotron. Counts per minirhizotron were
standardised by dividing by the appropriate number of
ladders read per period, giving the number of white
roots per ladder. Whilst root counts, summed over
depth, do not contribute to our understanding of the
distribution of root depths, analysis of this variable
was included to set the scene as to the overall treatment
effects and to highlight the extra information gained
by the more sensitive approach of examining depth
distributions.
Statistical analysis
The summary statistics were analysed by analysis of
variance using Genstat 5.3 software (NAG Ltd., Oxford, UK). Analysis of variance was used to assess
14
Figure 2. Average densities by depth of ‘white’ tree roots in May (—) and June (- - - -). For (a) Low Frequency of N; no herbicide, (b) Low
Frequency of N; with herbicide, (c) High Frequency of N; no herbicide (d) High Frequency of N; with herbicide. The data were smoothed by
fitting cubic smoothing splines.
the effects of herbicide, level of nitrogen, frequency
of nitrogen application and time on each of mean,
skewness and total number of white roots per ladder.
Since herbicide was applied to groups of trees (each
group comprising of two trees with minirhizotrons)
and minirhizotron data was analysed at two time periods sampled, the structure of the analysis was a nested
design (tree groups, trees within groups, time within
tree groups and time within trees). Root density data
from the minirhizotrons were log transformed prior to
statistical analysis. To allow for differences due to operator variation, operator identifiers were included as
covariates in the analysis of counts of white roots per
ladder. These covariates were derived by calculating,
for each minirhizotron, the proportion of ladders read
per period by each of the three operators recording
counts of roots. However, operator identifiers were not
statistically significant and so were omitted from the
final analysis.
Root density data from soil coring was log transformed prior to being analysed using an analysis
of variance in the same nested design as for the
minirhizotron data. As there were no significant effects of nitrogen treatment, only means across all N
treatments are presented.
Results
Minirhizotron data
Number of white roots per ladder
Treatment means relating to root counts, summed over
the depth of the minirhizotron, are shown in Table 1.
Trees receiving the high level of nitrogen (averaged
over the two time periods), showed higher counts
15
Table 1. Effects of Nitrogen and Herbicide on differences in Counts per ladder (log transformed) of
‘white’ cherry roots in 2 time periods (pre-herbicide application and post herbicide application). H+
refers to herbicide applied, H− refers to no herbicide. The N levels refer to a Low N total supply (72 kg
ha−1 yr−1 ), High N total supply (144 kg ha−1 yr−1 ), Low Frequency of application (4 applications)
and a High Frequency of application (12 applications)
Variable
Treatment
PreHerbicide
(Period 1)
PostHerbicide
(Period 2)
Difference
Counts per ladder
Low N, H+
Low N, H−
High N, H+
High N, H−
0.93
0.58
1.08
1.12
1.07
0.86
1.58
1.40
0.14
0.28
0.50
0.28
0.14
Low Frequency, H+
Low Frequency, H−
High Frequency, H+
High Frequency, H−
1.09
1.23
0.92
0.47
1.21
1.30
1.45
0.96
0.12
0.07
0.52
0.49
0.14
Counts per ladder
Standard
error of
Difference
(P=0.05) than trees under low N (log (counts) were
1.29 and 0.86 for high and low levels of nitrogen, respectively). Numbers of tree roots increased with time,
the period after herbicide application (June) showing much higher (P<0.001) counts of tree roots than
the period prior to herbicide application (May). Log
(counts) were 0.93 and 1.23 for May and June, respectively. Increased numbers in the post-herbicide period
(June) were more evident with the high frequency of
N application than with the low frequency (Table 1),
as indicated by the significant interaction (P<0.05)
between frequency of nitrogen application and time.
Total numbers of white roots, summed over the depth
of the minirhizotron, showed no significant effect of
herbicide.
Mean: Distribution of tree roots
Treatment means relating to mean of depth distribution of tree roots are presented in Table 2. Where herbicide was applied, mean depth of tree roots was less
after herbicide application (June) than before (May),
whereas in the absence of herbicide, mean depth of
tree roots was greater in June than in May, as indicated by the significant interaction (P<0.01) between
herbicide treatment and time. This effect was more
pronounced with high level of N application than with
low level of N application (Table 2), although the
evidence for this was weak (P=0.086).
Figure 3. Average densities by depth of ‘white’ grass roots in May
(—) and June (- - - -). For (a) With herbicide and (b) No herbicide.
The data were smoothed by fitting cubic smoothing splines.
Skewness: Distribution of tree roots
Treatment means relating to skewness of depth distribution of tree roots are given in Table 2. Changes
in skewness over time were positive (from −0.122 to
16
Table 2. Effects of Nitrogen and Herbicide on differences in Mean of depth distribution (cms) of ‘white’
cherry roots and on Skewness of depth distribution of ‘white’ cherry roots in 2 time periods (pre herbicide
application and post herbicide application). H+ refers to herbicide applied, H− refers to no herbicide.
The N levels refer to a Low N total supply (72 kg ha−1 yr−1 ), High N total supply (144 kg ha−1 ), Low
Frequency of application (4 applications) and a High Frequency of application (12 applications)
Variable
Treatment
Mean
of depth distribution
Low N, H+
Low N, H−
High N, H+
High N, H−
15.00
12.86
14.54
12.65
14.59 −0.41
13.28
0.42
12.71 −1.83
14.02
1.37
0.65
Low Frequency, H+
Low Frequency, H−
High Frequency, H+
High Frequency, H−
14.51
13.85
15.03
11.66
13.64 −0.87
14.01
0.16
13.65 −1.38
13.29
1.63
0.65
Mean
of depth distribution
Skewness
of depth distribution
Skewness
of depth distribution
PreHerbicide
(Time 1)
PostHerbicide
(Time 2)
Difference
Standard
error of
Difference
Low N, H+
Low N, H−
High N, H+
High N, H−
−0.218
0.210
−0.026
0.234
−0.139
0.079
−0.093 −0.303
0.384
0.410
0.026 −0.208
0.123
Low Frequency, H+
Low Frequency, H−
High Frequency, H+
High Frequency, H−
−0.138
0.029
−0.106
0.415
−0.125
0.013
−0.056 −0.085
0.370
0.476
−0.011 −0.426
0.123
0.123) for trees receiving herbicide, but negative (from
0.22 to −0.034) for trees which did not receive herbicide (P<0.001 for interaction of herbicide treatment
and time). This change in skewness reflects an upward shift in the mode of the root distribution for trees
where herbicide was applied and a downward shift for
trees where herbicide was not applied. Positive and
negative changes in skewness over time (for trees receiving or not receiving herbicide, respectively), were
more evident with high frequency of N application
than with low frequency of N (P<0.01 for herbicide
by time by N frequency interaction).
Depth distributions
Average distributions of depths of ‘white’ cherry roots,
for particular treatment combinations, are shown in
Figure 2. This illustrates the shift in tree root density
profile nearer to the surface with time where herbicide
had been applied, whereas it moves into deeper zones
with time in the absence of herbicide. It also illustrates how this trend is similar for both frequencies of
N application, but that the effect is most marked at
the higher frequency of application. Figure 3, derived
in a similar way to Figure 2, illustrates the temporal
change in grass root density profile for trees where
herbicide was applied and also for those where no
herbicide was applied.
Root coring data
Grass root length densities were concentrated in the
top 5 cm in May (Figure 4a). In July, the pasture
roots were still concentrated at the surface but reduced
growth generally at the surface and increased growth
at other depths meant the trend was less pronounced
(Figure 4a). Herbicide significantly (P<0.05), reduced
surface grass roots in July, 70 days after the herbicide application. In July, tree root distributions were
greatest (P<0.05) in the 5–15 cm depth (Figure 4b),
contrasting with the grass root distributions, and they
were unaffected by herbicide application.
17
Figure 4. Effect of soil depth on root length density from soil coring of (a) grass roots (circle) and (b) ‘white’ tree roots (triangle) on 2 sampling
dates, with (open symbols) and without (solid symbols) herbicide treatment. Data were pooled over all N treatments. Bars represent s.e.d. for
herbicide, ∗ represents a significant difference, p<0.05 in ANOVA.
Discussion and conclusions
Methodology
Numbers of tree roots increased over time, reflecting
the increasing size of the trees in the establishment
phase and the time of year when the trees were actively growing (Campbell et al., 1994b). The increase
in number of white roots was greatest with the greatest
rate of addition of N, due to a faster plant growth rate.
The novel approach adopted in this study highlighted
changes in root location over time, observed from the
minirhizotron data, with differences due to both herbicide and nitrogen, which was not evident from the root
coring data. Both mean depth and skewness of depth
distribution showed effects of herbicide treatment over
time, with temporal effects more pronounced where
numbers of tree roots increased in the post-herbicide
period.
Co-existence of root systems
The different root distribution between trees and grass
(Campbell et al., 1994a) reflects that root systems of
plants can avoid being in the same competitive space
(Eastham and Rose, 1990; Mou et al., 1995) as is
suggested by the results of our study. Results suggest
that the tree roots were avoiding the surface soil when
grass roots were present, leading to a rejection of the
original null hypothesis. Caldwell et al. (1996) found
that at the small scale, shrub and grass roots tended
to avoid each other. Bould and Jarrett (1962) found
that Timothy (Phleum pratense) and ryegrass (Lolium
perenne) restricted apple (Cox’s orange pippin) tree
growth due to competition for nitrogen, which was reflected in a reduced N concentration in the tree leaves.
Watson (1988), working with maple, ash and oak,
in an urban environment showed that the presence of
grass competition in the root zone of trees inhibits fine
root development of trees. Evidence from the distribu-
18
tion of forest trees suggests that tree roots will, without
understory competition, colonise the surface layers of
soil (Nambiar, 1983). Also, in orchards, where herbicide is applied in large areas, fruit tree roots often
colonise the surface soil, and the vertical distribution
can be similar to our study with the greatest density
of rooting being found in the 10–20 cm soil depth
(Atkinson, 1977).
The altered root distribution of the ‘white’ cherry
tree roots due to the herbicide application, as demonstrated in this study, therefore, has many implications
for future tree growth and management and interacts
with factors such as N supply, soil moisture and stage
of tree development.
Acknowledgements
Root depth distribution
In our study, changes through time in the depth distribution of tree roots were dominated by the herbicide
treatment but also influenced by the availability of
N. When grass was untreated with herbicide, ‘white’
tree roots grew deeper with time, particularly for
trees receiving an increased availability of N. When
the grass was reduced, ‘white’ tree roots tended to
grow nearer the surface, again particularly for trees
with increased N availability. The increase in ‘white’
tree root numbers in the surface, and the change in
the pattern of root distribution towards the soil surface after herbicide had partially removed grass root
competition, demonstrates the ability of tree roots to
grow in shallow depths after competition has been removed. This change in root distribution may reflect a
proliferation response to the increased N availability
in that soil zone once the grass was removed (Caldwell et al., 1996), reflect a greater availability of soil
water (Watson et al., 1992) or as a response to the
combination of both water and nitrogen (Goode and
Hyrycz, 1976). Where the surrounding grass was not
removed, the average ‘white’ tree root depth increased
with time as the competition from the grass roots remained in the surface horizons. Reflecting greater tree
root growth at depths when grass is present, Atkinson
and White (1980) showed a greater uptake of 32 P from
90 cm depth by trees under grass, than trees which had
received a herbicide treatment. Other workers have
also found deeper tree root distribution when under
a cover crop, e.g. Bjorkman and Lundeberg (1971).
However, this reduction in deep rooting as a consequence of herbicide application may not be totally
advantageous (Conner et al., 1987) as the altered root
spatial distribution can reduce tree stability (Helliwell,
1989; Stokes et al., 1995) and increase susceptibility to drought (Stuckey, 1961). The presence of the
grazing animal has also been shown to increase soil
compaction around the base of the tree (Eason et al.,
1992; Wairiu et al., 1994), which would have more
pronounced effects if the grass is removed.
This work was funded by the Scottish Executive Rural
Affairs Department. The authors would like to thank S
Pratt and E Reid for technical assistance.
References
Atkinson D 1977 Some observations on the root growth of young
apple trees and their uptake of nutrients when grown in herbicide
strips in grassed orchards. Plant Soil 46, 459–471.
Atkinson D and G C White 1980 Some effects of orchard soil
management on the mineral nutrition of apple trees. In The Mineral Nutrition of Fruit Trees. Eds. D Atkinson, JE Jackson, RO
Sharples and WM Waller. pp 241–254. Butterworths, UK.
Bjorkman E and Lundeberg G 1971 Studies of root competition in a
poor pine forest by supply of labelled nitrogen and phosphorus.
Stud. For. Succ . 91, 16.
Bould C and Jarrett R M 1962 The effect of cover crops and NPK
fertilizers on growth, crop yield and leaf nutrient status of young
dessert apple trees. J. Hortic. Sci. 37, 58–82.
Buckland S T, Campbell C D, Mackie-Dawson L A, Horgan G
W and Duff E I 1993 A method for counting roots observed
in minirhizotrons and their theoretical conversion to root length
density. Plant Soil 153, 1–9.
Caldwell M M, Manwaring J H and Durham S L 1996 Species
interactions at the level of fine roots in field – influence of soil
nutrient heterogeneity and plant size. Oecologia 106, 440–447.
Campbell C D, Mackie-Dawson L A, Reid E J, Pratt S M, Duff E I
and Buckland S T 1994a Manual recording of minirhizotron data
and its application to study the effect of herbicide and nitrogen
fertilizer on tree and pasture growth in a silvopastoral system
Agrofor. Syst. 26, 75–87.
Campbell C D, Atkinson D, Jarvis P G and Newbould P 1994b Effects of nitrogen fertilizer on tree/pasture competition during the
establishment phase of a silvopastoral system. Ann. Appl. Biol.
124, 83–96.
Connor D J, Sands R and Strandgard M 1989 Competition for
water, light and nutrients in agroforestry associations of Pinus
radiata and pasture. In Meteorology and Agroforestry. Eds. WS
Reifsnyder and TO Darnhofer. pp 451–462. Proceedings of International Workshop, Nairobi, Kenya, February 0–13, 1987.
ICRAF.
Davies R J 1987 Trees and weeds. Weed control for successful
tree establishment. Forestry Commission Handbook 2. HMSO.
London. pp 36.
Eason W R, Newman E I and Chuba G H 1991 Specificity of interplant cycling of nutrients: the role of mycorrhizas. Plant Soil
137, 267–274.
Eason W R, Tomlinson H F and Hainsworth C 1992 Effect of ground
vegetation on root distribution of ash trees. Asp. Appl. Biol. 29,
225–231.
19
Eastham J and Rose C W 1990 Tree/pasture interactions at a
range of tree densities in an agroforestry experiment.1.Rooting
patterns. Aust. J. Agric. Res. 41, 683–695.
Goode J E and Hyrycz K J 1976 The effect of nitrogen on young,
newly-planted apple rootstocks in the presence and absence of
grass competition. J. Hort. Sci. 51, 321–327.
Helliwell D R 1989 Tree roots and the stability of trees. Arboricul.
J. 13, 243–248.
ICRAF 1993 International Centre for Research in Agroforestry:
Annual Report 1993. Nairobi, Kenya. 208 pp.
Khatamian H, Pair J C and Carrow R 1984 Effects of turf competition and fertilizer application on trunk diameter of nutrient
comosition of Honeylocust. J. Arboricul. 10, 156–159.
Ludovici K H and Morris L A 1997 Competition-induced reductions
in soil water availability reduced pine root extension rates. Soil.
Sci. Soc. Am. J. 61, 1196–1202.
Mackie-Dawson L A and Atkinson D 1991 Methodology for the
study of roots in field experiments and the interpretation of results. In Plant Root Growth, An Ecological Perspective. Ed. D
Atkinson. pp 25-47. Blackwell, Oxford.
Mackie-Dawson L A, Pratt S M, Buckland S T and Duff E I 1995
The effect of nitrogen on fine root persistence in cherry (Prunus
avium). Plant Soil 173, 349–353.
Mou P, Jones R H, Mitchell R J and Zutter B 1995 Spatial distribution of roots in sweetgum and loblolly pine monocultures and
relations with above-ground biomass and soil nutrients. Funct.
Ecol. 9, 689–699.
Nambiar E K S 1983 Root development and configuration in intensively managed radiata pine plantations. Plant Soil 71, 37–47.
Stokes A, Fitter A H and Coutts M P 1995 Response of young trees
to wind and shading – effects on root architecture. J. Exp. Bot.
46, 1139–1146.
Stuckey I H 1961 Root growth of Taxus. Am. Nurseryman 114, 117–
114.
Wairiu M, Mullins C E and Campbell C D 1994 Soil physical
factors affecting the growth of sycamore (Acer pseudoplatanus
L.) in a silvopastoral system on a stony upland soil in North-East
Scotland. Agrofor. Sys. 24, 295–306.
Watson G W 1988 Organic mulch and grass competition influence
tree root development. J. Arboricul. 14, 200–203.
Watson C A, Hooker J E, Atkinson D, Birley M A and Newbould P
1992 Effects of vegetation management on nitrate loss to ground
water from agroforestry systems. Asp. Appl. Biol. 29, 407–412.
Section editor: R. Aerts