Tree Roots in Agroforestry: Evaluating Biomass and Distribution

with Ground Penetrating Radar

by

Kira Alia Borden

A thesis submitted in conformity with the requirements

for the degree of Master of Science in Forestry

Faculty of Forestry

University of Toronto

© Copyright by Kira Alia Borden 2013

ii

Tree Roots in Agroforestry: Evaluating Biomass and Distribution

with Ground Penetrating Radar

Kira Alia Borden

Master of Science in Forestry

Faculty of Forestry

University of Toronto

2013

Abstract

The root systems of five tree species (Populus deltoides × nigra clone DN-177, Juglans nigra,

Quercus rubra, Picea abies, and Thuja occidentalis) are described following non-intrusive

imaging using ground penetrating radar (GPR). This research aimed to 1) assess the utility of

GPR for in situ root studies and 2) employ GPR to estimate tree root biomass and distribution in

an agroforestry system in southern Ontario, Canada. The mean coarse root biomass estimated

from GPR analysis was 54.1 ± 8.7 kg tree-1 (± S.E.; n=12), within 1 % of the mean coarse root

biomass measured from matched excavations. The vertical distribution of detected roots varied

among species, with T. occidentalis and P. abies roots concentrated in the top 20 cm and J. nigra

and Q. rubra roots distinctly deeper. I evaluate these root systems based on their C storage

potential and complementary root stratification with adjacent crops.

iii

Acknowledgments

I am grateful for the guidance and support of my co-supervisors Dr. Marney Isaac and Dr.

Sean Thomas. They both continually amaze and inspire me with their expertise and passion for

their work. Additionally, I appreciate the contributions to my graduate research made by

committee members Dr. Marie-Josée Fortin and Dr. Jing Chen.

Stephanie Gagliardi was extremely integral in completing field and lab work. I thank all

those associated with the Isaac and Thomas labs, notably Vasuky Thirugnanassampanthar for

assistance in the lab and Jake Munroe and Janise Herridge becoming last minute field assistants. I

thank Ian Kennedy, Tony Ung, and the administration staff from the Faculty of Forestry as well as

Chai Chen, Tom Meulendyk, Tony Adamo, and the administration staff at UTSC.

Importantly, my research benefited from the collaborative nature of the project with those

at the University of Guelph Agroforestry Research Station. I thank Dr. Andrew Gordon, Dr. Naresh

Thevathasan, and their staff and students for all the logistical and field work support. A special

acknowledgment to Amy Wotherspoon for her exceptional organization, hard work at the field site

during ‘the excavations’, and graciously providing me with root biomass data.

I am grateful for funding support from the Faculty of Forestry, Agriculture and Agri-

Food Canada’s Agriculture Greenhouse Gases Program, and the Natural Sciences and

Engineering Research Council. Additionally, the Centre for Global Change Science Graduate

Award enhanced my graduate experience by allowing me to attend a short course in Montpellier,

France and present my research at the International Symposium for Root Research in Dundee,

Scotland.

Lastly, to my family and friends who gave me encouragement during the last two years

while I was out ‘looking for tree roots with radar’, I thank you.

iv

Table of Contents

Abstract ........................................................................................................................................... ii

Acknowledgments .......................................................................................................................... iii Table of Contents ........................................................................................................................... iv List of Tables ................................................................................................................................. vi List of Figures ............................................................................................................................... vii List of Appendices ......................................................................................................................... ix

List of Abbreviations ...................................................................................................................... x

Chapter 1 : Tree Roots in Agroecosystems ................................................................................. 1 1.1 Introduction ....................................................................................................................... 1

1.1.1 Current ecological issues for agriculture in Ontario ........................................... 1 1.1.2 Temperate tree-based intercropping ...................................................................... 2

1.1.3 The importance of tree root studies ....................................................................... 3 1.1.4 Ground penetrating radar for tree root studies..................................................... 5

1.2 Thesis approach ................................................................................................................ 7

Chapter 2 : General Methods ...................................................................................................... 9

2.1 Case study: University of Guelph Agroforestry Research Station ............................... 9 2.1.1 Site conditions during radar survey ...................................................................... 9

2.2 Tree species ...................................................................................................................... 12

2.3 Radar survey ................................................................................................................... 13

Chapter 3 : Estimating coarse root biomass with ground penetrating radar in a tree-

based intercropping system ................................................................................................... 17

3.1 Abstract ............................................................................................................................ 17 3.2 Introduction ..................................................................................................................... 18

3.2.1 Ground penetrating radar for root biomass estimation ...................................... 19 3.3 Materials and Methods ................................................................................................... 20

3.3.1 Radargram processing ......................................................................................... 20

3.3.2 GPR index–biomass relationship ........................................................................ 20 3.3.3 Coarse root biomass estimates ............................................................................. 22

3.3.4 Root carbon content ............................................................................................. 24 3.3.5 Statistical analysis ................................................................................................ 25

3.4 Results .............................................................................................................................. 26 3.4.1 GPR images and index–biomass relationship ..................................................... 26

3.4.2 Coarse root biomass ............................................................................................. 27 3.4.3 Root system C content .......................................................................................... 32

3.5 Discussion ......................................................................................................................... 32

3.5.1 The GPR index–biomass relationship ................................................................. 32 3.5.2 Biomass and carbon estimates of tree root systems ............................................ 35 3.5.3 Application and limitations of GPR in tree-based intercropping ....................... 36

3.6 Conclusions ...................................................................................................................... 38

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Chapter 4 : Evaluating vertical distributions of tree roots in a tree-based intercropping

system with ground penetrating radar ................................................................................. 39 4.1 Abstract ............................................................................................................................ 39 4.2 Introduction ..................................................................................................................... 40

4.2.1 Root distribution in TBI systems ......................................................................... 41 4.3 Materials and Methods ................................................................................................... 42

4.3.1 Coarse root distribution measurements with GPR ............................................. 42 4.3.2 Accuracy testing ................................................................................................... 43 4.3.3 Root distributions ................................................................................................. 43

4.3.4 Fine root distribution in crop rows...................................................................... 44 4.3.5 Statistical analysis ................................................................................................ 45

4.4 Results .............................................................................................................................. 45 4.4.1 GPR detection frequency of coarse roots ............................................................ 45

4.4.2 Coarse root distribution ....................................................................................... 46 4.4.3 Root distribution into crop rows .......................................................................... 51

4.4.4 Fine root distribution ........................................................................................... 51 4.5 Discussion ......................................................................................................................... 53

4.5.1 Coarse root detection ........................................................................................... 53 4.5.2 Tree root distribution in TBI systems .................................................................. 54 4.5.3 Tree and crop root stratification .......................................................................... 56

4.6 Conclusions ...................................................................................................................... 57

Chapter 5 : Conclusions .............................................................................................................. 59 4.7 The use of GPR for tree root study ............................................................................... 59 4.8 Tree-based intercropping ............................................................................................... 61

4.9 Final conclusions ............................................................................................................. 62

References ..................................................................................................................................... 63 Appendices .................................................................................................................................... 72

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List of Tables

Table 1 Relational equations between specific root biomass (W) representing dry weight of 10

cm long root segments (g) with root diameter (D) (cm) (n=20 per species). ............................... 23

Table 2 Analysis of covariance for root biomass measured in exposed profiles and GPR response

index. Displayed are the results for data inclusive of all species and the corrected relationship

that removed species main effect on remaining pooled species data. Significant results (p<0.05)

are in bold. .................................................................................................................................... 28

Table 3 GPR estimated coarse root biomass (BGPR) (kg tree-1; mean ± S.E.) with corresponding

excavated biomass of five tree species (kg tree-1; mean ± S.E). Paired t-tests completed on the

means for each species and across all study trees between BGPR and excavated biomass. Also

shown are corresponding calculated allometric estimates of coarse roots dependent on DBH and

the species class and group. .......................................................................................................... 29

Table 4 Carbon concentration (%) and C content (kg C tree-1) of the coarse root system of five

tree species (25 years old). Carbon concentration values are reported as total carbon of dry root

weight (mean ± S.E.) (n=3, except Populus sp. and Picea abies n=2) following i) conventional

oven dry sample preparation or ii) volatile inclusive methodology. Carbon content of the trees’

root systems were calculated using BGPR and the species-specific coarse root C concentration

(volatile inclusive). ....................................................................................................................... 34

Table 5 Minimum, maximun and mean (m) depth of coarse roots detected by GPR for five tree

species (mean ± S.E.) (n=3 except n=2 for Populus sp. and Picea abies). Total tree and

subdivided crop row and tree row data. Values followed by the same letter in a column for each

parameter indicates non-significant (p<0.05) result (Tukey HSD). ............................................. 48

Table 6 Cumulative distribution coefficient (β) for five tree species at the University of Guelph

Agroforestry Research Station, Canada. The distribution coefficients (β) were calculated using

species’ means of pooled detected coarse roots in 0.10 cm depth increments and fitted to the

function Y = 1- βd where Y is proportion of roots at depth (d). Results from subsetted

distribution data for coarse roots located in crop or tree rows are also presented. Depth (m) to

estimated cumulative 95 % of coarse roots (d95) presented. ......................................................... 50

vii

List of Figures

Figure 1 Conceptual diagram of thesis objectives and structure. .................................................. 8

Figure 2 Satellite image of the University of Guelph Agroforestry Research Station, Guelph,

Ontario, Canada. Site was established in 1987 as a tree-based intercropping experimental site.

Insert is a schematic diagram of the planting design. ................................................................... 10

Figure 3 Image of intercropping rows at the University of Guelph Agroforestry Research

Station, Guelph, Ontario, Canada. ................................................................................................ 11

Figure 4 Image of 1000 MHz ground penetrating radar (GPR) unit used for geo-image, or

radargram, data collection during this study. Unit pictured being pulled along soil surface with

attached odometer measuring distance along transect near Juglans nigra. .................................. 15

Figure 5 (a) Image of grid set-up for GPR survey of Quercus rubra in tree-based intercropping

system at the University of Guelph Agroforestry Research Station, Guelph, Ontario, Canada. (b)

Plan-view schematic of GPR data collection grid design. The base of tree stem is represented by

the circle approximately in the centre of the grid. Red lines indicate GPR data collection

transects in x and y directions with 10 cm spacing. ...................................................................... 15

Figure 6 Radargram of transect 0.70 m from Picea abies stem. Signal velocity was 0.09 m ns-1.

....................................................................................................................................................... 16

Figure 7 Plan view of interpolated radagrams assembled in grid orientation and visualized for

signal response within depth 0.30 to 0.325 m. Grid shown is surrounding Picea abies. The

magnitude of reflected signals are represented on colour scale from no reflection detected (dark

blue) to high reflection (red). ........................................................................................................ 16

Figure 8 GPR data processing sequence with soil profile (0.25 × 1.0 m) and exposed roots

(circles) of Juglans nigra (a) and the equivalent GPR geo-image (b) with applied background

removal (c) surface reflections or banding is reduced. Hyperbola migration focuses root

reflections to foci (c). The final data processing step is the Hilbert transformation whereby

magnitude of reflection is brought into one phase (e). The extracted GPR index (area within an

intensity range (175 to 255); cm2) is measured (f) to develop a GPR index–biomass relationship.

....................................................................................................................................................... 21

Figure 9 Transformed data used to test for species main effect and interactive effect with GPR

index on measured biomass (ANCOVA). Solid symbols represent corrected pooled species data

with no significant species main effect (p=0.20), or interactive effect with GPR index (p=0.68),

on biomass (Table 2). Open circles are isolated data points collected below Thuja occidentalis

representing the corrected data used for that species GPR index–biomass estimation equation

(p<0.0001). .................................................................................................................................... 30

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Figure 10 GPR index (area above a threshold; cm2) as a predictor of dry weight coarse root

biomass (g) determined from exposed soil profiles and correlated with equivalent section of GPR

radargram. Corrected relationships displayed from i) pooled data (solid symbols inclusive of

Populus sp., Juglans nigra, Quercus rubra, and Picea abies) identified by solid line (y = 0.214x

– 4.72; r = 0.55; n=51) and ii) isolated Thuja occidentalis (open circles and dotted line) (y =

0.038x – 4.62; r = 0.95; n=12). ..................................................................................................... 31

Figure 11 Positive correlation (solid line) between coarse root biomass estimated by GPR (kg

tree-1) and coarse root biomass measured from matched excavations (kg tree-1) (R2=0.75;

p=0.0003; RMSE=14.4 kg; n=12). The 1:1 relationship also shown (dotted line). ...................... 33

Figure 12 Mean detection frequency of coarse roots grouped by diameter size class (n=3 except

n=2 for Populus sp. and Picea abies). No significant differences between species’ means of each

diameter class (ANOVA) or between diameter class of same species (paired t-test) (p>0.05).

Bars represent ± S.E. of the mean. ................................................................................................ 47

Figure 13 Mean detection frequency at each soil depth interval (n=13 exposed profiles)

measured from subset of matched soil profiles and radargrams. Means with same letter are not

significantly different (Kruskal-Wallis) (p>0.05). Bars represent ± S.E. of the mean. ................ 47

Figure 14 Total detected coarse root frequency by depth to 0.80 m for each species (n=3 except

Populus sp. and Picea abies n=2). Bars represent ± standard error of the mean. ......................... 49

Figure 15 Fine root length density (RLD; mean ± S.E. cm cm-3) by depth inclusive of 3 sampling

distances (1.0, 1.5, and 2.0 m) from tree stem into crop rows. Five tree species (n=18 except

n=12 for Populus. sp. and Picea abies). Samples collected during spring May-June 2012. Same

letters represent non-significant differences among species’ means for each sampling depth using

non-parametric Kruskal-Wallis test (p>0.05).. ............................................................................. 52

ix

List of Appendices

A1: Bulk density of soil within tree study plots (mean ± S.E.; n=36 except n=24 for Populus sp.

and Picea abies)……………………………………………………………………………..…...72

A2: GPR estimated (BGPR), excavated, and allometrically derived coarse root biomass (kg

tree-1)...…………………………………………………...………………………………..…..…72

x

List of Abbreviations

BGPR coarse root biomass estimated from GPR analysis (dry weight; kg tree-1)

β cumulative proportion of root distribution coefficient

d95 depth to 95% of the cumulative root frequency

DBH tree stem diameter at 1.3 m aboveground (cm)

EM electromagnetic

GHG greenhouse gases

GPR ground penetrating radar

R:S root:shoot

TBI tree-based intercropping

1

Chapter 1 : Tree Roots in Agroecosystems

1.1 Introduction

1.1.1 Current ecological issues for agriculture in Ontario

The population of Ontario is projected to grow 28.6 %, an additional 17.4 million people,

by the year 2036 (Ontario Ministry of Finance 2013). This growth will occur predominantly in

Southern Ontario where urban expansion and rich agricultural soils overlap (Francis et al. 2012).

Consequently, there will be additional strain on the landscape from intensifying agriculture that

can negatively impact the environment, for instance increasing erosion into waterways and

releasing greenhouse gases (GHG) into the atmosphere (Paustian et al. 1997). Furthermore, the

resilience of landowners to withstand economic pressures from the expansion of urban

development will be challenged (Francis et al. 2012). Therefore, future landscape management

must aim to ensure stable and sufficient economic incentives for landowners while reducing the

environmental externalities commonly associated with intensive agriculture, acknowledging that

agriculture has a major role in GHG mitigation (FAO 2007; Jose 2009; Smith et al 2012).

Current practices that mediate soil degradation and GHG emissions include low or no-till

practices (Paustian et al. 1997), increased use of cover crops (Rosenzweig and Hillel 2000), and

afforestation on marginal lands (Paustian et al. 1997; Laganière et al. 2010; Foote and Grogan

2010). Commonly, these applied management techniques protect soils and increase inputs and

the stabilization of organic matter (Rosenzweig and Hillel 2000).

Agroforestry is an alternative land management approach that can further incorporate

organic matter into agricultural soils and capture atmospheric CO2. Agroforestry, broadly

described as agricultural systems that incorporate woody perennials, are more prevalent in

tropical regions in part due to access to tree species that are fast growing or N2 fixing (Jose et al.

2004). While these desirable tree qualities are limited in temperate regions, there are other

2

ecological benefits derived from the presence of trees in agricultural landscapes. For example,

compared to conventional monocropped agriculture, increases in biodiversity, soil fertility,

nutrient cycling, erosion control, water quality, and C storage have been reported in temperate

agroforestry systems (Dixon et al. 1994; Schroth 1999; Jose et al. 2004; Thevathasan and Gordon

2004; Oelbermann et al. 2004; Thevathasan et al. 2008; Jose 2009). In Ontario, agroforestry is

commonly found in the form of windbreaks and riparian buffers, but there is further potential in

the adoption of multi-species systems that integrate trees more directly within agricultural fields

such as tree-based intercropping (TBI) (Gordon and Williams 1991; Thevathasan et al. 2008).

1.1.2 Temperate tree-based intercropping

In TBI, or alley cropping, trees and crops are typically planted in alternating rows. Tree

species selection and planting design (e.g. stem spacing and crop row widths) can be modified

across sites and over time (e.g. through thinning practices) depending on ecological constraints

and economic incentives (Gordon and Williams 1991). Ecological constraints such as tree-crop

competition for soil moisture can depress crop yields (Jose et al. 2004), while economic

incentives and the valuation of the crops and tree products can influence relative importance to

the landowner. For example, timber or other tree products such as nuts might offset the loss in

crop yield (Gordon and Williams 1991). Simultaneously, C sequestration in trees on agricultural

landscapes is increasingly studied as a potential means to capture atmospheric CO2 (Winjum et al

1992; Dixon et al. 1994; Montagnini and Nair 2004; Thevathasan and Gordon 2004; Isaac et al

2005; Peichl et al.2006; Oelbermann et al. 2004; Nair et al. 2009; Evers et al. 2010) and future C

credit incentives offer promise for diversified income to landowners (FAO 2007). Therefore,

with the increased complexity of multi-functional systems such as TBI there are a multitude of

interacting factors, both ecological and economic, to consider when attempting to ‘optimize’ the

system.

3

After decades of research, Nair (2011) voiced concern that there still remains a deficiency

of research and quality of measurements pertaining to the biophysical processes occurring in

agroforestry. However, there have been a number of published studies contributing to our

understanding of temperate TBI systems from the University of Guelph Agroforestry Research

Station, Ontario, Canada. Prior studies have been completed on interspecies competitive

interactions (Reynolds et al. 2007; Clinch et al. 2009), C storage potentials (Thevathasan and

Gordon 2004; Peichl et al. 2006), N inputs from leaf litter (Thevathasan and Gordon 1997),

microclimate regulation (Clinch et al. 2009), earthworm populations (Price and Gordon 1999),

soil fertility (Bambrick et al. 2010), and mychorrhizal communities (Bainard et al 2012).

Additionally, some in situ investigations of tree root systems have been conducted. The root

system of Populus deltoides × nigra clone DN-177 is the most documented with biomass data

from two complete excavation studies (Thevathasan and Gordon 2004; Peichl et al. 2007), one of

which also excavated the root system of Picea abies (Peichl et al. 2007). However, these

belowground studies were limited to very low replication (n≤3) due to methodological

constraints. Consequently, thorough root system studies are lacking, resulting in a gap in the

literature pertaining to temperate TBI systems and for agroforestry more broadly (Oelbermann et

al 2004; Nair 2011).

1.1.3 The importance of tree root studies

The distribution of roots measured on the vertical profile, referred to in this thesis as root

distribution, is functionally an important area of research in TBI systems. Competition for

resources can occur aboveground for light (Reynolds et al. 2007), belowground for soil resources

(Jose et al. 2000), or as an interactive effect between the two (Freschet et al. 2013). As TBI is

commonly cultivated with fertilizer use, thus meeting nutrient requirements of the crops,

belowground competition is often attributed to reduced soil moisture that in turn inhibits nutrient

4

availability for the crops (Jose et al. 2000; Fletcher et al. 2012). Thus, as crop roots prevalently

occupy shallower soil, trees with root systems deeper than crop roots might mediate some

belowground competition (Schroth 1999; Jose et al. 2001; Mulia and Dupraz 2006).

Furthermore, with an increased stratification of rooting zones between species in agroforestry

systems, tree roots can act as a ‘safety net’ capturing nutrients leached below the crop roots thus

maximizing total resource use of a system (Van Noordwijk and Pernomosidhi 1995; Jose et al.

2004; Bergeron et al. 2011; Thevathasan et al. 2012). Finally, determining the biomass of tree

roots within TBI systems is essential for accurate C budgets of these systems and by failing to

include tree roots in biomass and C inventories, more than 20% of trees’ biomass is overlooked

(Cairns et al. 1997; Mokany et al. 2006; Brunner and Godbold 2007).

To study root system distribution, conventional sampling techniques include

minirhizotrons, profile walls, and soil cores (Vogt et al. 1998; Polomski and Kuhn 2002).

However, due to the spatial heterogeneity and the inherent fractal nature of root systems, these

techniques generally have low accuracy particularly for coarse roots (diameter > 2mm) (Schroth

and Kolbe 1994; Taylor et al. 2013). Conversely, partial or complete root system excavations can

capture this heterogeneity, but they are time consuming, destructive, and non-repeatable. This

has led to biomass study in agroforestry to adopt root:shoot (R:S) ratios or allometric equations

generated from studies in forest ecosystems (e.g. Kirby and Potvin 2007; Chauhan et al. 2011).

However, generalities of tree biomass allocation may produce inaccuracies when applied to

agroforestry, or TBI sites specifically, due to variation of tree root growth in cultivated scenarios

(Nair 2011; Kuyah et al. 2012). Owing to the challenges associated with the inaccessibility to

study tree roots in situ, the belowground component of trees is known as ‘the hidden half’ (Vogt

et al. 1998; Bhattachan et al. 2012) and consequently there is a pressing need for techniques that

non-intrusively measure tree root systems.

5

1.1.4 Ground penetrating radar for tree root studies

Over the last 15 years, ground penetrating radar (GPR) has been tested and applied as a

geo-imaging tool to detect trees’ coarse roots under controlled conditions (Barton and Montagu

2004; Dannoura et al. 2008; Hirano et al. 2009; Bassuk et al. 2011; Cui et al. 2013; Tanikawa et

al. 2013; Guo et al. 2013b; Guo et al. 2013c) or in field experiments (Hruska et al. 1999; Butnor

et al. 2001; Butnor et al. 2003; Stover et al. 2007; Samuelson et al. 2008; Zenone et al. 2008;

Samuelson et al. 2010; Hirano et al. 2012; Isaac and Anglaaere 2013; Raz-Yaseef et al. 2013;

Day et al. 2013). GPR emits electromagnetic (EM) signals into the ground and records the

reflected signals’ amplitude, polarity, and travel time, which can be used to interpret

belowground features. EM signals are reflected where there is contrast of dielectric permittivity

between two media such that:

𝑅 =√𝑘1−√𝑘2

√𝑘1+√𝑘2 (1)

where R is the reflection coefficient and k denotes the dielectric constant, of the first medium (k1)

and second medium (k2) (Davis and Annan 1989). Importantly, water has a high dielectric

constant compared to soil (k water = 80 vs. ɛ dry sand = 5) (Davis and Annan 1989). Therefore,

as coarse roots can contain higher water content than the surrounding soil matrix, coarse roots

provide the necessary interface for radar signal reflections (Hirano et al. 2009; Guo et al. 2013b;

Guo et al. 2013c). Depth of GPR signal penetration is limited by the frequency of the GPR unit

and the electrical conductivity of the subsurface (Davis and Annan 1989). Additionally, the

water and clay content of the soil will influence the rate of radar signal attenuation due to their

dielectric properties (Butnor et al. 2001). Thus, ideal conditions for GPR study of coarse roots

are well drained soils with low clay content and with coarse roots of sufficient water content

(Guo et al. 2013a).

6

As a GPR unit operates along a transect, sequential signal responses can be compiled to

create an interpreted subsurface image of the soil profile, or radargram. A hyperbolic reflection

pattern is generated in the radargram as the GPR moves above a coarse root perpendicular to the

direction of GPR travel. These root reflection patterns can be correlated to biomass, as addressed

in Chapter 3, or identified across multiple radargrams to chart the coarse root distribution, as

addressed in Chapter 4.

The benefit of this approach is that it provides researchers the opportunity for repeated

measurements of coarse roots and the capacity to examine the uniqueness of tree root system

response along gradients of biotic and abiotic constraints (Isaac and Anglaaere 2013). Recently,

the first application of GPR to measure distribution of coarse roots in tropical agroforestry

systems was used for comparative analysis of tree rooting patterns across different edaphic and

management conditions (Isaac and Anglaaere 2013). This study was the first to use GPR for

multi-species tree root investigation, specifically at a temperate agroforestry site, and conducted

both an analysis of biomass and root distribution.

7

1.2 Thesis approach

Throughout my thesis, there are two main objectives: 1) to investigate the utility of GPR

to quantify tree roots in situ, and 2) to contribute to the knowledge of tree root architecture in

temperate TBI systems. I organized my thesis chapters not by these objectives, but in the two

forms of measurements made. Chapter 3 focuses on amount, specifically root biomass and C

content whereas Chapter 4 focuses on location, specifically vertical root distribution. My

approach to data analysis was different between these chapters with distinct methodological and

ecological applications (Figure 1). As such, I derived my research questions for Chapter 3:

1) Can ground penetrating radar estimate coarse root biomass of trees in the multi-

species scenario of a TBI system and how accurate are such measures?

2) Does root biomass vary between tree species in TBI systems thereby making some

trees preferential for C storage purposes?

And for Chapter 4:

1) Can ground penetrating radar accurately describe the vertical distribution of coarse

roots in the studied TBI system?

2) How do the vertical root distributions vary between tree species in this system, and

notably within crop rows, thereby making some trees preferential for optimal

belowground resource use?

As the study site, study trees, and GPR data collection protocol are common to both Chapters 3

and 4, these components are described first in Chapter 2: General Methods.

8

Figure 1 Conceptual diagram of thesis objectives and structure.

9

Chapter 2 : General Methods

2.1 Case study: University of Guelph Agroforestry Research Station

GPR data and root samples were collected below 13 individual trees at the University of

Guelph Agroforestry Research Station, Guelph, Ontario, Canada (44°32'28” N, 80°12'32” W;

elevation 325 m) (Figure 2). The 30 ha site was established in 1987 as an experimental

agroforestry systems system (specifically tree-based intercropping). A variety of tree species are

planted in tree rows that are spaced 12.5 or 15 m apart, in between which a conventional crop

rotation is practiced under no-till cultivation and annual intercropping with Zea mays (maize),

Glycine max (soybean), Triticum aestivum (winter wheat), or Hordeum vulgare (barley)

(Thevathasan and Gordon 2004) (Figure 3). The soil is classified as Grey-Brown Luvisol with a

sandy loam texture (65% sand, 25% silt, and 10% clay) (Oelbermann and Voroney 2007). The

Ap horizon continues to a depth of 28 to 53 cm (Price and Gordon 1999) and a moraine till is

located approximately 1 m below the soil surface.

2.1.1 Site conditions during radar survey

Water content of coarse roots and soil within study plots was determined by weighing

samples before and after drying at 65˚C for 7 days. Coarse root gravimetric and volumetric

(assuming cylindrical root shapes) water contents was 62 ± 1% (mean ± S.E.; n=65) and 76 ±

11% (n=93) respectively. Soil gravimetric and volumetric (using known soil sampler volume of

100 mL) water content of the soil at the site was 9 ± 1% and 12 ± 1% respectively (mean ± S.E.;

n=20) and did not vary significantly by depth. Thus, the necessary soil to root water content

gradient was satisfied for GPR detection and interpretation (Hirano et al. 2009; Guo et al. 2013c)

with coarse roots containing more than six times more water than the soil on a volume basis. Soil

bulk density ranged between 0.98 ± 0.02 g cm-3 (mean ± S.E.; n=36) and 1.20 ± 0.04 g cm-3

10

Figure 2 Satellite image (Google Earth 2013) of the University of Guelph Agroforestry Research

Station, Guelph, Ontario, Canada. Site was established in 1987 as a tree-based intercropping

experimental site. Insert is a schematic diagram of the planting design.

11

Figure 3 Image of intercropping rows at the University of Guelph Agroforestry Research

Station, Guelph, Ontario, Canada.

12

(n=24) in the top 0.20 m of soil and for soil at 0.40 and 0.60 m depths bulk density ranged

between 1.15 ± 0.03 (n=36) and 1.40 ± 0.03 (n=36) (Appendix 1).

2.2 Tree species

The 13 study trees included five species (Populus deltoides × nigra clone DN-177,

Juglans nigra, Quercus rubra, Picea abies, and Thuja occidentalis) that are commonly selected

for agroforestry systems in the region (Gordon and Williams 1991). Populus deltoides × nigra

clone DN-177 (hybrid poplar) has low-value wood, but poplar hybrids are the more commonly

studied species for calculating C storage potential in these systems as they are extremely fast

growing (Evers et al. 2010). While root architecture among poplar hybrids can vary, Populus

deltoides is known to have roots nearer to the surface, but with some deep vertical roots in sandy

and moist soil conditions (Burns and Honkala 1990). Juglans nigra L. (black walnut) is a

commonly intercropped tree in temperate regions valued for both its wood and nut production

(Jose and Gillespie 1998; Gordon and Williams 1991). The root system of J. nigra is known to

produce a both a distinct taproot and also have a strong lateral root system (Burns and Honkala

1990) and of note is the alleopathic exudates, juglone, from roots (Jose and Gillespie 1998).

Quercus rubra L. (red oak), is also valued for its wood, has a large root system that includes a

taproot and many oblique roots (Jose et al. 2001). Picea abies L. Karst. (Norway spruce) is

characterized by a strong horizontal spreading root system with vertically descending roots that

emerge from the lateral roots (Drexhage and Gruber 1998). For this study, these four

aforementioned tree species had stem diameters (DBH) ≥ 18 cm (see Appendix 2) and

aboveground heights > 7 m. The fifth study species was the multi-stem Thuja occidentalis L.

(eastern white cedar), which can have a root system that can grow in extremely shallow substrate

and is a common choice for hedgerows (Burns and Honkala 1990; Kelley et al. 1992). The T.

occidentalis examined this study had aboveground heights < 6 m. The trees were planted with 6

13

m in-row tree stem spacing, except T. occidentalis with 1 m spacing. Each species was replicated

with 2 or 3 randomly selected trees. All trees included in the study were approximately 25 years

old and were located in flat areas of the site.

2.3 Radar survey

Tree root detection typically occurs with a GPR antenna emitting a centre frequency of 500

MHz (e.g. Barton and Montagu 2004) up to 2000 MHz (e.g. Cui et al. 2013). For this study, we

used a 1000 MHz GPR unit (NogginPlus; Sensors and Software Inc., Mississauga, ON, Canada)

(Figure 4) for all data collection. Previous studies using this frequency have reported detection

coarse roots of diameter 0.5 cm and greater (Guo et al. 2013a).

The area around the base of each target tree was cleared of leaf litter and other organic

material. A 4.5 × 4.5 m grid frame was installed surrounding the base of each target tree so the

tree stem was situated in the approximate centre (Figure 5a). The grid frame was constructed of

plastic pipe, which served to anchor guide-rope at 0.10 m increments in both the x and y

directions (Figure 5a). To ensure straight and square transects, the GPR unit was pulled by an

attached handle so that the antennae remained alongside the grid guide-rope. Data collection of

13 trees occurred between April and June, 2012. Transect increments of 0.10 m were selected for

GPR data collection to reduce dependency on interpolation. Each set of tree GPR data were

collected on the same day and under consistent conditions with the GPR programmed to EM

signal emission intervals of 0.1 ns stacked with 16 traces every 5 mm along each transect.

To measure the average EM signal velocity in the subsurface, metal rods were inserted

horizontally at depths of 0.40 m into soil profiles adjacent to target trees. The travel time and

distance of the GPR signal to the rods were measured and thus average velocity of the radar

signal was calculated. Velocities were measured the day of data collection for each tree and

ranged between 0.08 and 0.10 m ns-1. Radar signal attenuation became severe at depths of

14

approximately 0.80 cm. Each radargram contained the radar signal response data adjusted to the

appropriate velocity, interpreting depth (Figure 6). The radargrams were associated with x and y

data from the orientation of the grid design (Figure 7) and were the starting point for the analyses

described in the subsequent two chapters.

15

Figure 4 Image of 1000 MHz ground penetrating radar (GPR) unit used for geo-image, or

radargram, data collection during this study. Unit pictured being pulled along soil surface with

attached odometer measuring distance along transect near Juglans nigra.

Figure 5 (a) Image of grid set-up for GPR survey of Quercus rubra in tree-based intercropping

system at the University of Guelph Agroforestry Research Station, Guelph, Ontario, Canada. (b)

Plan-view schematic of GPR data collection grid design. The base of tree stem is represented by

the circle approximately in the centre of the grid. Red lines indicate GPR data collection

transects in x and y directions with 10 cm spacing.

16

Figure 6 Radargram of transect 0.70 m from Picea abies stem. Signal velocity was 0.09 m ns-1.

Figure 7 Plan view of interpolated radargrams assembled in grid orientation and visualized for

signal response within depth 0.30 to 0.325 m. Grid shown is surrounding Picea abies. The

magnitude of reflected signals are represented on colour scale from no reflection detected (dark

blue) to high reflection (red).

17

Chapter 3 : Estimating coarse root biomass with ground penetrating

radar in a tree-based intercropping system

3.1 Abstract

Conventional measurements of tree root biomass in tree-based intercropping (TBI) systems may

not accurately represent the heterogeneity of rooting patterns or are highly destructive and non-

repeatable. In this study, I estimated total coarse root biomass using ground penetrating radar

(GPR) of 25-year-old trees inclusive of five species (Populus deltoides × nigra, Juglans nigra,

Quercus rubra, Picea abies, and Thuja occidentalis) at a TBI site in Southern Ontario, Canada.

Subsurface images generated by GPR were collected in grids (4.5 × 4.5 m) centred on tree stems.

The predictive relationship developed between GPR signal response and root biomass was

corrected for species effect prior to tree scale estimates of belowground biomass. Accuracy and

precision of the tree scale estimates was assessed comparing coarse root biomass measured from

complete excavations of the corresponding tree. The mean coarse root biomass estimated from

GPR analysis was 54.1 ± 8.7 kg tree-1 (mean ± S.E.; n=12), within 1 % of the mean coarse root

biomass measured from excavation, and for all trees there was a root mean square error of 14.4

kg between measured and estimated biomass with no detectable bias despite variable conditions

within the in-field and multi-species study. Root system C storage is reported by species,

calculated with species-specific root carbon concentrations, to range between 5.4 ± 0.7 to 34.8 ±

6.9 kg C tree-1 at this site. GPR is an effective tool for non-destructively predicting coarse root

biomass in multi-species environments such as temperate TBI systems.

18

3.2 Introduction

With more than 20 % of total tree biomass allocated belowground, tree roots comprise a

substantial but understudied component of biomass in many ecosystems (Cairns et al. 1997;

Mokany et al. 2006; Brunner and Godbold 2007). Ecological benefits derived from the presence

of tree roots, particularly in modified agricultural systems, include soil amelioration via root

exudation, turnover, and sloughing, improved water infiltration and aeration from root channels,

and prevention of erosion (Schroth 1999; Jose et al. 2004; Thevathasan and Gordon 2004; Jose

2009; Nair et al. 2009). Furthermore, the incorporation of trees into agricultural landscapes can

increase the C storage potential of a landscape, considerably within belowground biomass, and is

touted as a viable and potentially significant land-use approach to sequester atmospheric CO2

(Dixon et al. 1994; Isaac et al. 2005; Peichl et al. 2006; Bambrick et al. 2010; Kuyah et al. 2012).

While potential aboveground C sequestration in temperate agroforestry systems is estimated at

1.9 × 109 Mg C year−1 (Oelbermann et al. 2004), data are limited on the belowground

contribution. Improved approaches to chart and predict the belowground biomass are required to

fully capture the role of roots in C budgets for temperate TBI.

Biomass inventories within agroforestry systems are primarily focused on measuring the

aboveground component with a limited number of studies also calculating root mass (e.g.

Oelbermann et al. 2005; Peichl et al. 2006; Kirby and Potvin 2007; Moser et al. 2010; Kessler et

al. 2012). Conventional sampling techniques of tree root systems include minirhizotrons, profile

walls, and soil cores (Vogt et al. 1998; Polomski and Kuhn 2002). However, due to the spatial

heterogeneity and the inherent fractal nature of root systems, these techniques generally have low

accuracy particularly for coarse roots (diameter > 2 mm) (Schroth and Kolbe 1994; Taylor et al.

2013). Conversely, partial or complete root system excavations are time consuming, destructive,

and non-repeatable. This has led to biomass study to adopt root:shoot ratios (e.g. Jackson et al.

19

1996; Cairns et al. 1997; Mokany et al. 2006) or allometric equations (e.g. Kurz et al. 1996;

Jenkins et al. 2003) generated from biomass studies in forest ecosystems. However, generalities

of tree biomass allocation may produce inaccuracies when applied to agroforestry, or TBI sites

specifically, due to variation of tree root growth in cultivated scenarios (Nair 2011; Kuyah et al.

2012). Thus, there is a need for new methodologies to study and measure root biomass in TBI

systems.

3.2.1 Ground penetrating radar for root biomass estimation

Previous research in the use of ground penetrating radar (GPR) for biomass estimation

have been conducted in controlled experiments (Barton and Montagu 2004; Dannoura et al.

2008; Hirano et al. 2009; Cui et al. 2013; Guo et al. 2013c; Tanikawa et al. 2013) and in situ

within tree monocultures or two-species mixtures (Butnor et al. 2003; Stover et al. 2007;

Samuelson et al. 2008; Samuelson et al. 2010; Hirano et al. 2012; Raz-Yaseef et al. 2013; Day et

al. 2013). Generally, physical samples of root biomass equating to corresponding areas of GPR

radargrams have been compared to develop predictive relationships between a GPR signal

response index and root biomass (Butnor et al. 2003; Stover et al. 2007; Guo et al. 2013a). A

growing body of research has used this approach to explore root responses to variation in

atmospheric CO2 (Stover et al. 2007; Day et al. 2013), forest stand management techniques

(Butnor et al. 2003; Samuelson et al. 2008; Samuelson et al. 2010), and precipitation patterns

(Raz-Yaseef et al. 2013). However, the utility of GPR biomass estimation has yet to be tested in

a multi-species scenario or in an agroforestry system specifically. Therefore, the objectives of

this study were to test the utility of GPR for coarse root biomass estimation across a variety of

tree species and in field conditions and to calculate the C content of the belowground biomass in

a temperate TBI system.

20

3.3 Materials and Methods

See Chapter 2: General Methods for description of study site, study trees, and radar survey.

3.3.1 Radargram processing

Prior to image analysis of the GPR data, non-root anomalies (e.g. plane reflectors and

signal “noise”) were reduced with a sequence of noise reduction steps (DC shift, dewow, and

background removal) (Figure 8b and c). Subsequently, dipping features, such as hyperbolic

reflections of roots, were repositioned to their foci with a migration algorithm (2d FK migration

with Stolt equation using known signal velocity and the angle of incidence) (Figure 8d). Finally,

an envelope algorithm known as the Hilbert transformation (amplitudes of the reflected EM

waves are used to interpret the data into one phase) was applied so that reflectors are more

discernible (Figure 8e). In order to enhance subsurface root reflections representatively with

depth, a spreading and exponential compensation gain (SEC2) was applied to all processed GPR

radargrams based on the rate of energy decay, similar to methodology in Cui et al. (2013) and

Guo et al. (2013c). A colour palette of bipolar grey was selected for visualization whereby low

to high amplitude response was displayed as grey to white. All GPR data processing steps were

completed in EKKO_View Deluxe (Sensors and Software Inc.).

GPR radargrams were imported into ImageJ (US National Institutes of Health, Bethesda,

MD, USA) as 8-bit bmp files. Radargrams were standardized to ensure consistent measures of

distance within an image using a ratio of 400 pixels:1 m. The final data processing step measured

the number of pixels within a threshold range (ImageJ) and subsequently converted to a cross

sectional area (cm2) (Figure 8f), defined as the GPR index.

3.3.2 GPR index–biomass relationship

Following GPR data collection, soil profiles (0.25 m across and 1 m deep) were exposed

at distances ranging from 0.5 to 2.0 m from the tree stem (n=64) (Figure 8a).

21

Figure 8 GPR data processing sequence with soil profile (0.25 × 1.0 m) and exposed roots

(circles) of Juglans nigra (a) and the equivalent GPR geo-image (b) with applied background

removal (c) surface reflections or banding is reduced. Hyperbola migration focuses root

reflections to foci (c). The final data processing step is the Hilbert transformation whereby

magnitude of reflection is brought into one phase (e). The extracted GPR index (area within an

intensity range (175 to 255); cm2) is measured (f) to develop a GPR index–biomass relationship.

22

These soil profiles were orientated along GPR data transects. To calculate total coarse root dry

weight biomass in each excavated soil profile, the diameters of all coarse roots crossing through

the exposed profiles were measured and applied to species-specific relationships of root dry

weight biomass to root diameter as described in Hirano et al. (2012) and Guo et al. (2013b)

(Table 1). Relationships were developed from root samples of various diameters collected at

each tree and assuming a length of 10 cm, equivalent to the GPR transect spacing.

To determine the GPR index, three pixel intensity thresholds (165 to 255, 175 to 255, and

200 to 255) were selected based on visually delineating root features and minimizing the

incorporation of non-root anomalies. The area derived from each threshold level were compared

for their correlations to coarse root biomass. The pixel intensity threshold of 175 to 255 (Figure

8f) was ultimately selected to generate the area index of GPR signal response as it produced the

optimal correlation with measured biomass.

3.3.3 Coarse root biomass estimates

The radargram processing sequence as described in 3.3.1. Radargram processing was

applied to all GPR data within each tree grid. The resulting GPR index determined for each

transect was used to calculate the predicted root biomass using the GPR index–biomass

relationship. All transect biomass estimates were summed for total tree coarse root biomass

(BGPR; kg tree-1). Since tree roots are best detected when crossing between 45˚ and 135˚ to the

plane of the radargram (Butnor et al. 2001; Tanikawa et al. 2013), the biomass estimates from

grid transects in both x and y directions were included to maximize root detection by capturing

roots irrespective of direction of growth.

An additional estimate of coarse root biomass for the study trees was completed using

conventional allometric equations from Jenkins et al. (2003) that employed measured DBH and

23

Table 1 Relational equations between specific root biomass (W) representing dry weight of 10

cm long root segments (g) with root diameter (D) (cm) (n=20 per species).

Tree species Biomass relationship

to diametera

r

Populus sp. W = 2.9138 × D1.8183 0.9959

J. nigra W = 3.5694 × D1.933 0.9936

Q. rubra W = 4.9623 × D1.8976 0.9980

P. abies W = 3.7016 × D1.8715 0.9993

T. occidentalis W = 3.0168 × D1.8269 0.9901

a It was assumed that root diameter was constant for 10 cm to be comparable to GPR transect

spacing of 10 cm.

24

parameters associated with species class (hardwood or softwood) and species group (hardwoods:

Populus sp. = “aspen/alder/cottonwood/willow”, J. nigra = “mixed hardwood”, and Q. rubra =

“hard maple/oak/hickory/beech”; softwoods: P. abies = “spruce”, and T. occidentalis =

“cedar/larch”) (Jenkins et al. 2003). The coarse root biomass was estimated such that:

BGBratio = exp(β0 + β1/DBH) (2)

where β0 and β1 are parameters fitted from species class data (Jenkins et al. 2003), DBH is the

diameter at breast height (cm), and BGBratio is the ratio of coarse root biomass to aboveground

biomass (AGB) whereby:

AGB = exp(β0 + β1 lnDBH) (3)

where AGB is total aboveground biomass (kg), DBH is the diameter at breast height (cm), and β0

and β1 are parameters fitted from species group data (Jenkins et al. 2003).

3.3.4 Root carbon content

Coarse roots were randomly selected during complete excavation. Sample preparation

and analysis follow C volatile-inclusive methodology as suggested by Thomas and Martin (2012)

as volatile compounds lost during oven drying are being realized as a non-negligible amount of

C necessary for more accurate C content estimates (Lamlom and Savidge 2003; Thomas and

Malczewski 2007; Martin and Thomas 2011). Roots were placed in air-tight bags and transported

in a cooler to the laboratory at which point roots were washed to remove soil and stored in air-

tight bags at -5°C. Coarse roots were cut into cylindrical segments ~1 cm in length to retain

representative proportions of root tissue. Root samples were dried in an 8 L freeze dryer

(Labconco Co., Kansas City, MO, USA) for seven days. The largest diameter roots were

weighed for constant mass on the final day to ensure complete drying.

For each tree species (n=3, except P. abies and Populus sp. n=2), coarse root samples

were prepared as composite samples inclusive of four coarse root diameter classes (0.2 to 0.5,

25

0.5 to 1.0, 1.0 to 2.0, and > 2.0 cm). Samples were ground in a ball grinder (Restch MM400

Mixer Mill) and stored in snap-cap 1 mL containers at -5 °C. Total C for each sample was

determined using a CHN analyzer (Thermo Flash 2000). Samples were weighed on a

microbalance for total sample mass. Elemental analysis calibrations were completed prior to

each sample run using aspartic acid. Known standards (SRM 133317, Thermo Scientific) were

tested during analysis to confirm instrument accuracy.

As biomass estimates are reported on a dry weight basis, the C concentrations (%)

measured from freeze-dried samples were converted onto a dry weight basis whereby:

C = [MC / (MF – (VMF × MF))] × 100 (4)

where MF was the mass of the freeze-dried sample used for elemental analysis and MC was the

mass of C in MF. The species’ mean volatile mass fractions (VMF) applied to equation 4 was the

species-specific fraction of biomass lost during heating methods and was calculated whereby:

VMF = (MF – MH) / MF (5)

where an additional subset of freeze-dried samples were weighed (MF) and oven dried at 105 °C

for 48 hours and weighed again after drying (MH). Carbon concentration analysis was repeated

with oven dried roots to evaluate methodology. Finally, the total root C content for each tree

species was calculated by applying the resultant C concentration values of coarse roots to GPR-

derived estimates of coarse root biomass.

3.3.5 Statistical analysis

The GPR index–biomass relationship was developed by correlating the GPR response

(area of processed radargrams above an intensity threshold; cm2) and the coarse root biomass (g)

from spatially matched subsurface soil profiles to a depth of 1 m to develop the predictive

equation. Cook’s distance identified a datum with significant influence on the regression. The

datum represented an excavated soil profile section containing an extremely large J. nigra root (>

26

10 cm) of non-cylindrical shape and deemed justifiable to be discarded due to the uncertainty of

applying a diameter–biomass relational equation (Table 1). An analysis of covariance

(ANCOVA) tested for species main effect on biomass as well as the interactive effect of species

and GPR signal response (GPR index × species) on biomass in order to identify required

corrected predictive equations among species.

An assessment of precision of BGPR was completed by comparing BGPR to the coarse root

biomass measured from matched excavated study plots (Wotherspoon et al. unpublished data)

using paired t-tests on means and a linear regression for all study trees. Differences among

species for BGPR and C concentration were tested using one-way analysis of variance (ANOVA).

Prior to parametric tests, data were confirmed for equality of variance using Bartlett test

and for normal distribution of residuals using Shapiro-Wilk test. Statistical analyses were

completed in R v.2.14.2 (R Foundation for Statistical Computing, Vienna, Austria) and the level

of significance was set at p<0.05.

3.4 Results

3.4.1 GPR images and index–biomass relationship

Signal noise and planar reflections, specifically from surface reflections, were reduced

following the image processing sequence (Figure 8b and c). Hyperbola migration and the Hilbert

transformation were successful in emphasizing root reflections in the radargrams (Figure 8d and

e). The selected image intensity threshold (pixel intensity between 175 and 255) delineated these

areas of high GPR signal response (Figure 8f). This allowed for detectable roots to be converted

quantitatively to the cross sectional area (cm2) on the radargram, which became the ‘GPR index’.

The area bounded in detected root signals within the subset of radargram profiles ranged from

1.56 cm2 to 615.44 cm2.

27

Measured coarse root biomass in the exposed soil profiles were positively correlated

with the GPR index extracted from the matched radargrams (r=0.47; n=63). However, there was

a species main effect on biomass (p = 0.0002) (Table 2) that necessitated the development

of corrected relationships based on species. When the data collected below T. occidentalis were

separated from the other four species (‘pooled’), the GPR index remained significantly correlated

to biomass (p<0.0001). There was no species main effect or GPR index and species interactive

effect on biomass for the remaining pooled species data (Table 2; Figure 9). The resulting GPR

index–biomass predictive equation for the corrected pooled species was y = 0.214x – 4.7

(r=0.55; n=51) and the GPR index–biomass predictive equation of the corrected T. occidentalis

was y = 0.039x – 4.6 (r=0.95; n=12). These two relational equations were used for biomass

estimation at the tree scale (Figure 10).

3.4.2 Coarse root biomass

BGPR was 54.1 ± 8.7 kg tree-1 (mean ± S.E.) (n=12), regardless of species, and the mean

coarse root biomass measured from excavation was 54.8 ± 8.3 kg tree-1 (n=12), and not

significantly different (p=0.876; Table 3) (see Appendix 2). One tree replication of J. nigra was

omitted for concern of it being a source of error due to an unusually small measurement of its

excavated biomass of 8.3 kg (close to 10% to coarse roots measured from either of the other two

J. nigra trees). The remaining two replications of J. nigra resulted in equivalent means of BGPR

and excavated biomass. BGPR of Q. rubra was a slight overestimate of 4 % and the BGPR of P.

abies was the largest overestimate of 24 %. In contrast, the BGPR of Populus sp. was 54.6 ± 6.0

kg (n=2) opposed to an excavated mean of 71.9 ± 10.8 kg (n=2), an underestimation of 32 %.

Thuja occidentalis had a BGPR of 11.8 ± 1.5 kg tree-1 (n=2), an underestimation of the excavated

amount by 16 %. The resulting correlation of BGPR and excavated coarse root biomass resulted in

a linear relationship with no evident bias of prediction capabilities of the GPR methodology

28

Table 2 Analysis of covariance for root biomass measured in exposed profiles and GPR response

index. Displayed are the results for data inclusive of all species and the corrected relationship

that removed species main effect on remaining pooled species data. Significant results (p<0.05)

are in bold.

df SS MS F P

All data:

GPR index 1 25.63 25.63 23.12 <0.0001

species 4 29.69 7.42 6.70 0.0002

GPR index × species 4 2.35 0.59 0.53 0.713

residuals 53 58.74 1.11

Corrected pooled data

without T. occidentalis:

GPR index 1 31.80 31.80 23.735 <0.0001

species 3 6.52 2.17 1.622 0.198

GPR index × species 3 2.05 0.68 0.510 0.678

residuals 43 57.61 1.34

29

Table 3 GPR estimated coarse root biomass (BGPR) (kg tree-1; mean ± S.E.) with corresponding

excavated biomass of five tree species (kg tree-1; mean ± S.E). Paired t-tests completed on the

means for each species and across all study trees between BGPR and excavated biomass. Also

shown are corresponding calculated allometric estimates a of coarse roots dependent on DBH and

the species class and group.

Tree species

BGPR

(kg tree-1)

Excavated

(kg tree-1) n t-test p-value

Allometric

(kg tree-1)a

Populus sp. 54.6 ± 6.0 71.9 ± 10.8 2 -3.60 0.172 90.2 ± 3.5

Juglans nigra 75.0 ± 14.4 75.0 ± 1.4 2 0.003 0.998 56.8 ± 5.3

Quercus rubra 77.0 ± 15.4 74.0 ± 5.7 3 0.296 0.796 44.1 ± 2.3

Picea abies 62.0 ± 9.8 50.1 ± 21.6 2 1.000 0.500 35.9 ± 12.8

Thuja

occidentalisb 11.8 ± 1.5 14.0 ± 4.0 3 -0.403 0.726 12.3 ± 4.7

all study trees 54.1 ± 8.7 54.8 ± 8.3 12 -0.160 0.876 44.6 ± 7.9

a Allometric equations from Jenkins et al. (2003). b Due to 1 m stem spacing for T. occidentalis, 1 or 2 additional trees were located within the 3

replicates of the root study area. The trees located near the study plot boundary were assumed to

contribute 50% of their root biomass and adjusted accordingly during analyses.

30

Figure 9 Transformed data used to test for species main effect and interactive effect with GPR

index on measured biomass (ANCOVA). Solid symbols represent corrected pooled species data

with no significant species main effect (p=0.20), or interactive effect with GPR index (p=0.68),

on biomass (Table 2). Open circles are isolated data points collected below Thuja occidentalis

representing the corrected data used for that species GPR index–biomass estimation equation

(p<0.0001).

31

Figure 10 GPR index (area above a threshold; cm2) as a predictor of dry weight coarse root

biomass (g) determined from exposed soil profiles and correlated with equivalent section of GPR

radargram. Corrected relationships displayed from i) pooled data (solid symbols inclusive of

Populus sp., Juglans nigra, Quercus rubra, and Picea abies) identified by solid line (y = 0.214x

– 4.72; r = 0.55; n=51) and ii) isolated Thuja occidentalis (open circles and dotted line) (y =

0.038x – 4.62; r = 0.95; n=12).

32

(r2=0.75; p=0.0003) and a root mean square error (RMSE) of 14.4 kg (Figure 11). Overall, the

estimates derived from the applied allometric equations were less accurate or precise than BGPR,

underestimating the mean excavated biomass by 19 % (Table 3), and resulted in a weaker

correlation (R2=0.60; RMSE=16.7 kg).

3.4.3 Root system C content

Among the five species, the concentration of C in the coarse roots was 45.9 ± 0.6 %

(mean ± S.E.; n=5 species) (Table 4). Carbon concentrations varied from 44.7% to 48.1 % (in J.

nigra and P. abies, respectively), though no significant variation was found among species

(p=0.361). Of note, the volatile inclusive methodology captured and additional 2.1 ± 0.8 %

(mean ± S.E.; n=5 species) of C lost during oven drying methods. The C content of tree root

systems using coarse root estimates from GPR data ranged between T. occidentalis with 5.4 ±

0.7 kg C tree-1 (mean ± S.E.; n=3) to Q. rubra 34.8 ± 6.9 kg C tree-1 (n=3) (Table 4), although no

significant variations were detected among species (p=0.361). Overall, the mean C content of

tree root systems at this site was estimated at 25.7 ± 5.4 kg C tree-1 (n=5 species), which scales to

the landscape level as 2.9 Mg C ha-1.

3.5 Discussion

3.5.1 The GPR index–biomass relationship

The use of a linear GPR index–biomass relationship was suitable in this study due to low

variability of coarse root water content (Guo et al. 2013c) and a large difference between root

and soil water contents (Hirano et al. 2009). A correlation of r=0.89 between biomass from soil

cores and GPR index was found in a study completed by Samueslon et al. (2008). They used a

1.5 GHz unit in a Pinus taeda plantation on sandy loam soils and correlated subsurface data

exclusively to a depth of 30 cm. The correlation from Day et al. (2013), also using a 1.5 GHz

GPR unit, was r=0.69 between biomass from soil cores and a GPR index inclusive of subsurface

33

Figure 11 Positive correlation (solid line) between coarse root biomass estimated by GPR (kg

tree-1) and coarse root biomass measured from matched excavations (kg tree-1) (R2=0.75;

p=0.0003; RMSE=14.4 kg; n=12). The 1:1 relationship also shown (dotted line).

34

Table 4 Carbon concentration (%) and C content (kg C tree-1) of the coarse root system of five

tree species (25 years old). Carbon concentration values are reported as total carbon of dry root

weight (mean ± S.E.) (n=3, except Populus sp. and Picea abies n=2) following i) conventional

oven dry sample preparation or ii) volatile inclusive methodology. Carbon content of the trees’

root systems were calculated using BGPR and the species-specific coarse root C concentration

(volatile inclusive).

Tree species

C concentration of

coarse roots (%)

i) conventional dry

C concentration of

coarse roots (%)

ii) volatile inclusive

Coarse root C

content from BGPR

(kg C tree-1)

Coarse root C

content at site

level

(Mg C ha-1)a

Populus sp. 43.6 ± 2.1 45.8 ± 1.1 25.0 ± 2.7 2.8

Juglans nigra 44.5 ± 1.0 44.7 ± 0.3 33.5 ± 6.4 3.7

Quercus rubra 42.2 ± 0.2 45.2 ± 1.0 34.8 ± 6.9 3.9

Picea abies 47.8 ± 1.2 48.1 ± 0.6 29.8 ± 4.7 3.3

Thuja

occidentalis 41.4 ± 0.4 45.8 ± 2.3 5.4 ± 0.7 0.6

Average

(n=5 species) 42.2 ± 1.1 45.9 ± 0.6 25.7 ± 5.4 2.9

a Study site tree stem density is assumed to be 111 trees ha-1.

35

data to a depth of 60 cm in a scrub-oak ecosystem co-dominated by Quercus myritfolia and

Quercus geminate on sandy soils in Florida, USA. Although it should be expected that GPR

index–biomass correlations will be reduced when there is an increase of depth of radar analysis

and an increase of the variability in subsurface conditions, here I include roots to a depth of 1 m

while still maintaining a reasonably strong correlation (r=0.55) inclusive of four different tree

species. The corrected relationship for T. occidentalis showed a very strong correlation (r=0.95)

in part due to the shallower root system of this relatively smaller tree species.

The utility of GPR biomass estimation across a landscape would be greater given the

applicability of one GPR index–biomass relationship to apply to all radargram data. From the

results of this study, two corrected predictive equations were appropriate in order to remove any

significant species effect on predicted biomass. Similarly, Butnor et al. (2003) developed

corrected relationships of GPR index–biomass for two contrasting scenarios of fertilizer use or

no fertilizer use in a Pinus taeda stand, which altered the soil conditions for radar signals. Given

reasonably consistent subsurface conditions (e.g. clay content) and soil-root moisture gradients,

corrected GPR index–biomass relationships may be required for scenarios of distinct biomass

gradients, a reality for temperate TBI systems.

3.5.2 Biomass and carbon estimates of tree root systems

Inclusion of the fraction of volatile C lost during high-heat (105 ˚C) drying was

confirmed for improved accuracy of C content estimates. The highest root C concentration was

for coniferous P. abies, consistent with previous reported trends where coniferous trees have a

higher concentration of C than deciduous trees in temperate regions (IPCC 2006; Thomas and

Martin 2012). Peichl et al. (2006) reported coarse root C concentrations, using conventional

oven-drying methods, of 13-year-old P. abies at 51 %, which is ~3 % greater than those found in

our study. Conversely, carbon concentration for Populus sp. roots found by Peichl et al. (2006)

36

(43 %) were ~3 % lower than our reported values inclusive of volatiles, but proximal to the

results from oven dry methods. Bert and Danjon (2006) detected variation between the interior

root “wood” and the exterior root “bark” (bark + phloem) of Pinus pinaster, with the root bark

was ~3 % greater in concentration. They also reported diameter-dependent variation notably for

roots < 4 cm (Bert and Danjon 2006). I did not test for variation within root tissues, but

acknowledge that there may be within-root variation of C concentrations dependent on the ratio

of root components, which would be inherently affected by the diameter of root samples used

during elemental analysis. Additional sources of variability in C concentration of tree roots may

arise from variation in sampling and C analysis protocol as well as physiological variation

(Lamlom and Savidge 2003), such as tree root carbohydrate storage (Bert and Danjon 2006).

In order to show the C storage potential of trees in temperate tree-based intercropping

systems, system level root C quantification was calculated using the current hardwood tree

density of 111 trees ha-1. However, it should be noted that if only coniferous trees are integrated,

the tree density will be much higher due to lower spacing used for coniferous trees. The

estimated root C content at this site indicates an increase of belowground C storage over the last

12 years when compared to the root C content of the average reported values for 13-year-old

Populus sp. and P. abies at the same site (1.8 Mg C ha-1) (Peichl et al. 2006). In temperate TBI

systems, stem density, species composition, and the age of trees are highly variable. Thus

reporting species root biomass and root C content at the tree scale is valuable for operational

purposes that are specific to these variables (Thevathasan and Gordon 2004).

3.5.3 Application and limitations of GPR in tree-based intercropping

There are limitations to the amount of biomass GPR can detect. For example, coarse roots

smaller than 1.0 cm in diameter are less likely to cause radar signal response than larger roots (≥

1 cm) (Hirano et al. 2012), coarse roots located deeper than GPR signal penetration can be

37

undetected or misinterpreted by the GPR signal response (Hirano et al. 2009), and coarse roots

located outside of the field of the radar signals, such as directly below the stem, will be

undetected (Samuelson et al. 2008). As a result of these detection limitations there is an

anticipated bias towards GPR underestimation of coarse root biomass, assuming proper

calibration and appropriate conditions. With the exception of Populus sp., this bias was not seen

in our results suggesting that the subsurface conditions and the morphological characteristics of

the root systems at this study site were conducive for radar study. However, some detected

biomass might be attributed to false positive GPR signal response incurred from in-field

conditions.

Although GPR does require some destructive calibration sampling, such as soil cores, the

amount of physical sampling required to estimate the coarse root biomass is drastically reduced

compared to conventional studies in TBI systems. During the current study, 243 m2 of surface

area were scanned with GPR, an area equating to over 13,500 soil cores (of 15 cm diameter).

GPR techniques can provide more thorough understanding of the heterogeneity of the root

systems without total excavation in agroforestry systems (Isaac and Anglaaere 2013) and unlike

destructive sampling, this method of root data collection can be repeated, critical for temporal-

scale studies on root system dynamics (Norby and Jackson 2000). I tested the use of pre-

established species-based allometric and root:shoot equations, a more traditional approach to

quantify root biomass, and found less accurate estimates of the excavated root biomass as

compared to the GPR estimates. Generalized equations derived from forest ecosystem data might

be unsuitable for trees in agricultural landscapes where variations in management (e.g. planting

density and fertilizer application) can induce differences in biomass allocation. Results from this

study suggest that the overall precision of the allometric estimate was outperformed by GPR

estimations in comparison to the excavated biomass and supports the need for more site and

38

species-specific tree root data. Recent advancements in modelling radar signal response given

variable root and soil conditions have been reported following controlled experiments (Guo et al.

2013b; Tanikawa et al. 2013). With these advancements, there is potential of enhancing accuracy

of root estimation for in-field conditions and lessening the need for destructive sampling for

calibrations.

3.6 Conclusions

Coarse root biomass of 12 trees, inclusive of five species, was accurately estimated with

the use of GPR at a TBI site in Southern Ontario, Canada. Therefore, the use of GPR technique

to quantify belowground biomass at the system level with diverse tress species may be of

importance. Subsequently, C content of tree root systems was quantified using species-specific

coarse root C concentrations. This was the first in-field study to test the robustness of GPR as a

coarse root biomass estimation tool across multiple species. Corrected predictive relationships

between GPR signal response and root biomass were required to remove species effect, namely

isolating data from a species with a distinctly smaller and shallower root system. I argue that this

technology can be suitable for use in temperate TBI systems under well drained, sandy loam

soils. Ultimately, these results contribute to furthering methodological techniques of GPR root

study for direct quantification of belowground biomass and C storage in agroforestry systems

and other tree-based ecosystems.

39

Chapter 4 : Evaluating vertical distributions of tree roots in a tree-

based intercropping system with ground penetrating radar

4.1 Abstract

Within tree-based intercropping (TBI) systems, tree root architecture contributes to important

ecological interactions, such as belowground resource competition that can influence the success

of adjacent crops. Yet the belowground component of trees in TBI systems remains largely

understudied due to methodological constraints. I used ground penetrating radar (GPR) to detect

coarse root locations and extracted fine roots from soil cores to determine the root distributions

below thirteen study trees of five species (Quercus rubra, Juglans nigra, Populus sp., Picea

abies, and Thuja occidentalis) at a TBI site in Guelph, Ontario, Canada. Coarse root locations

were identified across the radargrams (visualized as radar signal reflections) from 4.5 × 4.5 m

survey grids and provided root distribution data in the soil profile. Coarse roots detected by GPR

accounted for 80.6 ± 0.8% (mean ± S.E.; n=13) of large coarse roots (≥ 1 cm) and 43.4 ± 0.5%

(n=13) of small coarse roots (< 1 cm) that were later exposed in a subset of matched soil profiles.

There was significant variation in mean depth of detected coarse roots among species at the tree-

scale (p=0.001) and also for coarse roots detected solely in crop rows (p=0.031). Fine root

densities at each sampling depth varied among species (p<0.0001). Results suggest that a

conventionally applied asymptotic curve that describes the cumulative fraction of coarse roots

with depth might not be appropriate in describing the rooting profile of trees in TBI systems. I

evaluate tree species root distribution in the context of selection and management of trees that

promotes complementary root stratification between trees and crops.

40

4.2 Introduction

Important belowground ecological processes in tree-based intercropping (TBI) systems,

such as nutrient and water acquisition, are highly influenced by tree root architecture (Ong et al.

1991; Schroth 1999; Jose et al. 2001; Jose et al. 2004; Oelbermann et al. 2004). Furthermore,

roots play a significant role in increasing soil aggregate stability (Udawatta et al. 2008) and C

sequestration potential as biomass storage and as contributors to soil organic matter (Peichl et al.

2006; Oelbermann and Voroney 2007; and see Chapter 3). However, the benefits of carbon

storage and soil quality must balance with the economic requirements for TBI, specifically the

yield demands for adjacent crops (Schroth 1999; Livesley et al 2000; Thevathasan and Gordon

2004). Reductions in yields were identified at the study site in Southern Ontario, particularly for

corn (Thevathasan and Gordon 2004; Reynolds et al. 2007). While these reductions were

attributed to aboveground competition for light (for corn in particular as it is a C4 plant)

(Thevathasan and Gordon 2004; Reynolds et al. 2007), belowground competition for soil

resources was reported following tree root exclusion experiments in other temperate TBI systems

(Gillespie et al. 2000; Miller and Pallardy 2001). Furthermore, other studies found belowground

tree-herbaceous interactions to be more influential than competition for aboveground resources

(Bloor et al. 2007; Fletcher et al. 2012).

There remains conflicting evidence of the degree to which above and belowground

competition influence tree-crop interactions. However, it is generally understood that minimizing

belowground competition is critical in successful TBI systems whereby a stratification of

resource pools, spatially or temporally, is desired (Schroth 1999; Van Noordwijk and

Purnomosidhi 1995; Livesley et al. 2000). Additionally, deep-rooted trees can encourage water

uptake and hydraulic redistribution from areas of moist soil to dry soil or from deeper soils to

shallower soils (Oliveira et al. 2005; Fernández et al. 2008; David et al. 2013). Tree roots may

41

capture nutrients that have leached beyond the crop rooting zones (Das and Chaturvedi 2008;

Bergeron et al. 2011). For example, Dougherty et al. (2009) found lower amounts of nitrate run-

off from the Guelph TBI system compared to a control monocrop field, suggesting that tree root

systems might be acting as a ‘safety net’ (Jose et al. 2004).

4.2.1 Root distribution in TBI systems

The distribution of a trees’ roots are dependent upon internal factors (i.e. genotypic) and

external factors (Puhe 2003). External factors can include wind (Tamasi et al. 1995), interspecies

competition (Upson and Burgess 2013), and edaphic conditions (Sudmeyer et al. 2004; Isaac and

Anglaaere 2013). Thus, site-specific understanding of root distribution is required to explain

belowground processes generally in TBI systems and arguably for the Guelph site specifically.

Current knowledge of root distribution in TBI systems is based on limited research due the

difficulty of studying roots in situ (Livesley et al. 1999; Taylor et al. 2013).

Knowledge of tree root distribution can be used to better infer zones of competition or

complementarity and ultimately contribute to decision making of species selection, planting

design, and identifying if management prescriptions are required to mediate belowground tree-

crop interactions. However, studying tree roots in situ is an acknowledged methodological

challenge. Fine roots provide the primary surface area for water and nutrient absorption by plants

(Jackson et al. 1997). Some advancements of non-intrusive study of tree fine roots include the

use of electrical conductance (Aubrecht et al. 2006; Čermák et al. 2006) or modeling. However,

as tree fine root distribution is more uniform than tree coarse roots (Danjon and Reubens 2008;

Taylor et al. 2013), for this study fine roots were assumed to be accurately assessable through

soil sampling techniques, specifically soil cores, which result in a measured value for fine roots

in a known soil volume (root length density; cm cm-3). Coarse roots are methodologically more

challenging to study than fine roots. Generally, destructive sampling is involved in TBI research

42

(e.g. measurements from exposed roots in a trench wall (Upson and Burgess 2013)), which only

capture a portion of the root system’s distribution, or partial or complete harvesting (Das and

Chaturvedi 2008), which is labourious and non-repeatable. Thus, GPR offers an alternative to

reduce destructive sampling, while potentially capturing more of the coarse root system. GPR

has been used in tropical agroforestry to detect distribution of roots in mixed-species systems at

certain distances from trees (Isaac and Anglaaere 2013). Raz-Yaseef et al. (2012) estimated

biomass with depth at the plot-level, using a similar form of analysis as described in Chapter 3.

However, this study is the first to chart vertical root distribution at the tree-scale. Accordingly, I

hypothesized that GPR can accurately locate the distribution of the study trees’ coarse roots due

to the conducive subsurface conditions for radar survey at the study site. Furthermore, the

vertical root distribution of trees can be charted. In doing so, tree root distribution can be

compared among species and importantly in crop rows where tree-crop interactions might be

greatest.

4.3 Materials and Methods

See Chapter 2: General Methods for description of study site, study trees, and radar survey.

4.3.1 Coarse root distribution measurements with GPR

GPR geo-images were compiled into the grid orientation in which they were collected in

GFP_Edit (Sensors & Software, Mississauga, ON, Canada). Prior to image analysis, non-root

anomalies (e.g. plane reflectors and signal “noise”) were minimized by applying a sequence of

processing steps (DC shift, dewow, background removal). A spreading and exponential gain

(SEC2) was applied to enhance delineation of reflection patterns with depth (EKKO_View

Deluxe; Sensors & Software). Subsequently, all geo-images were examined for root reflections.

Root reflections were visually identified and recorded using EKKO_Interp (Sensors &

Software), which provided the x,y,z coordinates for each detected root. This coordinate data was

43

used to quantify coarse root distribution in the vertical profile for each tree as well as for

subsetted coarse roots detected below equivalent areas of crop rows or tree rows. Detected coarse

roots located in the T. occidentalis tree rows were not analyzed as these trees were planted at 1 m

spacing.

4.3.2 Accuracy testing

In order to assess the level of accuracy of the technique, randomly selected soil profiles

equating to 1 m2 were exposed along subsets of matched GPR transects and root locations

crossing the profiles were compared to identified root reflections in radargrams for each study

tree (n=13). A positive detection was confirmed when a coarse root was found within 0.10 m and

root diameter was measured using digital callipers. When one detected coarse root matched with

multiple roots in same exposed area, the largest diameter root was assumed as the positive

detection and the smaller diameter root(s) as missed detection(s). GPR coarse root detection

frequency was the proportion of positive coarse root detections compared to the total number of

detected and missed coarse roots. When no root was found where an identified reflection was

observed in the radargram, a false positive was counted.

4.3.3 Root distributions

For each tree, detected coarse roots were pooled in 0.10 m depth intervals from 0 to 1 m.

Root counts were converted into a proportion of roots detected within each depth increment so

that all increments summed to 1. Vertical root distribution was fitted to the function described by

Gale and Grigal (1987) and Jackson et al. (1996):

Y = 1 - βd (6)

where Y was the cumulative proportion of roots at depth (d) and β was the distribution

coefficient. A greater proportion of roots closer to the surface is common with lower β while a

44

higher β is indicative of a greater proportion of detected roots distributed deeper in the profile.

This curve (Equation 6) assumes tree roots have an asymptotic increase of cumulative root

distribution with depth and thus projects a fraction of roots to depths beyond that of the

maximum detected root.

4.3.4 Fine root distribution in crop rows

Soil cores were collected using a metal cylindrical sampler (100 cm3) inserted

horizontally into exposed soil profiles at three distances from the tree stem (0.5, 1.0, 1.5, and 2.0

m) and at four depths (10, 20, 40, and 60 cm). Sampling was completed along two transects into

the crop rows originating from the tree stem. Soil cores were stored in air-tight bags at 5°C until

processing. Samples were wet sieved through mesh sized 2.0, 1.0, 0.5, and 0.25 mm (as

suggested by Livesley et al. (1999) for fine root length measurements) and fine roots were

manually collected using forceps. Dead roots were omitted, characterized with a loss of plasticity

and often very dark in colour (Das and Chaturvedi 2008, Gwenzi et al. 2011). Soil samples were

collected prior to the germination of soybean crops, controlling for tree fine roots in the crop

rows. The exception was for T. occidentalis sampled last, but soybean roots were differentiated

as very light in colour and with distinctive morphology (e.g. with nodules) from the fine roots of

T. occidentalis. Fine root samples were scanned with flatbed scanner at 600 dpi and length was

measured using WinRHIZO (Regents Instruments, Montreal, Canada). Root length density

(RLD) was calculated such that:

𝑅𝐿𝐷 = 𝐿 𝑉⁄ (7)

where L is the measured length (cm) within a given volume V (100 cm3).

45

4.3.5 Statistical analysis

GPR detection frequencies were grouped by diameter class (< 1 cm or ≥ 1 cm), species,

and depth intervals. One-way analysis of variance (ANOVA) was used to test for differences by

species and depth. When detection frequencies did not meet parametric assumptions, Kruskal-

Wallis test was used and subsequent post hoc test for species’ pairwise variation using the

‘agricolae’ package in R. Paired t-tests compared means of detected large and small coarse roots

by species.

Un-pooled GPR detected root depth data were used to identify the minimum, maximum,

mean, and skewness of detected coarse root depths for each tree as well as for the subsets of

detected coarse roots located below crop or tree rows. Skewness (a measure of departure from

symmetry) of detected roots were calculated using the ‘moments’ package in R. Larger positive

skewness suggests a right-skew distribution with a longer ‘tail’ to the right, or with depth, while

a skewness value closer to 0 indicates symmetrical distribution of root depths. ANOVA was

completed for each statistic across the five species and, when significant, were followed by

Tukey HSD. Paired t-tests were used to test for variation between roots in the cropped rows and

the tree rows. Species’ means of fine roots at each sampling depth followed non-normal

distribution and were tested for significant variation using Kruskal-Wallis. Assumptions of

normality (Shapiro-Wilk) and equal variance were met prior to parametric tests. Statistical

analyses were completed in R v.2.14.2 (R Foundation for Statistical Computing, Vienna,

Austria) and the level of significance was set at p<0.05.

4.4 Results

4.4.1 GPR detection frequency of coarse roots

The frequency of coarse root detection was 51.3 ± 0.3 % (mean ± S.E.; n=13) with a

distinctly higher detection frequency (80.6 ± 0.8 %; n=13) for coarse roots ≥ 1 cm and a lower

46

detection frequency (43.4 ± 0.5 %; n=13) for smaller coarse roots < 1 cm. Smaller coarse roots

were consistently under-detected compared to larger coarse roots for each tree species although

not significantly (Figure 12). There were no significant differences between detection

frequencies across species for either diameter class. Detection frequencies varied significantly by

depth (p=0.015) (Figure 13) as it became increasingly difficult to accurately identify radar

reflections of coarse roots further down the soil profile. Overall, false positives accounted for

26.8 ± 0.3 % (n=13) of the identified coarse root reflections. Of these false positives, most were

located above 0.50 m (84 %).

4.4.2 Coarse root distribution

There was significant variation across species for both the mean depths of detected coarse

roots (p=0.009) and the maximum depths of detected coarse roots (p=0.001) (Table 5).

Furthermore, all species-specific distribution coefficients (β) (Table 6) indicated a similar species

pattern in regards to rooting depth distributions.

The J. nigra and Q. rubra had the deepest root systems (Figure 14) with mean detected

root depths of 0.29 ± 0.01 and 0.29 ± 0.02 m (n=3) respectively and maximum detectable rooting

depths of 0.67 ± 0.05 and 0.70 ± 0.01 m respectively (mean ± S.E.; n=3) (Table 5). These

maximum depths were further extrapolated asymptotically to 0.95 m for the depth at which 95 %

of roots are predicted (d95) (Table 6). While the Populus sp. had a shallower coarse root

distribution with a mean coarse root depth of 0.21 ± 0.02 m (n=2) and a lower β resulting in d95

of 0.70 m, proximal to its maximum detected root depth (0.66 ± 0.01 m). Therefore, the deep

rooted Populus sp. had a less evenly distributed coarse root system that was concentrated closer

to the surface, which was further supported by the highest skew (0.68 ± 0.19) associated with

more roots at shallow depths and a longer ‘tail’ of detected roots deeper in the profile (Table 5).

The coarse roots of the P. abies followed a similar vertical distribution pattern to the Populus sp.,

47

Figure 12 Mean detection frequency of coarse roots grouped by diameter size class (n=3 except

n=2 for Populus sp. and Picea abies). No significant differences between species’ means of each

diameter class (ANOVA) or between diameter class of same species (paired t-test) (p>0.05).

Bars represent ± S.E. of the mean.

Figure 13 Mean detection frequency at each soil depth interval (n=13 exposed profiles)

measured from subset of matched soil profiles and radargrams. Means with same letter are not

significantly different (Kruskal-Wallis) (p>0.05). Bars represent ± S.E. of the mean.

48

Table 5 Minimum, maximum, and mean (m) depth of coarse roots detected by GPR for five tree

species (mean ± S.E.) (n=3 except n=2 for Populus sp. and Picea abies). Total tree and

subdivided crop row and tree row data. Values followed by the same letter in a column for each

parameter indicates non-significant (p<0.05) result (Tukey HSD).

Distribution

parameter Species

Total tree Crop row Tree row

Minimum (m) Thuja occidentalis* 0.03 ± 0.01a 0.06 ± 0.01a NA

Picea abies 0.03 ± 0.01a 0.05 ± 0.03a 0.05 ± 0.01a

Populus sp. 0.04 ± 0.01a 0.07 ± 0.01a 0.06 ± 0.01a

Quercus rubra 0.05 ± 0.00a 0.04 ± 0.02a 0.05 ± 0.00a

Juglans nigra 0.04 ± 0.01a 0.06 ± 0.01a 0.04 ± 0.01a

Maximum (m) Thuja occidentalis* 0.45 ± 0.05 a 0.35 ± 0.02a NA

Picea abies 0.54 ± 0.05 ab 0.49 ± 0.10a 0.48 ± 0.10a

Populus sp. 0.66 ± 0.01 b 0.51 ± 0.10a 0.45 ± 0.02a

Quercus rubra 0.70 ± 0.01 b 0.61 ± 0.09a 0.56 ± 0.01a

Juglans nigra 0.67 ± 0.05 b 0.62 ± 0.02a 0.56 ± 0.06a

Mean (m) Thuja occidentalis* 0.17 ± 0.02 a 0.18 ± 0.03 a NA

Picea abies 0.21 ± 0.02 a 0.20 ± 0.01 ab 0.20 ± 0.03 a

Populus sp. 0.22 ± 0.01 ab 0.22 ± 0.03 ab 0.20 ± 0.02 a

Quercus rubra 0.29 ± 0.02 b 0.27 ± 0.03 ab 0.27 ± 0.03 ab

Juglans nigra 0.29 ± 0.01 b 0.30 ± 0.02 b 0.32 ± 0.03 b

Skewness Thuja occidentalis* 0.65 ± 0.15a 0.26 ± 0.55a NA

Picea abies 0.43 ± 0.08a 0.60 ± 0.49a 0.84 ± 0.32a

Populus sp. 0.68 ± 0.19a 0.58 ± 0.00a 0.49 ± 0.35a

Quercus rubra 0.44 ± 0.04a 0.49 ± 0.22a 0.19 ± 0.19a

Juglans nigra 0.21 ± 0.16a 0.21 ± 0.16a -0.25 ± 0.42a

* T. occidentalis represents coarse root distribution of tree row with 1 m stem spacing.

49

Figure 14 Total detected coarse root frequency by depth to 0.80 m for each species (n=3 except

Populus sp. and Picea abies n=2). Bars represent ± S.E. of the mean.

50

Table 6 Cumulative distribution coefficient (β) for five tree species at the University of Guelph

Agroforestry Research Station, Canada. The distribution coefficients (β) were calculated using

species’ means of pooled detected coarse roots in 0.10 cm depth increments and fitted to the

function Y = 1- βd where Y is proportion of roots at depth (d). Results from subsetted

distribution data for coarse roots located in crop or tree rows are also presented. Depth (m) to

estimated cumulative 95 % of coarse roots (d95) presented.

species Total tree Crop row Tree row

β d95 (m) β d95 (m) β d95 (m)

Thuja occidentalis* 0.944 0.52 0.946 0.54 NA NA

Picea abies 0.958 0.70 0.955 0.65 0.950 0.58

Populus sp. 0.958 0.70 0.958 0.70 0.953 0.62

Quercus rubra 0.969 0.95 0.966 0.87 0.966 0.87

Juglans nigra 0.968 0.92 0.969 0.95 0.971 1.02

* T. occidentalis represents coarse root distribution of tree row with 1 m stem spacing.

51

but slightly more symmetric, inferred from a shallower maximum detected root depth (0.54 ±

0.05 m) and smaller skew (0.43 ± 0.08). The T. occidentalis had the shallowest and most

concentrated root system with a mean detected coarse root depth (0.17 ± 0.02 m; n=3) close to

half that of the J. nigra or Q. rubra as well as a high skew value (0.65 ± 0.15).

4.4.3 Root distribution into crop rows

The mean depth of detected coarse roots varied significantly across species both in crop

rows (p=0.031) and in tree rows (p=0.020), but generally had similar root distribution regardless

of row (crop vs. tree) with no significant variation between summary statistics (Table 5). Coarse

root distribution below crop rows was similar to that of the entire tree root system: the mean

detected root depth of J. nigra and Q. rubra were deeper (0.30 ± 0.02 m and 0.27 ± 0.03 m

respectively; n=3) compared to Populus sp. (0.22 ± 0.03 m; n=2), P. abies (0.20 ± 0.01 m; n=2),

and the shallow rooted T. occidentalis (0.18 ± 0.03 m; n=3) (Table 5).

For the deciduous trees, maximum detected coarse root depths were slightly deeper

below crop rows opposed to those growing below tree rows, although not significantly and d95

did not vary between tree and crop sections by more than 0.10 m.

4.4.4 Fine root distribution

RLD varied significantly across species for each sampling depth (p<0.0001) (Figure 15).

At 0.10 m depth, P. abies and Populus sp. had RLD of 2.98 ± 0.57 (mean ± SE; n=12) and 2.74

± 0.35 cm cm-3 (n=12) respectively and were significantly greater than J. nigra, Q. rubra, and T.

occidentalis that had RLD of 1.78 ± 0.31 cm cm-3 (n=18), 1.62 ± 0.38 cm cm-3 (n=18), and 1.09

± 0.17 cm cm-3 (n=18) respectively. Between the sampling depths of 0.10 and 0.20 m, RLD

decreased for all species; Populus sp. RLD decreased the least (11 %) and T. occidentalis

decreased the most (58 %) over this 0.10 m depth increment. At 0.40 and 0.60 m sampling

depths, all tree species had RLD < 1 cm cm-3 where Populus sp. and Q. rubra had the largest

52

Figure 15 Fine root length density (RLD; mean ± S.E. cm cm-3) by depth inclusive of 3

sampling distances (1.0, 1.5, and 2.0 m) from tree stem into crop rows. Five tree species (n=18

except n=12 for Populus. sp. and Picea abies). Samples collected during spring May-June 2012.

Same letters represent non-significant differences among species’ means for each sampling depth

using non-parametric Kruskal-Wallis test (p>0.05).

53

RLD values of 0.70 ± 0.24 and 0.33 ± 0.07 cm cm-3 respectively. At 0.60 m, Populus sp.

remained the species with the highest RLD (0.56 ± 0.13 cm cm-3) while Q. rubra was the most

consistent at these lower soil depths with no change in RLD from 0.40 m. Similarly, P. abies had

only a 13 % decrease in RLD between the 0.40 and 0.60 m sampling depths.

4.5 Discussion

4.5.1 Coarse root detection

The detection frequency for coarse roots less than 1 cm in diameter was almost half that

for larger coarse roots. Given consistent moisture gradients, smaller coarse roots are less likely to

be detected by GPR than larger coarse roots (Butnor et al. 2001; Hirano et al. 2012). Hirano et al.

(2012), the only other in-field study to report on such data, had a detection frequency of 6.6 %

for coarse roots less than 1 cm in diameter of Pinus thunbergii on sandy soils in Japan. The

detection frequency of large coarse roots at the Guelph TBI system was approximately 25 %

above that reported by Hirano et al. (2012). Potential variation may be induced from substrate

and biotic conditions required for optimal GPR detection of tree roots between study sites

(Butnor et al. 2001; Hirano et al. 2012), but also by experimenter bias as a higher detection

frequency might occur simultaneously with an increase in false positives. The greater number of

false positives in the shallower soil noted in my study compounded with decreased detection

frequency with depth suggests a bias for shallower roots to be over-represented relative to deeper

roots in the vertical profile. I suggest further research into determining and applying a

compensation factor to detected roots to accommodate detection errors. Furthermore, to address

the under-representation of deeper roots, a root distribution curve can be applied to extrapolate

the distribution data to lower depths (Gale and Grigal 1987; Jackson et al. 1996), supplementing

the GPR data.

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4.5.2 Tree root distribution in TBI systems

Applying the asymptotic function (Equation 6) projected a larger proportion of roots

nearer to the surface than was observed, which suggests that this function is limited in describing

root distribution at this site. As such, I suggest future research into modeling vertical rooting

patterns for trees in TBI systems, which might exhibit rooting patterns different to trees in forest

systems. However for this study, the resulting asymptotic curves can elucidate some information

for comparative analysis of rooting depths among species (e.g. d95) and general distribution

patterns. Specifically, if roots are more evenly distributed in the profile, the cumulatively

proportion will increase less rapidly with depth and the resultant β value will describe a more

gradual asymptotic curve (higher β values). In this TBI system, among all five species, β values

were greater than 0.94, and according to Gale and Grigal (1987), are representative of intolerant

or early successional species with deeper rooting profiles (mean β = 0.95) despite the bias of

GPR detection towards coarse roots at shallower depths. Although the five tree species in the

TBI system are inclusive of tolerant, intermediate, and intolerant species (Baker 1949), these

trees were planted simultaneously on agricultural and sandy loam soils and thus tree roots might

be more spatially exploitive of the subsurface (Sudmeyer et al. 2004).

J. nigra and Q. rubra were the most deeply rooted of the study trees. Jose et al. (2001) at

a TBI system in Indiana, USA on silty loam soils found that 10 year old Quercus rubra root

systems had a greater rooting density than 10 year old Juglans nigra determined from complete

excavations. The tap roots of both species were observed to descend to 0.90 m, but the authors

found that lateral roots of J. nigra were constrained mostly in the top 0.30 m while the lateral

roots of Q. rubra were located to a depth of 0.60 m (Jose et al. 2001). In the present study, J.

nigra and Q. rubra had similar root distribution. It should be noted that compared to J. nigra, Q.

rubra had a lower coarse root GPR detection frequency (although not significantly), but more

55

root biomass (see Chapter 3). Consequently, while the rooting depths of all tree species were

underestimated, it can be speculated that Q. rubra might have a more extensive and deep root

system relative to the other tree species.

At the Guelph site in 1998, 95% of 10 year old Populus sp. roots were located within a

depth of 0.4 m (within the A and B horizons) measured in a trench wall study (Gray 2000).

Results from the current study mark a sharp increase in rooting depth for Populus sp. over the

last 15 years of approximately 0.30 m. Bergeron et al. (2011) report higher nutrient capture in

Populus deltoides × nigra clone DN-3333 and Populus deltoides × nigra clone DN-3570 in TBI

systems in Quebec compared to crop monoculture, suggesting optimal root stratification to

minimize nutrient leaching. Similarly, in my study, the roots of Populus sp. were prevalent in

shallower soils but had a comparable maximum rooting depth with the other two deciduous trees,

suggesting that these species had a tendency toward a root distribution favourable for rapid

nutrient cycling and the Dougherty et al. (2009) findings of a ‘safety net’ effect at the site are

better supported by the high fine root density at lower depths for this species.

Generally, root systems of coniferous P. abies grow more prevalently at shallow depths,

although many deviations from this trend have been described (Puhe 2003). My study confirms

this previous data; coarse roots of the P. abies were more shallow compared to the deciduous

trees, but when isolated for roots solely growing in crop rows, were akin to the Populus sp.. The

other conifer species, T. occidentalis, is also known to have a shallow root architecture (Kelly et

al. 1992), a root distribution pattern exhibited by T. occidentalis in my study. However, fine root

density was distinctly less for T. occidentalis than the other four species, consistent with the

much smaller coarse root biomass as described in Chapter 3, thus might not cause substantial

belowground competition.

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4.5.3 Tree and crop root stratification

Crop roots are most prevalent in the top 0.30 m of soil (Jose et al. 2001; Jose et al. 2004),

with exceptions such as corn roots that have been found to depths of 0.60 m in a temperate TBI

system (Jose et al. 2001). Therefore, inherently tree and crop roots do occupy the same shallow

soil layers, but tree root systems that can proliferate at lower depths might minimize

belowground tree-crop competition and maximize the use of belowground resources. To induce

further tree-crop root stratification in the soil profile, management such as pruning of shoots,

roots, or both may be a means to reduce tree root presence (Salas et al. 2004; Siriri et al. 2010;

Carrillo et al. 2011). Intervention to root growth, such as installing root barriers or root pruning,

has resulted in improved yield responses for the adjacent crop (Jose and Gillespie 1998; Gillespie

et al. 2000; Jose et al. 2000; Miller and Pallardy 2001). Jose and Gillespie (1998) and Jose et al.

(2000) found that the installation of a root barrier improved corn crop yields adjacent to J. nigra

compared to alleys without a root barrier. Similarly grain yield for corn both near J. nigra and Q.

rubra were significantly improved when roots were pruned to 1.2 m or polyethylene barriers

were inserted to a depth of 1.2 m between trees and crop rows (Gillespie et al. 2000). It was

observed in 1998 at the Guelph site, that the coarse roots of 10 year old Populus sp. were

suppressed in the top 0.10 m of soil in crop rows opposed to in the tree rows and it was

suggested that tillage practices that had included plowing to a depth of 0.20 m may have driven

roots to lower depths (Gray 2000). However, after 20 years of no-till practices at the Guelph site,

suppression of shallow roots in crop rows was not detected in this study.

The Populus sp. and P. abies had the highest fine root densities in the crop rooting zone

(0.10 and 0.20 m). However, measurements from this study are static and may not be

representative of temporally dynamic fine roots. Samples were collected from the beginning of

spring with the Populus sp. and P. abies, followed by the J. nigra and Q. rubra, and finally the T.

57

occidentalis at the end of spring. Therefore, these results did not describe the fine root

distribution once crop plants were established. Mulia and Dupraz (2006) found significant

variation of fine root distribution with season for Juglans regia × nigra cv. NG23 on silty

alluvial soils and Populus euramericana cv. I214 on sandy alluvial soils in France. They also

remarked on how Populus sp. trees begin to bud earlier in spring than J. nigra. Thus, Populus sp.

might initially exploit the top soil before crop establishment and the fine roots of J. nigra

growing later in the season may preferentially grow further down the profile (Mulia and Dupraz

2006). This observation might partially explain the variations of fine root density between

species at the time of this study, specifically for the Populus sp, and J. nigra. Other examples of

fine root adjustments have been observed supporting stratification of roots between species.

Dawson et al (2001) reported an upward shift of fine roots of Prunus avium where herbicide was

applied, reducing grasses, and conversely a downward shift of root distribution in areas where no

herbicide was applied. Therefore, while knowledge of coarse root distribution can provide

insight into belowground tree-crop interactions spatially, absorbing roots need further study at a

finer temporal scale to fully understand the magnitude of adjustments made in these TBI

systems, if any, that might inherently improve soil resource use efficiency.

4.6 Conclusions

When subsurface conditions are favourable for radar study, an analysis of reflection

patterns in GPR radargrams can efficiently measure the distribution of coarse roots in TBI

systems. Prior to interpreting the distribution data there should be a thorough evaluation of the

accuracy of coarse root detection by GPR, notably with depth, and should include false positives.

Accuracy data will better inform the experimenter of any bias and possible need to compensate

the detected distribution data as appropriate. During this study, over half of the coarse roots were

identified (51 %), with improved accuracy for larger coarse roots and roots at shallower depths.

58

From GPR detected coarse roots, the cumulative proportion of tree roots at the Guelph TBI site

were lower by depth than those that have been estimated for forested ecosystems. In this regard, I

suggest further study for alternative functions to describe vertical root distribution in TBI

systems. Moreover, coarse and fine roots of the 25-year-old study trees (Quercus rubra and

Juglans nigra in particular) grew below the presumed crop rooting zones, suggesting that

belowground resources are being better optimized in these systems. Tree root system plasticity

under crop rows compared to tree rows was inconclusive at this TBI system. Further research

should target seasonal changes of fine root distribution and resource pool up take on a spatial and

temporal scale.

59

Chapter 5 : Conclusions

My thesis is a culmination of findings derived from two research objectives that were

commonly threaded in both of my data chapters. First, I evaluated of the utility of GPR use to

quantify tree root systems. Secondly, I completed a comparative investigation of the

belowground component of tree species in temperate agroforestry (Figure 1). Under these

themes, I summarize my findings, their significance, and discuss limitations and directions for

future research.

4.7 The use of GPR for tree root study

My research advances in-field GPR study of trees roots. This work is the first to test the

robustness of a single GPR index–biomass relationship to estimate coarse root biomass across

multiple tree species in field conditions, an approach that will further support GPR data

acquisition as an efficient means of measuring root systems. Furthermore, GPR biomass

estimates were compared to data of the biomass from matched total root system excavation

(Wotherspoon et al. unpublished data), which provided the most thorough assessment of GPR

precision to date and was unique to this study. Furthermore, this is the first study to test GPR

detection accuracies of tree root distribution across multiple species in field conditions, a novel

methodology to evaluate tree root system suitability in TBI systems.

Using the methods presented in my thesis, radargrams could efficiently and non-

destructive be collected in four hours for an entire tree root system, including site preparation

and set up. Presently, while GPR does require initial destructive sampling to calibrate for

biomass estimation or evaluating detection accuracy, the amount of physical sampling required is

drastically reduced compared to conventional coarse root measurements. During the current

study, more than 260 m2 of surface area were scanned with GPR, an area equating to over 14,500

soil cores (15 cm diameter). GPR techniques can provide more thorough understanding of the

60

heterogeneity of the root systems without total excavation in agroforestry systems (Isaac and

Anglaaere 2013) and unlike destructive sampling, this method of root data collection can be

repeated, critical for temporal studies on root system dynamics (Norby and Jackson 2000).

Lastly, the framework of analyses described in my thesis could be replicated to amass more root

data, if subsurface conditions are appropriate, across a variety of temperate TBI systems or

broadly to other treed ecosystems.

Recent advancements in modelling radar signal response under variable soil and root

conditions (specifically water content and root angle) have been reported following controlled

experiments (Guo et al. 2013b; Tanikawa et al. 2013). With these advancements, there is

potential to improve accuracy for root biomass estimation in field conditions and lessen the need

for destructive sampling during calibration (Guo et al. 2013b). Moreover, the link between

distribution and biomass analyses are increasingly realizable. Currently, there is a separation of

the literature surrounding in-field GPR biomass estimation (e.g. Stover et al. 2007) and

distribution analysis (e.g. Isaac and Anglaaere 2013). The closest synthesis to date being that of

Hirano et al. (2012) with estimated root biomass extracted from reflection parameters and

visually linked to confirmed locations of Pinus thunbergii coarse roots along exposed soil

profiles in order to estimate total biomass. Appropriate for my research objectives, I made a clear

delineation between biomass and distribution analyses, but I believe it is important to emphasize

this as a future area of research for instance to study tree root anchorage and structural adaptation

(Danjon and Reubens 2008).

In a root study of trees grown in shelterbelts in southwest Australia, Sudmeyer et al.

(2004) found that tree root densities decreased more gradually in deep sandy soils opposed to a

more sudden decrease in clay subsoil. The deep well drained, fairly homogenous Ap layer at the

Guelph study site is conducive for deeper rooting of tress, while notably these soils might also be

61

more conducive for GPR survey. At approximately 1 m depth there is a moraine till (with a

gradient of soil and till above) that can obstruct root growth, while also reducing GPR signal

penetration due to the heterogeneous material. Thus, it is interesting to note that soil conditions

appropriate for deeper root growth were also conducive for GPR study.

4.8 Tree-based intercropping

As one of the first studies to compare tree species’ coarse root distribution in a temperate

agroforestry system, my research contributes towards the overall understanding of belowground

tree-crop interactions in temperate TBI systems. It also contributes to a limited body of research

that seeks to quantify C storage in these systems and specifically in Ontario (Thevathasan and

Gordon 2004; Peichl et al. 2006). Furthermore, C concentration determined from a volatile-

inclusive methodology was a first time application in an agroforestry system and one of the first

to be applied to tree roots, enhancing the accuracy for complete C inventories of temperate TBI

systems.

Results from my thesis can contribute to informed decisions on species selection for TBI

sites in Ontario that could incorporate tree root C storage and distribution criteria. In Chapter 3,

the observable differences in root biomass and resulting C storage allowed for an evaluation of

tree species according to their potential C storage. In Chapter 4, both statistically significant

variation of descriptive statistics, such as mean detected coarse root depth, as well as observable

trends permitted a similar evaluation of tree species based on the suitability of rooting profiles.

Generally, the root systems of J. nigra and Q. rubra were the most suitable for TBI systems

based on the criteria of C storage and crop root complementarity. While wood from Populus sp

is less valuable compared to wood from the other deciduous trees in this study, Populus sp does

have large C storage potential (Evers et al. 2010). Although GPR estimates undervalued this

benefit due to the underestimation of root biomass for that species as discussed in Chapter 3.

62

The root distribution for Populus sp. in the crop rows are more similar to that of P. abies

and combined with fine root distribution suggest higher competition with crops in the top 0.30 m

of soil compared to J. nigra or Q. rubra. During my analysis of tree root distribution, it was

assumed that crop roots are static and predominately occupy the top 0.10 to 0.30 m in the soil

profile, while perhaps deeper for corn (Jose et al. 2001). This assumption needs to be further

explored as crop roots might exhibit plasticity according to site-specific conditions such as

competitive effects from trees. In a TBI system in France, 50% of wheat roots adjacent to

Populus euramericana clone I214 were found to be above 0.15 m whereas next to J. nigra wheat

roots were deeper with 50% found above 0.40 m (Mulia and Dupraz 2006). Thus, it is critical for

further research to relate root structure, as described in this thesis, to function such as a more

extensive tree fine root survey or indirectly using stable isotope signatures to determine

preferential depth of water uptake of the intercropped trees (Fernández et al. 2008; Isaac and

Anglaaere 2013).

4.9 Final conclusions

Adoption of tree-based intercropping (TBI) can ameliorate ecosystem properties,

sequester atmospheric CO2, and can offer landowners diversified economic sources. However,

more widespread acceptance of multi-functioning TBI are likely to be limited without more

sophisticated and comprehensive scientific research that can parameterize these systems. My

research provides direct quantification of five species’ root systems, which can be used towards

tree species selection and management prescriptions. Moreover, I advanced the utility of GPR

for coarse root study and contributed towards general methodology of this novel technique.

Ultimately, this thesis can be used as a framework to complete further investigation of tree root

biomass and distribution in temperate TBI systems and in other tree ecosystems.

63

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Appendices

A1: Bulk density of soil within tree study plots (mean ± S.E.; n=36 except n=24 for Populus sp.

and P. abies).

depth (cm) T. occidentalis P. abies Populus sp. Q. rubra J. nigra

10 1.05 ± 0.03 1.07 ± 0.03 1.05 ± 0.05 1.09 ± 0.02 0.98 ± 0.02

20 1.05 ± 0.02 1.02 ± 0.04 1.20 ± 0.04 1.14 ± 0.02 1.04 ± 0.02

40 1.28 ± 0.05 1.25 ± 0.03 1.31 ± 0.04 1.28 ± 0.03 1.15 ± 0.03

60 1.38 ± 0.03 1.33 ± 0.04 1.38 ± 0.05 1.40 ± 0.03 1.23 ± 0.03

A2: GPR estimated (BGPR), excavated, and allometrically derived coarse root biomass (kg tree-1).

Tree Stem diameter (cm) Estimate (kg) Excavate (kg) Allometric (kg)

Populus sp. 34.0 60.5 82.6 93.7

Populus sp. 32.9 48.6 61.1 86.7

J. nigra 25.9 89.3 73.5 51.5

J. nigra 22.9 83.0 8.3b 38.1

J. nigra 25.7 60.6 76.3 62.1

Q. rubra 21.4 80.7 79.0 44.3

Q. rubra 22.1 48.8 62.7 47.9

Q. rubra 20.5 101.6 80.4 40.1

P. abies 24.9 71.7 71.7 48.7

P. abies 18.0 52.2 28.5 23.1

T. occidentalisa 16.7 8.9 20.7 20.9

T. occidentalisa 9.6 14.0 6.9 11.1

T. occidentalisa 8.5 12.5 14.4 4.9

a DBH of T. occidentalis is the mean ± S.E. of the largest stem from the primary trees located in

study plots. T. occidentalis is a multi-stem tree with 1 m tree spacing. Root biomass is inclusive

of multiple (2 or 3) trees within study plots.

b Unusually low root biomass reported from excavation was noted in Chapter 3 as a likely source

of error and omitted during analysis.